Engineering, Technology & Applied Science Research

Using a Fuzzy Model to Evaluate Risks caused by Variation Orders in Construction Projects

Allam Abu Mwais — 2025-02-02

The construction industry is the main driving sector for any country's economy. However, it is too risky due to the many risks and challenges in this industry. One of the main challenges in construction projects is variation orders, as they lead to conflicts between project parties and cause poor project performance. This study aims to evaluate the risks associated with variation orders in construction projects using fuzzy risk assessment. The targeted population is the construction contractors in Palestine. A questionnaire survey was used to collect data on the severity and frequency of the 22 identified factors of variation orders. Then, a fuzzy logic system was developed to rate these factors. The factor risk level was determined by connecting the relationship between the severity and frequency indices using If-Then rules. The determination of risk level is a critical task in the proposed fuzzy system, which depends on the complex combinations of all If-Then rules. Risk is determined not only by the If-Then rules but also by the weight of each rule. This study shows that the factors with the highest risk levels are scope change by the client, client financial problems, unavailability of required materials, poor design documents, and modification of specifications. The results can serve as guidelines for project participants who need to prepare and implement a comprehensive and effective risk management plan to meet project goals. To improve project performance, construction parties are recommended to implement the proposed method to assess risks related to construction projects. Finally, the proposed risk model demonstrates the ability to evaluate risk levels by aggregating rules and combining the project risk factors using a fuzzy logic model and MATLAB.

An Improved Laxity based Cost Efficient Task Scheduling Approach for Cloud-Fog Environment

Praveen Kumar Mishra — 2025-02-02

Task scheduling is critical in fog computing, as it has to assign workloads to fog nodes to save costs and execution times. This study emphasizes the allocation of jobs received from clients to suitable nodes through a proposed scheduling technique, which is deployed on layer 2 servers within a cloud-fog environment. Laxity-based Cost-efficient Task Scheduling (LCTS) is proposed for contemporary task scheduling difficulties, such as balancing cost and delay with optimal energy utilization. The results show that the proposed strategy decreased execution time and cost more than Round Robin (RR) and Genetic Algorithm (GA). Furthermore, the proposed method was less expensive than cloud-based IoT solutions. Compared to GA and RR, the simulation results showed that cost and execution time were reduced by 6.99%-17.36% and 4.58%-9.09%, respectively.

Comparative Evaluation of YOLO Models on an African Road Obstacles Dataset for Real-Time Obstacle Detection

Pison Mutabarura — 2025-02-02

Public datasets are used to train road obstacle detection models, but they lack diverse and rare object classes found on African roads, negatively impacting the performance of models trained on them. Although attempts have been made to create custom datasets to train road obstacle detection models, they lack the unique challenges posed by African wildlife and livestock commonly encountered on African roads. This leads to poor performance of road obstacle detection systems in the African context. This study presents a custom dataset with rare African object classes and compares the performance of three YOLO models on it using mean Average Precision (mAP). The images were collected from multiple sources to ensure a wide range of scenarios. Offline data augmentation was applied to increase dataset diversity and simulate real-world road scenarios. The models were trained and evaluated, with YOLOv5 demonstrating superiority over the other two models, with an object detection accuracy of 94.68% mAP at an Intersection over Union (IoU) threshold of 0.5 with data augmentation. Offline data augmentation significantly improved all models' object detection accuracy, especially for YOLOv3. The results reveal the effectiveness of the custom dataset and highlight the importance of data augmentation in improving object detection.

Causes and Effects of Change Orders for Construction Projects in Palestine

Ibrahim Mahamid — 2025-02-02

The construction sector is vital to Palestinian economy, contributing significantly to its growth and development. Its complex nature encompasses human, non-human, and other elements and often necessitates change orders, which are inevitable, regardless of the project size, type, or characteristics. Change orders lead to massive delays and cost overruns impacting project timeline and profitability. The ccurrent study explores and ranks the causes and impacts of change orders in Palestinian construction projects from contractors' and consultants' perspectives. The findings revealed that internal factors related to the owner were the primary source of change orders. The major five causes were ranked based on the Relative Importance Index as follows: "use of duplicated documents from previous projects," "change in plan and scope by owner," "owner's financial difficulties," "poor site investigation before the design stage," and "errors and omissions in design." Similarly, the study presents the top five impacts of change orders as follows: "time overruns," "cost overruns," "rework and demolition," "delay in payment by the owner," and "disputes between contract parties." This study holds particular importance for the construction sector, offering valuable insights into managing the change orders to meet the projects objectives in terms of schedule, budget, and quality.

An Enhanced Convolutional Neural Network (CNN) based P-EDR Mechanism for Diagnosis of Diabetic Retinopathy (DR) using Machine Learning

Munawar Hussain — 2025-02-02

This study focuses on Diabetic Retinopathy (DR), a disease caused by diabetes that affects the retina of the eye and eventually leads to blindness. Diabetes development progresses to retinopathy and must be addressed at an early stage for effective treatment. Currently, DR is classified as Non-Proliferative DR (NPDR) and Proliferative DR (PDR). This study proposes an Enhanced DR (P-EDR) method based on CNN using a high-resolution dataset benchmark of retinal images. Initially, the data were preprocessed by normalization, augmentation, and resizing to improve image quality and feature extraction. Evaluation was based on accuracy, specificity, sensitivity, and AUC-ROC. The proposed CNN-based P-EDR outperformed advanced ML strategies such as Support Vector Machine (SVM), Random Forest (RF), Probabilistic Neural network (PNN), and Gradient Boosting Machine (GBM) that were executed and compared to diagnose and classify DR. The proposed P-EDR extracts features such as a hemorrhage of the NPDR retina image to identify the disease using image processing for classification. P-EDR provides significant features from images in detection and classification, making it a successful model for diagnosing DR with improved accuracy of 93%, sensitivity of 92%, specificity of 94%, and AUC-ROC of 0.97%. These results highlight the potential of a P-EDR-based machine learning model to support ophthalmologists with the early and precise detection of DR, eventually helping with appropriate treatment and prevention of vision loss.

Αn Experimental and Numerical Study on the Drying of Celery (Apium Graveolens L.) Growing in Southern Tunisia

Nadia Nasfi — 2025-02-02

The present work experimentally investigates the curves of the drying kinetics of celery leaves (Apium Graveolens L.) in a drying convective oven. These curves were determined at 50°C, 60°C, and 70°C. For the fitting of the experimental results, the Lewis, Handerson, and Pabis, Page, Midilli &Kucuk, Logarithmic, Modified Page, Wang, and Singh and Two Terms models were used. The Midilli & Kucuk model provided the best fit for the experimental results. The effective water diffusion coefficient (D_eff) varied from 3.65×10^-10 m²/s to 7.29×10^-10m²/s in the considered temperature range. The higher temperature gave a higher effective water diffusion coefficient and Drying Rate (DR). The activation energy calculated using an exponential expression based on the Arrhenius equation was 31.72 kJ/mol. The Characteristic Drying Curve (CDC) was also determined as a three-degree polynomial.

Torque Measurement System Design for Step Motor 23HS30-3004S

Tran Vu Minh — 2025-02-02

This article describes the methodology employed in the development of a prototype system designed for torque measurement. The system consists of two components. Α rotating component, the transmitter, and a fixed component, the receiver. The torque data, obtained from a strain gauge bridge, are processed and transmitted to the receiver through a transformer-operated device. Rotary transformers are deployed to supply power to electronics that are mounted on shafts. This ensures continuous and stable data transmission without requiring physical connections. Additionally, the accuracy of the design model is verified through design equations and simulation results, reinforcing the system’s feasibility and reliability for real-world applications.

The Effect of Adding Waste Tire Rubber on Compressive Strength, Impact Resistance, and Damping Ratio of Fiber-Reinforced Foamed Concrete

Oday Asaad Abd — 2025-02-02

Research was conducted to investigate the effects of incorporating optimal proportions of Waste Tire Rubber (WTR) on the compressive strength, impact resistance, and damping of fiber-reinforced Foamed Concrete (FC) modified with a Super-Plasticizer (SP). In this study, four FC types with a density of 1100 kg/m³ were produced: conventional FC, modified FC with SP, polypropylene (PP) fiber-reinforced FC, and fiber-reinforced rubberized FC (containing SP, PP, and WTR). To evaluate the effect of density on the FC properties, two additional fiber-reinforced rubberized FC mixtures were produced with densities of 800 and 1400 kg/m³. The sand in the FC was partially replaced with WTR at optimum ratios of 50% for coarse WTR (4.75–10 mm) and 34% for fine WTR (≤ 2.36 mm). Additionally, 53 kg/m³ of cement was substituted with fly ash. The results indicated that the addition of SP enhanced the properties of the fresh and hardened FC. For a given density of 1100 kg/m³, adding WTR led to decreased consistency and strength while increased the impact and damping compared to the reference containing only SP and PP. However, the fiber-reinforced rubberized FC mix with SP showed improvements of 79.5%, 3700%, and 21.45% in compressive strength, impact resistance, and damping, respectively compared to conventional FC (without SP and PP). With the exception of the damping ratio, the compressive strength and impact resistance increased when the rubberized FC density was elevated.

Hybrid Multi-Criteria Decision Making Methods: Combination of Preference Selection Index Method with Faire Un Choix Adèquat, Root Assessment Method, and Proximity Indexed Value

Nguyen Trong Mai — 2025-02-02

This study presents an investigation into the hybridization of Multiple Criteria Decision Making (MCDM) methods. The Preference Selection Index (PSI) method is used in two distinct ways: first, for its traditional purpose of ranking alternatives, and second, to calculate criteria weights. These criteria weights are utilized to rank the alternatives provided by other MCDM methods, including the Faire Un Choix Adéquat (FUCA), Root Assessment Method (RAM), and Proximity Indexed Value (PIV), resulting in the creation of three hybrid models: FUCA-PSI, RAM-PSI, and PIV-PSI. The effectiveness of these hybrid approaches is tested by ranking 20 Vietnamese cities based on their digital transformation efforts. The results demonstrate that the hybrid approaches produce a highly correlated ranking, as evidenced by the Spearman rank correlation coefficient found among these methods, with the lowest being 0.8571. Both the PSI method and the three hybrid models identified the same top alternative, confirming the reliability and accuracy of the rankings.

Predictive Maintenance Algorithm of a 6DOF Robotic Arm using Gradient Boosting Regression

Louie Villaverde — 2025-02-02

The objective of this study is to develop a predictive maintenance algorithm for the ABB IRB 4600, a 6-axis robotic arm, using digital simulations. A variety of tests were conducted using SolidWorks, including calculations pertaining to stress, strain, fatigue, and heat. The simulations included an analysis of the materials used in the construction of the robotic arm, which are gray cast iron and aluminum alloy. The robotic arm was tested in three positions—picking, raised, and placing—with loads of 100 kg, 200 kg, and 300 kg, respectively. The findings indicated that elevated stress, strain, and displacement levels diminish the robot's operational lifespan and accelerate its deterioration over time. The placing position was found to experience the greatest stress, displacement, and strain. The fatigue test also demonstrated that after 10 million cycles, the arm had accumulated damage. The gradient boosting regression algorithm was selected as the Machine Learning (ML) algorithm for the study following a comparison of the performance of various ML regression models. This finding underscores the significance of predictive maintenance in preventing breakdowns and extending the robot's lifespan.

Modeling and Optimization of Hydropower Plant Operations in the Northern Vietnam Power System by 2030

Luong Ngoc Giap — 2025-02-02

The Northern Vietnam Power System (NVPS) has a long history, with over a century and a quarter of development. The NVPS has experienced a significant expansion in its total power generation capacity. In 1954, the total capacity was 31.5 MW. By the end of 2023, the total capacity of the NVPS reached 29,537 MW, with hydropower accounting for 9,764 MW, representing 33.06%. In 2023, the NVPS generated 90.380 billion kWh of electricity. Over the past decade, the annual growth rate of commercial electricity has averaged between 10% and 12%. Northern Vietnam is endowed with vast hydropower potential, particularly across its four major river basins: the theoretical maximum capacity of the Bang Giang-Ky Cung, Red River, Ma River, and Ca River basins is 20,598 MW. Hydropower plays a significant role in ensuring energy security due to its low production cost, capacity to rapidly meet peak demand, and status as a clean energy source. This study proposes an optimal operational model for hydropower plants in the NVPS for the period from 2025 to 2030. The results demonstrate that hydropower will continue to be crucial for meeting the growing energy demand while minimizing operational costs. Additionally, it supports the integration of renewable energy sources into the power grid, thereby underscoring the strategic importance of hydropower in maintaining system stability and promoting sustainable development in the region.

Performance Analysis of Effective Retrieval of Kannada Translations in Code-Mixed Sentences using BERT and MPnet

H. P. Rohith — 2025-02-02

Translating Kannada-English (Kn-En) code-mixed text is a challenging task due to the limited availability of Kannada language resources and the inherent complexity of the dataset. This study evaluates the effectiveness of the sentence transformer model, utilizing pre-trained multilingual MPNet and Bidirectional Encoder Representations from Transformers (BERT) architectures, in generating sentence embeddings to enhance translation accuracy. It encodes both code-mixed sentences and their corresponding Kannada translations into high-dimensional embeddings. By employing cosine similarity, it maps input sentences to their closest translations, encoding 2000 code-mixed sentences and their translations using both the MPNet and BERT models. The findings indicate that the MPNet model proved to be more effective, achieving a model accuracy of 98%, compared to BERT's 88%. Moreover, MPNet outperformed BERT in terms of Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores, attaining 85.0 and 80.0, respectively, while BERT scored 65.3 and 58.7. These results highlight the advanced capabilities of MPNet in translating code-mixed languages and its potential applicability to a broader range of multilingual Natural Language Processing (NLP) tasks.

The Impact of Construction Activities on the Stability of Highway Slopes

Fouad Baziz — 2025-02-02

This study concerns the case of a landslide that occurred at kilometer point PK 208 of the East-West Highway in the northeastern part of Algeria, located in the eastern region of Constantine Province. The primary objective is to understand the behavior and mechanisms of this complex landslide phenomenon. The phenomenon's relationship to various factors that influence this activity, including permanent causes, such as challenging terrain, the geological nature, and the evolving characteristics of the soil under the influence of various climatic conditions or human activities, was investigated. The study examined the impact of the East-West Highway construction at PK 208 on the stability of slopes composed of clayey and marly soils. Field investigations were conducted, and various studies on landslides were analyzed. A monitoring system was employed to track subsurface and surface movements, as well as changes in the groundwater table level. Additionally, numerical modeling using PLAXIS software was performed to evaluate the impact of construction activities, particularly the rise in the groundwater table, on slope stability. The obtained results demonstrated that the position of the groundwater table plays a crucial role in the stability of these structures, underscoring the importance of considering local hydrogeological conditions in the planning and execution of such projects. It is concluded that the complexity of such phenomena in slopes with similar geological, geomorphological, hydrogeological, and geotechnical characteristics is a significant issue that requires particular attention during the planning and execution of such projects.

Determination of Best Input Factors in Powder-Mixed Electrical Discharge Machining 90CrSi Steel using Multi-Criteria Decision Making Methods

Van Tung Nguyen — 2025-02-02

This article outlines the results of a Multi-Criteria Decision Making (MCDM) analysis conducted on the Powder-Mixed Electrical Discharge Machining (PMEDM) process for cylindrical parts fabricated from 90CrSi tool steel, using graphite electrodes. The study aims to identify the optimal input factors to simultaneously minimize Surface Roughness (SR) and Electrode Wear Rate (EWR), while maximizing Material Removal Speed (MRS). Five input factors were selected: powder concentration (C_P), pulse-on time (T_on), pulse-off time (T_off), pulse current (IP), and servo voltage (SV). Experimental data were generated using the Taguchi method with an L18 design. The optimization process was performed using the Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Evaluation by an Area-based Method of Ranking (EAMR) methods. Criteria weights were calculated utilizing the Entropy and the Multi-Expert Ranking Evaluation with Compensation (MEREC) techniques. The analysis identified the best PMEDM input factor, providing an optimal solution for enhancing the efficiency of machining cylindrically shaped components.

A Context-Enhanced Model for Fake News Detection

Majdi Beseiso — 2025-02-02

News published on social networks has a notable impact on changing people's perceptions on various topics. However, all news available on social media may not be genuine and might come from unverified sources. The prevalence of fake news is an inevitable concern that needs to be addressed effectively. This study presents an ensemble algorithm to improve fake news detection tools. Long-Short-Term-Memory (LSTM) and an ensemble of LSTM and Convolutional Neural Networks (CNN) were used. The proposed model used bidirectional LSTM layers and CNN convolutional 2D layers with kernel sizes of 2, 3, and 4 for 2-gram, 3-gram, and 4-gram tokens. The results obtained show an accuracy of 96.7% and 97.3% on a fake news dataset using the LSTM model and CNN-LSTM model, respectively, significantly improved from the maximum accuracy of 94.88% reported in a previous study. Embedding layers yielded significant improvements when paired with extended word sequences and pre-trained embedding vectors. Diverse tokenization methods with and without pre-trained embedding layers were also considered. The ensemble model achieved a 10.03% improvement in predictive accuracy on the Liar dataset, compared to the 6.08% improvement reported in a previous study using the same dataset.

Locating the Source of Information in Social Networks using Critical Nodes

Karima Mouley — 2025-02-02

Locating the information source within social networks is crucial to understand information propagation. The source can be detected based on specific nodes known as observation nodes, and identifying them is a critical challenge that can significantly affect the accuracy of identification. To address this issue, this study proposes a novel source detection approach based on the Susceptible-Infected (SI) model and the Critical Node Problem (CNP). CNP involves identifying a subset of nodes within a graph whose removal results in the maximum reduction of a given connectivity metric, thereby isolating significant areas within the graph. A heuristic algorithm was developed, grounded in the maximal independent set for general graphs to solve the CNP, allowing the identification of the most crucial observation nodes that enhance the accuracy and using the data recorded from them to estimate the localization of the source. Experimental evaluations on various real-world networks showed that the proposed approach achieved a source detection accuracy of up to 89%, outperforming existing methods. These results demonstrate the robustness of the proposed approach, highlighting its potential to significantly improve accuracy in network-based source localization tasks across multiple applications.

A Fog Computing and Blockchain-based Anonymous Authentication Scheme to Enhance Security in VANET Environments

Zahraa Sh. Alzaidi — 2025-02-02

Authentication of vehicles and users, integrity of exchanged messages, and privacy preservation are essential features in VANETs. VANETs are used to collect information on road conditions, vehicle location and speed, and traffic congestion data. The open exchange of information within VANETs poses serious security threats. Furthermore, existing schemes have higher communication and computational costs, making them incompatible with resource-constrained VANET applications. This study proposes a multifactor authentication and privacy-preserving security scheme for VANETs based on blockchain and fog computing to meet all these requirements. The proposed scheme uses fingerprints and Quick Response (QR) codes as a multifactor to authenticate vehicle users and fog-cloud computing techniques to reduce the computational burden on RSUs and improve service quality and resilience. Additionally, the scheme synchronizes a consistent ledger across all RSUs using blockchain technology to store and distribute vehicle authentication statuses. Through a thorough comparison with relevant current protocols, the scheme shows a much-reduced computing expense and communication burden in situations with high vehicle density within a timeframe of 6.3846 ms and 544 bytes for communication costs. In addition, the proposed scheme demonstrates a successful balance between efficacy and complexity, protecting confidentiality, anonymous authentication, and ensuring integrity and conditional tracking. Formal and informal security analysis showed that the proposed scheme is more reliable, practical, and secure against many hostile attacks, such as modification attacks, 51% attacks, Sybil attacks, and MITM attacks.

Seismic Fragility Assessment of Base-Isolated Nuclear Power Plant Structures

Trong-Ha Nguyen — 2025-02-02

Base isolators constitute solutions for improving the seismic performance of civil and nuclear engineering structures. This paper evaluates the seismic fragility of based-isolated nuclear power plant structures using the proposed fragility curves. A finite element model of the structures is developed deploying SAP2000, a structural analysis program. For constructing fragility curves, a set of ground motions is employed to perform nonlinear time-history analyses associated with Incremental Dynamic Analyses (IDA). Three Damage States (DS) are defined based on the shear deformation of base isolators. Finally, the maximum likelihood estimation technique generates a set of fragility curves for DS. Additionally, a comparison of fragility curves between IDA and Cloud Analysis (CA) is presented.

Assessing the Influence of Brick Powder as Filler in Asphalt Hot Mixes

Zainab M. Hussein — 2025-02-02

This study investigated the efficacy of utilizing waste Brick Powder (BP) as a partial or complete replacement for the filler in Hot Asphalt Mixes (HAM). BP was used to substitute Portland Cement (PC) in varying proportions: 25%, 50%, 75%, and 100%. The mixes were evaluated based on Marshall properties, Indirect Tensile Strength (ITS), and Tensile Strength Ratio (TSR). The findings revealed increased Marshall stability, stiffness, and ITS in the mixes containing BP. The flow decreased for HAM containing BP, particularly for those with a complete replacement of cement having utilized BP as the filler, indicating an improved ability of the HAM to withstand loads. The tests conducted at 25, 40, and 60 °C showed that the ITS increased steadily with an increased BP proportion, which is beneficial for rutting resistance, as high service temperatures influence rutting, and high ITS corresponds to a longer rutting life of the asphalt mix. The effect of improving the tensile strength at 60 °C was higher than at 25 °C and 40 °C. Additionally, the BP mixes demonstrated greater resilience to moisture effects compared to the reference mix. The use of BP as an alternative filler for PC did not significantly impact the volumetric properties of the HAM. It was determined that BP could be successfully added to the HAM at a 100% replacement rate as a filler, with an ideal asphalt content of 5.6%. The results suggest that the processing and management of waste bricks can be a sustainable development strategy.

Detecting Acute Lymphocytic Leukemia in Individual Blood Cell Smear Images

Ruba Baluabid — 2025-02-02

Acute Lymphocytic Leukemia (ALL) is a form of blood cancer that mainly affects lymphocytes and white blood cells. The severity of this cancer varies and progresses quickly, requiring immediate and intensive treatment and making a quick and accurate diagnosis essential. This study presents a diagnostic model for the diagnosis of ALL using deep learning. YOLOv8 achieved 95% accuracy when trained on the C-NMC dataset and 94% when trained on the ALL-IDB2 dataset while maintaining generalization. YOLOv8 outperformed other models such as SVM, ResNet-50, a hybrid model that integrates ResNet-50 with the SVM classifier, and DenseNet121. YOLOv8, with its strong architecture, can efficiently extract intricate patterns from medical imaging data and diagnose ALL. The proposed model can potentially reduce pathologist workloads and improve patient diagnosis. This research contributes to the field by providing a reliable tool for automated leukemia detection, paving the way for further advances in medical image analysis.

Effects of Rubber Particle Size and Substitution Rate on the Behavior of Eco-Friendly Rubberized Concrete

Sayed Najeeb AlMousawi — 2025-02-02

One of the world's largest tire graveyards is located in the Al-Salmi area of Kuwait, where over 42 million discarded waste rubber tires have been accumulated over a time period of 17 years. This study aims to develop sustainable, cost-effective building materials for the construction industry, utilizing waste rubber as a partial substitute for fine and coarse aggregates in concrete mixtures. Three types of untreated rubber particles were used: powder rubber (P) with a diameter between 0.4 and 0.6 mm, crumb rubber (CR) with a diameter between 0.6 and 2 mm, and 2.6 and 3.5 mm respectively, and rubber chips (CH) with a diameter ranging between 2 and 18 mm. Fine aggregates were replaced by P and CR, while coarse aggregates were replaced by CH, at a substitution rate of 10 and 20% by volume. The impact of rubber particles on workability was assessed on fresh rubberized concrete, while the compressive strength was evaluated at 7, 14, and 28 days. Microstructural analysis using Scanning Electron Microscopy (SEM) was also conducted to collate the macroscopic behavior with internal structural changes. The results showed that increasing the rubber content and particle size led to reductions in workability and compressive strength. Large rubber particles, particularly chips, caused gaps and microcracks in the matrix, exhibiting poor adhesion at the Interfacial Transition Zone (ITZ). These findings demonstrate the potential of rubberized concrete as an eco-friendly alternative, with optimization needed for practical applications.

Enhanced Real-Time Object Detection using YOLOv7 and MobileNetv3

Sara Ennaama — 2025-02-02

Object detection serves as a crucial element in computer vision, increasingly relying on deep learning techniques. Among various methods, the YOLO series has gained recognition as an effective solution. This research enhances object detection by merging YOLOv7 with MobileNetv3, known for its efficiency and feature extraction. The integrated model was tested using the COCO dataset, which contains over 164,000 images across 80 categories, achieving a mAP score of 0.61. Additionally, confusion matrix analysis confirmed its accuracy, especially in detecting common objects such as 'person' and 'car' with minimal misclassifications. The results demonstrate the potential of the proposed model to address the complexities of real-world scenarios, highlighting its applicability in various scientific and industrial domains.

An AI-Driven Framework for Optimizing Business Intelligence across Organizational Hierarchies

Mohammed Shaban Thaher — 2025-02-02

In today's global trade landscape, Artificial Intelligence (AI) significantly enhances productivity and transforms business processes across sectors. This research investigates the role of business intelligence in improving service delivery within corporate entities. By applying Deming's methodology, strategies to optimize decision-making processes are identified, and hidden insights are revealed through advanced data analysis techniques. A Data Flow Diagram (DFD) illustrates the development stages and system implementation, offering practical guidance for general managers. The findings provide actionable insights that enhance efficiency and decision-making in organizational contexts.

An Innovative Approach on Recycle Foam Concrete as a Sustainable Alternative with the addition of Nano Titanium Dioxide TiO2 on the Properties of Foam Concrete

Baraa Zuhier — 2025-02-02

Sustainability and construction waste recycling have become crucial topics today in response to the growing environmental challenges and the increasing accumulation of waste. Therefore, it is essential to explore innovative solutions that improve the sustainability of concrete mixes. An effective approach is the use of Lightweight Foamed Concrete (LFC), a revolutionary new material that is considered a viable solution for the reduction of the weight of conventional concrete. This research focuses on the study of the effect of replacing 50% of virgin sand by volume with Recycled Foam Concrete (RFC) waste crushed at four gradation levels with aggregate sizes between 12.5-9.5 mm, 9.5-4.75 mm, 4.75-2.36 mm, and 2.36-1.18 mm, and the effect of adding 0.5% Nano titanium dioxide TiO₂ by weight of cement. The water-to-cement and cement-to-aggregate ratio were maintained at 0.45 and 1:1.3, respectively. Nanoparticles are incorporated into Foam Concrete (FC) to enhance its strength, due to their beneficial properties, such as their small particle size and high reactivity. The results conclude on the optimal sizes of RFC with the addition of Nano TiO₂ for use in FC mixes that enhance compressive strength and increase carbonation compared to traditional FC mixes.

Dynamic Assessment of a Railway Bridge using Operational Modal Analysis and Fast Fourier Transform: A Comparative Study with Finite Element Analysis

Riza Suwondo — 2025-02-02

This research investigates the dynamic behavior of a railway bridge using both experimental and numerical methods. Field tests were conducted to capture the bridge response to live loading conditions with acceleration data collected via uniaxial accelerometers placed at critical locations along the structure. The dynamic characteristics, including the natural frequencies and mode shapes, were determined using two analytical techniques: Fast Fourier Transform (FFT) and Operational Modal Analysis (OMA). While FFT provides a frequency domain analysis, OMA enables the estimation of modal parameters, such as natural frequencies, mode shapes, and damping ratios, using the bridge's response to operational forces. The results revealed that the fundamental frequency obtained from the OMA (2.163 Hz) was higher than that obtained from the FFT (1.95 Hz) and the Finite Element Analysis (FEA) model (1.65 Hz). Additionally, the OMA produced mode shapes that were closely aligned with those predicted by the FEA, validating the accuracy of the numerical model. This study highlights the advantages of OMA over FFT, particularly the ability to capture mode shapes, and underscores the importance of integrating OMA with FEA for a comprehensive dynamic assessment of bridge structures. These findings contribute to the growing body of knowledge on structural monitoring and provide practical insights into improving bridge safety and performance.

Mutual Information-based Feature Selection Strategy for Speech Emotion Recognition using Machine Learning Algorithms Combined with the Voting Rules Method

Hamza Roubhi — 2025-02-02

This study proposes a new approach to Speech Emotion Recognition (SER) that combines a Mutual Information (MI)-based feature selection strategy with simple machine learning classifiers such as K-Nearest Neighbor (KNN), Gaussian Mixture Model (GMM), and Support Vector Machine (SVM), along with a voting rule method. The main contributions of this approach are twofold. First, it significantly reduces the complexity of the SER system by addressing the curse of dimensionality by integrating a focused feature selection process, resulting in considerable savings in both computational time and memory usage. Second, it enhances classification accuracy by using selected features, demonstrating their effectiveness in improving the overall performance of the SER system. Experiments carried out on the EMODB dataset, using various feature descriptors, including Mel-frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), and Linear Prediction Cepstral Coefficients (LPCC), showed that the best performance was achieved by GMM, with an accuracy of 85.27% using 39 MFCC features, compared to an accuracy of 82.55% using a high-dimensional vector with 111 features. Furthermore, applying the Joint Mutual Information (JMI) selection technique to extracted MFCC features reduces the vector size by 23.07% while improving the accuracy to 86.82%. These results highlight the effectiveness of combining the feature selection process with machine learning algorithms and the voting rules method for the SER task.

Revolutionizing Diagnostic Insights: Exploring Advanced Image Processing Techniques and Neural Networks in Traditional Indian Medicine

R. Srinivasan — 2025-02-02

The Siddha and Ayurveda traditional Indian medicine practices utilize non-invasive diagnostic methods, such as Neikuri and Taila Bindu Pariksha, for patient diagnosis through urine analysis. While these methods have proven effective for centuries, their accuracy highly depends on the subjective experience of practitioners. To address this limitation, this study explores the use of advanced image processing techniques and deep learning, specifically Convolutional Neural Networks (CNNs), to automate and enhance diagnostic image analysis. This study utilized five pre-trained CNN models, namely DenseNet, ResNet, VGG-19, Inception, and EfficientNet, on a dataset of Neikuri images acquired from a Siddha medical institute, to standardize and improve the accuracy of patient diagnosis. The comparative evaluation revealed DenseNet as the best-performing model, achieving a classification accuracy of 93.33%, while Inception v3 followed with 90.5%. This study highlights the potential of integrating modern neural networks with traditional diagnostic practices, paving the way for more objective, efficient, and accessible healthcare solutions in traditional Indian medicine.

A Parametric Study of GFRP Composite Beams with Encased I-Section using 3D Finite Element Modeling

Fahad M. Bahlol — 2025-02-02

Glass Fiber Reinforced Polymer (GFRP) materials play a crucial role in the construction industry due to their lightweight properties, corrosion resistance, and high strength. Furthermore, the GFRP reinforcement ratio is a significant factor in the strength design philosophy that governs the design of flexible members. This study presents a parametric investigation of the performance of concrete composite beams reinforced and encased with pultruded GFRP. This study investigates the effect of concrete compressive strength and GFRP reinforcement ratio on the structural behavior of composite beams with encased GFRP sections under static loads. To achieve this objective, five simply supported models were numerically simulated using the Abaqus software. The reference model comprised normal concrete with a 30 MPa compressive strength, 0.42% GFRP longitudinal reinforcing ratio, and transverse steel rebars, with the GFRP I-section encased in the center of the cross-section. The other models maintained similar properties and geometries but varied in reinforcement ratio (0.85% and 1.2%) and compressive strength (25 MPa and 20 MPa). The results showed that increasing the reinforcement ratio in composite beams with encased GFRP sections improved the ultimate capacity by approximately 29% and 41% for 0.85% and 1.2% ratios, respectively, compared to the reference beam. Conversely, reducing compressive strength below 30 MPa decreased maximum load by about 16% and 23% for 25 MPa and 20 MPa values, respectively, in relation to the reference beam.

Hydro-Forming of U-Shaped Parts with Branches

Mai Thi Trinh — 2025-02-02

Tube parts formed by hydroforming technology have many outstanding advantages, such as high mechanical properties, fast forming time, and the ability to shape complex parts. U-shaped tube parts with branches are applied in various fields, such as the automotive, electronics, and medical industries. The current challenges in hydroforming technology for manufacturing these parts lie in controlling and determining suitable process parameters to ensure the highest product quality. Through numerical simulations and theoretical calculations, with the input material being a copper tube made from CDA110, this study has investigated the parameters of die cavity fluid pressure and punch displacement. Based on these results, a regression equation for the relationship between fluid pressure and punch displacement was established, which allows for determining the appropriate ranges for fluid pressure and punch displacement to successfully hydroform U-shaped parts with branches. The research results aim to assist engineers in reducing the time required to identify suitable process parameters for hydroforming similar-shaped parts.

A Stacking Ensemble Model with Enhanced Feature Selection for Distributed Denial-of-Service Detection in Software-Defined Networks

Tariq Emad Ali — 2025-02-02

The proliferation of Distributed Denial of Service (DDoS) attacks poses a significant threat to network accessibility and performance. Traditional feature selection methods struggle with the complexity of network traffic data, leading to poor detection performance. To address this issue, a Genetic Algorithm Wrapper Feature Selection (GAWFS) is proposed, integrating Chi-squared and Genetic Algorithm (GA) approaches with a correlation method to select the most correlated features. GAWFS effectively reduces feature dimensions, eliminates redundancy, and identifies crucial and correlated features for classification. Detection accuracy is further improved by employing a stacking ensemble model, combining Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) as base models, with Random Forest (RF) as the metamodel. The proposed classifier achieves impressive accuracies of 99.86% for training data and 98.89% for test data, representing improvements of approximately 5% and 40%, respectively, over previous studies. The training time was also reduced to 2,593 s, a substantial improvement of approximately 29.92%. Validation on various benchmark datasets confirmed the efficacy of the proposed approach, underscoring the importance of the enhanced feature selection method and the stacking ensemble model against DDoS attacks.

Smart Dustbins: Real-Time Monitoring and Optimization for Waste Management in Smart Cities through IoT Devices

Sreerama Murty Maturi — 2025-02-02

The Internet of Things (IoT) provides a technological foundation for the development of smart cities. The technology has yielded novel possibilities for urban governance. The IoT offers a practical and cost-effective means of gathering vast quantities of data, which can be applied across a range of industries with the aim of increasing production. One of the most pressing challenges currently facing the development of smart cities is the issue of waste collection beyond the municipal corporation. The IoT has the potential to streamline the waste collection process across a smart city. The proposed model is designed to facilitate more efficient location determination of smart dustbins through the use of IoT enabling technologies. The primary objective of the proposed models is to monitor the status of the smart dustbin. The smart dustbins are equipped with a GPS module, a GSM module, ultrasonic sensors, and a Liquid-Crystal Display (LCD). The primary function of the proposed model is to ascertain whether the smart dustbin is full, medium, or empty. In the event that the smart dustbin is full, the GPS location and status of the smart dustbin are automatically transmitted via SMS by GSM module. This SMS is sent to the relevant authorities of the Municipal Corporation, who are then responsible for collecting the waste from the smart dustbin. The SMS contains longitude and latitude values, which are used to identify the correct route map for the smart dustbins. Ultimately, the city can be transformed into a smart city by using IoT-enabled smart dustbins.

Development of a Green Corrosion Inhibitor from Lophatherum Gracile Extract for Steel Protection

Muhammad Imran Haris — 2025-02-02

Corrosion constitutes a significant challenge in the context of infrastructure development, with particular implications for steel materials. One common method for preventing corrosion is the application of inhibitor materials. Inhibitors are classified into two main categories: organic inhibitors and inorganic inhibitors. Inorganic inhibitors are costly and may have adverse environmental effects. Consequently, organic inhibitors that are cost-effective and environmentally benign were developed. One plant that has the potential to be used as an organic inhibitor is Lophatherum gracile B. (Lophatherum gracile Brogn), due to its antioxidant compounds that can prevent corrosion. The objective of this research is to analyze the effect of the Lophatherum gracile B. extract inhibitor on the corrosion rate and its inhibition efficiency on reinforcing steel. The weight loss method was employed to determine the corrosion rate in a 3% sodium chloride (NaCl) medium, with concentration variations of 0%, 2%, and 4% over a duration of 24, 72, and 96 hours. The findings indicated that the lowest corrosion rate was observed at the 4% concentration, while the highest rate was noted at the 0% concentration. The inhibition efficiency of the Lophatherum gracile B. extract was determined to be greater than 66%. The qualitative analysis of the macro photo material structure indicated that the steel surface treated with Lophatherum gracile B. extract exhibited a reduced level of corrosion in comparison to the control sample. Furthermore, the tensile strength testing demonstrated that the decline in the tensile strength of steel could be attenuated through the use of inhibitors. These findings suggest that the Lophatherum gracile B. extract is an effective inhibitor material for reinforcing steel.

Blockchain Non-Fungible Token for Effective Drug Traceability System with Optimal Deep Learning on Pharmaceutical Supply Chain Management

Shanthi Perumalsamy — 2025-02-02

In recent times, the number of fake drugs has increased dramatically, which has resulted in millions of victims severely affected by poisoning and treatment failures, resulting in a need for Drug Supply Chain (DSC) traceability. The DSC is generally reluctant to share traceability data and includes several parties having heterogeneous interests. Moreover, existing provenance and traceability systems for DSCs need more trust, data sharing transparency, and separated data storage. By realizing decentralized, trustless systems, a decentralized Blockchain (BC)-based solution is proposed to tackle these constraints. BC is an immutable, decentralized, shared network that allows management directly through a peer-to-peer (P2P) network without the necessity of a central authority to check transactions. This study proposes a new Blockchain Non-Fungible Token-based Drug Traceability with Enhanced Pharmaceutical Supply Chain Management (BNFTDT-EPSCM) model. The proposed BNFTDT-EPSCM model presents transparent and more secure reporting of changes in the operating condition of transported pharmaceutical products to prevent drug recalls. The Ethereum BC enables transactions and computational services using the cryptocurrency Ether (ETH). Simultaneously, an enhanced Byzantine fault-tolerant consensus (RB-BFT) leverages a reputation system to address reliability issues of primary nodes and reduce communication complexity inherent in the Practical Byzantine algorithm (PBFT). The BNFTDT-EPSCM model presents a decentralized solution using Non-Fungible Tokens (NFTs) to improve the traceability and tracking capabilities of the standard serialization process. In addition, the BNFTDT-EPSCM model employs a Deep Belief Network (DBN) approach to perform the inbound logistics task prediction process. Finally, the Tasmanian Devil Optimization (TDO) method is utilized to enhance the hyperparameter tuning of the DBN approach. A detailed set of simulations was executed to examine the effectiveness of the BNFTDT-EPSCM approach, demonstrating a higher throughput at the highest user count of 6000 and achieving 551.22 TPS, significantly outperforming existing models.

Improving Intrusion Detecction Systems by using Deep Learning Methods on Time Series Data

Asma Ahmed A. Mohammed — 2025-02-02

Intrusion Detection Systems (IDSs) are the cornerstone of cybersecurity, monitoring network traffic to find abnormal suspicious activities. Traditional IDSs usually face challenges in adapting to the cyber threats that evolve day by day, leading to very high false positive rates and missed detections. This study focuses on enhancing the performance of an IDS system by integrating deep learning techniques with time series data. The efficiency of RNN, CNN, and LSTM networks was evaluated in detecting intrusions in real-time. The experimental results showed that hybrid models, especially the CNN+RNN+LSTM combination, performed best with a 0.86 F1 score, 0.92 precision, and 0.79 recall, indicating that hybrid deep learning methods can improve detection accuracy while reducing false alarms, opening a resilient future for cybersecurity.

A Positive Output Buck-Boost Converter with Extensive Linear Conversion Ratio Adjustment Range

Nguyen Van Ban — 2025-02-02

The article introduces a non-isolated positive output buck-boost converter that is capable of operating across a wide duty cycle range. The converter exhibits low nonlinearity in its voltage gain relative to D, thereby enhancing its capability to regulate output voltage or effectively regulate current over a broad duty cycle spectrum. A comparison of the performance of the proposed converter with existing converters is presented, with a particular consideration of the DC gain, voltage and current stresses on switches and diodes, and inductor currents. The comprehensive calculations include ideal and practical voltage gains, current assessments, stress analysis on components, parameter design, efficiency, and brief discussions on the discontinuous conduction mode and boundary conditions. The converter exhibits peak efficiencies of approximately 98.98% in boost mode and 99.16% in buck mode, which represent a notable advancement in power electronics. It is noteworthy that the converter displays minimal current and voltage overshoot in buck mode with a conversion ratio below 3. The overshoot relative to the normalized current is less than 1, reaching a minimum of 0.21, while the voltage overshoot is as low as 0.51. This contributes to superior buck mode efficiency. A laboratory model was developed with great care to validate the converter's experimental outcomes and theoretical evaluations. This provides reassurance about the reliability of the research.

Equilibrium Moisture Effects in Silica Gel Adsorption/Desorption

Amir Abubaker Musa — 2025-02-02

This research involves a numerical analysis focused on examining how the initial moisture content and equilibrium moisture content affect a Regular Density (RD) silica gel-packed bed, in terms of the composite mass and heat transfer dynamics during the adsorption and desorption processes. The detailed interaction between the air and the desiccant material is thoroughly represented through the development of the governing drying kinetics, mass conservation, and energy conservation equations, thereby establishing a solid mathematical foundation to clarify these essential processes. All numerical simulations are meticulously executed using the Finite Volume Element method, allowing for a detailed analysis of the mass and heat transfer phenomena within the RD silica gel-packed bed. The utilization of this advanced computational technique enables a deeper understanding of the complex interactions between the moisture content and transfer process efficiency in the desiccant system. This research culminates in the development of correlations that serve as predictive tools. An accurate estimation is facilitated by both the removal/addition of humidity from/to the air and the maximum enthalpy released/absorbed by the RD silica gel medium throughout the adsorption and desorption phases. These correlations, rooted in the equilibrium and initial moisture content of the RD silica gel medium, provide valuable insights into optimizing the performance and efficiency of desiccant systems in various operating conditions. There are intricate relationships between moisture content and transfer processes. This study contributes to advancing the understanding of desiccant systems, paving the way for more efficient and sustainable air treatment technologies.

Multi-Objective Optimization of a Two-Stage Helical Gearbox with Two Gear Sets in First Stage to Reduce Volume and Enhance Efficiency using the EAMR Technique

Tran Quoc Hung — 2025-02-02

This study presents the results of a work employing the Evaluation by an Area-based Method of Ranking (EAMR) methodology to address the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox comprising two gear sets in the initial stage. The objective of this study is to identify the most critical design parameters for minimizing the volume of the gearbox while optimizing its efficiency. In this study, three key design parameters were selected for analysis: the wheel face width coefficients X_ba for the first and second stages, as well as the gear ratio of the first stage u₁. Furthermore, the EAMR technique was employed to address the Multi-Criteria Decision-Making (MCDM) challenge, with the entropy method used to ascertain the weight criterion for resolving the MOOP. The study's findings offer valuable insights into the optimal values for three primary design parameters, which are essential for the development of a two-stage helical gearbox with two gear sets in the initial stage.

Moar: A Swimmer Motion Swimming Style Identification Model using Deep Learning

Atheer Al-Majnoni — 2025-02-02

Athletes in various sports, such as swimming, are increasingly using motion capture to identify and optimize their movement techniques. However, traditional motion capture systems tend to be expensive and limited. Computer vision-based methods have emerged as alternatives to identify four swimming styles: freestyle, backstroke, breaststroke, and butterfly. However, previous models did not identify flaws in swimmer movement. A significant challenge is the lack of labeled swimming video datasets that indicate these flaws. To overcome this challenge, this study collected and labeled a dataset of swimmer flaws and integrated them with the publicly available dataset SwimXYZ. Then, YOLO models were trained on the generated data. The YOLOv8s model demonstrated an impressive mean average precision (mAP@0.50) of 98% in the detection of swimming style and 95% in the simultaneous detection of swimming style and the identification of incorrect movements. This model can be used in real-time applications to help swimmers evaluate and improve the accuracy of their techniques.

An Estimation Method for the Overthrowing Moment of Vehicles on the Car Deck of the Ro-Ro Ferry

. Alamsyah — 2025-02-02

Ensuring passenger safety and comfort is crucial for maintaining trust in sea transportation, especially as ship accidents remain a concern. One critical area for improvement is the vehicle fastening systems on Roll-On-/Roll-Off (Ro-Ro) ferries, which must account for fastening techniques and vehicle positioning to prevent shifting or overturning during adverse conditions. This study calculates the shear and overturning moments acting on vehicles when a ferry experiences rolling motion due to beam waves, using numerical simulations to evaluate the impact of vehicle placement. Wave heights were adjusted to reflect operational area conditions, and vertical and lateral accelerations were derived based on vehicle dimensions, type, and enter of gravity. A case study of a Ro-Ro ferry operating on the Bira-Pamatata route revealed that Bus 2, located at the rear centreline, exhibited the highest rolling moment of 14,370 N·m, while Ayla 21, positioned at the front centreline, had the lowest moment of 1,141 N m. These results demonstrate that vehicle mass, dimension, and vertical and horizontal center of gravity significantly influence rolling and share moments.

A GPS - Integrated Slide Controller Application for Quadrotor Tracking in Straight Trajectory

Anh Le Hoang — 2025-02-02

Unmanned Aerial Vehicle (UAV) trajectory guidance is an important area in modern aviation and automatic control, requiring the UAV to maintain precise position and velocity along the trajectory despite environmental fluctuations. This article presents the quadrotor hardware and method for developing a trajectory tracking control algorithm, using a sliding mode controller combined with GPS data. The controller' design is based on the nonlinear model of the system, integrating the nonlinear sliding mechanism with information from GPS to ensure that the system follows the target trajectory. The stability of the proposed method is proven through Lyapunov's theorem. The controller is verified through simulation and experiments. The results show that the proposed algorithm helps the quadrotor stabilize the tilt angle and track the trajectory with small errors.

Model-Free Swing-Up and Balance Control of a Rotary Inverted Pendulum using the TD3 Algorithm: Simulation and Experiments

Trong-Nguyen Ho — 2025-02-02

The Rotary Inverted Pendulum (RIP) system is a highly nonlinear and under-actuated mechanical system, which presents significant challenges for traditional control techniques. In recent years, Reinforcement Learning (RL) has emerged as a prominent nonlinear control technique, demonstrating efficacy in regulating systems exhibiting intricate dynamics and pronounced nonlinearity. This research presents a novel approach to the swing-up and balance control of the RIP system, employing a RL algorithm, Twin Delayed (TD3) Deep Deterministic Policy Gradient (DDPG), obviating the necessity for a predefined mathematical model. The physical model of the RIP was designed in SolidWorks and subsequently transferred to MATLAB Simscape and Simulink for the purpose of training the RL agent. The system was successfully trained to perform both swing-up and balance control using a single algorithm for both tasks, representing a significant innovation that eliminates the need for two or more separate algorithms. Additionally, the trained agent was successfully deployed onto an experimental model, with the results demonstrating the feasibility and effectiveness of the model-free TD3 approach in controlling under-actuated mechanical systems with complex dynamics, such as the RIP. Furthermore, the results highlight the sim-to-real transfer capability of this method.

Efficient Liver Segmentation using Advanced 3D-DCNN Algorithm on CT Images

S. Subha — 2025-02-02

According to the latest global cancer statistics for 2022, liver cancer ranks as the ninth most common disease in women. Segmenting the liver and distinguishing it from tumors within it pose a significant challenge due to the complex nature of liver imaging. Common imaging methods such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), and Ultrasound (US) are employed to distinguish liver tissue from liver tumors after collecting a sample. Attempting to partition the liver and tumor based on grayscale shades or shapes in abdominal CT images is not ideal because of the overlapping intensity levels and the variability in the location and shape of soft tissues. To address this issue, this study introduces an effective method for liver image segmentation using a 3D deep Convolutional Neural Network (3D-DCNN). The process involves several stages. First, liver images undergo preprocessing to enhance image quality, including median filtering, adaptive filtering, and converting them to grayscale. The feature extraction phase focuses on extracting four sets of features, such as the Local Binary Pattern (LBP) and the Gray-Level Co-occurrence Matrix (GLCM). Additionally, an Iterative Region Growing (IRG) technique is developed to improve the Dice Similarity Coefficient (DSC) prediction by enhancing the quality of the input images obtained from segmented images. This method enables the segmentation of the liver in abdominal CT image volumes and can subsequently be used to segment liver tumor images to evaluate the performance of the proposed 3D-DLNN approach. This method was implemented in MATLAB, and its performance was evaluated using various metrics. In experimental analysis, the proposed technique outperformed other methods, including Jaccard with JISTS-FCM, Fuzzy C-Means (FCM), and FCM with Cluster Size Adjustment (FCM-CSA).

Smart Contract-Enhanced Residual GRU with Merkle-Damgard Cryptography for IoT Attack Detection

T. Nishitha — 2025-02-02

As IoT continues to expand, the security of connected devices remains a critical concern, particularly in the face of DDoS attacks. This study introduces a novel approach that leverages blockchain technology through smart contracts integrated with an advanced attack detection mechanism. Central to this approach is the Enhanced Residual Gated Recurrent Unit (ERGRU) architecture, designed to effectively identify and mitigate DDoS attacks within IoT networks. The Adaptive Coati Optimization Algorithm (ACOA) was used to adjust the hyperparameters of the ERGRU model, such as the learning rate and the number of GRU neurons, to further improve detection accuracy. In addition, the proposed framework uses a one-way compression function to generate secure hashes for input data, utilizing the Merkle-Damgård cryptography technique to ensure data integrity and confidentiality. The proposed solution was tested through a rigorous process using a DDoS dataset. Performance was assessed by focusing on metrics such as processing time, data integrity rate, and confidentially rate. The results demonstrate the effectiveness of the proposed smart contract-based framework in providing a durable and efficient protection mechanism against DDoS attacks in IoT environments.

Punch Force Classification using K Means and a Data Logging System

Lilik Anifah — 2025-02-02

Much research has been conducted worldwide on the recognition and monitoring of punches in martial art sports during the training process. The performance of the punching movements can be accurately analyzed based on the collected data. The current study aims to classify the punches on a punching bag using K Means based on a data logging system. Its stages are hardware design, hardware implementation, hardware testing, learning, and testing. The FSR 402 sensor was used to measure the punching force. GY6500 MPU6500 was also utilized to identify and measure the reaction force of these punches. The data were collected utilizing K Means and were subsequently tested. The results revealed that the system exhibited good performance, proven by its accuracy of 93.6%, precision of 0.933, recall of 0.934, and F1 score of 0.934. Based on these results, it seems that the classification of right and left punch forces can be efficiently carried out. This system can help users analyze the punching bag training process, and thus improve their performance.

Integration of Deep Learning with Fox Optimization Algorithm for Early Detection and Classification of Tomato Leaf and Fruit Diseases

K. Sundaramoorthi — 2025-02-02

Tomato is a common vegetable crop extensively cultivated in the farming lands in India. The hot climate of India is perfect for its development, but particular weather conditions along with many other aspects affect the growing of tomato plants. Apart from these natural disasters and weather conditions, plant diseases consist a major issue in crop production. Precisely classifying leaf and fruit diseases in tomato plants is a vital step toward computerizing processes. Traditional disease detection models for tomato crops often fall short in predictability. To address this, Machine Learning (ML) and Deep Learning (DL) models have been developed, presenting advanced classification capabilities and the ability to manage the vast variability in agricultural data that conventional computer vision models struggle with. This work presents an Integration of DL with Fox Optimization Algorithm (FOA) for the Recognition and Classification of Tomato Leaf and Fruit Diseases (IDLFOA-DCTLFD). The major objective of the proposed IDLFOA-DCTLFD model is to enhance the detection and classification outcomes of tomato leaf and fruit diseases. At the initial stage, the Median Filter (MF) model is used for pre-processing and the Efficient Channel Attention-SqueezeNet (ECA-SqueezeNet) model is employed for feature extraction. For the hyperparameter tuning process, the proposed IDLFOA-DCTLFD technique implements the FOA. Finally, a Wasserstein Generative Adversarial Network (WGAN) is utilized for the detection of tomato leaf and fruit diseases. The IDLFOA-DCTLFD method is experimentally examined in a tomato leaf and fruit dataset. The experimental validation of the IDLFOA-DCTLFD methodology portrayed a superior accuracy value of 98.02%, surpassing the existing techniques.

Α Solar-Integrated Wireless Charging System for Electric Vehicles

Harpreet Kaur Channi — 2025-02-02

This paper presents a well-integrated system combining photovoltaic (PV) energy harvesting and Wireless Power Transfer (WPT) technology to develop a Solar Wireless Electric Vehicle Charging System (SWEVCS). With the growing adoption of Electric Vehicles (EVs), the demand for efficient and sustainable charging infrastructure has become a critical issue. The proposed system utilizes photovoltaic panels as a clean renewable energy source to charge EVs, eliminating the need for physical cables. The system performance is evaluated using MATLAB simulations, considering key parameters, such as solar irradiance, power output, battery State of Charge (SOC), charging current, and voltage. The results indicate a peak power output of approximately 500 W during midday, and a high SOC of up to 100% by late afternoon. The charging current reaches almost 5 A, demonstrating the high system’s efficiency in wireless energy transfer/WPT. The concerns of this study are the prospects of SWEVCS in minimizing reliance on the power grid while promoting Renewable Energy Solutions (RES) for EV charging. Future work will address scalability challenges and further improvement of WPT efficiency to advance this innovative charging technology.

Charpy Impact Test Result Comparison on Reinforcing Materials used in Continuous Filament 3D Printing

Balazs Molnar — 2025-02-02

With the growing industrial demand for materials that can withstand dynamic loads, composite 3D printing, particularly utilizing continuous fiber reinforcements, presents a promising solution. This study investigates the toughness of three fiber-reinforced materials, namely carbon fiber, Kelvar, and fiberglass, by conducting Charpy impact tests. The results reveal that fiber-reinforced 3D materials significantly outperform standard 3D printed components, with fiberglass showing the highest toughness. These findings demonstrate that fiber-reinforced 3D printed materials offer a viable alternative for applications requiring high toughness and dynamic resistance.

Α Study on the Possibility of Replacing Roller Bearings in CM 120L Concrete Mixers with Necuron 1050 Sliding Bearings

Mirela Romanet — 2025-02-02

This paper presents the results of the study on the possibility of replacing the roller bearings of CM 120L concrete mixers with sliding bearings made of Necuron 1050. In order to carry out the study, the radial stresses located in planes normal to the axis of symmetry of a sliding bearing in the cantilever of the material Necuron 1050, were determined using two analytical methods, the finite element method and the electro-resistive tensometry method. An experimental stand was constructed to determine the mechanical and tribological characteristics of the sliding bearings in static and dynamic regimes. The difference between the maximum stress values determined by the two analytical methods is 1.057 MPa, and the difference between the results obtained by Finite Element Analysis (FEA) for static and dynamic stress is 3.093 MPa. The results of the study show that it is recommended to replace a roller bearing with a sliding bearing made of Necuron 1050 polyurethane material.

An Energy-Efficient Hybrid LEACH Protocol that Enhances the Lifetime of Wireless Sensor Networks

Malik Adnan Jaleel — 2025-02-02

A Wireless Sensor Networks (WSN) comprises of little, low-power sensors, which have a low battery limit and are often utilized in unfavorable conditions. These sensors are data processing and networking devices. The battery life, processing power, communication range, and memory of sensors, which are tiny, self-contained devices, are just a few of their limits. They also have transceivers, which gather environmental data and transmit it to a base station. There are various strategies accessible to expand the existence of a WSN and diminish energy utilization. LEACH (Low Energy Adaptive Clustering Hierarchy) is a successful and widely used method. The protocol clusters the network in order to conserve energy The cluster head, which is a single-region indicative node, receives fusion cluster data once the sensor nodes are divided into clusters. In our study, we provide a novel method for reducing sensor node energy consumption and extending network lifetime. Our research aims to increase network lifespan by reducing energy consumption based on the restrictions of the LEACH and its related algorithms.

Optimization of Stock Predictions on Indonesia Stock Exchange: A New Hybrid Deep Learning Method

Bernadectus Yudi Dwiandiyanta — 2025-02-02

This study presents a new method for predicting stock prices on the Indonesia Stock Exchange (IDX) using a hybrid deep learning model. The proposed model combines historical price data, consisting of open, high, low, and close values, with technical indicators such as Moving Average (MA), Simple Moving Average (SMA), and Exponential Moving Average (EMA). The proposed model offered improved accuracy and efficiency using distinct architectures of Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) to process the datasets. The results showed that the proposed hybrid model significantly outperformed traditional single-architecture models in terms of R², achieving 0.98 for the BBCA stock, surpassing models using only RNN or GRU. In addition, comparable improvements were observed with additional equities, such as the PT. Bank Mandiri Tbk (BMRI) and PT. Bank Negara Indonesia Tbk (BBNI) stocks, achieving an R² of 0.99, demonstrating the proficiency of the proposed model in capturing the complex dynamics of the stock market. The results demonstrated the significant potential of combining historical data and technical indicators into the modeling procedure to predict stock prices. This process can benefit investors and economic forecasters in the stock market. The results could be further expanded by classifying datasets and investigating different sets of models to improve the performance of financial forecasting.

Enhancing Data Streaming Clustering Algorithms for AutoML in Cloud Environments: A Novel Design Approach

Madhuri H. Parekh — 2025-02-02

The objective of this revision is to enhance existing AutoCloud clustering technology, which demonstrates optimal performance when dealing with clusters of specific dimensions and arrangements. AutoCloud uses the TEDA framework to break down the clustering challenge into two smaller problems, called micro cluster and macro cluster. AutoCloud is an innovative method that eliminates the requirement for any pre-existing understanding of datasets, where clusters can develop and combine when new information and explanations are presented. This study proposes an experimental configuration to generate microclusters and data clouds without imposing a certain topology on static datasets. MLAutoCloud uses a modified distance-based technique, utilizing the big data framework and incorporating the adjusted random index value with the TEDA framework for streaming data. The MLAutoCloud technique yielded optimal cluster numbers and achieved excellent data collection results, as seen in the test results on different datasets. Estimating thickness despite changes in the underlying assumptions is a process that could modify the variables used to provide data. The MLAutoCloud method is an effective way to generate a cloud clustering algorithm in the data streaming section.

The Impact of Hexane on the Dynamic Viscosity of Kazakhstans’s Heavy Oil

Alexandr Logvinenko — 2025-02-02

The Republic of Kazakhstan has significant amount of heavy oil and natural bitumen reserves. Some of that reserves have high viscous oil more than 1000 mPa*s at shallow depth. The main recovery technique is thermal methods using steam or hot water injection. One of the variants to improve thermal methods is to add liquid or gaseous solvent. In our research we consider hexane as an additive solvent. The purpose of this study is to determine how hexane affects to dead heavy oil of Sarybulak field within the context of dynamic viscosity reduction. Mix of heavy oil and hexane in various proportions are obtained. Also, dependences of heavy oil dynamic viscosity on the hexane content are considered.

Enhancing Free Space Optical System Performance through Fog and Atmospheric Turbulence using Power Optimization

Vijayashri V. Belgaonkar — 2025-02-02

Free Space Optical (FSO) communication is gaining traction as a pivotal technology for next-generation communication systems, offering extremely high data rates, unlicensed bandwidth, and rapid transmission capabilities. However, its performance is significantly hindered by atmospheric factors such as turbulence and fog. This paper presents a comprehensive model for FSO communication designed to optimize performance under various atmospheric conditions. We assess channel capacity across a spectrum of weak to strong atmospheric turbulence using a Gamma-Gamma channel distribution. To mitigate channel losses, our system employs Wavelength Division Multiplexing (WDM) and multi-beam Multiple Input Multiple Output (MIMO) technologies. Results indicate that the integration of diversity techniques and WDM substantially enhances system performance in adverse weather conditions. Furthermore, power optimization is achieved through the implementation of optical amplifiers and feedback mechanisms from the receiver to the transmitter to adjust the transmitter power in accordance with received Bit Error Rate (BER). The proposed power-optimized WDM MIMO system demonstrates a remarkable BER of 1.48884e-15, while extending the transmission link distance to 2500 meters with a Q factor of 21.5 even under strong atmospheric turbulence conditions.

Dynamic Association Mining Techniques for the Faster Extraction of High Utility Itemsets from Incremental Databases

Subba Reddy Meruva — 2025-02-02

Financial and market analysis applications require the mining of strong-utility itemsets. Finding frequent itemsets with high utility patterns is vital for such wide applications. Recent utility-based mining methods were successfully used in the current study to identify high value itemsets from static datasets. Stream databases or incremental databases update the itemsets at regular intervals (schedulers). Incremental Mining-based High Utility Itemset (IM-HUI) algorithms improve the methodologies based on High Utility Itemset (HUI) methods. The proposed technique refines the itemset values and updates the HUIs based on incremental schedulers. It reduces the time and space while mining HUIs over dynamic databases. The efficacy of the proposed work is compared experimentally to that of existing mining techniques on benchmark datasets.

Investigating the Impact of EDM Parameters on Surface Roughness and Electrode Wear Rate in 7024 Aluminum Alloy

Safaa Kadhim Ghazi — 2025-02-02

Electrical Discharge Machining (EDM) is a significant process in the industry for machining hard metals. It is a time-consuming and costly method that requires a high level of expertise to operate effectively. EDM is considered one of the unconventional operating processes due to its unique characteristics and the challenges associated with its implementation. One of the challenges facing researchers is determining the optimal parameters for achieving high surface quality while minimizing equipment consumption and associated costs. In addition to being classified as one of the operating processes, EDM processes are also classified as electro-thermal processes that affect surface quality. In this study, the influence of EDM parameters on surface roughness and electrode wear rate when machining aluminum alloy type 7024 was investigated. A total of 27 experiments were conducted to evaluate the impact of three parameters at three levels. The parameters under investigation include current, pulse on time, and pulse off time. Subsequent analysis of the results by variance analysis revealed that the most influential parameter for both surface roughness and electrode wear rate is electric current, with a rate of influence of 74%. The results were then subjected to further analysis using variable effect graphs to identify the optimal variables for achieving the best results. Finally, neural networks were employed to predict the results, with an accuracy of up to 99%.

Underwater Image Enhancement using Convolution Denoising Network and Blind Convolution

Shubhangi Adagale-Vairagar — 2025-02-02

Underwater Image Enhancement (UWIE) is essential for improving the quality of Underwater Images (UWIs). However, recent UWIE methods face challenges due to low lighting conditions, contrast issues, color distortion, lower visibility, stability and buoyancy, pressure and temperature, and white balancing problems. Traditional techniques cannot capture the fine changes in UWI texture and cannot learn complex patterns. This study presents a UWIE Network (UWIE-Net) based on a parallel combination of a denoising Deep Convolution Neural Network (DCNN) and blind convolution to improve the overall visual quality of UWIs. The DCNN is used to depict the UWI complex pattern features and focuses on enhancing the image's contrast, color, and texture. Blind convolution is employed in parallel to minimize noise and irregularities in the image texture. Finally, the images obtained at the two parallel layers are fused using wavelet fusion to preserve the edge and texture information of the final enhanced UWI. The effectiveness of UWIE-Net was evaluated on the Underwater Image Enhancement Benchmark Dataset (UIEB), achieving MSE of 23.5, PSNR of 34.42, AG of 13.56, PCQI of 1.23, and UCIQE of 0.83. The UWIE-Net shows notable improvement in the overall visual and structural quality of UWIs compared to existing state-of-the-art methods.

Enhancing Colorectal Polyps Detection using Transfer Learning on DICOM Metadata

Khadija Hicham — 2025-02-02

Colorectal and Rectum Cancer (CRC) presents significant global health challenges, necessitating early detection and precise diagnosis to achieve effective treatment and better patient outcomes. Transfer learning techniques have shown considerable promise, especially in cancer detection. This study presents a CRC prevention system based on a fusion of a pre-trained VGG16 model with dense layers for metadata processing. Experiments were performed using the CT Colonography dataset from The Cancer Imaging Archive (TCIA), applying preprocessing and class weighting to address class imbalance. The system was evaluated using accuracy, loss, recall, precision, F1-score, and AUC. This study investigated the impact of integrating DICOM patient metadata to enhance the proposed CRC prevention system. The findings indicate that the proposed MetaVGGNet model outperformed the standard VGG16, achieving greater accuracy (82%) and a marginally lower loss. This successful application has the potential to enhance CRC diagnosis and treatment and underscores the importance of incorporating metadata into deep learning classification systems, offering avenues for more effective and dependable diagnostic tools in CRC management.

Analysis of Critical Success Factors of Agile Software Projects based on the Fuzzy Delphi Method

Fuye Zhang — 2025-02-02

Agile software development initiatives have gained widespread recognition both domestically and internationally, particularly in the Chinese software industry. However, traditional enterprises often face challenges, such as inadequate project management and lower success rates, which can be attributed to a limited understanding of agile methodologies and effective implementation of agile practices. To address these challenges and identify the Critical Success Factors (CSFs) in agile software projects, an extensive literature review was conducted. As a result, a CSFs model for agile projects in China was constructed. The aim of this study is to evaluate the CSFs model using the Fuzzy Delphi Method (FDM). The research involved 30 authoritative experts from the Chinese agile software development industry and academia, each with more than 10 years of relevant industry knowledge and experience. The FDM was applied to collect data through questionnaires and verify theoretical success factors and dimensions in three rounds of the survey. Finally, a total of 28 factors were analyzed and ranked to develop an optimized CSFs model that has a significant impact on agile software development in China. The research findings provide a feasible set of CSFs for the effective implementation of agile software projects in China. This CSFs model also offers valuable insights for the broader adoption of agile practices in China, with the potential to greatly improve the success rate of agile software development and implementation.

Control Design for Electronic Voltage Stabilizer

Nguyen Thi Diep — 2025-02-02

Vietnam's electrical infrastructure has undergone notable advancements in recent times. Nevertheless, concerns pertaining to under-voltage, over-voltage, and voltage fluctuations persist in rural, mountainous, and industrial regions. This results in the inefficient operation of electrical equipment, disruptions in industrial production processes, data loss, and equipment damage. This paper puts forth a novel control design methodology for electronic voltage stabilizers, with the objective of mitigating the detrimental effects of grid voltage fluctuations on electrical apparatus. A compact and cost-effective AC-AC converter is proposed as a means of regulating the compensation voltage. The paper puts forth the use of the state-space averaging method for system modeling and proposes the combination of feedback and feed-forward controllers to achieve high accuracy and rapid response times. Furthermore, the moving average method is proposed for measuring the Root Mean Square (RMS) voltage, which significantly enhances the control response speed and accuracy. A 10 kVA single-phase electronic voltage stabilizer was constructed and tested in a laboratory setting under a range of grid voltage conditions, from 150V to 290V, and with varying loads. The results demonstrate that the electronic voltage stabilizer is effective in maintaining the load voltage within the desired range. The maximum response time recorded was found to be half a cycle of the grid voltage in the simulation and one cycle in the experiments, which is a significantly faster response time than that of similar designs. Furthermore, the low total harmonic distortion provides additional confirmation of the effectiveness of the designed electronic voltage stabilizer.

Context Management Life Cycle for Internet of Things: Tools, Techniques, and Open Issues

Kirti Vijayvargia — 2025-02-02

The advent of the Internet of Things (IoT) and the concomitant development of smart systems has rendered context-aware computing an emerging field of research. The IoT facilitates the large-scale integration of Machine-to-Machine (M2M) communication systems, largely independent of human intervention. The context of a situation, encompassing factors, such as mood, location, and activity, is typically taken into account by humans in an implicit manner, influencing their subsequent actions. Similarly, IoT based smart systems require context data acquired through the use of sensors. The primary challenge lies in the adaptation of context information through the proper modeling and analysis of the vast and heterogeneous sensor data. The phases of context acquisition, modeling, reasoning, and dissemination are collectively referred to as the context management life cycle. The principal aim of this paper is to provide a comprehensive overview of the current state of the art in each phase of the context management life cycle. This study presents a comprehensive review of the tools, techniques, algorithms, and architectures documented in the relevant literature, with a focus on research papers and articles published between 2010 and 2024. The discussion and open issues section at the end of the paper offer insights for future researchers engaged in the study, development, implementation, and evaluation of techniques and approaches for context management in IoT.

Optimization of PLA 3D Printing Parameters using a Combined SMART-MOORA Multi-Criteria Decision-Making Approach

Pham Ngoc Linh — 2025-02-02

This paper presents an optimization study of 3D printing parameters for Polylactic Acid (PLA) using a combined SMART-MOORA multi-criteria decision-making approach. The research focused on three key performance characteristics: tensile strength, strain, and modulus. By employing the Taguchi L₂₇ orthogonal array, the authors conducted 27 experimental trials, varying the printing temperature, print speed, layer height, and bed temperature. The Simple Multi-Attribute Rating Technique (SMART) method was utilized to assign weights to the criteria, emphasizing tensile strength due to its significance in structural applications. Subsequently, the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method was applied to rank the experiments based on the weighted criteria. The findings demonstrated that experiments with high tensile strength and strain values were ranked the highest, underscoring the importance of balancing strength and flexibility in optimizing 3D-printed parts. The sensitivity analysis confirmed the robustness of the optimization results, as the rankings remained stable even when the importance of the criteria was adjusted. This study showcases the effectiveness of the SMART-MOORA approach in optimizing 3D printing parameters, providing a framework to enhance the mechanical performance of PLA parts.

Drone Localization using Global Navigation Satellite System and Separated Feature Visual Odometry Data Fusion

Riza Agung Firmansyah — 2025-02-02

The localization system is the most important part of the overall drone navigation system. The Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) is the main device commonly used in a drone. However, under certain conditions, GPS or GNSS may not function optimally, such as in situations of signal jamming or enclosed environments. This paper implemented a new approach to address this issue by combining GNSS data with Visual Odometry (VO) through Machine Learning (ML) methods. The followed process consists of three main stages. First, performing speed and orientation estimation using VO. Second, performing left and right feature separation on the images to generate a more stable and robust estimation of speed and rotation. Third, refining speed and orientation estimation by integrating GNSS data through ML-based data fusion. The proposed method strives to enhance drone localization accuracy, despite disruptions or unavailability of GNSS signals. The research results indicate that the introduced method significantly reduces Absolute Translation Error (ATE) compared to utilizing VO or GNSS separately. The average ATE produced reached 4.38 m and an orientation of 8.26°, indicating that this data fusion approach provides a significant improvement in drone localization accuracy, making it reliable in operational scenarios with limited GNSS signals.

Cognitive Fish Swarm Optimization for Multi-Objective Routing in IoT-based Wireless Sensor Networks utilized in Greenhouse Agriculture

D. Deepalakshmi — 2025-02-02

This research presents the working mechanism of Cognitive Fish Swarm Optimization (CFSO) for multi-objective routing and channel selection in Internet of Things (IoT)-based Wireless Sensor Networks (IWSNs). CFSO is inspired by the collective intelligence and cooperation observed in fish swarms. The model involves three main components: perception, cognition, and behavior. Each fish in the swarm perceives the network conditions by gathering information from its surrounding environment, including signal strength, channel availability, and network congestion. The fish then utilizes its cognitive abilities to evaluate different routing paths and channel options based on specific objectives, namely energy efficiency, packet delivery ratio, and delay. This evaluation process involves analyzing historical information and utilizing heuristics to create notified results. Each fish adapts its behavior by adjusting its movement pattern and selecting optimal routing paths and channels. This adaptive behavior is critical for achieving reliable and efficient data transmission in IWSNs. The fish swarm balances exploration and exploitation strategies to search for optimal solutions comprehensively. Exploration allows for discovering new paths and channels, while exploitation focuses on refining the best-known solutions. The efficiency of the CFSO method in enhancing data transmission efficiency in greenhouse agriculture applications was validated through extensive simulations in the NS-3 network simulation framework. The findings suggest that the CFSO method is a promising technique for addressing routing and channel selection challenges in IWSN by leveraging the collective intelligence of fish swarms. The CFSO model portrayed a superior throughput and Network Lifetime (NLT) values of 71.34% and 77.20%, respectively, significantly outpacing SSEER and CRP across overall node counts.

A Numerical Study of Concrete Composite Circular Columns encased with GFRP I-Section using the Finite Element Method

Abbas Allawi — 2025-02-02

This paper presents ABAQUS simulations of fully encased composite columns, aiming to examine the behavior of a composite column system under different load conditions, namely concentric, eccentric with 25 mm eccentricity, and flexural loading. The numerical results are validated with the experimental results obtained for columns subjected to static loads. A new loading condition with a 50 mm eccentricity is simulated to obtain additional data points for constructing the interaction diagram of load-moment curves, in an attempt to investigate the load-moment behavior for a reference column with a steel I-section and a column with a GFRP I-section. The result comparison shows that the experimental data align closely with the simulation results regarding the ultimate strength, deformation, and failure modes, thereby validating the accuracy of the considered models. On the other hand, the numerical results of the column specimens under 50 mm eccentric load demonstrated that, in that case, the ultimate load of the columns decreased. The capacity of the reference column, a column with steel I-section, and a column with GFRP I-section decreased to 67%, 63%, and 64%, respectively compared with the columns tested under concentric load. The analytical investigation predicted the load-carrying capacity and bending moment capacity of the specimens with good accuracy. Based on the experimental curves, and the high strength found in the specimens that use the steel I- and GFRP I-sections, a good agreement between the numerical simulation and the experimental results was noticed.

Building Information Modelling (BIM) as an Efficient Solution to Middle Eastern Construction Project Delays

Saba Jabbar Kadhuim Almayyahi — 2025-02-02

The construction and building industry is considered one of the essential players in the economic sector that contributes significantly to creating jobs and generating financial resources. This study provides a review of the construction industry's capability available in the Middle Eastern countries, the development process, and its importance for the economic growth. It analyzes what causes construction projects to go over budget and behind schedule in an attempt to draw conclusions that can be applied to a broader body of research. Delays are costly in terms of time, money, quality, and safety. Some adverse outcomes of project delays are disputes between employer and contractor, lower revenue and productivity, and incomplete projects. This research investigates the reasons behind the construction setbacks in the Middle East and what can be done to rectify the situation. Moreover, it examines the Building Information Modeling (BIM) as a tool to reduce the construction delays. The BIM permits the digital construction of facilities before their physical structure, which aids in time management, reduces risks, improves security, and resolves problems.

Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning-based ECG Signal Classification on Internet of Things Environment

Mohamed Tounsi — 2025-02-02

The Internet of Things (IoT) in healthcare relates to implementing interconnected devices and systems for collecting and sharing healthcare information in real time. The integration of IoT in healthcare has the potential to enhance patient outcomes, reduce healthcare costs, and improve the efficacy of medical services. Electrocardiogram (ECG) is a non-invasive heart monitoring method that has become widely accessible due to user-friendly, low-cost, and lead-free wearable heart monitors. However, relying on overworked caregivers for manual monitoring is inefficient. This study develops a Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning (CLSSA-EDL) technique for ECG signal classification in IoT healthcare. The objective of CLSSA-EDL is to detect and classify ECG signals to support decision-making in the IoT healthcare environment. The CLSSA-EDL approach employs the DenseNet201 feature extraction method, with hyperparameters optimally selected by the CLSSA system. For ECG signal detection and classification, an ensemble model using a Stacked Autoencoder (SAE), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) is utilized. The CLSSA-EDL technique was evaluated on a benchmark ECG dataset, achieving an accuracy of 98.7%, sensitivity of 97.5%, and specificity of 99.1%, demonstrating superior performance compared to recent algorithms.

Verification of the Finite Element Model of a Moving Load Passing Over a Single Irregular Suspended Load in the Dynamic Analysis of a Beam System

Tran Thi Thuy Van — 2025-02-02

This article compares the variants of dynamic models of mobile load to describe the joint oscillations of span structures and vehicles on road bridges, taking into account the irregularities of the road surface. Using a known solution to the beam system oscillation problem, when the sprung load moves through a single unevenness, the joint modeling application of an inert mobile load and a span structure in the LS-Dyna FE complex using contacts is considered. The proposed method eliminates the need to use special plugins to describe the car dynamics and allows considering the the separation of the wheel from the road surface. At the same time, the use of contacts to create dynamic models of vehicles in the FEM is complicated by the lack of a verified way to account for road surface irregularities. In bridge calculations, spatial modeling of an elastic pavement layer with irregularities leads to the fact that the rigidity of the span structure varies in length depending on the micro profile. An effective way to solve this problem is to use solids with orthotropic material properties to describe the geometry of irregularities. Due to the unequal mechanical properties of the material along and across the beam, the layer with irregularities adequately transfers the load from the vehicle model to the supporting structures while not affecting the rigidity of the span structure. A good coincidence of the results of solving the dynamic problem by the proposed method in LS-Dyna with the results obtained by other authors in the SAP2000 program shows the possibility of using contacts for the dynamic calculation of bridge structures considering the irregularities of the road surface.

Implementation of a Deep Learning ANN-based Algorithm utilizing the IEEE 34 Bus Test System to Investigate the Effects of Distributed Generation on Fault Diagnosis in Distribution Networks

Parach Daniel Deng — 2025-02-02

Electrical distribution systems are undergoing significant modifications since the application of new technologies. New possibilities for automated, dependable, and efficient electrical power grids have been made possible by the technological advancement. While new technologies might improve electrical network performance and offer creative solutions to future network difficulties, they can also have unintended consequences that need to be carefully studied and considered. A recent technological advancement that enhances power grid performance is Distributed Generation (DG). While DG unit integration has measurable benefits for electrical grids, its significant effects on power network protection systems create many questions and difficulties about the proper way to identify and isolate distribution network faults. The DLANN-based approach looks into the ways the integration of DGs affects fault identification and location. This method involves two steps: first, three-phase currents are constantly analyzed for detection, and Discrete Wavelet Transform (DWT) is utilized to extract the currents' features. The second step is classification employing Artificial Neural Networks (ANNs) to pinpoint the defective stages. Counting the shorted phases will reveal the sort of short circuit. The MATLAB programming environment is utilized in the development of the fault identification and classification technique. The fault type (one, two, or three phases), fault resistance, fault location bus, fault distance, and the DG type (upstream or downstream) are all considered. The methodology is used on a modified IEEE 34-bus test system, and four scenarios, one with combined DGs units, one with IBDGs, one with SBDGs, and one without DGs, are modeled. As per the simulation results, 100% fault detection and classification accuracy were obtained, whereas the average fault location accuracy attained without DGs, with IBDGs, SBDGs and combined DGs for selected nodes were 99.94%, 99.91%, 99.86%, and 99.88%, respectively

Enhancing Semantic Search Precision through the CBOW Algorithm in the Semantic Web

Ashraf F. A. Mahmoud — 2025-02-02

The Semantic Web enhances data interoperability and enables intelligent information retrieval through structured data representation. However, challenges remain in achieving high precision in semantic search. This paper uses the Continuous Bag of Words (CBOW) model to enhance semantic search precision. By generating rich word embeddings, CBOW enables a better understanding of contextual relationships among terms within semantic queries. Our approach has been evaluated using the websites intended to be used as a sample for testing the efficiency of semantic information retrieval, demonstrating significant improvements in search precision compared to traditional methods. The findings indicate that integrating CBOW into semantic search frameworks can lead to more relevant and accurate search results, paving the way for future advancements in Semantic Web technologies.

Effect of Powdered Nano-Local Banded Iron Formation (BIF) Rock on Some Mechanical Properties of Cementitious Concrete

Khaled M. Osman — 2025-02-02

Banded Iron Formation (BIF) rocks are a significant source of iron ore, and they can also be used in the production of cementitious materials. However, the BIFs of the Egyptian Eastern Desert (ED) are not currently employed in steel iron manufacturing due to their elevated silica content and the technical challenges and high cost associated with extracting iron ore from them. Furthermore, the incorporation of nano-sized particles of BIF into cementitious mortar may impart specific characteristics that could enhance mechanical strength and durability, or even contribute to sustainability. This study examines the impact of nano- and powder-based materials derived from locally sourced BIF rocks on the properties of cementitious concrete when used as partial replacements for Ordinary Portland Cement (OPC). A comprehensive evaluation was conducted on concrete mixtures with varying cement replacement ratios, 1, 2, 3, and 4%, to assess the impact on key mechanical properties at different curing ages, 7, 28, and 90 days. The concrete samples exhibited significant enhancements in mechanical properties at all curing periods. The 2% Nano-BIF replacement yielded the most notable increase. Furthermore, X-Ray Diffraction (XRD) and Transmission Electron Microscopy (TEM) analysis demonstrated that the Interfacial Transition Zone (ITZ) between the cement paste and aggregates exhibited a robust compacted bond, indicating that local nano-BIFs have the potential to serve as an effective additive for enhancing the mechanical properties of cementitious concrete.

A Comprehensive Review on Biomedical Image Classification using Deep Learning Models

Mohamed Tounsi — 2025-02-02

Medical imaging is one of the most efficient tools for visualizing the interior organs of the body and its associated diseases. Medical imaging is used to diagnose diseases and offer treatment. Since the manual examination of a massive number of Medical Images (MI) is a laborious and erroneous task, automated MI analysis approaches have been developed for computer-aided diagnostic solutions to reduce time and enhance diagnostic quality. Deep Learning (DL) models have exhibited excellent performance in the MI segmentation, classification, and detection process. This article presents a comprehensive review of the recently developed DL-based MIK classification models for various diseases. The current review aims to assist researchers and physicians of biomedical imaging in understanding the basic concepts and recent DL models. It explores recent MI classification techniques developed for various diseases. A thorough discussion on Computer Vision (CV) and DL models is also carried out.

CuLOA-based Data Encryption with Tuned Key for Privacy Preservation in the Cloud

Rajkumar Patil — 2025-02-02

Preservation of data privacy in cloud computing involves securing sensitive data during analysis and storage. Conventional approaches often use techniques such as encryption and differential privacy, but they can be computationally intensive and may still risk data leakage through indirect inferences. These limitations necessitate advanced methods to balance efficiency and robust privacy. To address this, this study proposes a novel approach using DL-based fine-tuned keys for encrypting the data, aimed at preserving data privacy in the cloud through the Coati Lyrebird Optimization Algorithm (CuLOA) approach. Initially, sensitive data is randomly chosen from the database. Then, the optimal key is derived using the CuLOA approach. This key, along with the sensitive data is input into the SqueezeNet model, which generates a fine-tuned optimal key. Subsequently, the sensitive data are encrypted and stored in cloud storage. Finally, the encrypted data and the optimally tuned key are employed in the data decryption process to recover the original. A comparative experimental analysis showed that the proposed CuLOA approach was better than previous schemes.

Optimal CNN Model for Obstructive Sleep Apnea Detection using Particle Swarm Optimization

Thanh-Huong Tran — 2025-02-02

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder with significant health risks. It is characterized by the narrowing of the upper airway during sleep, leading to vibrations in the airway structures and the production of snoring sounds. Recently, Convolutional Neural Networks (CNNs) have been leveraged to extract meaningful features from snoring sound data, enabling early and accurate detection of OSA. The effectiveness of these neural network optimizations depends on the starting values of the model, the gradient algorithm used, and the complexity of the problem. This study introduces an improved Particle Swarm Optimization (PSO) strategy that linearly adjusts the learning rate coefficient to enhance accuracy and convergence speed. Our approach was evaluated on a collected and pre-processed dataset based on the PSG-Audio database. Experimental results demonstrate that our method significantly outperforms the conventional optimization algorithm and existing PSO techniques, achieving a remarkable accuracy of 99.1%. These findings confirm the potential of our optimized model for OSA detection.

Sustainable Development of an Optimized Design Model for Groundwater Purification Units: A Solution for Irrigation Use in Rural Communities

Wael S. Al-Rashed — 2025-02-02

Groundwater is an essential resource for both irrigation and drinking water, particularly in arid and semi-arid regions where it often serves as the only dependable source. However, its quality is increasinlgy threatened by factors such as urbanization, population growth, and the overuse of chemical fertilizers in agriculture. hese challenges are particularly acute in Saudi Arabia, where groundwater quality deterioration poses significant obstacles to sustainable water use. This study proposes an optimized design for groundwater purification units aimed at improving water quality for irrigation. The proposed systems integrate coagulation with advanced purification methods, including nanofiltration or sand filtration, to effectively remove contaminants and enhance groundwater suitability for agricultural use. Nanofiltration excels in removing dissolved salts, organic molecules, and microorganisms, while sand filtration offers an economical solution for reducing suspended solids and turbidity By addressing critical water quality challenges, the model ensures more sustainable agricultural practices and a cleaner water supply for local communities. This research underscores the need for effective water management and purification strategies to safeguard groundwater as a reliable and safe resource for future generations, especially in regions like Saudi Arabia that face severe water scarcity and pollution pressures.

Exploring LDoS Attack Detection in SDNs using Machine Learning Techniques

Ali Osman Mohammed Salih — 2025-02-02

This study investigates the application of machine learning algorithms for detecting Low-Rate Denial-of-Service (LDoS) attacks within Software-Defined Networks (SDNs). LDoS attacks are challenging to detect due to their similarity to normal network behavior. This study evaluates the performance of algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), and BIRCH clustering in this challenge. The results show that the LR and BIRCH algorithms outperformed other approaches, achieving a detection accuracy of 99.96% with minimal false positive and negative rates. The models demonstrated a fast detection time of 0.03 seconds, highlighting the potential of machine learning to improve SDN security. The study recommends future work to validate these findings in real-world environments to strengthen security systems.

Performance Evaluation of Magnesia-Type Refractory Brick Waste as Complete Replacement for Fine Aggregate and Filler in Asphalt Concrete Wearing Course Mixtures

Syukuriah Syukuriah — 2025-02-02

Infrastructure development has been rapidly increasing in recent times, and the use of waste materials as aggregates in this process has positively impacted regional and national economies. This study investigates the use of magnesia-type Refractory Brick (RB) waste as a substitute for fine aggregate and filler in Asphalt Concrete-Wearing Course (AC-WC) mixtures. The RB waste is generated from the kiln walls of nickel smelting furnaces and is used to completely replace natural sand by weight. The study compared Marshall empirical values, such as stability, yield, and Marshall quotient (MQ), volumetric characteristics, such as Void In the Mix (VIM), Void in Mineral Aggregate (VMA), and Void Filled with Bitumen (VFB), and Ultrasonic Pulse Velocity (UPV) of AC-WC mixtures containing natural sand at asphalt percentages of 5.0%, 5.5%, 6.0%, 6.5%, and 7.0%. The findings reveal that the optimum Marshall properties were achieved with RB waste at a 5% asphalt content, compared to 6.0% for natural sand. Furthermore, the AC-WC mixture incorporating RB waste exhibited sufficient strength and durability to withstand traffic loads, suggesting that the complete replacement of natural sand with RB waste significantly influences the properties of AC-WC asphalt, promoting the environmentally friendly and economical reuse of waste materials in the industry.

Diminishing Environmental Impact in the Construction Industry: The Use of Brick Coarse Aggregates Instead of Natural Coarse Aggregates

Saloua Filali — 2025-02-02

The rapid growth of infrastructure, urbanization, and industrialization has increased global concrete demand, putting pressure on natural resources and creating ecological challenges. In response, using Brick Waste (BW) as a substitute for natural aggregates in concrete offers a promising solution to enhance sustainability in construction materials. This study specifically investigates the replacement of Natural Coarse Aggregates (NCA) with Brick Coarse Aggregates (BCA) at substitution rates of 25%, 45%, 65%, and 85%. The experimental results show that replacing 25% of NCA with BCA leads to a 12% decrease in workability and a 2.48% reduction in density compared to a control concrete mix. In its hardened state, this substitution results in a slight decrease of 6.45% in compressive strength (fc). At higher substitution rates, such as 85%, the decrease is intensified, with a 32% reduction in workability, 7.93% in density, and 50.32% in compressive strength, all compared to the control concrete after 56 days. The present study also emphasizes a significant correlation between the measured compressive strength and that estimated by non-destructive methods, such as the Schmidt Rebound Hammer Test. Optimizing substitute materials is crucial for achieving high performance while ensuring environmental benefits. This research proposes an innovative approach to sustainable construction, providing a unique opportunity to reconcile performance and sustainability in the construction sector. The importance of this work lies in its potential to transform waste management practices and promote more ecological construction materials.

Assessing the Effects of Libyan Iron Slag on Self-Compacting Concrete Characteristics

Nurdeen Mohamed Altwair — 2025-02-02

The current study addresses the growing environmental issue of waste from blast high furnaces, particularly iron and steel plants in Libya. It investigates the fresh and mechanical properties of Self-Compacting Concrete (SCC) by substituting conventional aggregate with slag aggregate. The fresh properties of SCC were assessed using slump flow diameter, T50 flow time, J-ring, and L-box tests. Its mechanical properties were also evaluated, including compressive strength, flexural strength, splitting tensile strength, and Ultrasonic Pulse Velocity (UPV). Various replacement ratios were tested, 30%, 60%, and 100% for coarse aggregate, 10%, 20%, and 30% for fine aggregate, and combinations of coarse and fine aggregate at specified ratios. The results indicated that higher slag powder content slightly increased the setting times. The coarse slag aggregate proportions negatively impacted the filling ability, while fine aggregate proportions enhanced it. The passing ability decreased when 60% of coarse slag was used as a replacement, but it improved with a 100% coarse slag replacement. Interestingly, replacing 60% of coarse aggregate with slag enhanced compressive strength. Meanwhile, the best flexural and splitting tensile strengths were observed with 20%-30% replacements of both coarse and fine aggregates with slag. All slag aggregate mixtures were classified as of excellent quality based on UPV assessments, highlighting their potential as sustainable construction materials.

Application of the PSI Method in Selecting Sustainable Energy Development Technologies

Tran Van Dua — 2025-02-02

The development of renewable energy is not only an urgent solution for addressing climate change but also a driving force for sustainable economic growth. The transition to clean, inexhaustible energy sources not only helps to reduce greenhouse gas emissions and protect the environment but also ensures national energy security, creates employment opportunities, and enhances the quality of life for individuals. Presently, various technologies exist for sustainable energy development, each characterized by multiple criteria, complicating the evaluation of their performance. This study presents a straightforward method for identifying the best option among eight sustainable energy development alternatives: hydropower, geothermal, biomass, wind, solar, concentrated solar power, coal technology, and oil-fired power plants, each of which is characterized by 17 distinct criteria. The simple method utilized is the Preference Selection Index (PSI) method, which eliminates the need for criteria weighting. This absence of criteria weight calculation in the PSI method distinguishes it from other ranking techniques that typically require such calculations. Therefore, the PSI method significantly simplifies the comparison of the available options compared to other ranking methods, as it bypasses the need for criteria weight calculations. The optimal option identified through the PSI method was also compared with the optimal option identified using 6 other methods: Multi Atributive Ideal Real Com parative Analysis (MAIRCA), Evaluation Based on Distance from Average Solution (EDAS), COmplex PRroportional ASsessment (COPRAPS), Multiobjective Optimization On the basis of Ratio Analysis (MOORA), Proximity Indexed Value (PIV), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Notably, all employed methods consistently identified geothermal energy as the optimal choice.

Experimental Investigation of Concrete-filled Steel Tube Beams with Transverse Openings

Ali Mohammed Abdulridha — 2025-02-02

Modern building construction requires numerous pipes and ducts for services like air conditioning and electricity, often accommodated by web openings in beams. This study investigates the structural performance of Concrete-Filled Steel Tube (CFST) beams with transverse openings, which can affect their load-carrying capacity and behavior. Eight CFST beams and four Hollow Steel Tube (HST) beams were tested under two concentrated loads, including four CFST and two HST beams with transverse openings. The present research examines how openings impact load capacity, failure modes, ductility, strain, and Energy Absorption (EA) across varying cross-sections and Depth-to-thickness (D/t) ratios. The results show that transverse openings significantly affect CFST beams more than HST beams. The load-carrying capacity of CFST beams was reduced by up to 18.6%, while HST beams exhibited reductions of only up to 3.77%. Ductility and EA followed similar trends, with CFST beams experiencing reductions of up to 20% in ductility and 30.7% in EA. The HST beams showed relatively minor decreases of 2.54% in ductility and 14.1% in EA. The failure of CFST beams with openings was characterized by steel rupture through the openings. The effect of openings increased with higher D/t ratios. Despite the reductions caused by the openings, the overall enhancement in all studied aspects provided by the concrete filling in CFST beams with transverse openings remained significant.

DTXG-RF-based Intrusion Detection System for Artificial IoT Cyber Attacks

Shayma Wail Nourildean — 2025-02-02

The swift advancement of networking technology and the rising incidence of cyber-attacks have made effective cybersecurity a critical priority. The primary concern with IoT networks is their susceptibility to vulnerabilities. IoT security necessitates the substantial involvement of artificial intelligence as a security technology to mitigate these challenges. Cyberattacks are evolving in sophistication, consequently posing greater obstacles in the precise detection of intrusions. An Intrusion Detection System (IDS) is a device or software application that monitors the activities of network systems for malicious actions or policy breaches and produces reports. The primary objective of an IDS is to efficiently identify attacks. Moreover, it is imperative to identify attacks at an early stage to mitigate their effects. Machine learning models have become increasingly popular in IDSs due to their capacity to process substantial data volumes and identify patterns in real time. Machine learning involves building an algorithm to identify consistent patterns within a dataset. This study aimed to build an IDS using an ensemble machine learning (DTXG-RF) model and compare it with DT, XGBoost, KNN, RF, NB, and CatBoost on the CIC-IoT-2023 and a Ransomware dataset. The results showed that the proposed DTXG-RF outperformed other machine learning models with accuracy reaching 95.06%.

Analysis of the Excess Deaths in Ecuador caused by the COVID-19 during 2020 and 2021

Marco E. Benalcazar — 2025-02-02

Ecuador became one of the most affected countries in the world from the COVID-19 pandemic in 2020: the number of deaths during March and April of 2020 suggests that the pandemic in this country was much worse than the studies reported by the Ecuadorian national institutions. A number of studies concerning the number of excess deaths have been conducted, but they used a limited amount of data because of the time they were done. Additionally, these studies do not provide a way of comparing results with those of other countries since they use the raw number of excess deaths, and not a relative measure. This study fills all these gaps by presenting an analysis of the excess deaths (raw and per 100000 inhabitants). For this analysis, Long Short-Term Memory (LSTM) Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) were used to do a forecasting. These methods were trained using the data of deaths in Ecuador before the pandemic, over a period of 5 years. The methodology used for this work takes steps from recognized guidelines from CDC and the University of Melbourne to compute the excess deaths. In 2020, Ecuador had an excess death of 42009 ± 5823 people, which means an excess death within 207.14 to 273.81 people per 100,000 inhabitants. The decrease of deaths due to land traffic accidents, congenital malformations, deformities, chromosomal abnormalities and HIV in 2020 was 435. Additionally, the causes with the highest excess deaths were respiratory insufficiency, influenza and pneumonia, ischemic heart diseases, hypertensive diseases and mellitus diabetes. In order to contrast these numbers, a computation of the excess (decrease) of deaths was computed.

DeepMelaNet: Advancing Melanoma Stage Classification in Skin Cancer Diagnosis

Md Sadi Al Huda — 2025-02-02

Melanoma skin cancer is a global public health threat due to its increasing rates and the possibility of severe outcomes if not adequately addressed. Melanoma is caused by ultraviolet radiation and, among its two stages, malignant is more dangerous than benign. The diagnosis of melanoma is typically based on visual inspection and manual methods carried out by experienced physicians. However, this method is usually slow and has a high probability of error. Deep-learning-based models can offer better and low-cost treatments for people with melanoma. This study aimed to develop a deep-learning model to classify melanoma skin cancer in its early stages. This study presents a modified deep-learning model, named DeepMelaNet, to correctly classify skin cancer images as benign or malignant. The proposed classifier achieved an accuracy of 93.40%, a precision of 98%, a recall of 94%, and an F1 score of 93% on a dataset of 10,000 melanoma skin cancer images, offering a practical solution that can help healthcare professionals in early skin cancer prediction.

Powertrain Design and Modeling for a Fuel Cell Hybrid ElectricVehicle

Youssef Dhieb — 2025-02-02

The objective of this study was to develop a Fuel Cell Hybrid Electric Vehicle (FCHEV) powertrain with the aim of enhancing battery usage autonomy. The vehicle, which participated in the Eco-Marathon competition as a prototype, incorporates batteries, a Direct Current (DC) electric motor, and a Proton Exchange Membrane (PEM) fuel cell. The design permits the operation of the fuel cell to be conducted in a more efficacious and fuel-efficient manner. The study employs the MATLAB-Advisor software to construct powertrain models that are then validated in laboratory settings. These models are subsequently compared with the performance of the actual FCHEV prototype and adapted for use in automotive applications. The FCHEV power model calculates instantaneous energy consumption using input variables, such as vehicle speed, acceleration, and road gradient. Furthermore, Real Cycle drive was carried out to improve the trade-off between energy consumption, fuel cells, battery State of Charge (SOC) dynamics, and battery power smoothness, while ensuring that all essential limitations were met. The addition of a fuel cell to an electric car model enhances its range by 250%, significantly improving its adoption and usage.

Investigation of the Spatiotemporal Distribution of PM10, PM2.5, and PM1 from Motor Vehicles in Roadside Environments

Vera Surtia Bachtiar — 2025-02-02

This study examines the spatial and temporal distribution of PM₁₀, PM_2.5, and PM₁ concentrations in Padang City, Indonesia, focusing on the impact of motor vehicle emissions. Measurements were conducted at distances ranging from 5 m to 100 m from major roadways and at different times of the day to evaluate the effects of traffic patterns and meteorological conditions on air quality. The findings revealed that Particulate Matter (PM) concentrations are significantly higher near roads, with PM₁₀ peaking at over 55 μg/m³ in the afternoon at 5 m from the roadway. Similarly, PM_2.5 and PM₁ also reach the maximum levels of 45 μg/m³ and 35 μg/m³, respectively, during peak traffic hours. While meteorological factors, such as temperature, wind speed, relative humidity, and pressure, exhibit weak correlations with the PM levels, traffic volume emerges as the primary contributor to air pollution. These results underscore the need for effective traffic management and emission reduction strategies to mitigate pollution and protect public health. The current study’s recommendations include enhancing roadside air quality monitoring, and conducting further research on seasonal variations and the specific contributions of different vehicle types to PM pollution dynamics.

An Intuitive Approach on Transfer Learning with an IBF+IHP Model for Stroke Classification and Prediction

Talekar Rohini — 2025-02-02

A cerebral stroke can have significant health ramifications. Efficient stroke prevention requires precise prevention and prompt detection of risk factors. This study introduces a novel predictive modeling technique that uses uncomplicated spatial filter maps and ensemble approaches to enhance stroke risk prediction. The proposed approach utilizes ensemble approaches along with comprehensible spatial filter maps to uncover significant spatial patterns in brain imaging data. The ensemble approach employs a multitude of prediction models to enhance the accuracy of stroke risk forecasts. The experimental findings demonstrate that spatial filter maps and ensemble techniques surpass traditional models in predicting performance. This study showcases the potential of spatial filters to include several patient data to accurately predict stroke risk with a 98% success rate.

A Comparative Study of Fine-Tuning Deep Learning Models for Leaf Disease Identification and Classification

Bh. Prashanthi — 2025-02-02

Innovative agricultural solutions are needed to detect and classify leaf diseases early across crop species and environments. This study compares deep learning approaches, focusing on Convolutional Neural Networks (CNN) and Vision Transformers (VTs), to identify leaf diseases early and accurately for scalable crop management and productivity. Optimizing CNNs, Explainable Transfer Learning (ETPLDNet) using ResNet50 architecture, and LEViT leaf disease diagnosis are compared. The CNN model, optimized with dynamic hyperparameters, achieved an impressive 99.58% accuracy for leaf disease classification, demonstrating its effectiveness in feature extraction and classification precision. On the other hand, the VT-based LEViT model, which leverages self-attention mechanisms and Explainable AI (XAI), achieved 95.22% accuracy but offers enhanced interpretability and generalization capabilities due to its transformer-based architecture. This distinction illustrates that while CNNs excel in accuracy, VTs provide a more transparent decision-making process and better handle the complex variances in plant leaf diseases, making them ideal for precision agriculture. The combined use of CNNs and VTs showcases the strengths of each model, with CNN focusing on high classification precision and VTs offering improved interpretability and adaptability for various leaf disease conditions. The use of XAI enables the models to highlight important areas in plant leaf images that influence the model's decisions, offering a transparent and interpretable decision-making process that allows researchers and farmers to understand why a particular diagnosis or classification was made. This ability to visualize and explain the reasoning behind the model predictions is crucial to increasing trust in AI-driven solutions in agriculture. By combining the high precision of CNN and the interpretability of VT with XAI, this study offers a robust approach to improving crop disease management and precision agriculture.

Shear Capacity of Self-Compacting Concrete Beams provided by External Steel Plate using Z Stirrups

Douaa Kasim — 2025-02-02

The stirrups are the primary factor that resists the shear forces, while other parameters like aggregates and dowels contribute less than 7% of the shear strength. Shear connectors transmit the shear forces in composite beams between steel parts and concrete, and these connectors can be utilized as vertical legs to assist the stirrups in their performance. The results indicate that decreasing the shear span to effective depth (a/d) ratio from 3 to 2.5 led to a 24% increase in the crack load and a 16% increase in the maximum load value. Additionally, the shear connectors have a significant effect on increasing the shear capacity, and this effect becomes more pronounced as their lengths increase, effectively replacing the function of the stirrups. Furthermore, the change in the shape of the stirrups does not significantly affect the shear capacity, and the decrease in endurance can be compensated by reducing the distance between the stirrups. It was also observed that the horizontal parts of the stirrups may have a limited and negligible effect, suggesting that the stirrups could potentially be replaced by I-shaped alternatives.

Sustainable Design of Raft Foundations: Analyzing Embodied Carbon and Cost Impacts

Riza Suwondo — 2025-02-02

The construction industry is a significant contributor to global carbon emissions, necessitating the adoption of sustainable design practices. This study investigates the embodied carbon and cost implications of raft foundations, focusing on the effects of different concrete grades, K300, K400, and K500, and slab thicknesses. A comprehensive methodology, guided by BS EN 15978, was employed to assess the carbon emissions across the product, construction, and end-of-life stages. Additionally, a cost analysis was conducted, reflecting typical construction expenses relevant to the Indonesian context. The findings revealed that increasing the concrete grade consistently leads to higher embodied carbon and costs, with K300 demonstrating the lowest values across all thicknesses. Moreover, thicker slabs exacerbate both the environmental and financial impacts, highlighting the trade-offs inherent in material selection and design choices. The study concludes that a strategic balance between structural requirements, cost efficiency, and environmental sustainability can be achieved by utilizing lower-grade concrete, where high strength is not essential. These insights contribute to the discourse on sustainable construction practices, advocating for informed decision-making in raft foundation design to minimize the carbon footprint while maintaining economic viability.

Enhanced Image Tampering Detection using Error Level Analysis and CNN

Ramesh Gorle — 2025-02-02

This paper introduces a novel approach to image tampering detection by integrating Error Level Analysis (ELA) with a Convolutional Neural Network (CNN). Traditional forensic methods, such as ELA and Residual Pixel Analysis (RPA), often struggle to detect subtle or advanced manipulations in digital images. To address these limitations, this method leverages ELA to highlight compression-induced variations and CNN to extract and classify spatial features indicative of tampering. The dataset, consisting of both authentic and tampered images, was preprocessed to generate ELA representations, which were then used to train a CNN model designed to distinguish between authentic and manipulated regions. Extensive experimentation was performed on the CASIA v2.0 dataset, demonstrating significant improvements in detection accuracy, precision, and recall. The proposed framework achieved a detection accuracy of 96.21%, outperforming established deep learning models such as VGG16, VGG19, and ResNet101. These results underscore the potential of combining ELA and CNN in advancing image forensics, offering a robust solution to ensure the integrity of digital content in an era of sophisticated digital manipulation.

Reducing an Indonesian Auto Part Production Cycle Time using the PDCA Approach: A Case Study

. Jonny — 2025-02-02

Constituting the biggest challenge in any manufacturing company’s product delivery, operational excellence can be accomplished by obtaining the best Quality, Cost, and Delivery (QCD). The delivery parameter has been an issue for the ABC Company, an Indonesian auto part manufacturer, having automotive manufacturers as customers, since its cycle time has been found to be lower than its takt time. This could jeopardize the company’s status in that it could degrade its customer service level. Thus, the company immediately initiated a quality control circle, especially when it was revealed that its production cycle time in Core Assy Line 3 was 49 s more than its targeted 41.7 s takt time. The team was then committed to reducing it to 39.2 s, accounting for a 20% reduction. A method named 8 steps and 7 tools was deployed under the Plan-Do-Check-Act (PDCA) approach to solve the specific problem. This method guided the team to find five root causes of the problem, while five solutions were equivalently provided. As a result, the achieved cycle time reduction was from 49 to 38.9 s, that is, a 21% decrease, capable of securing the company’s relationship with its customers and granting an order increase.

Evaluating the Impact of Aggregate Size and Sediment Type on Clogging and Permeability of Pervious Concrete

Eduardi Prahara — 2025-02-02

Pervious Concrete (PC) is increasingly being used in sustainable drainage systems owing to its ability to manage stormwater. However, sediment accumulation can clog pores and reduce their permeability. This study investigates the effect of various sediment types, sand, clay, gravel, and oil, on the permeability of pervious concrete samples with three different aggregate size ranges: 12.5–19.1 mm, 9.5–12.5 mm, and 4.75–9.5 mm. The concrete samples were subjected to sediment loads ranging from 0 g to 100 g, and the permeability was measured after each sediment addition. The objective was to assess the impact of sediment type and quantity on permeability reduction, and to evaluate the role of aggregate size in resisting clogging. The results demonstrate that larger aggregates maintained higher permeability and were less affected by sediment accumulation, whereas smaller aggregates experienced significant clogging and rapid permeability loss. Oil had the least impact on the permeability, whereas gravel and sand caused the greatest reduction. Permeability stabilisation occurred after sediment accumulation reached 60–70 g for all samples. These findings highlight the importance of aggregate size selection in pervious concrete designs to enhance long-term performance and resistance to clogging. Larger aggregates from 12.5 to 19.1 mm were shown to be the most effective in maintaining permeability, even under sediment load.

Advancing Healthcare: A Comprehensive Review and Future Outlook of IoT Innovations

Rabeya Khatoon — 2025-02-02

Rapid innovation leading to better patient outcomes have been driven by recent breakthroughs in the Internet of Things (IoT), which have drastically changed the healthcare sector. In order to highlight the importance of IoT in healthcare applications, this article presents a user-friendly and integrated approach for bibliometric analysis. Traditional bibliometric methods often rely solely on Web of Science (WoS) or Scopus, limiting the scope of analysis. To address this issue, the proposed approach uses the R program Bibliometrix to combine data from seven databases, namely Scopus, WoS, IEEE, ACM Digital Library, PubMed, Science Direct, and Google Scholar (GS). After having developed an inclusion/exclusion criterion, 2,990 journal papers published between 2011 and 2022 were subjected to a thorough literature review and bibliometric analysis. This study demonstrates that the healthcare industry is highly interested in the IoT, as well as the rapid growth of research into IoT healthcare applications, blockchain, Artificial Intelligence (AI), 5G telecoms, and data analytics. Authentication methods, fog computing, cloud-IoT integration, cognitive smart healthcare, and other essential topics are further examined by employing co-citation network analysis. In addition to illuminating potential avenues for further investigation, these results provide academics with a comprehensive picture of where IoT research in healthcare is standing at the moment. The output of the conducted analysis shows that there has been a dramatic uptick in publishing since 2017, with most of the articles appearing in prestigious journals related to computer science. By integrating data from multiple databases, the proposed methodology represents a significant advancement in bibliometric analysis, enabling a more comprehensive exploration of IoT's impact on healthcare, and facilitating a deeper understanding of the emerging trends and critical themes in this rapidly evolving field.

Enhancing Security in Wireless Sensor Networks: A Machine Learning-based DoS Attack Detection

Ghadeer Al Sukkar — 2025-02-02

The Internet of Things (IoT) is based on Wireless Sensor Networks (WSNs), which are essential for many applications. Denial of Service (DoS) attacks are a major risk for WSNs due to their open architecture and limited resources. This paper investigates how different Machine Learning (ML) methods can be used to identify DoS attacks and mitigate their effects. The predictions from several models were combined using the ensemble method to increase overall accuracy, while explainable Artificial Intelligence (AI) techniques were also deployed to enhance transparency and understanding. To compare the performance of both hard and soft ensemble methods, the WSN Dataset (WSN-DS) and the WSN Blackhole, Flooding, and Selective Forwarding (WSN-BFSF) dataset were utilized. The ensemble techniques aggregated predictions from multiple models to improve overall accuracy, while both showed high accuracy for both datasets. With an accuracy of 98.12%, the soft ensemble technique slightly outperformed the hard ensemble technique for the WSN-DS dataset, which had an accuracy of 97.97%. For the WSN-BFSF dataset, the hard ensemble technique achieved an accuracy of 99.967%, while the soft ensemble technique achieved an excellent accuracy of 100%.

Reducing the Error of Measurement of the Potentiometric Method of the Conductive Liquid Level Meter

Andrey Smirnov — 2025-02-02

This article examines the linearization of a nonlinear transfer characteristic in a two-component sensor, with the potentiometric level meter serving as a case study. The conducted analysis employs a method of interpolation involving two interrelated variables: the liquid level readings are contingent upon both the liquid level and the liquid conductivity, while the liquid conductivity readings are influenced by both the liquid conductivity and the liquid level. The objective of the current paper is to identify a mathematical approach that enhances the precision of the measurement. A methodology for linearizing the nonlinear transfer characteristic of a conductivity level meter was established through the integration of two conversion correction tables and piecewise quadratic interpolation with iteration in the form of a table algorithm. This approach resulted in a reduction in the measurement error compared to the interpolation methods without iteration.

Assessing Factors Impacting Electric Vehicle Adoption in Saudi Arabia: Insights on Willingness to Pay, Environmental Awareness, and Perceived Risk

Maher Toukabri — 2025-02-02

As Saudi Arabia seeks to transition toward sustainable energy, the adoption of Electric Vehicles (EVs) is a key component in reducing carbon emissions and combating climate change. This study explores the factors driving EV adoption, focusing on Willingness To Pay (WTP), Environmental Awareness (EA), Perceived Risks (PR), and Product Attributes (PA). Using a structured survey distributed to 365 respondents, the obtained data were analyzed through the SPSS 27 software, employing regression analysis and factor analysis. The results reveal that WTP and EA are significant predictors of Perceived Value (PV), which, in turn, positively influences consumers’ intention to purchase EVs. Conversely, PR negatively impacts Purchase Intention (PI), though these risks are mitigated by favorable PA. The findings highlight a gap between consumer interest in EVs and the existing infrastructure, suggesting that addressing these concerns is crucial for widespread EV adoption in Saudi Arabia. These insights provide actionable recommendations for policymakers and businesses aiming to enhance consumer confidence and facilitate the growth of the EV market in the region.

The Cyclical Effects on Labor Force Participation: A Study of the Saudi Labor Market using ARDL Techniques

Jumah Ahmad Alzyadat — 2025-02-02

This article assesses the cyclical effects on Labor Force Participation (LFP) in the Saudi labor market. The Discouraged Worker Effect (DWF) and Added Worker Effect (AWF) for men and women LFP rates were examined by applying the Autoregressive Distributed Lag (ARDL) method. The results showed the existence of AWF in the long run in the case of women. They also confirmed that the women's LFP rate is countercyclical to economic activity and procyclical to unemployment and inflation rates. However, there is no evidence of the impact of the unemployment rate and economic cycles on women's decision to participate in the labor force in the short run. Moreover, the results support the invariance unemployment hypothesis for men's decision to participate in the labor force. This means that these factors do not affect men's decision to join the workforce, but other factors make them look for work to increase income. This study also suggests that women can be motivated to enter the labor market if their chances of finding work are improved through an active economic policy that creates jobs for both men and women.

The Delumping Method as a Key Factor in obtaining a characterized Hydrocarbon Fluid using the Example of Kazakhstani Oil

Bolatbek Khussain — 2025-02-02

The composition of hydrocarbon systems plays an essential role in ensuring reliability in field operation processes, the design of oil onshore facilities, and the availability of reliable information about the PVT properties of systems. In this article, experimental and numerical methods are used to obtain data on the component composition of the extracted oil products and their PVT properties. These methods help obtaining a detailed composition and phase state of the hydrocarbon system. The numerical delumping method is utilized in this article. It is worth noting that all calculations were made on the basis of data on the component composition of Kazakhstani oil samples. The results and comparative evaluation show that the proposed delumping mix-method has excellent consistency with the data obtained with experiments and with the Nichita’s analytical approach. In the future, the results on the detailed composition can improve and link the compositional modeling of the reservoir with the modeling of surface structures.

Q_YOLOv5m: A Quantization-based Approach for Accelerating Object Detection on Embedded Platforms

Nizal Alshammry — 2025-02-02

The deployment of deep learning models on resource-constrained embedded platforms presents significant challenges due to limited computational power, memory, and energy efficiency. To address this issue, this study proposes a novel quantization method tailored to accelerate object detection using a quantized version of the YOLOv5m model, called Q_YOLOv5m. This method reduces the model's computational complexity and memory footprint, allowing for faster inference and lower power consumption, making it ideal for real-time applications on embedded systems. This approach incorporates advanced weight and activation quantization techniques to balance performance with accuracy, dynamically adjusting precision based on hardware capabilities. The efficacy of Q_YOLOv5m was confirmed, exhibiting substantial enhancements in inference speed and a reduction in model size with negligible loss in object detection accuracy. The findings underscore the capability of Q_YOLOv5m for edge applications, including autonomous vehicles, intelligent surveillance, and IoT-based monitoring systems.

Challenges and Opportunities for Building Information Modeling in Facility Management: A Case Study from Egypt

Mohamed Salah Ezz — 2025-02-02

The objective of this study is to evaluate the obstacles encountered when using Building Information Modeling (BIM) in Facility Management (FM) within the context of Egypt. The research methodology employs a case study approach, using a single case study to investigate the phenomenon of interest. A comprehensive literature review was conducted, resulting in the identification of 42 challenges to BIM usage in FM. These challenges were classified into five primary groups and formed the basis for a five-point Likert scale questionnaire, which was utilized to collect insights from FM professionals in Egypt. The survey participants included facilities and maintenance managers, as well as BIM employees. The data collected were also analyzed deploying the Impact Effect Index (EI) method. Furthermore, the EI findings indicated that the primary difficulties were the integration of building system design with BIM, the establishment of handover requirements, the integration specifications between the FM and BIM, and securing accurate and reliable data. The category with the highest EI was challenges related to BIM implementation in FM. The research identifies the significant challenges affecting BIM adoption in FM in Egypt, thereby promoting the development of BIM implementation strategies. Consequently, the findings hold practical importance for various stakeholders within the construction sector in Egypt.

Sustainable Transportation Solutions in Remote Areas: Static Analysis of Vertical Axis Wind Turbines for Enhanced Efficiency of Wind-Powered Cars

Youssef Kassem — 2025-02-02

It is imperative that a sustainable transportation system, powered by renewable energy resources, be implemented in order to mitigate the impacts of climate change and enhance living standards. A Wind-Powered Car (WPC) is a vehicle that employs a connection between the vehicle and wind turbine blades, thereby leveraging the advantages of wind kinetic energy. The energy is then conveyed directly to the car's wheels via a system of mechanical connections and gears, enabling the vehicle to move without the use of fossil fuels. The absence of an internal combustion engine results in the generation of negligible emissions. The primary objective of this study is to examine the static aerodynamic drag of nine WPC designs with diverse blade configurations of Vertical Axis Wind Turbines (VAWT). To achieve this objective, Autodesk Computational Fluid Dynamics (CFD) was employed to model the aerodynamic drag of WPC designs at varying wind speeds of 4 m/s, 6 m/s, and 8 m/s. The comparative analysis revealed that model 8, featuring a 3-blade Savonius wind turbine without a circular end plate, demonstrated superior efficiency among all car models. This is evident in its ability to generate the highest mechanical power compared to other blade designs. These findings contribute to the understanding of aerodynamic performance in VAWT cars, offering valuable insights for further design optimization. Furthermore, the results highlight model 8 as a promising solution for sustainable transportation, aligned with SDG 7 and SDG 11, through the development of clean and efficient wind-powered vehicles.

An Enhanced Random Forest (ERF)-based Machine Learning Framework for Resampling, Prediction, and Classification of Mobile Applications using Textual Features

Shahbaz Hussain — 2025-02-02

The amount of mobile applications is increasing rapidly, and it is difficult for software developers to identify the numerous key factors that affect their rating and performance. This study presents a machine-learning framework to improve decisions in adding new features to mobile applications and enhancing overall performance. A dataset of app attributes from the Apple AppStore was used, exploiting NLP techniques to preprocess the textual information and develop an Enhanced Random Forest (ERF) framework to assess and forecast ratings for multifunctional apps and investigate the connections between features and user ratings. The ERF model was compared with other renowned ML methods including Decision Trees (DT), Naive Bayes (NB), CNN, and ANN. The experimental results showed that the proposed model predicts app ratings more effectively compared to other complex models. The proposed model achieved precision, recall, and F1-score of 92.76%, 99.33%, and 95.93%, respectively.

Enhancing 5G Performance: A Study of an MIMO Antenna with Elliptical Ring Polarization

P. Ramya — 2025-02-02

This paper discusses the design and evaluation of a four-element Multiple-Input, Multiple-Output (MIMO) antenna array with Circular Polarization (CP), tailored explicitly for sub-6 GHz 5G wireless systems. The proposed antenna utilizes an elliptical ring slot with a variable width in the ground plane and an asymmetric feed line layout to achieve orthogonal CP radiation in both the forward and backward directions. Three interconnected strip lines are incorporated into the ground plane for enhanced performance, increasing isolation and ensuring effective linkage between the antenna components and their respective grounds. The antenna exhibits strong isolation, a low Envelope Correlation Coefficient (ECC), and minimal Channel Capacity Loss (CCL), making it ideal for MIMO applications. The analysis confirms the antenna's stable CP gain and operational efficiency throughout the sub-6 GHz band, meeting the rigorous standards of contemporary 5G networks.

Factors Shaping the Net Asset Value of Saudi Real Estate Investment Trusts

Soumaya Hechmi — 2025-02-02

This study investigates the impact of the dividend per unit, loan-to-value ratio (LTV), operating performance, and liquidity on the financial performance of Saudi Real Estate Investment Trusts (REITs), as measured by Net Asset Value (NAV). The study examines a sample of 17 listed Saudi REITs over a five-year period, 2019-2023, resulting in 85 observations. Through multiple regression analysis, the results demonstrate a significant positive relationship between NAV and dividend per unit, operating performance, liquidity, and size as a control variable. In contrast, a negative relationship is found between LTV and NAV. These findings provide important insights for investors and portfolio managers, enhancing their understanding of the key factors that drive the financial performance of Saudi REITs.

Exploring the Adverse Impact of Smartphone Use on Young Individuals' Self-Esteem: A Structural Equation Modeling Approach based on Five Temperaments

J. Snegha — 2025-02-02

Excessive Smartphone Use (ESU) has emerged as a major social concern, with widespread reliance on smartphones and the Internet resulting in various detrimental effects. This research aimed to analyze the primary psychological factors that affect ESU among individuals using Structural Equation Modeling (SEM). Smartphone addiction levels were evaluated using the Smartphone Addiction Scale, along with five different temperamental traits, namely, Depressive, Cyclothymic, Hyperthymic, Anxious, and Irritable, using the Affective Temperament Measure. Self-esteem was examined using the Rosenberg self-esteem scale. Of 376 participants aged 16 to 23, 88.4% showed signs of ESU, with certain temperamental traits being more prevalent. Negative impacts were especially noticeable among women. Self-esteem levels were found to be 15.7% high, 23.8% moderate, and 60.5% low. The results emphasize the harmful effects of excessive smartphone use on emotional and cognitive well-being, particularly in those with lower self-esteem. This study highlights the need to understand these relationships and create strategies to mitigate the negative effects of smartphone overuse.

Load Frequency Control Design for Complex Power Systems Implementing Integral Single-Phase Sliding Mode Control

Anh-Tuan Tran — 2025-02-02

This paper proposes a Decentralized Integral Single-phase Sliding Mode Control (DISSMC) for Load Frequency Control (LFC) in Complex Power Systems with Multi-Source generation (CPSMS), integrating reheat, hydro, gas, and wind turbines. A generalized structure is employed to model Multi-Area Linked Power Systems (MALPS), providing a realistic representation of diverse power plants. The proposed method formulates an integral Sliding Surface (SS) to mitigate the chattering phenomena and ensures finite-time stability through a continuous control law. Additionally, a single-phase technique eliminates the reaching phase, ensuring immediate trajectory control. The MATLAB/Simulink simulations validate the DISSMC's effectiveness in stabilizing frequency fluctuations and managing load variations across three interconnected regions, each hosting different power plant types. The results highlight the proposed controller's robustness and adaptability to dynamic load and renewable energy source fluctuations.

Digital Transformation in Higher Education Obstacle Assessment and Development of Strategies against Cybersecurity Threats: The Case of Moroccan Universities

Abdelilah Chahid — 2025-02-02

Digital Transformation(DT) in higher education has become essential in improving both educational delivery and operational efficiency. However, this transition also exposes institutions to increasing cybersecurity threats, often associated with various barriers reported in the literature. Although these barriers have been widely studied, no research has yet systematically prioritized them in the academic context. This study, conducted within the framework of DT in Morocco, addresses this gap by classifying and prioritizing these barriers to better understand how they contribute to the spread of cybersecurity threats. Using methodologies such as the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP), we not only prioritized the major barriers but also developed specific strategies to counter the resulting threats, revealing significant variations in the prioritization of cybersecurity strategies. These differences arise from the complex interactions between the barriers identified by the ANP, highlighting the importance of considering interdependencies when developing effective cybersecurity strategies.

Discharge Coefficient of a Compound Weir with a Triangular underneath Gate for Different Geometric and Hydraulic Conditions

Abdulrahman Seraj Almalki — 2025-02-02

It is common practice in the field of irrigation systems to use composite hydraulic structures, which are constituted of two distinct parts. The initial component, represented by two rectangles, is responsible for the overflow regime, while the subsequent component, represented by a triangular gate, is responsible for the underflow regime. In order to measure, direct, and control the flow, both components are required. The present study investigates the flow through a combined two-rectangle weir with a below-triangular gate across the channel, which serves as a control structure. The weir's upper rectangle has a constant width, designated as b₁ and measuring 20 cm, while the lower rectangle has a variable width, designated as b₂ and comprising the values of 8 cm, 10 cm, and 12 cm. The depth of the lower rectangle, z, is also a variable with values of 6 cm, 9 cm, and 11 cm. The dimensions of the triangular gate are 15 cm in height and 0, 60, 90, or 120 degrees in vertex angle. The aforementioned dimensions were employed interchangeably as geometric conditions and the disparate water heads h₂ as hydraulic conditions. Additionally, the compound weir devoid of a gate (θ = 0) was used for varying water heads. The results demonstrated that the dimensions of the weir and gate had an impact on the discharge that went through the two rectangular weirs and gates. In terms of discharge capacity, the combined structure was observed to be more effective than a classic weir, with the ability to convey a discharge that was two to ten times greater. An empirical formula was developed to predict the discharge coefficient, C_d, for the combined structure, based on the given geometric and hydraulic conditions. It should be noted that the results and analysis of this study were limited to the tested dataset.

A Study on the Use of Necuron-Type Polymer Plastics utilized for Sliding Bearing Manufacturing

Ion Nae — 2025-02-02

This paper presents research on the characteristics of the thermoplastic materials used in the manufacture of sliding bearings. Two types of materials from the category of thermosetting polyurethanes with the commercial names Necuron 1050 and Necuron 1300 were investigated. Tests were conducted to determine their physical and mechanical characteristics, such as density, hardness, tensile strength, compressive strength, and longitudinal modulus of elasticity. The results obtained during the study attest to the quality of the materials in the thermosetting polyurethane category in terms of hardness, tear resistance, and compression resistance. The materials in the category of thermosetting polyurethanes stand out as the right choice for applications that require mechanical strength, durability, and resistance to environmental factors.

Clustering Commuter Behavior based on Automated Fare Collection (AFC)

Dwijoko Purbohadi — 2025-02-02

This paper examines the application of the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method to cluster Automated Fare Collection (AFC) transaction data from train travelers in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek) in Indonesia. To enhance the clustering process, the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction and the DenseClus library are employed. In this study, different combinations of hyperparameters are used to identify the optimal configuration for producing distinct clusters with a high concentration and noticeable distinction. The results demonstrate that the utilization of HDBSCAN on UMAP-reduced data effectively, discerning unique trip patterns and emphasizing notable disparities in travel distance, time, and length among various clusters. The UMAP intersection method showed notable efficacy in maintaining the local structure of the data, resulting in the development of distinct and meaningful clusters. In addition, categorical data were transformed into numerical formats using hashing techniques, efficiently tackling the difficulties posed by a high number of categories and assuring efficient data processing. The results reveal vital insights into the application of density-based clustering to intricate transportation data, with major implications for enhancing route planning and capacity management for Jabodetabek commuters.

Enhanced Variational Graph Convolutional Networks with Multi-Scale Convolutions and Attention Mechanisms for Dynamic Network Analysis

Aabid Ahmad Mir — 2025-02-02

The dynamic and constantly evolving landscape of cyber threats demands innovative methods capable of adapting to the complex relationships and structures inherent in network data. Traditional methods often struggle to adequately capture the intricacies of dynamic networks, especially in terms of evolving temporal dynamics and multiscale dependencies. The proposed solution, Enhanced V-GCN, combines the structural insights of Graph Convolutional Networks (GCNs) with the temporal modeling capabilities of Variational Autoencoders (VAEs), further augmented by multiscale convolutions and attention mechanisms. Multiscale convolutions enable the model to aggregate information across broader neighborhood ranges, while attention mechanisms prioritize the most critical nodes and edges, dynamically adapting to changes within the network. This enhanced approach allows V-GCN to effectively capture both nodal and structural patterns, significantly improving performance in node classification tasks. The Enhanced V-GCN model has demonstrated superior performance in node classification, outperforming baseline models with an accuracy of 98.00%, precision of 97.93%, recall of 98%, and an F1-score of 97.92%, indicating robust classification capabilities and exceptional generalization across diverse network structures.

Behavior and Strength Prediction of Concrete Beams reinforced with GFRP Bars and subjected to High Temperature

Maan Hatam Saeed — 2025-02-02

The behavior and strength prediction of concrete beams reinforced with Glass Fiber-Reinforced Polymer (GFRP) bars at high-temperature conditions are examined in this work. Twelve beams burnt at 500°C and 700°C were reviewed as part of the experimental methods, and were contrasted with four more unburned beams. The parameters chosen in this study consist of the type of main bar material, protection type against fire, concrete cover thickness, and the burning temperature. The experimental results indicate that the stiffness of all samples diminishes with rising burning temperatures. This is attributable to the degradation of concrete during the fire being exposed to, leading to an increase in beam deflection under the same load. The plastering of 1 cm was better than the fire-resistant dye as a form of protection against burning, while All beams experienced flexural failure.

Solving Multi-Criteria Shortest Path by Optimization with Morphological Filters

Amar Kateb Hachemi Amar — 2025-02-02

The challenge of determining the shortest path within a multimodal transportation network involves identifying the most efficient travel route while considering various interconnected modes of transportation, such as roads, railways, and public transit. This problem becomes increasingly complex when numerous criteria and modes are involved, complicating the decision-making process. This study proposes a novel approach to computing the shortest path in multimodal networks, focusing on four modes of transportation: metro, trams, buses, and taxis. The optimization criteria include distance, travel time, and monetary cost. The proposed method utilizes a new metaheuristic called Optimization by Morphological Filters (OMF), inspired by image processing techniques. This approach was compared with the Genetic Algorithms (GA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Experiments were carried out using graph models of multimodal transport networks that closely resemble real-world scenarios varying in size. Furthermore, the proposed method was evaluated using a real network from the city of Lyon, France. The results demonstrate that the OMF approach performs well in terms of convergence to optimal solutions and computation time.

Multi-Objective Optimization of the Turning Process using the Probability Method

Tran Van Dua — 2025-02-02

This research aims to determine the optimal values of the cutting parameters when solving the turning process multi-objective problem. Three cutting parameters are considered in this study: spindle speed (n_w), feed rate (f), and depth of cut (a_p). A turning experiment series was conducted on a conventional lathe, with nine experiments having been designed according to the Taguchi experimental design matrix. In each experiment, the values of the three parameters changed and the material Removal Rate (Q) was measured. The Probability method was used to solve the multi-objective optimization problem. The Method based on the Removal Effects of Criteria (MEREC) technique was employed to calculate the weights of the criteria. The results of the optimization problem using the Probability method were also compared with those obtained using other methods, including the Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vlsekriterijumska optimizacijaI KOmpromisno Resenje (VIKOR), Multi-Atributive Ideal-Real Comparative Analysis (MAIRCA), Evaluation by an Area-based Method for Ranking (EAMR), Complex Proportional Assessment (COPRAS), Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), Proximity Indexed Value (PIV), and Combined Compromise Solution (COCOSO). All the methods converged on the same unique solution to the multi-objective optimization problem. The optimal values for the parameters were: n_w = 1350 rev/min, corresponding to a feed rate of 0.13 mm/rev, and a depth of cut of 0.4 mm. When machining with these optimal cutting parameters, the resulting values for Ra, RE, and Q were 1.057 µm, 0.03 mm, and 13225.68 mm²/min, respectively.

Control of a Doubly Fed Induction Generator for Variable Speed Wind Energy Conversion Systems using Fuzzy Controllers optimized with a Genetic Algorithm

Mourad Guediri — 2025-02-02

This paper presents a comprehensive study of a wind turbine system operating under variable wind conditions, utilizing a Doubly Fed Induction Generator (DFIG) connected to the grid. The DFIG is controlled via a rotor-side transducer, allowing for independent regulation of the conductors to manage both active and reactive power flows effectively. The control strategy focuses on generating reference voltages for the rotor to ensure that active and reactive power align with the desired targets, optimizing the tracking of the maximum power point to maximize electrical output. The research analyzes the system's dynamic performance under fluctuating wind conditions, emphasizing control strategies for managing active and reactive energy. A notable innovation is the integration of fuzzy logic and genetic algorithm into the control strategy for the wind turbine's switching mechanism, which enhances system performance and efficiency. Simulation results demonstrate that this approach provides higher efficiency, improved performance, and greater stability compared to the traditional Proportional-Integral (PI) controllers. Advanced artificial intelligence methods, such as fuzzy genetic algorithm control, were employed and the proposed system's effectiveness was validated with Matlab/Simulink simulations.

Nuclear Energy Future in Mitigating Climate Change and Achieving Sustainable Development

Samah Abdullah Abd El-Azeem — 2025-02-02

The growing global population and rising developmental demands have led to increased electricity consumption, primarily from fossil fuel-powered stations, contributing to climate change and necessitating urgent sustainable development. This situation, exacerbated by global warming and its adverse environmental impacts, highlights the escalating demand for energy. As the awareness of climate change has intensified, nuclear energy has gained renewed attention as a key player in mitigating its effects. With global energy consumption being expected to rise substantially, expanding nuclear power plants has become a priority. Nuclear energy presents an opportunity to reduce reliance on fossil fuels while providing a clean and reliable source of electricity. This study investigates the role of nuclear energy in electricity generation, emphasizing its potential as a low-carbon energy source. It underscores the critical need to advance technologies, foster innovation, and enhance safety protocols to ensure energy security. The current study also advocates for increased funding and investment, the establishment and enforcement of effective policies and regulatory frameworks, and the development of human resources and infrastructure. Additionally, it recommends that governments prioritize research into innovative nuclear technologies, collaborate internationally, and accelerate the development and deployment of nuclear energy to support sustainable development.

Processing used Aluminium Production Granular Filters to Produce Concrete

Petr O. Bykov — 2025-02-02

This article presents the findings of experimental studies on the involvement in the processing of spent filters from ash and slag waste used in the refining of primary aluminum as a filler for concrete production. The processing of spent granular filters was conducted in three stages. The first stage involved the preliminary processing of filter grains to remove aluminum scrap. The second stage entailed the metallurgical processing of separated aluminum scrap through remelting in an induction crucible furnace and subsequent refining. The third stage focused on the production of a concrete mixture comprising crushed spent filter grains, quartz sand, bauxite sludge, screenings of crushed rocks with a fraction of 20 mm–30 mm, and Portland cement. This mixture was used to create samples of building products. The test results indicate that the tensile strength of the concrete samples for building products ranges from 20.89 MPa to 37.75 MPa, depending on the Portland cement content. This strength corresponds to that of heavy concrete.

Automated Glaucoma Detection Techniques: A Literature Review

Wisal Hashim Abdulsalam — 2025-02-02

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing on ML and DL techniques were selected. The best performance metrics obtained using ML recorded in the reviewed papers, were for the SVM, which achieved accuracies of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, DRISHTI-GS, and sjchoi86-HRF databases, respectively, employing the REFUGE-trained model, while when deploying the ACRIMA-trained model, it attained accuracies of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36%, in the same databases, respectively. The best performance metrics obtained utilizing DL recorded in the reviewed papers, were for the lightweight CNN, with an accuracy of 99.67% in the Diabetic Retinopathy (DR) and 96.5% in the Glaucoma (GL) databases. In the context of non-healthy screening, CNN achieved an accuracy of 99.03% when distinguishing between GL and DR cases. Finally, the best performance metrics were obtained using ensemble learning methods, which achieved an accuracy of 100%, specificity of 100%, and sensitivity of 100%. The current review offers valuable insights for clinicians and summarizes the recent techniques used by the ML and DL for glaucoma detection, including algorithms, databases, and evaluation criteria.

A Study on the Optimization of FDM Parameters for the Manufacturing of Compression Specimens from recycled ASA in the Context of the Transition to the Circular Economy

Dragos Gabriel Zisopol — 2025-02-02

The present study investigates the optimization of the FDM parameters, that is, the height of the deposited layer in one pass (L_h) and the filling percentage (I_d), for the manufacture of compression specimens from recycled ASA (rASA) in the context of transitioning to the circular economy. The Anycubic 4Max Pro 2.0 3D printer was utilized, where compression specimens were additively manufactured from rASA 45 using the following variable parameters: L_h = 0.10 mm, 0.15 mm, and 0.20 mm, and I_d = 50%, 75%, and 100%. All compression specimens were tested on the Barrus White 20 kN universal testing machine. It was found that the Compressive strength (Cs) is influenced by the two considered variable parameters of the Fused Deposition Modeling (FDM), L_h and I_d, but the overwhelmingly influencing parameter is I_d. According to the results of the FDM parameter optimization for the manufacture of compression specimens from rASA, L_h= 0.10 mm and I_d = 100%.

A Novel Lucas-based Clustering Optimization for Enhancing Survivability in Smart Home Design

Kanaka Raju Rajana — 2025-02-02

This study presents a novel Lucas-based topology optimization framework to enhance network survivability in smart homes, particularly against random node failures. As the proliferation of interconnected devices in the Internet of Things (IoT) environments increases, so does the vulnerability of these networks to node failures, which can significantly disrupt connectivity and functionality. By integrating the mathematical properties of Lucas numbers with advanced graph theory concepts, specifically the Trimet Graph Optimization (TGO) model, this framework systematically addresses the challenges posed by random node failures. The proposed model optimizes network topologies to ensure robust connectivity and resilience, allowing smart home networks to maintain operational integrity even under adverse conditions. Simulations and theoretical analyses demonstrate the effectiveness of this approach, highlighting its potential to improve the reliability of smart home networks.

Application of the RUSLE Modeling Tool for Quantification of Soil Erosion towards Sustainable Planning: The Case of Kosi River Basin, Bihar, India

Rakesh Kumar — 2025-02-02

Soil erosion is a pressing global issue, affecting approximately 2.6 billion people across over 100 countries. It occurs from natural processes and human activities such as intensive agriculture and deforestation. In India, the National Bureau of Soil Survey and Land Use Planning estimates that around 146.8 million hectares of soil have been degraded. Preliminary analysis indicates an average soil erosion rate of 16.4 tons per hectare per year, leading to an annual loss of 5.3 billion tons nationwide. The Kosi River, which frequently shifts its course, exacerbates soil erosion issues in Northern Bihar. This study employs the Revised Universal Soil Equation (RUSLE) to estimate soil loss in the Kosi Basin, covering an area of 1,370,873.485 hectares, utilizing a 30-year rainfall dataset from the Indian Meteorological Department (IMD). Furthermore, various remote sensing data reveal that 0.20% of the area is at very high risk, while 65.88% is classified as having low to shallow risk for soil erosion. These results intend to guide regional planning and land use management in Bihar, emphasizing the importance of the soil erosion prevention model for effective environmental management.

Finite Element Analysis of CM247LC Superalloy for Gas Turbine Blade Application

Tejan Chavan — 2025-02-02

The objective of this article is to conduct a comparative analysis of the various materials used in the production of gas turbine blades. The materials under investigation include CM247LC, Nimonic 80A, and Inconel 738. The selected blade materials are required to demonstrate exceptional resistance to high temperatures and corrosion. It is determined that the most appropriate material for the construction of a gas turbine blade is a nickel-based superalloy. For the purposes of Finite Element Analysis (FEA), the aforementioned materials are defined as nickel-based superalloys. A comprehensive analysis of these materials was conducted using the ANSYS 2024 R2 student edition and a combination of structural and vibrational analyses was carried out. The deformation observed in CM247LC and Nimonic 80A exhibited nearly identical values of 0.965 mm and 0.884 mm, respectively. The results of the vibrational analysis indicated that all materials successfully circumvented the natural frequency as well as the operational natural frequency of 50 Hz, thereby ensuring the safe operation of the gas turbine blade. The findings demonstrated that the CM247LC satisfied both criteria for material selection, making it the most suitable material for gas turbine blade applications when compared to alternative materials. This is due to its comparatively lower deformation despite experiencing a greater magnitude of centrifugal force.

Deep Learning-assisted Automatic Modulation Classification using Spectrograms

Hamza Ouamna — 2025-02-02

With the increasing demand for reliable and efficient V2X (Vehicle-to-Everything) communications in cognitive radio environments, spectrum sharing becomes imperative. In this context, accurate modulation classification serves as a fundamental component for efficient spectrum sensing and allocation. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) trained on spectrograms of BPSK and QPSK modulation schemes for automatic modulation classification in V2X scenarios. Experimental results demonstrated the effectiveness of the proposed CNN-based framework in accurately classifying modulation schemes in V2X communications.

Digital Health Transformation in Saudi Arabia: Examining the Impact of Health Information Seeking on M-Health Adoption during the COVID-19 Pandemic

Nasser Aljohani — 2025-02-02

This study investigates the intention of Saudi Arabian users to adopt mobile health (m-health) applications through the lens of the United Theory of Acceptance and Use of Technology (UTAUT) framework. The research highlights the growing importance of m-health solutions in Saudi Arabia, especially in the context of the country's Vision 2030 development agenda and the accelerated adoption of e-health/m-health technologies during the COVID-19 pandemic. The key findings indicate that health information seeking, and social influence are significant factors driving users' intentions to adopt m-health applications, while Performance Expectancy (PE) is not a primary driver. Additionally, Effort Expectancy (EE) positively influences users' behavioral intentions. Improving health information features in these applications could facilitate broader adoption. This research contributes to the existing literature on information systems and m-health adoption by shedding light on the critical factors influencing user intentions in a developing context. However, the study does not account for all potential technological and external variables that could affect adoption behavior. Future research, particularly qualitative or mixed-method studies, should explore the impact of age on m-health adoption, as the current findings primarily reflect younger users.

Improving Residential Complex Project Performance using Information and Communication Technology supported by the DEXi Method

Maytham Basim Abdulhussain — 2025-02-02

A complex project involves a significant level of risk, uncertainty, and complexity due to several variables, including the project's size, duration, scope, and interdependencies. The use of Information and Communication Technology (ICT) leads to increasingly better and more sustainable results in the progress of complex tasks. Specialized Information Technology (IT) software packages are available on the market to meet the specific needs of the construction industry. The main objective of this research is to identify the factors that have the greatest impact on complex projects and to explore the utilization of ICT applications to improve productivity. The first step of the research methodology is to assess the effectiveness of ICT in construction. The second step is to specify the responses of the participants in complex projects. The final step involves employing a verified computational methodology to identify the parameters that influence the effectiveness of ICT use in the construction sector. This study utilized survey data, expert comments, in-depth interviews, and exploratory research to assess the impact of management styles on ICT performance metrics. The research also drew on previous work in the construction project area to enhance its findings. According to the results of the Relative Importance Index (RII), the most significant component in the use of ICT was the technical calculation based on time. The analysis showed that improving the efficiency of subcontractors and suppliers' coordination yielded a rank value of 0.869. The special computational software called Decision Expert (DEXi) method is also used to facilitates decision making based on specific criteria. Ultimately, this study concludes that ICTs are crucial for improving the efficiency and effectiveness of sustainable project implementation in all aspects.

Comprehensive Analysis of a YOLO-based Deep Learning Model for Cotton Plant Leaf Disease Detection

Sailaja Madhu — 2025-02-02

Diagnosis of cotton plant diseases is essential to maintain agricultural sustainability and output. This study proposes a YOLO-based deep learning model for leaf disease detection to maximize cotton plant leaf disease detection accuracy. This method ensures a comprehensive evaluation of cotton plant health by combining various image processing techniques, improving the accuracy of disease identification. This study provides a viable path to improve crop health monitoring and management in cotton farming systems and emphasizes the importance of utilizing cutting-edge image processing techniques in agricultural activities. ROC curve performance and classification metrics were better for YOLOv5 than for VGG16 and ResNet50, as it had the highest F1 score (99.21%), recall, and precision. Consistent performance in classification tests was demonstrated by all models, which showed balanced precision, recall, and F1 scores. ResNet50 marginally outperformed VGG16 in terms of true positive rates, F1 score (98.88% vs. 98.65%), recall, and precision. More sophisticated models, such as YOLOv5 and ResNet50, showed higher efficiency and accuracy than VGG16, which makes them more appropriate for applications demanding low false positive rates and high precision. The proposed YOLO-based method improves the accuracy of disease identification, ensuring a thorough assessment of cotton plant health using image processing techniques. The results show that the proposed approach is quite successful in correctly detecting and classifying a variety of diseases that affect cotton plants.

Vibroarthrographic Signal Classification for Knee Joint Disorder Detection using Tunable Q-factor Wavelet Transform based on Entropy Measures

Krishna Sundeep Basavaraju — 2025-02-02

Research on Vibroarthrographic (VAG) signals presents a promising means for the early diagnosis of knee joint disorders. However, the classification problem for these signals faces serious issues due to their complex and dynamic nature. This study proposes a novel method for decomposing and analyzing VAG signals based on a Tunable Q-factor Wavelet Transform (TQWT) and entropy-based measures. TQWT is used to preprocess and decompose VAG signals recorded during knee motion into subbands. Different entropy metrics, such as approximate entropy, sample entropy, fuzzy entropy, slope entropy, and so on, were computed over different subbands of the signal to capture significant signal features. Effective features were selected using Recursive Feature Elimination (RFE) and then classified using ensemble classifiers such as XGBoost, Ensemble Random Forest (ERF), and RF-logistic regression. The classification accuracy of the proposed sample entropy method was 87.64% and had 90% sensitivity, 86.36% specificity, and 0.88 AUC-ROC. These results demonstrate the ability of the TQWT-based approach to discriminate knee joint abnormalities. Future work will explore performance scaling with larger datasets and apply it to other joint disorders.

Temperature Dependence Evaluation of CO2 Adsorption on Eagle Ford Shale using Isothermal Models: A Comparative Study

Zaheer Hussain Zardari — 2025-02-02

This study investigates the CO₂ adsorption capacity of the Eagle Ford (EF) shale under varying temperatures, utilizing six isothermal adsorption models: Langmuir, Freundlich, Dubinin-Radushkevich (D-R), Sips, Toth, and Brunauer-Emmett-Teller (BET). The shale sample was characterized through Total Organic Carbon (TOC) analysis, X-ray diffraction (XRD), BET surface area analysis, and Field Emission Scanning Electron Microscopy (FESEM) to assess its organic content, mineral composition, pore structure and elemental composition. CO₂ adsorption experiments were conducted using a volumetric method at pressures up to 12 MPa and temperatures of 35°C, 55°C, and 70°C. The results revealed that the adsorption capacity increased with pressure but decreased with rising temperature, which is consistent with the exothermic nature of CO₂ adsorption. Among the models, Freundlich and Sips provided the best fit for most temperature conditions, highlighting the heterogeneous nature of the shale surface, while the Langmuir, Toth, and D-R models performed well but with slight deviations. The BET model exhibited the poorest fit. Overall, the findings suggest that the EF shale has significant potential for CO₂ storage, especially at lower temperatures, with Freundlich and Sips models being the most reliable for predicting adsorption behavior in EF shale formations.

Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region

S. Vimalkumar — 2025-02-02

Soil moisture is a critical determinant of the maize crop health and productivity. With over 60% of India's maize cultivation concentrated in South Indian states, accurately forecasting soil moisture is essential for optimizing irrigation and enhancing agricultural output. This study introduces an Improved Hybrid Machine Learning (IHML) model that integrates and optimizes Machine Learning (ML) models to deliver superior predictive performance. By leveraging data from key maize-growing districts in South India, the IHML model demonstrates enhanced convergence rates and accuracy compared to traditional ML approaches. The research framework is grounded in comprehensive correlation evaluations, which inform parameter selection and model architecture. Extensive comparisons reveal that the IHML model significantly outperforms individual ML models in forecasting soil moisture with higher precision. These findings highlight the potential of IHML models to advance smart farming practices and enable precise irrigation management, paving the way for improved crop yield and sustainable agriculture.

Performance Analysis of a Swastika shaped MIMO Antenna for Wireless Communication Applications

Polavarapu Sushma Chowdary — 2025-02-02

A novel wideband 4-port Multiple-Input Multiple-Output (MIMO) circular patch antenna is presented for operation in a resonant frequency band from 1.76 to 9.06 GHz used for PCs, Wireless Local Area Networks (WLANs) and satellite applications. The structure of the design consists of microstrip fed slot antenna considered on the four corners of the FR-4 substrate. To reduce mutual coupling and enhance the parameters of the MIMO antenna, a swastika-shaped ground is embedded. The simulated and experimental results show that the design achieves good performance, isolation >15 dB and radiation efficiency greater than 85%. The radiation patterns, surface currents, gain and channel capacity are also investigated. This circular shaped patch antenna is applicable to WLAN and satellite communication applications. Each circular patch on the four corners is connected to a 50 Ω microstrip feedline. A ring slot is etched on the circular patch and a small rectangular slot is embedded to the etched circular patch. The design provides good radiation patterns and the operating bands are obtained at 1.76 GHz, 2.4 GHz, 3.5 GHz and 9.06 GHz attaining reflection loss of -16 dB, -22 dB, -24 dB, -15 dB. The structure of the proposed design is simulated and evaluated in High-Frequency Structure Simulator (HFSS) software. The MIMO structure is fabricated on the FR-4 substrate. The minimum proximity between simulated and measured results is observed.

Event Detection and Classification in Tweets using Deep Learning

Malika Noui — 2025-02-02

Online social networks have become important sources of information and contextual data in all areas of life, including finance, elections, social events, health, sports, etc. Recently, the detection and classification of useful events presented in tweets has attracted a lot of interest. However, due to the inherent challenges associated with the nature of the events to be detected or classified, traditional approaches have not yielded satisfactory results. The use of deep learning-based text word embedding representations, such as Word2Vec, GloVe, FastText, and BERT, has shown significant efficacy in improving detection performance by considering the semantic context. This study proposes a model that uses an LSTM stacked on top of BERT representations to effectively detect and classify events in tweets. To this end, a dataset of about 310,000 event-related tweets has been collected and categorized into 50 event types based on a selected set of representative keywords. Multiple experiments were carried out on the collected dataset to evaluate the performance of the proposed model. The proposed model attained an overall accuracy greater than 94.3% and an F1 score of more than 90%, achieving state-of-the-art results in the classification of most of the event categories.

An Enhanced Document Source Identification System for Printer Forensic Applications based on the Boosted Quantum KNN Classifier

Shahlaa Mashhadani — 2025-02-02

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing characteristics effectively. This study proposes leveraging quantum-inspired computing to improve KNN classifiers for printer source identification, offering better accuracy even with noisy or variable printing conditions. The proposed approach uses the Gray Level Co-occurrence Matrix (GLCM) for feature extraction, which is resilient to changes in rotation and scale, making it well-suited for texture analysis. Experimental results show that the quantum-inspired KNN classifier captures subtle printing artifacts, leading to improved classification accuracy despite noise and variability.

Investigation of the Gaussian Process with Various Kernel Functions for the Prediction of the Compressive Strength of Concrete

Hoang Ha — 2025-02-02

The Compressive Strength of Concrete (CSC) is a critical parameter for evaluating the quality of concrete used in various construction projects, including buildings, bridges, and roads. The primary objective of this study is to examine the efficacy of a Gaussian Process (GP) Machine Learning (ML) model employing two kernel functions: Radial Basis Function (RBF) and Polynomial (POL), for predicting the CSC, considering readily quantifiable parameters. Based on these kernel functions, two models were created for this prediction, GP-RBF and GP-POL. The modeling process employed a total of 369 concrete sample data, including compressive strength values and eleven other physico-mechanical properties, collected from the Cua Luc bridge project in Vietnam. This dataset was partitioned into a training set (70%) and a testing set (30%) for model training and validation. Various validation metrics, including R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the models. The findings of this study demonstrated that both models GP-RBF and GP-POL exhibited strong performance in predicting CSC, with GP-POL demonstrating marginal superiority over GP-RBF. Consequently, it can be concluded that POL is more efficacious than RBF in training the GP model for CSC prediction.

Evaluating the Impact of Weighting Methods on the Stability of Scores for Alternatives in Multi-Criteria Decision-Making Problems

Nguyen Thi Dieu Linh — 2025-02-02

Criteria weights play a crucial role in Multi-Criteria Decision Making (MCDM) problems when selecting the best alternative from a set of options. This study aims to compare three objective weighting methods: MEthod based on the Removal Effects of Criteria (MEREC), Entropy, and Symmetry Point of Criterion (SPC). These methods were applied to a case study involving the ranking of eight sustainable energy development alternatives, each characterized by seventeen criteria. Four representative MCDM methods, the Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Proximity Indexed Value (PIV), and Root Assessment Method (RAM), were also deployed. The results revealed that the Entropy method provided the most stable and consistent performance, followed by the MEREC method, with the SPC method showing the least stability.

Repair and Strengthening of Cantilever Continuous Bridges using External Prestressed Cables: The Case Study of the Tan De Bridge in Vietnam

Dac Duc Nguyen — 2025-02-02

Continuous cantilever bridges utilizing cross-sectional box girders have been widely used worldwide, as well as in Vietnam, with outstanding advantages, such as the ability to cover large spans, good torsional resistance, high stability, and low maintenance costs. The arrangement of the prestressed cables in the span can use all internal cables (cables arranged in a box cross-section) or a combination of internal wires and external cables (cables arranged in a box cavity). The arrangement of the external cables helps reduce the cross-sectional area and self-weight of the span, leading to savings in foundation costs and an increase in the ability to exceed the span. However, the external cables degrade and become damaged. Therefore, repair and strengthening solutions are required to ensure the longevity of the project. This study focused on researching solutions to restore and enhance span structures using external prestressed cables through specific projects implemented in Vietnam.

Properties of GFRP Bars Subjected to High Temperature

Maan Hatam Saeed — 2025-02-02

This study evaluates the properties of Glass Fiber Reinforced Polymer (GFRP) bars exposed to high temperatures. An experimental program was carried out, which investigated 30 samples burned at different temperatures, 300 °C, 500 °C, and 700 °C, and compared them with additional unburned samples. The chosen parameters in this study consist of the concrete cover thickness and the burning temperature. The experimental results demonstrated that at a temperature of 300 °C, burning did not significantly affect the tensile strength of the covered samples, as it exhibited a decrease between 0% and 7%. In contrast, at a temperature of 500 °C, burning significantly influenced the specific samples’ tensile strength, as its decrease ranged between 0 and 30%. At 700 °C, burning substantially impacted the covered samples’ tensile strength, causing a reduction ranging from 2% to 58%, contingent on the concrete cover thickness. It was generally observed that the samples’ tensile strength decreased as the burning temperature increased, and that although significant alterations in the tensile characteristics of the uncoated GFRP bars were noted at 300 °C, the critical threshold for the coated GFRP bars was identified around 500 °C.

Design of a Miniaturized Dual-Band Antenna using Slotted Techniques for 2.45/5.8 GHz Microwave Band RFID Utilizations

Younes El Hachimi — 2025-02-02

In this study, a compact dual-band antenna for Radio Frequency Identification (RFID) readers operating at 2.45 GHz and 5.8 GHz is proposed. It is mounted on a FR4 substrate with a dielectric permittivity of 4.3. The proposed structure occupies a total area of 728 mm², with dimensions of 28×26 mm². The antenna features a rectangular radiating patch with integrated slots to enhance performance. A precisely tuned rectangular transmission line feeds the antenna, ensuring efficient signal coupling. The ground plane design incorporates a geometry derived from the slot technique, consisting of a rectangular loop, a square loop, and an attached rectangular element. This slot-based approach facilitates antenna miniaturization and enables the generation of two distinct resonant frequencies, significantly improving overall performance. The antenna was designed, simulated, and analyzed using the CST Microwave Studio (CST MWS) tool. The resulting structure is compact and straightforward, exhibiting suitable gain, coherent radiation patterns, and efficient impedance matching. Its design allows easy integration into handheld RFID readers operating in the microwave band.

Performance Analysis of Ronier Fibers (Borassus Aethiopum) with Silica Fume on the Mechanical Properties of Concrete

Saint Jacques Le-Majeur Mandelot-Matetelot — 2025-02-02

This research examines how Silica Fume (SF) and Ronier Fibers (RF) (Borassus aethiopum) affect concrete's mechanical and durability properties. Natural fibers are sustainable and have the potential to improve concrete performance. Thus, their inclusion into concrete has attracted considerable research. This study used SF as an additional cementitious material at a replacement rate of 10%. RF were utilized at 0.5%, 1%, and 1.5% by weight of cement, including Untreated (UN) and Treated (TRT) forms. An alkali treatment was utilized to increase the adherence of TRT fibers to the cement matrix. In addition to the durability traits, like Water Absorption (WA) and resistance to chemical attack, the mechanical qualities, including compressive strength, tensile strength, and flexural strength, were measured. The findings showed that while SF increased the composite's strength and durability, the addition of RF, especially in the TRT form, significantly increased the concrete's tensile and flexural strengths. The ideal mechanical strength-to-durability ratio was found to be 1% TRT fiber and 10% SF content. Moreover, fiber treatment strengthened the fiber-matrix bond by decreasing the WA and enhancing the resilience to harsh environmental factors. This study’s results revealed that using SF along with RF offers a viable homogenous and compact concrete matrix, making it appropriate for use in environmentally friendly construction projects.

Enhancing Ad Hoc Network Security using Palm Vein Biometric Features

Abdelnasser Mohamed — 2025-02-02

This study proposes an innovative approach to securing ad hoc networks through palm vein biometric authentication, addressing critical security vulnerabilities in decentralized wireless communications. The research introduces an Adaptive Fusion Biometric Key Generation (AFBKG) framework that seamlessly integrates palm vein biometric features with state-of-the-art cryptographic protocols. The methodology implements a comprehensive six-stage process, incorporating Near-Infrared (NIR) imaging at 850 nm wavelength, advanced image preprocessing techniques, and deep learning-based feature extraction using a fine-tuned Convolutional Neural Network (CNN), culminating in a robust 512-dimensional feature vector. A rigorous performance evaluation was conducted, which demonstrated exceptional results, achieving 98% authentication accuracy with a 0.1% False Acceptance Rate (FAR) and 95% spoofing resistance. The AFBKG algorithm significantly outperforms traditional security methods, demonstrating 95% authentication strength and 92% resistance to Man-in-the-Middle (MITM) attacks while maintaining minimal key management complexity (15%). The system's superior scalability (90%) and computational efficiency (10% overhead) compared to conventional biometric approaches are noteworthy. These findings establish palm vein biometric authentication as a cutting-edge solution for enhancing ad hoc network security, offering substantial improvements over traditional password-based systems and alternative biometric methods.

Sentiment and Emotion Modeling in Text-based Conversations utilizing ChatGPT

Pradeep Mullangi — 2025-02-02

Emotional Intelligence (EI) constitutes a vital element of human communication, and its integration into text-based dialogues has gained great significance in the modern digital era. The present paper proposes an innovative method for modeling sentiment and emotion within text-based conversations using the ChatGPT language model. The advancements in sentiment and emotion recognition are centered on the role of EI in text-based conversational models. The study underscores the significance of diverse datasets, including Interactive Emotional Dyadic Motion Capture (IEMOCAP), MELD, EMORYNLP, and DAILYDIALOG, for training and evaluating emotion detection algorithms. IEMOCAP and MELD offer detailed emotional annotations, EMORYNLP emphasizes sensitive dialogue scenarios, and DAILYDIALOG encompasses a wide range of everyday interactions, providing distinct advantages for capturing emotional subtleties. The proficiency of different emotion categorization models, including ChatGPT and models with four levels of detail, is demonstrated through their capacity to understand and respond to emotions aptly. The crucial role of conversational AI with sophisticated EI in fostering empathy and context-sensitive interactions is emphasized.

Vehicle Body Vibration and Noise Impact on Driver and Passengers Analysis: The Case of the 29-Seat Thaco Garden TB79s Bus

Vu Hai Quan — 2025-02-02

Noise, Vibration, and Harshness (NVH) are critical factors influencing the comfort and satisfaction of vehicle occupants. NVH encompasses the sound levels generated during vehicle operation, the vibrations transmitted through the vehicle structure, and the perceived harshness of the ride quality. This study focuses on the NVH analysis of the Thaco Garden TB79_S, a 29-seat passenger vehicle. The Finite Element Method (FEM) is applied to simulate the impact on the vehicle as it traverses two types of road surfaces, characteristic of Vietnam: rumble strips with varying frequencies and uneven roads with random excitation. The results reveal that the Sound Pressure Level (SPL) is within the frequency range from 0 to 200 Hz while the vehicle navigates on rumble strips. Further analysis of the vehicle's body and the air mass within the cabin identifies a resonance phenomenon at the driver's ear location at 176 Hz, resulting in a maximum SPL of 166.92 dB. Additionally, the study examines the noise formation within the cabin during a 25-second period as the vehicle travels over random road surfaces. The maximum sound pressure at the driver's position reaches 0.24 psi, with the SPL fluctuating primarily between 130 and 155 dB.

Assessing the Mechanical Properties of Open-Graded Asphalt Mixtures

Shams Ali Ahmed — 2025-02-02

In consideration of the escalating vehicular intensity and the substandard material properties of pavements in Iraq, particularly with regard to their impact on wet-weather accident rates and noise pollution in urban areas, there is an urgent need for an analysis of Open-Graded Asphalt (OGA) mixes to address the environmental and safety concerns. While OGA mixtures offer the dual advantages of reducing stormwater runoff and enhancing wet skid resistance, they are also more prone to raveling due to their high porosity. To enhance the performance of OGA mixes, various methods have been employed, including the incorporation of recycled polymers. The primary objective of this research is to evaluate the durability and strength properties of OGA mixes through laboratory testing using the Recycled Polyvinyl Chloride (RPVC) polymer. Laboratory tests were conducted on OGA mixes to ascertain the Marshall stability, resistance to abrasion, permeability, tensile strength, and moisture-induced damage. The mix designs were executed in accordance with the design procedure proposed by the National Cooperative Highway Research Program (NCHRP) for a range of 5.5%–7.0% asphalt content. RPVC was used in various proportions (2%, 4%, 6%, and 8%) by the weight of the base binder. The experimental findings demonstrated that the incorporation of RPVC led to enhanced Marshall stability and Indirect Tensile Strength (ITS) in porous asphalt concrete, surpassing the performance of conventional asphalt mixes. Additionally, the OGA mixture exhibited significant improvements in raveling resistance and moisture susceptibility. The study concluded that the Optimal Binder Content (OBC) of RPVC could enhance the pertinent engineering properties of OGA mixtures without compromising their permeability.

Effective Diabetes Prediction using an IoT-based Integrated Ensemble Machine Learning Framework

Rashi Rastogi — 2025-02-02

Diabetes, a prevalent chronic disease, affects a significant global population. Identifying and being aware of key variables promptly may substantially enhance results for both patients and public health efforts. Systematic methods such as monitoring diabetic patients allow the collection of extensive data from diabetic patients. When it comes to keeping track of a patient's health, IoT sensors, such as those used in diabetic patient monitoring systems, are invaluable. Blood glucose levels, body temperature, and location of a diabetic patient can be tracked and recorded through a monitoring device. In addition to monitoring patients, these data can be classified using Machine Learning (ML) methods. This study applies three ML models to three different diabetes datasets and analyzes their performance. According to the results, the fine-tuned random forest model achieved higher accuracy, i.e., 89%, 90%, and 99%.

Enhancing Cloud Data Center Security through Deep Learning: A Comparative Analysis of RNN, CNN, and LSTM Models for Anomaly and Intrusion Detection

Shimaa A. Ahmed — 2025-02-02

Cloud data centers form the backbone of modern digital ecosystems, enabling critical operations for businesses, governments, and individuals around the world. However, their high connectivity and complexity make them prime targets for cyberattacks, leading to service disruptions and data breaches. This paper investigates the use of deep learning techniques, namely Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance cloud data center security. By employing these models for anomaly detection and intrusion prevention, the study performs a comparative analysis. The results indicate that the LSTMs achieved the highest ROC AUC score (0.90), demonstrating better detection of persistent threats. These findings highlight the potential of deep learning to revolutionize cloud security by providing scalable, accurate, and proactive measures against evolving cyber threats.

Residual Stress and Distortion Analysis for TMCP Steel Grade EH36 Butt Welding Parts in GTAW-SMAW Hybrid Welding Process using Finite Element Method

Sumeth Nuchim — 2025-02-02

The present work evaluates residual stress and distortion in Thermo-Mechanical Control Process (TMCP) grade EH-36 steel plates subjected to hybrid Gas Tungsten Arc Welding (GTAW) and Shielded Metal Arc Welding (SMAW) processes Specimens measuring 650 × 170 mm with a thickness of 12 mm were utilized. Finite Element Method (FEM) analysis was employed to model residual stress and distortion, a critical step in optimizing the manufacturing process of mechanical structures and parts in shipbuilding. The FE model was developed using ANSYS software incorporating a heat source model with a user-defined subroutine to represent an ellipsoidal moving weld torch with front and rear power density distribution. Heat losses due to radiation and convection were accounted for, while mechanical boundary conditions were applied to restrict rotation and displacement but allow material deformation. Thermal analysis demonstrated close agreement between experimental thermocouple data and numerical simulations, with a temperature deviation of only 5%. Residual stress analysis using X-Ray Diffraction (XRD) revealed that ultrasonic stress relief reduced the maximum residual stress from an average of 193.4 MPa to 39.1 MPa Distortion analysis showed that the maximum FEM deformation was 0.2873 mm, with a 12% deviation from coordinate measuring machine (CMM) results, while the minimum FEM deformation was 0.031922 mm, differing by 3%. The larger deviation occurred in areas with peak distortion, attributed to variations in mechanical restraint positioning, which significantly influence material deformation during cooling.

Deep Learning-Driven Ontology Learning: A Systematic Mapping Study

Asma Amalki — 2025-02-02

Today, ontologies are the widely accepted framework for managing knowledge in a manner that supports sharing, reuse, and automatic interpretation. Ontologies are fundamental to various Artificial Intelligence (AI) applications, including smart information retrieval, knowledge management, and contextual organization. However, the rapid growth of data in various domains has made ontology acquisition and enrichment, time-consuming, labor-intensive, and expensive. Consequently, there is a need for automated methods for this task, commonly referred to as ontology learning. Deep learning models have made significant advancements in this field, as they can extract concepts from vast corpora and infer semantic relationships from wide-ranging datasets. This paper aims to explore and synthesize existing research on the application of deep learning techniques to ontology learning. To achieve this, a Systematic Mapping Study (SMS) was conducted, encompassing 2765 papers published between 2015 and September 2024, from which 47 research papers were selected for review and analysis. The studies were systematically categorized according to eight refined criteria: publication year, type of contribution, empirical study design, type of data used, deep learning techniques implemented, domain of application, focused ontology learning tasks, and evaluation metrics and benchmarks.

Enhancing Traffic Counting in Rainy Conditions: A Deep Learning Super Sampling and Multi-ROI Pixel Area Approach

Elly Warni — 2025-02-02

In Intelligent Transportation Systems (ITS), adaptive traffic control relies heavily on precise, real-time traffic data. Controllers use information such as vehicle count, vehicle density, traffic congestion, and intersection wait times to optimize traffic flow and improve efficiency. Traffic cameras collect and process this data, but environmental factors like rain can degrade the performance of data retrieval systems. We propose a vehicle detection method that integrates pixel area analysis with Deep Learning Super Sampling (DLSS) to enhance performance under rainy conditions. Our method achieved an accuracy of 80.95% under rainy conditions, outperforming traditional methods, and performing comparably to specialized methods such as DCGAN (93.57%) and DarkNet53 (87.54%). However, under extreme conditions such as thunderstorms, the method's accuracy dropped to 36.58%, highlighting the need for further improvements. These results, evaluated using the AAU RainSnow Traffic Surveillance Dataset, demonstrate that our method improves traffic data collection in diverse and challenging weather conditions while identifying areas for future research.

A Qualitative Approach for Enhancing Fundus Images with Novel CLAHE Methods

Vijaya Madhavi V. — 2025-02-02

Glaucoma is a progressive eye disease. This study presents a custom technique to enhance retinal fundus images to detect glaucoma. Contrast enhancement is a crucial stage in medical image analysis to improve the visual impression of diseases. CLAHE is a common technique to improve images. Clip Limit (CL) and subimages may restrict the potential benefits of the typical approach and pose difficulties. This study introduces Enhanced CLAHE and Automated CLAHE to address the shortcomings of the base method. These methods demonstrate progress in improving retinal landmarks in various ways by looking directly at the in-depth description of retinal images. The proposed methods, along with the baseline CLAHE, were compared using quality assessment tools such as the Peak-Signal-to-Noise Ratio (PSNR). The results help to determine the degree of contrast enhancement and the overall richness of the image.

Introduction to Predictive Maintenance Application using Machine Learning: The Case of the Injection System of a Diesel Engine

Zineb Znaidi — 2025-02-02

Diesel engines are crucially important in various fields, particularly in the automotive sector, as they ensure a reliable supply of mechanical energy. However, injection system failures, which are among the most recurrent failures, can lead to performance deterioration and increased pollutant emissions and maintenance costs. Therefore, adopting an effective maintenance strategy to analyze and predict such failures would significantly improve the efficiency of these engines. Based on collected data from engines by reliable sensors, the application of predictive maintenance coupled with a machine learning model allows effective prediction of failures for optimal appropriate maintenance. This study presents an approach to diagnosing the injection system of automotive diesel engines using a test bench based on data from temperature sensors installed on engine cylinders. These temperature data exhibit unusual variations in the event of an injection system failure. The Random Forest (RF) algorithm was employed to analyze these data and establish a clear relationship between cylinder temperatures and failure. The proposed model can detect failures associated with the injection system. Performance evaluation, particularly after parameter tuning, underscores the model's efficacy, achieving an accuracy exceeding 97%.

Project Delivery System Selection using the AHP Multi-Criteria Decision Making Method

Buroog Basheer Mahmood — 2025-02-02

This study aims to select the most suitable project delivery method for the implementation of construction projects in Iraq. The descriptive analytical approach was used to determine the importance of the criteria considered in this study, namely cost, project duration, and quality, according to owners, contractors, and consultants. A field study was used utilizing questionnaire survey and interviews to determine the degree of importance of these criteria. Then, data regarding 28 projects for the period from 2022 to 2024 were was collected to measure the performance of these criteria. Univariate data analyses were performed to assess the performance of Integrated Project Delivery (IPD) along with the deriving priority scales based on the Analytic Hierarchy Process (AHP) theory. The results of the AHP indicate that the IPD system ranked first with a preference rate of 34.5%.

A Control Strategy for Salient-Pole Permanent Magnet Axial-Gap Self-Bearing Motors Considering Stator Inductance Variation

Ngo Kien Trung — 2025-02-02

Axial-Flux Self-Bearing Motors (AFBMs) are distinguished by considerable variations in inductance resulting from axial displacement. Conventional controllers are frequently engineered to accommodate a wide range of inductance changes. Nevertheless, this methodology is often suboptimal, particularly when inductance is not accurately determined. This paper puts forward a novel inductance approximation function for the stator, designed to minimize modeling errors. Furthermore, an advanced control strategy is presented, based on a sliding mode controller with online adaptive parameter tuning. The experimental results demonstrate the efficacy of the proposed control strategy, exhibiting enhanced performance, stability, and robustness across a range of operating conditions.

Explainable Machine Learning Algorithms to Predict Cardiovascular Strokes

Afia Fairooz Tasnim — 2025-02-01

Cardiovascular disease has been more common throughout the past several decades. Cardiovascular disease detection methods use machine learning algorithms to assess data and provide accurate cardiac diagnosis. An accurate and comprehensive assessment of cardiovascular risk is essential to improve cardiovascular protection and reduce the frequency and severity of heart attacks and strokes. This paper proposes a machine learning-based autonomous strategy for the diagnosis of cardiovascular disease. Some preprocessing methods were applied to improve the results and accuracy. Finally, lazy prediction was used to find the best model by applying a neural network and two ensemble models. The best accuracy of 99% was obtained with the HistGradientBoosting (ensemble) classifier, which obtained respectable results with a higher accuracy rate. This model can enhance the ability to predict cardiovascular disease with better accuracy.

Analysis of the Seasonal Change on the Sediment Transport in the Primary Channel of Saddang Irrigation Area

Gatok Hasan Syamsuddin — 2025-02-02

Irrigation channels are a vital component of an agricultural system, functioning to distribute water to agricultural land and support food security. The Saddang irrigation area, one of the main irrigation regions in Indonesia, plays a crucial role in enhancing agricultural productivity. However, the sediment accumulation in irrigation channels can significantly impact water distribution efficiency and environmental quality. The current research aims to analyze the influence of flow parameters during the rainy and dry seasons, examine the relationship between seasonal changes and sediment transport, and assess the impact of sediment discharge in the primary channel of the Saddang irrigation area. The tests conducted included sieve analysis, specific gravity, sediment concentration, and hydrometer analysis. Based on the research results, the average sediment discharge during the rainy season in the primary channel of the Saddang irrigation area is 2,702 tons/day, while during the dry season, the average sediment discharge is 100 tons/day. The ratio of the average sediment discharge between the rainy and dry seasons is 27:1. Additionally, linear equations were obtained to predict sediment discharge in the primary channel of the Saddang irrigation area. For the rainy season, the linear equation used is y = 857.67x - 1618.3, while for the dry season, it is y = 38.811x - 79.937. These equations have a validation rate of 98% between the linear equation and the actual calculation results, indicating high accuracy in modeling sediment discharge in the channel.

Enhancing Network Access Control using Multi-Modal Biometric Authentication Framework

Abdelnasser Mohammed — 2025-02-02

This study presents an innovative multi-modal biometric authentication framework that integrates Deep Learning (DL) techniques with zero-trust architecture principles for enhanced network access control. The framework employs a three-tier fusion strategy (feature-level, score-level, and decision-level) incorporating facial, fingerprint, and iris recognition modalities. The system architecture implements a sophisticated multi-layered approach utilizing the ResNet-50 based Convolutional Neural Network (CNN) architecture for facial recognition, CNN-based minutiae extraction for fingerprint processing, and 2D Gabor wavelets with DL-based feature extraction for iris analysis. The experimental validation using established datasets, namely Labeled Faces in the Wild (LFW), CelebA, FVC2004, NIST SD14, CASIA Iris V4, and UBIRIS v2, demonstrates exceptional performance with 99.47% authentication accuracy, 0.02% False Acceptance Rate (FAR), and 0.15% False Rejection Rate (FRR). The framework resulted in a 68% reduction in fraudulent access attempts. It achieved a mean authentication time of 235 ms (SD=28 ms), representing a 45% improvement over traditional systems. The resource efficiency analysis showed significant reductions in system overhead: 32% in CPU utilization, 28% in memory consumption, and 45% in network bandwidth requirements. The scalability testing confirmed a linear performance scaling up to 100,000 concurrent authentication requests. The statistical test of significance through t-test confirmed the framework's significant improvements over existing solutions (p-value<0.001). This study establishes an effective framework to address network access control challenges across various sectors, particularly in high-security environments requiring robust authentication mechanisms.

Blockchain-enabled Secure Data Communication Protocols for 5G Networks

Mohanad Sameer Jabar — 2025-02-02

With the further expansion of 5G networks, a main priority continues to shift towards secure and efficient protocols for data transmission. Traditional 5G security mechanisms, such as 3GPP AKA protocols, have limitations in scalability, latency, and resilience against cyber threats, making them quite unsuitable for complex high-density 5G environments. This study proposes a Secure Blockchain-based Data Transmission Protocol (SBDTP) with the decentralized and tamper-resistant feature of blockchain, combined with a hybrid consensus mechanism driven by Proof of Stake (PoS) or Practical Byzantine Fault Tolerance (PBFT). In this respect, this study contributes to state-of-the-art research efforts in the field of enhancing data integrity, authentication, and confidentiality with reduced latency and energy consumption in 5G applications. Extensive simulations showed that SBDTP outperformed previous solutions by a large margin. This protocol reduces latency to 50-80 ms, increases throughput to 900 pps, allows up to 1000 nodes without performance degradation, and reduces energy consumption to 0.8 J per node. It also maintains a very close-to-perfection data integrity check rate of ~100% and a very minimal privacy loss rate of less than 1%, showing strong security that could serve well for real-time 5G applications such as IoT networks, autonomous vehicles, and smart cities. These results show that SBDTP offers an efficient and secure solution for data transmission over 5G networks, outperforming traditional and blockchain-based methods while fulfilling the tight requirements posed by next-generation networks. In the future, the protocol should be optimized for scalability, including further advanced privacy techniques to widen its adaptability to diverse 5G applications.

Production of Sustainable Foam Concrete using Foam Concrete Block Waste as Partial Cement Replacement

Ban Abdulkarim Salman — 2025-02-02

As the global population continues to grow, the cost and environmental challenges associated with traditional construction materials, like cement and river sand, are becoming increasingly significant. The present study examines an alternative environmentally friendly approach to typical building materials. It thus aims to evaluate the feasibility of using foam concrete block waste as a partial replacement for cement to create eco-friendly foamed concrete products. The experimental program involved the deployment of specific machines to prepare finely ground foam concrete waste with a particle size equivalent to that of cement. The replacement levels were 0%, 10%, 20%, and 30% by weight of cement. The effects of the different replacement ratios on the foamed concrete properties were investigated and compared with the control cement-foamed concrete.

Advancing Sentiment Analysis: Evaluating RoBERTa against Traditional and Deep Learning Models

Pongsathon Pookduang — 2025-02-02

This research evaluates the performance of various sentiment analysis models, including traditional machine learning approaches (Naive Bayes, KNN, CART), a deep learning model (LSTM), and the transformer-based model RoBERTa using an Amazon book reviews dataset. ROBERTa outperformed all other models, achieving an accuracy of 96.30% and an F1-score of 98.11%, underscoring its superior ability to process complex and semantically diverse textual data. Traditional models, while computationally efficient, demonstrated limitations in capturing nuanced textual relationships, and the LSTM model, although competitive, faced scalability challenges and overfitting issues. These results demonstrate how transformer-based architectures such as RoBERTa offer advantages in real-world applications, particularly in e-commerce and social media sentiment analysis. This study underscores the superior capabilities of RoBERTa for sentiment analysis, particularly in processing semantically diverse and context-rich textual data that traditional models struggle to capture. Future work will explore optimizing RoBERTa's computational efficiency and expanding its applications to multilingual and cross-domain sentiment analysis tasks.

A Numerical Study of the Behavior of Concrete Columns constructed with Recycled Aggregate reinforced with GFRP Rebars

Ahlam Sader Mohammed — 2025-02-02

This research presents the findings of a numerical simulation conducted using the ABAQUS/CAE Finite Element (FE) software, with the purpose of investigating the behavior of short concrete columns constructed using recycled aggregate reinforced by Glass Fiber Reinforced Polymer (GFRP) bars. The numerical validation technique included an analysis of the experimental data of twenty columns constructed utilizing recycled aggregate reinforced by steel or GFRP rebars. Additional aspects, such as the column length, acting as an indicator of column slenderness, and the configuration of the column section, were also investigated. The results revealed a significant correlation between the failure loads and axial displacement of the computational models and those derived from the experimental methods. It was found that the increase in the column length was inversely proportional to its load carrying capacity. The drop percentage in the load carrying capacity was 6% and 11% for columns with length of 1100 mm and 1500 mm, respectively, compared to the reference square column with 700 mm length. The drop percentage in the load carrying capacity was 6.9% and 12.7% for columns with lengths of 1100 mm and 1500 mm, respectively, compared to the reference circle column with 700 mm length.

Effect of Iron Loading on Quiescent Crystallization of Syndiotactic Polypropylene/Iron Composites

Naveed Ahmad — 2025-02-02

The present study investigates the crystallization kinetics of the syndiotactic polypropylene/iron (sPP/Fe) composites using the rheological and Differential Scanning Calorimetry (DSC) techniques to evaluate the impact of varying iron content. Rheology, which is particularly sensitive under slow crystallization kinetics, was employed to complement the widely used DSC method. The current study aimed to integrate the aforementioned approaches to provide a comprehensive understanding of how the iron content influences the crystallization behavior of the sPP composites. Non-isothermal and isothermal crystallization experiments revealed that the increasing iron content significantly enhanced the crystallization and melting temperatures, indicating improved thermal stability and crystallinity. The rheological measurements, carried out using an Atomic Rheometric Expansion System (ARES), demonstrated higher sensitivity than the DSC at low iron concentrations, providing a more precise detection of crystallization kinetics. The results showed excellent agreement between the two techniques, confirming the robustness of rheology as a complementary method. This study underscores the importance of the iron content in tailoring the thermal and mechanical properties of sPP composites and highlights the value of integrating rheological methods with traditional thermal analysis for polymer characterization.

Determination of Best Input Parameters for Internal Grinding SKD11 Tool Steel using MCDM

Anh Tuan Nguyen — 2025-02-02

This article presents the results of an optimization study on the determination of the best input parameters for the internal grinding process when processing cylindrical-shaped parts of SKD11 tool steel. For this purpose, three Multi-Criteria Decision Making (MCDM) methods including the Evaluation based on Distance from Average Solution (EDAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and Multi Attribute Utility Theory (MAUT) were used to solve the MCDM problem, while the entropy method was applied to find the criteria weights. Three objectives, namely the Surface Roughness (SR), Material Removal Rate (MRR), and wheel life (Tw), were also investigated. Six input factors, involving the coarse dressing depth (a_r), coarse dressing times (n_r), fine dressing depth (a_f), fine dressing times (n_f), non-feeding dressing (n₀), and dressing feed rate (S_d), were examined. Additionally, the Taguchi method with the L16 (4⁴+2²) design and the Minitab R19 program were deployed to design this study’s experiment and investigate its outcomes. The MCDM work was successfully solved and the best process factors are proposed.

Analysis of Factors affecting Construction Project Tender Winning in Small Qualification Contractors

Rosmariani Arifuddin — 2025-02-02

Finding employment in the construction sector almost always involves a tender process, which is essential for the construction entrepreneurs because the company's continuity depends on it. Tender qualifications and requirements must fulfill several stages, namely the administrative, qualification, technical, and price evaluation. The purpose of this research is to predict the relationship between certain variables. The Partial Least Square (PLS) method is deployed since it can directly analyze the latent variables, indicator variables, and measurement errors and determine the complexity of the relationship between several variables and their indicators. Eight main factors were successfully obtained: Regulations, Company Qualifications, Administration, Equipment Resources, Human Resources, Construction Safety Plan, Financial, and Technology and Information Systems. Using the SEM-PLS also resulted in estimating the significance value of the factors influencing the highest tender winner determinants: Human Resources obtained a value of 84.5% and the Financial variable attained a value of 82.3%. This research is expected to assist contractors in getting acquainted with the level of significance of the tender winners’ determinants for the construction projects. Contractors can also prepare and improve the Human Resources factor by conducting advanced training, certification, and competency development that supports its technical capabilities. Contractors need to obtain a deep understanding of the tender requirements, and with a high experience level they can make more appropriate and competitive offers.

Usage of Internet of Things in Iraqi Higher Education: An Extension of Information System Success Model

Hayder Salah Hashim — 2025-02-02

The usage of Internet of things (IoT) in higher education is still emerging especially in developing countries. The purpose of this study is to examine the information and the system and service quality on the Usage of IoT (UIoT) among students and academic staff and non-academic staff. The study, based on Information System Success model (ISS), proposes that Information Quality (IQ), System Quality (SYSQ), and Service Quality (SQ) have a positive impact on UIoT. The research further proposes that IoT awareness acts as a moderator. The data were collected with a use of a questionnaire. Stratified random sampling was used and the data collected from a sample of 423 participants completed a process of validation and pilot testing. The data analysis was conducted using Smart PLS 4. The findings of the study indicate that SQ, IQ, and SYSQ do have positive effects on UIoT. IoT awareness moderated the effect of IQ only on UIoT. To increase the UIoT, it is advised to focus on enhancing the awareness about the IoT and provide reliable information.

A Multidimensional Approach for Formal Modeling and Analyzing Medical Cyber-Physical Systems

Ayoub Bouheroum — 2025-02-02

The combination of integrated software controlling devices, networking capabilities, and sensing/actuation technologies in Medical Cyber-Physical Systems (M-CPS) highlights some specific research challenges. The major challenge is to formally ensure the confidentiality of the data or resources they handle. This study tackles this problem by proposing a formal approach that combines CA-BRS (Control Agent and Bigraphical Reactive Systems) and BPMN (Business Process Model Notation) to specify and analyze CPS in general, while respecting several dimensions. The structural dimension of the CPS, representing the space (physical and cyber entities) in which agents exist and interact, is defined with BRS. Control agents constitute the virtual dimension and observe and control the physical and cyber entities of their environment. The complex and adaptive behavior of CPS (behavioral dimension) is defined through several types of rules, each managing a possible evolution of a CPS component (physical, cyber, or virtual). Two distinctive perspectives are associated with the semantic interpretation of these rules: the states perspective and the activities perspective. This study focuses on the activities perspective that specifies the behavior of control agents with a BPMN activity diagram. This highlights how these two models (CA-BRS and BPMN) complement each other to assist the designer in defining formal models for CPS. Additionally, it reveals how to provide the CA-BRS model with means to control unauthorized access to an electronic health record system.

Application of Multi Criteria Decision Making Methods for the Determination of the Best Dressing Factors for Surface Grinding Hardox 500

Le Duc Bao — 2025-02-02

This study applies Multi-Criteria Decision-Making (MCDM) methods to identify the optimal dressing parameters for the surface grinding of Hardox 500 steel. The investigation focuses on three key objectives: Surface Roughness (SR), Material Removal Rate (MRR), and Wheel lifespan (L_w). Five dressing variables were considered: non-feeding dressing (n_n), fine dressing depth (d_f), fine dressing times (n_f), rough dressing depth (d_r), and rough dressing times (n_r). Three MCDM methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Simple Additive Weighting (SAW), and Evaluation based on Distance from Average Solution (EDAS)—were employed to solve the MCDM problem. Additionally, the Entropy technique was used to determine the criterion weights. A total of 16 experimental runs were conducted based on the L16 (4⁴ x 2¹) design configuration. The analysis identified Option 7 as the optimal dressing mode, characterized by the input parameters: d_r = 0.02 mm, n_r = 3 times, d_f = 0.05 mm, n_f = 3 times, and n_n = 0. To validate the consistency of rankings obtained from the three MCDM methods, the Spearman’s rank correlation coefficient (R) was employed. The results demonstrated a strong correlation among the rankings, confirming the reliability of the proposed approach. These findings provide a robust framework for optimizing surface grinding parameters to enhance performance and productivity.

Vernacular Materials for Thermal Comfort

Ruma Kalla — 2025-02-02

This study proposes Dulmera sandstone cooling tiles as a novel solution to the prevalent heat absorption problems in building construction, particularly in hot climates, such as that of Bikaner, Rajasthan. The objective is to enhance building energy efficiency and indoor comfort while promoting sustainable practices. The development of Dulmera slim tiles uses the natural cooling properties of Dulmera sandstone, with research focusing on its heat absorption and emission characteristics. This approach integrates traditional knowledge with modern manufacturing techniques, addressing heat-related building challenges. The findings indicate that these tiles significantly reduce heat absorption and emission, leading to decreased reliance on energy-intensive cooling systems, lower electricity consumption, and reduced greenhouse gas emissions. Additionally, the tiles enhance indoor comfort, boost occupant well-being, and improve productivity. The innovation's distinctiveness stems from its incorporation of locally available materials and state-of-the-art manufacturing methodologies, providing a culturally sensitive and sustainable solution that safeguards Rajasthan's architectural heritage. Consequently, Dulmera sandstone cooling tiles emerge as a valuable and scalable solution for analogous climates worldwide, fostering resilience and environmental consciousness in the built environment.

A Deep Ensemble Gene Selection and Attention-guided Classification Framework for Robust Cancer Diagnosis from Microarray Data

Sara Haddou Bouazza — 2025-02-02

Microarray technology has enabled unprecedented insight into cancer diagnosis through large-scale gene expression analysis. However, the high dimensionality and complexity of microarray datasets pose significant challenges, as only a small subset of genes is typically informative, with the remainder introducing noise and complicating classification. Traditional gene selection methods, including filter, wrapper, and hybrid techniques, have achieved promising results but often fail to capture complex gene interactions, suffer from computational inefficiencies, or lack interpretability. This study presents DEGS-AGC (Deep Ensemble Gene Selection and Attention-Guided Classification), a novel integrated framework for gene selection and classification. DEGS-AGC is designed to address these limitations through two primary components: Deep Ensemble Gene Selection (DEGS), which leverages ensemble learning with Random Forest, XGBoost, and Deep Neural Networks to select relevant genes while reducing redundancy via sparse autoencoders, and Attention-Guided Classification (AGC), where an attention mechanism dynamically assigns weights to genes to improve interpretability and classification precision. The DEGS-AGC framework was evaluated against traditional methods, using consistent classification models for robust comparisons. Evaluation metrics demonstrated the potential of DEGS-AGC as an effective tool for high-dimensional biomedical data analysis. The results highlighted the ability of DEGS-AGC to offer accurate, interpretable, and computationally feasible solutions for cancer diagnosis, advancing the development of data-driven personalized approaches in healthcare.

Enhancing the RC4 Algorithm by Eliminating the Initiative Vector (IV) Transmission

Waleed Abdelrahman Yousif Mohammed — 2025-02-02

The Rivest Cipher RC4 encryption algorithm is commonly utilized to generate keys of varying lengths. Despite its rapid processing speed, vulnerabilities within the algorithm have made it susceptible to exploitation, allowing attackers to compromise it within a matter of minutes. This paper introduces an innovative approach to address the vulnerabilities of the RC4 encryption algorithm by employing an Initiative Vector (IV). The proposed method incorporates a lengthy random text without transmitting an initialization vector. The proposed solution was rigorously validated, demonstrating performance comparable to existing solutions while simultaneously expanding the range of potential solutions and mitigating security threats. Further exploration into the use of a complex equation is recommended for calculating the swapping value j while maintaining the same high level of performance.

A Study on the Nonlinear Characteristics of Hydro-Pneumatic Suspension Systems for Mining Dump Trucks

Le Xuan Long — 2025-02-02

In the current study, nonlinear characteristics of Hydro-Pneumatic Suspension (HPS) systems for mining dump trucks are proposed and analyzed for the ride comfort of off-highway vehicles. To analyze these characteristics, a mathematical HPS model was used to determine the vertical elastic and damping forces. Then, a two-degrees-of-freedom (2-DOF) quarter-vehicle dynamic model of a mining dump truck was proposed to analyze the nonlinear characteristics of the HPS systems implemented in a MATLAB/Simulink environment under low-frequency excitations of road surfaces. An experiment was set up to measure the vibration accelerations at the upper and lower positions of the HPS systems to verify the proposed HPS mathematical model. The experimental and simulation results demonstrated that both the time- and frequency-domain accelerations were consistent with the laws of physics and exhibited errors within acceptable ranges, thereby demonstrating the reliability of the proposed mathematical model. The simulation results showed that the elastic force increased rapidly during the compression process and increased slowly during the rebound process, whereas the damping force increased very slowly during the compression process but increased rapidly during the rebound process owing to the effect of the backflow valve. The results of the force characteristic curve analysis of the HPS systems with different excitation frequencies also revealed that when the vibration excitation frequency increased, the elastic, damping, and vertical total forces of the front and rear HPS systems increased quite rapidly.

Phytochemical Analysis and Antioxidant Activity Assessment of Methanolic Extract from Jasmine Flowers

Amal H. Al-Bagawi — 2025-02-02

This study offers a comprehensive analysis of the phytochemical composition and antioxidant properties of the methanol extracts from Jasmine flowers. Employing a combination of advanced techniques, including Gas-Chromatography Mass-Spectrometry (GC-MS), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and antioxidant activity assays, the research uncovered key insights into the bioactive potential of Jasmine. The GC-MS analysis identified nine distinct compounds, including major constituents, such as 2-Phenylthiolane (44.12%), Cyclohexene, 3-ethenyl- (25.88%), Acetaldehyde (12.70%), and N-Methylallylamine (10.31%) among others. The FTIR spectra revealed significant functional groups, including O-H and C-C stretches, suggesting the presence of phenolic compounds. The SEM imaging highlighted the morphological changes in the Jasmine flower powder, showing expanded oil glands post-pre-treatment, which enhanced the oil extraction process. The methanol extract exhibited a strong antioxidant activity, as evidenced by the DPPH radical scavenging assay. These findings position Jasmine flowers as a promising natural source of phytochemicals, particularly antioxidants, with potential for further pharmacological and industrial applications. Future studies could focus on isolating and evaluating additional bioactive compounds for their therapeutic potential.

The Impact of Data Preprocessing Order on LASSO and Elastic Net Capabilities

Geneveive Yii Ven Tang — 2025-02-02

The Food Security Index (FSI) evaluates affordability, accessibility, utilization, and food availability. However, previous research on food security in Malaysia has primarily focused on production, neglecting a detailed analysis of economic factors. The Overnight Policy Rate (OPR), set by Bank Negara Malaysia (BNM), regulates economic activity by controlling the interest rate at which commercial banks borrow and lend overnight. This study explores the impact of data preprocessing sequences on the performance of LASSO and Elastic Net regression models in predicting Malaysia's FSI. Using macroeconomic data from 2010 to 2023, this study evaluates the effects of different sequences of outlier detection and missing data imputation. The findings reveal that the LASSO model achieves the highest accuracy and the lowest error rates with outlier detection performed after imputation. This study underscores the importance of preprocessing order in enhancing model reliability and provides insight into the economic factors that influence food security in Malaysia. The results show that OPR reduces Malaysia's FSI by 0.151 units, while inflation increases it by 0.022. The LASSO regression model offers a novel perspective on the economic factors influencing food security, providing a more comprehensive understanding of food security in Malaysia.

Enhanced Automatic License Plate Detection and Recognition using CLAHE and YOLOv11 for Seat Belt Compliance Detection

. Sutikno — 2025-02-02

Traffic accidents caused by seat belt violations remain a severe problem in low-income countries. Identifying the vehicles of these violators is vital for enhancing safety. Therefore, this research develops a vehicle license plate detection and recognition system to support this problem. The proposed system was divided into three subsystems: windshield detection, license plate detection, and character recognition. The windshield detection subsystem used the You Only Look Once (YOLOv11) model. License plate detection combined the determination of the Region Of Interest (ROI) and YOLOv11. Meanwhile, character recognition combined the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and YOLOv11. YOLOv11 is the latest version of YOLO, which is faster and more efficient than the previous version, and CLAHE enhances the contrast of the image dataset, improving its quality. The dataset was collected from highways and toll roads in Semarang, Indonesia. The test results for windshield detection showed that the YOLOv11n model produced higher precision and faster detection time than YOLOv11m and YOLOv8m. The test results for license plate detection showed that the proposed method achieved perfect precision and recall. Meanwhile, the test results for character recognition indicated that the proposed method produced higher precision and average precision than YOLOv11n alone. The proposed method can produce precision and average precision for character recognition of 0.922 and 0.931, respectively. This research can potentially be used for automatic and real-time identification of car license plates for violators who do not wear seat belts on the highway.

Design and Implementation of a ROLAP Cube in Scalable Distributed Data Structure

Amel Mechri — 2025-02-02

The Scalable Distributed Data Structure (SDDS) is a data model specifically designed for distributed environments. An SDDS file comprises records that are dynamically distributed across servers using an SDDS algorithm. A notable feature of SDDS is the removal of a centralized addressing component, simplifying client-server communication and reducing both the message count and data access time in distributed systems. This work also explores a Data Warehouse (DW) within a decision support system, where multidimensional data are represented as a cube and managed through Relational Online Analytical Processing (ROLAP). Although extensive research has been conducted in both the data warehousing and SDDS fields, no prior studies have combined these two areas. This paper introduces a novel approach to implementing a ROLAP cube within an SDDS using the Linear Hashing algorithm (LH*), which eliminates centralized addressing, enabling direct client-server communication and improving performance by reducing inter-site message exchanges. This work demonstrates the feasibility of this method and its positive impact on data processing efficiency in distributed systems.

Robust Direction-of-Arrival Estimation using improved Coprime Array for Wireless Communication Applications

K. S. Shashidhara — 2025-02-02

In wireless communication systems, robust and accurate Direction-of-Arrival (DOA) estimation is essential for tasks such as beamforming and interference suppression. This research presents advancements in the Multiple Signal Classification (MUSIC) algorithm leveraging enhanced coprime sensor arrays for DOA estimation. Coprime arrays, characterized by their non-uniform spacing derived from coprime integers, offer superior angular resolution compared to traditional uniform arrays. By exploiting this unique array geometry, the proposed method enhances the spatial localization capabilities of the MUSIC algorithm, thereby improving signal detection and mitigation of interference. Experimental validation demonstrates the efficacy of the approach in various signal environments, highlighting its potential to enhance the performance and reliability of wireless communication systems.

Impact of Change Orders on Cost Overruns and Delays in Large-Scale Construction Projects

Jatiaryo Sidiq Ramadhan — 2025-02-02

This study investigates the impact of Change Orders (CO) on construction project performance, focusing on cost overruns and project delays. Partial least square structural equation modeling was used to analyze the relationships between key causal factors, including design changes, planning errors, and project outcomes. Data were collected from 127 construction practitioners involved in large-scale projects managed by PT XYZ, a leading Indonesian contractor. The analysis identifies that design changes contribute to 56.5% of cost overruns and 40% of project delays, while planning errors account for 34.5% of cost overruns and 23.1% of delays. These findings highlight the critical importance of improving project planning accuracy and enhancing design management processes to reduce the adverse effects of CO. Structured protocols for managing CO, better coordination among stakeholders, and adopting advanced technologies are recommended to minimize their effect. These insights are particularly relevant for large-scale projects where CO frequently disrupt budgets and timelines. By addressing these issues, project managers can enhance overall performance and reduce risks associated with cost and time escallations. This research provides practical strategies applicable to various construction contexts, supporting more efficient project delivery and better management of CO.

Long-Term Monitoring of Cable Tension Force in Cable-stayed Bridges using the Vibration Method. The Case Study of Binh Bridge, Vietnam

Ha Hoang — 2025-02-02

The tension force of a cable is an important parameter used to ensure the stable and safe working of cable-stayed bridges. This parameter needs to be strictly controlled throughout the operation of the bridge by installing direct force measurement sensors or indirect determination methods through analyzing vibration data of the oblique cables. This article introduces a method for predicting the cable tension value of a cable-stayed bridge using vibration-based data ready for application to in-service cable-stayed bridges. A method is proposed for calculating the effective length of stay cables while considering the influence of damping devices to improve the accuracy of cable tension assessment. The database to verify the model is a series of data on vibration testing in 32/80 cables of the Binh Bridge (Hai Phong) through inspections from 2007 to 2019. The bridge was damaged in 2010 and was completely repaired in 2012. The tension force monitoring during the damaged event, compared with that before and after the repair gives realizable results. The tension force was reduced over time and was recovered after the repair. Using vibration data to estimate the tension force is a fast and cost-effective method that helps minimizing risks during the exploitation and operation of cable-stayed bridges.

Evaluating AES Security: Correlation Power Analysis Attack Implementation using the Switching Distance Power Model

Hassen Mestiri — 2025-02-02

Cryptographic circuits play a critical role in safeguarding confidential information and ensuring secure communication, contributing to the resilience of digital infrastructure under SDG 9 (Industry, Innovation, and Infrastructure). These circuits store encryption keys for the Advanced Encryption Standard (AES) algorithm, including AES-128, AES-192, and AES-256, which are widely used in applications such as online banking and secure messaging platforms. This paper examines the effectiveness of Correlation Power Analysis (CPA), a side-channel attack technique that exploits power consumption patterns in cryptographic circuits, to highlight the challenges of implementing secure encryption systems. The study illustrates the CPA attack procedure against AES implemented on the SASEBO-GII FPGA platform. Experimental results reveal that while the CPA attack based on the Hamming Weight (HW) power consumption model fails to extract the encryption key, the Switching Distance (SD) power consumption model successfully recovers the entire key with a 100% success rate using approximately 4000 power traces. These findings underscore the vulnerability of cryptographic circuits to advanced side-channel attacks and emphasize the need for robust countermeasures to ensure secure data protection, thereby advancing secure and sustainable digital environments under SDG 11 (Sustainable Cities and Communities).

A Deep Learning CNN-GRU-RNN Model for Sustainable Development Prediction in Al-Kharj City

Fahad Aljuaydi — 2025-02-02

This study introduces an advanced Deep Learning (DL) framework, the Convolutional Neural Network-Gated Recurrent Unit-Recurrent Neural Network (CNN-GRU-RNN). This model is engineered to forecast climate dynamics extending to the year 2050, with a particular focus on four pivotal scenarios: temperature, air temperature dew point, visibility distance, and atmospheric sea level pressure, specifically in Al-Kharj City, Saudi Arabia. To address the data imbalance problem, the Synthetic Minority Over-Sampling Technique was employed for Regression along with the Gaussian Noise (SMOGN). The efficacy of the CNN-GRU-RNN model was benchmarked against five regression models: the Decision Tree Regressor (DTR), the Random Forest Regressor (RFR), the Extra Trees Regressor (ETR), the Bayesian Ridge Regressor (BRR), and the K-Nearest Neighbors Regressor (KNNR). The models were evaluated using five distinct metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The experimental outcomes demonstrated the superiority of the CNN-GRU-RNN model, which surpassed the traditional regression models across all four scenarios.

Improving the Engineering Properties of Highly Expansive Soil by adding Psyllium Seed Biogel

Sondos Kareem Al-Mousawi — 2025-02-02

This study investigates the use of environmentally sustainable materials, particularly biopolymers, to enhance the engineering properties of expansive soils. Psyllium Seed (PS) biogel, added in four percentages, 0.4%, 0.8%, 1.2%, and 1.6% by dry weight of soil, was evaluated as a biopolymer additive for highly expansive soil. A series of tests were conducted on treated and untreated soil samples. The results revealed a slight decrease in the Atterberg limits with 1.6% PS biogel, where the liquid limit (LL) reached 75.3%, the plastic limit (PL) dropped to 32.65%, and the plasticity index (PI) was reduced to 42.65%. The swelling potential decreased significantly by 76% at 1.6% PS biogel. Compressibility improved with a 52.3% reduction in the compression index (Cc) and a 96% reduction in the recompression index (Cr) at 1.6% PS biogel content. The Unconfined Compressive Strength (UCS) increased, with the best improvement being observed at 0.8% PS biogel (81.6%), and continued enhancement with longer curing periods. Elasticity also improved, with the strain at failure increasing by 83.75% at 1.2% PS biogel. The SEM analysis confirmed that 0.8% PS biogel rearranged the clay particles and reduced voids, leading to enhanced UCS and reduced swelling. These findings highlight the PS biogel as an environmentally sustainable and effective material for improving the engineering properties of expansive soils.

Impact of Variable Speed Limits on Crash Frequency and Crash Rate: Stimulation by Flow Rate and Percentage of Heavy Vehicles

Atheer N. Al-Nuaimi — 2025-02-02

The current transportation system has faces a high number of crashes on road networks, often resulting in damages and human losses. According to WHO, these incidents cost most countries approximately 3% of their GDP. This underscores the need to address the weaknesses in road networks and provide efficient countermeasures to reduce these incidents and consequent losses. This study focuses on Variable Speed Limits (VSL) as a crucial factor that could influence road safety. To model the impact of VSL on the Crash Frequency (CF) and Crash Rate (CR), the researchers also considered other relevant traffic characteristics, such as the Percentage of Heavy Vehicles (PHV) and Average Daily Traffic (ADT). For the purposes of this research, the Mandali-Baqubah highway in Iraq was selected as a case study for the potential implementation of VSL. The statistical regression approach was utilized to model the Safety Performance Functions (SPF) for the road crash count. The findings of the current study revealed that the VSL are positively and significantly associated with CF and CR.

A Deep Learning with Metaheuristic Optimization-Driven Breast Cancer Segmentation and Classification Model using Mammogram Imaging

M. Sreevani — 2025-02-02

Cancer is the second leading cause of death globally, with Breast Cancer (BC) accounting for 20% of the new diagnoses, making it a major cause of morbidity and mortality. Mammography is effective for BC detection, but lesion interpretation is challenging, prompting the development of Computer-Aided Diagnosis (CAD) systems to assist in lesion classification and detection. Machine Learning (ML) and Deep Learning (DL) models are widely used in disease diagnosis. Therefore, this study presents an Optimized Graph Convolutional Recurrent Neural Network based Segmentation for Breast Cancer Recognition and Classification (OGCRNN-SBCRC) technique. In the preparation phase, images and masks are annotated and then classified as benign or malignant. To achieve this, the Wiener Filter (WF)-based noise removal and log transform-based contrast enhancement are used for preprocessing. The OGCRNN-SBCRC technique utilizes the UNet++ method for segmentation and the RMSProp optimizer for parameter tuning. In addition, the OGCRNN-SBCRC technique employs the ConvNeXtTiny Convolution Neural Network (CNN) approach for feature extraction. For BC classification and detection, the Graph Convolutional Recurrent Neural Network (GCRNN) model is used. Finally, the Aquila Optimizer (AO) model is employed for the hyperparameter tuning of the GCRNN approach. The simulation analysis of the OGCRNN-SBCRC methodology, using the BC image dataset, demonstrated superior performance with an accuracy of 99.65%, surpassing existing models.

Harnessing Deep Learning and Technical Indicators for Enhanced Stock Predictions of Blue-Chip Stocks on the Indonesia Stock Exchange (IDX)

Bernadectus Yudi Dwiandiyanta — 2025-02-02

Given the limitations of existing models in accurately predicting stock prices, particularly in emerging markets such as Indonesia, this study aimed to evaluate the effectiveness of deep learning models in forecasting stock prices using blue-chip company shares traded on the Indonesia Stock Exchange (IDX). The main focus lies in combining historical stock data with a series of existing technical indicators, optimizing their integration to improve prediction accuracy. The accuracy of this method is reflected in a comprehensive evaluation of model performance using robust metrics, including R², Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Empirical results show the superiority of models integrating technical indicators compared to models relying only on historical data. The LSTM model showed the most significant improvement, with R² for ASII stock jumping by 14.59% after incorporating technical indicators. The prediction accuracy of the GRU model for BBCA shares increased significantly, as shown by a decrease of 45.16% in MSE. These findings underscore the critical role of feature selection in developing prediction models. Integrating technical indicators with historical stock data increases prediction accuracy and provides additional tools for informed decision-making.

Hierarchical Deep Learning for Robust Cybersecurity in Multi-Cloud Healthcare Infrastructures

Tariq Emad Ali — 2025-02-02

Patient safety is in danger because healthcare networks are more susceptible to cyberattacks as they become more intricate and linked. By altering data transmitted between various system components, malicious actors can hack into these networks. As cloud, edge, and IoT technologies become more widely used in contemporary healthcare systems, this difficulty is predicted to increase. This study presents a Combined Hybrid Deep Learning Framework with Layer Reuse for Cybersecurity (CHDLCY) to address this issue. This system is built to detect malicious actions that modify the metadata or payload of data flows across IoT gateways, edge, and core clouds quickly and precisely. The CHDLCY's is a unique design demanding less training time, while bigger models at the core cloud profit from a cutting-edge layer-merging method. The core cloud model is partially pre-trained by reusing layers from trained edge cloud models, which drastically reduces the number of training epochs required from 35 to 40 to just 6 to 8. Thorough tests demonstrated that CHDLCY not only accelerates the training phase but also achieves remarkable accuracy rates, ranging from 98% to 100%, in identifying cyber threats. The proposed approach offers a significant improvement over previous models in terms of training efficiency and generalizability to new datasets.

Enhanced Prediction of Intensive Care Unit Length of Stay using a Stack Ensemble of Machine Learning Models

Ashok Kumar Tella — 2025-02-02

The Length of Stay (LoS) refers to the time between a patient's hospital admission and discharge. LoS is considered to increase as the complexity of the disease increases. A prolonged stay in the Intensive Care Unit (ICU) can consume clinical resources and be labor intensive. Models that correctly predict LoS are needed to help medical experts make better decisions. To define an ideal process system, healthcare models must consider the patient's condition, availability of beds, resources, etc. These predictions can also help insurance companies manage their budgets. Existing models deploy machines and deep learning techniques to predict LoS. However, there is a need for improvement, considering the features associated with the process. This study presents machine learning algorithms, such as SVM and a stack ensemble, with improved accuracy over existing models. Experiments were carried out on a benchmark dataset, MIMIC-III, specific to ICU patients. The SVM model achieved an accuracy of 93.88%, while the stack ensemble model showed an improved accuracy of 94.70%. The results show that combining machine learning models achieves better prediction rates, which helps healthcare professionals make better decisions.

Leveraging Community-based Approaches for Enhancing Resource Allocation in Fog Computing Environment

Alasef M. Ghalwah — 2025-02-02

Efficient resource allocation in fog computing environments is essential to address the increasing demand for high-performance and adaptable network services. Traditional methods lack granular differentiation based on traffic characteristics often resulting in suboptimal bandwidth utilization and elevated latency. To enhance network efficiency, this study applies a community-based resource allocation approach leveraging the Louvain algorithm to dynamically cluster network nodes with similar traffic demands. By forming communities based on bandwidth and latency needs, this approach enables a targeted resource distribution, aligning each community with optimized pathways that address specific requirements. The results indicate notable performance gains, including a 14% increase in bandwidth utilization affecting the download and a reduction in latency by an average of 23% for time-sensitive applications. These improvements highlight the effectiveness of the proposed approach in managing diverse network demands, improving data flow stability, and enhancing the overall performance of fog computing infrastructures. These findings underscore the potential for community-based resource allocation to support scalable, adaptable, and secure resource management, positioning it as a viable solution to meet the complex needs of IoT and other distributed network systems.

Improved Whale Optimization Algorithm for Dynamic Optimal Power Flow with Renewable Energy Penetration

Kukuh Widarsono — 2025-02-02

This paper proposes the Improved Whale Optimization Algorithm (IWOA) method to solve the Dynamic Optimal Power Flow (DOPF) problem in Renewable Energy Systems (RES), taking into account the influence of Energy Storage (ES). The convergence performance of the Whale Optimization Algorithm (WOA) algorithm is enhanced by incorporating Fuzzy Logic Control (FLC) in the exploration phase. The FLC assists the IWOA agents in finding the optimal weight values more quickly. The weights generated by the IWOA method are dynamic, continuously adjusting based on the deviation or error in the agents' movement during each iteration, which is essential for locating the global optimum. This paper primarily uses FLC to determine the weights, accelerating the IWOA optimization process. The effectiveness of this method is tested on the IEEE 30-bus system with the integration of PV and ES. The findings of this study demonstrate the superiority of the IWOA method over the WOA algorithm, resulting in a lower generation cost of $25/day and a faster convergence time.

Mechanical Strength Optimization of HDPE Biocomposite with Water Hyacinth Fiber Reinforcement using a Dispersing Agent

Kusuma Dewi — 2025-02-02

This study investigates the impact of a polyamine amides dispersing agent (BYK W-980) on the mechanical performance of the High-Density Polyethylene/Water Hyacinth Fiber (HDPE)/(WHF) composites. The dispersing agent was employed to improve the fiber distribution, enhance the fiber-matrix interaction, and reduce the fiber agglomeration, which negatively affects the mechanical properties of the composite. The Scanning Electron Microscopy (SEM) analysis revealed that the dispersing agent, particularly DA2, effectively minimized fiber agglomeration and promoted a more uniform fiber distribution within the HDPE matrix. The density testing indicated a reduction in porosity and an increase in composite density following the dispersing agent treatment. The mechanical testing demonstrated significant improvements with DA2 yielding the optimal results: a 19.54% increase in tensile strength, a 24.33% increase in flexural modulus, and an 18.53% increase in impact strength. The X-ray Diffraction (XRD) analysis showed an increase in the crystallinity index of the WHF, suggesting enhanced structural regularity, which supported the observed improvements in mechanical performance. Overall, the utilization of the polyamine amides dispersing agent, particularly DA2, significantly enhanced the mechanical properties and fiber-matrix interaction of the HDPE/WHF composites.

An Interpretation of the Cumulative Impact of FASTag on the Reduction of Environmental Pollution and Traffic Delays at Toll Booths: A Case Study

Anil Kumar Chhotu — 2025-02-02

Traffic congestion has been identified as a significant contributor to the environmental degradation and elevated fuel consumption. The causes of traffic delays are multifaceted, with toll booths positioned on highways being a notable factor. This study aims to analyze the impact of toll booths on traffic congestion, with a particular focus on the case study of the highway toll booth in question. The analysis will include a comparison of the delay times before and after the implementation of the FASTag system. The study will also undertake an economic evaluation to assess the cost implications of delay. The findings of this study indicate that congestion is more pronounced in the presence of manual toll booths compared to automated systems. The present study uses the 'Chakiya Toll Plaza on National Highway 27A, located between Motihari and Muzaffarpur, as a case study. This toll booth has been initially operated with a manual toll collection system, but has since transitioned to the FASTag implementation. A comprehensive analysis encompasses both traffic volume and delay studies, with a focus on their environmental and economic ramifications. The analysis reveals that for each Passenger Car Unit (PCU), the environmental cost is 8.3 rupees and the fuel cost is 15.34 rupees. The idle time cost is calculated as 8.34 rupees. The overall cost of the manual toll collection delay, including all the aforementioned factors, is found to be 52.3 rupees. However, the implementation of FASTag significantly reduces this cost to. 8.20 rupees.

Geotechnical Properties of Soil-Lightweight Aggregate Mixtures

Wurood Aljboori — 2025-02-02

This study investigates the stabilization and strengthening of clayey soils using several Lightweight Aggregates (LWAs), including Lightweight Expanded Clay Aggregate (LECA), Ponza (Pumice), and Thermostone. LWAs were incorporated into the soil to evaluate their impact on critical geotechnical parameters, such as compaction, consolidation, Unconfined Compressive Strength (UCS), and shear strength. The results revealed that incorporating LWAs effectively reduces the soil density, increases the void ratios, and enhances certain soil properties. LECA demonstrated the most substantial impact in decreasing soil density and increasing porosity, achieving a maximum density reduction of 60% and a void ratio increase of 75% at a 60% addition. While LWAs enhanced the internal friction angle by up to 90% -with Thermostone showing the highest increase at a 60% addition- cohesion diminished across all concentrations. The UCS peaked with a 94% increase at a 10% LECA addition but decreased with higher LWA percentages due to the porous nature of the additives disrupting the soil matrix. Optimal performance was observed with LWA concentrations between 10% and 30%, balancing improved strength and soil integrity. These findings suggest that LWAs can effectively stabilize and strengthen clayey soils, particularly in applications requiring reduced weight and enhanced shear strength, provided that the mixing ratios are carefully calibrated to align with project-specific requirements.

Broadband Rectangular Microstrip Antenna with Slits for W-Band Applications

AbdulGuddoos S. A. Gaid — 2025-02-02

This work introduces a broadband rectangular patch antenna optimized for efficient data transmission in the W-band, particularly for 5G applications. By integrating two I-shaped slits with the radiating element, the antenna achieves an impressive performance, exhibiting wide bandwidth and excellent radiation characteristics. Utilizing Rogers RT5880 as the substrate material with a relative permittivity (εr) of 2.2, a small antenna with a size of 3.7 × 4.1 × 0.16 mm³ is realized. Extensive simulations are conducted using CST software in both frequency and time domains to optimize the antenna. The results show a notable 16% fractional bandwidth from 80.75 GHz to 94.79 GHz, with dual resonance frequencies at 84.5 GHz and 91.5 GHz, primarily a result of the incorporated slits. At 84.5 GHz, the antenna demonstrates an outstanding reflection coefficient of -66.37 dB, a Voltage Sanding Wave Ratio (VSWR) of 1.00096, a gain of 9.71 dBi, a directivity of 9.75 dB, and a high radiation efficiency of 91.8%. Similar trends are observed at 91.5 GHz, where the return loss remains at an impressive value of 55.92 dB and the VSWR maintains a very low value of 1.0032, indicating continued excellent impedance matching. While the gain (6.98 dBi) and directivity (7.05 dB) are slightly lower at this frequency, the radiation efficiency remains remarkably high at 94.9%, indicating efficient energy utilization. The wide bandwidth of the proposed design enables high data transfer rates, a crucial requirement for 5G networks. This translates to significant improvements in network capacity, allowing for more connected devices and data traffic. Additionally, the design exhibits excellent signal transmission characteristics, ensuring reliable data transfer. Finally, the antenna's compact size and efficient radiation have the potential to reduce power consumption in 5G devices, contributing to improved battery life and sustainability.

Enhancement of the Shear Capacity of RC Deep Beams with Ultra-High Performance Fiber-reinforced Concrete

Ahmed Abd Elghany — 2025-02-02

This research investigates the shear behavior of Reinforced Concrete (RC) deep beams strengthened with Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC). For this purpose, eight RC deep beams were fabricated and tested for failure. One beam served as a control beam (un-strengthened), while the remaining seven deep beams were strengthened utilizing various strengthening schemes. This experimental study primarily focused on the thickness of the UHPFRC layer, Steel Fiber (SF) volume fraction, and strengthening schemes (jacketing, bilateral layers, and strips exclusively in the shear zone). The experimental findings demonstrated that UHPFRC significantly enhanced the shear capacity, toughness, and stiffness of the RC deep beams. The performance of the strengthened beams exhibited improvements in ultimate shear strength, stiffness, and toughness of about 43.6%, 102.2%, and 171.3%, respectively, higher than that of the un-strengthened deep beam. UHPFRC U-jacketing is a highly effective method for strengthening the RC deep beams. Incorporating SF into the UHPFRC mixture improved the shear properties of the strengthened specimens and delayed fracture propagation. Finally, the shear capacity of the strengthened specimens was compared to the values predicted by the analytical approaches presented in earlier research.

Machine Learning-Driven Soft Sensor Implementation for Real-Time Fault Detection in CDU of Oil Refinery

Mothena Fakhri Shaker AlRijeb — 2025-02-02

Soft sensors in oil refineries provide operators with important insights into the behavior and performance of processes using real-time and historical data to generate predictions. This data-driven strategy makes it easier to make wise decisions for detecting faults, thus improving process optimization and control. The Crude Distillation Unit (CDU) imposes very harsh working environments for measuring instruments, imposing both the use of a very robust sensory system and periodic maintenance procedures, which are time-consuming and costly. Notwithstanding such precautions, faults in those measuring devices, such as temperature and pressure sensors, still occur, and the presence of a sensor fault deteriorates the efficiency, productivity, and reliability of the refinery process. Recent works focused only on some fault types (e.g., bias and drift), ignoring others. This study presents the design of a soft sensor to detect all possible fault types in the real-time processing of an oil refinery. This method used actual data collected from the Salahuddin oil refinery in Iraq, several preprocessing methods, and a machine-learning approach. The proposed soft sensor was designed using several stages, including data collection, preprocessing, clustering, and classification. In the classification stage, an approach based on a Bagged Decision Tree (BDT) and Support Vector Machine (SVM) was implemented to classify the detected faults. The proposed soft sensor was trained and tested using actual data, achieving a high fault detection and classification result of 99.96%.

The Role of Economic and Environmental Variables in Green Growth: Evidence from Saudi Arabia

Ihsen Abid — 2025-02-02

Saudi Arabia, as one of the world’s leading oil producers, faces critical challenges in transitioning to sustainable economic growth. The heavy reliance on oil exports, coupled with rapid urbanization and environmental degradation, underscores the urgent need for green growth strategies tailored to the Kingdom’s unique socioeconomic and environmental context. This study aims to investigate the factors influencing the Green Growth Index (GGI), which measures sustainable economic growth, and analyze the short-term and long-term relationships between key variables such as environmental technology diffusion, carbon emissions, financial development, GDP per capita, and urbanization. The research employs the Autoregressive Distributed Lag (ARDL) model to assess the effects of various explanatory variables on the GGI, considering both immediate and delayed impacts. The model also incorporates an Error Correction Model (ECM) to evaluate the short-term dynamics and long-term equilibrium adjustments. It is found that the diffusion of environmental technologies and urbanization positively influence GGI in the short term, while CO₂ emissions are also linked to growth in the short run. However, financial development negatively impacts green growth in the long term, and GDP per capita has no significant effect. The ECM indicates that urbanization and emissions are major short-term drivers, while other factors show minimal short-run influence. This paper provides new insights into the dynamics of green growth by highlighting the roles of urbanization, environmental technologies, and emissions, offering valuable policy implications for sustainable development. The findings contribute to the understanding of the complex relationships that shape green growth in both the short and long term.

Classification using Enhanced Spectral and Spatial Transformer with Grasshopper Optimization

Pilligundla Niharika — 2025-02-02

Hyperspectral image (HSI) classification plays a crucial role in remote sensing, allowing the identification of various land cover types. Traditionally, Convolutional Neural Networks (CNNs) have been widely used for this purpose. However, they often face challenges related to high training parameter requirements and limited capacity for feature extraction, affecting their overall effectiveness. To overcome these challenges, this study proposes a novel approach integrating the Enhanced Deep Spectral and Spatial Transformer (EDSST) with Grasshopper Optimization (GHO). EDSST leverages transformer architecture to perform advanced spectral and spatial feature extraction, effectively mitigating the limitations of CNNs. This method improves feature abstraction and classification performance by reducing the number of training parameters while implementing a self-focusing mechanism. This approach incorporates a Classification Head (CH) with an orthogonal softmax activation function to accurately classify hyperspectral images. The proposed method was rigorously evaluated using the Salinas dataset, a benchmark in HSI classification research. The results show substantial improvements over existing techniques, achieving an accuracy of 99.5472%, precision of 99.5574%, recall of 99.5267%, and an F score of 99.6145%. These findings not only demonstrate the effectiveness of the proposed method in HSI classification but also highlight its efficiency and robustness, offering a promising solution for future applications in remote sensing and environmental monitoring.

Application of Continuous-Discrete Conversion and Balanced Truncation Algorithm to the Order Reduction Problem of Unstable Systems

Ngo Kien Trung — 2025-02-02

Since the model order reduction problem was first posed, numerous order reduction algorithms have been proposed in a variety of approaches. However, the majority of these algorithms have been developed to reduce the order of stable systems. In certain practical applications, such as high-order controller design, the original system may be unstable. Consequently, there is a need for order reduction algorithms capable of reducing the order of both stable and unstable systems. The present paper focuses on introducing a Continuous-Discrete (CD) transformation-based Balanced Truncation (BT) algorithm, which has the capacity to reduce the order of both stable and unstable systems. The efficiency of the improved BT algorithm is demonstrated by the simulation results.

A Blockchain-based Landslide Mitigation Recommendation System for Decision-Making

Djarot Hindarto — 2025-02-02

Landslides are catastrophic natural disasters that could threaten the structural integrity of a building, imposing hazards to engineering and human life. This study proposes a TOPSIS landslide disaster mitigation recommendation system integrated with blockchain. New approaches to data provenance, transparency, and informed decision-making are explored in the context of geospatial blockchain. The decision-making process is carried out using the multicriteria evaluation method, which considers soil stability, rainfall, vegetation density, proximity to rivers, and slope. The results yielded promising precision, recall, accuracy, and F1 scores (91%, 93%, 95%, and 95%, respectively), suggesting that the model could make accurate and impartial prioritization predictions. Blockchain ensures data transparency, immutability, and security, and TOPSIS ranks mitigation strategies from worst to best to determine the better solution. The proposed approach is essential to predict regions that are prone to landslides and enables the appropriate management of relaxation measures. This application of blockchain technology can provide trust, reliability, and speed in decision-making while reducing landslides.

Learning and Uncertainty Compensation in Robotic Motion Systems using Li-Slotine Adaptive Tracking and Intelligent Adaptive Control

Vo Thu Ha — 2025-02-02

This paper examines adaptive control strategies for stabilizing robots, focusing on Li-Slotine adaptive control and Iterative Learning Control (ILC). Both methods handle uncertainties through learning and compensation, ensuring stability and precision. Li-Slotine control, based on Lyapunov theory, dynamically adjusts parameters for asymptotic stability in uncertain systems. ILC improves performance in repetitive tasks by refining control inputs using tracking errors, making it suitable for robotics and manufacturing. While Li-Slotine excels in real-time adaptation and robustness to disturbances, its computational demands challenge high-degree-of-freedom systems. ILC enhances accuracy through iterative learning but is sensitive to noise and requires careful tuning. MATLAB simulations and experimental results demonstrate the effectiveness of both approaches. Future work will explore hybrid frameworks that combine the adaptability of Li-Slotine with the data-driven refinement of ILC to provide robust solutions for complex, dynamic robotic systems.

Utilization of a Deep Convolutional Neural Network for the Binary Classification of Chest X-Ray Pneumonia

Haydr Nataq Taha Al-Azzawi — 2025-02-02

Pneumonia remains a significant global health concern, necessitating efficient diagnostic tools. This study presents a novel Convolutional Neural Network (CNN) architecture, CuDenseNet, designed for the binary classification of Chest X-Ray (CXR) images as either having pneumonia or normal (healthy). Unlike models that rely on transfer learning from pre-trained architectures, CuDenseNet is trained from scratch and incorporates three parallel DenseNet paths of varying depths, enhancing feature extraction and classification accuracy. The model was evaluated on a combined dataset of 11,708 CXR images, achieving exceptional performance metrics of 99.1% accuracy, 99.7% precision, 99.1% recall, and an AUC of 99.7%. The comparative analysis demonstrates that CuDenseNet outperforms state-of-the-art pre-trained models such as VGG19 and ResNet50 while providing superior adaptability. These results underscore the potential of CuDenseNet as a robust and reliable tool for automated pneumonia diagnosis, with significant implications for clinical applications and future research in medical imaging.

Research on the Application of the Model Order Reduction Algorithm in Designing a Robust Controller for the Balance System of a Self-Balancing Two-Wheeled Bicycle

Ngo Kien Trung — 2025-02-02

This paper focuses on the design analysis and control of a Self-Balancing Two-Wheeled Bicycle (SBTWB) model. The difficulty of the two-wheeled bicycle balance control problem is that the two-wheeled bicycle model is uncertain and is continuously affected by disturbances. Many different control methods have been proposed to design an SBTWB balance controller, but the most suitable algorithm is the robust control algorithm. However, the robust controller of an SBTWB is often complex and of higher order, which affects the quality of the control process. This study introduces a Model Order Reduction (MOR) algorithm based on the preserving dominant poles and applies this algorithm to simplify the 15th order robust controller of the balance control system of an SBTWB. Through comparison and evaluation, it is shown that the 5th-order controller or the 4th-order controller can replace the 15th-order robust controller. Through a simulation of the control system using the 4th-order controller, it is demonstrated that the proposed 4th-order controller ensures a stable balance of the SBTWB, while the 4th-order controllers according to other order reduction methods cannot maintain the balance of the SBTWB. The simulation results show the effectiveness of the order-reduction algorithm based on the conservation of dominant pole points and the robust control algorithm for the SBTWB.

Investigation of the Radar Cross-Section and its Optimization Potential for ADAS Tests

Robert Magai — 2025-02-02

The objective of this study is to examine the Radar Cross Section (RCS) of instruments designed for Autonomous Driving Systems (ADAS) testing, with the intention of comparing the results to those of actual human subjects. The RCS values of both dummy and platform objects were documented at varying distances and positions, with the objective of ascertaining the extent to which dummies can serve as substitutes for human values in vehicle radar sensing tests. The findings, substantiated by graphical representations and statistical analyses (e.g., Pearson and Spearman correlation), reveal a moderately strong positive correlation between the RCS and human values, which is statistically significant. The outcomes of the tests demonstrate that the developed instruments can substitute for real human radar cross-section values within the range of 5-15 m. However, as the distance increases, larger deviations are observed. These discrepancies underscore the necessity for a refinement of the dummy design in future ADAS tests, ensuring that distance-sensitive tests accurately reflect real human measurements.

Optimizing Fly Ash and Cement Ratios for Immobilizing Lead and Arsenic in Liquid Hazardous Waste

Diki Surya Irawan — 2025-02-02

Improper handling of hazardous and toxic industrial wastes can directly impact human health and the environment. To overcome this problem, the PT Prasadha Pamunah Limbah Industri (PPLI) provides industrial waste processing services including Stabilization and Solidification (S/S) treatment using cement and fly ash mixtures. This research evaluated the efficiency of S/S in reducing the heavy metal content of lead (Pb) and arsenic (As) in liquid hazardous waste, using waste, fly ash, and cement ratios of 1:1:1, 1.5:1:0.5, and 2:0.5:0.5, tested with the Toxicity Characteristic Leaching Procedure (TCLP). The results demonstrated that the 1:1:1 and 1.5:1:0.5 ratios effectively reduced the Pb and As content, achieving reduction efficiencies of 99.50% and 99.34% for the 1:1:1 ratio, and 99.36% and 99.19% for the 1.5:1:0.5 ratio, respectively. However, the 2:0.5:0.5 ratio produced a brittle mixture prone to leachate formation and it is not recommended. According to the TCLP results, the 1:1:1 and 1.5:1:0.5 ratios met the environmental quality standards outlined in Government Regulation No. 22 of 2021 on Environmental Protection and Management. The statistical analysis using an independent T-test indicated no significant difference in the reduction efficiency between the two effective ratios, suggesting that either can be reliably used. This study addresses existing research gaps by applying the S/S method to liquid hazardous waste, an area previously focused on soil applications. Besides, it demonstrates that high immobilization rates exceeding 99% can also be achieved for Pb and As using cement and fly ash. This study provides a novel perspective on S/S applications, enhancing the efficiency and applicability of hazardous waste treatment processes.

Optimization of Rubber Sheet Rolling Machine Parameters using a Taguchi-based TOPSIS Linear Programming Model

Surasit Phokha — 2025-02-02

The Multi-Response Optimization (MRO) problem is a critical aspect of the engineering design, particularly in improving process efficiency and product quality. This study focuses on optimizing the parameters for a rubber sheet rolling machine, a vital component of Thailand's natural rubber industry. The objective is to enhance its operational efficiency and product consistency by addressing key criteria, such as production time and rubber sheet thickness. A novel approach integrating the Taguchi method and the Technique for Order Preference by Similarity to Ideal Solution Linear Programming (TOPSIS-LP) model is proposed. The Taguchi method systematically designs experiments, while the Preference by Similarity to Ideal Solution (TOPSIS) model consolidates multiple performance indicators into a single optimal solution. Optimal roller gaps of 4.5 mm, 3.0 mm, 2.0 mm, and 0.1 mm for the first, second, third, and fourth roller pairs, were, respectively, identified. The results demonstrated a reduction in rubber sheet thickness to 2.06 mm (5.94% improvement) and production time to 9.71 seconds per sheet (1.33% improvement) compared to the original settings. The qualitative analysis confirmed the robustness and reliability of the optimized parameters, achieving consistent results across various evaluation methods. This study presents a significant advancement in the MRO problem, offering a robust framework applicable to similar challenges in industrial settings. The findings provide a foundation for future automation and optimization efforts, driving sustainable improvements in the manufacturing efficiency and product quality.

Determination of Zea Mays Plant Fertility Level in Automatic Fodder Systems using Supervised Learning based on GLCM and Physical Feature

. Haryanto — 2025-02-02

The problem during the dry season is the availability of animal feed, especially for cattle. One of the efforts made is to use fermented feed and corn fodder. Automated feedstock monitoring and control is one of the technologies that has been developed. This study proposes a method to determine the fertility of Zea May sp plants in automatic fodder using supervised learning based on Self-Organizing Map (SOM), Gray Level Co-occurrence Matrix (GLCM), and physical features. The results showed that the system worked satisfactorily, where both methods achieved an accuracy of 93.5% on 3-day Zea Mays fodder using SOM and the highest on 12-day Zea Mays fodder using both methods with an accuracy of 96%. Although this system has shown good performance using both SOM and K-means, in some conditions, K-means achieved higher performance. These contributions are expected to help farmers provide animal feed.

Numerical Simulation of Natural Convection in a Chamfered Square Cavity with Fe3O4-Water Nanofluid and Magnetic Excitation

Rached Nciri — 2025-02-02

This study delves into the numerical exploration of the MagnetoHydroDynamic (MHD) characteristics of an Fe₃O₄-Water nanofluid contained within a chamfered square enclosure under the influence of an external magnetic field. The enclosure, characterized by distinct hot and cold imposed temperatures on its side walls, features both straight and chamfered sections. The orientation of magnetic field lines was manipulated by varying the angular placement of the magnetic source. The computational framework for nanofluid dynamics is mathematically formalized through a dimensionless formulation of the Navier-Stokes equations derived from their dimensional counterparts. A comprehensive numerical analysis was conducted employing the Finite Element (FE) method, a. The interaction between the Hartmann number and the angular placement of the magnetic source was analyzed, with a specific focus on nanofluid isotherms, temperature profiles, and velocity magnitude distributions. The results were thoroughly investigated and extensively discussed.

AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry

Md. Habibur Rahman Sobuz — 2025-02-02

The need to develop ecologically friendly sustainable building materials is made apparent by the worldwide construction industry's substantial contribution to global greenhouse gas emissions. The use of supplemental materials in concrete is one potential solution to lessen the environmental footprint. Thus, the purpose of this work is to use Machine Learning (ML) algorithms to forecast and create an empirical formula for the Compressive Strength (CS) of concrete with supplemental materials. Six distinct ML models—XGBoost, Linear Regression, Decision Tree, k-Nearest Neighbors, Bagging, and Adaptive Boosting—were trained and tested using a dataset that included 359 experimental data of varying mix proportions. The most significant factors used as input parameters are cement, aggregates, water, superplasticizer, silica fume, ambient curing, and supplemental material. Several statistical measures, such as Mean Absolute Error (MAE), coefficient of determination (R²), and Mean Square Error (MSE), were used to evaluate the models. XGBoost model outperformed the other models with R² values of 0.99 at the training stage. To ascertain how the input parameters affected the outcome, feature importance analysis using Shapely Additive exPlanations (SHAP) was conducted. It was demonstrated that curing age and cement type significantly affected the strength of concrete with high SHAP values. By eliminating experimental procedures, reducing the demand for labor and resources, increasing time efficiency, and offering insightful information for enhancing sustainable manufacturing of concrete, this research advances the low-cost production of concrete in the USA construction industry.

A Review on the Performance of Concrete Beams reinforced with GFRP Bars and Internally reinforced with Different Meshes

Ban Abd Abbas Khudhair — 2025-02-02

The incorporation of geogrid or Glass-Fiber Reinforced Polymer (GFRP) mesh into concrete structures presents a novel approach to leveraging the geosynthetics and Fiber Reinforced Polymer (FRP) composites in structural components. Geogrid, steel, and GFRP mesh can enhance the post-cracking ductility and load-bearing capacity of Reinforced Concrete (RC) beams, depending on the specific type and properties of the mesh utilized. The use of reinforcing meshes offers the advantage of reducing the size of structural elements due to their lower weight compared to steel bars, thereby decreasing the overall weight of the structure.

Optimizing Investment Portfolio Allocation: Analyzing Trends and Dynamics of Alternative Investments in Estate Planning

Vijai Pillarsetti — 2025-02-02

Private Equity (PE) plays a unique role in estate planning due to its potential for high returns and complexity. Therefore, understanding its pricing dynamics is essential for effectively incorporating it into an estate plan. The current research examines the complexities of optimizing investment portfolio allocation by analyzing the pricing dynamics of PE funds in secondary markets. The study acknowledges the challenges posed by the inherent risks, illiquidity, and long-term nature of PE investments. It focuses on the impact of PE investments on Limited Partners' (LP) optimal allocations. Typically, LPs commit capital to PE funds, which is gradually called and eventually distributed back to them. The research reveals that the PE investments significantly influence LPs' optimal allocations, with differing strategies being observed among LPs with varying risk aversion levels. The study's findings reveal that PE allocations do not consistently decrease in risk aversion, suggesting the presence of nuanced decision-making processes. Depending on an LP's risk aversion, they adopt one of two distinct investment strategies. A conservative LP, characterized by higher risk aversion, holds relatively more liquid reserves of stocks and bonds compared to illiquid PE investments. This LP tends to be unconstrained and to remain close to an interior optimum, leaving it largely unaffected by the illiquid and long-term nature of PE investments. Moreover, the model extends to incorporate a secondary market for PE partnership interests, allowing an exploration into the implications of trading in this market and the pricing dynamics of the Net Asset Value (NAV) and unfunded liabilities. The proposed analysis provides valuable insights into the intricate interplay between the risk aversion, PE investments, and secondary market dynamics, offering guidance to LPs navigating the complex landscape of alternative investments.

The Optimization of Rotary Bending Die Process: Criteria for the Metal Sheet Angles and Springback Effects

Vu Duc Quang — 2025-02-02

Rotary die bending enables the precise fabrication of sheet metal components across various bending angles, offering high dimensional accuracy, structural stability, and reduced springback. This study employs explicit and implicit numerical simulations using the Abaqus software to analyze the rotary die bending process and springback behavior of SUS304 stainless steel sheets. Five key criteria are investigated: desired angle post-springback (a_si), springback factor (k_si), forming stress (Von Mises) at the required bending angle (S_r), residual stress (Von Mises) after springback (S_b), and equivalent plastic strain (PEEQ). These criteria enable accurate predictions of material behavior during rotary die bending, including elastic-plastic deformation and stress distribution. The insights gained support a more flexible design process, enhance the precision of sheet metal bending, and ensure that the final product meets the specified requirements. This research serves as a valuable reference for professionals working with sheet metal components made from various metals and bimetal sheets. Additionally, it informs strategies to mitigate or eliminate residual stress in bent parts, improving reliability and manufacturability.

Investigation of the Optimal Output Parameter Equation in a Small Ethanol-Fueled Engine

Nguyen Xuan Khoa — 2025-02-02

Biofuels are increasingly recognized as an urgent solution to reducing engine emissions and achieving sustainable development goals. Among them, ethanol fuel stands out as a promising candidate due to its clean combustion properties and high energy regeneration potential. This study investigates the optimal equations for the engine output parameters to enhance power and improve the overall engine performance. The findings demonstrate that optimizing the ignition angle allows the engine to achieve a higher power output, significantly reduce the fuel consumption, and minimize the emissions of harmful pollutants, such as CO, NOx, and HC. This research provides a solid foundation for the application of ethanol as a viable alternative fuel in internal combustion engines, paving the way for cleaner and more environmentally friendly engine technologies that meet stringent emission standards. Furthermore, the derived equations and insights offer practical implications for improving the existing engine systems and guiding the development of advanced biofuel-powered engines for future applications.

Performance of Asphalt Concrete Mixtures Containing Nickel Slag

Nurul Azizah — 2025-02-02

Recent advancements in material technology have led to an increased interest in using alternative materials in the asphalt mixtures. One such material is Nickel Slag (SN), a byproduct of nickel ore smelting. With the growing volume of slag produced during nickel smelting, research has focused on using SN as a component in pavement materials to reduce the steel waste accumulation. The primary objective of this study is to explore the optimal use of SN as a coarse aggregate in asphalt concrete mixtures, aiming to achieve the maximum asphalt content. The study also evaluates the impact of SN on the stability, volumetric characteristics of the asphalt mixtures, and Ultrasonic Pulse Velocity (UPV) wave patterns. The research involved Marshall testing using a Universal Testing Machine (UTM) and UPV testing. The results indicated that SN mixtures reached maximum stability at 5.8% asphalt content and demonstrated higher stability values than conventional mixtures. As a coarse aggregate replacement, SN enhances the resistance to permanent deformation due to its hardness, interlocking properties, and the silica content that improves adhesion to the asphalt. Incorporating SN into asphalt mixtures improves mix stability, reduces industrial waste, conserves natural resources, and enhances road infrastructure quality. This method supports the principles of sustainable development.

Leveraging Deep Reinforcement Learning for Effective PI Controller Tuning in Industrial Water Tank Systems

Vijaya Lakshmi Korupu — 2025-02-02

This paper addresses the level control problem in water tank systems by proposing a Deep Deterministic Policy Gradient (DDPG) algorithm to automatically tune the parameters of a Proportional-Integral (PI) controller. The integration of the PI controller with the DDPG algorithm leverages the strengths of both methods, enabling the algorithm to learn optimal controller gains through the exploration of the state-action space and reward feedback from the system. The proposed approach eliminates manual tuning, automates gain adaptation to varying system states, and ensures a robust performance under uncertainties and disturbances. The validation results demonstrate that the DDPG-tuned PI controller outperforms the manually tuned controller using the PID Tuner app in Simulink, achieving no overshoot, faster settling times, and enhanced robustness. These findings highlight the potential of Reinforcement Learning (RL) for adaptive control in industrial applications, particularly for systems with dynamic and uncertain environments.