Engineering, Technology & Applied Science Research

Deep Learning with Semantic Segmentation Approach for Building Rooftop Mapping in Urban Irregular Housing Complexes

2025-04-04T07:03:14+00:00

This research investigates the application of the Deep Learning (DL) U-Net architecture for building rooftop segmentation in densely populated urban areas with irregular housing patterns. The research explores the effectiveness of two loss functions - Binary Cross Entropy (BCE) and Dice Loss (DLs) - to optimize the segmentation accuracy. The present study utilized Small-Format Aerial Photography (SFAP) images processed into orthophotos with a final ground sampling distance of 5 cm. The study area, located in Bogor, Indonesia, features both regular and irregular housing patterns, making it an ideal testing ground for the segmentation model. The U-Net model, having been utilized EfficientNetB6 as the encoder and having been trained with augmented data, demonstrated stable performance across metrics, such as accuracy, precision, recall, and F1-score. The results show that the DLs function outperformed BCE, achieving an average Intersection over Union (IoU) score of 96.8% compared to the 87% score for BCE, indicating that DLs is more effective for this application. The study further enhances the segmentation results by converting the raster data into a vector format using the Ramer-Douglas-Peucker (RDP) algorithm, which simplifies and smooths the polygonal shapes of the segmented rooftops. The combination of the U-Net, DLs and RDP algorithm provides high accuracy results and high usability of the segmentation outputs in practical applications, such as urban planning and disaster management scenarios where accurate rooftop delineation is critical.

Firefly Algorithm-based Optimization of Control Parameters in DC Conversion Systems

2025-04-04T07:03:59+00:00

Sustainable energy and electric vehicles require DC-DC converters in renewable energy systems, EV charging, and smart grids. In this context, buck converters are crucial, providing efficient voltage regulation and reliable performance in these advanced energy systems. While Proportional-Integral (PI) controllers are widely adopted for their simplicity and dependability, they often rely on manual parameter tuning, limiting their adaptability and responsiveness. To address this limitation, this research introduces a digital control strategy that optimizes the PI parameters using the Firefly Algorithm (FA). This optimization significantly enhances the stability and reduces the oscillations in the DC-DC buck converter. A MATLAB/Simulink simulation model is utilized to validate the proposed approach, and the results demonstrate that the FA-optimized control parameters substantially improve the converter's performance, making it highly suitable for high-demand applications in advanced energy systems.

Application of the One-Step Second-Derivative Method for Solving the Transient Distribution in Markov Chain

2025-04-04T07:04:58+00:00

Markov chains are an application of stochastic models in operation research, helping the analysis and optimization of processes with random events and transitions. The method that will be deployed to obtain the transient solution to a Markov chain problem is an important part of this process. The present paper introduces a novel Ordinary Differential Equation (ODE) approach to solve the Markov chain problem. The probability distribution of a continuous-time Markov chain with an infinitesimal generator at a given time is considered, which is a resulting solution of the Chapman-Kolmogorov differential equation. This study presents a one-step second-derivative method with better accuracy in solving the first-order Initial Value Problems (IVPs) compared to other approaches found in the literature, which is verified by the obtained solutions. The determination of the transient solutions for Markov chains is presented using the proposed method. The results show better accuracy in solving the transient distribution in Markov chains, which implies that there is an improved assurance in adopting this approach in future studies of the Markov chain modeling process for predicting future events based on the current state of a process. Future studies on Markov chain modeling could adopt the introduced method to predict future events based on the current state of a process.

Exploring Customer Engagement in Social Commerce: A Literature Review of Frameworks, Pathways, and Emerging Trends

2025-04-04T07:05:14+00:00

Customer Εngagement (CE) in social commerce (s-commerce) has become a focal point for businesses seeking to build long-term consumer relationships in digitally mediated environments. This literature review synthesizes existing research on CE within s-commerce, examining theoretical foundations, key antecedents, mediating and moderating factors, and engagement outcomes. The key findings indicate that among the most important variables for CE is the social support, platform interactivity, and hedonic motivations, while trust and satisfaction act as crucial mediators that bridge initial engagement drivers and long-term outcomes, like brand loyalty and Word-of-Mouth (WοM). Furthermore, the current review discusses the role of moderators, such as demographic characteristics and platform-specific features, in conditioning the impact of CE efforts. It is revealed that engaged consumers not only exhibit increased loyalty and advocacy, but also contribute to the co-creation and community-building efforts within the s-commerce. Future research is encouraged to explore emerging technologies, like Artificial Intelligence (AI) and Virtual Reality (VR), cross-cultural variations in CE, and the ethical concerns surrounding the data privacy and personalization. This review contributes a comprehensive synthesis of CE in s-commerce, positioning it as a strategic asset for brands aiming to foster a sustainable CE in digital ecosystems.

A Robust Neural Network against Adversarial Attacks

2025-04-04T06:58:25+00:00

The security and dependability of neural network designs are increasingly jeopardized by adversarial attacks, which can cause false positives, degrade performance, and disrupt applications, particularly on resource-constrained Internet of Things (IoT) devices. Τhis study adopts a two-step approach: first, designs a robust Convolutional Neural Network (CNN) that achieves high performance on the MNIST dataset, and second, evaluates and enhances its resilience against advanced adversarial techniques such as Deepfool and L-BFGS. Initial evaluations revealed that while the proposed CNN performs well on standard classification tasks, it is vulnerable to adversarial attacks. To mitigate this vulnerability, APE-GAN, an innovative adversarial training technique, was employed to re-train the proposed CNN, significantly improving its robustness against adversarial attacks while optimizing performance for embedded systems with limited computational resources. Systematic experimentation demonstrates the effectiveness of APE-GAN in enhancing both the accuracy and resilience of the proposed CNN, outperforming conventional methods and establishing it as a pioneering solution in adversarial machine learning. By integrating APE-GAN into the training process, this research ensures the secure and efficient operation of the proposed CNN in real-world IoT applications, marking a significant step forward in addressing the challenges posed by adversarial attacks.

Structural Performance of Double castellated Steel Beams with Innovative Opening Configuration

2025-04-04T07:02:00+00:00

This research delves into the structural performance of castellated steel beams featuring expanded webs, emphasizing how design variables, such as the number of openings and cutting angle, affect the load capacity, deflection, and stiffness. Castellated beams are engineered to improve the strength-to-weight ratio of the standard I-beams by cutting and reconstructing the web into hexagonal, rectangular, or circular openings. This configuration not only boosts the beam load capacity and minimizes weight, but also maintains or enhances stiffness, rendering it well-suited for long-span structures. In the present study, seven beam specimens were tested under one-point load conditions, including six castellated beams. The key findings demonstrate that reducing the number of openings significantly increases the load capacity and reduces deflection. The castellated beams outperformed RB-S by up to 68.9% in the load-carrying capacity at Service Limit Deflection (SLD). Additionally, the study examined the influence of cutting angles (58°, 52°, and 45°) on the beam performance, concluding that a 52° angle provided the best balance between strength and stiffness. The results underscore the importance of optimizing castellated beam designs for large-scale construction applications, where balancing weight reduction, structural integrity, and serviceability is crucial.

Flood Risk Assessment and Mitigation Strategies for the Sinjai and Tangka River Catchments in Indonesia using Hydraulic Modeling and Spatial Analysis

2025-04-04T06:59:55+00:00

This study aims to address the persistent flooding in Sinjai District, Indonesia, by developing a comprehensive spatial flood risk model. The development of this model was conducted using hazard, vulnerability, and capacity index data. The research first determines the weighted scores of the hazard, vulnerability, and capacity indices to categorize areas into high, medium, and low flood risk zones as a reference in developing flood mitigation. The study further uses the Unmanned Aerial Vehicle (UAV) technology to facilitate the collection of topographic data, which are then used as an input in the hydrological and hydraulic analysis. The overarching objective of this research is to provide insights and recommendations to policymakers, with the aim of informing effective strategies for reducing the flood risk. The results indicate that low-risk areas encompass 564.45 hectares, medium-risk areas extend to 645.83 hectares, and high-risk areas cover 46.19 hectares. The proposed flood control measures include the construction of retention ponds, embankments, and river normalization. These interventions have the potential to reduce up to 507.12 hectares, equivalent to 40.36% of the inundation area, thereby reducing the impact of flooding. The findings provide important guidance for policymakers in making decisions, devising mitigation strategies, and promoting sustainable development in the region.

Behavior of Concrete Beams encasing castellated Steel Sections with Different Opening Shapes

2025-04-04T07:05:34+00:00

This study investigates the behavior of concrete-encased castellated steel beams featuring various aperture geometries and shear stud connector configurations. Five Composite Castellated Beam (CCB) specimens were tested under two-point loading conditions, including one control specimen with a solid steel section and four specimens with castellated steel beams encased in Normal-Strength Concrete (NSC). The castellated beams featured either Hexagonal (H) or Rectangular (R) openings, and the shear stud connectors provided either Full (F) or Partial (P) interaction between the steel and concrete components. The research objectives were to determine the maximum load capacity for each sample under applied loads, analyze the resulting deformations, and assess the impact of the opening shape and shear connections on the beam performance. The results showed that the H opening improved the load-bearing capacity by 19% and reduced the deflection and horizontal displacement by 21.47% and 12.86%, respectively, compared to the R opening sample. Specimens with F interaction exhibited a higher load capacity and lower deflection and horizontal displacement than those with P interaction. The F configuration increased load tolerance by 2.44% and decreased the deflection and horizontal displacement rates by 4.17% and 5.86%, respectively, relative to the P configuration. The findings demonstrate the influence of aperture geometry and shear connections on the structural performance of concrete-encased castellated steel beams, providing insights for optimizing their design in composite construction.

Buckling of Rectangular Plates with Embedded Stiffeners under Shear Stress

2025-04-04T06:57:48+00:00

This study analyzes the shear buckling behavior of rectangular plates with embedded stiffeners, employing the Finite Element Method (FEM) in ABAQUS. A total of 98 plate models were examined under shear stresses to assess the influence of key design parameters, including stiffener height, shape, location, and plate aspect ratio, on shear buckling resistance. The findings indicate that the optimal position for the embedded stiffener is at the center of the plate, irrespective of the configuration, resulting in an enhancement in shear resistance ranging from 14% to 32% compared to the unstiffened reference plates. Stiffeners positioned at one-third and one-quarter of the plate length yield more modest improvements, with shear resistance increases of 3% to 9% and 1% to 6%, respectively. For plates with an aspect ratio of 0.5, the optimal stiffener height was determined to be 30 mm, resulting in a 33% increase in shear resistance. Conversely, for plates with an aspect ratio of 1, the optimal stiffener height was found to be 40 mm, yielding a 76% increase in shear buckling resistance. The influence of the stiffener shape was also examined, with trapezoidal stiffeners demonstrating the most substantial enhancement (33% increase) for aspect ratio 0.5, and circular stiffeners exhibiting the most significant improvement (75% increase) for aspect ratio 1. The analysis further revealed that increasing the aspect ratio from 0.5 to 1 led to a substantial reduction in the shear buckling resistance, with a decrease of 64%. These findings underscore the crucial impact of stiffener parameters and aspect ratio on the shear buckling performance of stiffened plates, providing valuable guidance for the design and optimization of thin-walled structures subjected to shear loading.

Machine Learning-enhanced Direction-of-Arrival Estimation for Coherent and Non-Coherent Sources

2025-04-04T07:04:49+00:00

Accurate Direction-Of-Arrival (DOA) estimation for both coherent and non-coherent sources remains a critical challenge in array signal processing, particularly under sparse sensor configurations. This study introduces a novel 3D coprime array method that enhances source separation and spatial resolution. By leveraging a unique joint diagonalization framework with a full-rank Toeplitz matrix, the proposed approach effectively decorrelates coherent sources while preserving the accuracy of uncorrelated signals. A machine learning model can be employed to further refine the DOA estimates, utilizing a regression model or neural network to predict DOA based on features extracted from the covariance matrix. A new cost function, independent of the number of sources, is proposed to increase robustness in complex environments. Extensive simulations demonstrate that the proposed technique significantly outperforms established algorithms, including 3D Unitary Root-MUSIC, modified Root-MUSIC, ECA-MURE, and FBSS. The results reveal substantial improvements in Root Mean Square Error (RMSE) across various Signal-to-Noise Ratios (SNRs), affirming the method's effectiveness. Additionally, the approach's adaptability to different scenarios makes it suitable for real-world applications. These advances pave the way for improved applications in Unmanned Aerial Vehicles (UAVs), radar systems, and next-generation communication networks.

Enhanced Capon-MUSIC Integration for improved DOA Estimation with Coprime Arrays in Disaster Management

2025-04-04T07:06:19+00:00

This paper introduces an enhanced Capon beamforming approach that is further integrated with the Multiple Signal Classification (MUSIC) method to achieve superior Direction of Arrival (DOA) estimation using coprime arrays. The proposed enhancement to the standard Capon beamformer focuses on improving its robustness against steering vector mismatches, which significantly boosts its performance in complex signal environments. By optimizing the beamformer's spatial filtering capability, the improved method mitigates signal distortion and enhances resolution. Building on this enhanced Capon beamformer, this study integrates it with the MUSIC method to leverage the strengths of both approaches. The coprime array configuration allows for an increased number of virtual sensors, enabling higher degrees of freedom and improving the resolution of both coherent and uncorrelated signals. This combined Capon-MUSIC framework provides an efficient solution for accurate DOA estimation, even in scenarios where traditional methods fail. The effectiveness of this hybrid approach is evaluated in disaster management applications, where precise signal localization is crucial for tasks such as emergency communication, search and rescue operations, and resource deployment. The simulation results demonstrate that the integrated method outperforms conventional techniques, delivering improved accuracy, robustness, and computational efficiency, making it ideal for real-world disaster response scenarios.

Innovative Fault Detection for AES in Embedded Systems: Advancing Resilient and Sustainable Digital Security

2025-04-04T06:59:39+00:00

The AES algorithm is commonly used in embedded systems for security purposes, but its robustness can be compromised by natural and malicious faults, leading to potential information leakage. Various fault detection schemes have been proposed to protect it against differential fault analysis attacks. These schemes aim to detect and mitigate any potential vulnerabilities in the AES algorithm, ensuring system security. The implementation of fault detection schemes aligns with Sustainable Development Goal (SDG) 9, which focuses on building resilient infrastructure and promoting inclusive and sustainable industrialization. Enhancing the security of embedded systems through these measures contributes to creating a more secure and sustainable digital environment for all. This study introduces a new fault-parity detection scheme that involves comparing the correct parity of the rounded output with the predicted parity based on AES processing steps. The strengths and weaknesses of this scheme in defending against fault attacks are also discussed. The experimental results demonstrate that the proposed fault detection scheme achieves an impressive fault coverage of 99.999%. Implemented on the Xilinx Virtex-5 FPGA, the scheme was compared to existing methods in terms of fault coverage, area overhead, frequency degradation, and throughput. These results highlight the ability of the proposed scheme to strike a balance between implementation cost and AES security.

Reconfigurable Memristive Pulse Generator Based on Pulse Shaping for Ultra Wideband Communication

2025-04-04T07:01:05+00:00

This paper presents a novel reconfigurable memristive Pulse Generator (PG) designed for Ultra-Wideband (UWB) applications, leveraging the advanced pulse shaping techniques. The proposed design aims to improve the efficiency and flexibility of UWB communication systems, thereby contributing to the achievement of the "Sustainable Development Goal 9: Industry, Innovation, and Infrastructure" by promoting technological advances in the field of communications. The design utilizes the CMOS 0.18 µm technology operating at 1.8 V to achieve high performance and low power consumption. By employing constant resistance and dynamic resistance modulation, the proposed PG supports various modulation schemes, including Frequency-Shift Keying (FSK) and On-Off Keying (OOK), enhancing its adaptability and efficiency. The transmitter demonstrates significant energy efficiency with a low-duty cycle impulse approach, operating within the lower UWB band (3-5 GHz) to minimize interference. The simulation results indicate that the UWB generator achieves high data rates and improved spectral efficiency while maintaining compliance with the FCC regulations. This makes it ideal for integration into IoT devices, wearable technology, and other battery-powered applications. Therefore, the proposed design has the potential to enhance connectivity and data transmission capabilities, ultimately supporting the development of more efficient and reliable communication networks worldwide.

Optimal Placement of Internet of Things Gateways in Modern Electric Vehicle Charging Communication Systems

2025-04-04T07:03:18+00:00

This paper presents the use of the optimal placement and number of the Internet of Things (IoT) gateway method to support home Electric Vehicle (EV) charging scheduling within an IoT system. A research was conducted for two scenarios. In scenario 1, a single IoT gateway was placed, while in scenario 2, the optimal number of IoT gateways was placed. The evaluation method for both scenarios utilized random placement, Equally Distributed Placement (EDP), and Genetic Algorithm (GA) placement. The optimization result ensures that the Path Loss (PL) value in the communication system does not exceed the specified PL threshold. This research aims to minimize the IoT gateways while ensuring quality data transmission, specifically maintaining a data rate above 31.72 kbps and a throughput of 24 kbps. The results indicate that both the random placement and EDP require more than three IoT gateways. Meanwhile, the GA placement requires only three IoT gateways, making it a more cost-effective communication solution.

A Study on the Influence of Enclosure Temperature Control on the Printing of ABS Filament in a Three-Dimension Printer

2025-04-04T07:01:21+00:00

Fused Deposition Modeling (FDM) is a newest technique in additive manufacturing, capable of producing complex 3D parts efficiently and cost-effectively without using complicated or expensive dies. One of the most popular materials adopted in 3D printers is the ABS filament, which, despite its high mechanical properties, is susceptible to warping defects which can result in print failures. The objective of this study is to eliminate or minimize the warping defects by installing a temperature control system within the enclosure to prevent rapid cooling during the 3D printing process. The adopted design system uses a thermostat controller, a wire heater, and an enclosure to regulate temperature as required. A series of experimental tests were carried out using a range of sample shapes and sizes at three distinct controlled temperatures (40 °C, 50 °C, and 60 °C). The maximum measured error of 1.463 mm was observed at 40 °C. This variation was attributed to insufficient temperature control and a substantial sample volume of 2,827.44 mm³. Conversely, the minimum error of 0.223 mm was identified at higher temperatures of 60 °C, with a reduced sample volume of 530.14 mm³. The study determined that warping, in addition to layer shifting at vertical levels, is a significant contributor to the warping error. The present study recommends the use of an externally controlled temperature in order to enhance the quality and precision of 3D-printed ABS components.

Behavior of the Alternative Types of USBR Basin with Changes in the Geometric Characteristics of the Straight Walls of the Stilling Basin

2025-04-04T07:02:38+00:00

A stilling basin is a vital energy dissipator structure that transitions supercritical flow from a dam spillway into subcritical flow to protect downstream (ds) riverbeds from the scouring caused by high-velocity water. This study evaluates the impact of wall configurations and middle blocks on energy dissipation efficiency in stilling basins by modifying wall shapes and incorporating middle blocks. Five cases were tested: flat walls with middle blocks (Case 1), small zigzag walls without and with middle blocks (Cases 2 and 3), and large zigzag walls without and with middle blocks (Cases 4 and 5). A total of 55 experiments were conducted with discharges ranging from 0.010 m³/s to 0.020 m³/s. The average energy dissipation rates were 61.1%, 58.5%, 65.2%, 63.6%, and 64.9% for Cases 1, 2, 3, 4, and 5, respectively. Case 3, featuring small zigzag walls with middle blocks, demonstrated the highest energy dissipation efficiency, outperforming the other cases. This research highlights innovative designs for stilling basins, enhancing energy dissipation efficiency and mitigating the ds scouring effects.

A Comparative Analysis of CNNs and ResNet50 for Facial Emotion Recognition

2025-04-04T06:59:48+00:00

Anger, disgust, fear, happiness, sadness, surprise, and neutrality are some of the basic facial emotions that researchers worldwide consider for recognition. Detection of these emotions is important in the present era due to the digital transformation of many processes and human communication. This study analyzes emotion detection methods using the capabilities of deep learning techniques such as a Convolutional Neural Network (CNN) and Residual Network 50 (ResNet-50). The FER2013 benchmark dataset for facial emotion recognition was used for training and testing purposes, along with a few other private images. This study aimed to compare and analyze the performance of the two methods based on several comparative factors, such as architectural differences, feature extraction capability, training dynamics, model performance, computational efficiency, and hardware configuration. The experimental results showed that the ResNet-50 model was significantly more accurate than the CNN, with an accuracy of 85.75% compared to 74%. Although ResNet-50 has higher computational costs, its robustness and accuracy make it the optimal choice for facial emotion recognition tasks. This research provides valuable insight into the capabilities and trade-offs of these models for face emotion recognition techniques.

Resource Utilization and Repetitive Construction Scheduling using the Discrete Event Simulation Method

2025-04-04T07:00:32+00:00

The efficient utilization of resources in repetitive construction projects is crucial to optimizing schedules and reducing costs. This study investigates the application of Discrete Event Simulation (DES) to enhance resource utilization in scheduling repetitive construction activities, specifically the construction of Type-36 houses in Palu City, Indonesia. Traditional project-based planning methods, such as the Critical Path Method (CPM), are compared with production-based planning approaches. The study shows that although CPM can effectively determine project completion times, it falls short in optimizing resource allocation, particularly in scenarios involving repetitive tasks. By employing the DES tool SIMUL8, the study models the construction process and simulates various resource allocation scenarios to identify the optimal resources required. The results indicate that DES provides a more dynamic and accurate analysis, allowing real-time adjustments and improved resource management. The study concludes that integrating DES into construction project management can significantly enhance efficiency, reduce project duration, and optimize resource utilization.

A Dual-Band Implantable Antenna for Mobile Systems

2025-04-04T07:06:40+00:00

In this paper, a miniaturized dual-band implantable antenna is presented for mobile communication services, as well as industrial and scientific applications. The proposed antenna, with compact dimensions of 37 × 35 × 1.6 mm³, operates efficiently at dual frequencies of 3.7 GHz and 6.2 GHz. It features a circular metal patch with rectangular-shaped slots, enabling stable radiation patterns, broadband impedance matching, and robust performance. The antenna achieves a gain of 3.47 dBi, a directivity of 4.5, and an efficiency of 86%, making it well-suited for 5G technology and other high-frequency applications. Compared to existing designs, the proposed antenna demonstrates significant improvements in efficiency and bandwidth while maintaining a compact size. These antennas are essential for handling the increased data demands and diverse frequency requirements of modern mobile systems. Future trends in wireless communication are rapidly evolving, addressing challenges, such as interference reduction, increased data demands, and enhanced network performance. The proposed antenna, with its high efficiency and compact design, aligns with these trends and is well-positioned to meet the requirements of the next-generation communication systems.

Simulation Modeling of Kinematic Structures of Parallel Mechanisms

2025-04-04T06:58:43+00:00

The present study offers an in-depth examination of the current advancements and the prevailing state of parallel kinematic structures, with a particular emphasis on Delta robots. The central focus of this study is the design solution of the Delta robot model in the Pro/ENGINEER system. The study explores the design and strength calculation of the individual components of the Delta robot, in addition to determining its workspace. Parallel mechanisms are distinguished by their unique kinematic structure, which is embodied by a closed kinematic chain. The terminal effector of the mechanism is then connected to the base by multiple arms. This configuration offers distinct advantages, primarily in terms of enhanced rigidity and related properties. The direct kinematic problem is employed to derive the coordinates of the end effector from the known positions of the actuators. While this task is not essential for the robot's movement per se, it is used during initialization and calibration, when the positions of the actuators are known, and the position of the end effector must be determined to move it to a specific location. The inverse kinematic problem, on the other hand, plays a critical role in continuous calculations during movement, as it transforms the world coordinates of the end effector into joint coordinates. This task is essential for robot control, as it enables the calculation of the positions of the individual actuators, given the desired position of the end effector.

Design of a Compact Millimeter Wave Antenna for 5G Applications based on Meta Surface Luneburg Lens

2025-04-04T07:05:51+00:00

As the demand for fast and reliable wireless connectivity increases, the 5G technology has emerged as a promising solution. This study focuses on enhancing the gain and return loss performance of 5G wireless communication systems, with a particular emphasis on the Meta Surface Luneburg technique. In this work, a compact millimeter-wave antenna operating at a frequency of 28GHz dedicated to 5G applications is proposed and designed. The introduced design utilizes a metasurface Luneburg technique in order to obtain reduced size, high gain, and less return loss. The proposed antenna is implemented on a 40 × 40 × 0.5 mm³ RT Duroid 5880 Lossy substrate with a relative dielectric constant of ε_r = 2.2 and a loss tangent of 0.0068. Two-unit cells are strategically arranged in an array on the substrate to form a Luneburg meta-lens, which transforms spherical wavefronts into planar wavefronts. This configuration enables the antenna to achieve a directed beam at 28 GHz. The antenna is simulated, and key parameters, such as gain and return loss are analyzed. The results show that the antenna achieves a gain of 7.9 dBi and a return loss of less than -10 dB, demonstrating its suitability for 5G applications.

Virtual Reality and Augmented Reality Technology in Bridge Engineering: A Review

2025-04-04T07:01:17+00:00

Improving the quality of specialized disciplines within the construction sector is a necessary and inevitable trend. Each field has distinct characteristics, leading to differences in the adoption and application of technologies, which, in turn, drive tailored research approaches. Bridge construction, as a critical subdomain, frequently involves large-scale, complex projects that draw interdisciplinary attention from experts across various fields, extending beyond construction alone. This interdisciplinary nature underscores the importance of advanced technologies for management, design, construction, and communication, particularly in making specialized knowledge accessible and practical for diverse stakeholders. Virtual Reality (VR) and Augmented Reality (AR) have emerged as transformative tools to address these challenges. Despite extensive research on VR and AR in the broader construction industry, studies focused on their application in bridge construction remain limited. This paper consolidates existing research, examining VR and AR in the context of bridge construction. It provides a comprehensive overview of their benefits, challenges, equipment requirements, and emerging trends, providing insight to guide future research and support the practical implementation of VR and AR in this field.

Large Language Models for Arabic Sentiment Analysis and Machine Translation

2025-04-04T07:04:12+00:00

Large Language Models (LLMs) have recently demonstrated outstanding performance in a variety of Natural Language Processing (NLP) tasks. Although many LLMs have been developed, only a few models have been evaluated in the context of the Arabic language, with a significant focus on the ChatGPT model. This study assessed three LLMs on two Arabic NLP tasks: sentiment analysis and machine translation. The capabilities of LLaMA, Mixtral, and Gemma under zero- and few-shot learning were investigated, and their performance was compared against State-Of-The-Art (SOTA) models. The experimental results showed that, among the three models, LLaMA tends to have better comprehension abilities for the Arabic language, outperforming Mixtral and Gemma on both tasks. However, except for the Arabic-to-English translation, where LLaMA outperforms the transformer model by 4 BLEU points, in all cases, the performance of the three LLMs fell behind that of the SOTA model.

Harnessing Explainable Artificial Intelligence (XAI) based SHAPLEY Values and Ensemble Techniques for Accurate Alzheimer's Disease Diagnosis

2025-04-04T07:03:50+00:00

Machine Learning (ML) is a dynamic method for managing extensive datasets to uncover significant patterns and hidden insights. ML has revolutionized numerous industries, from healthcare to finance, and from entertainment to transportation. Ensemble classifiers combined with Explainable AI (XAI) have surfaced as a significant asset in the field of Alzheimer's Disease (AD) diagnosis. Boosting EC techniques coupled with Shapley Additive Explanations (SHAP) offers a powerful approach to AD diagnosis. This paper investigates boosting ensemble ML schemes, such as XGBoost, LightGBM, and Gradient Boosting (GB), for AD diagnosis and SHAP for feature selection. The proposed scheme achieved efficient results, with an accuracy of more than 94% with minimum features for the detection process.

An Audit System Causality Model for Construction Safety Management System Assessment in Building Projects using Integrated Design-Build

2025-04-04T07:04:29+00:00

Increasing the implementation of Construction Safety Management Systems (CSMSs) has proven to be an effective strategy for preventing construction accidents. Unfortunately, the current safety audit system is still not fully developed at each stage of the project life cycle, especially in integrated design-build contracts for construction projects. Based on this phenomenon, the objectives of the current research were developed, which include identifying the indicators and sub-indicators of the performance assessment audit system, and designing a causality model of the indicators and sub-indicators of the assessment audit system in each project life cycle, especially for the integrated design-build contract. The research method comprises a literature review and expert validation to obtain the indicators and sub-indicators for the safety system audit. In addition, a perception survey was conducted through with questionnaires being distributed to safety engineers and safety personnel in several construction projects. The questionnaire data were analyzed using Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to develop a causality model for the safety indicators and sub-indicators. The study results obtained five indicators and eighty-six sub-indicators in the safety audit system that significantly influenced the performance of safety implementation in construction projects. Moreover, he causality model obtained Y1 = 0.2518X1 - 0.0308X2 - 0.3523X3 + 0.4188X4 - 0.0693X5 including: X1 leadership and worker participation in construction safety, X2 construction safety planning, X3 construction safety support, X4 construction safety operational, and X5 construction safety performance evaluation. The results of this study are expected to act as a reference, especially for service providers implementing the elements of the audit system.

Behavior of Glass Fiber Reinforced Polymer Concrete Panels

2025-04-04T07:06:36+00:00

Glass Fiber Reinforced Polymer (GFRP) bars have gained popularity as a corrosion-resistant alternative to traditional steel reinforcement in Reinforced Concrete (RC) elements. This study investigates the flexural behavior of PRC panels reinforced with GFRP bars. The study variables included the GFRP reinforcement ratio and the number of embedded steel section distributions. Six concrete panels were fabricated, each measuring 2500 mm in length, with a rectangular cross-section of 750 mm in width and 150 mm in thickness. All panels were reinforced with GFRP bars and divided into two groups based on the reinforcement ratios of 0.532% and 0.266%. For each group, one panel served as the control specimen, while the remaining two were internally strengthened with embedded steel box sections, one with 2 steel sections and the other with 4 sections. The parametric study highlighted the effects of the reinforcement ratio and the inclusion of internal I-section steel shapes on the flexural performance of the panels. Compared to non-strengthened control slabs, the addition of steel elements significantly improved the structural performance, as evidenced by reductions in deflection, strains, and crack widths, as well as an increase in the ultimate load capacity and flexural stiffness at the ultimate loading stage. These findings underscore the effectiveness of combining GFRP reinforcement with embedded steel shapes to enhance the structural performance of PRC panel slabs.

Securing Virtual Machines using Cloning in Cloud Services

2025-04-04T07:05:30+00:00

Cloud Computing (CC) is now a service available to everyone, where all computing resources are made available as a service over the internet, based on the user's needs. Virtualization is a critical component of cloud services that significantly reduces costs and improves resource utilization by creating Virtual Machines (VMs), which are essentially virtual resources that can serve multiple users simultaneously. VMs are subject to security threats and attacks such as malware, and it is very important to create a secure environment for VMs to run seamlessly. In this novel strategy, we create cloned instances for the VMs so that instead of using the VMs directly to run the application, we allow one of the cloned VMs to run it. If something happens to that cloned VM instance, another cloned VM takes over without interrupting the VM's functionality. This provides security for the VM in the cloud environment.

Integrative Deep Learning for Enhanced Acute Lymphoblastic Leukemia Detection: A Comprehensive Study on the ALL-IDB Dataset

2025-04-04T07:01:40+00:00

Acute Lymphoblastic Leukemia (ALL) is a malignant neoplasm defined by the rapid proliferation of early lymphoid progenitors (lymphoblasts) within the bone marrow and peripheral blood. Due to its aggressive course, prompt and accurate diagnosis is essential and has a profound impact on patient outcomes. This study proposes an integrative deep learning method for ALL detection using the Acute Lymphoblastic Leukemia Image Database (ALL-IDB). This is accomplished by fusing one modified clinical data CNN integrated through an attention mechanism with another modified pre-trained CNN for image analysis. The performance of the proposed model was evaluated using the ALL-IDB1 and ALL-IDB2 datasets, achieving 99.2% accuracy with AUC at 0.998%. By incorporating clinical with image data, an overall increase of 2.3% in accuracy and 0.007 in AUC was observed. The results show that using deep learning to detect ALL is accurate and possible, laying the foundations for AI-based diagnoses of hematological cancers to be more accurate.

Α Python-based Evaluation of Kazakhstan's Fields for Carbon Capture, Utilization, and Storage Projects

2025-04-04T07:03:55+00:00

The purpose of this study is to evaluate the feasibility of different oil fields in Kazakhstan for Carbon Capture, Utilization, and Storage (CCUS) projects using advanced algorithms in Python. Using automated methods, the approach greatly simplifies and accelerates the selection process, allowing efficient analysis of large data sets. Taking into account key geological and operational parameters, with particular emphasis on the importance of the Dykstra-Parsons coefficient, the study presents a comprehensive ranking system for evaluating reservoir suitability. This coefficient is critical to accurately assess the fluid displacement efficiency, which significantly influences the selection of candidates for Enhanced Oil Recovery (EOR). The results show that the inclusion of the Dykstra-Parsons coefficient improves the accuracy of field evaluation by accounting for key reservoir heterogeneity factors along with conventional properties. The comparative analysis shows that this approach provides more reliable field selection compared to the existing methods that do not consider this parameter, thereby improving the efficiency of CO₂ storage projects.

Analyzing the Impact of Data Resampling on Stroke Prediction using Machine Learning

2025-04-04T07:01:56+00:00

This study focuses on stroke prediction using machine learning algorithms and evaluates the impact of different resampling techniques, including original, under-sampling, and over-sampling, on classification performance. The classifiers used in this study include Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and K-Nearest Neighbor (KNN). Each model was trained and evaluated using performance metrics such as accuracy, precision, recall, F1-score, and AUC. The results demonstrate that RF trained on the oversampled dataset achieved the best performance with an accuracy of 94.31%, a precision of 93.52%, a recall of 95.27%, an F1-score of 94.39%, and an AUC of 98.46% on the test set. These findings highlight the effectiveness of oversampling in handling imbalanced datasets and the superiority of RF in stroke prediction tasks compared to other classification methods and resampling techniques.

Adaptive Pixel Deviation Absorption Technique for Efficient Video Surveillance using Deep Convolutional Neural Networks

2025-04-04T06:58:13+00:00

Monitoring human activity in industries is a great challenge and numerous methods use various features, such as sketch, position, color, and shape features. However, these methods do not achieve the expected accuracy in classifying the activity of people in the environment. This study presents an Adaptive Pixel Deviation Approximation with Deep Convolutional Neural Networks (APDA-DCNN) model to increase classification accuracy. The method starts with local feature-approximation-based normalization of video frames. Then, global value segmentation is used to group the features of the frame. From the image segmented, the human features along with texture and region pixel deviation features are extracted. The APDA-DCNN model trains the CNN model to convolve the texture features into one-dimensional features by convolving in two layers. The output layer neurons estimate Texture Similarity (TS), Sketch Level Similarity (SLS,) and Pixel Deviation Similarity (PDS) against various classes. Using the values of TS and PDS, the model estimates the Activity Weight (AW) against various classes to select the most dominant. The APDA-DCNN model increases the accuracy of activity classification to achieve higher video surveillance performance.

A Review of Embankment Design on Artificial Islands by Dredge Material to Mitigate Flooding

2025-04-04T07:07:01+00:00

Sedimentation in Lake Tempe, South Sulawesi, Indonesia, has led to significant reductions in the water storage capacity, necessitating dredging efforts and the reuse of Dredged Material (DM) for artificial island construction. This study focuses on designing stable embankments for these islands, which face heightened risks of failure due to the use of low-quality backfill materials and the extreme hydrological conditions. The objective is to determine the required embankment height and assess the effectiveness of geosynthetics and bamboo piles in enhancing slope stability. A comprehensive approach was employed, combining hydrological and geotechnical analyses. The hydrological analysis, based on the 20-year (Q₂₀) and 50-year (Q₅₀) return periods, determined embankment heights of 8.36 meters and 9.2 meters, respectively. The geotechnical analysis using slope stability models revealed that geosynthetic reinforcement significantly outperformed bamboo piles, achieving safety factors well above the critical threshold (1.25) compared to the sub-threshold values for the bamboo piles. These findings underscore the critical role of geosynthetics in mitigating failure risks and enhancing the resilience of embankments constructed with DM.

Unraveling the Impact of Climate Change on Food Security in Malaysia: Insights from Vector Error Correction Modeling

2025-04-04T07:01:13+00:00

This study examines the influence of climate variables on paddy production in Malaysia, focusing on historical data from 1980 to 2016. The employed methodology incorporates Multiple Linear Regression (MLR) to identify the critical predictors, Johansen cointegration tests to explore the long-term relationships, and Vector Error Correction Models (VECMs) alongside Granger causality tests to analyze the dynamic interactions among variables. The performed analysis reveals consistent patterns in mean rainy days and rainfall amounts, indicating a relatively stable climate. In contrast, mean 24-hour temperatures show an upward trend, while mean 24-hour relative humidity exhibits a decline. The findings identify the mean rainfall amount and 24-hour relative humidity as significant predictors of the paddy production. The advanced analytical techniques confirm two long-term cointegrating relationships among the variables. Granger causality tests reveal a bidirectional relationship between the mean rainfall amount and paddy production, suggesting mutual predictability. Conversely, the mean 24-hour relative humidity exhibited a unidirectional relationship, predicting paddy production but not vice versa. These findings underscore the critical role of climate variables, particularly rainfall and humidity, in shaping the paddy cultivation outcomes in Malaysia.

Pretrained Convolutional Neural Network for Fruit Classification Analysis of Pineapple Plantation Images

2025-04-04T07:06:27+00:00

The adoption of precision agriculture in pineapple farming has a significant impact by increasing the yield and reducing the input resources while improving the management of pineapple crops. The intersection of advanced drone technology and cutting-edge artificial intelligence has reformed fruit crop management through revolutionary levels of automation, precision fruit detection, yield estimation, and crop health detection. However, the capability for obscuring the detection of subtle features to better manage occlusions and complex environments in images captured by drones at certain heights with drones is challenging to distinguish, thus hindering an accurate object analysis for fruit-environment differentiation. The proposed work uses Deep Learning (DL) techniques to classify pineapple fruit images captured ten meters above the ground. This is achieved specifically through the use of pretrained models and Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) due to their ability to learn robust interpretations from images for object classification tasks. This paper evaluates the capabilities and accuracies of four pretrained models, namely ResNet-101, ResNet-50, Inception-ResNet-v2, and VGG-19, to detect and classify the pineapple fruit amidst the complex background and varying lighting conditions. By evaluating the pretrained models for pineapple fruit classification using comprehensive metrics (True Positive Rate (TPR), False Positive Rate (FPR), Accuracy (ACC), Recall (REC), Precision (PRE), F1-score), the results reveal that the Faster R-CNN architecture with the VGG-19 pretrained model outperformed the other architectures, demonstrating the best performance in pineapple fruit detection with an ACC of 0.7924 (79.24%), a PRE of 0.9990 (99.90%), a REC of 0.7930 (79.30%), and an F1-score of 0.8839 (88.39%). The effectiveness of this model in overseeing complex scenarios suggests potential improvements in classification accuracy compared to other pretrained models, while acknowledging performance variability across various architectures.

Streamlined Traffic Prognosis using Flexible Reservoir Sampling and Regression Methods

2025-04-04T06:58:55+00:00

The increased use of Internet of Things (IoT) technologies has resulted in an exponential increase in real-time data streams, particularly in smart city applications, especially for traffic management. Accurate prediction of traffic parameters in such environments is critical for optimizing traffic flow, reducing congestion, and enabling efficient resource management. This study presents an approach to the prediction of traffic intensity and occupancy using IoT streaming data, time series analysis, and machine learning algorithms. The proposed method includes preprocessing steps such as data interpolation to handle missing values and temporal alignment, followed by feature extraction and model training using a combination of regression and sampling techniques. Experiments were carried out on a real-world IoT traffic dataset, and the results show significant improvements in the prediction accuracy in terms of MAPE values. It also predicts the complex event of congestion, using a rule-based algorithm. The proposed method can pave the way for smarter and more efficient urban infrastructure.

Gated Cross-Modal Fusion Mechanism for Audio-Video-based Emotion Recognition

2025-04-04T07:05:17+00:00

Due to its potential uses in security, surveillance, mental health monitoring, and human-computer interaction, artificial emotion recognition employing video and audio modalities has attracted a lot of attention. This study focuses on optimal cross-modal fusion techniques to enhance the precision and robustness of multimodal audio-video-based emotion recognition. Specifically, this study introduces a gated cross-modal fusion mechanism in audio-video-based emotion recognition, known as Compact Bilinear Gated Pooling (CBGP). The novelty of this work is that CBGP fusion is being applied to the emotion recognition task for the first time to integrate the extracted features and reduce the dimensionality of the audio and video modalities using 1DCNN and 3DCNN deep neural architectures, respectively. This novel approach was tested and verified on three benchmark datasets: CMU-MOSEI, RAVDESS, and IEMOCAP, each containing multimodal data representing a range of emotions, including happy, sad, fear, anger, neutral, and disgust. Experimental results show that CBGP consistently outperformed state-of-the-art fusion techniques, such as early fusion, late fusion, hybrid fusion, and others. CBGP extracts the relevant features, leading to higher accuracy and F1 scores due to its dynamic gating mechanism that selectively emphasizes relevant feature interactions. This study suggests that the integration of gating mechanisms within fusion processes is vital to improve emotion recognition. Future work will focus on extending these findings to real-time applications, exploring multitask learning frameworks, and enhancing the interpretability of multimodal emotion recognition systems.

Load-Deflection Relationship of White Hollow Core reinforced Concrete Panels under Static Load

2025-04-04T07:02:19+00:00

The main objective of this research is to examine the load-deflection behavior of White Concrete-Hollow Core Slab (WC-HCS) panels, which were made using white cement, crushed Limestone (LS) as sand, and coarse LS aggregate. The panels were subjected to symmetrical two-point static loads and the results were compared with Normal Concrete-Hollow Core Slab (NC-HCS) panels, which were made utilizing cement, sand, and gravel. The flexural response of steel-reinforced Hollow Core Slab (HCS) was also evaluated. The experimental study involved casting 12 HCS specimens. Each slab had dimensions of 1000 mm length and 450 mm width, with varying thicknesses of 80 mm, 100 mm, and 120 mm. All slabs featured a constant hollow diameter of 32 mm. The slabs were divided into four groups based on three variables: slab thickness, steel reinforcement ratio, and concrete type, namely White Concrete (WC) or Normal Concrete (NC). Concrete mechanical properties, including compressive strength, splitting tensile strength, modulus of rupture, and modulus of elasticity, were studied. Non-destructive testing was performed using Ultrasonic Pulse Velocity (UPV) to assess the concrete quality. The results showed that, for the slabs with varying thickness but the same reinforcement ratio, the deflection decreased by 16.8%, 63.77%, 63.44%, 3.18%, and 57.7%. For NC-HCS slabs, deflection reductions of 23.4% and 47.9% were observed. When varying the reinforcement ratio while maintaining the same slab thickness, the deflection in WC-HCS slabs decreased by 53.5% at 80 mm thickness and by 4.6% at 100 mm thickness, whereas it increased by 18.8% at the same thickness. At 120 mm thickness, the deflection increased by 17.3% and then decreased by 38.3%. The study also explored the effect of concrete type. For slabs with 80 mm thickness, the deflection increased by 48% in WC-HCS compared to NC-HCS. At 100 mm thickness, the deflection decreased by 29.7%, while at 120 mm, it increased by 5.8%.

Predicting Stability and Flow Properties of Hot Mix Asphalt using Field and Laboratory Tests in Wasit Governorate

2025-04-06T07:15:25+00:00

The Mechanical Marshall test and statistical analysis have been used to examine pavement site measurements extracted from 43 cores of the Kute-Badra asphalt road section. The study included a theoretical and a practical aspect, and was based on the practical aspect of the Marshall Test. A carrying device was used to perform laboratory tests on hot asphalt samples, and the results were compared with the indices of Iraqi specifications for the largest value for stability and flow. It was found that the hot asphalt mixture matches the Iraqi and international.

A Comparative Study between Single-loop and Dual-loop Tracking Control Schemes for MPPT of Solar Tracking System

2025-04-04T07:05:06+00:00

Solar energy is one of the principal renewable energy sources for electric power generation. However, maximizing the power extraction from solar Photo-Voltaic (PV) systems remains a challenge due to their inherent low conversion efficiency. To address this issue, a Maximum Power Point Tracking (MPPT) controller is necessary to optimize power extraction in a PV system. This paper aims to conduct a comparative study between two distinct MPPT control schemes. The first is a simple single-loop system employing the Perturb and Observe (P&O) algorithm. The second is an advanced dual-loop system integrating the P&O algorithm with a Proportional-Integral (PI) controller. The comparison evaluates the systems' performance in terms of steady-state accuracy. For this purpose, a MATLAB/Simulink model of a stand-alone PV panel was developed. The proposed MPPT schemes were then implemented on this PV system under varying environmental conditions to assess their ability to track the MPPT. The simulation results indicate that the dual-loop control scheme outperforms the single-loop scheme in terms of steady-state performance, particularly during abrupt environmental changes.

Modal Dynamic Response of a Darreius Wind Turbine Rotor with NACA0018 Blade Profile

2025-04-04T07:02:43+00:00

The global wind energy industry achieved a significant milestone by reaching a total capacity of one terawatt (TW) by the end of 2023, underscoring the increasing importance of wind energy as a sustainable energy source (Global Wind Energy Outlook, 2022). This study focuses on the simulation and dynamic analysis of an H-Darrieus wind turbine rotor using 3D Finite Element Analysis (FEA). Key structural parameters, including natural frequencies, associated vibration modes, and mass participation rates, were determined to optimize the rotor performance. A novel blade design is proposed in this work, offering a lighter and more robust alternative to traditional rotor blades manufactured from composites, like fiberglass-polyester, fiberglass-epoxy, or combinations with wood and carbon. The lighter design enhances the startup performance at low wind speeds, while the improved strength and fixing mechanisms ensure resilience against the increasingly severe sandstorms reported in recent years. The vibration dynamics of the rotor under critical wind loads were analyzed using the SolidWorks Simulation software, yielding highly satisfactory results. The stability and reliability of the rotor were validated, as the dynamic performance indices, and the quality criteria meet the requirements for optimal operation.

Advanced Object Tracking in Video Surveillance Systems with Adaptive Deep SORT Enhancement

2025-04-04T07:04:37+00:00

Object tracking is a crucial feature of video surveillance systems that are essential for maintaining awareness and detecting potential threats. Advanced solutions are needed to overcome the obstacles associated with video object tracking, including the complexity of everyday environments and the massive amount of data. Traditional tracking algorithms often struggle with the complexity of dynamic situations, necessitating the use of deep learning methods. This paper presents an innovative deep learning-based object tracking system that uses Multi-Level Glow-Worm Swarm Convolution Neural Networks (MLGS-CNNs) to detect objects in video frames. Subsequent object tracking is facilitated by the adaptive Deep Simple Online Real-time Tracking (DeepSORT) algorithm by incorporating an optimized Kalman filter instead of a conventional Kalman filter. The Waterwheel Plant Optimization (WPO) method is used to tune the noise covariances of the Kalman filter to further improve the tracking accuracy. Comprehensive performance criteria, including metrics such as Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), Integrated Detection and False-alarm Rate (IDF1), Mostly Tracked (MT), and Mostly Lost (ML), are used to evaluate the effectiveness of our method.

Hysteresis Compensation Application in Magnetostrictive Inkjet Print-head

2025-04-04T07:00:07+00:00

This study presents the experimental hysteresis compensation method implemented in a magnetostrictive inkjet print-head, utilizing the Terfenol-D, a giant magnetostrictive material. The distinctive features between the input energy and the output displacement are known as the inherent hysteresis characteristics in a ferromagnetic material, which cause major obstacles to the output performance. Therefore, an appropriate compensation method is necessary to reduce the Hysteresis Loss (HL). Previous research has focused on mathematical models such as the Preisach or the Jile – Atherton models. However, such models are complicated, and it is thus challenging for them to control hysteresis in a real-time system. This paper solves the aforementioned problem based on the charging and discharging of an RC-circuit, which is known as the experimental compensation method. In the experiment, an attempt to compensate for hysteresis at the frequencies of 5 Hz and 100 Hz is made. For each frequency, different ranges of the capacitance value are selected to find the resistance value. A resistor with the value of 50 Ω is chosen and integrated into the compensation circuit. Through the experiment, optimal capacitance values of 335 μF and 10.75 μF are obtained at the considered frequencies. The results are attained using PEDOT: PSS and silver nanoparticle ink to validate the droplet formation. In both cases, the droplet formation is estimated and calculated in terms of the droplet diameter, tail length, droplet volume, and breaking time.

The Effect of Due Professional Care, Norms, Ethics, and Attitude on Audit Quality

2025-04-04T06:59:58+00:00

The current study extends the Theory of Planned Behavior (TPB) to examine the factors influencing high-quality financial reports in Iraqi organizations. The employed variables include Subjective Norms (SN), Audit Quality (AQ), Attitude (ATT), Auditor Ethics (AE), and Due Professional Care (DPC). Data were collected from 109 professionals using an online-based survey and were analyzed deploying the Partial Least Square-Structural Equation Modeling (PLS-SEM) approach. The quantitative findings exhibit that AE, DPC, and ATT can predict high-quality audit reports. In contrast, no statistically significant correlation was observed between SN and AQ. It was concluded that auditors must perform their tasks with expertise, competence, and attention to detail to achieve outstanding AQ. Furthermore, firms and regulatory bodies must prioritize the strengthening of these aspects to improve audit performance and stakeholders’ trust.

Copper Recovery from Scrap Electrical Cables based on an Environmentally Sustainable Gravity Separation Technique

2025-04-04T07:00:51+00:00

The aim of this study is to recover copper from scrap cable wires through a sustainable gravity separation technique. Initially, the scrap wires were shredded in such a way, that the plastic on them was completely removed. The shredded mixture of the copper and plastic pieces was poured on a shaking table, where separation of copper and plastic was caused. The copper was collected in the concentrate zone and the plastic pieces were collected in the tailings zone. The study also examines the influence of the shaking table inclination at 1º, 3º, and 5º, and that of the wash water flow rate, ranging from 9 to 34.70 ml/sec, on the stratification of the copper plastic mixture. It was found that increasing both the shaking table inclination and the wash water flow rate improved copper recovery. However, at the maximum angle of 5º and wash water flow rate of 34.70 ml/sec, the recovered copper grade decreased due to the plastic pieces’ contamination. The most favorable results were obtained at the shaking table inclination of 3º and wash water flow rate of 20.50 ml/sec, which resulted in a copper concentrate with a copper recovery of 93.5% and a copper grade of 98%. Additionally, it was found that wash water can be recycled, which, in a commercial setting, could save up to 590 L of fresh water per 8 hours.

Exploratory Data Analysis and Water Potability Classification using Supervised Machine Learning Algorithms

2025-04-04T07:06:53+00:00

This study investigates the critical task of assessing water potability using supervised machine-learning techniques. The problem statement involves accurately predicting water potability based on chemical and physical parameters, which are crucial for public health and environmental sustainability. Exploratory Data Analysis (EDA) highlighted significant insights into feature distributions and correlations, guiding preprocessing steps and model selection. The Synthetic Minority Oversampling Technique (SMOTE) was applied to mitigate class imbalance, ensuring robust model training. Three classification algorithms, namely Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF), were evaluated, with RF exhibiting superior performance after Optuna hyperparameter tuning, achieving an accuracy of 68%. Based on the performance of RF and KNN, a weighted voting-based ensemble technique achieved an accuracy of 71%. This study emphasizes the importance of leveraging machine learning to support water quality assessment, offering reliable tools for decision-making in public health and environmental management.

An Innovative IoT Framework using Machine Learning for Predicting Information Loss at the Data Link Layer in Smart Networks

2025-04-04T07:04:04+00:00

In smart networks, data are becoming increasingly complex, and enhancement methods are required to ensure data integrity and reliability. This paper proposes a novel IoT framework using machine learning for the prediction and mitigation of information loss at the data link layer, where conventional methods have many limitations. These methods cannot handle dynamic networking conditions and complex data traffic on any network, yielding smaller accuracy with a high false positive ratio. This work proposes a Machine Learning-based Information Loss Prediction Framework (ML-ILPF) using machine learning algorithms such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks to overcome these challenges. These models analyze the historical data of the network to identify anomalies and predict possible loss. Compared to traditional methods, the proposed ML-ILPF outperformed both Static Threshold-Based Methods (STBM) and Basic Statistical Models (BSM) with an increase of 0.25% in accuracy and a reduction of 0.30% in false positives. This improvement shows real strength in the inclusion of machine learning in IoT frameworks toward smarter and more reliable network management. ML-ILPF is a promising solution that can help predict information loss at DLLS and improve the reliability and efficiency of smart networks, opening the window for more resilient IoT applications.

A Heuristic Approach for Solving Robotic Assembly Line Balancing Problems

2025-04-04T06:59:51+00:00

This study, proposes a heuristic algorithm to balance Robotic Assembly Lines (RAL). A flexible line is assumed in which robots can be allocated to any station, perform any task, and have fixed setup costs. To consider both robot allocation costs and limiting the number of stations, the current work aims to minimize the system cost, which includes new station and robot allocation costs. It evaluates the performance of the algorithm with a large set of randomly generated samples and conducts statistical analyses to summarize, compare, and draw conclusions. The experimental results demonstrate the efficacy of the proposed algorithm in addressing large-scale problems in a reasonable timeframe.

A Model of the Degrading Solute Transport in Porous Media based on the Multi-Stage Kinetic Equation

2025-04-04T07:06:45+00:00

A mathematical model of solute transport in porous media with two adsorption zones was developed, incorporating balance and kinetic equations, and initial and boundary conditions. The model was enhanced to account for multistage deposition kinetics in both adsorption zones, and numerical methods were employed to solve the problem. An algorithm using the finite difference method was presented as a solution. Computer experiments were conducted to ascertain the effect of different parameters of the model on solute transport, and the results of the study were analyzed. The primary focus of this study is to assess the impact of parameters within the kinetic equations on the transport process. These parameters are crucial in determining the intensity of adsorption during various stages of the process. The findings of this study demonstrate that multistage deposition kinetics significantly influences both the transport and adsorption processes. Furthermore, the presence of two distinct intensity areas in the concentration profile of the adsorbed substance is observed. This phenomenon is attributed to the effects of multistage kinetics.

Enhancing Early Detection of Skin Cancer in Clinical Practice with Hybrid Deep Learning Models

2025-04-04T07:01:36+00:00

Skin cancer is a significant global health issue where early detection is essential to improve outcomes. This study evaluates hybrid deep learning models that combine CNN architectures (MobileNetV2, ResNet-18, EfficientNet-B0, and others) with metadata (age, lesion localization) for classification using the SLICE-3D subset of the ISIC 2024 dataset. MobileNetV2 achieved a recall of 99.2% and an accuracy of 97.7%, while EfficientNet-B0 demonstrated a recall of 98.5% and an accuracy of 97.2%, making them ideal for telemedicine in resource-limited settings due to their low computational demands. ResNet-18 and DenseNet-121, with recalls of 99.0% and 98.7%, respectively, excelled in clinical applications but required greater computational resources. These hybrid models show great potential as accessible and accurate tools for improving skin cancer detection. Future work should validate these findings on diverse datasets and optimize preprocessing to further enhance sensitivity and early diagnostic accuracy.

A Hydraulic Performance Model of Khassa Chai River under Varying Flow Conditions

2025-04-04T07:03:05+00:00

The increasing deterioration of rivers highlights the need for effective restoration mechanisms and flow management strategies to sustain the local ecosystems. This study evaluates the hydraulic performance of rivers under varying flow conditions using the Hydraulic Engineering Center-River Analysis System (HEC-RAS) model, with the Khassa Chai River as a case study. A Differential Global Positioning System (DGPS) survey was conducted along a 14 km distance, capturing 73 cross-sections of the river. The model results indicate that several reaches are at risk of flooding, revealing the need for river restoration to ensure that the cross-sections can accommodate the design discharge (1200 m³/s). These findings emphasize the importance of sediment removal and channel maintenance to enhance the river’s hydraulic capacity.

Evaluation of the Structural Performance Behavior of Hotel Building Y in Palu City, Central Sulawesi, Indonesia using the Pushover Analysis Method after the 2018 Earthquake Event (SNI Approach for Earthquake-Resistant Buildings)

2025-04-04T07:02:11+00:00

The purpose of this research is to assess the structural performance level resulting from the assessment through ETABS software based on FEMA 440 regulations on the planning of the Y Hotel building in Palu City, Central Sulawesi, Indonesia after being analyzed by the pushover analysis method after the 2018 earthquake. The study subject is the upper structure of the Hotel Y building, namely the columns, beams, plates, and roofs. Data analysis techniques were carried out according to the rules of load simplification. Three-dimensional modeling of the building was conducted using the ETABS softwar before the analysis and pushover stages for performance evaluation. In accordance with the design of the Hotel Y building, the total height is 56 m with 15 stories, using reinforced concrete structures to provide optimal stability and strength. The basic Y-shape was chosen to maximize space distribution and increase visual appeal. The material qualities used were Fc' 35 Mpa and 30 Mpa, Fy' = 400 Mpa, Ec = 4700√(fc'), BJTP 24, and BJTD 40. The analysis of loading calculations was conducted in accordance with SNI 1727: 2020 and SNI 1726: 2019 standards, including dead load, live load, fixed additional load, rain load, roof live load, wind load, and earthquake load. The results of this study obtained an effective base shear force greater than the plan shear force, namely V

Efficient COVID-19 Detection using Optimized MobileNetV3-Small with SRGAN for Web Application

2025-04-04T06:57:44+00:00

Rapid and accurate detection of COVID-19 from medical images, such as X-rays and CT scans, is critical for timely diagnosis and treatment. This paper presents an innovative approach that combines Super-Resolution Generative Adversarial Network (SRGAN) for image enhancement with an optimized MobileNetV3-Small model to achieve efficient and high-accuracy classification. The proposed method significantly reduces computational complexity while maintaining performance. Specifically, the optimized MobileNetV3-Small model achieves 99.5% accuracy for X-ray images and 99.8% accuracy for CT images with only ~0.8M parameters and ~2.5 MB memory usage, making it highly suitable for real-time web applications in resource-constrained environments. Comparative analysis with related works demonstrates that the proposed approach outperforms other models in terms of accuracy, efficiency, and lightweight design. The results highlight the potential of the proposed method as a practical solution for rapid COVID-19 detection, contributing to the development of accessible and scalable diagnostic tools.

Effect of Openings on the Performance of Continuous RC One-Way Slabs

2025-04-04T07:01:44+00:00

Nowadays, slab openings have become necessary for service purposes in most new and old constructions, therefore, their effect on the slab strength should be extensively investigated. This paper explores the effect of such openings on the performance of a one-way continuous Reinforced Concrete (RC) slab. Fourteen slabs with different opening categories were cast. The main parameters studied in this research were the opening size, opening location, and slab thickness. The study focused on two span slabs, including openings with and without the proposed diagonal reinforcement at the corners. All slabs had identical spans and widths whereas two thicknesses were used. Openings of varying sizes were introduced at different locations within the spans. The results indicate a significant reduction in load capacity and ductility for the slabs with openings. The largest reduction occurred when the largest opening was placed near the mid-support, resulting in a 21.3% decrease in load capacity. Conversely, the smallest reduction was observed in the slab with the same large opening but with additional reinforcement, resulting in only a 1.74% decrease in load capacity.

Temperature Field Determination during Bridge Pier Construction

2025-04-04T07:06:14+00:00

Thermal deformation resulting from the hydration of cement in the concrete mix is a primary cause of thermal cracking in structures, particularly in mass concrete elements, and more specifically in the abutments and piers of bridges. These cracks are most likely to occur at an early age, when the temperature rise within the concrete is most pronounced. The formation of thermal cracks is especially problematic in the early stages of construction, as the temperature differential between the interior and surface of the structure can lead to significant stresses. Therefore, understanding the temperature and stress fields during the construction of bridge piers is crucial for identifying effective strategies to mitigate thermal cracking. This paper investigates the underlying causes of thermal crack formation in bridge piers and applies the Finite Element Method (FEM) to analyze the temperature field during the construction process under typical Vietnamese conditions. The findings of this study aim to provide practical solutions for preventing thermal cracking in bridge pier structures during the construction phase, ensuring the durability and integrity of the bridge over time.

Investigation of the Compressive Strength of Fly Ash–based Geopolymer Concrete cured by Oven and Microwave Radiation

2025-04-04T06:52:28+00:00

This study investigates the influence of three different curing methods -oven, microwave, and hybrid microwave–oven- on the compressive strength of Fly Ash (FA)-based geopolymer concrete. Five mixtures with an Alkaline Liquid (AL) to FA ratio varying from 0.6 to 1.0, combined with different curing conditions, were tested to evaluate the highest compressive strength values. The results revealed that the maximum compressive strength was observed at 30.2 MPa for oven-curing at 80 ℃ for 16 hours, 13.7 MPa for microwave curing at 400 W for 10 minutes, and 33.1 MPa for hybrid curing (microwave at 400 W for 10 minutes) followed by oven at 80 ℃ for 8 hours. These findings indicate that the hybrid curing method is an optimal solution, developing higher compressive strength in a shorter time compared to traditional curing methods.

Experimental Study of the Effect of Wire Fiber Addition on the Mechanical Strength of Preplaced Aggregate Concrete

2025-04-04T06:56:50+00:00

Preplaced Aggregate Concrete (PAC) is a specialized type of concrete, with low tensile strength, making it prone to brittle failure, used in the construction industry. One effective way to enhance durability and minimize cracking is by incorporating fibers into the mix. This study explores the use of wire fibers, produced by cutting wire ropes into pieces approximately 1 mm in diameter and 60 mm in length. Five different concrete mixtures were tested to evaluate the mechanical strength of PAC and Preplaced Aggregate Wire Fiber Concrete (PAWFC). The wire fiber content varied at 0%, 0.25%, 0.5%, 0.75%, and 1% of the total concrete volume, while the proportions of cement, fine and coarse aggregates, water, and Super Plasticizer (SP) remained constant. A mortar mix with a cement-to-sand ratio of 1 and a water-to-cement ratio of 0.45 was used. The mechanical properties, including compressive strength, tensile strength, and modulus of rupture were assessed following the ASTM standards. The results showed that adding wire fiber significantly improved the mechanical strength of PAWFC. Specifically, the optimum 1% wire fiber addition increased compressive strength by 11.23%, tensile strength by 36.85%, and modulus of rupture by 17.23%.

Towards the Development of Gaussian Clustering Algorithm Technology to Extend the Lifetime of MANETs

2025-04-04T07:05:47+00:00

Mobile Ad hoc Networks (MANETs) are infrastructure-independent wireless networks where nodes communicate directly or through relays without a central base station. Routing protocols employed in MANETs face numerous challenges due to their limited resources. Cross-layer optimization is fundamental to conserving energy and achieving quality of service parameters. However, reducing end-to-end diversity conflicts with power consumption, creating a problem when trying to improve network lifetime. In this work, a Lifetime Enhancement Routing (LER) protocol, which selects the most efficient path to the destination using residual energy and cost exchange metrics, is proposed. LER primarily reduces node overutilization and load to prolong the network lifetime. The proposed MANET performance optimization technique is Gaussian clustering algorithm with one of the deep learning (RNN) techniques as a combined technique. The simulation results show that the proposed protocol significantly reduced energy consumption and augmented the ability to send data through the best path available in the network with a high efficiency of up to 92%.

Assessment of Route Choice Models for Dynamic Traffic Assignment using Microscopic Simulation

2025-04-04T06:59:35+00:00

Travel forecasting models predict changes in the travel patterns and propose improvements. This study evaluates the parameters in route choice models, such as binomial, proportional, multinomial logit, and C-logit in microscopic simulation-based dynamic traffic assignment. The average Geoffrey E. Havers (GEH) index of each route choice model was tabulated when comparing the simulated flow of the junctions with the observed flow. The results indicated that the binomial model generally yields the lowest GEH value, but since the model does not consider the travel costs in the decision process, it is not suitable for traffic impact studies. As for the proportional and multinomial logit models, the K-Shortest Path (K-SP) value has greater impact on the assignment results. With a K-SP value of 1, the proportional and multinomial logit models generated the lowest GEH index when the alpha factor was set to 3.0 and the scale factor was set to 25, respectively. Lastly, for the C-logit model, the assignment results are more sensitive to the calibration of the scale factor and beta values compared to the K-SP and gamma factor. A lower GEH index is always observed for the scale factor of 25 and the combination with a beta value of either 0.1 or 0.15, regardless of the values of gamma and the initial K-SP. When comparing the calibrated models with the original model, the C-logit model showed higher deviations, whereas the logit and proportional models showed no significant differences. These findings highlight the importance of parameter calibration, apart from providing significant insights into route choice modeling, especially in replicating the real route choice behavior of motorists in Malaysia.

Design of Telescopic Soft Gripper for Mangosteen Harvesting

2025-04-04T06:57:23+00:00

This research presents a telescopic soft gripper designed to assist farmers in the harvesting of mangosteens. Subsequent to the outbreak of a pandemic, like the novel coronavirus (SARS-CoV-2), producers are encountering a workforce shortage during the harvesting of mangosteen. The labor crisis is currently becoming more acute, but the competition for mangosteen exports is increasing, requiring the expansion of mangosteen agricultural areas, which will ultimately give rise to a labor shortage. The gripper is fabricated from hyperelastic material, a material that offers flexibility and softness, making it ideal for delicate objects. Conventional soft grippers lack the telescopic design, which enables extension and retraction, allowing for the handling of various object sizes. The gripper's design optimization involved the evaluation of nine models with different internal inclinations and material thicknesses. Finite Element Analysis (FEA) was employed to simulate the deformation and stress responses. The optimized model, in comparison to nine other models, possesses a thickness of 2.25 mm and an internal inclination of 10 degrees, facilitating high deformation with acceptable stress. It has been demonstrated that the gripper can deform up to 132 mm, a finding that has been validated through experimentation. The experimental validation was conducted to corroborate these findings, demonstrating the gripper's capacity to securely grasp objects with diameters ranging from 50 mm to 70 mm and weights up to 22 N. Furthermore, the gripper's efficacy was assessed in a mangosteen harvesting scenario, where it demonstrated a capability to successfully harvest the fruit within a span of two seconds. The gripper's design is characterized by its compactness, low production cost, and ease of use, rendering it highly practical for agricultural applications in confined spaces. The telescopic soft gripper under consideration offers a versatile and scalable solution for harvesting a wide range of crops of varying sizes, positioning it as a valuable tool for future agricultural automation.

Crushed Stone Utilization in replacing Silica Sand in Ultra-High Performance Concrete

2025-04-04T06:58:05+00:00

Ultra-High Performance Concrete (UHPC) offers superior load-bearing capacity and durability, yet its reliance on natural Silica Sand (SS) contributes to high production costs and environmental concerns. This study examines the feasibility of substituting SS with Crushed Stone (CS) aggregates in UHPC production. Through a combination of theoretical analysis and experimental investigation, an optimal mixture is identified, and the effects of CS aggregates on key UHPC properties, including flowability, air bubble content, and compressive strength, are evaluated. The experimental results indicate that UHPC incorporating CS aggregates achieves compressive strengths exceeding 130 MPa at 28 days. The Scanning Electron Microscopy (SEM) analysis reveals that the Interfacial Transition Zone (ITZ) surrounding CS aggregates exhibits lower local stiffness due to the predominance of calcium hydroxide (CH) and ettringite crystals. Furthermore, the microstructural analysis identifies the presence of elongated particles (accounting for up to 32% of the mixture) and microcracks within the CS aggregates, which contribute to a reduction in compressive strength. Consequently, UHPC produced with CS aggregates achieves approximately 84% of the compressive strength of UHPC utilizing SS aggregates. Despite this reduction in mechanical performance, the cost-effectiveness of CS-based UHPC is significantly superior, with a 29% reduction in the overall production costs and a 16% improvement in the cost-to-performance ratio compared to SS-based UHPC. These findings demonstrate that CS aggregates provide a viable and economically advantageous alternative to SS in UHPC production, offering significant cost savings while maintaining the essential mechanical properties required for structural applications.

An Integrated Remote Sensing and GIS Road Condition Assessment Framework

2025-04-04T07:00:44+00:00

Road infrastructure is essential for supporting socio-economic activities but faces deterioration due to high traffic volumes, unpredictable weather, poor drainage, and inadequate maintenance. Traditional visual assessment methods are often time-consuming and subjective. In contrast, Geographic Information Systems (GIS) provide a more precise and efficient approach to road condition assessment, including drainage analysis. This study integrates Remote Sensing and GIS to develop an innovative virtual road condition assessment framework that combines pavement distress evaluation with drainage analysis. The research was conducted on selected roads within Jomo Kenyatta University of Agriculture and Technology (JKUAT). Using high-resolution drone imagery, field surveys, and GIS-based analysis, road conditions were assessed through pavement distress mapping, flow accumulation, curvature analysis, and road attribute evaluation. The results revealed that Innovation Street exhibited the most severe distresses, while Technology Street had predominantly minor to moderate deterioration. Commonly identified distresses included rutting, potholes, longitudinal and transverse cracking, weathering, and alligator cracking. The Quantum Pavement Condition Index (QPCI) effectively identified distress hotspots requiring urgent maintenance, demonstrating the framework’s potential to enhance road maintenance planning and decision-making. This study highlights the value of integrating GIS and remote sensing for efficient, data-driven infrastructure management, offering a scalable and resource-efficient approach for improving road maintenance strategies.

A Comprehensive Study of Deep Learning Models for Intrusion Detection in IoT Devices

2025-04-04T07:04:54+00:00

The Internet of Things (IoT) has revolutionized how people interact with the world, but the increasing complexity of cyberattacks poses significant challenges in detecting intrusions. Failure to prevent intrusions can compromise IoT security services, including data confidentiality, integrity, and availability. For this reason, this study employs four deep learning models: A Deep Neural Networks (DNN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long-Short-Term Memory (LSTM) network. The multiclassification performance of each model was evaluated using the Bot-IoT dataset. This study also addresses the bias towards the DDoS/DoS category in the Bot-IoT dataset, using the SMOTE technique to mitigate overfitting. The LSTM model achieved an excellent balance between performance and efficiency, outperforming state-of-the-art deep learning Intrusion Detection System (IDS) approaches on the same dataset, achieving a multiclass classification accuracy of 99.97%.

Utilizing Recycled Polyethylene Terephthalate Waste in Geopolymer Concrete Applications

2025-04-04T07:01:25+00:00

Concrete has experienced a marked increase in usage for road construction over the past decade, largely due to its durability. This study proposes an innovative method for producing eco-friendly and sustainable cement mortar, using municipal waste and industrial by-products. The study investigates the use of Polyethylene Terephthalate (PET) plastic waste as a fiber to enhance the mechanical characteristics of geopolymer concrete, which is based on Fly Ash (FA) and Rice Husk Ash (RHA). The investigation focused on the mechanical characteristics of geopolymer concrete, including its flexural and compressive strengths. The study incorporated four distinct geopolymer concrete mixtures containing PET plastic waste into the fly ash and rice husk ash-based geopolymer concrete: 0% PET plastic waste (SN), 0.25% PET plastic waste (SA), 0.50% PET plastic waste (SB), and 0.75% PET plastic waste. Using a 100 mm x 100 mm x 400 mm block for flexural strength testing and a 10 cm x 20 cm cylinder for compressive strength testing, the tests were conducted seven- and twenty-eight-days following air curing. The flexural test results indicated a decline in average flexural strength value with every 0.25% PET addition, reaching a 6.48% decrease. Compression testing revealed a negative correlation between the addition of PET and the compressive strength of the material. Specifically, an increase of 0.25% to 0.5% in the PET content resulted in an average reduction of 24.22% in compressive strength. Conversely, the compressive strength exhibited an increase of 10.91% between the 0.75% and 0.5% range of PET.

A Multi-Language NLP Model for Inclusive Digital Healthcare Marketing and Patient Communication

2025-04-04T07:05:01+00:00

Digital healthcare systems integrate Natural Language Processing (NLP) to make advances in the ways patients engage and communicate. However, multilingual access to a wide variety of languages has been an ongoing problem. This study introduces a multilingual NLP model for digital healthcare marketing and patient communication, designed to help patients obtain health information across languages. This work addresses essential multilingual issues in the healthcare context, such as providing a language-adaptive function using state-of-the-art semantic processing. The model introduces linguistic diversity for personalized healthcare marketing to help develop more personal relationships with patients. The model was evaluated across languages to determine whether it provides practical benefits in enabling clear and culturally attuned communication. This model has the potential to help create a linguistically inclusive healthcare environment, helping patients understand their health conditions and treatment options, and increasing overall patient satisfaction.

DC Conduction and Dielectric Behavior of (Bi,Pb)-2223 Superconductor when adding Ag Nanoparticles

2025-04-04T07:00:47+00:00

In this paper, the dielectric behavior of Bi_1.7Pb_0.3Sr₂Ca₂Cu₃O_10+δ ceramic superconductor and the effects of Ag nanoparticle addition were studied at room temperature. The X-ray diffraction pattern reveals that all samples crystallize in orthorhombic structures. The optimal value of critical transition temperature (Tc) was found at addition weight percentage x = 1.2 wt%. The influence Ag nanoparticle adding for changing the Bi-2223 phase's dielectric characteristics were studied along with the determination of the capacitance for ranging frequencies from 50 Hz to 1 MHz. The results show that the dielectric constant decreases with increasing Ag nanoparticle content, indicating the enhancement of the carrier concentration in the CuO₂ planes. The presence of Cu atoms in the CuO₂ and the Bi₂Sr₂O_5-_δ charge reservoir layers in (Bi,Pb)-2223 superconductors gives them partially insulating/conducting characteristics, probably improving the doping efficiency of the mobile charge carriers.

Real Time Electrical Load Prediction and Management through Deep Learning and Reinforcement Learning Techniques

2025-04-04T07:04:25+00:00

Real-time electrical load prediction and management are critical to ensuring the stability and reliability of modern power systems, especially as global energy demand continues to grow. This research presents a groundbreaking solution by combining a hybrid deep learning approach with reinforcement learning to address the challenges of accurate forecasting and adaptive energy distribution. The proposed framework integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, leveraging their strengths to capture both spatial and temporal patterns in electrical load data. This hybrid model delivers highly accurate load forecasts and effectively handles complex and nonlinear consumption patterns that traditional methods fail to address. In addition to accurate forecasting, the research employs the Soft Actor-Critic (SAC) reinforcement learning algorithm, which enables adaptive decision-making for real-time load management. By dynamically adapting to fluctuating grid conditions, the SAC algorithm optimizes energy distribution, reduces peak demand stress, and enhances overall system efficiency. This integrated approach ensures that energy resources are allocated more effectively, improving grid stability and minimizing waste. The methodology is validated through rigorous experimentation using real-world datasets, such as the PJM dataset, and performance metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and overall system efficiency. This research not only advances predictive analytics in electrical load management, but also provides utilities and consumers with a scalable and practical solution to optimize energy consumption, integrate renewable energy sources, and promote sustainability. The proposed hybrid deep learning and reinforcement learning framework serves as a vital tool for future energy systems, paving the way for smarter, more resilient power grids.

Performance of Expansive Soil blended with Waste Marble Dust and Natural Pozzolana for Road Subgrade

2025-04-04T06:58:47+00:00

This research examined the effects of Natural Pozzolana (NP) on expansive soil blended with Waste Marble Dust (WMD), focusing on improving its engineering properties. The NP was sourced from Kanzenze, Rubavu, Rwanda, oven-dried, ground into powder, and sieved to 0.452 mm. WMD was added to the soil in 5% increments (5%-30%), with the optimal dosage found at 25%. The California Bearing Ratio (CBR) and Unconfined Compressive Strength (UCS) tests showed that untreated soil had a CBR of 1.1%, UCS of 93.213 kN/m², a Plasticity Index (PI) of 39.5%, and linear shrinkage of 15.21%. Adding 25% WMD increased the CBR to 4.82% and UCS to 163 kN/m² after 7 days of curing, reaching 190 kN/m² and 219.5 kN/m² after 14 and 28 days, respectively. PI decreased to 25.38%, and linear shrinkage reduced to 13.93%. However, these values were below the standards of Kenya's Pavement Guidelines. Incorporating 20% NP also enhanced soil properties, with CBR increasing to 10.4%, UCS reaching 184.76 kN/m² after 7 days, 223.38 kN/m² after 14 days, and 371.819 kN/m² after 28 days. PI decreased to 13.93%, and linear shrinkage dropped to 11.5%. These results met the requirements of 15% PI and 5% CBR. The study results suggest that the combined use of WMD and NP significantly enhances the strength of expansive soils.

Effect of Cement Kiln Dust and Sugarcane Bagasse Ash on Black Cotton Soil to be used as Road Subgrade Material in Flexible Pavement Construction

2025-04-04T06:58:39+00:00

Cement, lime, and Fly Ash (FA) are the major traditional soil stabilizers. Cement production contributes 0.8-0.9 tons of carbon emissions per ton of cement, while lime production generates around 1.2 tons of CO₂ per ton of cement. FA is not readily available in all regions, necessitating the exploration of alternative stabilizing agents. Cement Kiln Dust (CKD) and Sugar-Cane Bagasse Ash (SCBA) are waste products from cement and sugarcane production, respectively. This study investigated the use of CKD and SCBA to stabilize black cotton soil. CKD was incorporated into the soil at 0, 2, 4, 6, 8, and 10% for standard Proctor compaction, consistency limits, Free Swell Index (FSI), Unconfined Compressive Strength (UCS), and California Bearing Ratio (CBR) testing. The optimal CKD content based on UCS and CBR was 6%, while the optimal CKD-SCBA composite was 6% CKD and 10% SCBA. The third part of the Kenyan Road Design Manual (KRDM III) categorizes subgrades by strength based on the CBR, ranging from S1 to S6. Subgrades classified as S1 exhibit the lowest strength (CBR of 2-5%), while S6 denotes the highest strength (CBR of 30% or greater). The untreated black cotton soil, with a CBR of less than 2%, was unsuitable as a subgrade. The CKD-SCBA composite improved the soil's CBR to 16.43%, upgrading it to an S4 subgrade, which can reduce the pavement thickness and associated costs. Other enhancements included an increase in UCS from 97.5 kPa to 555.81 kPa, a reduction in the FSI from 86% to 45%, and a reduction in Plasticity Index (PI) from 26.18% to 15.26%.

Structural Performance and Optimization of 3D-Printed PLA Lattice Structures for Sustainable Design in Load-Bearing Applications

2025-04-04T07:03:46+00:00

This study explores the structural performance and optimization of 3D-printed Polylactic Acid (PLA) lattice structures, focusing on octapeak, hexstar, and dodecahedron designs, for potential load-bearing applications. Through compression testing, the load-displacement behavior of each structure type was analyzed, examining key characteristics such as peak load capacity, deformation patterns, and failure modes. The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) was employed to assess each configuration based on two primary criteria: compressive strength and mass. This analysis yielded insights that were structure-type specific as well as overall across all nine samples. Among the hexstar configurations, sample 4 attained the highest rank due to its exceptional load-bearing capacity, making it the optimal choice for high-strength applications. Within the octapeak and dodecahedron groups, samples 2 and 7, respectively, demonstrated balanced performance, suitable for applications prioritizing mass efficiency over maximum strength. In the overall ranking, hexstar emerged as the top-performing structure, with its configurations consistently balancing strength and mass effectively, while octapeak and dodecahedron offered viable alternatives for lighter, less load-intensive uses. The findings demonstrate the utility of the PROMETHEE method in optimizing lattice structure configurations for specific engineering applications, thus contributing to the advancement of sustainable design in additive manufacturing. This research provides a framework for selecting 3D-printed structures that meet application-specific criteria for compressive strength and material efficiency.

Optimized Machine Learning for Cancer Classification via Three-Stage Gene Selection

2025-04-04T07:05:10+00:00

Gene selection from high-dimensional microarray data presents challenges such as overfitting, computational inefficiency, and feature redundancy. Despite significant advances, existing methods often suffer from limitations in scalability and interpretability, especially for precision oncology. This study introduces a novel Three-Stage Gene Selection (3SGS) strategy that addresses these issues through a combination of filter-based methods (signal-to-noise ratio, correlation coefficient, ReliefF) with accuracy-driven refinement and redundancy reduction. The 3SGS approach identifies minimal but highly predictive gene subsets, achieving 100% accuracy for leukemia and 98% for prostate cancer using only 3-4 genes. Compared to traditional methods, 3SGS enhances efficiency and interpretability, establishing itself as a scalable and robust solution for cancer classification.

Efficient ECG Arrhythmia Detection on FPGA using Machine Learning and Fiducial Windowing

2025-04-04T07:04:07+00:00

This work presents an efficient FPGA-based system for real-time detection of ECG arrhythmias using machine learning and fiducial windowing techniques. The proposed system integrates FPGA hardware acceleration to achieve low latency and high energy efficiency while maintaining superior classification accuracy, making it well-suited for portable health monitoring devices. ECG signals are preprocessed with a Butterworth filter to remove noise, followed by feature extraction through Discrete Wavelet Transform (DWT). The fiducial windowing method identifies key ECG components such as the P-wave, the QRS complex, and the T-wave, allowing the extraction of clinically relevant features. These features are then classified using a machine learning model implemented on an FPGA, allowing for rapid and accurate arrhythmia detection. The hardware-based solution significantly outperforms traditional software implementations in terms of real-time performance and power consumption. The proposed system achieved an impressive accuracy of 99.7%, a processing speed of 0.723 s, and a power consumption of 0.42 mW. The design was implemented using Xilinx Vivado 2022 EDA tools on the Xilinx PYNQ FPGA platform. This study demonstrates the potential of FPGA-based machine learning systems for efficient and reliable real-time ECG analysis, paving the way for advanced wearable health monitoring applications.

Mixing Time Optimization of Chitosan-Bentonite Composites for Sustainable Clay Liner Applications

2025-04-04T06:56:17+00:00

Bentonite-chitosan composites offer promising potential as clay liners due to their low permeability and enhanced mechanical properties. However, the extended mixing times required for optimal composite performance pose challenges for large-scale applications. This study investigates the effects of varying mixing times on the properties of bentonite-chitosan composites to optimize their performance while improving practicality. The composites were prepared by mixing bentonite with chitosan in acetic acid and sodium tripolyphosphate (STPP) solutions for varying durations. Characterization tests, including FTIR, TGA, and SEM-EDX, were conducted to assess the chemical interactions, thermal stability, and morphology. The plasticity was evaluated through the Liquid Limit (LL) and Plasticity Index (PI), while the permeability was tested using the falling head method at 16 kN/m³ density and 10% water content. The results indicated that longer mixing times, particularly 2 hours in acetic acid and 4 hours in STPP, resulted in the lowest permeability (1×10⁻¹² m/s) and the best structural integrity. However, shorter mixing times, such as 2 hours in acetic acid and 2 hours in STPP, also provided acceptable performance, offering a practical alternative. Pure bentonite, while exhibiting low permeability, lacked the structural integrity achieved by chitosan-enhanced composites. Future research should focus on evaluating the long-term durability of these composites under field conditions, their scalability, and performance in sand-bentonite mixtures, emphasizing the role of optimized mixing times in improving composite performance.

An Experimental Study to Assess the Void Impact on the Ultimate Bearing Capacity of a Strip Footing Sitting on a Reinforced Slope

2025-04-04T06:54:08+00:00

This research presents the findings of experimental laboratory models carried out on strip footing situated on a slope with an underlying cavity, considering both unreinforced and geogrid-reinforced soil. Tests were performed on a small-scale footing model subjected to vertical-centric loads. The research encompasses several parametric investigations, varying the cavity depth (H/B), the horizontal distance between the center of the footing and the cavity's center (X/B), and the number of geogrid layers (N). The detailed experimental results indicate that the presence of the cavity diminishes the soil-bearing capacity and undermines slope stability. Furthermore, an increase in cavity depth (H/B), horizontal distance ratios (X/B), and the number of geogrid layers (N) has been shown to result in an enhancement in bearing capacity. Additionally, a variety of failure mechanisms have been observed, with the size of the failure surface and void deformation shape depending on the location of the void and reinforcement layers. In general, the failure area is primarily formed in the direction of the closest void from the foundation and spreads towards the slope.

Calibration and Temperature Compensation of a Low-Cost Capacitive Soil Moisture Sensor for Precision Irrigation in Thailand

2025-04-04T07:03:00+00:00

Low-cost capacitive soil moisture sensors have potential application in precision irrigation in Thailand. However, these sensors require proper calibration and are affected by soil temperature fluctuations that reduce their measurement accuracy. This study developed and validated a combined calibration and temperature compensation approach for the commercially available soil stick sensor. The calibration was performed using soil samples ranging from sandy clay loam to silty clay. A temperature compensation equation was developed by measuring the sensor responses under varying soil temperatures and moisture content levels in outdoor conditions. The sensor performance was assessed against a reference Time-Domain Reflectometry (TDR) sensor (TRIME-PICO64) and evaluated based on continuous field measurements for 14 days. The temperature compensation equation reduced the diurnal temperature effects through a linear correction model. The calibration showed a piecewise linear relationship between the Relative Voltage (V_R) and volumetric water content (q_V) with a strong correlation. The performance of the calibrated soil stick sensor was comparable to the TDR sensor, with the Confidence Index values exceeding 0.8. These findings indicated that the calibrated and temperature-compensated low-cost capacitive sensors could provide accurate soil moisture measurements for precise irrigation scheduling.

TWFSL-MM: Few-Shot Learning using Meta-Learning and Metric-Learning for Disease Detection in Azadirachta Indica

2025-04-04T06:59:00+00:00

Few-Shot Learning (FSL) is one of the emerging and promising approaches used in machine learning for image classification and prediction. This work proposes a Two-Way Five-Shot Learning with Meta-learning and Metric-learning (TWFSL-MM) model that can detect plant diseases with limited data, reducing the cost of implementation and improving the quality of Azadirachta Indica. The proposed method addresses the drawbacks of FSL by employing meta-learning and metric-learning approaches. Experimental results showed that the proposed model achieved an accuracy of 92.09%, an average loss of 0.18, an average precision of 0.94, a recall of 0.93, and an F1 score of 0.93. FSL is a promising strategy for plant disease detection, achieving higher accuracy with a limited dataset. The TWFSL-MM model outperforms other state-of-the-art models, demonstrating its potential to improve crop yields and quality.

Smart Healthcare Applications: Detecting DDoS Attacks Efficiently using Hybrid Firefly Algorithm

2025-04-04T07:01:32+00:00

The rapidly growing and emerging Smart Healthcare Applications (SHA) are reducing the burden on the existing healthcare system caused by limited medical infrastructure and increasing number of diseases. Bio-inspired anomaly-based detection systems are still affected by false positive rates because the approaches are synchronized with user-defined parameters that are unpredictable, resulting in convergence rate, discovery and utilization disparities, algorithm complexity, and unrealistic results. One of the most well-known and effective nature-inspired swarm intelligence metaheuristic algorithms is the Firefly Algorithm (FA). In this work, we propose a Hybridized Firefly Algorithm (HFA) that combines the advantages of the FA and Particle Swarm Optimization (PSO). The bio-inspired HFA is designed to mitigate Distributed Denial-of-Service (DDoS) attacks in SHA. We compare our algorithm with other DDoS attack resistant methods and conclude that our hybrid approach outperforms the existing FAs in terms of accuracy, error prediction, and attack detection time. The statistical results demonstrate the improved accuracy and effectiveness of our proposed HFA model with a higher accuracy of 94.9%, error prediction of 6%, and detection time of 1.12 ms compared to existing DDoS attack detection methods. The proposed HFA methodology is a decentralized architecture, more effective, highly reliable, and available for real-time SHA in terms of monitoring and detecting attacks.

Physical and Mechanical Properties of Geopolymers with the Addition of NaOH modified Bemban Fiber (Donax Canniformis)

2025-04-04T06:58:09+00:00

The increasing demand for sustainable and eco-friendly materials has driven research into natural resource utilization. This study explores the development of geopolymer mortar incorporating fly ash, bemban fiber, and natural kaolin minerals. Due to its hydrophilic nature, bemban fiber requires alkalization treatment to enhance bonding strength within the geopolymer matrix. This research investigates the effect of 3% NaOH alkalization for 2 hours on the fiber’s properties. The results indicate that alkalization significantly enhances the physical and mechanical performance of bemban fiber. The optimal composition of 1.5% bemban fiber with a 70:30 metakaolin-to-fly ash ratio improves key properties, including water absorption (2.75%), porosity (5.80%), compressive strength (32.58 MPa), and splitting tensile strength (10.78 MPa). These findings are supported by Fourier Transform Infrared Spectroscopy (FTIR), which confirms geopolymerization through Si-O-Si asymmetric stretching vibrations at 974 cm⁻¹. Additionally, X-Ray Diffraction (XRD) analysis identifies a dominant quartz phase, while Scanning Electron Microscopy (SEM) reveals strong fiber-matrix bonding. This study highlights the potential of bemban fiber-reinforced geopolymers as a sustainable alternative for cement-based materials, promoting green construction practices.

GIS Combined with Multivariate Analysis in Supporting Digitalization Supply Chain Management of Halal Products: The Case Study of MSMEs in West Java Indonesia

2025-04-04T06:57:27+00:00

Micro, Small, and Medium Enterprises (MSMEs) struggle to compete in the global market, especially those producing halal products. Information system support is one solution that can solve the complex supply chain problem from producers to consumers. The objective of this study is to develop a Geographic Information System (GIS) supported by multivariate analysis for the digitalization of Supply Chain Management (SCM) of halal products in MSMEs located in West Java, Indonesia. Six attributes were evaluated: information technology, human resources, collaborative relationship, halal certificate, and SCM implementation as independent variables, and halal products as dependent. The results of the ANOVA test show that the six attributes are significantly different (p<5%). In addition, based on the Pearson correlation, only the collaborative relationship, human resources, and halal certificate attributes correlate more than 80%. The SCM analysis shows that supplier, manufacturing, and raw materials factors are the main levers in the halal product supply chain. A GIS-based information system was successfully developed, with satisfactory user acceptance results for usability, efficiency, reliability, and functionality. The results of this study indicate that this GIS-based system can help distribute halal products in MSMEs, especially those in the West Java area, Indonesia.

Enhancing Algorithmic Techniques for Streamlined Complex Graph Structures in Big Data Visualization

2025-04-04T07:01:52+00:00

With the rapid expansion of data applications, particularly in large and complex graph structures, effective visualization and analysis tools are essential. This paper addresses the "Hair Ball" problem, where excessive node and edge intersections hinder the clear interpretation of networks. To mitigate this issue, an efficient algorithm based on the K_3,4 bipartite graph model is proposed. The model is systematically compressed to reduce intersecting edges while preserving essential structural relationships. The algorithm was tested on various datasets, ranging from small synthetic networks to large real-world graphs. The results demonstrate significant reductions in visual complexity and improved clarity. Key performance metrics, including edge density reduction and observer feedback, validate the scalability and practical applicability of the proposed approach in big data environments. By simplifying intricate graph structures, this method offers a versatile and effective solution for applications in network analysis, data visualization, and related fields.

Experimental Investigation of the Rutting Potential of Polymer-modified Asphalt Binders using the Multiple Stress Creep Recovery Test

2025-04-04T06:55:50+00:00

Polymer-modified binders have become increasingly important in enhancing the durability and strength of asphalt flexible pavements, allowing them to withstand higher traffic volumes, heavier loads, and extreme weather conditions. Although the Dynamic Shear Rheometer (DSR) test is widely used, it is inadequate to accurately capture the viscoelastic properties of polymer-modified asphalt binders. As a result, the Multiple Stress Creep Recovery (MSCR) test, a recently developed method for assessing the high-temperature performance of asphalt binders, is expected to replace the DSR for short-term aged binders. In this study, binders comprising 40/60 pen unmodified bitumen, a hard polymer-modified binder, and a softer polymer-modified binder, were evaluated using MSCR testing under various stress and temperature conditions. The MSCR results showed that incorporating Styrene–Butadiene–Styrene (SBS) modifiers into the bitumen significantly enhanced the permanent deformation resistance of the modified binders by reducing the non-recoverable compliance and increasing the recovery percentage. Moreover, a comparison of the MSCR results at the 3.2 kPa stress level with the AASHTO standard confirmed that the examined asphalt binders were modified with an elastomeric polymer of acceptable quality.

CFRP Strengthening of Circular Geo-Polymer Concrete Slab with and without Openings

2025-04-04T06:56:09+00:00

The current study investigates the eco-friendly concrete and specifically Geopolymer Concrete (GPC), and its behavior in reinforced concrete circular slabs both with and without openings. It also examines GPC strength utilizing Carbon Fiber Reinforced Polymer (CFRP) sheets under punching shear. Slag-based GPC was used to cast the slabs. The experimental part included testing six circular slabs divided into two groups with a diameter of 700 mm and a thickness of 70 mm, and a cast circular column with dimensions of 150 x 150 mm at the top face in the middle. The slab components of these samples were strengthened with a distorted 8 mm diameter dispersed across the section of 75 mm c/c. The circular column was reinforced by 5Ø6mm bars, with a 2Ø6@50mm tie to prevent local failure in the column before the slab. The investigated experimental variables included the column location and the strengthening schemes. Measurements were made for the first cracking load, mid-span vertical deflections, and ultimate load capacity. Also, the crack patterns were marked, and the failure mode was observed. Furthermore, the mechanical properties of the slag-based GPC were studied. The results showed that the modulus of rupture and modulus of elasticity were about 3.2 and 29725 Mpa, respectively, and the compressive Strength (fcu) about 45 Mpa. Each slab's initial crack appeared at a load between 23 and 50 kN of its ultimate capacity.

An Adaptive Approach to Multimedia Adaptation in Context-Aware Pervasive Systems

2025-04-04T06:59:18+00:00

In pervasive systems, contextual constraints can hinder the smooth execution of multimedia documents. Several adaptation approaches have been proposed that operate as client-side, server-side, proxy-based or peer-to-peer applications. Most of these approaches rely on centralized mechanisms that adapt content at a single point. While centralized mechanisms offer several advantages, they also introduce significant drawbacks related to resource overhead. In this work, we propose a novel hybrid approach for multimedia document adaptation that efficiently combines multiple adaptation categories, such as client-side, server-side, proxy-based, and peer-to-peer, to alleviate some of their limitations. The main objective is to improve the quality of service in adaptation processes. The proposed process can operate on different devices, enabling self-reconfiguration by seamlessly switching from one adaptation category to another, depending on the computation context of the users, i.e., the device capabilities. To validate our proposal, we focus on three key aspects: first, real-world prototyping applied to the adaptation of multimedia content for e-learning materials; second, an assessment of performance in terms of response time, considering the number of adaptation requests and the available computing resources of each device; and third, a comparison with related work. The results show that as the number of adaptation requests increases, our approach outperforms traditional methods that rely on a single location. Specifically, it achieves response time reductions of up to 53.3% over the client/proxy/server hybrid scheme, 66.6% over the client/proxy hybrid scheme, and 74.3% over the client-only scheme. These findings are highly satisfying and encouraging, demonstrating that distributed computation can effectively optimize resource consumption, reduce response time, and maintain high scalability and robustness.

Behavior of Piles subjected to Combined Axial and Lateral Loading

2025-04-04T07:00:25+00:00

Pile foundations are employed to sustain both vertical and horizontal loads in various geotechnical applications, including coastal and offshore engineering. The contemporary design methodology analyzes the response of piles under combined horizontal and vertical loads independently and then superimposes them. This simple analytical method does not account for the combined loads' coupling effect. The number of studies on this subject is limited and the findings thus far are unclear about the effect of vertical loads on the lateral response of piles. In this paper, a number of model experiments were performed under different load conditions using the particle image velocimetry technique in well-graded sandy soil with a relative density of 65%. The results indicate that the presence of a low vertical load improves the lateral behavior of piles with L/D ratio (20, 25) due to the soil densification effect, and when the vertical load becomes 60 and 80% it results in a declining of the pile's lateral capacity. In piles with L/D ratio equal to 30, the P-∆ effect is more significant than the soil densification impact, which produces more pile deformation. This study also discusses the lateral displacement along the pile shaft using the PIV technique.

Enhancing Security in Healthcare Frameworks using Optimal Deep Learning-based Attack Detection and Classification for Medical Wireless Sensor Networks

2025-04-04T07:01:47+00:00

Wireless Sensor Networks (WSNs) have modernized healthcare, providing vital sign collection and real-time patient monitoring. Healthcare WSNs are vulnerable to cyberattacks, such as false data injection, sensor manipulation, and data eavesdropping, which can disrupt monitoring and endanger patient lives. Traditional Intrusion Detection Systems (IDSs) based on static signatures struggle with evolving threats. Deep Learning (DL)-based IDSs, combined with Feature Selection (FS), offer a more adaptive and effective solution, improving attack detection and protecting patient data. This work presents an innovative Pigeon-Inspired Optimizer-based Feature Selection with Deep Learning-based Attack Detection and Classification (PIOFS-DLADC) method, which focuses on creating an optimal DL framework for attack detection and classification in healthcare WSNs. Initially, patient health data (actual input data) undergo preprocessing using the one-hot encoding system. Then, the PIOFS method selects key features from sensor data streams, reducing dimensionality and improving model efficiency. Furthermore, an attention-based Bidirectional Gated Recurrent Unit (BiGRU) method captures long-term dependencies and prioritizes features for accurate attack classification. The Coati Optimization Algorithm (COA) is employed to tune the hyperparameters of the DL models. The model efficiently explores the hyperparameter space, optimizing the performance for attack detection and classification. Validated on a healthcare WSN dataset, the PIOFS-DLADC model demonstrated an accuracy of 96.78%, which is superior to existing approaches.

The The Effect of Rainfall Intensity on Slope Stability: An Analytical Study using Numerical Modeling

2025-04-04T06:52:49+00:00

Slope instability causes landslides, which have a detrimental effect on infrastructure and the environment while contributing to significant damage both in terms of people and property. The present article offers a thorough examination of slope stability by SEEP/W and SLOPE/W software programs. Using numerical simulations based on three different models, the work plan analyzed changes in moisture content, Pore Water Pressure (PWP), and factor of safety (F.S.) to assess the impact of a set of hydrological and engineering factors, such as rainfall intensity (I), soil permeability (K), and slope angles (S). Based on the results, PWP rises to 185.84 kPa and the F.S. drops to 1.233 as rainfall intensity exceeds 80 mm/h. Additionally, longer rainfall intervals (3 days) result in a 20% reduction in F.S. as compared to short rainfall periods. The study also found that steep slopes (30° or more) greatly enhance the chance of falling apart, particularly in highly permeable soils, while the rapid water seepage along gradients caused by highly permeable soils increases the danger of collapse. The findings suggest that, in addition to enhancing soil qualities in high-permeability areas and utilizing efficient drainage systems to lower pore pressure, engineering projects should consider the impact of heavy rainfall and extended precipitation. Furthermore, a novel mathematical equation was developed to calculate the F.S. of slopes, incorporating key parameters, such as rainfall intensity, slope angle, and soil type. This equation underwent rigorous statistical analysis, achieving the highest accuracy rate, and can serve as a robust tool for slope stability assessment in diverse environmental and engineering scenarios.

Assessment of the Impact of Pile Characteristics on the Horizontal Displacement of Retaining Walls under Heavy Rainfall: A Case Study in Vietnam

2025-04-04T06:58:00+00:00

This study evaluates how pile characteristics influence the Horizontal Displacement (Ux) of river retaining walls in Ho Chi Minh City, Vietnam, during heavy rainfall, which floods the walls, while the river water level remains at its lowest. The study utilizes Finite Element Method (FEM) in combination with statistical methods, such as linear regression and Pearson correlation, to examine the effects of pile factors. These factors are the Number of Piles (NoP), Pile Spacing (PS), Pile Diameter (PD), and Pile Type (PT) based on the Ux of the river retaining walls. Finite element simulations are conducted across different scenarios to evaluate the impact of the aforementioned factors under dynamic environmental conditions. The study results show significant variation in the Ux of the retaining walls based on each factor. PS and PD have a strong influence on Ux, with correlation coefficients of 0.585 and -0.549, respectively. This indicates that a larger PS increases displacement, while a smaller PD also leads to greater displacement. In contrast, NoP has a weak correlation with Ux. The linear regression models suggest that these factors do not have an equal impact on the retaining wall stability. It is concluded that optimizing the pile characteristics, particularly PS and PD, can help minimize Ux. This enhances the stability of river retaining walls under harsh climatic conditions.

Building Information Modeling-based Cost Estimation

2025-04-04T06:58:52+00:00

This study conducts a comparative analysis between a Business Information Modeling (BIM)-based and traditional cost estimation methods on the building structure of the Guntung Payung Banjabaru Health Center, Banjabaru City. The Tekla Structures (TS) software is utilized to carry out structural work calculations, including concrete, steel, and iron work, as well as the AHSP/HSPK working drawings from 2021. The BIM analysis results regarding the calculation time and cost differences were compared with the calculation results obtained with Microsoft Excel and AutoCAD. The two methods’ findings demonstrate that the cost difference is 12% for the concrete work and 13% for the iron/rebar work. It can be concluded that the BIM cost estimation method is more efficient, because its processing time is shorter and the calculation results are closer to the project needs compared to the conventional method.

Analysis of the Mechanical Properties of SMAW welded Joints on API 5LX42 Steel Pipelines

2025-04-04T07:04:46+00:00

This paper examines the performance of gas pipeline welding in relation to the mechanical properties and metallurgical structure of the weld bead. Our research focuses on API 5L X42 steel pipes, which are commonly used for transporting hydrocarbons, particularly in the welded joints of these pipes. We conducted tests to characterize a weld created using the Shielded Metal Arc Welding (SMAW) method for gas pipelines. This included a microstructural analysis of the heterogeneous zone and the production of standardized specimens taken perpendicular to the welding direction, which were then subjected to uniaxial tension to assess the properties of both the base material and the filler metal under various stress conditions. Our findings indicate that the welded joint exhibits hardening compared to the base metal due to thermal effects, and we observed that the elongation in the welded specimens is significantly lower than that of the unwelded specimens.

Unenhanced Sparse Vector-based Embedding Method for Sentiment Analysis

2025-04-04T06:55:41+00:00

Natural language processing is one of the most trending fields in research, with sentiment analysis being one of the well-known problems in the field. Many methods have been proposed to handle text-based sentiment data, with social networks acting as one of the main data sources and research targets. An important step in designing a text-based model is the embedding method, which helps in the representation of the inputs. This study presents a novel static text embedding method to represent text inputs and compares its sentiment classification performance with some well-known text embedding methods. The results are on par with existing embedding methods, achieving a promising classification accuracy of 90.66%.

Performance of uPVC RC-filled Pipe Columns exposed to Thermal Cyclic Loading

2025-04-04T06:59:27+00:00

This study analyzes the performance of unplasticized Polyvinyl Chloride (uPVC) RC-filled pipe columns when exposed to thermal cyclic loading. The exposure simulates hot environments with a peak temperature of 60 °C for 28, 56, and 112 days of Heating-Cooling Cycles (HCC). The experimental analysis focuses on uPVC residual strength after thermal cyclic loading and load-carrying capacity, ductility, and stress-strain behavior of uPVC RC-filled pipe columns. Tensile tests of extracted uPVC specimens after exposure demonstrated no change in ultimate tensile strength but a progressive decline in elastic modulus, reducing by 17.24% and 24.56% after 28 and 56 cycles, respectively. The uPVC confined exposed samples exhibited an initial increase in load-carrying capacity by a factor of 1.39 compared to the unexposed unconfined samples at ambient temperature. However, this increase was followed by a gradual decline to 1.32 and 1.26 at 56 and 112 cycles, respectively. Despite this, the load-bearing capacity of the confined samples was still higher than that of the unexposed, confined samples. Thermal cycling significantly reduced maximum lateral and axial strain at failure by up to 85% at 112-HCC, with ductility declining by 50.9% at 28 cycles and continuing to decrease gradually at higher cycles. Failure modes shifted from ductile in confined unexposed samples to brittle and explosive in thermally cycled samples, highlighting reduced confinement effectiveness. Predictive models demonstrated a high degree of accuracy in estimating peak strength and strain, with average absolute errors of 0.16% and 7.65%, respectively. Long-term projections suggest that confinement effectiveness could decrease to 1.06, which is the ratio of confined exposed strength (f_ccx) to unconfined unexposed strength (f_co) after 50 years of thermal exposure, emphasizing the necessity for enhanced design guidelines to optimize the performance of uPVC-confined RC columns in fluctuating temperature environments, particularly in consideration of thermal cyclic loading.

Interpretable AI for Liver Cancer Detection: Cascaded CNN & GLCM Integration

2025-04-04T06:54:31+00:00

Liver cancer has significantly high mortality, especially in regions such as Africa and Asia. Early detection enhances treatment options, but indications are frequently not apparent until advanced stages. This research introduces an explainable AI (XAI) approach using a cascaded Convolutional Neural Network (CNN) combined with Gray Level Co-occurrence Matrix (GLCM)-based texture features to segregate non-cancerous from malicious tumors. The CLD system was used for assessment, and the approach was examined using the TCIA dataset, demonstrating higher accuracy and interpretability compared to prevailing techniques. XAI methods, such as feature importance and model visualization, were employed to provide details on the decision-making process of the model, ensuring transparency and reliability in clinical applications.

Point of Interest Recommendation using Implicit Trust based on Combining Ratings and Check-ins of Smartphone Users

2025-04-04T06:57:52+00:00

This paper introduces a hybrid model called Implicit Trust based on Combining point-of-interest Ratings and user Check-ins (ITCRC) to address the cold-start challenges commonly associated with trust-based collaborative filtering methods. The model combines Point of Interest (POI) ratings and user check-ins to estimate implicit trust, facilitating location recommendations in a Location-Based Social Network (LBSN). In the Yelp dataset, the ITCRC model's trust and prediction matrices are calculated using Trust based on Ratings (TR), Trust derived from Check-ins (TC), and Trust based on the Hybridization of ratings and check-ins (TH), as well as three approaches derived by adapting O'Donovan's trust formula to the LBSN context. These six approaches are then compared using sparsity metrics and evaluation parameters such as RMSE, precision, and recall. The comparisons revealed that the TH approach significantly reduces the data sparsity of the prediction matrix by 36.08%, the TR and TC approaches improve the relevance of the recommendations (0.77% of precision and 0.99% of recall), and the OR, OC, and OH approaches improve the prediction accuracy by 0.2% in terms of RMSE.

Modeling and Manufacturing of a Flexible Socket for Above-Knee Amputation Prosthesis

2025-04-04T06:56:32+00:00

There is a high incidence of Above-Knee (AK) amputations in Iraq due to factors, such as war, birth defects, explosives, and accidents. The development of advanced prosthetic solutions is critical in improving the comfort and life quality of the amputees. Traditional prosthetic sockets for AK amputations often cause discomfort, limit mobility, and increase the risk of secondary health issues, such as scoliosis. Creating a socket that balances both support and flexibility for the amputee, addressing these concerns while also ensuring comfort and ease of use is a major challenge. The objective of this project is to design a prosthetic socket combining stiff and flexible materials to optimize comfort, support, and functionality for AK amputees. The goal is to develop a socket that alleviates back discomfort, reduces the risk of scoliosis, and improves suspension while being easier to wear and more aesthetically pleasing. This project employs a composite material approach, using a combination of silicon, carbon, perlon, and laminate to create the socket. The socket is designed in two parts: the first part utilizes rigid materials to support the weight of the amputee, while the second part incorporates flexible materials to allow for muscle movement during gait. Tensile tests were conducted to determine the mechanical properties of silicon with perlon, and the performance of the flexible socket was evaluated in terms of pressure distribution and comfort. The tensile testing of silicon with perlon yielded a Young's modulus of 0.0165 GPa, a yield strength of 0.283 MPa, and an ultimate tensile strength of 1.386 MPa. Additionally, the maximum F-socket pressure measured in the anterior region was 490 kPa.

Intrusion Detection in a Digital Twin-Enabled Secure Industrial Internet of Things Environment for Industrial Sustainability

2025-04-04T06:54:51+00:00

Research focuses on sustainable development for the smart industry environment, where new challenges emerge every day. Digital Twins (DT) have gained substantial attention from an industrial growth point of view. This is because it significantly contributes to the predictive maintenance, simulation, and optimization of the Industrial Internet of Things (IIoT), ensuring its sustainability in future industries that demand unprecedented flexibility. Current research discusses the possibility of using DT for intrusion detection in industrial systems. By integrating DT and IIoT, physical elements become virtual representations and enhance data analytics performance. However, a lack of trust between the parties involved and untrustworthy public communication channels can lead to various types of attacks and threats to ongoing communication. With this motivation in mind, this study develops a Binary Arithmetic Optimization Algorithm with Variational Recurrent Autoencoder-based Intrusion Detection (BAOA-VRAID) for DT-enabled secure IIoT environments. The proposed BAOA-VRAEID technique focuses on the integration of DT with the IIoT server, which collects industrial transaction data and helps to enhance the IIoT environment's security and communication privacy. The BAOA-VRAID technique uses BAOA to designate an optimal subset of features to detect intrusions. The VRAE classification model with the Harris Hawks Optimization (HHO) algorithm-based hyperparameter optimizer is used for intrusion detection. The BAOA-VRAID method was tested on a benchmark dataset, showing that it significantly outperformed other contemporary methods.

A Mobile Application for the Detection of Pre-Carious Lesions in Peruvian Patients based on YOLOv7

2025-04-04T07:06:48+00:00

Dental cavities represent a significant global health challenge, particularly in low- and middle-income countries, where early detection and diagnosis can substantially improve clinical outcomes. This study presents the development of a mobile application that utilizes YOLOv7 to detect early carious lesions on intraoral images, intending to provide dental professionals with a tool for timely diagnosis and intervention. The research was carried out in three key phases: analysis of YOLOv7, system development, and validation. The application was trained in a real clinical environment in Peru in collaboration with two independent dentists and their patients in two private clinics. Intraoral images were collected and processed from 40 participants, ensuring complete adherence to the ethical and privacy standards required for clinical studies. The experimental results demonstrated that the application achieved an average accuracy of 94%, with both accuracy and Positive Predictive Value (PPV) exceeding 90% in most cases. The results demonstrated consistent diagnostic accuracy and efficiency, validating the application's performance. Patient surveys reflected high satisfaction, with average scores of 4.4 for usability, 4.2 for efficiency, and 4.6 for functionality. Similarly, dentists rated the usability, functionality, and efficiency of the application with average scores of 4.5. These findings highlight the potential of the application to improve clinical workflows and accuracy in detecting early carious lesions.

Forecasting Multi-Level Deep Learning Autoencoder Architecture (MDLAA) for Parametric Prediction based on Convolutional Neural Networks

2025-04-04T07:06:32+00:00

This study presents a data-driven framework for anomaly detection, which is a significant process in modern computing, as the detection of an abnormal signal can prevent a high-risk decision. The proposed Multi-Level Deep Learning Autoencoder Architecture (MDLAA) is used to encode high dimensional input data using CNNs for anomaly detection in High Dimensional Input Datasets (HDDs). MDLAA is based on unsupervised learning, which has a strong theoretical foundation and is widely used for the detection of anomalies in HDDs, but a few limitations significantly reduce its performance. The proposed MDLAA combines multilevel convolutional layers and data preprocessing. The performance of the proposed model was evaluated on a benchmark dataset. Using feature engineering, the proposed algorithm assists in the detection of anomalies that are present in data structures, especially when compared to the ResNet101 feature extractor. The results show that given adequate data, the proposed technique outperformed other previously implemented deep learning approaches and classification models, showing an overall improvement of 2.3% in terms of MSE, F1-score, precision, and accuracy.

An Experimental Investigation on the Performance of a Double Slope Single-Stage Solar Still tested in Cape Town, South Africa

2025-04-04T07:05:56+00:00

Desalination systems have emerged as an alternative solution to the global water crisis, with many countries using them to alleviate freshwater scarcity. Various types of desalination systems exist, including solar desalination, which is the focus of this study. This research aimed to design, construct, and test a Double Slope Single-Stage Solar Still (DSSSSS) under real environmental conditions in Cape Town, South Africa. The system incorporated an Evacuated Tube Solar Collector (ETSC) to enhance its performance. The DSSSSS was tested during day and night. The experiment took place in October and November 2023, during the spring season in South Africa. The water depth was maintained at 50 mm using a float valve, and a 220 V water circulation pump ensured continuous seawater flow between the basin and the ETSC. The system was tested for 12 days, the highest production obtained per day was 513 ml on a day when the maximum outdoor temperature was 30 ^oC. The minimum distillate produced was 140 ml on a day that had a maximum temperature of 22 ^oC and that was one of the coldest days during the testing period. A total of 2142 ml of distillate was produced during daytime and 1679 ml at night, amounting to 3821 ml over the testing period. Salinity and conductivity tests were conducted on both the raw seawater and the distillate to compare water quality before and after the purification process.

Residual Shear Strength and Other Geotechnical Properties of Clay Mixed with Different Sand Ratios

2025-04-04T07:02:35+00:00

The current research investigates the residual shear strength of clay soils to which different percentages (5, 10, 15, and 20%) of sand are added, playing a pivotal role in determining the stability of steep slopes on these soils. The residual shear strength was compared with the shear strength calculated from the direct shear test and the unconfined compression strength test. According to the results, the clay soil’s swelling ranged from medium to high, and its engineering characteristics were similar to that of all clay soils found in most sloped areas and hills in Mosul city. Also, a notable reduction was observed in the plasticity index with an increase in the sand percentage. In the residual shear test, a rise in shear coefficients (effective angle of internal friction (Φ') and cohesion(c')) was demonstrated with an increase in the percentage of sand addition up to a certain limit (15%), which is the same percentage that gave values for (c and Φ) in the direct shear test.

Enhancing 3D Printing Workflows through Multi-Objective Optimization and Reinforcement Learning Techniques

2025-04-04T06:55:38+00:00

Integrating Machine Learning (ML) with optimization algorithms in 3D printing, also known as Additive Manufacturing (AM), has revolutionized the creation and production of complex structures. This integration has significantly boosted material efficiency, print quality, and optimization of the entire process. This paper delves into details on improving 3D printing design and production workflows using advanced ML techniques such as neural networks, Reinforcement Learning (RL), and optimization techniques, such as topology optimization and genetic algorithms. The proposed framework offers a 15-25% reduction in print time and material consumption and a 10-20% improvement in predictive accuracy over existing methods. Additionally, the results of the multiobjective optimization reveal an aligned improvement in cost-effectiveness, structural strength, and mechanical performance. Stress-strain analysis showed that optimized designs can achieve up to a 12% increase in yield strength, while defect rates decrease by up to 30% by applying dynamic RL for parameter adjustments. The results validate the effectiveness of these hybrid models, emphasizing their potential to boost reliability, efficiency, and scalability in additive manufacturing processes.

Optimizing Vehicle Ride Comfort using GA-LQR Control in In-Wheel Suspension Systems

2025-04-04T07:02:51+00:00

Controlled suspension systems, particularly active in-wheel suspension systems, are increasingly adopted in electric and autonomous vehicles due to their compact design and adaptability to various operating conditions. This study proposes the implementation of Linear Quadratic Regulator (LQR) controllers to improve vehicle smoothness and safety criteria. Genetic Algorithms (GA) are employed to optimize the weighting parameter values in the objective function in LQR controller, which allow them to adapt to the vehicle's condition. The simulation results demonstrate that the proposed controller model enhances system performance by up to 14% in comparison with conventional models. These findings suggest that the proposed system significantly enhances the feasibility of meeting user requirements in modern vehicle applications.

Comparison between No-Tillaga and Conventional Farming Systems and their Impact on Some Performance Indicators and Wheat Yield

2025-04-04T06:58:17+00:00

A field study was conducted in loamy agricultural soil of Baghdad Governorate, Tarmiyah District, during the 2021-2022 growing season. The research aimed to evaluate the feasibility and impacts of no-tillage wheat planting systems on plant growth and yield. The experiment examined three factors: tractor operational speed, seed rate, and seeder configurations. Three speed levels 4.23, 6.54, and 8.37 km/h, two seed rates 100 and 140 kg/ha, and three seeder setups were tested. The indicators evaluated included operational productivity, draft force, soil bulk density, and 1000 grain weight. The Randomized Complete Block Design (RCBD), with a nested arrangement and three replicates, was employed. The mean comparisons carried out used the Least Significant Difference (LSD) at the 0.05 significance level. The highest operational productivity of 1.87 ha/h and 1000-grain weight of 48.7 g were achieved with the 8.37 km/h speed, 140 kg/ha rate, and the Z seeder. Conversely, the 4.23 km/h speed, 100 kg/ha rate, and Z seeder resulted in the lowest draft force of 125.76 kgf and soil bulk density of 1.030 mg/m³.

Diabetic Retinopathy Detection using the Genetic Algorithm and a Channel Attention Module on Hybrid VGG16 and EfficientNetB0

2025-04-28T11:30:01+00:00

Diabetic Retinopathy (DR), a result of diabetes, requires early detection to reduce the impact of the disease on vision. This study introduces a new system whose architecture is based on a combination of VGG16 architecture with EfficientNetB0 as well as an added body structure, which is the Channel Attention Module (CAM), to strengthen the channel maps and thus achieve improved classification accuracy. For further efficiency and consistency, the system employs a genetic algorithm for image normalization. The system shows great potential for improving clinical decision making and patient examination results when used in the diagnosis of DR. The evaluation results confirm the reliability of the system and the feasibility of using it in daily practice to address the acute challenge of early detection of DR. The model is well trained with a test dataset of 2900 images and demonstrates high accuracy of 95%. This high accuracy clearly shows the high reliability of the proposed hybrid model which is also confirmed by the precision and recall values. The achieved precision is 0.96 for class 0 and 0.94 for class 1, and the achieved recall is 0.94 for class 0 and 0.97 for class 1.

Analysis of Wind-Solar Power Development and Policy Strategies for Carbon Neutrality in Hunan Province

2025-04-04T06:56:54+00:00

Against the backdrop of global carbon neutrality, the energy transition in Hunan's power sector is crucial. This study employs the Low Emissions Analysis Platform (LEAP) model analysis to evaluate the electricity production structure and environmental impacts under three scenarios: Baseline Scenario (BAS), Policy Support Scenario (PSS), Deep Emission Reduction Scenario (DES), focusing on the development of wind and solar energy. The results indicate that the increasing installed capacity of wind and solar power significantly improves Hunan's power generation structure and environmental conditions. By 2060, under the PSS scenario, wind and solar power will have achieved an installed capacity of 128 GW, contributing 23.6% to electricity generation, while pollutant emissions (CO₂, NO₂, SO₂, PM_2.5) will have been reduced by 21% compared to the BAS scenario. Under the DES scenario, wind and solar power capacity will have risen to 251 GW, with solar generation having reached 212 TWh, while thermal power's share will have declined to 9.8%. Pollutant emissions will have decreased by nearly 80% compared to the BAS scenario. Furthermore, the study proposes targeted strategies to address challenges in Hunan's wind and solar development, including the establishment of integrated wind-solar energy storage systems, the advancement of smart grid infrastructure, and the development of a market-oriented trading system for wind and solar power. These strategies aim to enhance infrastructure, improve technology integration, and establish a resilient and economically viable renewable energy framework, ensuring Hunan's successful achievement of carbon neutrality goals.

Efficient Removal of Nickel, Zinc, Chromium, and Cobalt from Acid Mine Drainage using Constructed Wetlands

2025-04-04T07:00:16+00:00

This study evaluates the effectiveness of Constructed Wetlands (CWs) in treating Acid Mine Drainage (AMD), focusing on the removal of Ni, Zn, Cr, and Co. Two CW configurations were tested: CW-I (unplanted) and CW-II (planted with Alocasia odora and Spirodela polyrhiza). Over 12 months, both systems operated at Hydraulic Retention Times (HRTs) of 24, 48, and 72 hours. CW-II consistently outperformed CW-I, achieving 88.1% Zn and 67.8% Cr removal at 72 hours. Ni removal improved to 44.3%, while Co, though less effectively removed, reached 28.3%. . The statistical analysis confirmed that both HRT and vegetation significantly influenced metal removal efficiencies. The enhanced performance of CW-II highlights the critical role of phytoremediation in the pollutant uptake. These findings demonstrate that vegetated CWs offer a scalable, eco-friendly alternative for AMD treatment, with potential applications in broader environmental remediation efforts. Further research should focus in plant optimization, real-world validation, and substrate and system design.

Efficiency Assessment of Cruciform Steel Columns: Balancing Axial Capacity and Weight

2025-04-04T06:55:29+00:00

Cruciform steel columns, also known as king and queen cross sections, have gained attention in structural engineering for their ability to overcome the limitations of traditional I-sections and H-sections. These limitations include reduced stability due to slenderness effects and excessive material usage in tall columns. Designed to enhance axial load capacity while minimizing weight, cruciform sections offer a more efficient, cost-effective, and sustainable alternative for modern construction. This study assesses the efficiency of cruciform steel columns by comparing their design axial capacities to their unit weights. Using AISC 360-16 provisions, single supported columns with heights of 2 m, 3 m, 4 m, and 5 m were analyzed. The results show that all sections perform similarly for short columns (2 m), with efficiency differences of less than 8% due to the minimal impact of slenderness. However, at 3 m, the I-section is 20% less efficient than the H-section and 30% less efficient than cruciform sections. For taller columns (4 m and 5 m), cruciform sections outperform conventional sections, with the king cross-section proving to be 30% more efficient than the H-section and superior to the queen cross-section. These findings highlight the structural and material advantages of cruciform sections, particularly in applications requiring tall columns with significant slenderness effects. By reducing material usage while maintaining high load-bearing capacity, cruciform sections enhance sustainability by lowering both carbon and construction costs. This study provides valuable insights for optimizing steel column designs to achieve more sustainable and cost-effective construction.

Predictive Modeling of Saturated Hydraulic Conductivity using Machine Learning Techniques

2025-04-04T06:53:17+00:00

The hydraulic conductivity of saturated soil is a critical parameter for understanding various engineering challenges related to groundwater. Machine learning techniques offer powerful methods to address complex nonlinear regression problems. This study developed three models, namely a Multilayer Perceptron Neural Network (MPNN), a Support Vector Machine (SVM), and a Tree Boost, to predict field saturated hydraulic conductivity using easily measurable soil properties, such as hydraulic conductivity, clay/silt ratio, soil saturation percentage, d90 of grains, liquid limit, plastic limit, soil pH, hydrocarbon anions, chloride ions, and calcium carbonate content. Soil samples were collected from two locations: the El-Nubaria and Sinai regions, located in the western delta of Egypt. To evaluate the performance of these models, five distinct metrics, namely Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Scatter Index (SI), and Correlation Coefficient (R), were employed along with a Taylor diagram. Among the models tested, the Tree Boost model demonstrated exceptional accuracy in predicting field-saturated hydraulic conductivity, having a lower SI (0.085) compared to the SVM (0.192) and MPN (0.226) models. Moreover, the Tree Boost model exhibited a higher R value (0.99) than SVM (0.981) and MPN (0.974). The Tree Boost results were compared with those of previous models. The findings highlight the effectiveness of the Tree Boost model and suggest its potential as a reliable tool for estimating field-saturated hydraulic conductivity and generating highly accurate predictions.

Improving the Performance of DVB-T2 in High-Speed Train Communication Systems

2025-04-04T06:58:34+00:00

The development of Digital Video Broadcasting-Terrestrial (DVB-T2) technology has spread worldwide and been implemented in various wireless communication channel environments, including High-Speed Train (HST) communication. The HST channel's characteristics, including extremely high speeds of up to 300 km/h and the presence of numerous multipaths, significantly degrade the performance of DVB-T2. This paper proposes a method to address these challenges by employing Bose-Chaudhuri-Hocquenghem (BCH) and block code LDPC channel coding, along with Minimum Mean Squared Error (MMSE) channel estimation and equalization. The proposed approach integrates HST channel modeling with a DVB-T2 communication system that has been modified to mitigate the effects of Doppler and multipath fading. The BCH outer code is designed to address burst errors, while the LDPC inner code is engineered to handle random errors. The efficacy of the channel estimation and MMSE equalization methods in HST channels has been demonstrated, ensuring the preservation of the orthogonality of the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers used and the mitigation of Inter-Carrier Interference (ICI). To this end, we have simulated the performance of this research on HST channels with various digital modulation types (16-QAM, 64-QAM, and 256-QAM) and also the effect of the number of multipath fading scatterers and the variation of train speed. The results of this research demonstrate a significant BER reduction at Signal-to-Noise Ratio (SNR) 15 dB, where the BER value reaches 10-3 when using 16-QAM. Among the various modulation options examined, 256-QAM emerged as the most effective, demonstrating a performance enhancement of up to 85% compared to the baseline scenario without the proposed mitigation. The research findings underscore the significance of train speed in Bit Error Rate (BER) performance, indicating that an increase in train speed leads to a deterioration in BER performance. Furthermore, the study highlights the impact of the number of scatterers on BER performance, demonstrating that an increase in scatterers results in a decline in BER performance. The findings of this research further reinforce the efficacy of DVB-T2 integrated with high-speed rail transportation applications, which have begun to show significant growth in numerous countries.

The Effect of Nano Materials on the Rheological Properties of Asphalt Binder

2025-04-04T06:56:21+00:00

This study investigates the effects of nano-silica, nano-chitosan, and nano-clay as asphalt binder modifiers, focusing on their physical and rheological properties. The nanomaterials were incorporated at concentrations of 2.5%, 5.0%, 7.5%, and 10% by weight. This research was driven by the growing adoption of nanomaterials, many of which are known for their toxicity and environmental impact. The results demonstrated a significant improvement in both physical and rheological properties, particularly in rutting resistance. Nano-silica demonstrated the greatest enhancement, leading to a reduction in permeability values, an increase in softening point, viscosity, and overall resistance to permanent deformation. Nano-chitosan exhibited a similar trend but with slightly lower performance values than nano-silica. Nano-clay provided the least improvement, though it still contributed to increased asphalt binder stiffness and reduced temperature sensitivity. Overall, the findings confirm that nano-silica, nano-chitosan, and nano-clay can improve asphalt binder properties, extending pavement lifespan and performance under increasing traffic demands. Further studies are recommended to explore the long-term aging resistance, fatigue behavior, and field applications of these nanomaterial-modified asphalt binders.

Design and Optimization of a Compact Inset Feed Microstrip Antenna for 5G Applications with Enhanced MIMO Performance

2025-04-04T06:55:58+00:00

This study presents the design, simulation, and optimization of a compact inset feed microstrip antenna for fifth-generation (5G) applications. With dimensions of 6.2 × 8.4 × 1.57 mm³, the proposed antenna utilizes a Rogers RT5880 substrate (ε_r = 2.2, loss tangent = 0.0013) and operates at resonant frequencies of 28 GHz and 26 GHz. The design, performed using CST Microwave Suite 2018, achieves an operational bandwidth of 5.368 GHz (25.144–30.512 GHz), with a relative bandwidth of 19.3%. At 28 GHz, the antenna exhibits a return loss of -25.166 dB and a gain of 7.33 dB, while at 26 GHz, it achieves a return loss of -13.2 dB and a gain of 7.88 dB. Enhancements using a 2-by-1 MIMO configuration, including inverted, mirrored, and nearby arrangements, were investigated. The inverted configuration demonstrated the highest gains of 8.15 dB and 7.96 dB at 26 and 28 GHz, respectively. The proposed antenna demonstrates applicability in compact mobile devices, Internet of Things (IoT) systems, and smart city infrastructure, underlining its practical relevance.

Diagnosis and Classification of Depressive Disorders using ML and DL Models

2025-04-04T06:57:03+00:00

The diagnosis and classification of depressive disorders pose significant challenges in mental healthcare, mainly due to overlapping symptoms, subjective evaluations, and variations in patient presentations. Traditional diagnostic approaches often lack objectivity and fail to capture the complex nature of depression across diverse populations. This study introduces a comprehensive framework that leverages advanced Machine Learning (ML) and Deep Learning (DL) models to improve the accuracy and reliability of diagnosing depressive disorders. Using the SAMM (Spontaneous Micro-Facial Movement) dataset, comprising 11,800 high-resolution facial images capturing spontaneous facial expressions, the proposed framework integrates dual embedding methods (GloVE and BERT) with hierarchical attention mechanisms for feature extraction. Parallel processing streams of LSTM and CNN architectures allow the recognition of intricate patterns across multimodal data. Experimental results showed superior performance across key metrics, achieving an accuracy of 94%, precision of 92%, recall of 93%, F1-score of 92.5%, and an AUC-ROC of 0.96. The proposed framework provides an efficient, interpretable, and scalable solution to advance mental health diagnostics, addressing the urgent need for objective and standardized tools in psychiatric care.

Actor Optimization Algorithm: A Novel Approach for Engineering Design Challenges

2025-04-04T06:54:13+00:00

In this paper, a novel human-based metaheuristic algorithm called Actor Optimization Algorithm (AOA) is introduced. AOA mimics the behaviors of an actor when playing a role. The main idea in designing AOA is derived from a specific behavior of the actor including (i) simulating the movements and dialogues of the given role and (ii) practicing to better present the assigned role. The theory of AOA is stated and mathematically modeled in the phases of exploration and exploitation. The performance of AOA to address real-world applications is evaluated on the CEC 2011 test suite. The optimization results show that AOA, with its high ability in exploration, exploitation, and balancing during the search process, achieved suitable results. In addition, the performance of AOA was challenged by comparing it with 12 known metaheuristic algorithms. Result comparison showed that the proposed AOA outperformed the competing algorithms by 100% (in all 22 optimization problems) of the CEC 2011 test suite. The simulation results show that AOA has a successful performance in handling optimization tasks in real-world applications by achieving better results in competition with the compared algorithms.

A Fine-Tuned BART Pre-trained Language Model for the Indonesian Question-Answering Task

2025-04-04T07:00:02+00:00

The information extraction process from a given context can be time consuming and a Pre-trained Language Model (PLM) based on the transformer architecture could reduce the time needed to obtain the information. Moreover, PLM is easily fine-tuned to accomplish certain tasks, one of which is the Question-Answering (QA) task. In literature, QA tasks are generally fine-tuned using encoder-based PLMs, such as the Bidirectional Encoder Representations from Transformers (BERT), where the generated answers come from the extraction process of the context. In order to be able to return more abstract answers, a PLM with Natural Language Generation (NLG) capability, such as the Bidirectional and Auto-Regressive Transformer (BART), is needed. In this study, we aim to fine-tune the NLG PLM using BART to build a more abstractive generative QA task. Based on the experimental results, the fine-tuned BART model performs well with an 85.84 F1 score and a 59.42 Exact Match (EM) score.

Exploring the Eco-Friendly Potential of Local Aggregates: A Study on the Use of Senoni Stone and Mahakam Sand in Asphalt Concrete Mixes

2025-04-04T06:56:39+00:00

This study investigates the potential environmental benefits of employing locally produced materials, such as Senoni Stone (SS) and Mahakam Sand (MS), in asphalt concrete mixtures, aiming to achieve a balance between environmental sustainability and performance requirements. Since aggregateσ constitute 75%-85% of the total weight of asphalt mixtures, it serves as a primary component. The aggregates used in the specific mixtures are required to meet the Indonesian National Standard (SNI) specifications. In the current work, the Asphalt Concrete Wearing Course (AC-WC) combination was examined using SS as the coarse aggregate and MS as the fine aggregate, both of which originate from East Kalimantan. The volumetric metrics analyzed include Void in Mix (VIM), Void Mineral Aggregate (VMA), and Void Filled Bitumen (VFB). Asphalt concentration varied, with values of 4.5%, 5%, 5.5%, 6%, and 6.5% with increments of 0.5%. VIM values decreased as the asphalt quantity increased, with the corresponding VIM values being 9.26%, 4.51%, 4.33%, 4.17%, and 3.25%. The VMA values recorded for each asphalt content were 19.18%, 16.05%, 16.97%, 17.92%, and 18.56%, respectively, while the VFB values for the same contents were 51.7%, 71.87%, 74.49%, 76.68%, and 77.86%. It is therefore recommended that locally sourced materials, such as SS and MS, be prioritized in asphalt concrete mixtures to support sustainable construction practices. This approach not only reduces environmental impact, but also enhances resource efficiency and strengthens local economies.

Effect of Nozzle Diameter and Raster Angle on the Mechanical Properties of 3D Printed Nylon/ Carbon Fibers

2025-04-04T06:57:39+00:00

Fused Deposition Modeling (FDM) is classified as the most commonly used 3D printing process due to its low cost, wide range of material selection, and high accuracy. As an additive manufacturing method, FDM selectively deposits a melted plastic material layer by layer to produce a 3D object according to a geometry defined by a CAD model. The 3D printing process parameters, including infill density, printing speed, and printing orientation, have a huge effect on the mechanical properties of the 3D printed parts. Thus, finding the optimum 3D printing parameters is a very significant task that enriches the FDM 3D printing process, resulting in 3D printed parts with augmented mechanical performance. The present study investigates the effects of the FDM injector’s nozzle diameter and printing path direction (raster angle) on the mechanical properties of the nylon/carbon fiber composite 3D printed parts. The two targeted parameters are optimized through experimental tests on the elastic and flexural strength. Their impact on the nylon/carbon fiber composites’ microstructure is also explored deploying Scanning Electron Microscopy (SEM). The findings provide a comprehensive understanding of the mechanical performance of nylon/carbon fiber composite 3D printed parts. In addition, inspecting the internal microstructure of the materials, especially at the interface zone between the nylon and carbon fiber, provides an explanation of the material composites’ failure mechanism under various loads.

Evaluating Flood Susceptibility through integrated Geospatial Techniques in Thailand’s Monsoon Regions

2025-04-04T07:05:43+00:00

This study integrated Geographic Information Systems (GIS) with the Potential Surface Analysis (PSA) method to assess and map flood hazards in the repeatedly flooded Khuan Khanun District of Phatthalung Province, Thailand. Amid the escalating global concerns about climate change impacts this research addressed critical gaps in local disaster preparation and resilience strategies demanded to face the increased precipitation and intensified flooding documented in recent IPCC reports. By synthesizing data considering multiple parameters such as slope, elevation, water and road density, land use, and soil drainage, this work delineated flood-prone areas, emphasizing their susceptibilities and the specific environmental dynamics that influence hydrological behavior. The findings revealed that over 66% of Khuan Khanun District fell within high to very high flood risk zones, accentuating the urgent need for enhanced flood mitigation measures. The significance of comprehensive flood risk management is hilighted, having incorporated advanced mapping techniques to inform effective policymaking and community-engaged planning, aiming to bolster regional resilience against recurrent threat of flooding.

An Efficient Technique to Improve Fault Categorization in Transmission Lines

2025-04-04T07:00:36+00:00

Machine Learning (ML) has become an essential tool for solving complex problems in the domain of electrical engineering. The ability to examine a large dataset, search for paths, and predict trends has enabled much progress. The strength of the research lies in the use of ML algorithms for fault classification in transmission lines. The ML models take into account the presence of a faulty voltage or current while the fault is occurring. This research confirms the efficiency of the algorithms built with ML techniques. A faulted transmission line, simulated in the MATLAB/Simulink environment, is used and techniques such as Decision Tree (DT) and Random Forest (RF) are implemented for classification purposes. The Receiver Operating Characteristic (ROC) curve, Precision-Recall (PR), and confusion matrix demonstrate the efficiency of the proposed algorithm. The converged technique optimizes the fault categorization and increases both precision and effectiveness by detecting faults within the transmission lines.

Investigation of V2O5 and CeO2 Nanoparticles: Synthesis, Characterization, and Application in Ammonium Removal from Aqueous Solutions

2025-04-04T07:00:29+00:00

In this study, vanadium pentoxide (V₂O₅) and cerium dioxide (CeO₂) nanoparticles were synthesized using hydrothermal and autoclave methods, respectively. The nanoparticles underwent thermal treatment at 90 °C and 400 °C, followed by structural and compositional analysis through X-Ray Diffraction (XRD). The surface morphology was examined using field emission Scanning Electron Microscopy (SEM), while Atomic Force Microscopy (AFM) was employed to assess the nanoscale surface roughness. The Fourier Transform Infrared Spectroscopy (FTIR) identified the functional groups, and the UV/Visible spectrometry evaluated their optical properties. The ammonium removal efficiency of the synthesized nanoparticles was also investigated. The results indicated that vanadium pentoxide exhibited the highest ammonium removal efficiency at 90 °C and 400 °C, with nanoparticles treated at 400 °C demonstrating enhanced performance compared to those treated at 90 °C.

Optimizing Asphalt Mixture Performance with Modified Buton Asphalt and Recycled PET using the Response Surface Methodology

2025-04-04T06:53:51+00:00

In order to enhance asphalt-concrete mixtures, additives are used to improve durability and resilience to repeated road loads. The present study uses plastic waste as an additive and aims to find the optimal parameters for blending asphalt with additives made from Plastic Bottle Waste (PET) using Response Surface Methodology (RSM). The RSM approach, guided by the Design-Expert 8.0.6 software (Stat-Ease, Inc., Minneapolis, MN, USA), involved the design and analysis of a series of 17 experiments, employing a three-factorial Box-Behnken Design. The experimental design incorporated three primary independent variables, with the first (X1) being the ratio of PET to Modified Buton Asphalt (MBA), and the dependent variable (Y) corresponded to the Marshall characteristic, which serves as a measure of the reaction output. The results indicate that the incorporation of PET-based additives significantly enhances the stability, flow, and void characteristics of the asphalt mixture. The optimal mixture was achieved at an X1 of 2.0%:6.25%, which resulted in an improvement of Marshall stability by 47.89% compared to conventional asphalt mixtures. Furthermore, the addition of PET improved asphalt resistance to deformation and fatigue cracking. Consequently, it is recommended that further investigation be conducted into the potential of PET-based additives for large-scale applications in road construction, with a focus on long-term performance and environmental impacts.

Effect of Carbodiimide Crosslinking on Gelatin-Carboxymethylcellulose-Polycaprolactone Scaffold Properties for Wound Dressing Applications

2025-04-04T07:05:39+00:00

This research investigated the feasibility of fabricating 3D porous scaffolds from gelatin, carboxymethylcellulose (CMC) and polycaprolactone (PCL) using the freeze-drying technique for wound dressing applications. The scaffolds were crosslinked using Dehydrothermal Treatment (DHT) and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) combination with N-hydroxysuccinimide (NHS). Their morphological and mechanical properties were analyzed to determine the optimal condition. For DHT crosslinking, the results demonstrated that the average pore size ranged from 127.25 to 150.77 µm, which was smaller than in non-crosslinked scaffolds. The porosity ranged from 64.84% to72.08%, decreasing as CMC content increased. The gelatin scaffold with 35% (w/w) CMC and 30% (w/w) PCL exhibited the best overall properties. It provided the highest average pore size and porosity, and a compressive strength of 47.39 MPa, which was higher than non-crosslinked scaffold. Under EDC/NHS conditions, the average pore size ranged from 145.40 µm to 184.80 µm and porosity from 70.24% to 74.48%. These characteristics indicate a larger pore size and porosity compared to the DHT crosslinked scaffold Although its compressive strength was lower than that of the DHT crosslinked scaffold, it remained higher than that of the non-crosslinked scaffold. Therefore, it can be implied that the gelatin scaffold with 35% CMC and 30% PCL is suitable for use as a skin substitute in wound dressing applications

Local Binary Pattern in the Frequency Domain: Performance Comparison with Discrete Cosine Transform and Haar Wavelet Transform

2025-04-04T06:55:20+00:00

This study presents a new method that aims to improve iris recognition performance by amplifying high-frequency components in the frequency domain, considering that iris images naturally contain high-frequency details. The Haar Wavelet Transform (HWT) and Discrete Cosine Transform (DCT) are used to enhance these components and an inverse transformation is applied to obtain iris images with more details. As input, the brightness values of the 8 neighboring pixels around each central pixel are used. These values are transformed into the frequency domain, the high-frequency band is amplified, and the data are reconstructed. Feature vectors are then generated using the Local Binary Pattern (LBP) algorithm, which is fed with the enhanced images. These feature vectors are formed using a combination of local histograms rather than a global LBP histogram, which are normalized to ensure consistency. The generated feature vectors are divided into a 70% training set and a 30% test set and are tested using K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF) algorithms. The proposed method provides a significant performance improvement in terms of accuracy compared to traditional approaches. While both HWT and DCT yield similar results, it has been observed that HWT is much faster. In this study, a comparison is made in terms of both speed and accuracy. Two different public iris datasets, MMU1 and MMU2, are used. This work not only introduces an innovative approach to iris recognition, but also makes a significant contribution to the manipulation of pixel brightness values in the frequency domain, with the findings being expected to guide future research.

Efficient Load Balancing for Future Dense Networks using Radio over Fiber Infrastructure and applying Different Learning Rates

2025-04-04T06:54:59+00:00

Reinforcement Learning (RL) can lead to effective Load-Balancing (LB) mechanisms, as traditional methods cannot always provide an optimal solution in cellular networks. This study proposes an RL-based LB scheme for a dense network that uses radio over fiber infrastructure. The proposed technique is based on LB constraints in the action space that maintain zero violation during the learning process. In this technique, a Deep Q-Network agent was chosen to search for an optimal policy to maximize the expected cumulative long-term reward to satisfy the constraints. This study uses the number of user entities per base station in the dense network as constraints to maintain average throughput based on the Signal-to-Noise Ratio (SNR) generated from the radio frequency signals of the network. The proposed method outperformed at an SNR of 38 dB with a throughput of 32 Mbps for a 20 MHz channel bandwidth for macro- and microcells in the dense network. Furthermore, this study examined the effect of different learning rates as hyperparameters in the system. The proposed approach shows that when the agent was trained with a learning rate of 1e-3, the network performed well by obtaining a higher CDF compared to a learning rate of 1e-5. In addition, the system achieved higher rewards for a learning rate of 1e-3 with or without LB constraints, confirming the efficiency of the proposed scheme. The simulation results showed that CDF was 4% higher when using constraints compared to without constraints.

Minimum Surface Roughness Prediction of Grinding SKD11 Steel with the Response Surface Methodology

2025-04-04T06:55:08+00:00

Surface roughness is a very important technical index of mechanical components that meets requirements for improving performance efficiency and reducing corrosion behavior in its working environment. Surface roughness is generally achieved by cutting methods using machine tools and cutting tools. The grinding process is a special finishing method using thousands of abrasives bonded together to form a grinding wheel for the finishing process with cost-effectiveness, productivity, and high quality. By controlling technological parameters, the grinding process produces products with high-quality of the roughness surfaces. However, it is difficult to compose a set of processing parameters for processing a hardened steel material such as the grinding process. This paper demonstrates a method of enhancing the quality of the surface roughness of SKD11 engineering steel material by controlling the grinding process parameters. The optimal grinding condition is conducted by using response surface methodology to analyze practical experiments designed by the Box-Behnken method to minimize the surface roughness of the final product during the grinding process. The results show that the prediction model of the surface roughness can be achieved with the minimum surface roughness of Ra of 0.591 µm and the processing parameters kit of v of 5 m/min, s of 3 mm/stroke, and t of 0.013 mm, respectively, via using the design of experimental method and the regression analysis law. The research hopes that the results will be referenced in the development of manufacturing technology for products that require high-quality index of surface roughness in the near future.

Deep Feature Extraction and Classification of Diabetic Retinopathy Images using a Hybrid Approach

2025-04-04T06:53:43+00:00

Hybrid approaches have improved sensitivity, accuracy, and specificity in Diabetic Retinopathy (DR) classification. Deep feature sets provide a more holistic analysis of the retinal images, resulting in better detection of premature signs of DR. Hybrid strategies for classifying DR images combine the strengths of extracted deep features using pre-trained networks and Machine Learning (ML)-based classifiers to improve classification accuracy, robustness, and efficiency. Perfect pre-trained networks VGG19, ResNet101, and Shuffle Net were considered in this work. The networks were trained using a transfer learning approach, the pre-trained networks were chosen according to their classification accuracy in conjunction with the Softmax layer. Enhanced characteristics were extracted from the pre-trained networks' last layer and were fed to the machine learning-based classifier. The feature reduction and selection methods are essential for accomplishing the desired classification accuracy. ML-based kNN classifier was used to classify DR and a PCA-based feature reduction approach was utilized to obtain optimized deep feature sets. The extensive experiments revealed that ResNet101-based deep feature extraction and the kNN classifier delivered enhanced classification accuracy. It is concluded that combining deep features and the ML-based classifier employing a hybrid method enhances accuracy, robustness, and efficiency. The hybrid approach is a powerful tool for the premature identification of DR abnormalities. The PCA-kNN classification algorithm, which employs features obtained from the ResNet101, attained a peak classification accuracy of 98.9%.

Development of High-Performance Concrete with Advanced Materials for Sustainable Building Applications

2025-04-04T06:52:05+00:00

Developing High-Performance Concrete (HPC) with advanced materials is crucial for achieving superior concrete that aligns with sustainable building practices. The use of innovative materials enhances both fresh and hardened properties, offering improved workability and strength. This study explores the impact of incorporating advanced materials into concrete mixtures by evaluating the performance of different compositions. Three mix variations were prepared by adjusting the types and dosages of admixtures. The first mix used a conventional Type F superplasticizer, while the other two applied advanced materials at varying dosages. Slump tests were conducted on fresh concrete and cylindrical specimens (10x20 cm) were tested to measure unit weight and compressive strength after 7, 14, and 28 days. Results indicate that the use of advanced materials significantly improves concrete performance, even at lower dosages compared to traditional superplasticizers. This research confirms that incorporating advanced materials improves both workability and compressive strength of concrete. The findings suggest that these materials offer a sustainable solution for developing high-performance concrete with enhanced durability and reduced material consumption. Consequently, their integration in construction can contribute to more sustainable, efficient, and resilient building structures. Further research is recommended to explore the long-term effects of advanced materials on concrete performance under various environmental conditions. The study highlights the potential of advanced material technologies as a transformative approach in concrete quality management within the construction industry.

Elevated Temperature Performance of a Novel Eco-Friendly Cementitious Material

2025-04-04T06:55:03+00:00

This study presents the development of a novel environmentally friendly cementitious material and examines its behavior at elevated temperatures. Ceramic Waste Powder (CWP) and Fly Ash (FA) were utilized as substitutes for cement at a concentration of 10%. Four distinct mix designs were formulated and evaluated. The flow table test was implemented to study the workability of the mortar, while 120 cubes and 39 specimens were shaped into briquettes. The specimens’ compressive strength, mass loss, and tensile strength were analyzed after exposure to temperatures ranging from 150 °C to 700 °C, deploying Field Emission Scanning Electron Microscopy (FESEM) testing. The research findings indicate that CWP can function as a sustainable material in construction, diminishing the carbon footprint of construction materials and alleviating the environmental damage resulting from CWP disposal in landfills. Furthermore, it was found that FA can be combined with pulverized ceramic to substitute cement, resulting in a sustainable cement mortar. It was concluded that sustainable mortar production was attained while substituting 20% of cement with alternative materials.

Analysis of the Nondeterministic Dynamic Structural Behavior of a Steel Wind Tower when Subjected to Wind Loadings

2025-04-04T07:02:26+00:00

This study presents an in-depth investigation into the structural dynamics response of a wind tower designed to support a 2 MW onshore wind turbine. The tower's finite element model was developed using the Finite Element Method (FEM), utilizing the ANSYS software and considering the wind loadings on the rotor and tower and the effect of the geometric nonlinearities and soil-structure interaction, aiming to obtain a realistic representation of the structure's dynamic behavior. The stochastic nature of the wind loadings was considered, and a statistical analysis was carried out on the structure's dynamic responses. Then, an extensive parametric study was performed, considering several basic wind velocities to assess the steel tower's dynamic structural behavior based on horizontal displacements, von Mises stresses, and the fatigue service life. The results showed that within the operational limit of the turbine, the investigated tower complies with the recommended limits specified in the current wind tower design standards. However, for higher basic wind speeds, the wind tower's structural design does not meet these requirements.

Advanced Correlations for Predicting Wax Precipitation in Crude Oil: A Study on Melting Point and Solid-State Transition Temperatures

2025-04-04T07:03:32+00:00

This study presents an in-depth investigation into the fusion properties, specifically the melting point and solid-state transition temperature, of crude oil samples from five distinct fields in Kazakhstan. These properties are critical for understanding and predicting wax precipitation, which poses significant challenges in the petroleum industry, particularly in cold climates where wax deposition can obstruct pipelines. Using advanced analytical techniques, including gas chromatography and pour point testing, new correlations were developed to more accurately predict these fusion properties for Kazakhstani crude oil. The proposed correlations outperform the existing models, offering closer alignment with the experimental data across a wide range of hydrocarbon compounds. The novelty of this research lies in its tailored approach, which integrates experimental data, existing predictive models, and Python programming to develop a region-specific solution for Kazakhstani crude oil. By addressing the limitations of generalized models, the study highlights the importance of adapting predictive frameworks to specific oil compositions and regional conditions. These findings have substantial implications for the optimization of crude oil transportation and storage in cold environments, where wax deposition is a prevalent issue. The improved accuracy of the proposed correlations enables better predictability of wax-related flow assurance problems, contributing to more efficient and safer operations in the oil and gas industry. Furthermore, this work establishes a robust methodological framework that can be extended to other crude oil types and operational scenarios, paving the way for advancements in predictive modeling of wax precipitation under diverse environmental conditions.

Control Strategy of OLTC using Quantum Binary Particle Swarm Optimization to Improve the Voltage Stability Index

2025-04-04T07:02:23+00:00

Efficient voltage regulation in distribution and transmission systems heavily relies on transformers with On-Load Tap Changers (OLTC). This study introduces a novel optimization technique, called Quantum Binary Particle Swarm Optimization (QBPSO), to optimize transformer tap settings to improve voltage stability and reducing power losses. QBPSO combines the principles of quantum computing with binary particle swarm optimization, enhancing the algorithm's exploration and exploitation capabilities. Utilizing the Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method for power flow analysis, this research evaluates the performance of the proposed method on the IEEE 34-bus 20 kV radial distribution system. The results indicate a significant reduction in the Voltage Stability Index (VSI) from 0.2257 to 0.2069, a decrease in power losses from 21.756 kW to 19.1573 kW, and an improvement in the average voltage from 19.0047 kV to 19.9453 kV. A comparative analysis with Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Quantum Differential Evolution (QDE) demonstrates that QBPSO achieves superior performance in computational efficiency and voltage stability enhancement. These results highlight the effectiveness of QBPSO as a powerful tool for optimizing OLTC settings, contributing to the reliability and efficiency of power distribution systems.

Integrating the Root Assessment Method with Subjective Weighting Methods for Battery Electric Vehicle Selection

2025-04-04T06:52:10+00:00

The global automotive industry is actively transitioning towards the production of BEVs (Battery Electric Vehicles) to significantly reduce carbon emissions and address climate change. In the context of a world striving for sustainable development, selecting the right BEV has become a crucial decision for consumers. This study pioneers the application of the RAM (Root Assessment Method) method for BEV selection among 10 available options. Each electric vehicle is described by 11 criteria, with weights calculated using two subjective weighting methods: the ROC method and the RS (Rank Sum) method. Regardless of the weighting method employed for the criteria, the RAM method consistently identifies the same optimal BEV. Furthermore, the top-ranked electric vehicles obtained using the RAM method in conjunction with either the ROC or RS weighting methods exhibit a high degree of similarity to those determined using other ranking methods and different criteria weighting approaches.

DLKS-MQTT: A Lightweight Key Sharing Protocol for Secure IoT Communications

2025-04-04T06:53:31+00:00

The increasing reliance on Message Queuing Telemetry Transport (MQTT) as a lightweight messaging protocol for Internet of Things (IoT) applications requires robust security mechanisms that address resource constraints while ensuring data integrity, confidentiality, and authenticity. This paper proposes the Dynamic Lightweight Key Sharing for MQTT (DLKS-MQTT) mechanism, a novel approach that integrates ephemeral key generation, streamlined authentication, and lightweight cryptographic operations to enhance the security of MQTT-based IoT communications. The mechanism employs a 128-bit key generated using a Linear Congruential Generator (LCG), providing robust resistance to brute-force and cryptanalytic attacks while maintaining computational and energy efficiency. Through extensive performance evaluations, DLKS-MQTT demonstrates significant improvements: reducing CPU energy consumption to 0.000002 mJ, achieving an execution time of 0.40 s, and minimizing communication overhead to 60 bytes, outperforming existing methods such as Dynamic Lightweight Authentication for MQTT (DLA-MQTT), Improved Ciphertext Policy-Attribute-Based Encryption (ICP-ABE), and Secure MQTT (SMQTT). The use of ephemeral session keys and nonces ensures protection against replay and Man-in-the-Middle (MitM) attacks, whereas lightweight hashing guarantees message integrity without burdening resource-constrained devices. This work establishes DLKS-MQTT as a practical, scalable, and secure solution for modern IoT networks, offering a balance between performance and security.

The Conception of Fundus Multi-Disease Dataset (FMDD) using Multi-Spectral Generative Adversarial Networks

2025-04-04T06:59:14+00:00

The World Health Organization (WHO) reports that 2.2 billion people are affected by visual impairment. Early detection and diagnosis of ocular pathologies can help predict visual impairment. Over the past twenty years, many fundus image datasets have become publicly available due to technological advances. These datasets have primarily focused on the detection of common ocular pathologies such as age-related macular degeneration, diabetic retinopathy, and glaucoma, and recent research in fundus diseases has highlighted the importance of detecting multiple fundus diseases. Existing datasets such as ARIA and RFMiD mainly contain images of the most common pathologies and very few images related to rare pathologies. The existing public datasets have problems in multi-disease classification, such as less data under some under-represented diseases, low-quality photos, and class imbalance among several classes. The main objective of our research is to construct a Fundus Multi-Disease Dataset (FMDD) with 20 courses of ocular diseases from publicly available datasets and with the application of Multi-Spectral Generative Adversarial Networks (MSGANs). The resulting dataset is balanced for all image classes.

Development of an Extension for CityGML 3.0 for Fire Prevention Projects

2025-04-04T07:02:55+00:00

To elaborate on a technical fire and disaster prevention project, information about the building and its surroundings is key to determining which safety measures should be used. The use of 3D models that integrate GIS and BIM has been a promising tool for this type of analysis, which requires information from different sources in the same environment. This study presents the construction of a conceptual model for an extension of CityGML (Application Domain Extension - ADE) with the necessary information to elaborate a technical fire and disaster prevention project, following the standards of the Fire Department of the state of Paraná, Brazil. In addition to information from IFC models, information obtained from mapping carried out by the UFPRCampusMap (UCM) project at the Federal University of Paraná was also added. To build the ADE, IFC and UCM conceptual model formats were studied, as well as their geometric and semantic correspondence with CityGML. New classes were created, classes for specific attributes, and subclasses derived from existing classes. For a total of 86 attributes, 21 have full correspondence, 27 partially correspond, and 38 have no correspondence. The correspondence of real-world objects to IFC was much greater than that of CityGML, since the latter had more generic classes regarding the interior of the building. Using this model, it will be possible to implement this extension in 3D models, as a suggestion for future studies.

Adaptive Gait Control for Quadruped Robots on Varied Slopes via ARS Algorithm

2025-04-04T07:02:46+00:00

Ensuring stable walking for quadruped robots on unknown slopes is a critical challenge in robotic navigation. This study introduces a novel gait planning algorithm that leverages data from an Inertial Measurement Unit (IMU) for terrain slope estimation, offering a cost-effective alternative to visual sensors. The proposed approach integrates a trot gait with an elliptical foot trajectory, enabling efficient movement across varied slopes. Using the Augmented Random Search (ARS) algorithm, we fine-tune the elliptical trajectory parameters to achieve precise and adaptive foot placements. Additionally, the robot dynamically adjusts its posture in real time to maintain stability by aligning with desired joint angles during slope traversal. Simulation results validate the effectiveness of the proposed algorithm, demonstrating its ability to ensure stable and adaptive locomotion on slopes of up to 11 degrees. This work highlights the feasibility of using low-cost hardware and advanced algorithms to address complex terrain navigation challenges.

Dimensional Stability and Moisture Content of White Teak Wood Treated with Nano-SiO2 and Furfuryl Alcohol

2025-04-04T06:54:17+00:00

The objective of this study is to assess the impact of nanoparticle impregnation on the moisture content and dimensional stability of white teak wood derived from Southeast Sulawesi, Indonesia. The impregnation method was employed to process white teak wood samples, with varying concentrations of Furfuryl Alcohol (FA) and nano-SiO₂. The wood samples underwent examinations to quantify the moisture content, dimensional changes, and mechanical properties after treatment, including Weight Percent Gain (WPG), Bulking Effect (BE), Anti-Swelling Efficiency (ASE), and Water Uptake (WU). The results indicated that the dimensional stability of white teak wood was considerably enhanced with the impregnation process, as evidenced by a decrease in swelling, compared to the untreated wood. Additionally, the moisture content of impregnated wood decreased significantly, suggesting improved moisture resistance. These findings demonstrate that the application of nano-SiO₂ and FA as impregnation materials offers a viable solution for enhancing the quality and durability of white teak, potentially expanding its applications in the construction sector.

Volumetric and Rutting Analysis on Degradation of Aggregate and Asphalt Reclaimed Asphalt Pavement

2025-04-04T07:03:36+00:00

The performance of a hot asphalt mixture as a road surface layer is subject to deterioration due to the effects of traffic loads and weather conditions. The process of asphalt aging is characterized by a decline in physical properties and alterations in aggregate composition. To maintain the integrity of the road surface, the surface layer is periodically removed and recycled. This study aims to simulate the process of aggregate and asphalt degradation as an asphalt aging process. The study proposes a model with five levels of asphalt physical property decline and four levels of Coarse Aggregate (CA), arranged into 20 variations. The analysis results indicate that a decrease in the percentage of CA reduces stability and increases air cavities, while an increase in asphalt penetration on specific CAs increases cavity volume and stability. The correlation test results demonstrate that the mixture with optimal CA strengthens resistance to deformation, especially at low-temperature conditions. However, at temperatures of 40 °C and 55 °C, the initial deformation rate increases sharply. The degraded mixture exhibits a decrease in deformation resistance at high temperatures. The findings of this study can be used to ascertain the optimization of the residual value of the performance of asphalt concrete mixtures for recycling processes involving reconditioning aggregate and asphalt.

Enhancing Indoor Positioning Accuracy using a Hybrid Li-Fi/Wi-Fi System with Deep Learning Support

2025-04-04T06:52:57+00:00

This study proposes a new indoor positioning system that utilizes Li-Fi/Wi-Fi technology and the Received Signal Strength (RSS) triangulation method, aided by a Deep Neural Network (DNN) for better system accuracy. The proposed system uses several Light-Emitting Diodes (LEDs) as light emitters and photodetectors as receivers to determine the position of a user in an indoor environment. Photodetectors measure the RSS of a Li-Fi or Wi-Fi signal, which is then used to calculate the distance between the light sources and the user. RSS values are entered into a DL model to improve the accuracy of the positioning system by predicting the location of the user in more detail. The proposed system was experimentally tested and the results show that this method can achieve high positioning accuracy. The main objective of this work was to locate the mobile user within a room equipped with Li-Fi technology and obtain the best possible coverage of service to the user. In the first stage of data simulation, the triangulation technique achieved average errors of 2.174×10^-14 cm, 6.450×10^-14 cm, and 4.657×10^-11 cm for the x, y, and z axes, respectively. This indicates the proximity of the simulation results to the actual ones. In the second stage, when RSS triangulation was applied with noise effects, the average error was 2.060×10^-3 cm, 4.565×10^-3 cm, and 5.110× 10^-3 cm for the x, y, and z axes, respectively. A DL technique was used to handle noise, and the greatest error for the x, y, and z axes was 2.520 cm, 2.260 cm, and 4.230 cm in a 6×4×3 m indoor environment.

Study and Analysis of a New Five-Dimensional Hyper-Chaotic System

2025-04-04T06:56:35+00:00

This study presents a novel Five-Dimensional (5D) hyper-chaotic system to increase the degree of disorder in a system, which comprises 10 positive chaotic parameters as well as complex chaotic dynamic properties. The system’s basic properties and dynamic behavior are investigated based on the existence of equilibrium points, Lyapunov Exponents (LEs), chaotic attractors, dissipative properties, symmetry, waveform analysis, Kaplan-Yorke dimensions, bifurcation properties, and sensitivity to initial conditions. The new system has 5 LEs in addition to 2 points of unstable equilibrium. According to the study's findings, the Maximal positive Lyapunov Exponent (MLE) and Kaplan-Yorke estimated values are 6.85408 and 4.37292, respectively. The results show that the innovative system exhibits highly complicated, unstable, and unpredictably unstable properties.

Explainable Artificial Intelligence with Single Layer Feedforward Neural Network and Improved Crowned Porcupine Optimization Algorithm for Classification Problems

2025-04-04T06:56:12+00:00

The increasing occurrence of network intrusions calls for the development of advanced Artificial Intelligence (AI) techniques to tackle classification challenges in Intrusion Detection Systems (IDSs). However, the complex decision-making processes of AI often prevent human security professionals from fully understanding the behavior of the model. Explainable AI (XAI) enhances trust in IDSs by providing transparency and assisting professionals in interpreting data and reasoning. This study explores AI techniques that improve both accuracy and interpretability, strengthening trust management in cybersecurity. Integrating performance with explainability improves decision-making and builds confidence in automated systems for classifying network intrusions. This study presents an Explainable Artificial Intelligence Kernel Extreme Learning Machine Improved with the Crowned Porcupine Optimization Algorithm (XAIKELM-ICPOA) approach. Initially, the proposed XAIKELM-ICPOA method preprocesses the data using min-max scaling to ensure uniformity and improve model performance. Next, the Kernel Extreme Learning Machine (KELM) model is employed for classification. The Improved Crowned Porcupine Optimization (ICPO) method is used to optimize KELM hyperparameters, improving classification performance. Finally, SHAP is employed as an XAI technique to provide insights into feature contributions and decision-making processes. The XAIKELM-ICPOA method was evaluated on the NSL-KDD dataset, achieving an accuracy of 96.82%.

Comparative Life Cycle Assessment of Monocrystalline and Multicrystalline-based Grid-Connected Photovoltaic Systems with Uncertainty Analysis

2025-04-04T06:56:29+00:00

A comparative Life Cycle Assessment (LCA) of Photovoltaic (PV) systems in Shah Alam, Malaysia using different PV module technologies, i.e. monocrystalline silicon and multicrystalline silicon installed was conducted in this paper to evaluate the energy consumption and global warming impacts using CED and IPCC methods. Several energy and global warming-related indicators were also determined and uncertainty, contribution, and sensitivity analyses were performed. The results show that multi-Si PV system outperforms the mono-Si PV system after taking into account the data uncertainty. The global warming impacts were found to be 47 and 54.7 g CO₂-eq/kwh for multi-Si and mono-Si systems, respectively. The contribution analysis shows that the PV module is the major contributor of each system. It is also highlighted that increased irradiation, extended system lifetime, and reduced module degradation rates could improve the overall performance. This study provides valuable insights into the environmental performance of different PV module technologies, offering guidance for optimizing PV system design and promoting sustainable energy development.

Discrete Migratory Bird Optimizer with Deep Learning Driven Cyclone Intensity Prediction on Remote Sensing Images

2025-04-04T07:06:56+00:00

Tropical Cyclones (TCs) are extreme climatic conditions that can crucially disrupt human life. Heavy rainfall and resilient winds that follow these systems can result in severe consequences for property and hamper social and economic growth in respective areas. Thus, accurate assessments of TC intensity is paramount for practical applications and theoretical research in predicting and preventing disasters. Satellite Cloud Images (SCIs) are a primary preferable and effective data source for the study of TCs. Efficient and accurate estimation of TC intensity is often challenging despite the remarkable success in different SCI-based studies. Recently, Machine Learning (ML) and Deep Learning (DL) methods have shown significant potential and gained fast development against big data, especially with images. Considerable progress has been made in applying Convolutional Neural Networks (CNNs) to predict and evaluate the intensity of TCs. This study focuses on developing a Discrete Migratory Bird Optimizer with Deep Learning Dirven Cyclone Intensity Prediction (DMBODL-CIP) technique on remote sensing images to estimate the intensity levels of TCs. To accomplish this, the DMBODL-CIP technique initially undergoes preprocessing in two phases: Bilateral Filtering (BF) and Adaptive Histogram Equalization (AHE)-based noise removal and contrast enhancement. The DMBODL-CIP technique utilizes a deep CNN-based SqueezeNet model for the feature extraction process. Then, a Deep Belief Network (DBN) model is used to predict TC intensity. Finally, the DMBO technique is employed for optimal hyperparameter selection of the DBN model, which assists in improving the overall prediction results. The proposed DMBODL-CIP approach was evaluated on a cyclone image dataset and a comparison study showed an RMSE of 6.02 kt outperforming existing techniques.

Advanced Techniques for Transit Priority at Roundabouts utilizing Signal Metering

2025-04-04T06:59:23+00:00

Transit priority strategies frequently focus on conventional Transit Signal Priority (TSP) at intersections, overlooking the distinct operational characteristics of roundabouts, including signalized, metered, and yield-based control methods. This study introduces a new approach, Transit Metering Signal Priority (TMSP), which uses metering signals to provide Public Transport Vehicles (PTVs) preference at roundabouts. A distinguishing feature of TMSP is its compatibility with the existing yield or metering control strategies employed by roundabouts, allowing them to maintain these methods without the full signalization of all approaches for priority allocation to PTVs the latter involves. The efficacy of the proposed TMSP model is assessed through numerical experiments, with yield control (no priority) serving as the baseline. Comparisons are drawn between conventional TSP and TMSP scenarios under varying congestion levels. The findings suggest that the proposed TMSP logic can lead to a reduction in bus delays by 2 sec to 16.6 sec, with minimal impact on general traffic, while also decreasing travel time variability by up to 19 sec (standard deviation). In comparison to TSP, TMSP exhibits clear advantages for public transportation by reducing delays and providing more stable travel times, while minimizing disruptions to the general traffic flow. The implementation of the TMSP method enhances the performance and reliability of public transport services, contributing to the development of more resilient and sustainable urban mobility systems.

Evaluation of Manning's Coefficients for the Al-Adhaim River Basin in Iraq utilizing Modern Techniques

2025-04-04T06:53:05+00:00

The current study presents an innovative analysis that combines climate and land use data to assess changes in Manning's coefficient within the Al-Adhaim River Basin (ARB) from 2017 to 2023. The primary objective is to calculate the hydraulic roughness coefficient (Manning coefficient, n) and evaluate its variations in relation to climate and land use changes. The ArcGIS and HEC-RAS software were utilized for the spatial and hydraulic analysis, respectively, as well as to calculate the arithmetic average for the entire study area. The results indicate an increase in temperature, humidity, and rainfall of 0.82 ^oC, 1.07 g/kg, and 0.41 mm/year, respectively, with a decrease in wind speed of 0.02 m/s. These climatic changes contributed to a 3.96% increase in crop area, while human activities led to a 0.72% rise in built areas and a 3.93% reduction in open areas. Manning's coefficient, despite its low value of 0.0695, demonstrated a strong relationship between its value and the aforementioned factors.

Key Drivers of Energy Consumption in the Gulf Cooperation Council Countries: A Panel Analysis

2025-04-04T06:53:09+00:00

The objective of this study is to examine the factors that influence energy consumption, a critical component of both industrial and everyday life that is projected to increase annually. The research focuses on Gulf Cooperation Council (GCC) countries and uses panel data from 1990 to 2023 to assess the impact of economic growth, trade openness, population growth, urbanization, inflation, and Foreign Direct Investments (FDI) on energy consumption. The findings, derived from a rigorous Augmented Mean Group (AMG) regression analysis, reveal a positive and significant relationship between economic growth, trade openness, and inflation on energy consumption. However, the study also outlines that FDI, population growth, and urbanization do not exert a significant influence on energy use. The analysis highlights a critical challenge faced by GCC countries: the need to balance high industrial energy consumption with sustainability goals. The study underscores the necessity of transitioning to sustainable practices, including energy efficiency and renewable energy adoption, as crucial elements for achieving long-term growth and climate commitments. This research contributes to the ongoing discourse on sustainable development in the region and underscores the necessity of incorporating environmental factors into economic growth strategies.

Deep Context-Aware Feature Extraction for Anomaly Detection in Surveillance Videos

2025-04-04T07:00:20+00:00

Surveillance video analysis plays a crucial role in ensuring public safety and security. Developing a context-aware framework for anomaly detection in surveillance videos is motivated by the need for enhanced security, safety, and efficiency in various domains. Context-aware anomaly detection depends on spatiotemporal features that help the model understand the context of anomalies in surveillance videos. This study aimed to provide a novel deep learning-based context-aware approach to feature extraction to detect anomalies in surveillance videos. The proposed method integrates ResNet50 for spatial feature extraction and 3D Convolutional Neural Network (CNN) for temporal feature extraction. This method identifies six anomalous activities, namely abuse, arrest, fighting, robbery, shooting, and road accidents, using the UCF-Crime dataset. The proposed integrated ResNet50 and 3D CNN model achieves promising accuracy for the six classes, such as 95% for abuse, 93% for arrest, 95.22% for fighting, 94.44% for robbery, 93% for shooting, and 94.22% for road accidents. By combining spatiotemporal features, the proposed model detects anomalies in behavior and unexpected movements, which makes it useful for security monitoring where deviations from normal behavior indicate anomalous events. This research contributes to advancing the capabilities of surveillance systems, enhancing public safety, and enabling proactive security measures in diverse urban environments.

An Optimized Color Image Watermarking Scheme based on HD and SVD in DWT Domain

2025-04-04T07:04:41+00:00

Digital watermarking is considered a trustworthy strategy for proving ownership of valuable digital files such as audio, image, and video documents. Most of the prevailing image watermarking systems embed grayscale or binary image watermarks, while only a few use color images as watermarks. In this paper, we develop a secure, imperceptible, and robust optimized semi-blind color image watermarking technique that uses color images as watermarks. It is based on Hessenberg Decomposition (HD) and Singular Value Decomposition (SVD) in the Discrete Wavelet Transform (DWT) domain. First, the color host image and color watermark image in RGB space are converted to YCbCr space, and then the watermark data are embedded into the luminance component (Y) of the host image. In this work, the principal component of the watermark's luminance (Y) is implanted into the associated singular value of the host image with an appropriate scaling factor that optimizes the robustness-imperceptibility tradeoff. The Artificial Bee Colony (ABC) algorithm is used to find the appropriate scaling factors. To further enhance the security, the Arnold transformation is used to scramble the Y channel of the watermark before it is injected into the host image. As demonstrated by the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) metrics, the proposed scheme exhibits high invisibility and is robust to most image processing manipulations, geometric operations, and combinational attacks. Compared to various existing color image watermarking schemes that use color images as watermarks, it shows higher imperceptibility and robustness.

Effect of Metal Corrosion on the Maximum Deflection of I-Section Steel Beams subjected to Sudden Short-Duration Constant Force

2025-04-04T07:06:10+00:00

This paper aims to determine the effect of metal corrosion on the maximum deflection of I-section steel beams subjected to sudden constant force in a short duration. To calculate this type of deflection, a model was developed and subsequently combined with a metal corrosion model, considering the beam’s cross-section and stiffness reduction. A series of investigations of the maximum steel beam deflection in different corrosive environments were carried out. The results showed that the maximum deflection of I-section steel beams varied from 6.63% (in rural environments) to 29.62% (in marine environments) after 100 years. These findings highlight the importance of considering long-term corrosion effects when designing steel structures, particularly those exposed to sudden and sustained forces in a short time.

Cat Breed Classification with YOLOv11 and Optimized Training

2025-04-04T06:53:26+00:00

Accurate identification of cat breeds poses a significant challenge due to subtle inter-breed differences and intra-breed variability. This study leverages YOLOv11, the latest version of the YOLO family, to address these challenges through advanced deep-learning techniques. By training on a dataset consisting of five distinct cat breeds (Persian, Maine Coon, Siamese, Pallas's Cat, and Bengal), the model demonstrates exceptional capability in identifying nuanced breed-specific features. Data augmentation techniques were employed to enhance the dataset's diversity, while various optimization algorithms (Adam, Adamax, NAdam, AdamW, RAdam, RMSProp, and SGD) were evaluated to optimize the performance of the model. Experimental results showed that RAdam and SGD emerged as the top-performing optimizers, achieving an average recall of 96.8%, precision of 97.2%, and mAP50 of 98.1%, significantly outperforming other optimization methods. In contrast, RMSProp exhibited the lowest performance, particularly in terms of precision and mean Average Precision (mAP50). Additionally, data augmentation techniques were applied to enhance the diversity of the dataset, improving the robustness of the model. These findings highlight the effectiveness of YOLOv11 in cat breed classification, with potential applications in pet identification, animal conservation, and veterinary diagnostics.

Generalization to Type 2 of PSO-Optimized Type 1 PD Fuzzy Controller and its Application to a Quadrotor UAV

2025-04-04T07:00:56+00:00

This study presents a new Type 2 fuzzy logic (interval-valued fuzzy logic) control system for a Vertical Take Off and Landing (VTOL) quadrotor. The goal of the control design is to obtain robust and stable tracking of the desired angles in the presence of disturbances and noises. The membership functions relative to the linguistic variables of the fuzzy if-then rules are chosen to control the quadrotor to track a reference trajectory. The Particle Swarm Optimization (PSO) method is used to optimize controller-free parameters. The results obtained from an extensive simulation study show that the quadrotor can operate autonomously in flight with stable orientation. The performance of the controller was evaluated for both Type 1 and Type 2 fuzzy controllers. The simulation results show the effectiveness of the proposed controller and its better performance.

Ball Detection and Color Identification for a Mobile Robot using a 2D Camera

2025-04-04T07:00:11+00:00

In this study, a novel method is developed to help the mobile robot system accurately detect and recognize the color of a ball in environments with light disturbances using deep learning. The YOLOv8 algorithm is applied to detect the ball and identify its color. The effectiveness of the algorithm is tested in various lighting conditions and when the balls are inside a silo and when they are outside. The developed algorithm identifies balls even when they are partially obscured by shadows.

LoRe-GRNN: A Hybrid Deep Learning Framework for Real-Time Anomaly Detection and Stress Distribution Prediction in 3D Printing Processes

2025-04-04T06:55:33+00:00

Advanced 3D Printing (A3P) revolutionizes manufacturing with precision, speed, and innovation, unlocking limitless design possibilities and superior material performance for next-generation industrial and creative applications. A3P epitomizes a paradigm shift in manufacturing, seamlessly merging additive fabrication with advanced 3D printing to construct intricate geometries unattainable through conventional methods. However, inherent challenges persist, including structural deformations in Stereolithography (SLA) and nozzle occlusions in Fused Deposition Modeling (FDM), necessitating intelligent intervention. This study introduces LoRe-GRNN, a groundbreaking Deep Learning (DL) framework for real-time anomaly detection and stress distribution prediction. Leveraging a novel fusion of Longformer-Reformer (LoRe) architectures with Gated Recurrent Neural Networks (GRNN), the system optimizes feature extraction and predictive accuracy. A meticulously curated 3D model repository, synergized with Finite Element (FE) simulations, enhances SLA stress predictions, while an integrated multisensory module ensures FDM process monitoring. The hybrid approach demonstrates unparalleled precision, achieving 99.23% anomaly detection accuracy, significantly mitigating computational overhead compared to traditional FE simulations. This transformative framework enhances the resilience of additive manufacturing, heralding an era of intelligent, high-fidelity, and resource-efficient 3D printing systems.

Enhancing Flood Prediction in Urban Areas: A Machine Learning Approach for Makassar City

2025-04-04T07:05:25+00:00

Accurate and rapid predictions regarding urban flooding, are essential in supporting risk mitigation efforts. Flood phenomena have the potential to cause extensive damage and disrupt the functions of economic and governmental sectors. However, these impacts can be minimized through comprehensive planning and preparation to reduce potential losses. Machine learning techniques have emerged as a promising method for predicting complex hydrological processes. This research develops a flood prediction model by comparing seven machine learning algorithms, namely Logistic Regression, Linear Discriminant Analysis, k-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machine, AdaBoost, and Random Forest. The results show that Random Forest has the highest performance, demonstrating the reliability of Random Forest in processing complex urban flood datasets. This model is expected to enhance disaster preparation and contribute significantly to flood risk management in urban areas.

Evaluation of Reactive Powder Concrete Strength using Various Curing Methods

2025-04-04T06:52:23+00:00

This study examines the strength development of Reactive Powder Concrete (R-P-C) under various curing methods. The R-P-C mixture was prepared using a ratio of 1:0.25:1.11 by weight of high-quality ordinary cement, Silica Fume (S-F), and fine Sand (S), along with 2% by concrete volume of micro steel fibers. In addition to Normal Curing (R-N) utilized as a control approach, three alternative curing methods were evaluated: Autogenous + Normal Curing (R-AN), Steam + Normal Curing (R-S), and coating with a Water (W)-based liquid curing compound (R-C). The results indicated that R-S significantly enhanced the compressive strength of R-P-C as the curing duration increased from 1 to 3 days. The strength improvements at 7, 28, and 90 days were measured at 13.99%, 15.97%, and 16.47% for 1 day of R-S; 15.42%, 17.44%, and 18.48% for 2 days; and 17.52%, 18.49%, and 20.04% for 3 days. This method accelerated chemical reactions within the cement matrix, having promoted stronger bonds and higher early-age strength, making it the most effective technique for maximizing the strength gain. R-AN for 2 days, followed by 26 days of W immersion, also proved beneficial, having increased compressive strength by 10.85%, 11.05%, and 12.2% at 7, 28, and 90 days, respectively. This method effectively retained moisture, having facilitated optimal chemical reactions and steady strength development. Similarly, (R-C) improved compressive strength by 12.55%, 12.71%, and 13.82%, having minimized evaporation and maintained internal moisture. Furthermore, improvements in compressive strength were accompanied by proportional increases in flexural and splitting tensile strengths across all curing methods.

The Effect of the Hydrophilic and Hydrophobic Behavior of Polymeric Fibers on Some Properties of Reactive Powder Concrete

2025-04-04T06:54:22+00:00

This study compares the interface bonding properties of Polyvinyl Alcohol (PVA) fibers and Polypropylene (PP) fibers with a Reactive Powder Concrete (RPC) matrix. The chemical composition and microstructure of the reaction were characterized using Scanning Electron Microscopy (SEM) to understand the influence of PVA and PP fibers on their surrounding matrix. The Interfacial Transition Zone (ITZ) between the fibers and the RPC matrix was examined in detail. The hydrophilic and hydrophobic behavior of PVA and PP fibers affected the tensile strain capacity and flexural strength properties of the concrete mixes. Two strength grades of RPC mixes were used (30 MPa and 60 MPa, both with 1% fiber content of PVA or PP). The PVA fibers showed superior bonding with the RPC matrix compared to the PP fibers. The 60 MPa PVA mix achieved the highest strain capacity of 13.8%. The 30 MPa PVA mix had a maximum flexural strength enhancement of 4.3%, while the 60 MPa PVA mix demonstrated a 23% increase. Such enhancement can broaden the use of RPC with PVA fibers in structural members subjected to tensile and flexural stresses, while its significant strain capacity lessens the likelihood of microcrack formation.

Current Practices in Quality Assessment of Systematic Reviews in Computing: Exploring Automation Potential

2025-04-04T07:03:23+00:00

Systematic Literature Reviews (SLRs) play a crucial role in evidence synthesis within computing research. However, the quality of SLRs can vary significantly, affecting their reproducibility and trustworthiness. This study addresses the problem of poorly understood practices in SLR quality assessment. It investigates the current landscape of quality instruments used to assess SLRs in computing by analyzing 97 tertiary studies across various computing domains. The analysis focuses on identifying the dominant quality instruments, and examining reported modifications or adaptations made to them. A qualitative analysis is conducted on the interpretations and scoring of widely utilized quality criteria. The analysis reveals diverse interpretations and potential inconsistencies in the application of quality instruments, owing to the absence of concrete examples. The findings provide valuable insights for both SLR authors and consumers in computing research, pointing out the most widely deployed quality instruments, common customization and interpretive practices, and potential areas for improvement. This study contributes to the ongoing discussions on enhancing SLR quality in computing, forming the basis for automating the quality assessment process.

Refined Theories for Beam Bending: A Simplified Approach to Structural Analysis

2025-04-04T06:54:55+00:00

This study develops a refined beam theory that improves upon classical models by accurately capturing transverse shear deformation without requiring shear correction factors. The proposed approach maintains the simplicity of the Bernoulli-Euler theory while achieving higher precision in predicting transverse deflections, axial stresses, and shear stresses. A linearly elastic, homogeneous, and isotropic material with a uniform rectangular cross-section is assumed. The accuracy of the proposed theory is validated through comparisons with advanced shear deformation theories, showing that it provides reliable results with reduced computational complexity. Furthermore, the theory's applicability is demonstrated through case studies, showcasing its effectiveness in practical structural design and analysis Numerical comparisons indicate minimal percentage differences, with a maximum deviation of -0.37% for simply supported beams and -0.82% for fully clamped beams in transverse deflection predictions. The results align well with advanced shear deformation theories and two-dimensional elasticity solutions, confirming the model’s reliability. This theory enhances structural analysis, particularly for thick and shear-deformable beams, with potential extensions to anisotropic materials, dynamic loading, and complex boundary conditions in future research.

Dual-Branch Convolutional Neural Network for Image Comparison in Presentation Style Coherence

2025-04-04T07:04:20+00:00

Image comparison is an important task that is part of the pipeline in many different computer vision applications. Maintaining style coherence across presentation slides is essential for professionalism and effective communication. Inconsistent design elements, such as varying fonts, colors, borders, and logo placements, can disrupt the visual flow and diminish the overall impact. This study introduces a novel approach to automate the validation of presentation slide coherence using a Dual-Branch Convolutional Neural Network. The model is trained to calculate a similarity score between image slides based on key design parameters, including font consistency, color schemes, border styles, and layout alignment. The proposed CNN architecture is specifically designed to compare two inputs representing slide images for binary classification. Unlike traditional Siamese networks that rely on identical branches and a distance metric for feature comparison, the proposed dual-branch architecture concatenates feature embeddings from two specialized branches and processes them through fully connected layers for final classification, allowing more targeted and nuanced feature extraction and coherence evaluation. The model was evaluated on a custom image dataset comprising 6000 images synthesized following specific design guidelines for style coherence of image features to ensure consistency and variety in the dataset while maintaining a balance for comparative tasks. The experimental results demonstrate significant improvements over the baseline Siamese network across all key metrics. Specifically, the proposed model achieved an accuracy of 0.85 compared to 0.81 for the baseline Siamese network, Jaccard similarity 0.76 vs 0.72, Kappa coefficient 0.69 vs 0.62, and ROC AUC 0.87 vs 0.81. Additionally, precision increased from 0.73 to 0.77 and the F1-score reached 0.87, reflecting a stronger balance between precision and recall. This work provides a significant contribution to automated design evaluation, offering a flexible and modular architecture that supports multi-view analysis and captures intricate visual patterns and discrepancies. By addressing key limitations of traditional approaches, the proposed model provides a robust tool to ensure style coherence in professional presentations, paving the way for more efficient and accurate design validation processes.

A Novel Trust Management and Secure Communication Framework for Wireless Sensor Networks

2025-04-04T06:57:08+00:00

In Wireless Sensor Networks (WSNs), ensuring secure and reliable communication amidst various cyber threats is a pivotal challenge. Existing security methods often struggle with high computational demands and do not adequately address the unique characteristics of WSNs, such as the limited energy resources and their susceptibility to specific types of attacks like blackhole and Sybil attacks. The proposed Lightweight MG-Net Model addresses security and performance challenges in WSNs by integrating a Trust Model, Anomaly Detection, and Secure Communication protocols into a novel hybrid deep learning framework. This framework combines MobileNet, which utilizes depthwise separable convolutions for efficient spatial feature extraction, with Gated Recurrent Units (GRUs) to capture temporal dependencies, enabling precise real-time anomaly detection with reduced computational demands. Trust management leverages a modified EigenTrust algorithm, dynamically updating trust scores based on node interactions to optimize reliability across network operations. The anomaly detection component was rigorously trained using a labeled dataset that includes various attack scenarios such as blackhole attacks, where detection accuracy exceeds 97.5%, and Sybil attacks, highlighting its robustness against sophisticated threats. Secure communications are upheld by the Datagram Transport Layer Security (DTLS) protocol, ensuring data integrity and confidentiality with an encryption success rate of 97%. Operational performance metrics are evaluated through simulations, showcasing the system’s efficiency with a detection latency under 2 s and energy consumption that is 30% lower than traditional security frameworks. Overall, the Lightweight MG-Net Model enhances WSN security without compromising on efficiency, demonstrating significant advancements in trust management, anomaly detection accuracy, and secure, low-latency communications.

Cost-Effective Real-Time Obstacle Detection and Avoidance for AGVs using YOLOv8 and RGB-D Sensors

2025-04-04T06:54:46+00:00

Recent advances in obstacle detection and avoidance technologies have significantly enhanced robotic navigation capabilities. This study presents a real-time obstacle detection and avoidance system leveraging YOLOv8 and RGB-D sensors. The system integrates Microsoft Kinect V1 to capture RGB and depth images, employing YOLOv8 for efficient real-time object detection and classification. Depth data are utilized to calculate object distances and positions, allowing accurate navigation decisions. Implemented on the Pioneer 3DX robot, the system demonstrates high efficiency, reliability, and adaptability. With a training dataset, the model achieves exceptional performance, attaining an accuracy of 92.6% across all object classes and a [email protected] of 95%, However, the system was primarily tested in structured indoor environments, which may limit its generalization to unstructured outdoor settings. This cost-effective solution offers a practical approach to enhancing autonomous navigation and obstacle avoidance in real-world applications.

WDM-RoF Architecture for Low-Cost and Large-Coverage 5G Applications

2025-04-04T06:52:45+00:00

This paper presents an enhanced Wavelength Division Multiplexing (WDM) method based on the Radio over Fiber (RoF) architecture to achieve cost-effective, long-distance, and high-coverage communication for 5G systems. The proposed model addresses the critical challenges of increasing the number of channels and transmitting the Radio Frequency (RF) signal along 72 km distance while ensuring enhanced data rates and reduced latency across extensive coverage areas. Performance evaluations demonstrate that the 8-channel model (80 Gbps) has quality factors of 6.8, 7.5, 7.8, and 7.7, and the minimal Bit Error Rate (BER) is 2.9E-10, 2.2E-11, 2.2E-12, and 4.2E-12. The 16-channel model (160 Gbps) has quality factors of 5.6, 6.5, 6.8, and 6.9 with minimal BERs of 2.9E-8, 2.2E-10, 2.2E-10, and 4.2E-11, respectively. The 32-channel model (320 Gbps) has quality factors of 5.4, 6.1, 6.3, and 6.2 with minimal BERs of 2.9E-8, 2.2E-11, 2.2E-10, and 4.2E-10. The results highlight the potential of the proposed Wavelength Division Multiplexing Radio over Fiber (WDM-RoF) model to serve as a robust backbone for next-generation mobile networks, meeting the demands of 5G communication.

Experimental Validation of Intelligent MPPT Control for Photovoltaic Energy Chain

2025-04-04T06:57:19+00:00

This paper presents a comparative study of two Maximum Power Point Tracking (MPPT) algorithm techniques for a Photovoltaic (PV) system, which includes a PV generator, a DC-DC boost converter, and a resistive load. The study compares the performance of Artificial Neural Networks (ANN) and Perturb and Observe (P&O) algorithms in extracting maximum power under both stable and variable climatic conditions. To this end, simulation tests are performed using MATLAB Simulink, with a focus on energy efficiency and response time in different scenarios. The findings are validated through a hardware setup using the LAUNCHPAD-XL 28F379D and C2000 embedded coder. The results demonstrate that the ANN-based MPPT technique outperforms the traditional P&O method, particularly under rapidly changing environmental conditions, highlighting its superior efficiency in PV systems. Additionally, the ANN algorithm has been shown to exhibit enhanced adaptability to variable irradiance and temperature, thereby ensuring more stable and consistent power output across a broad spectrum of operating conditions.

The Effect of Aluminum Wire Winding Patterns on Partial Discharge Occurrence in 24 kV Insulator Strings

2025-04-04T06:53:22+00:00

This study investigates Partial Discharge (PD) on aluminum wire insulators for 24 kV transmission lines. Using the IEC 60270 charge measurement, PD was evaluated on line post 57-2, line post 57-3, and pin post 56/57-2 insulators. A metallic pipe modeled the transmission line, with aluminum wire wound in straight and curved patterns as per Thailand's PEA standards. A test voltage of 10–25 kV was applied, and the Phase Resolved Partial Discharge (PRPD) analysis identified corona discharge as the primary PD type. The results showed increased PD magnitude and frequency at higher voltages, peaking at 90° and 270° phase angles. In addition, the pin post insulator and straight winding produced the highest PD. This study highlights the impact of wire winding patterns on PD, an often-overlooked factor. The simple, cost-effective evaluation method provides insights for optimizing insulation, maintenance, and real-time monitoring in high-voltage systems, benefiting applications such as cable spacers and lightning arresters.

Optimizing Energy Efficiency in Battery-powered IoT Devices through Hardware Optimization and Voltage Scaling

2025-04-04T07:03:41+00:00

With the rapid proliferation of battery-powered Internet of Things (IoT) devices, optimizing energy efficiency has become a critical challenge, especially in wireless communication modules. This work focuses on the power consumption analysis of the ESP32-WROOM module, a widely used wireless communication component in IoT applications. By evaluating five key interfaces, UART, I2C, SPI, I2S, and Wi-Fi, over different CPU clock frequencies (40 MHz, 80 MHz, 160 MHz, 240 MHz) and operating voltages (3 V and 3.3 V), this study provides a comprehensive understanding of how these parameters affect the energy efficiency in battery-powered IoT systems. The primary contribution of this work is the identification of critical trade-offs between clock frequency, voltage scaling, and power consumption across different interfaces. The findings reveal that higher CPU frequencies lead to increased power consumption across all interfaces, with I2S consuming the highest current (up to 64.6 mA) at 240 MHz and 3.3 V. Wi-Fi, often considered a power-intensive interface, showed significant current surges, particularly during connection establishment, with a peak current of 280 mA at 240 MHz and 3 V. These variations highlight the importance of effective voltage regulation during link establishment to mitigate power inefficiencies. Additionally, the voltage differential between 3 V and 3.3 V was found to influence overall power consumption, although certain interfaces at higher frequencies demonstrated marginal efficiency improvements when operating at 3.3 V. This highlights that while voltage selection is important, clock frequency adjustments have a more profound effect on power consumption. This work provides actionable insights for developers aiming to optimize power consumption in IoT applications. The findings provide guidance for selecting appropriate operating frequencies and voltage levels, contributing to significant energy savings and extended battery life in energy-constrained IoT and embedded systems.

Painting Training Based Optimization: A New Human-based Metaheuristic Algorithm for Solving Engineering Optimization Problems

2025-04-04T06:58:30+00:00

This study introduces a completely different perspective on optimization through the development of a novel human-based metaheuristic algorithm named Painting Training Based Optimization (PTBO). Inspired by the intricate and iterative human activities observed during painting training, PTBO models these creative and systematic processes to effectively address optimization challenges. The algorithm's foundation is rooted in the concepts of exploration and exploitation, which are essential for achieving a balance between searching the solution space widely and refining promising areas. The theoretical framework of PTBO is comprehensively described, followed by detailed mathematical modeling of its two-phase operation. To evaluate its capability, the algorithm is tested on 22 constrained optimization problems sourced from the well-regarded CEC 2011 test suite. The experimental results show that PTBO excels at producing competitive and high-quality solutions. A comparative analysis with 12 other well-known metaheuristic algorithms underscores PTBO's superior performance, particularly in handling complex benchmark functions. The results show that the proposed PTBO approach outperformed competing algorithms in all (22) optimization problems of the CEC 2011 test suite. The findings highlight PTBO's effectiveness in solving real-world optimization problems, showcasing its potential to outperform existing methods. By offering a completely different optimization approach, PTBO contributes a significant and innovative tool to address challenges in engineering and other applied domains.

A Comprehensive Study on the Homomorphic Encryption for Secure Image Data Processing

2025-04-04T06:57:11+00:00

In the contemporary digital landscape, the integrity and confidentiality of data have become paramount concerns. This study presents a comprehensive framework for secure image data processing using homomorphic encryption. The proposed approach involves image preprocessing, logistic regression model training, feature extraction, and polynomial approximation to accommodate the constraints of homomorphic encryption algorithms. Sensitive data, encrypted via homomorphic algorithms, is embedded within images to ensure its concealment during computational operations. Subsequent encryption of the image using the asymmetric Rivest-Shamir-Adleman (RSA) algorithm further secures the encapsulated sensitive data. Through experimental data and analysis, the performance and speed of homomorphic encryption are compared against traditional methods, validating its efficacy in encrypted image data processing.

Characteristics and Microstructure of Geopolymer Mortars incorporating Ground Granulated Blast Furnace Slag and Calcined Dolomite Powder: A Sustainable Solution for Construction Materials

2025-04-04T06:55:53+00:00

The global demand for environmentally sustainable and cost-effective materials that reduce carbon emissions and energy consumption has significantly risen. In this context, geopolymer binders, primarily sourced from industrial by-products or agricultural waste, have emerged as viable alternatives to traditional Ordinary Portland Cement (OPC). This study examines the characteristics and microstructure of two types of geopolymer mortars: one utilizing an alumina-rich binder, namely calcined clay, and the other employing a silica-rich binder, namely rice husk ash. Both mortar types incorporate a consistent 30% Ground Granulated Blast Furnace Slag (GGBFS), with Calcined Dolomite Powder (CDP) added in varying proportions of 10%, 15%, 20%, and 25%. A total of eight geopolymer mortar mixes, along with a reference mix consisting of 100% OPC, were prepared and evaluated for setting time, flowability, compressive strength, flexural strength, and dry density. Additionally, microstructural analysis was conducted using electron microscopy techniques. The results indicated that the clay-based geopolymer mortars outperformed those based on rice husk ash. Notably, the mixes containing 30% GGBFS, 50% calcined clay, and 20% calcined dolomite powder, as well as those with 30% GGBFS, 45% calcined clay, and 25% calcined dolomite powder, exhibited performance levels comparable to, or slightly exceeding, those of the reference mix.

Enhancing Data Security in IOT-based UAV Networks through Blockchain Integration

2025-04-04T06:58:20+00:00

There is great potential for utilizing Unmanned Aerial Vehicle (UAV) networks for commercial, military, and civil purposes. Therefore, as network volumes increase, communicating within UAV networks poses serious cybersecurity issues. Integrating Blockchain with UAV communication networks can offer a scalable and secure communication method. The proposed approach to a protected and accessible interaction method for peer-to-peer UAV networks integrates blockchain technology, allowing safe, decentralized, and cooperative communication between several entities. This study presents a new consensus-building technique to protect UAV network communications, integrating public key cryptography with blockchain Elliptic Curve Diffie-Hellman (ECDH) using the Secure Hash Algorithm (SHA) to preserve data integrity and secure key exchange to provide confidentiality.

A Novel Hempcrete utilizing Calcium Aluminate Cement as a Binder

2025-04-04T06:53:35+00:00

Hempcrete is an eco-friendly, carbon-negative building material composed of hemp shives, a binder, and water. While traditionally bound with lime, its mechanical limitations hinder structural applications. This study explores the use of Calcium Aluminate Cement (CAC) as an alternative binder to enhance hempcrete's mechanical properties. Results indicate that CAC improves early strength, reduces setting time, and enhances compressive, flexural, and elastic properties compared to lime-based hempcrete. Microstructural analysis reveals denser packing and reduced porosity in CAC-based hempcrete, contributing to higher strength. However, CAC increases material costs and affects thermal and acoustic properties due to its density. Despite these challenges, CAC-based hempcrete is promising for applications requiring faster setting, higher strength, and improved durability, though further modifications, such as aggregate additions and compaction, may optimize performance.

Fortifying Future IoT Security: A Comprehensive Review on Lightweight Post-Quantum Cryptography

2025-04-04T06:54:38+00:00

This paper presents lightweight Post-Quantum Cryptography (PQC), identifying its importance for a shift from traditional cryptographic schemes, vulnerable to quantum threats, to efficient PQC algorithms. Lattice-based cryptography stands out owing to its small key sizes and computational efficiency, with CRYSTALS-Kyber and NTRU algorithms being substantial representatives for Internet of Things (IoT) applications. However, PQC implementation in IoT environments has various obstacles to overcome. Minimizing energy consumption, scalability, and hardware limitations remain key challenges for PQC smooth integration into these resource-constrained networks. The present review analyzes the state-of-the-art PQC, makes security and performance comparisons among leading algorithms, and evaluates optimization techniques aimed at reducing resource overheads. Algorithmic refinement, hardware acceleration, and hybrid cryptography are also discussed as methods for mitigating these challenges. The results indicate that continuous research and development efforts should be made to improve the PQC technologies, and thus achieve their practical deployment in IoT systems. Quantum threats in IoT will be, hence, prevented with the employment of secure and scalable IoT ecosystems in a post-quantum world.

Hybrid 3D U-Net and Attention Mechanisms for Whole Heart Segmentation from CT Images

2025-04-04T06:55:11+00:00

Accurate delineation of heart structures from multimodal images is crucial for the treatment and investigation of different cardiovascular diseases. Automated whole-heart segmentation remains a challenging task due to its complex structure and imbalances in sample data. Convolutional Neural Networks (CNNs) are popular due to their efficiency in segmenting medical images. However, they often struggle to capture long-range dependencies and lack the precision needed for complex anatomic structures such as the heart. To overcome these limitations, this study presents a hybrid 3D U-Net framework that utilizes residual connections with attention mechanisms to improve feature learning and localization of cardiac structures. Residual connections stabilize training in deeper networks and attention blocks focus on relevant regions, refining segmentation quality. This network focuses on relevant regions and uses attention blocks to enhance quality. The proposed architecture was evaluated on 40 volumetric CT images of the Multi-Modality Whole Heart Segmentation (MM-WHS) dataset, achieving an average dice score of 85%. These results demonstrate the effectiveness and high accuracy of the proposed method for delineating cardiac substructures, offering potential clinical utility for automated cardiac analysis.

Design and Optimization of a Multi-Core Fiber Optic Communication System for Height-Capacity Data Transmission in Iraq’s Urban Environment

2025-04-04T07:04:34+00:00

Iraq's industry has gone through various transformation phases and has seen tremendous growth during the recent years. To sustain such growth, the infrastructure should be highly efficient. Fiber optic technology is a main component in the networks because it provides high bandwidth and high speed, thus providing support for current and emerging technologies. To the best of our knowledge, various research works carried out in Iraq so far have not touched on the point of effective improvement in the performance of the fiber optic communication system. The concept behind this research is the design of a Radio over Fiber system using the Optisystem simulator, focusing on how to improve the performance of a multi-core fiber optic communication system by improving the transmission capacity and enhancing the reception system to raise the quality of the received signal and obtain a lower bit error rate. The simulation results showed that there was much enhancement in the quality of transmission, reducing the bit error rate by 10 times in comparison with previous systems while providing better signal clarity. These improvements are in line with recent advances in optical fiber technology used in similar studies globally.

A Numerical and Experimental Simulation Study of the Fluid Flow Characteristics of the Past Horizontal Stabilizer and Elevator of the Microlight Aircraft Model

2025-04-04T06:57:35+00:00

A microlight aircraft named PPH-Unhas was developed at Hasanuddin University in Makassar, Indonesia, in 2020. This study aims to produce the characteristics of the lift coefficient (C_L), drag coefficient (C_D), and flow simulation on the horizontal stabilizer and elevator model of the PPH-Unhas microlight aircraft. Numerical simulations were conducted using a Computational Fluid Dynamics (CFD) program, and experiments were performed in a low-speed wind tunnel. The microlight aircraft model was made of three pieces adapted to the PPH-Unhas aircraft prototype and then tested by treating five levels of airflow velocity (V): 14, 16, 18, 20, and 22 m/s. Each speed level was treated with seven levels of angle of attack (α), namely, -15°, -5°, 0°, 10°, 15°, 20°, and 25°. Each α level was treated with six levels of change in the aircraft elevator deflection angle (δ): -15°, 0°, 10°, 20°, 30°, and 45°. The results showed that the maximum values of C_D and C_L were obtained at δ = 45°, whereas the maximum value of C_L/C_D was obtained at δ = 45°.

Research Progress Knowledge Mapping on Transit Oriented Development (TOD) Typology

2025-04-04T06:55:46+00:00

This paper presents a global research on the different aspects of Transit Oriented Development (TOD) typology, conducted from 2007 to 2024. The knowledge mapping and research theoretical framework were derived from procuring datasets, obtained from the Web of Science (WoS) and Scopus databases, with a total of 112 articles on TOD Typology having been selected. Bibliometrix (Biblioshiny) programming utilizing R Studio was employed to process and analyze the datasets, enabling open-source visualization. Criteria, such as the most prominent authors, most cited publications, and most productive nations were visualized in the findings. A significant rise in publications related to TOD typology research was observed, with China and the USA being the most significant contributors. Τhe most productive and most cited researchers along with the most prolific Universities in TOD typology were identified. The most relevant journal source with the largest number of publications was also identified, while the most popular keywords utilized were "Typology", "Land Use", and "Transit Oriented Development".

Exploring Different Annotation Schemes for Single and Consecutive Named Entity Recognition in the Arabic Biomedical Domain using Transformer Models and Contextual Semantic Embeddings

2025-04-04T06:56:59+00:00

Named Entity Recognition (NER) is an important task for Natural Language Processing (NLP) in the Arabic biomedical field. However, most works on NER in the Arabic biomedical domain suffer from some limitations, such as the inability to capture the context and semantics within texts. Moreover, only a few research studies have efficiently handled biomedical consecutive entities in the Arabic language. To overcome these limitations, this study proposes an efficient method to build contextual models for biomedical NER tasks that capture context and semantics in Arabic text using transformer models and semantic embeddings. The extracted embeddings are combined with machine learning methods, including SVM, Decision Tree (DT), and AdaBoost, to identify both single and consecutive named entities accurately. Furthermore, the effect of seven annotation schemes, namely IO, IOB, IE, IOE, BI, BIES, and IOBES, was studied to determine the most suitable for Arabic biomedical NER. The experimental results showed that the BERT and AraBERT models when fine-tuned for the Arabic biomedical NER outperform well-known machine learning methods in terms of accuracy and F1 score. The findings across various annotation schemes highlight the effectiveness of the IO scheme for simple (single) entities, while IOBES and BIES annotation schemes are better suited for recognizing multi-token entities.

Multi-Criteria Decision Making for Prioritizing Project Manager Skills according to Construction Project Success Factors

2025-04-04T06:56:01+00:00

The possession of specific skills by the project manager is a critical factor in the success of construction projects at every stage of development. The objective of this research is to identify and prioritize the specific skills that a project manager must possess to ensure a successful construction project. A comprehensive theoretical review was conducted, leading to the identification of 22 soft skills and 10 technical skills that are essential for project managers to ensure the success of construction projects. A preliminary questionnaire was used to evaluate the relative importance and interrelationships among the identified skills. This was followed by an expert questionnaire, which was assessed utilizing two analytical methods: the Stepwise Weight Assessment Ratio Analysis (SWARA) for skills and the main success factors of the construction project, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for skills according to the success factors. The preliminary questionnaire revealed that all project manager skills were deemed to be of significant importance, with percentages ranging from 0.7 to 0.818. Additionally, a moderate to weak correlation was observed between soft skills, with values ranging from 0.005 to 0.686, while for technical skills, a medium correlation was observed (0.707). The top five skills were identified as coordination, general knowledge of project management, communication, dealing with others, and organization. The TOPSIS technique revealed the preference order of soft skills: coordination skill (0.98), supervision (0.552), and general knowledge of project management (0.473). Regarding the preference order of technical skills, the following were revealed: legal experience (0.672), oral skills and listening (0.369), and planning, strategic planning, and goal setting (0.359). The findings of this study assist those responsible for making decisions concerning the most essential skills required for a project manager and provide a framework for selecting a project manager based on these competencies.

Implementation of ARIMA with Min-Max Normalization for predicting the Price and Production Quantity of Red Chili Peppers in North Sumatra Province considering Rainfall and Sunlight Duration Factors

2025-04-04T06:59:09+00:00

Red chili peppers are a vital agricultural commodity in the North Sumatra province, playing a significant role in Indonesia's economy. Fluctuations in chili prices affect farmers, consumers, and overall economic stability. This study leverages time series forecasting using the ARIMA model to predict red chili pepper prices and production, incorporating weather factors such as rainfall and sunlight duration. The dataset spans March 2021 to December 2023 and includes historical records of chili prices, production levels, and weather conditions. The analysis reveals a strong correlation between price fluctuations and production trends: Prices tend to rise when production declines and fall when yields increase. Additionally, production is influenced by weather conditions, where excessive rainfall damages crops and reduces yields, while balanced rainfall and sunlight duration support optimal growth. The ARIMA model demonstrates its effectiveness in capturing these patterns, providing actionable insights for farmers and policymakers to predict price changes and optimize production strategies. By integrating data-driven forecasting with weather analysis, this research contributes to more adaptive and informed decision-making in the agricultural sector.

Combining the Entropy Method and Genetic Algorithm in the Multi-Objective Grinding Process

2025-04-04T06:52:01+00:00

Grinding is a commonly used method for machining products that require high precision in the mechanical engineering industry. This study conducts multi-objective optimization of the grinding process for SUS440C steel on a surface grinding machine. A total of 15 experiments were designed by deploying the Box-Behnken method. In each experiment, the values of three cutting parameters, namely workpiece speed, feed rate, and depth of cutting, varied, while four objectives, involving surface roughness (Ra), cutting force component in the x-direction (Fx), cutting force component in the y-direction (Fy), and cutting force component in the z-direction (Fz), were measured. The entropy method was used to calculate the weights of the objectives, and the Genetic Algorithm (GA) was employed to solve the multi-objective optimization problem. According to the results, the optimal values of 5 m/min, 3 mm/stroke, and 0.0198 mm were, respectively, obtained for the workpiece speed, feed rate, and depth of cut. Corresponding to these cutting parameter optimal values, the values attained for the Ra, Fx, Fy, and Fz objectives were 0.612 mm, 10.126 N, 13.621 N, and 4.112 N, respectively.

Optimizing Township Government Administration with Genetic Algorithms for a Green and Sustainable Rural Future

2025-04-04T07:01:09+00:00

India's Sustainable Rural Development (SRD) policy is a pivotal step toward achieving equitable growth and strengthening rural governance structures. This study focuses on enhancing Gram Panchayat governance by proposing an Improved Genetic Algorithm (IGA)-based approach to streamline decision-making, optimize resource allocation, and foster participatory rural development. Household survey data collected in Karnataka (November 2022) inform the analysis, emphasizing best practices in rural governance and policy implementation. The findings highlight the critical role of local leadership, particularly village heads and Gram Panchayat Secretaries, in effectively overseeing SRD initiatives. Key recommendations include appointing village heads as SRD directors, deploying specialized officers to priority rural areas, and adopting standardized governance protocols. Between 2018 and 2022, performance metrics such as economic growth, citizen engagement, environmental sustainability, and rural wastewater management were evaluated. Results demonstrate that integrating IGA into rural development strategies significantly enhances resilience and sustainability outcomes. The proposed framework underscores the potential for improved funding mechanisms and administrative efficiency in addressing pressing rural challenges. This research provides valuable insights for policymakers, contributing to the advancement of India’s SRD goals and fostering a more inclusive and sustainable rural future.

Intelligent Digital Controller Design and Experimental Implementation for a Digital Hydraulic Lifting System

2025-04-04T06:54:26+00:00

This paper examines the enhancement of hydraulic performance through the replacement of a proportional valve with a Digital Valve Unit (DVU) for flow control purposes. The study underscores the significance of proportional hydraulic valves in precise flow regulation, while concurrently acknowledging that efficiencies in flow control may adversely affect the operational efficacy of actuators, particularly in hydraulic lifting systems. The digital valve system uses simple on/off solenoid valves arranged in parallel and integrated with an intelligent controller, designed utilizing a Programmable Logic Controller (PLC) to compete with conventional valves. To this end, both theoretical and experimental evaluations were conducted to compare the performance of the digital and proportional valve systems. The intelligent digital controller operates with discrete flow rates based on geometric sequence, ensuring precise modulation, while the proportional valve uses continuous flow regulation in a closed-loop system. The results revealed that the digital valve system had a 97.8% improvement in energy efficiency, referring to its lower differential pressure. The digital system also exhibited superior linearity and reduced pressure drop at low flow rates; however, its large package size posed a challenge. The proposed configuration was tested through simulations and practical implementation with the PLC, demonstrating its flexibility in adapting to various flow control needs in hydraulic lifting systems. In conclusion, the Digital Hydraulic System (DHS) offers a promising solution for meter-in flow control, providing enhanced performance in hydraulic applications.

Reducing Embodied Carbon of Paving Blocks with Landfill Waste Incineration Ash: An Eco-Cement Life Cycle Assessment

2025-04-04T06:56:24+00:00

This study examines the embodied carbon of paving blocks by substituting Portland Composite Cement (PCC) with landfill waste incineration ash at 0%, 25%, 50%, 75%, and 100% replacement levels. Using Life Cycle Assessment (LCA) and mechanical testing, the embodied carbon value was calculated per ISO 14040 standards. Results show that a 50% replacement achieves a 33% reduction in embodied carbon (120 kgCO₂e/m³ vs. 180 kgCO₂e/m³ for conventional paving blocks) while maintaining compressive strength within SNI criteria. These findings highlight the potential for eco-cement paving blocks to support sustainable construction and inform policies promoting low-carbon building materials.

Exploring the Impact of Annotation Schemes on Arabic Named Entity Recognition across General and Specific Domains

2025-04-04T06:53:38+00:00

Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP) that involves identifying and classifying entities into predefined categories. Despite its importance, the impact of annotation schemes and their interaction with domain types on NER performance, particularly for Arabic, remains underexplored. This study examines the influence of seven annotation schemes (IO, BIO, IOE, BIOES, BI, IE, and BIES) on arabic NER performance using the general-domain ANERCorp dataset and a domain-specific Moroccan legal corpus. Three models were evaluated: Logistic Regression (LR), Conditional Random Fields (CRF), and the transformer-based Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model. Results show that the impact of annotation schemes on performance is independent of domain type. Traditional Machine Learning (ML) models such as LR and CRF perform best with simpler annotation schemes like IO due to their computational efficiency and balanced precision-recall metrics. On the other hand, AraBERT excels with more complex schemes (BIOES, BIES), achieving superior performance in tasks requiring nuanced contextual understanding and intricate entity relationships, though at the cost of higher computational demands and execution time. These findings underscore the trade-offs between annotation scheme complexity and computational requirements, offering valuable insights for designing NER systems tailored to both general and domain-specific Arabic NLP applications.

LoCoNet: A Low-Complexity Convolutional Neural Network Model for Efficient Fire Detection in Outdoor Environments

2025-04-04T06:51:57+00:00

Early Fire Detection (FD) is essential, yet preventing damage to human life and property presents challenges. This study introduces a reliable and fast FD framework using a new Convolutional Neural Network (CNN) model called Low-Complexity Network (LoCoNet). The LoCoNet model deals with color images of 24×24 pixels, highly decreasing memory usage and processing time. The structure of the LoCoNet model consists of three convolutional layers, each utilizing a kernel size of 1×1, followed by a max-pooling layer, effectively halving the data size. Next, a flattening layer transforms the data into a 1-D vector. Then, a fully connected dense layer follows, and a dropout layer randomly deactivates 50% of its neurons during training. Finally, the output layer classifies the images according to the probability of fires occurring, predicting whether there are fires. K-fold cross-validation with various K values divided the dataset into training and testing sets. Multiple CNN models were investigated, and their results were compared to estimate their performance. According to the experimental results, the proposed LoCoNet model surpasses others in accuracy, processing speed, and memory usage, achieving an accuracy of approximately 99%, consuming about 2.86 s in model training, and using only 81.25 KB of memory. Compared to related approaches, the proposed LoCoNet model significantly decreases computational complexity while achieving high accuracy with minimal processing time.

Limit and Shakedown Analysis of Thin Plates under Random Strength by Probabilistic Constrained Programming

2025-04-04T07:04:16+00:00

This work presents a new model for the shakedown analysis of Kirchhoff plates under uncertain conditions of the plastic moment by the direct method. The stochastic models of the plastic moment are normal or lognormal distribution. New formulations are derived to compute the lower bound and upper shakedown loads and a dual algorithm is established to calculate the upper and lower bound shakedown load factors simultaneously for a chosen structural reliability level. An example is examined to illustrate the algorithm and shows robust results of the stochastic analysis.

Comparative Analysis of Machine Learning Models for Sentinel-2 based Classification of the Bornean Heath Forest

2025-04-04T06:54:00+00:00

Bornean heath forests, known as hutan kerangas, are fragile ecosystems that face significant anthropogenic threats. This study integrates Sentinel-2 satellite imagery with Machine Learning (ML) models to accurately classify these forests and assess their current spatial distribution. The Random Forest (RF) and Gradient Tree Boost (GTB) models achieved the highest classification performance, with overall accuracy scores of 96.66% and 96.69%, respectively, and Kappa coefficients of 0.945. These metrics were obtained using a test dataset with an 80:20 train-test split and validated through a 5-fold cross-validation process, ensuring the robustness of the models. Compared to previous studies employing unsupervised classification with Landsat-9 data, this approach demonstrates improved classification reliability and spatial accuracy. The findings highlight the substantial potential of combining remote sensing technologies with advanced ML techniques for large-scale ecosystem monitoring. This approach provides valuable insights for conservation planning and sustainable management of Bornean heath forests, addressing the growing environmental pressures that threaten their integrity.

Secondary Control for Cyber-Physical Interconnected Microgrid Systems

2025-04-04T06:51:52+00:00

This paper presents a comprehensive analysis of the design, control frameworks, and practical implementation of interconnected microgrids, with an emphasis on improving system resilience and reliability. To overcome architectural challenges, a hierarchically distributed control system is proposed, featuring both microgrid-level and system-wide control layers. A multi-agent system is employed to oversee control elements in each microgrid and to facilitate cooperation with neighboring grids. Additionally, the paper introduces a Cyber Hardware-in-the-Loop (CHIL) framework, designed to function as a real-time simulation platform for the cyber-physical aspects of these systems, integrating network emulation, real-time power system dynamics, and multiagent control coordination.

Deep-View X-Modalities Visio-Linguistics (DV-XML) Features Engineering Image Retrieval Framework

2025-04-04T06:53:56+00:00

This research proposes an advanced framework for efficient image retrieval by integrating visual and linguistic modalities into a unified system. The Deep-View X-Modalities Visio-Linguistics (DV-XML) framework is designed to handle user queries that include both text and image inputs while allowing modifications to align with user preferences. By employing a multimodal Content-Based Image Retrieval (CBIR) system, the framework combines features extracted by a ResNet-50 model for images and a Bidirectional Encoder Representations from Transformers (BERT) model for textual data. These features are harmonized using an inductive learning-based fusion methodology within Multi-Layer Perceptrons (MLPs). A novel Reverse Re-ranking (RR) algorithm enhances retrieval accuracy by optimally aligning the combined representations with the target images during inference. Extensive evaluations on the Fashion-200K and MIT-States datasets demonstrate the model's superior performance compared to baseline CBIR methods. This work advances the field by efficiently merging dual modalities and streamlining the retrieval process with innovative RR strategies, setting a benchmark for future research in multimodal image retrieval systems.

Comparing Subjective Weighting Methods in Multi-Criteria Decision-Making: An Application to Electric Bicycle Ranking

2025-04-04T06:51:26+00:00

Ranking the various electric bicycle models available in the market, each with different specifications, is a complex task. The importance of criteria in this process depends on subjective weighting methodsbecause the assigned weights to the criteria are based on the decision-maker's subjective priorities. This study compares three subjective weighting methods, namely the Rank Order Centroid (ROC) method, the Rank Sum (RS) method, and a method based on the Lagrange multiplier (referred to as the Lagrange method). These methods share the common characteristic of deriving weights from the evaluation of criteria, yet they differ in their specific formulas. The three methods were applied to assign weights to the criteria used in evaluating seven electric bicycle models across 10 different criteria. The weights were calculated under 10 different scenarios, each reflecting a change in the prioritization of criteria. For each scenario, four Multi-Criteria Decision-Making (MCDM) methods were used to rank the electric bicycles: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Ranking of Alternatives with Weights of Criterion (RAWEC), Particle Image Velocimetry (PIV), and Root Assessment Method (RAM). The comparison of weighting methods was based on the average Spearman rank correlation coefficient between the MCDM rankings obtained using different weighting methods. The findings indicate that the ROC and Lagrange methods outperformed the RS method.

A Comprehensive Review of Collaborative Robotics in Manufacturing

2025-04-04T07:02:30+00:00

Collaborative manufacturing, which integrates robots and human workers, aims to enhance productivity, flexibility, and safety by automating tasks and enabling Human-Robot collaboration in manufacturing processes. The present review encompasses a systematic search strategy, entailing advancements in collaborative robotics, Human-Robot Interaction (HRI), safety and risk assessment, applications, and case studies, as well as challenges and limitations, utilizing keywords, such as "collaborative manufacturing," "human-robot collaboration," "co-bots," "industrial robots," and "manufacturing automation" in prominent databases. The key findings and conclusions emphasize the potential of collaborative manufacturing to revolutionize the industry, while also acknowledging existing challenges, such as the need for specialized training, high costs of Collaborative Robots (Cobots), and the potential for job displacement. Future research directions, including the development of cost-effective robots, intuitive interfaces, adaptable safety standards, accessible training programs, and strategies to mitigate job displacement, are also outlined. Addressing these challenges and implementing the proposed strategies could lead to a transformative impact on the manufacturing industry. Cobot integration reduces downtime, enhances worker safety, and optimizes overall performance.

Optimized YOLOv8 for Automatic License Plate Recognition on Resource Constrained Devices

2025-04-04T06:57:31+00:00

This paper presents an optimized Automatic License Plate Recognition (ALPR) system designed for resource-constrained devices, leveraging YOLOv8 for real-time object detection and Optical Character Recognition (OCR) to extract license plate information under challenging conditions such as low-light, motion blur, and occlusions. Unlike traditional ALPR systems that rely on high computational resources, our approach balances detection accuracy, processing speed, and efficiency. The system is evaluated on three benchmark datasets: the Chinese City Parking Dataset (CCPD) with 250,000 images under diverse conditions, the UFPR-ALPR Dataset (Universidade Federal do Paraná, Brazil) containing 4,500 real-world traffic images, and the RodoSol-ALPR Dataset with 20,000 highway surveillance images for high-speed license plate recognition. Among various YOLOv8 variants tested, YOLOv8-s achieved the best performance, with a mean Average Precision (mAP) of 99.3% while sustaining over 30 Frames Per Second (FPS), making it suitable for real-time ALPR applications. Furthermore, image sharpening and contour segmentation techniques improved OCR recognition accuracy by 5.1% under low-light conditions, improving robustness. Comparative analysis against state-of-the-art OCR-based ALPR methods (EasyOCR, FastOCR, and CR-NET) demonstrated that our approach surpasses existing models in both recognition rate and computational efficiency. These findings establish YOLOv8 as a highly effective and deployable solution for intelligent transportation, surveillance, and law enforcement applications requiring real-time license plate recognition with minimal computational overhead.

Enhancing IoT Security for Sustainable Development: A Parity Checking Approach for Fault Detection in PRESENT Block Cipher

2025-04-04T06:55:24+00:00

The PRESENT lightweight block cipher designed for resource-constrained environments exhibits vulnerabilities to fault injection attacks. By deliberately introducing errors during the computation, attackers can potentially recover secret keys or bypass security measures. Various fault models, including single- and multi-bit faults targeting different stages of the cipher, have been explored, demonstrating the feasibility of such attacks. Consequently, robust countermeasures, such as error detection codes, parity checks, and hardware redundancy, are essential to enhance the fault resistance of PRESENT implementations and maintain security in real-world deployments. This paper presents an enhanced fault detection scheme for the PRESENT lightweight block cipher, designed to provide a high level of protection against a wide range of fault injection attacks. The proposed scheme focuses on detecting both simple and multiple fault attacks, addressing scenarios that target one or more bytes. A comprehensive analysis of the detection capabilities is performed, considering various fault multiplicities and injection methods. This innovative approach contributes to the advancement of secure and reliable systems, in line with the focus of SGD 9 on fostering innovation. The proposed scheme is extensively evaluated through simulations, demonstrating its ability to detect a significant percentage of injected faults. A hardware implementation on a Xilinx Virtex5-XC5VFX70T FPGA platform is explored, analyzing the trade-off between security, area, and performance. The results show that the proposed scheme achieves high fault coverage while maintaining reasonable resource utilization without impacting operating frequency. A comparison with existing techniques highlights the advantages of the proposed approach.

Leveraging a Modified Contrastive Language-Image Pre-training Model to Align Images and Text for Generating Remedy Text for Malus Pumila Lamina Images

2025-04-04T06:57:57+00:00

The increasing threat of leaf diseases to the productivity of precision farming necessitates systematic, logical, and scalable leaf identification methodologies. Conventional plant disease detection approaches are often slow, inefficient, and limited in their applicability, restricting the effective management of leaf diseases. This research work recommends a hybrid multimodal model that uses different modes of activities for leaf disease detection and can integrate image and text data in a single frame to improve the accuracy and proficiency of disease classification. The text data include custom-generated remedy descriptors specifically designed for the proposed model. The latter combines Machine Learning (ML) techniques, such as OTSU thresholding, Gaussian filtering, and modified Contrastive Language-Image Pre-training (mCLIP), to classify diseased leaves and propose suitable remedial actions. The proposed mCLIP model combines image and label data to enhance the effectiveness of multi-class image classification and suitable remedy description generation. Unlike existing multimodal approaches that primarily output text describing image features, the proposed model generates remedy text as the output for specific diseases. This novel approach offers a comprehensive solution for leaf disease detection and renders optimistic results for real-time and automated disease identification in agricultural practices, facilitating early intervention and better crop management. The proposed model obtained an accuracy of 98.1%.

Road Surface Condition Identification with Deep Neural Networks and SVM Classifier

2025-04-04T06:54:04+00:00

Roads are people's main transportation mode, deeming them an important aspect of worldwide everyday life. However, weather conditions increasingly impact road infrastructure, necessitating improved road safety measures. Identifying road types enhances traffic management and safety, particularly as roads often sustain damage during the rainy season and require restoration that takes time. In many countries, weather conditions also affect road usability. This study proposes a Deep Neural Network (DNN) for automatic road classification Road Surface Images (RSI). ResNet-50 is employed for feature extraction, while additional features, such as Gray-Level Co-Occurrence Matrix (GLCM), correlation factor, and Histogram of Oriented Gradients (HOG) are integrated to improve detection accuracy. These features collectively form the GHR50 model. Next, the collected features are classified using a Support Vector Machine (SVM) classifier and the parameters are evaluated. The proposed GHR50 model achieves 97.39% accuracy in detecting road types, such as dry mud, fresh snow, and water-asphalt smooth, representing a 0.95% improvement over conventional Convolutional Neural Networks (CNNs).

Micro-Friction Stir Lap Welding of Aluminum and Copper: A Short Review

2025-04-04T07:03:27+00:00

This review article examines the recent progress in Micro-Friction Stir Lap Welding (μFSLW) of Al-Cu thin sheets, comparing the differences in tool geometry and processing parameters of macro-scale and micro-scale Friction Stir Lap Welding (FSLW) of Al-Cu plates. The effect of microstructural evolution, intermetallic formation, hardness distribution, mechanical joint strength, and electrical conductivity is discussed in detail. The most common defects in μFSLW, such as voids, tunnel defects, and hook formations, along with their impact on heat input and tool movement, are examined. Additionally, strategies to improve joint quality, including the addition of engineering interlayers (e.g. zinc foil) and nanoparticles (e.g. graphene), are explored as they mitigate brittle IMCs, improve grain structure, and enhance both mechanical and electrical properties. Important research gaps, regarding the effects of tool tilt angles and complex tool profiles on the mechanical and electrical joint properties, are highlighted as the potential benefits of assistive technologies, such as ultrasonic vibration, assistive heating and cooling, and assistive magnetic field. Future work is essential to enhance the μFSLW of Al-Cu, investigating complex tool geometries, and improving process parameters.

A Plant Design Heuristic Considering the Eventual Measurement of Currently Unknown Variables

2025-04-04T06:59:31+00:00

It is common practice for chemical plants to be sized using estimated parameter values that are uncertain at the design stage, but whose true values will be known once the plant is in operation. Moreover, not all design decisions are fixed once the plant is built, as some may be adjusted during operation. In this paper, we present a heuristic method for plant design under uncertainty that takes these characteristics into account. The problem is framed as selecting the best from a set of candidate designs, where each candidate design results from optimizing the plant for a set of possible values of the uncertain variables. Decision trees are used to select the best-performing alternative given the probability distribution of the uncertainties. A working example is presented that relates to the design of a heat-integrated reactor with uncertainty in the plant inlet composition. Candidate designs and optimal operation for different compositions are found by using the Solver add-in of MS Excel. It is concluded that decision trees allow post-construction operational adjustments and parameter uncertainties to be easily and clearly incorporated into the design process.

Effect of Suspended Hydrated Lime Mineral Filler on the Physical Properties of Asphalt Binder

2025-04-04T07:00:39+00:00

Although several studies have investigated the best additive to improve the properties of bitumen that contributes to asphalt pavement mixtures, selecting the best performing, cheapest, and most environmentally friendly material is not yet an easy task. Aging can be a chronic phenomenon that constantly threatens the durability and service life of asphalt pavement, and research efforts aim to mitigate its effects. Accordingly, this study selected a suspended and insoluble material as an additive to investigate its effect on the physical properties of asphalt binder before and after the aging process. Hydrated Lime Mineral Filler (HLMF) was added at different concentrations (2, 4, 6, 8, and 10 % by weight of asphalt) to investigate its ability to mitigate the effect of short-term aging. Penetration, softening point, and viscosity tests were performed on samples prepared under two conditions, short-term aging and non-aging. The results showed positive signs of improvement, with lower penetration values and higher softening point values achieved, as well as higher viscosity values measured at two standard elevated temperatures. The higher retained penetration values as well as the lower increment in Softening Point (SP), and Viscosity Aging Index (VAI) values showed that the suspended HLMF particles successfully enhanced the aging resistance of the asphalt binder. All AC-HLMF samples showed better performance than the unmodified AC asphalt sample. There were no significant performance variations for the selected HLMF concentrations, but AC-HLMF 6% was the best performer, recording a 9% RP and a 26% VAI improvement compared to base asphalt.

A Penta-band Antenna using Symmetrical DGS for RF Energy Harvesting in IoT Applications

2025-04-04T06:54:43+00:00

The Radiofrequency (RF) energy harvesting technology offers sustainable and non-maintenance power for wireless sensor networks, Internet of Things (IoT) devices, and low-power electronics. Its ongoing advances in improving efficiency are miniaturizing rectennas and making multiple-band antennas capture versatile and efficient electromagnetic radiation across various frequency bands. This paper uses a composed structure of three rings, a symmetrical Defected Ground Structure (DGS), and a shorting pin to construct a penta-band antenna. The antenna operates at 2.4 GHz, 5 GHz, 6 GHz, 7.4 GHz, and 8.7 GHz, with a wide bandwidth of 747 MHz, 286 MHz, 397 MHz, 760 MHz, and 1773 MHz, respectively. Thus, the proposed antenna can capture energy from diverse RF sources such as the IEEE 802.11be standard (WiFi-7), C-band satellite, and radar communications. Furthermore, the proposed antenna achieves a good gain of 2.31 dBi, 4.56 dBi, 4.16 dBi, 5.46 dBi, and 5.41 dBi at resonant frequencies of 2.4, 5, 6, 7.4, and 8.7 GHz, respectively, and a high radiation efficiency of over 90%. Based on the Fire Retardant 4 (FR4) substrate, the eventual size of the antenna is 41.5×37×1.6 mm, which is relatively compact for RF energy harvesting in IoT.

Solar Photocatalytic Degradation of Antibiotics in Wastewater by Advanced Oxidation Technology: Optimization using the Response Surface Methodology

2025-04-04T06:56:06+00:00

Amoxicillin, a widely used antibiotic, is increasingly recognized as an environmental threat due to its persistence in aquatic ecosystems and potential risks to human health. This study investigated the removal of amoxicillin from simulated pharmaceutical wastewater using a solar-powered photocatalytic process with titanium dioxide (TiO₂) in a tube-shaped reactor. The degradation efficiency was assessed by monitoring the reduction in amoxicillin concentration under varying experimental conditions. A Box–Behnken Design (BBD) was applied to evaluate the effects of key parameters, including: initial amoxicillin concentration (10-100 mg/L), TiO₂ dosage (50, 75, and 100 mg/L), hydrogen peroxide (H₂O₂) concentration (200-600 mg/L), and pH levels (3, 5, and 7). The results revealed an optimal degradation efficiency of 90.0% under the following conditions: pH=5, 10 mg/L of amoxicillin concentration, 75 mg/L of TiO₂ dosage, and 400 mg/L of H₂O₂ with a 150-minute exposure to solar irradiation. Statistical analysis using Analysis of Variance (ANOVA) yielded high model accuracy, with R² = 96.59%, adjusted R² = 93.18%, and predicted R² = 81.7%, indicating strong agreement between experimental data and model predictions. The findings confirm the effectiveness of solar-driven photocatalysis in degrading amoxicillin, highlighting its potential as a cost-effective and environmentally sustainable approach for pharmaceutical wastewater treatment.

Audio Enhancement for Gamelan Instrument Recognition using Spectral Subtraction

2025-04-04T06:53:47+00:00

Artificial intelligence has made significant progress in processing audio, text, and images, but noise remains a major challenge, especially in real-world audio data. This research presents a novel approach to improve audio classification by integrating noise reduction techniques with machine learning models. Focusing on the bonang barung, a traditional Javanese gamelan instrument, the study uses Mel Frequency Cepstral Coefficients (MFCC) and Mel spectrograms to identify the most effective features for classification, and the Multi-Layer Perceptron (MLP) model for the classification task. In addition, the spectral subtraction method is used to reduce noise, which resulted in significant improvements in audio quality, although some artifacts remain. The main contribution of this study is the integration of noise reduction with the MLP model to improve the classification performance. The MLP model successfully classified various bonang barung playing techniques, achieving a classification accuracy of 90% after noise reduction compared to 87.22% with noise, highlighting the importance of preprocessing steps, such as noise reduction. It is also demonstrated that MLP models can be a viable alternative to more complex deep learning models, such as CNN and RNN, for audio classification tasks. Overall, this research provides new insights into the role of noise reduction in audio analysis and offers potential advances in the field of audio classification.

CFD analysis of Hydrodynamic Characteristics of Tidal Current Turbines

2025-04-04T07:06:01+00:00

Malaysia uses mostly fossil fuels for electricity generation. This study examines the hydrodynamic performance of tidal turbines, especially designed for three sites in East Malaysia: Sibu, Kota Belud, and Pulau Jambongan. The performance characteristics of these site-specific turbines were analyzed with the use of Computational Fluid Dynamics (CFD) and were validated with experimental benchmarks. The results indicate that a power coefficient greater than 0.4 is possible at Tip Speed Ratios (TSR) between 4 and 7, with the best performance recorded at TSR 5 for inflow velocities between 0.75 and 1.35 m/s. This study highlights the potential of tidal energy as a sustainable resource for Malaysia and provides a basis for further development in the region.

Analysis of the Effect of High Opening Variation on the Performance of Castellated Beams using the Reduced Beam Section Method

2025-04-04T06:52:40+00:00

Castellated beams are widely used in structural applications due to their improved load-bearing capacity and material efficiency. This study examines the effect of the variations in the opening height of castellated beams using the Reduced Beam Section (RBS) method, aiming to determine the optimal opening height based on tension, strain, deflection, and stiffness. The modeling results show that increasing the opening height leads to higher stress and strain in the beam. Modeling with an opening height of 190 mm resulted in the highest tension of 311.03 MPa and strain of 0.016, while an opening height of 110 mm recorded a stress of 269.41 MPa and a strain of 0.003. The lowest deflection of 9.87 mm and the highest stiffness of 8.22 kN/mm were obtained at an opening height of 150 mm, rendering it the optimal opening height. It is concluded that this opening height provides the most efficient balance between tension, strain, deflection, and stiffness in castellated beams with RBS. Further research is proposed to analyze the fatigue behavior and long-term performance of the castellated beams with different opening configurations under dynamic loading conditions.

An Experimental Study on the Axial Compressive Behavior of Normal Concrete Composite Square Columns with UHPC Internal Cores

2025-04-04T06:54:35+00:00

The paper presents the results of experimental tests on Normal Strength Concrete (NSC) composite square columns with Ultra-High-Performance Concrete (UHPC) internal cores of four types with a total of 24 specimens, examining the shape, number of cores, and steel fiber content to create a promising model for studying the axial compressive behavior of UHPC-NSC composite square columns. This study examines UHPC concrete with a steel fiber content of 0, 1, and 2% by volume using materials available in Vietnam in the cores. The results show that the addition of steel fibers increased the bearing capacity of the structure, while also helping to accurately evaluate the interaction between the NSC and the UHPC core. This study makes an important contribution to UHPC research in the context of limited experimental data, opening opportunities for the development of more advanced construction technologies and is of practical significance for the construction industry.

A Scenario-based Case Study Approach to Pavement Rehabilitation using Life Cycle Analysis of Recycled Asphalt Materials

2025-04-04T06:52:37+00:00

This study evaluates the environmental and mechanical impacts of using Reclaimed Asphalt Pavement (RAP) and Crumb Rubber (CR) in asphalt rehabilitation. A Life Cycle Assessment (LCA) and laboratory testing, complemented by AASHTOWare analysis, were used to assess the mechanical properties and sustainability of various asphalt mixtures. The results show that a composition of 50% RAP and 30% CR offers the best balance of durability, cost efficiency, and environmental benefits. RAP improved rutting resistance, while CR enhanced elasticity and fatigue resistance at lower concentrations. Excessive RAP or CR caused brittleness and susceptibility to cracking. The LCA findings reveal reduced carbon emissions and material consumption, while the cost analysis showed significant savings in construction and maintenance. The AASHTOWare predictions confirmed improved service life and reduced pavement distress under varying traffic and climate conditions. This study demonstrates the potential of RAP and CR for sustainable, cost-effective pavement solutions while maintaining structural performance.

Modeling the Influence of Drivers’ Personal and Driving Characteristics on Traffic Sign Comprehension

2025-04-04T06:53:00+00:00

This study investigates the factors that influence drivers’ understanding of the 30 Traffic Signs (TSs) encountered on the street network of Al-Najaf governorate, Iraq. A random sampling survey using a structured questionnaire was carried out to interview a sample of 450 drivers. The questionnaire was designed to collect data regarding drivers’ personal and driving characteristics along with their TS understanding. The descriptive analysis revealed that the drivers’ comprehension level of regulatory, warning, and information TSs reached 57.5%, 53.4%, and 65%, respectively. In the predictive analysis, the IBM SPSS version 28 was utilized along with two multinomial logistic regression models to identify the investigated factors. The results indicate that drivers' TS comprehension is substantially affected by personal traits, such as age, gender, and previous TS knowledge, whereas driving experience, traffic violation history, and driver’s attention to TSs while driving are contributing driving characteristics. These findings stress the necessity for the development of educational schemes and training initiatives aiming to increase drivers’ TS understanding.

Optimized Energy-Efficient Knapsack Algorithm for Intelligent Cluster Head Selection in Wireless Sensor Networks

2025-04-04T07:05:20+00:00

Wireless Sensor Networks (WSNs) are vital for data collection, monitoring and environmental analysis. This study presents a new energy balancing method that uses a Cluster Head (CH) selection policy based on the residual energy state of nodes, involving uniform distribution of energy consumption, with the aim to increase network lifespan and performance. Calculations are performed with the Knapsack method, which considers energy constraints and optimizes resource allocation. Performance tests with NS2.34/2.35 show significant improvements. Important findings are the extended network longevity, with the proposed solution increasing network lifetime by 16%, increased data usage by 17%, reduced latency by 14%, improved coverage by widening the monitored locations by 20%. These findings show that the proposed energy-balancing algorithm can be used to increase the lifetime and performance of WSNs. This work contributes to the ongoing effort to improve WSN performance and sustainability, particularly in circumstances when energy efficiency is essential.

A Mathematical Model of the Magnetic Field in the Magnetic Circuit Window of the Transformer Converter on the Basis of Poisson and Laplace Differential Equations

2025-04-04T06:51:39+00:00

The technical and economic requirements for electrical devices are increasing, mainly aiming at ease of operation, increased efficiency and measurement accuracy, as well as cost reduction. These requirements can be met by conducting a comprehensive study of the components of each designed device and their characteristics. For a contactless current meter, which has found wide application in the power system, meeting the above requirements is of particular interest. However, t is impossible to achieve this without putting forward new approaches to the current converter study. Since the output signal of the current converter mainly depends on the position of the conductive conductor in the magnetic circuit window and on the structural parameters of the magnetic circuit, a new modeling approach is proposed in this paper. Analytical dependencies have been obtained which, unlike known analogues, allow one to evaluate the magnetic field image without discretizing the observed area, as well as by entering the initial values of the potential. The proposed dependencies allow the determination of the characteristic parameters of the field at any position of the conductor, while in the case of known methods it is necessary to discretize the observed area again for each new position, which leads to additional difficulties.

Α Supervised Hybrid Weighting Scheme for Bloom's Taxonomy Questions using Category Space Density-based Weighting

2025-04-04T06:53:13+00:00

Question documents organized based on Bloom's taxonomy have different characteristics than typical text documents. Bloom's taxonomy is a framework that classifies learning objectives into six cognitive domains, each having distinct characteristics. In the cognitive domain, different keywords and levels are used to classify questions. Using existing category-based term weighting methods is less relevant because it is only based on word types and not on the main characteristics of Bloom's taxonomy. This study aimed to develop a more relevant term weighting method for Bloom's taxonomy by considering the term density in each category and the specific keywords in each domain. The proposed method, called Hybrid Inverse Bloom Space Density Frequency, is designed to capture the unique characteristics of Bloom's taxonomy. Experimental results show that the proposed method can be applied to all question datasets, considering term density in each category and keywords in each cognitive domain. Furthermore, the accuracy of the proposed method was superior on all datasets using machine learning model evaluation.

An Experimental and Analytical Study of the Flexural Capacity of reinforced Geopolymer Concrete Beams

2025-04-04T07:03:09+00:00

This study investigates the flexural strength of reinforced Geopolymer Concrete (GPC) beams using experimental and analytical methods. Five sets of reinforced beams were cast, each containing three beams with dimension of 150 mm x 200 mm x 1200 mm. One set was made with conventional concrete, while the other sets were made with GPC with different percentages of reinforcement. In order to determine compressive strength, three cube samples of conventional and GPC were also cast. The experimental flexural capacity of the beams was evaluated using a four-point loading test. The analytical flexural capacity was predicted deploying the IS 456:2000 standard method for reinforced concrete beams. The findings revealed that the predicted flexural capacity was similar or lower than the experimental values for GPC beams. Therefore, the IS 456:2000 approach can be utilized to predict the flexural capacity of reinforced GPC beams.

Enhanced Text Detection in Natural Scenes using Advanced Machine Learning Techniques

2025-04-04T06:56:45+00:00

Text detection in natural scenes remains a fundamental challenge in computer vision, impacting applications from mobile navigation to document digitization. Traditional methods struggle with varying text orientations, complex backgrounds, and inconsistent lighting, while recent deep-learning approaches face computational efficiency challenges. This paper presents a novel hybrid machine-learning framework that combines traditional computer vision with advanced machine learning to achieve robust text detection. The framework integrates optimized preprocessing techniques, feature extraction methods, including Histogram Oriented Gradients (HOG) and Maximally Stable Extremal Regions (MSER), and a lightweight convolutional neural network for improved accuracy and efficiency. Experimental evaluation on benchmark datasets demonstrates superior performance, achieving 98% precision, 97.5% recall, and 97.8% F1-score, while maintaining real-time processing capabilities at 45 fps. The framework significantly outperforms existing methods in handling diverse text scenarios, establishing a new standard for natural scene text detection. This research contributes to the advancement of text detection technology and offers practical applications in augmented reality, autonomous navigation, and document processing systems.

Stator Inter-Turn Short Circuit Fault Estimation for a DFIG-based Wind Turbine

2025-04-04T07:02:07+00:00

This paper presents an Inter-Turn Short-Circuit (ITSC) fault detection and isolation method based on the Proportional-Integral observer (PIO) design. ITSC fault is one of the most common electrical faults in electrical machines, the early detection of which can significantly reduce maintenance costs and prevent wind turbine damage. Therefore, a fault detection and isolation method is proposed to evaluate the ITSC fault level affecting the stator windings of a Double-Fed Induction Generator (DFIG). First, a state-space model of the generator with an ITSC fault in the d-q reference frame is introduced. Based on this fault model, an unknown input observer described by a Takagi-Sugeno (TS) model is also used to detect and isolate the ITSC fault. This observer provides a good estimation of the unknown inputs despite the abrupt changes in wind speed and parameter variations in the DFIG. Finally, the effectiveness of the proposed ITSC fault estimation is highlighted through simulation on a 3-kW wind turbine system.

BiPoP: Bipolar Disorder Optimized Preprocessing Framework for Stress Disorder Identification through Gene Expression Data using Deep Learning

2025-04-04T07:01:01+00:00

Gene expression data are widely used in diagnosing diseases and identifying promising genes with the advancement in computational tools in biology. Gene Expression Omnibus (GEO) datasets provide the gene expression data for various diseases and disorders. For Bipolar Disorder, GSE46449 was obtained from the NCBI data repository. This study aimed to classify control (Normal) and case (Disordered) individuals from samples using Machine Learning (ML)/Deep Learning (DL) models. The preprocessing involved the removal of null values and normalization of gene expression values using R. The second step focussed on the selection of optimal features/genes from the gene expression dataset. The Pearson Correlation Coefficient (PCC) along with Principal Component Analysis (PCA) were used for feature selection. The samples were then classified using ML/DL models. A Multi-Layer Perceptron (MLP) was used to validate the optimal feature set to classify healthy and disordered individuals. The proposed Bipolar Disorder Preprocessing Framework (BiPoP) was validated for its targeted use, highlighting its multifunctional and fine-tuned approach to preprocessing and achieving a classification accuracy of 98.9%.

Economic and Environmental Scenario Analysis of a Finnish Wood-based Case Building

2025-04-04T06:52:54+00:00

The construction industry is a major contributor to Greenhouse Gas (GHG) emissions, highlighting the need for more sustainable building practices. While building costs often drive project decision-making, environmental impacts from material production to building operation are considered equally significant. Wood has emerged as a viable alternative to traditional construction materials, offering reduced carbon emissions and potential cost savings. This study aims to assess the environmental and economic performance of a wooden-framed educational building in Finland, with a focus on life-cycle carbon emissions and cost-effectiveness. The case building is a single-story structure with glulam external walls, beams, and columns. A Life Cycle Assessment (LCA) and cost analysis has been conducted using LCA tools provided by the Finnish Ministry of Environment, alongside a comparative scenario analysis involving alternative structural materials. Three alternative scenarios have been designed with different materials utilized for external walls and the structure, i.e., beams and columns. The findings reveal that wood-based structures can achieve substantial reductions in carbon emissions while remaining cost-competitive, particularly in early life-cycle stages compared to conventional reinforced concrete options. The results of this study partially challenge the widely recognized barrier to adopting greener building practices, namely the incremental cost of sustainable construction. Additionally, scenario analysis highlights the potential for hybrid structural systems to balance environmental benefits with economic feasibility. This research contributes practical insights into how contractors and policymakers can adopt wood and hybrid materials to support low-carbon construction goals.

Development of Green Concrete for Mining Roads using Incineration Residue Ash

2025-04-04T06:52:33+00:00

This study explores the potential of incorporating Incineration Residues Ash (IRA) as a partial replacement for Portland Composite Cement (PCC) in cement production at a mining site in Ceria Indotama Nugraha, Indonesia. Concrete mixtures with different percentages of IRA-0%, 25%, 50%, 75%, and 100%- were evaluated for workability and compressive strength under extreme conditions. The results show that replacing up to 50% PCC with IRA leads to compressive strength values similar to those of traditional concrete (30 MPa), with 25% IRA being the optimal percentage. Additionally, Life Cycle Assessment (LCA) analysis revealed that the environmental impact of concrete can be reduced with IRA replacement, especially regarding CO₂ emissions. These findings suggest that using incineration residues in concrete production can be a sustainable and possible alternative for mining road constructions.

The Impact of Cooling Water Way Length on Thermal Effluent Temperature Reduction in Coal-fired Steam Power Plants: The Case Study of the 2×150 MW Jeneponto Power Plant

2025-04-04T06:55:15+00:00

The thermal pollution caused by the usage of fossil energy sources has a significant impact on aquatic ecosystems that requires effective cooling systems for the reduction of temperatures such as the Cooling Water Way (CWW). This study examines the correlation between CWW length and temperature reduction at the Jeneponto power plant (2×150 MW) in Indonesia, focusing on two sections: box culvert (0-359 m) and open channel (359 m-1068 m). The box culvert segment, with minimal air-water interaction, achieves a temperature reduction gradient of 0.0006 °C/m, while the open channel segment shows a higher gradient of 0.0022 °C/m due to the enhanced cooling by convection, evaporation, and radiation. Linear regression models for both segments (R² is 0.9586 and 0.9961, respectively) highlight the important role of channel configuration in cooling efficiency. These findings provide valuable insights for optimizing CWW designs for effective thermal pollution control in coal-fired power plants worldwide.

Enhanced Depth-First Search Algorithm for Improving the Efficiency of Route Construction in Data Center Networks

2025-04-04T06:57:14+00:00

Data transmission is a critical component of data center networks, ensuring efficient and reliable data transfer between nodes. Using the shortest path for data transmission is a common approach in data center networks, as it helps minim. Nevertheless, this methodology may also give rise to some challenges, including those related to network congestion and heightened susceptibility to node failures. In light of the inherent self-similarity and multipath routing characteristics shown by the Fat Tree topology, this work proposed a modified search method aimed to enhance the efficiency of the depth-first search (DFS) method. The comparison is made between the modified algorithm and the original algorithm operating on the conventional network architecture seen in data centers. The findings highlight the distinctive advantages of the modified DFS method, demonstrating its enhanced scalability and effectiveness in minimizing latency. The modified DFS technique consistently excels in reducing energy usage under various network load circumstances.

Flexural Behavior of Reinforced Concrete Beams using Recycled Glass as a Sand Substitute

2025-04-04T06:51:35+00:00

Construction waste, representing a major part of the total solid waste generated in urban areas, contains glass and recycling this glass can partially replace sand as aggregate in concrete. This study experimentally investigates the flexural behavior of 7 Reinforced Concrete (RC) beams, utilizing Recycled Glass (RG) as fine aggregate to substitute 0%, 10%, 50%, and 100% of the sand component's weight in the concrete mix. The study identified the load at which the beams cracking initiated, the progression of the cracks, and the subsequent damage. It established relationships between load and Vertical Displacement (VD), as well as load and deformation in the Tension Zone (TZ) and Compression Zone (CZ) at the mid-span of the RC beam using a 4-point bending test. The research findings indicate that a 10% RG content can effectively substitute sand in concrete beams, with stirrups measuring 125 mm at the ends of the beam and 100 mm at the midpoint of the beam span.

Multi-Objective Optimization of Electric Distribution Systems with integrated distributed Generation using Deep Reinforcement Learning

2025-04-04T06:51:48+00:00

This paper proposes a method for optimizing the placement and capacity of Distributed Generators (DGs) in distribution systems based on Deep Reinforcement Learning (DRL). The objective of the method is to minimize power losses, investment costs, voltage deviations, and CO₂ emissions while ensuring strict compliance with system operating constraints. The proposed approach leverages the robust capabilities of DRL to handle nonlinear and complex-constrained problems, making it highly adaptable to various operational scenarios. Experimental results on standard distribution systems demonstrate that the proposed method outperforms traditional algorithms, significantly improving operational efficiency and enhancing the integration of renewable energy sources. This contributes to the development of smart grid systems and promotes sustainable energy solutions.

Refined Nonlinear Estimation of Effective Flexural Rigidity in Reinforced Concrete Beams using Curvature Integration

2025-04-04T06:51:17+00:00

Deflection control in Reinforced Concrete (RC) beams is a fundamental aspect of structural engineering. Most contemporary design codes estimate deflection using the effective moment of inertia formula, which remains largely consistent across various standards. However, an alternative and more precise approach involves computing deflection through the double integration of the moment-curvature relationship along the beam's length, offering superior accuracy but requiring significantly higher computational effort. This study evaluates deflection predictions obtained through experimental testing, conventional code-based calculations, and the moment-curvature double integration method. The findings demonstrate a strong correlation between the experimental data and the results from moment-curvature integration, whereas deflection estimates based on code formulations tend to be overly conservative. Therefore a comprehensive parametric study was performed, considering key parameters such as tensile and compressive reinforcement ratios, and span-to-depth ratio. Based on the study's findings, an empirical model is proposed to determine the effective moment of inertia, offering improved accuracy in deflection predictions while maintaining computational efficiency in RC beam analysis.

Quality of Life Analysis with WHOQOL-BREF in Disaster Preparedness for Flood-Prone Areas in Makassar City, Indonesia

2025-04-04T06:51:22+00:00

Floods are recurring disasters in urban areas, particularly in flood-prone regions, such as Makassar City, Indonesia. The preparation of residents for such events is crucial for reducing risks and enhancing resilience. This study aims to analyze the relationship between Quality of Life (QoL), as measured by the World Health Organization Quality of Life-BREF (WHOQOL-BREF), and disaster preparedness in flood-prone areas of Makassar City. A combination of conventional statistical methods and Structural Equation Modeling- Partial Least Squares (SEM-PLS) was used to analyze the data collected from 409 respondents across four sub-districts: Biringkanaya, Tamalanrea, Panakkukang, and Manggala. The findings indicate that a higher QoL correlates with improved disaster preparedness, suggesting that efforts to enhance residents' well-being can positively influence their readiness for floods. Based on these results, this study proposes integrating QoL factors into disaster preparedness programs to increase community resilience.

Evaluation of the Shear Strength of RC Beams Strengthened by Basalt Fiber Sheets

2025-04-04T06:52:14+00:00

The present study experimentally investigates the use of Basalt Fiber-Reinforced Polymer (BFRP) composites for the shear strengthening of High-Strength Reinforced Concrete (HSRC) beams. The objectives of this work are to investigate the contribution of BFRP in enhancing the shear capacity and ductility of HSRC beams across different shear span-to-effective depth (a/d) ratios and to assess the effect of the number of BFRP sheet layers on shear strength improvement. The experiment involved U-wrapped 90° BFRP strips applied to HSRC beams under shear. Two groups of a/d ratios were used, namely 2.8 and 2.4. A total of eight HSRC beams with and without shear reinforcement were tested under four-point loading. The results indicate that beams with a lower a/d ratio (2.4) exhibited higher ultimate load capacities and improved shear strength compared to those with a higher a/d ratio (2.8). Specifically, for a/d 2.8, the ultimate load capacity increased by 89% with two layers of U-wrapped 90° BFRP strips, and for a/d 2.4, it increased by 49% with the same strengthening compared to the control beams. The application of BFRP U-strips significantly enhanced structural performance, increasing ultimate load capacity and reducing deflections, particularly with multiple layers.

Lighting Dynamics for Emotional Perception: A Technology-Driven Virtual Simulation Approach

2025-04-04T06:51:31+00:00

This research examines the effects of light color (Light Color), light intensity (Intensity), and time (Time) on emotional responses through a simulation created with the Unreal Engine. The controlled experiment carried out included 162 participants, the emotional perception of whom was assessed following scenarios with differing lighting conditions. The two-way ANOVA results revealed the significant influence of Light Color, Intensity, and Time (p < 0.001), along with that of the Light Color-Intensity and Intensity-Time interactions on human perception. The fuzzy logic examinations demonstrated that warm, low-intensity lighting during the day yields the highest perception score (7.8), while cold (cool), high-intensity lighting at night produces the lowest (3.6). The predictive regression model achieved a good accuracy (R² = 0.96), showing that the ideal lighting combination improves emotional experience. This research provides novel perspectives on dynamic lighting design for cinematography, gaming, and immersive media. However, further research is required to scale these findings on a wider population and diverse spatial environments.

Modeling Turbulent Flow Velocity Profiles in Irregularly shaped Open Channels: A 3D Approach

2025-04-04T07:06:23+00:00

Numerically simulating turbulent open-channel flows represents a formidable challenge in Computational Fluid Dynamics (CFD), particularly when addressing the interplay of transient turbulence, irregular bathymetry, and dynamic free-surface interactions inherent to natural river systems. This study advances a three-dimensional nonlinear (k-ε) turbulence model to resolve flow dynamics, velocity distributions, and mass transport mechanisms in both meandering and straight open channels. The framework leverages cylindrical coordinate systems to accommodate curvilinear geometries, enabling precise representation of intricate channel boundaries. The governing equations are discretized with the finite volume method, with pressure-velocity coupling achieved through the SIMPLE algorithm. The nonlinear (k-ε) formulation is uniquely suited to capture anisotropic turbulence effects while maintaining computational efficiency, addressing a critical gap in conventional isotropic eddy-viscosity models. Key innovations include the development of a geometrically adaptive numerical framework capable of simulating flow in meandering channels with variable curvature and width-to-depth ratios. Parametric analysis reveals that secondary circulations, driven by curvature-induced centrifugal forces and bed roughness heterogeneity, profoundly influence the velocity profiles and scalar transport. The model successfully predicts flow separation at bends, velocity-dip phenomena beneath free surfaces, and pollutant dispersion patterns in compound channels. Validations against empirical datasets confirm the model’s fidelity in replicating turbulent kinetic energy distributions and Reynolds stress anisotropy. This study establishes the nonlinear (k-ε) model as a versatile tool for analyzing hydraulically complex environments, including sediment-laden rivers and vegetated wetlands. By integrating geometric adaptability with advanced turbulence closures, the framework bridges theoretical CFD advancements and practical applications in flood risk mitigation, eco-hydraulic engineering, and contaminant transport modeling. The findings underscore the necessity of resolving anisotropic turbulence and secondary flow mechanisms to achieve predictive accuracy in real-world, geometrically heterogeneous open-channel systems.

An Experimental and Analysis Comparison of Solid Wood Bridle Joints with Various Fastener and Retrofit Methods

2025-04-04T06:51:43+00:00

Teak (tectona grandis) is a material widely used for roof framing, known for its load-bearing capacity. Many buildings, including wooden ones, suffered significant damage after an earthquake due to failure of meeting technical requirements for seismic resistance. Therefore, it is necessary to strengthen the wooden roof trusses before a strong earthquake occurs. This study examines the structural behavior of L-type solid wood trusses under different fasteners, strengthening methods, and loading directions, and compares the experimental test with analysis methods. The test specimens consisted of teak L-joints with dimensions of 2 mm³ ×70 mm³ ×140 mm³ ×800 mm³ and a total of 32 pieces. Four types of fasteners were used: wooden plugs (4ø16 mm), bolts (4ø1/2"), nails (13ø3.76 mm), each with a length of 2.5", and screws (26ø3.50 mm) each with a length of 1.5". The retrofit materials were: L35.35.3 iron profile, C70.35.0.45 stainless steel, and 60.4 strip plate. The specimens were loaded in two directions: upright and sideways using a flexure tester with a maximum capacity of 150 kN and a maximum displacement stroke of 100 mm, which continued until peak load was reached, and then stopped after a load drop. The maximum load on the L-joint was found to be higher in the upright position than in the side-up position. The highest load capacities were achieved with the following fasteners: bolts, screws, nails, and wooden dowels, for both loading directions. Retrofitting with iron profile shows the greatest increase in load capacity for both loading directions. For right-up loading, retrofitting with strip plates is better than stainless steel, while for side-p loading, stainless steel retrofit is better than the strip plate. Failure modes were mainly shear cracks in the joint area originating from the bolt and pin holes. Failures were observed as breakage in wooden pins, and shear failure in nails and screws. The comparison of the maximum load capacity of the experimental test shows higher results compared to the results of the analysis calculation, with a ratio of about 1.20. The formula for calculating the load resistance of the joint, with a constant value of 73.11, in the literature review must be corrected to 70.80 for nail joints, 70.40 for bolt joints, and 62.10 for screw joints.

Transformer Hyperparameter Tuning for Madurese-Indonesian Machine Translation

2025-04-04T06:59:44+00:00

The main problem arising in using Neural Machine Translation (NMT) for the Madurese language is the limitation of training data due to the unavailability of an adequate parallel corpus. In addition, the model must overcome the difference in words caused by the level of politeness in the Madurese language (coarse, moderate, and smooth). The rules-based approach requires many rules to represent these differences. In contrast, the statistical approach relies on the frequency of words in the training data, which cannot accurately capture variations in politeness levels. To overcome this problem, a parallel corpus was created to provide adequate training data, and an embedding matrix based on Skip Gram with Negative Sampling (SGNS) was used to produce better word representations for processing with transformers. This study also employs two types of evaluation: model configuration based on dataset size (large and small) and two tokenization methods (word and subword levels). The best results were obtained with the large dataset using word-level tokenization, achieving 0.70% accuracy for entirely correct text, 78.87% for partially correct text, and a BLEU score ranging from 4.76 to 27.63 with a maximum n-gram value from 1 to 4. This approach improved translation accuracy and shows significant potential for developing NMT systems for languages with limited resources, such as the Madurese language.

Flexural Characteristics of Hollow One Way-Ferrocement Slabs

2025-04-04T06:52:19+00:00

In this study, five hollow one-way Ferrocement slabs were fabricated, cast, and tested under a four-point loading system. The ferrocement slabs were voided by waste plastic bottles. The main employed parameters were the void presence, void ratio for the waste plastic bottles, and fiber percentage. The waste plastic bottle void method relied on a hollow tube of steel wire mesh that was then filled with waste plastic bottles and installed at the mid-height of slab thickness aiming for the low stress position within slab depth. The ultimate load capacity, flexural stiffness and ductility, crack width, and profile deflection were measured and analyzed. The study demonstrated that the ferrocement voided slab load capacity was affected by the voids compared to the reference specimen, namely the ferrocement solid slab. However, the voided slabs using 1% Polypropylene (PP) Fibers demonstrated a good performance, which was approximately identical to that of the solid slab. From the results, it can be concluded that the addition of PP fibers to empty plastic bottle hollow slabs is an excellent method to increase the latter's total flexural stiffness. Compared to the solid slab, the voids in the slabs not only decreased the dead load, but also ductility and stiffness. Moreover, adding fibers improved these qualities making them similar to those of the original slab. Furthermore, it was observed that when using two lines of hollow plastic tube, the ultimate load capacity dropped by approximately 18.5% compared to that of the solid slab.

An Optimized Approach for Handwritten Arabic Character Recognition based on the SVM Classifier

2025-04-04T07:06:05+00:00

Optical Character Recognition (OCR) is an essential technology, capable of addressing complex challenges while simplifying numerous human activities. Although it emerged in the 1970s with various solutions, these efforts primarily focused on Latin-based languages, leaving other writing systems, such as Arabic, largely underexplored. In this context, this study proposes an innovative offline Arabic handwriting recognition system based on a structural segmentation method combined with the use of Support Vector Machines (SVM) for character classification. An in-depth review of different character segmentation methods was followed by an in-depth analysis of the OCR field. This study examined the challenges associated with normalization, a recurring issue in the processing of handwritten scripts. Finally, after comparing the unique characteristics of Arabic handwritten characters with existing segmentation techniques, an approach was developed based on a segmentation algorithm to improve the accuracy and efficiency of the recognition process.

Shear Punching Behavior for Flat Slabs with CFRP and Openings

2025-04-04T06:59:05+00:00

This study investigates the punching shear behavior of geopolymer flat slabs with transverse web openings reinforced with Carbon Fiber Reinforced Polymer (CFRP). Shear reinforcement plays a critical role in enhancing the slabs' resistance to punching shear failure, and the addition of transverse web openings allows for service apertures near the columns. In this study, three wooden molds were prepared to test 15 samples of geopolymer concrete under concentrated loading conditions. Each slab had dimensions of 70 cm × 70 cm × 7 cm, with 15 cm × 15 cm × 15 cm columns. The research models were divided into three groups: the first studied the effect of column location, the second examined the influence of openings near the columns, and the third evaluated the impact of CFRP reinforcement. The results showed that transverse web openings reduced the overall punching shear capacity of the slabs due to the loss of concrete in the geopolymer section. However, slabs reinforced with CFRP demonstrated superior performance, which was attributed to the excellent mechanical properties of the material. The full wrapping technique provided the most effective results among the various repair methods tested.