Engineering, Technology & Applied Science Research

Enhancing Filter Assembly and Printing Efficiency through Automation and Virtual Reality Integration

Peter Malega — 2024-12-01

This paper presents a comprehensive approach to improving the assembly and printing process of filters in a selected company. Currently, the company's process involves significant manual labor, which leads to inefficiencies and potential health risks for employees. The proposed solution integrates the use of pneumatic cylinders to automate the assembly and printing process, reducing manual effort and improving production efficiency. The fixture and filter were modeled using SOLIDWORKS 2022 and simulated in virtual reality with Pixyz Review software. The virtual reality simulation serves as an innovative training tool for new employees, enhancing understanding and accuracy in the assembly process. The study compares the current state, where manual handling results in a time-consuming process with a lower production rate, to the proposed automated system. The proposed solution eliminates unnecessary intermediate storage and manual fixture transfers, significantly reducing the total assembly and printing time from 24 s to 22 s per unit. This results in a 14.16 % increase in production efficiency, allowing the company to produce 1227.3 filters per shift compared to the current 1075 filters.

Intrusion Detection in IoT using Gaussian Fuzzy Mutual Information-based Feature Selection

Abdullah Hussain Abu Saq — 2024-12-01

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling real-time monitoring, data collection, and intelligent decision-making. However, the massive volume of data generated by these devices presents significant challenges for data processing and analysis. Intrusion Detection Systems (IDS) for IoT require efficient and accurate identification of malicious activities amidst vast amounts of data. Feature selection is a critical step in this process, aiming to identify the most relevant features that contribute to accurate intrusion detection, thus reducing computational complexity and improving model performance. Traditional Mutual Information-based Feature Selection (MIFS) methods face challenges when applied to IoT data due to their inherent noise, uncertainty, and imprecision. This study introduces a novel Fuzzy Mutual Information-based Feature Selection (Fuzzy-MIFS) method that integrates fuzzy logic with Gaussian membership functions to address these challenges. The proposed method enhances the robustness and effectiveness of the feature selection process, resulting in improved accuracy and efficiency of IDSs in IoT environments. Experimental results demonstrate that the Fuzzy-MIFS method consistently outperformed existing feature selection techniques across various neural network models, such as CNN, LSTM, and DBN, showcasing its superior performance in handling the complexities of IoT data. The results show that Fuzzy-MIFS increased the accuracy from 0.962 to 0.986 for CNN, from 0.96 to 0.968 for LSTM, and from 0.96 to 0.97 for DBN.

Cloud-Cyber Physical Systems: Enhanced Metaheuristics with Hierarchical Deep Learning-based Cyberattack Detection

Ahmad Taher Azar — 2024-12-01

Cyber-Physical Systems (CPS) integrate several interconnected physical processes, networking units, and computing resources, along with monitoring the processes of the computing system. The connection between the cyber and physical world creates threatening security problems, particularly with the growing complexities of transmission networks. Despite efforts to overcome this challenge, it remains challenging to analyze and detect cyber-physical attacks in CPS. This study mainly focuses on the development of Enhanced Metaheuristics with Hierarchical Deep Learning-based Attack Detection (EMHDL-AD) method in a cloud-based CPS environment. The proposed EMHDL-AD method identifies various types of attacks to protect CPS. In the initial stage, data preprocessing is implemented to convert the input dataset into a useful format. Then, the Quantum Harris Hawks Optimization (QHHO) algorithm is used for feature selection. An Improved Salp Swarm Algorithm (ISSA) is used to optimize the hyperparameters of the HDL technique to recognize several attacks. The performance of the EMHDL-AD algorithm was examined using two benchmark intrusion datasets, and the experimental results indicated improvements over other existing approaches.

Sensitivity Analysis of a Stator Current-based MRAS Estimator for Sensorless Induction Motor Drives

Mohamed S. Zaky — 2024-12-02

The sensitivity of speed estimators for parameter variations presents a significant challenge for sensorless Induction Motor (IM) drives, particularly at very low speeds. This paper examines the impact of parameter variations and the PI adaptation mechanism on the stator current-based Model Reference Adaptive System (MRAS). In contrast to the estimation of rotor flux, the MRAS method uses the observed stator current and the stator current estimate error within the adjustable IM model. The stability analysis for changes in machine parameters and PI controller gains is examined using small-signal perturbation. Additionally, a sensitivity analysis of stator and rotor resistance changes is included. A complete simulation using MATLAB/Simulink and experimental validation using a laboratory prototype based on the DSP-DS1103 are provided. The analytical, modeling, and measurement results reveal that the suggested observer responds well and provides precise speed estimation in all four quadrants of operation.

Three Leg Inverter Control based on Laguerre Model Predictive Controller

Adelhard Beni Rehiara — 2024-12-01

An inverter is a power electronic device that converts Direct Current (DC) to Alternating Current (AC). A three-leg inverter was used to convert DC to AC in a three-phase form. The increasing need to integrate renewable energy into the power grid has increased the demand for this type of converter. In this paper, a Laguerre-based Model Predictive Controller (LMPC) is proposed to control a three-leg inverter under load variation. The proposed LMPC algorithm was designed to optimize the inverter performance and was compared with traditional Sinusoidal Pulse Width Modulation (SPWM) methods. Simulations were conducted under various load conditions including resistive (R), capacitive (RC), and induction motor loads. The results demonstrate that the LMPC controller outperforms the SPWM methods, providing better performance and lower Total Harmonic Distortion (THD) values. The THDs of the voltage and current achieved by the LMPC controller were below 1.5%, complying with IEEE 519-2014 standards. However, the current THD increased to 7.18% under the induction motor load.

SMART Model: A Robust Approach for Cyber Criminal Identification using Smartphone Data

K. Swetha — 2024-12-01

The SMART (Smartphone Metadata Analysis for Recognizing Threats) model is a novel approach to the identification of prospective cyber criminals by analyzing smartphone data, with a particular emphasis on social media interactions, messages, and call logs. The SMART model, in contrast to conventional methods that depend on a wide variety of features, prioritizes critical parameters to ensure more precise and effective analysis. This model exhibits exceptional adaptability and robustness in a variety of data environments by employing sophisticated feature extraction and classification algorithms. This targeted approach not only improves the precision of threat identification but also offers a practicable solution for real-world cybersecurity applications, where data quality and consistency may vary.

Utilizing Waste Engine Oil and Soft Binder as Additives to Mitigate the Moisture Damage of Asphalt Mixtures

Mohammed Qadir Ismael — 2024-12-01

The deterioration of asphalt pavements caused by moisture is a significant concern for asphalt pavement construction companies. To improve this characteristic, this research aims to determine how rejuvenators and flexible compounds, affect the resistance of asphalt concrete to moisture. This study investigates the effects of incorporating Waste Engine Oil (WEO), an easily obtainable and economical substance, into a maturing mixture. The action of this substance resulted in strengthening the physical and chemical characteristics of the bitumen, as well as mitigating the adverse effects caused by moisture. The various degrees of bitumen penetration ranged from 40-50 to 80-1. Extremely small limestone dust particles measuring 19.0 mm, were utilized as mineral infill in the aggregate grade. To enhance the Marshall characteristics, treatments containing 0%, 2%, 4%, and 6% WEO by weight of binder were implemented after filtration. The most advanced Marshall had a WEO content of 6% and an asphalt grade ranging from 85-100, while stability and resistance to moisture degradation were observed. Compared to combinations lacking WEO, the compressive strength and the indirect tensile strength value, were determined to find the Index of Retained Strength (IRS) and Tensile Strength Ratio (TSR). This reduced moisture susceptibility as TSR% and IRS% values increased by approximately 1.22% and 0.9%, respectively.

Comparing Several Orifice Flange Shapes for Hydrodynamic Cavitation Treatment and COD Reduction in Textile Wastewater

Pratima Gajbhiye — 2024-12-02

Textile industry wastewater contains potentially harmful metals, such as nickel and copper, and has a high Chemical Oxygen Demand (COD). This study investigated the use of hydrodynamic cavitation to reduce COD and color levels in textile wastewater using various orifice plate designs, including 1-star, 1-circular hole, 5-star, and 5-circular hole patterns, combined with two orifice plates in succession. The results showed that the 1- and 5-circular hole arrangements led to significant reductions in COD (78% for 5-circular hole and 65% for 1-circular hole) and color (27% for 5-circular hole and 25% for 1-circular hole). The 1-star pattern design reduced COD by up to 79% and color by 33%, whereas the 5-star pattern design reduced COD by up to 60% and color by 20%. The study concluded that the most effective orifice plate for eliminating COD from textile wastewater is a combination of an 1-star pattern and a 5-circular pattern design. These findings demonstrate the potential of hydrodynamic cavitation as an effective method for reducing harmful pollutants in textile industry effluents.

Trajectory Tracking Control for a Quadcopter under External Disturbances

Cuong V. Nguyen — 2024-12-02

This paper initially discusses the application of a Sliding Mode Controller (SMC) for drone control, encompassing vertical takeoff and landing. Subsequently, the dynamic model of the quadcopter is formulated using the Newton-Euler method. Despite the challenges posed by the nonlinear characteristics of Unmanned Aerial Vehicles (UAVs), empirical evidence from previous tests and simulation studies underscores the efficacy of the SMC in delivering satisfactory performance and robust resistance against interference. Moreover, this research endeavors to present a quadcopter model and simulation, leveraging the SMC alongside the Newton-Euler formula to enhance control precision in the face of external magnetic disturbances affecting the UAV. Both the position and attitude of the UAV are governed by the SMC. The dynamic and control models of the quadcopter are implemented and visualized in MATLAB, culminating in results that substantiate the efficacy of the proposed controller across diverse scenarios. Furthermore, the performance of the proposed control method is compared with alternative methodologies such as PID, particularly in scenarios involving disturbances. The simulation results indicate promising and practical implications.

Increasing the Performance of Ring Foundation to Lateral Loads by using a Skirt Foundation

Balqees A. Ahmed — 2024-12-02

In numerous engineering foundation designs, the influence of lateral loads is frequently underestimated in calculations. Consequently, recent studies have increasingly concentrated on comprehending the behavior of soil and foundations when subjected to lateral load influences. The present study aims to examine the performance of ring foundations, a common engineering solution employed in the construction of tall and slender structures that are vulnerable to lateral loads, such as those exerted by wind forces. The objective of this study is to enhance the lateral resistance of ring foundations by incorporating skirt foundations. The efficacy of skirt foundations was evaluated through a series of tests conducted on sandy soils of varying densities, ranging from dense to medium loose sand. Subsequently, lateral loads were applied to the ring foundations, both with and without skirt foundations. The results demonstrated that the lateral resistance increased in proportion to the ratio between the inner and outer diameters. Furthermore, the improvement rate was enhanced by the addition of the skirt foundation. Additionally, the lateral resistance increased with increasing the skirt foundation depth, reaching a maximum of approximately 50-100%. Similarly, an increase in the skirt inclination ratio to 450 resulted in a lateral resistance increase of up to 650%.

A Novel Hybrid Approach for Global Maximum Power Point Tracking in Standalone PV Systems

C. Prasanth Sai — 2024-12-02

The efficiency of Photo-Voltaic (PV) systems is highly dependent on their ability to accurately track the Global Maximum Power Point (GMPP) under varying environmental conditions. Traditional Maximum Power Point Tracking (MPPT) methods often struggle with issues such as slow tracking speed, susceptibility to local maxima, and the need for complex parameter tuning, particularly in dynamically changing environments with Partial Shading Conditions (PSCs) and rapid irradiation changes. To address these challenges, this study introduces a hybrid approach that combines a modified Rao algorithm with the Perturb and Observe (P&O) method. The modified Rao algorithm was employed in the initial tracking stages to quickly locate the global vicinity, benefiting from its simplicity and the absence of algorithm-specific parameters, whereas the P&O method ensured precise tracking in the final stages. The performance of the proposed method was assessed on a PV array subjected to PSCs and compared with several well-known MPPT algorithms, such as Gray Wolf Optimization (GWO), JayaDE, and the Slime Mould Algorithm (SMO). The proposed approach was implemented and analyzed using the MATLAB/Simulink software.

Predicting Financial Distress in Indonesian Companies using Machine Learning

Farida Titik Kristanti — 2024-12-02

Predicting financial distress is essential in Indonesia's rapidly evolving economy, characterized by diverse business environments and regulatory challenges. This study evaluates four machine learning classifiers, XGBoost, Random Forest (RF), Support Vector Classification (SVC), and Long Short-Term Memory (LSTM), to predict financial distress among Indonesian companies. Two sampling methods, Random Under-Sampling (RUS) and Synthetic Minority Over-Sampling Technique (SMOTE), were used to address class imbalance. Empirical results indicate that the RF model trained with SMOTE sampling was the most effective, achieving an F1 score of 0.9632 and an accuracy of 0.96, while the XGBoost classifier with RUS sampling achieved a precision of 0.9716. These findings provide valuable insights into Indonesia's financial sector, guiding the selection of appropriate models for timely financial distress prediction and intervention.

Use of Geomatic Techniques for Mapping Suspended Solids in Aquatic Ecosystems: The Case Study of Guayas River, Ecuador

Jennyffer Rebeca Yepez Ramírez — 2024-12-02

Satellite images cover large remote areas and are useful for detecting and monitoring water bodies. In Ecuador, since 1950, the lower Guayas River basin has undergone significant natural and anthropogenic changes that have impacted its dynamics and sustainability. This study aims to analyze through in situ data and geomatic techniques the change that the river has undergone in a decade by mapping the Suspended Sediment Concentration (SSC). Increasing levels of pollution in the river have raised concerns, prompting various approaches to measure and mitigate sedimentation to maintain the sustainable quality of the watershed. The spatiotemporal variations of SSC in the Guayas River revealed a remarkable variability, influenced by the operation of reservoirs, changes in land use, erosion, and sedimentation causing SSC in 2013 to range from 64.82 to 707.06 mg/l in the satellite image of 9/16/2013 and from 87.58 to 933.36 mg/l in the image of 7/26/2023. Understanding this distribution is crucial for the environmental protection and sustainability of aquatic ecosystems. This study used Landsat 8 data, an atmospheric pre-correction, and a remote sensing model. The results indicate a creasing trend of SSC in the stretches of the Guayas River between 2013 and 2023, which allows the understanding of the spatiotemporal dynamics of suspended sediment transport.

Evaluating the Effectiveness of Wenner Mode Configurations for Resistivity-based Moisture Monitoring in Compressed Earth Bricks

Tuan Anh Nguyen — 2024-12-02

Compressed Earth Bricks (CEBs) have emerged as an eco-friendly construction material, although their properties are highly moisture dependent. This study investigated the applicability of electrical resistivity techniques for non-destructive moisture assessment in CEBs and determined the optimal electrode configurations for small-scale CEB samples. Various Wenner array electrode configurations, including Wenner Alpha, Beta, and Gamma arrangements, were tested on CEB specimens across a wide range of relative humidity levels. Numerical modeling using the finite element method was employed to simulate the current diffusion process in CEB samples. A mathematical formulation was developed to calculate the true electrical resistivity of the specimens based on the measured resistance and the geometric factors obtained from the numerical model. The results show that the electrical resistivity of CEBs exhibited a logarithmic relationship with moisture content, and the Wenner Alpha and Gamma configurations were proved to be the most suitable for small-scale samples. The proposed approach demonstrates the feasibility of continuous non-destructive moisture monitoring of CEBs to improve quality control.

Accelerating Plastic Pollution Mitigation through Sustainable Urban Infrastructure Development

Agus Salim — 2024-12-02

Plastic waste plagues both land and aquatic environments. The surface layer refers to the road pavement layer that comes into direct contact with a vehicle's wheel surface. The surface layer distributes wheel load far more evenly than the layer underneath. The surface layer design incorporates top-notch materials that can be used as additives to enhance the quality of the asphalt mixture. This study evaluates the asphalt mixture's durability using Marshall characteristics and Cantabria tests carried out in a lab setting. An ideal asphalt concentration of 5.25% is obtained. Low Density Polyethylene (LDPE) is added to the asphalt mixture at various contents, 0%, 2%, 4%, 6%, and 8%. The study's findings indicate that adding plastic waste to the asphalt mixture can enhance its performance. The two main mitigating actions are probably plastic prohibition laws and public awareness campaigns. To evaluate the possible environmental effects and resources utilized over the course of a plastic product's life span, researchers stress the importance of its life cycle assessment and circularity. Innovations are necessary to minimize, reuse, recycle, recover, and develop environmentally friendly plastic substitutes. By empowering and educating communities and citizens on how to reduce plastic pollution and use alternative plastic solutions, governments must enforce and promote collective action. This research must prioritize addressing plastic waste as a global issue.

An Experimental Investigation of Slab-Column Connection Strengthened with Steel Collar under Eccentric Load

Hanaa Abdulbaset Ali — 2024-12-02

Slab-Column (SC) connections refer to concrete reinforcing slabs that have consistent thickness and directly transfer loads to the support column. The absence of beams makes these connections distinct and economical compared to other systems. The most common type of failure in flat slab systems is punching shear, therefore, strengthening the SC region is necessary. The current study introduces a practical methodology that aims to enhance the punching shear strength of concrete flat slabs using steel collars. Nine 9-square reinforced concrete slab specimens with dimensions of 1400×1400×100 mm were cast and investigated under static load. Three specimens were tested using the axial load procedure, while six slabs were tested deploying an eccentric procedure. This article has studied two parameters to characterize the shear strength resistance for this type of slab: the steel collar model and the eccentricity loading effect. The study outlines the load-deflection relationship, failure mode, ultimate capacity, stiffness, cracking load, and the value of the failure angle. The test results illustrate a reduction in ultimate load by 26% and 60% due to the influence of eccentric load and unbalanced moment in group I, while the ultimate load increased by 34% and 61% in specimens strengthened with steel collars under the same eccentric load applied, proving the efficiency of the steel collar in the connected area of the slab column on enhancing the shear strength of the slab exposed to eccentric load and moments.

Human Remains Detection in Natural Disasters using YOLO: A Deep Learning Approach

Jyotsna Rani Thota — 2024-12-02

Natural catastrophes are defined as events whose precise location and timing are unexpected. Natural disasters can cause property damage and death. The NDRF has to coordinate rapid evacuation to help victims of natural disasters minimize their losses. In reality, the evacuation process is rather challenging. The journey begins with tackling challenging terrain and ends with equipment limitations. Most studies focus on classifying various types of disasters, estimating the amount of damage incurred during a disaster, and identifying victims in post-disaster situations. Many studies use image processing to locate victims in vulnerable locations. This study aims to establish a system for identifying human bodies after natural disasters to assist NDRF teams and volunteers find bodies in hard-to-reach areas. The You Only Look Once (YOLO) method is used in conjunction with artificial intelligence's computer vision algorithms and the Python programming language to effectively detect human bodies with an accuracy of 96%.

An Empirical Study on the Development and Assessment of a Supplier Selection Model based on the Analytical Hierarchy Process in the Al Kharj Industrial Sector

Teg Alam — 2024-12-02

Selecting a supplier is a critical strategic decision for supply chain management in today's global context. The process involves evaluating suppliers based on core competencies, pricing, delivery timeframes, location, data gathering, and related risks. Suppliers play a crucial role in an organization's profitability and stability. Finding the most optimal supplier can help industries reduce material expenses and maintain their competitive advantage. The supplier not only impacts the organization's profit margin but also its economic strength. Choosing a supplier requires considering qualitative and quantitative elements, making it a decision issue with several criteria. This study aims to create and evaluate a supplier selection model using the analytical hierarchy approach, focusing on a specific case study. When selecting the best supplier, it is crucial to consider tangible and intangible elements that may conflict. The supplier selection process considers several criteria, including qualitative and quantitative variables. The proposed methodology involved a literature review and informal interviews with industry experts and academics to establish the selection criteria. "Quality Supplier Co." was chosen due to the paramount importance of their quality. This research will comprehensively analyze several criteria to identify suppliers accurately.

Voting Strategies for Arabic Named Entity Recognition using Annotation Schemes

Ikram Belhajem — 2024-12-02

Named Entity Recognition (NER) seeks to identify and classify NEs into predefined categories and is an important subtask in information extraction. Many annotation schemes have been proposed to assign suitable labels for multiword NEs within a given text. This study proposes a method to combine the results of different annotation schemes (IOB, IOE, IOBE, IOBS, IOES, and IOBES) for Arabic NER (ANER). Three voting strategies are explored, namely, majority voting, weighted voting, and weighted voting-based Particle Swarm Optimization (PSO), applied to Conditional Random Fields (CRF) classifiers, each corresponding to a certain annotation scheme. The experimental results showed that majority voting can be considered an effective combination strategy to enhance the performance of ANER systems.

An Intelligent Optimazitation Method for Evacuation Route Planning in the Occurrence of Natural Disasters

Hafsah Nirwana — 2024-12-02

This research aims to design and apply intelligent optimization methods using various algorithms to find disaster evacuation routes. The efficiency and effectiveness of evacuation routes are essential in disaster situations to ensure the safety of the affected residents. This research focuses on developing an intelligent optimization method utilizing the Multi Vertex Multi Goals (MVMG) scheme to find optimal evacuation routes. In this scheme, multiple starting points and evacuation destinations reflect the actual conditions on the ground. The Ant Colony Optimization (ACO) algorithm was chosen because of its superiority in finding optimal solutions in dynamic and complex conditions. This research also compares the performance of ACO with traditional algorithms, such as Dijkstra and Breadth-First Search (BFS). The test results show that ACO consistently achieves the lowest evacuation time and the highest efficiency compared to the other two algorithms. In addition, this research opens opportunities for further research by considering complex factors, including traffic congestion and disaster-prone areas, to improve the robustness and application of optimization algorithms in more realistic and dynamic scenarios.

DeepYoga: Enhancing Practice with a Real-Time Yoga Pose Recognition System

Roli Bansal — 2024-12-02

The adoption of yoga as a holistic wellness practice is increasing throughout the world. However, in the absence of a personalized expert, especially in an online environment, there is a need for reliable and accurate methods for yoga posture recognition. Maintaining correct yoga postures is essential to reap holistic health benefits in the long term and address chronic medical issues. This paper presents DeepYoga, a novel approach to improve posture recognition accuracy with the support of deep learning models. The proposed approach uses a dataset of accurate yoga pose images encompassing five distinct poses. Landmarks extracted from the practitioner's body in the images are then used to train a Convolutional Neural Network (CNN) for accurate pose classification. The trained model is then used to detect yoga poses from real-time videos of yoga practitioners. Then, the system provides users with real-time feedback and visual suggestions, helping them improve physical alignment and reduce the risk of injury. The proposed method achieved an overall high accuracy of 99.02% in pose detection while trying to minimize the use of resources as much as possible to make it more accessible.

Enhancing the Scalability of Blockchain Networks using a Data Partitioning Technique

Basavaiah Lathamani — 2024-12-02

The scalability limitations of current blockchain systems slow down their broad adoption. This issue arises because transactions are processed sequentially, limiting throughput and increasing network delays. Additionally, even with advanced multicore technology, the Proof-of-Work (PoW) process is generally performed in a linear fashion. To address these challenges, this study proposes a static analysis-based data partitioning technique to enhance transaction performance and reduce network latency by allowing parallel processing of transactions, called Simultaneous Block-Level Transaction Execution in a Distributed Setting. This framework utilizes a master-slave system within a trusted node community. The master node analyzes transactions and partitions non-conflicting ones into separate groups, or shards, which are then distributed among slave nodes for parallel execution. Once transactions are completed, the community's combined computing power is used to perform PoW simultaneously. The miner subsequently broadcasts the newly created block to other network peers for validation, which can be performed either sequentially or in parallel. Validators ensure that they achieve the same state as specified in the block. Implementing this framework on a workload can result in a maximum speedup of 1.81x for miners and 1.80x for validators, with each block containing between 150 and 550 transactions and involving six community members. PoW is a consensus mechanism in which miners solve complex cryptographic puzzles to validate transactions. It ensures network security but is resource-intensive due to its high computational demands. In the proposed framework, the master node coordinates transactions, while the slave nodes process them in parallel. This approach maximizes resource utilization across nodes.

An Efficient Novel Model for Multi-Story Building Construction Quantity Estimation using Coupled MATLAB-Revit Software

Suha Falih Mahdi Alazawy — 2024-12-02

The nature of the construction business and building projects necessitates the capacity to manage extensive and intricate data and documentation. The processes for documenting, exchanging, and updating data constitute one of the principal administrative challenges being currently confronted by the construction project management. As a consequence of the continued reliance on paper-based processes evidenced in the Iraqi businesses and construction projects, a considerable volume of documentation is likely to accumulate, thereby increasing the complexity and time required for specific data to be retrieved. In this study, the Support Vector Machine (SVM) and Building Information Modeling (BIM) models were used to document projects by employing the MATLAB-Revit software. The findings demonstrate that the project timeline is also recorded because it is related to the suggested model, which is designed to produce an effective model that mimics reality. A comparative analysis of the data pertaining to the foundations, columns, walls (24 cm and 12 cm), floors, and slabs of four multi-story buildings, was conducted. This analysis was divided into three categories: estimated, SVM-BIM, and actual documentation. The findings indicated that the proposed model demonstrated a high degree of accuracy in predicting the material quantities required in building construction. These values were found to be in close proximity to, and aligned with, the actual documentation.

A Scalability Enhancement Scheme for Ethereum Blockchains: A Graph-based Decentralized Approach

Burhan Ul Islam Khan — 2024-12-02

Amidst the rising demands for data security across expansive networks, blockchain technology is witnessing an upsurge in its adoption, particularly within Internet of Things (IoT) applications, services, and smart cities. Blockchains offer an immutable property that bolsters security and aids in the structured management of distributed ledgers. Nevertheless, ensuring scalability remains a formidable challenge, especially within decentralized Ethereum systems. Current methods often fall short of offering tangible solutions, and the scrutiny of Ethereum-based cases reveals persistent deficiencies in addressing scalability issues due to inherent system complexities, dependency on resource-intensive consensus algorithms, lack of optimized storage solutions, and challenges in ensuring synchronous transaction validation across a decentralized network. This paper proposes a foundational scheme underpinned by a unique graph-based topology and hash bindings for nodes that join the system. The proposed scheme establishes an innovative indexing mechanism for all transactions and blocks within the IoT framework, ensuring optimal node accessibility. Transaction and block replications occur over the joining nodes' graphical structure, ensuring efficient subsequent retrieval. A standout feature of the proposed scheme is its ability to enable participating nodes to forgo retaining a complete ledger, making it non-reliant on individual node capabilities. Consequently, this facilitates a broader spectrum of nodes to participate in the consensus system, irrespective of their operational prowess. This study also offers a novel empirical model for Proof-of-Validation (PoV), which reduces computational intricacy and expedites the validation process in stark contrast to prevailing blockchain systems.

Embodied Carbon in Concrete: Insights from Indonesia and Comparative Analysis with UK and USA

Militia Keintjem — 2024-12-02

Concrete is the most widely used construction material globally. However, its production, particularly that of cement, is a significant source of carbon dioxide (CO₂) emissions, contributing to approximately 8% - 10% of the global anthropogenic CO₂ emissions. This study aims to analyze and compare the embodied carbon (eCO₂) of various concrete strength grades commonly utilized in Indonesia to offer insights for enhancing sustainability in the construction industry. The methodology involved designing concrete mixes according to Indonesian standards and calculating carbon emissions for each component. The findings revealed that the eCO₂ in the Indonesian concrete mixes was significantly higher than that reported in the UK and US databases. This higher carbon footprint emerges primarily due to the greater cement content found in the Indonesian mixes. Nevertheless, the current study demonstrated that using fly ash as a supplementary cementitious material can substantially reduce the eCO₂, with the mix containing fly ash showing a 42% reduction in emissions compared to the mix without fly ash. This research emphasizes the necessity for the Indonesian construction industry to adopt sustainable practices, including optimized mix designs and the use of low-carbon materials such as fly ash. In doing so, significant reductions in the carbon footprint of concrete can be achieved, contributing to the global efforts to mitigate climate change and to promote sustainability in construction practices.

Assessment of Shear Strength Models for Squat Rectangular Reinforced Concrete Shear Walls

Duc-Xuan Nguyen — 2024-12-02

The shear strength is a critical parameter in the design of Reinforced Concrete (RC) shear walls subjected to lateral loads. Numerous design codes and published studies have proposed formulas for calculating the shear strength of squat RC walls. However, there is a discrepancy between the calculated and experimental results. This study aims to evaluate various models for the calculation of the shear strength of rectangular squat RC walls using 312 databases collected from the literature. The shear strength of the RC walls was calculated using eight code- and empirical-based models, while the input parameters were obtained from the experimental database. The results were evaluated utilizing two statistical indicators: coefficient of determination and root-mean-squared error. The analysis of the results revealed that the model of C. K. Gulec and A. S. Whittaker is the optimal model, followed by the models of S. L. Wood and Eurocode-8 (EC8).

An Ensemble Approach to Improve the Performance of Real Time Data Stream Classification

Dhara Joshi — 2024-12-02

In the era of the Internet of Things (IoT), data stream mining has gained importance to make accurate and profitable decisions. Various techniques are used to gain insight into data streams, including classification, clustering, pattern mining, etc. Data are subject to changes over time. When this happens, predictive models that assume a static link between input and output variables may perform poorly or even degrade, which is called concept drift. This study proposes an ensemble architecture designed to improve performance and effectively detect concept drift in stream data classification. Using an ensemble approach, the proposed architecture incorporates three classifiers to improve accuracy and robustness against concept drift. The proposed architecture provides drift detection that ensures the model's continued performance by enabling it to be quickly modified to changing data distributions. Through comprehensive testing, the performance of the proposed algorithm was compared with existing methods, and the results demonstrate its superiority in terms of classification accuracy, precision, and recall and drift detection capabilities.

The Influence of Iron Particle Contamination on the Breakdown Characteristics of Circulating Mineral Oil under DC Voltage

Daniar Fahmi — 2024-12-02

The presence of metallic particles can lead to the degradation of transformer oil due to the intensification of the electric fields near the conductive components. The primary objective of this research was to investigate the impact of iron impurities on the electrical properties of Mineral Oil (MO), particularly under conditions of continuous flow. Five distinct samples with varying levels of contaminants were carefully prepared for analysis. A specialized chamber was designed to replicate the circulation conditions of oil within an operating transformer. The focus of the investigation was on the breakdown characteristics under DC voltages. The results indicated that higher concentrations of iron impurities were associated with a reduction in breakdown voltage although the circulation of oil exhibited a beneficial effect. To validate these findings, Finite Element Method (FEM)-based simulations were conducted. The analysis of the electric field distribution revealed that iron impurities amplified the electric field intensity, while circulation served to mitigate this effect. Furthermore, the simulations tracking the trajectory of iron particles demonstrated that circulation hindered the particles from reaching the electrodes, thereby diminishing discharge events and lowering the risk of dielectric failure. In conclusion, the circulation of MO enhanced its breakdown voltage, although the presence of iron contamination could still pose a risk under DC voltage conditions.

Hierarchical Models of Information Systems Security Metrics: A Comparative Sectoral Approach

Ansar Daghouri — 2024-12-02

Information system security metrics are critical in assessing and mitigating data protection risks. Executives must improve the security of their information systems. However, it is important to note that there is a wide variety of metrics available and that generic measurements may not be effective for the broader enterprise. This article provides an overview of information system security metrics and introduces a novel hierarchical model for them. Adopting a comparative approach across five sectors (health, finance, industry, government, and education), the Analytical Hierarchy Process (AHP) was used to design and evaluate the model in each sector context. The objective was to identify the variation in security criteria based on the sector. The results obtained confirm that the criteria weights vary according to the sector involving a change in the hierarchical evaluation model.

Investigation of the Ιmpact of HVOF Spraying Parameters on the Abrasion Resistance of Tungsten Carbide Coatings

Hong Tien Nguyen — 2024-12-01

For components operating under high pressure and high values of friction conditions, rapid wear is a common issue. Surface treatment measures are often employed to enhance the working lifespan of such components by improving their resistance to abrasion. This paper utilizes the Taguchi experimental design method in conjunction with Analysis of Variance (ANOVA) to assess the impact of spraying parameters on the abrasion resistance of the coating when applying the HVOF method. The sprayed material is WC HMSP1060-00+60% 4070, primarily consisting of Nickel and Tungsten Carbide, whereas the material for manufacturing the pressing screws is 1045 steel. The investigated spraying parameters include the spray Flow Rate (F), Spray Distance (D), and Oxygen/Propane Ratio (R). The experimental results are analyzed to determine the most suitable spraying parameters to achieve the highest abrasion resistance, and thereby improve the working lifespan of the components.

Evaluating Large Language Models' Proficiency in Answering Arabic GAT Exam Questions

Mohammad D. Alahmadi — 2024-12-02

The Saudi General Aptitude Test (GAT) aims to measure the analytical and inferential learning abilities of high school graduates seeking admission to higher education institutions. Given the need for effective preparation tools, this study investigates the potential of chat generative pre-trained transformers to assist students in preparing for the GAT, especially in Arabic. The primary objective is to assess the effectiveness of Large Language Models (LLMs) in answering questions related to mental and logical abilities, specifically in Arabic. The performance of GPT-4, GPT-4o, and Gemini was examined through 21 experiments to determine their accuracy in answering a range of GAT-related questions. The findings indicate that although GPT-4 and GPT-4o outperformed Gemini in providing accurate answers for the GAT, their current accuracy levels still require improvement.

The Effect of Digital Leadership Styles on Sustainable Performance: A Systematic Literature Review

Hadef Sultan Mutaeb Bin Dhaein Alghfeli — 2024-12-02

The importance of sustainable performance (SP) increased after the launch of the Sustainable Development Goals (SDGs) by the United Nations. Governments instructed organizations to participate in achieving the SDGs. However, there are varied levels of compliance and achievement correlated with SGs across countries, industries, and organizations. This study reviewed the literature and examined the role of leadership styles in SP. The findings showed that the number of articles on this topic has been constantly increasing, especially after 2017. Moreover, the number of studies on this field has been correspondingly increasing in developing and emerging economies, and particularly in manufacturing and small and medium enterprises. The findings also indicate that most of these studies are quantitative and have utilized an adequate sample size. The Resource-Based View (RBV) and stakeholder theories are widely deployed. Leadership styles are critical for SP. There are mixed findings, though, in terms of the effect of certain leadership styles on SP. Hence, more studies need to be carried out in different countries and industries, while different approaches are required to be put into service since having the right leadership style can improve SP.

Improving the RSA Encryption for Images by Introducing DNA Sequence Encoding

Ali Hennache — 2024-12-02

Recent research is focused on the exploitation of DNA-based molecules for data encryption due to their high capacity to store larger volumes of data and lower computation requirements [1, 2]. This study proposes a Hybrid Image Encryption method (HIE) that convolves DNA sequence encoding with the Rivest–Shamir–Adleman (RSA) algorithm to enhance the security of image encryption. The proposed scheme uses small prime numbers to encrypt the image, which is then encoded as a DNA sequence. Subsequently, the encrypted DNA sequence is stored in a physical medium. The encrypted DNA sequence can then be decrypted using the RSA algorithm and the corresponding private key to recover the original image. The results show that using small prime numbers for RSA encryption of an image and encoding it as a DNA sequence can enhance security and reduce computational time.

Flexural Behavior of High Volume Fly Ash-Self Compacting Concrete Beams to Normal Concrete Beams with Conventional Steel Reinforcement

Rosyid Kholilur Rohman — 2024-12-02

This research determines the comparison between the flexural behavior of Normal Concrete (NC) beams and High-Volume Fly Ash Self Compacting Concrete (HVFA-SCC) beams. The research data was obtained from full-scale beam tests using four-point loading. Tests were carried out on 6 NC and 6 HVFA-SCC beam specimens with dimensions of 150 mm × 250 mm × 2000 mm. The test specimens varied with main reinforcement of 12 mm, 16 mm, and 19 mm diameter. The results of the studies show that the crack patterns of the NC and HVFA-SCC beams are almost identical to those of the flexural failure mode, while the HVFA-SCC beam has greater ductility than the NC beam. The nominal flexural strength (Mn) of HVFA-SCC beams can be calculated using the Mn formula in ACI 318-19.

Decentralized Payment Framework for Low-Connectivity Areas Using Ethereum Blockchains

Burhan Ul Islam Khan — 2024-12-02

This paper presents a pioneering analytical framework for a secure payment system leveraging blockchain technology tailored to regions with suboptimal network connectivity. Contemporary payment mechanisms utilizing Ethereum are predominantly optimized for areas with robust network infrastructure, neglecting regions with less connectivity. To address this gap, the proposed model integrates novel security attributes and employs an analytical method to design a decentralized payment system. The framework facilitates communication between low-connectivity zones and Internet service providers through auxiliary nodes, creating a local blockchain network for residents, merchants, and auditors. A mathematical model quantifies operational costs, transaction processing, and synchronization of auxiliary nodes, ensuring a resilient and secure payment architecture. A unique aspect of the proposed approach is its robustness against auditor outages and network variability, coupled with an empirical analysis of incentive structures for auditors' block validation activities. Moreover, it delineates the minimum requirements for secure transaction completion. Empirical findings showed a significant improvement in system efficiency, including a 79% reduction in block time, a 28% increase in transaction throughput, a 30% decrease in energy consumption, a 68% shorter confirmation time, a 63% reduction in execution time, a 46% increase in block production rate, and 82% reduced network variability. This study's significant contribution lies in introducing a sustainable, cost-effective, and secure payment system for regions with inadequate network services.

Prediction of Higher Education Student Dropout based on Regularized Regression Models

Bouchra Bouihi — 2024-12-02

This study explores the critical topic of student dropout in higher education institutions. To allow early and precise interventions and to provide a multifaceted view of student performance, this study combined two predictive models for dropout classification and score prediction. At first, a logistic regression model was developed to predict student dropout at an early stage. Then, to enhance dropout prediction, a second-degree polynomial regression model was used to predict student results based on available academic variables (access, tests, exams, projects, and assignments) from a Moodle course. Dealing with a limited dataset is a key challenge due to the high risk of overfitting. To address this issue and achieve a balance between overfitting, data size, and model complexity, the predictive models were evaluated with L1 (Lasso) and L2 (Ridge) regularization terms. The regularization techniques of the predictive models led to an accuracy of up to 89% and an R² score of up to 86%.

Use of the SWAT Model to Simulate the Hydrological Response to LULC in a Binational Basin between Ecuador and Peru

Robinson Fabricio Pena Murillo — 2024-12-02

Land use change has played a crucial role in altering the hydrological behavior, making detailed assessments essential to ensure sustainable water resource management and the conservation of natural ecosystems. This study focuses on simulating the impact of different Land Use and Land Cover (LULC) scenarios for the years 1985, 1995, 2005, and 2015 on the water balance in the Puyango-Tumbes River basin, which spans across Ecuador and Peru, during the period 1981-2015. The results indicated an 18.3% increase in the grassland areas and a significant 38.2% reduction in the savanna zones, contributing to an annual 2.1% increase in the Evapotranspiration (PET) rates. These land use changes led to a 29.2% decrease in the Percolation (PERC), a 20.7% decrease in the Surface Runoff (SURQ), a 33% reduction in the Groundwater Flow (GW_Q), and a 26.6% decrease in the Annual Water Yield (WYLD), as well as a slight reduction of 0.9% in the Lateral flow (LAT_Q). These findings highlight the importance of considering land use changes to ascertain the sustainable management of natural resources, particularly in a transboundary basin.

Monitoring of a Low-Order Even Radial Vibrational Circumferential Mode in a Round Hollow Cylinder

Djamel Ouis — 2024-12-02

This paper presents a nondestructive testing method for assessing the structural integrity of cylindrical elements by monitoring a range of radial vibrational modes, with a specific emphasis on the so-called ovalling mode. The method involves exciting the cylinder with a single vibration source in the radial direction and measuring the response using two vibration sensors positioned diametrically on the cylinder's surface. The ovalling mode was extracted from the frequency response by adding the in-phase signals recorded by the sensors. Experiments conducted on a PVC pipe showed good agreement between the measured resonance frequency of the ovalling mode and its predicted value, calculated using the theory for thin cylindrical shells and Finite Element Method simulations. This research is an investigation into the potential and reliability of this nondestructive technique for detecting corrosion and strength-weakening defects in concrete building elements, steel pillars, and columns. The extent of the strength reduction can be determined by analyzing the change in the resonance frequency of the ovalling mode.

Analysis of the Water Balance in a Block of Seven Drainage Lysimeters under Field Conditions

Robinson Pena Murillo — 2024-12-02

Water Balance (WB) allows for assessing the deficit or excess of water. For this purpose, drainage lysimeters have a mechanism to collect and quantify the amount of water that infiltrates through the soil profile, thus evaluating crop evapotranspiration. This study describes the design, construction, and calibration of a block of 7 drainage lysimeters. The lysimeters were designed with a width of 1.97 m, length of 2.49 m, and depth varying from 0.60 m to 1.10 m. For construction, four sequential layers of soil, each 0.2 m thick, were extracted. The concrete resistance of the walls and floors was 210 kg cm^-2, and rhizotrons were installed on the inner wall of five of the lysimeters. Calibration included evaluating compaction in the first 3 layers, averaging 2.11, 5.18, and 7.91 kg cm^-2 respectively. Infiltration ranged from 5.6 to 10.2 mm h^-1. The moisture retention curve allowed determining the irrigation volume to reach Field Capacity (FC), plus an additional percentage of FC volume to produce drainage.

Enhancing the Geotechnical Properties of Expansive Soils through Coconut Shell Ash Treatment: An Experimental Investigation

Andryan Suhendra — 2024-12-02

Expansive Soils (ES) present a significant challenge to civil engineering projects worldwide due to their propensity to undergo volumetric changes in response to fluctuations in the moisture content. This study examined the potential of Coconut Shell Ash (CSA) as a soil stabilizer to mitigate the adverse effects of ES. The objective was to conduct a systematic evaluation of the impact of CSA on a range of soil properties, including the plasticity index, compressive strength, shear strength, swelling potential, and compaction characteristics, across a diverse array of soil types. This study adopted a comprehensive methodology, which involved the laboratory testing of soil samples with varying proportions of CSA. The tests included the determination of the Atterberg limits, the evaluation of the compaction properties, unconfined compression tests, and swelling tests. The findings revealed significant variations in the soil properties in response to the CSA content. The plasticity index responses exhibited a range of subtle changes, with a downward trend at lower CSA concentrations and more complex behaviors at higher concentrations. The compaction characteristics exhibited alterations in the optimum moisture content and maximum dry unit weight, indicating changes in soil density and stability. Similarly, the compressive and shear strength properties exhibited fluctuations with varying CSA content, underscoring the necessity for a comprehensive assessment of soil stability under CSA-treated conditions. Additionally, swelling tests demonstrated the potential of CSA to mitigate soil expansiveness, with lower swelling percentages observed in treated soils. This study highlights the importance of considering the soil type, CSA content, and engineering requirements to optimize the effectiveness of CSA in soil stabilization applications.

Analyzing Delay Factors in Iraqi Construction Projects: An ANP Approach with Najaf Governorate as a Case Study

Ali Moosa Alklkali — 2024-12-02

Construction projects in Iraq often experience delays owing to various factors, making it challenging to accurately estimate completion times. This study investigates the factors contributing to these delays, focusing on the Najaf Governorate as a case study. The primary objective of this study was to investigate issues related to the construction project completion duration, particularly considering Iraq's current unprecedented reconstruction and building efforts. The most significant factors influencing the completion duration were identified through statistical analysis of the samples and application of the Analytic Network Process (ANP) method. Data were collected and refined using a questionnaire designed according to a five-point Likert scale and administered in two rounds. The first round involved 28 participants, whereas the second round included 5 participants. The analysis revealed that contractor inefficiency, particularly from a financial perspective, is one of the most critical factors affecting construction progress and causing delays. The practice of accepting the lowest bids emerged as the second most crucial aspect leading to project delays in Iraq. Additionally, the study concluded that insufficient cash flow for the employer and the lack of technical expertise within the contractor company were the third and fourth most influential factors affecting the duration of construction projects, respectively. This study also highlighted that legal gaps in Iraq's legislative framework for construction projects are among the most significant causes of delays. Finally, the practice of subcontracting was identified as a significant contributor to both delays and poor work quality in the construction projects in Iraq.

Structural Behavior of Self-Compacting Bendable Mortar Beams reinforced by GFRP Bars under Monotonic Loads

Tabark Mohammed — 2024-12-02

Flexible or bendable concrete is an Engineered Cementitious Composite (ECC) that exhibits ductile material properties, in contrast to the brittle nature of conventional concrete. The material composition of conventional concrete is modified in order to impart a flexible nature to the material. This research presents the findings of an experimental study examining the flexural response of self-compacting bendable concrete beams reinforced by Glass Fiber Reinforced Polymers (GFRP) bars under a symmetrical two-point load. The experimental work comprised the casting of eight reinforced beams. The dimensions of all beams were identical: an overall height of 150 mm, a width of 100 mm, and a total length of 1,000 mm. The beams were classified into three groups based on the type of variables adopted and the type of the strengthening method employed, the percentage of steel fibers (1%, 1.5%, and 2%), and the percentage of Polyvinyl Alcohol (PVA) (0.2%, 0.3%, and 0.4%) were also considered. The following measurements were taken: the first cracking load, midspan vertical deflections, concrete surface strains, and the ultimate load capacity. Additionally, the crack patterns were recorded and the failure mode was observed, in addition to the mechanical properties of self-mortar bendable concrete (both fresh and hardened). The results indicated that an increase in the PVA ratio from 0.2% to 0.3% and 0.4% resulted in a notable rise in the modulus of rupture and modulus of elasticity, by approximately 16% and 40%, respectively, and 0.09% and 2%, respectively. The ductility of the beams increased with the steel fiber ratio due to enhanced flexural and splitting properties, which is a positive outcome. This allows for more caution to be exercised before the beam reaches its limit of stability. Furthermore, the value of deflection at maximum load increases with the increase of steel fiber content due to the increase in load capacity.

Navigating the Clouds: An Exploratory Research of Cloud Computing Adoption in Saudi Arabia's Small and Medium Enterprises

Nada Alamri — 2024-12-01

The adoption of cloud computing has become essential for SMEs, especially in the e-commerce industry, as it has the potential to improve the efficiency of operations. However, the existing literature indicates significant gaps, including a general lack of comprehensive research focused on cloud computing adoption for SMEs and very limited research on the Kingdom of Saudi Arabia (KSA) context. This study aimed to fill these gaps by evaluating the current status of cloud computing adoption by SMEs in KSA and identifying the challenges facing its implementation in the e-commerce sector. To achieve these objectives, the Technology-Organization-Environment (TOE) framework was employed, analyzing the technological, organizational, and environmental factors that affect cloud service adoption. This study incorporates insights from government agencies, service providers, and SMEs to provide a holistic view. Through a mixed-method approach, combining qualitative and quantitative data, this study offers a detailed understanding of the cloud computing landscape in KSA, providing valuable insights for stakeholders and policymakers.

Integrating FUCA, SRP, and OPARA Methods to Assess Faculty's Scientific Research Capacity

Thi Nhu Uyen Vo — 2024-12-02

Faculty's scientific research activities are not only a primary task besides teaching but also play a crucial role in knowledge development and enhancing education quality. Evaluating the scientific research capacity of the faculty in a department helps identify capabilities and promote a competitive spirit, thereby improving the effectiveness and reputation of the educational institution. This study evaluates the scientific research capacity of outstanding faculty members in a Vietnamese university department by integrating three methods: FUCA (Faire Un Choix Adéquat), SRP (Simple Ranking Process), and OPARA (Objective Pairwise Adjusted Ratio Analysis). The evaluation data are based on the number of Scopus-indexed scientific articles published in an academic year. Q1, Q2, Q3, and Q4 ranked articles are used as evaluation criteria for each faculty member. The weights of the criteria are calculated with the use of ROC (Rank Order Centroid) and RS (Rank Sum) weight methods. For both methods, two faculty members with outstanding scientific research achievements were identified.

Enhancing Information Technology Governance in Universities: A Smart Chatbot System based on Information Technology Infrastructure Library

Souad Ahriz — 2024-12-02

The rapid evolution of information and communication technologies has created a pressing need for higher education institutions to modernize their Information Technology (IT) governance practices. This article proposes an innovative solution to enhance the governance and efficiency of IT services while optimizing and personalizing user experience. The proposed solution consists of a chatbot using Artificial Intelligence (AI) and Natural Language Processing (NLP) combined with the Information Technology Infrastructure Library (ITIL) standard to automate the management of IT services in the digital work environment (ENT). Intended for students, teachers, and administrators, this chatbot provides reactive support by responding to requests, reducing waiting times, and improving satisfaction. It also helps decrease operational costs and the workload of support teams by autonomously handling recurring requests. Beyond efficiency improvements, the chatbot contributes significantly to IT governance by providing structured service management, improving decision-making through real-time data, and supporting compliance with governance frameworks. An online survey conducted among 120 students revealed slow processing of requests and unavailability of services, justifying the need for this chatbot. The chatbot was designed with advanced NLP and Machine Learning (ML) technologies. Preliminary tests demonstrate the chatbot’s response reliability, with an accuracy rate of 96% and a response time decrease to an average of 4.17 seconds. The use of chatbots has considerable potential for universities to improve the efficiency of digital services offered to students.

Wear Behavior of 2024 Aluminum Alloy Reinforced with SiC Particles

Abdulhakeem Amer Salman — 2024-12-02

The wear behavior of Aluminum Alloy (AA) 2024 reinforced with SiC particles were investigated at room conditions. Stir casting method was used to prepare the matrix composite of AA 2024 alloy reinforced with different weight percentages of SiC (3, 6, 9, 12 wt%). The wear behavior of both AA 2024 and AA 2024/SiC composite was studied using the reciprocating wear test. Loads of 2.5, 5, 7.5, 10, and 12.5 N were applied with sliding speeds of .0.55, 0.72, 0.88, 1.04 and 1.2 m/sec in dry sliding contact conditions. The microstructure and phase distribution were investigated using Optical Microscope (OM) and Scanning Electron Microscopy (SEM). The results demonstrate the presence of fine dendritic structure with semi-homogeneously distributed SiC particles in the alloy matrix. The micro-hardness increased with increasing SiC percentage. The highest value of hardness of 79.3 HV was found at 12 wt% SiC, from the initial 44 HV of the base alloy. The wear tests showed that the wear rate increased with increasing applied normal contact load at a contact sliding speed of 0.88 m/sec. The wear rate of the as cast material was 18.59 ×10^-8 g/cm. Adding the SiC particles decreased the wear rate to 13.78, 9.76, 7.98, and 5.81 ×10^-8 g/cm for 3, 6, 9, 12 wt% SiC addition at 12.5 N applied load, respectively. SEM examination of worn surfaces showed that severe and abrasive wear was exhibited at higher loads in the dry case while mild and adhesive wear were observed at lower loads in the lubricated condition.

Design and Failure Analysis of a Vacuum Pressure Vessel for Aerospace Applications using Finite Element Analysis (FEA)

Eiman Solangi — 2024-12-02

This paper provides detailed insights into the external pressure vessels. External pressure vessels are designed to perform under extreme operating conditions, so the selection of material and geometry along with stress analysis are fundamental for their optimal efficiency. The objective of this research is to provide a case study of the material, design, and stress generated in the pressure vessel to make it suitable for thermal and load-bearing applications in the aerospace industry. An aluminum alloy was chosen as the design material due to its low density and high strength. The modeling geometry of the alloy was constructed using the ASME section-VIII division-I div code. After the performed Finite Element Analysis (FEA), modeling was carried out deploying the ANSYS design modeler to obtain the stress concentration and failure mode of the model. The present study demonstrates the behavior of a structure under applied load and identifies the weak areas of its geometry. Based on the external stress at the center of the structure, the maximum and minimum stresses computed are 0.763 MPa and 0.00803 MPa, respectively. It was also found that the maximum strain is generated at the center of the structure and is equal to 1.0834e-6 mm/mm, the maximum deformation is equal to 0.00109 mm and also occurred at the center, while the shells of the model are unable to undergo any deformation. FEA results agree with the analytical results, as the errors for hoop stress and equivalent strain are 4.8% and 1.8%, respectively. Thus, the proposed method can be applied to predict the equivalent stress, equivalent strain, total deformation, and stress intensity, which are required for the structural integrity analysis of pressure vessels.

Leveraging Convolutional Neural Network (CNN)-based Auto Encoders for Enhanced Anomaly Detection in High-Dimensional Datasets

M. Aetsam Javed — 2024-12-02

This study presents an Auto-Encoder Convolutional Neural Network (AECNNs) approach for anomaly detection in high-dimensional datasets. Unsupervised learning-based algorithms have a strong theoretical foundation and are widely used for anomaly detection in high-dimensional datasets, but some limitations significantly reduce their performance. This study proposes an algorithm to address these limitations. The proposed AECNN combines various convolutional layers, feature extraction, dimensionality reduction, and data preprocessing and was evaluated using accuracy, precision, recall, and F1-score. The performance of the proposed model was evaluated using a large real benchmark dataset. The proposed CNN-based autoencoder distinguished anomalies with an AUC score of 0.83 and remarkable accuracy, precision, recall, and F1 score.

Analysis of Permanent Magnet Demagnetization during the Starting Process of a Line-start Permanent Magnet Synchronous Motor

Tuan Le Anh — 2024-12-02

Neodymium (NdFeB) rare earth Permanent Magnets (PMs) are widely used in the manufacturing of Line-Start Permanent Magnet Synchronous Motors (LSPMSMs). During the initial startup of LSPMSMs, irreversible PM demagnetization often occurs. This research provides a thorough examination of the PM demagnetization process during the initiation of a 15-kW, 3000-rpm LSPMSM, which was converted from a squirrel-cage Induction Motor (IM) featuring a 3-bar PM. Utilizing Ansys/Maxwell2D software for simulations and conducting experiments, this study reveals that upon starting the LSPMSM, demagnetization occurs at the two outermost PM pairs. This leads to unstable motor operation at synchronous speed, significant current fluctuations, and a substantial decrease in performance. The findings suggest that to ensure effective LSPMSM operation, it is crucial to either restrict frequent restarts or select an appropriate PM type to prevent partially irreversible demagnetization, thereby enhancing power efficiency.

Sensitivity Analysis of Multi-Parameter Magnetic Control Indicators for determining the Mechanical Properties of Steel

Alibek Zhakupov — 2024-12-02

This paper presents the results of a steel magnetic property study using a non-destructive method of determination of the mechanical properties of products made from structural steels 20, 09Mn2Si, and 25CrMoV. The influence of tempering temperature on the change of magnetic properties was analyzed and a statistical analysis of the input parameter significance, including coercive force, residual magnetic induction, and maximum magnetic permeability, was carried out. As a result, it was found that the greatest sensitivity for steels at tempering temperatures up to 400 °C is the coercive force and, above 300 °C, the residual magnetic induction. At the same time, the maximum magnetic permeability provides a correlation over the whole range of tempering temperatures, justifying the use of magnetic properties in the determination of mechanical properties.

Utilizing Chaotic Logistic Keys for LSB1 and LSB2 Message Steganography

Ahmad A. Sharadqh — 2024-12-02

This paper introduces a novel hybrid data steganography method that combines the new techniques of LSB1 and LSB2. The proposed method simplifies data-hiding and extraction operations by utilizing a patch method. A unique Private Key (PK) divides the message into two parts. The first part is processed using the LSB1 method, while the second one is treated with the LSB2 method. The PK information is utilized to create secret keys, namely key1 and key2. The keys are generated by converting two Chaotic Logistic Keys (CLKs) and establishing the sequence of cover Stego bytes for concealing and revealing data. The secret message is protected within a secure key area by utilizing the PK, which improves security by preventing unwanted access. The secret message's effective extraction depends significantly on the PK's content. Any alterations to the key during the extraction step will be deemed unlawful, possibly leading to a compromized secret key. Moreover, the suggested approach is followed and assessed by using different messages. The results are comprehensively studied, ensuring a robust evaluation of the quality, efficiency, and security improvements of the data steganography process. The experimental results confirm the data steganography's quality, efficiency, and security enhancements.

Online Multi-object Tracking with YOLOv9 and DeepSORT Optimized by Optical Flow

Djalal Djarah — 2024-12-02

To ensure reliable environmental perception in the realm of autonomous driving, precise and robust multi-object tracking proves imperative. This study proposes an innovative approach to multi-object tracking by combining YOLOv9's sophisticated detection capabilities with an enhanced DeepSORT tracking algorithm, enriched through the integration of optical flow. In the proposed method, the YOLOv9 detector acutely identifies objects in input images, and these detected entities are subsequently transmitted to the optimized DeepSORT tracking algorithm. The principal contribution of this study lies in improving the Kalman filter measurement model within DeepSORT by incorporating robust local optical flow, thus adding a velocity dimension to the filter's update vector. This novel approach significantly improves tracking resilience in the face of occlusions, rapid movements, and appearance changes. Evaluations on MOT17 and KITTI show substantial improvement gains of 2.42%, 2.85%, and 1.84% for HOTA, MOTA, and IDF1, respectively, on MOT17, and 1.94% in MOTA and 2.09% in HOTA on KITTI. The proposed method particularly excels in managing scenarios involving dense traffic and light variations, which are recurrent problems in dynamic urban environments. This enhanced performance positions the proposed solution as an essential component of future perception architectures for autonomous vehicles, promising safer and more efficient navigation in the complex real world.

Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data

Karma Dorji — 2024-12-02

Electricity load forecasting is an important aspect of power system management. Improving forecasting accuracy ensures reliable electricity supply, grid operations, and cost savings. Often, collected data consist of Missing Values (MVs), anomalies, outliers, or other inconsistencies caused by power failures, metering errors, data collection errors, hardware failures, network failures, or other unexpected events. This study uses real-world data to investigate the possibility of using synthetically generated data as an alternative to filling in MVs. Three datasets were created from an original one based on different imputation methods. The imputation methods employed were linear interpolation, imputation using synthetic data, and a proposed hybrid method based on linear interpolation and synthetic data. The performance of the three datasets was compared using deep learning, machine learning, and statistical models and verified based on forecasting accuracy improvements. The findings demonstrate that the hybrid dataset outperformed the other interpolation methods based on the forecasting accuracy of the models.

An Experimental and Analytical Study on the Compressive Behavior of Glass Fiber Reinforced Concrete (GFRC) confined with GFRP Composites

Abdelhakim Zendaoui — 2024-12-02

This study investigates the axial compression behavior of confined circular concrete columns through a combined experimental and analytical approach. It examines the influence of the concrete strength, 8.5, 16, and 25 MPa, internal glass fiber percentage, 0.3-1.2 %, and Glass Fiber Reinforced Polymer (GFRP) confinement thickness, 0.8, 1.6, and 2.4 mm. The Glass Fiber (GF) percentage and GFRP thickness have a significant impact on the results of the uniaxial compression tests exploring both the load-deformation behavior and crack propagation characteristics of the specimens, ranging from 90 to 110%. The proposed confinement model demonstrates excellent agreement with the experimental data for the ultimate axial strain and across the investigated range of concrete strengths.

New Solutions in Pipe Billet Production

Aray Zhakupova — 2024-12-02

This paper proposes a method for determining the speed of hollow steel billet casting to produce seamless oil and gas pipes. At the same time, the use of hollow billet excludes, in the technological process of pipe production, the piercing of the billet. The results of industrial production modeling were recalculated into natural quantities according to the Fourier number similarity criterion. The calculations determined that the optimal drawing speed and casting speed of a billet with a diameter of 210 mm are 2.01 m/min and 0.273 t/min, respectively. At the same time, productivity increased by 16%, compared to the one when casting a solid billet. To ensure the homogeneity of structure and mechanical properties over the entire cross-section of the hollow billet, the optimum ratio of coolant flow rates into the inner cavity and outside the billet in the secondary cooling zone was determined, considering the area of the cooled surface, which is equal to 1.46.

Nonlinear Backstepping Control for Three-Phase Single-Stage Grid-Tied Photovoltaic Systems via an LCL-Filter

Zakariae El Madani — 2024-12-02

This paper examines the modeling and nonlinear control of a Three-Phase Single-Stage Grid-Tied Photovoltaic System (TPSS-GTPS). The system structure is relatively simple, comprising a Photovoltaic (PV) generator connected to the grid through a three-phase Voltage Source Inverter (VSI) and an LCL filter designed to reduce harmonics in the grid current. The primary objective of the control system is to maximize the extraction of power from the Photovoltaic Generator (PVG) and deliver it to the utility grid with a Unity Power Factor (UPF), while ensuring the asymptotic stability of the closed-loop system. In order to achieve these objectives, a novel nonlinear controller was developed in the synchronous dq-frame following the backstepping approach. Evaluating the effectiveness of the designed controller, simulations were performed in the MATLAB/Simulink environment under various scenarios to consider the effects of irradiance and temperature on the PVG. The simulation results demonstrated that the controller successfully achieved all the specified objectives. Additionally, this study highlights the effectiveness of the LCL filter in reducing Total Harmonic Distortion (THD).

Evaluating the Impact of Oil Market Shocks on Sovereign Credit Default Swaps in Major Oil-Exporting Economies

Nadia Belkhir — 2024-12-02

This study analyzes the impact of oil market fluctuations on Sovereign Credit Default Swaps (SCDS) in three key oil-exporting economies: Saudi Arabia, Russia, and the United Arab Emirates (UAE). The study investigates how various oil shocks, namely demand, supply, and market risk, affect sovereign credit risk and how these effects are transmitted within and across these economies. Time-domain and frequency-domain analyses were used to categorize oil market shocks and structural break analysis was incorporated to account for significant global events. The findings indicate that Saudi Arabia is a primary source of credit risk volatility, influencing Russia and the UAE, with the latter being significantly affected as a net recipient of such risks. Structural breaks, such as those associated with the COVID-19 pandemic, introduce shifts in impact patterns. This study underscores the significant role of demand shocks in shaping sovereign credit risk across the countries examined. These insights are essential for policymakers, investors, and financial analysts focused on sovereign credit risk management in oil-exporting economies, highlighting the importance of considering structural changes in economic conditions.

ResNet-based Gender Recognition on Hand Images

Eren Yildirim — 2024-12-02

The use of biometric features for the surveillance and recognition of certain classes, such as gender, age, and race, is widespread and popular among researchers. Various studies have focused on gender recognition using facial, gait, or audial features. This study aimed to recognize people's gender by analyzing their hand images using a deep learning model. Before training, the images were subjected to several preprocessing stages. In the first stage, the joint points on either side of the hand were detected using the MediaPipe framework. Using the detected points, the orientation of the hands was corrected and rotated so that the fingers pointed upwards. In the last preprocessing stage, the images were smoothened while the edges were preserved by a guided filter. The processed images were used to train and test different versions of the ResNet model. The results were compared with those of some other studies on the same dataset. The proposed method achieved 96.67% recognition accuracy.

Application of LightGBM Algorithm in Production Scheduling Optimization on Non-Identical Parallel Machines

Khalid Ait Ben Hamou — 2024-12-02

Production scheduling plays a decisive role in supply chain management, directly influencing the operational efficiency and competitiveness of companies. This study explores the effectiveness of the LightGBM algorithm for production scheduling on non-identical parallel machines, comparing it to algorithms such as logistic regression, KNN, decision tree, and XGBoost. LightGBM was chosen for its speed of execution and its ability to handle large amounts of data. The results show that LightGBM outperforms the other models in terms of RMSE, MAE, explained variance score, and R² score for regression tasks, as well as in classification accuracy for certain features. Its superiority is attributed to its ability to efficiently handle data complexity while reducing computational complexity through its leaf tree growth technique. This study highlights LightGBM's potential for improving the efficiency of supply chain management systems and the challenges associated with computational scalability for large datasets. The results suggest that LightGBM is a robust and effective solution to optimize production scheduling, paving the way for future research in this field.

Improvement of NC Program Quality based on Shape Generation Motions and Feed Drives for Five-Axis CNC Machine Tools

Wiroj Thasana — 2024-12-02

Five-axis Computer Numerical Control (CNC) machine tools, integrated with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems, are used to machine complex parts and reduce trials and errors. However, these machine tools still rely on Numerical Control (NC) programs and often lack accuracy and precision due to poor quality when implemented in the machine. This research aims to enhance the quality of NC programs for five-axis CNC machine tools by focusing on shape generation motions and a closed-loop feed drive system with Proportional-Integral-Derivative (PID) control. The individual motions were mathematically described using 4x4 transformation matrices, incorporating kinematic motion deviations, end mill geometry, machining parameters, and cutting forces derived from virtual machining. Additionally, a closed-loop feed drive system with PID control was integrated with the new position and angular data of each axis from the shape generation motions model. The new NC programs were validated by machining an S-shaped part and measuring dimensional errors at 64 points before and after using a Coordinate Measuring Machine (CMM). The results indicate a substantial reduction in the standard deviations of form and angular errors within the NC program quality, totaling approximately 80.73%. Reductions are demonstrated in the standard deviations for the X, Y, A, and B axes, with decreases of 76.83%, 95%, 82.40%, and 68.72%, respectively indicating a significant improvement in the overall quality of the NC program.

Investigation of Acoustic Waves generated by the Detonation Gun of a Zenith-M modernized Powerful Antihail Station

Gagik Ayvazyan — 2024-12-02

In this paper, a detonation gun of an antihail station was used to study acoustic waves as they propagated upward. The station's power was enhanced by placing a Hartmann resonance tube into the conical nozzle of the gun. Propagation speed, pressure-time waveform, and acoustic spectrum of the acoustic waves were determined. The propagation speed was approximately 890 m/s (Mach number ~2.6), and the initial (near-ground) intensity was 130-160 dB in the low-frequency range up to 200 Hz, where the energy distribution is relatively uniform. The results show that the antihail station can effectively influence atmospheric processes, either promoting rain precipitation or preventing hail formation.

Optimizing UAV-IoT Network Integration: A Scalable Multi-Objective Communication Framework

Satnam kaur — 2024-12-02

In UAV-IoT systems, trajectory planning is crucial for maintaining effective communication, coordination, and energy efficiency. This challenge is further compounded when UAVs need to coordinate with IoT devices and maintain continuous communication. Existing approaches struggle with limited scalability and inefficient energy management in UAV-supported IoT networks, leading to increased latency and reduced data throughput as network size expands. This work introduces an energy-efficient framework using a multi-objective PathFinder algorithm designed to simultaneously handle transmission coordination between drones and IoT devices. The proposed approach facilitates collaborative decision-making for route planning and resource allocation by utilizing the Collaborative Index, which measures cooperative behavior among network nodes, emphasizing key node cooperativeness parameters. Furthermore, a multi-objective fitness function was constructed for effective path planning using the Collaboration Index of nodes in the path and the QoS of the path. To validate the efficacy of the proposed model, a series of simulations were conducted focusing on key performance indicators such as energy consumption, data delay, and task completion rates against existing state-of-the-art methods.

Strength and Thermal Properties of Hollow Foamed Concrete Blocks considering Various Parameters

Abubaker Mohammed Sulaiman — 2024-12-02

Hot weather is one of the main problems the residential building construction field faces, especially in the southern hemisphere. Therefore, there is an increasing need to produce materials capable of reducing the high temperature impact. These materials should be characterized by thermal insulation properties without, though, their mechanical properties and load resistance being affected. In this research, cuboid and hexagonal hollow concrete blocks were produced from lightweight foam concrete with different hole shapes and a hole ratio of 30%. An analytical study was conducted for 8 models using the ANSYS v16 program, while the compression behavior and thermal performance of the selected models were studied. Ordinary Portland Cement (OPC), water, sand, and foaming agents were deployed in the production of foamed concrete. In addition to the use of admixtures, materials, such as Superplasticizer (SP), Class F Fly Ash (FA), and Silica Fume (SF), were also utilized. Moreover, the effect of the hole’s shape and the method of bonding were studied. The compressive strength of the concrete blocks, bond shear strength, thermal conductivity, and thermal resistance were tested. It was found that the cuboid shape of the hole block H7 was the most acceptable compared to the other shapes, with a compressive strength of 3.75 MPa, thermal conductivity of 0.149 W/m.k, and a bond shear strength of 0.157 MPa. At the same time, it was found that using bonding adhesive material gave the best results, with the cuboid blocks being compared to using mortar and mechanical bonding.

Wastewater Quality Assessment at Amarah Sewage Treatment Plant: Implications for Agricultural Use and Environmental Safety

Nezar Hassan — 2024-12-02

This study examines the quality of treated wastewater from the Amarah Sewage Treatment Plant (ASTP) in Iraq for potential agricultural use and compares it to the water quality standards of Iraq, Egypt, and the USA. The effluent was analyzed over a five-month period for various parameters, including pH, Total Suspended Solids (TSS), Total Dissolved Solids (TDS), and concentrations of ions, such as chloride (Cl^-), sulfate (SO₄^-2), phosphate (PO₄^3-), nitrate (NO₃^-), and ammonia (NH₃). The findings showed that the pH and TDS measurements of the treated wastewater fell within the acceptable range, according to the regulations set by all three countries. The concentrations of Cl^-, SO₄^-2, PO₄^3-, NO₃^-, and NH₃in the treated wastewater were also within the acceptable limits set by the Iraqi standards. However, chloride and sulfate levels occasionally exceed permissible thresholds. The treated wastewater from ASTP is generally suitable for the irrigation of certain crops, but it is important to implement a monitoring and control system to ensure consistent water quality. Finally, investments are needed to improve treatment processes and establish educational programs for farmers to enhance their understanding of proper wastewater usage. These measures are crucial for protecting public health and conserving water resources, particularly in regions facing water scarcity.

Enhancing Sentiment Analysis of Indonesian Tourism Video Content Commentary on TikTok: A FastText and Bi-LSTM Approach

Dony Ariyus — 2024-12-02

Sentiment analysis is a method used to measure public opinion or the emotions of a group of people with similar interests based on their reactions to an event through text, images, videos, or audio on social media. However, such online data presents several challenges that can hinder the sentiment analysis process. These challenges stem mainly from the freedom that users have to post their content. Additionally, irrelevant opinions, often referred to as fake opinions, can also arise. The Bi-LSTM approach processes input sequences bidirectionally, allowing the model to capture information from both previous and subsequent contexts. This method is well-suited for sentiment analysis tasks due to its ability to recognize language nuances and relationships between different parts of the text. This study integrates a Bi-LSTM model with FastText word embeddings to filter out irrelevant opinions considered spam. The dataset consists of 150,351 TikTok comments taken from 100 popular videos related to tourist attractions. The experimental results show that the proposed Bi-LSTM model outperforms other models such as LSTM, CNN, GRU, MD-LSTM, and Peephole LSTM, achieving a test accuracy of 89.18%. Furthermore, when slang word translation is performed to convert slang into formal words, the Bi-LSTM model shows further improvement, with test accuracy reaching 93.10%, again surpassing the baseline models. These results demonstrate the robustness of the proposed method in handling noisy and informal language, thus improving the accuracy of sentiment analysis in the context of social media. This study provides a foundation for future research to improve sentiment analysis by addressing domain-specific challenges such as data imbalance and noise in social media data.

A High Gain Dual Band Hexagonal Metamaterial Inspired Antenna for 5G Applications

Sneha Talari — 2024-12-02

Microstrip patch antennas play an important role in wireless communications to improve speed. Some of their benefits are low cost, low profile, and easy to fabricate. Existing research on microstrip patch antennas examines difficulties such as limited diversity performance, low radiation characteristics, and low efficiency. To address these issues, metamaterials are used to enhance diversity. This study focuses on designing a dual-band microstrip patch antenna for 5G applications to meet user demands with high data rates and enhance communication speed. To improve isolation and gain, a hexagonal-shaped Split Ring Resonator (SRR) was added to the substrate of the antenna. The proposed antenna model operates at frequencies greater than 28 GHz. The antenna dimensions are 25×25×0.15 mm, designed on a polyamide substrate with a loss tangent tan δ of 0.004, dielectric constant ε_r of 4.3, and relative permeability of 1. The suggested model was evaluated using several performance parameters, such as reflection coefficient, axial ratio, Voltage Standing Wave Ratio (VSWR), gain, and radiation patterns. The proposed model has S-parameter values of -17.6192 dB and -20.3264 dB, and gain values of 7.1 dB and 7.3 dB at 28.900 and 33.7400 GHz, respectively.

Z-MSP: Zonal-Max Stable Protocol for Wireless Sensor Networks

Ahmed Harbouche — 2024-12-02

Clustering is a well-known energy enhancement approach used to prolong the lifetime of Wireless Sensor Networks (WSNs). However, it introduces another issue, which is the selection of the optimum number of clusters along with the appropriate cluster heads. In this paper, we study in detail the clustering approach and its impact on enhancing WSN lifetime. We provide a mathematical study that discusses the impact of clustering, where the WSN is divided into multiple zones, each zone functioning as an independent cluster. The WSN topology consists of 10 zones, all similar in area and density but differing in their distances to the base station. To prolong the WSN’s lifetime, we developed Z-MSP, an extension of MSP for Zonal WSNs. It maintains the highest stable period of MSP for the Z-WSN. Z-MSP prolongs the network's stable period by 315.625%, 315.625%, and 287.258%, and the lifetime by 245.340%, 237.277%, and 232.475%, with a very high throughput level compared to FBECS, E-CAFL, and LEACH-FC, respectively.

Mechanical Properties of Recycled Aggregate Concrete with Industrial Waste Ash

Uruya Weesakul — 2024-12-02

This study investigates the performance of concrete incorporating various recycled fine aggregates, including recycled brick aggregates, Fly Ash (FA), and Sugar Cane Bagasse Ash (SCBA). The test results showed that the mechanical properties were adversely affected when utilizing recycled brick or concrete aggregates, whereas FA or SCBA enhanced them. The water absorption potential of recycled bricks was proportional to the reduction in mechanical properties. FA and SCBA enhanced compressive strength and increased flexural strength up to 175.72% and 225.51%, respectively, at 20% replacement. The inclusion of recycled brick and concrete aggregates raised water absorption, while FA and SCBA significantly lowered it, improving the overall performance.

Structural Analysis of Latticed Steel Transmission Towers Subjected to Nondeterministic Wind Loads

Mariana Souza Rechtman — 2024-12-02

Lattice steel towers are commonly used to support overhead power transmission lines. However, the dynamic behavior of these structures is often overlooked in current design practices. Given that many accidents involving these towers occur even at basic wind speeds lower than those specified in the project, it is likely that dynamic actions play a significant role in these failures. This study proposes a method to accurately simulate the interaction between transmission line cables and towers under non-deterministic wind loads to assess displacements and forces in the steel towers. The study examines a transmission line system consisting of towers, conductors, shield wires, and insulators, featuring a central suspension tower of 32.86 m in height, flanked by two end towers with 450 m spans. Finite element modeling was developed to account for the dynamic characteristics of the wind. Wind loads were modeled as a random process based on their statistical properties. The results revealed significant differences in displacement and force values when comparing the results provided by static and dynamic analyses. The structural design of a base leg member indicated potential failure at higher wind velocities, highlighting the importance of considering the wind dynamic effects in the design.

Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts

Sitik Sakira Kamaruddin — 2024-12-02

Suicide is a global public health concern, with the World Health Organization (WHO) identifying it as the second leading cause of death among individuals aged from 15 to 29 years. In Malaysia, recent statistics indicate a 10% increase in suicide cases in 2023 compared to the previous year. Online forums, such as Reddit, have become platforms for sharing opinions on this matter. Extracting and automatically analyzing these discussions can provide valuable insights into public opinion concerning this issue. While the existing research primarily focuses on identifying suicidal ideation from posts, the work on discerning public opinion remains limited. This study scraped opinion posts on suicide from the Malaysian Reddit community. Sentiment Analysis (SA) was conducted using a lexicon-based sentiment analyzer and topic modeling was performed by deploying Latent Dirichlet Allocation (LDA). The analysis revealed the following insights: (1) predominantly negative sentiments were detected in the opinions, both overall and within identified topics and (2) topic modeling indicated two distinctive topics reflecting the different perspectives and concerns of the Muslim and non-Muslim communities. Specifically, the overall SA revealed that 58% of the posts were negative, 10% were neutral, and 32% were positive. Within the identified topics, the Muslim community expressed a notably higher percentage of negative sentiments at 64%, compared to the 50% found in the non-Muslim community. These findings offer evidence-based insights into public opinion regarding suicide in Malaysia, contributing to the understanding of societal perspectives on this critical issue.

Modeling of the Stress-Strain Quality of Hydroentangled Nonwoven Fabrics

Sana Ridene — 2024-12-02

Hydroentanglement is a mechanical bonding process designed to produce nonwoven fabrics with appearances and textures that resemble woven and knitted fabrics. Eleven samples of hydroentangled nonwoven fabrics with different compositions and weights were subjected to a series of uniaxial stress-strain tests. Models, ranging from the simple Kelvin to the more complicated Kelvin–Vangheluwe, were fitted to the experimental data to find a generalized and universal model. In this model, a nonwoven fabric was considered a nonlinear viscoelastic material. The combination of Kelvin and Vangheluwe models resulted in an excellent fit to the uniaxial stress-strain curves. The model-predicted results almost overlapped with the experimental data, an indication of its excellent accuracy in predicting the mechanical behavior of nonwoven fabrics.

Improved Automatic Drowning Detection Approach with YOLOv8

Nouf Alharbi — 2024-12-02

Although swimming is a popular activity that promotes relaxation and stress relief, drowning remains a serious global problem. According to the World Health Organization (WHO), drowning is the third most common cause of death. This study delves into implementing deep learning techniques for precise drowning detection. From this point of view, a drowning detection system was designed using the YOLOv8 model, which is a powerful tool for object detection and classification tasks. Using a large dataset, the YOLOv8 model was trained to recognize drowning patterns and movements and increase the likelihood of successful rescue operations by reducing response times and improving water safety. The proposed system uses deep learning techniques and YOLOv8 technology with data augmentation techniques to enhance the model's robustness to variations in lighting, pose, and background conditions. The system performance was evaluated using the Swimming and Drowning Detection dataset achieving 90.1% accuracy compared to 88.5% with YOLOv5.

A CFD Analysis to Investigate the Effect of Inserts on the Overall Heat Transfer Coefficient in a Concentric Tube Heat Exchanger

Naveedul Hasan Syed — 2024-12-02

In the present study, a U-bend concentric tube heat exchanger has been modeled using Computational Fluid Dynamics (CFD) through ANSYS Fluent software to study the influence of different variables on the overall heat transfer coefficient (U). Following the successful validation of the CFD model, an analysis was conducted to examine the impact of five distinct star-shaped inserts on U enhancement in the U-bend concentric tube heat exchanger. The analysis demonstrated that the maximum U values, which were 381.21 W/m²K and 468.96 W/m²K, were attained at a hot water flow rate of 0.007 L/s when 14 mm plain and twisted star-shaped inserts were, respectively, employed. The incorporation of the inserts resulted in the generation of a secondary fluid motion within the tube, which in turn induced turbulence and consequently enhanced the heat transfer rate. However, the turbulence generated within the tube was attributed to the high pressure drop occurring there. The pressure drop within the inner tube was found to be 129.27 Pa and 149.44 Pa for the plain and twisted star-shaped inserts, respectively. The impact of elevated pressure drops for all five star-shaped insert types was examined and revealed to be the greatest for the 7 mm twisted insert, which was identified as the optimal choice for operational use. This conclusion was based on the observation that the twisted insert exhibited the highest U (390.89 W/m²K) at a pressure drop of 35.30 Pa, achieved at a hot water flow rate of 0.007 L/s.

Optimizing Edge Computing for Activity Recognition: A Bidirectional LSTM Approach on the PAMAP2 Dataset

Anupama Bollampally — 2024-12-02

This study investigates the application of a Bidirectional Long Short-Term Memory (BiLSTM) model for Human Activity Recognition (HAR) using the PAMAP2 dataset. The aim was to enhance the accuracy and efficiency of recognizing daily activities captured by wearable sensors. The proposed BiLSTM-based model achieved outstanding performance, with 98.75% training accuracy and 99.27% validation accuracy. It also demonstrated high precision, recall, and F1 scores (all 0.99). Comparative analysis with state-of-the-art models, including Deep-HAR and CNN-BiLSTM-BiGRU, revealed that the proposed BiLSTM model surpassed their performance. These results highlight the potential of the proposed approach for real-time HAR applications in edge computing, particularly where accurate and efficient activity recognition is crucial.

A Customized Method for Recovery of Gaussian Beam Profile Emerging from Optical Fibers

Muhammad Tajammal Chughtai — 2024-12-02

Loss of the Gaussian beam profile is frequently observed when lasers are combined with either classical or modern optics. This alteration in the beam profile affects the coherence length of the beam and produces an unfavorable output in laser applications. Poor cleaving of the optical fiber end face is the main cause of this problem, especially when cleaving is performed using low-precision equipment or nonstandard methods. This profile deformation prevents the intended output, which leads to an unanticipated leap in the laser beam profile from one Transverse Electromagnetic Mode (TEM) to another. In this work a method is proposed to mitigate this effect by attaching an optically flat glass piece to the end face of the fiber and using index matching gel. By guaranteeing a uniform distribution of the index matching gel, this technique enhances the consistency of the laser beam and successfully restores the Gaussian beam profile. Laboratory test results show that this technology is a viable substitute for conventional fiber-cleaving techniques and is rapid, easy, inexpensive, and dependable. While successful in controlled situations, other improvements, such as optical adhesives, are needed to achieve stable performance in settings that are prone to vibration.

Evaluating Flexural Strength of Steel Fiber Reinforced Geopolymer Concrete using the ResNet Approach and Sensitivity Analysis

Tran Nhat Minh — 2024-12-02

The present study evaluates the performance of fiber-reinforced geopolymer, especially its flexural strength, using a Deep Learning (DL) approach, Deep Residual Network (ResNet), and the experimental work is presented. A total of 245 mixtures were employed to generate the data for the ResNet training and validating procedures. In the proposed model, the Fly Ash (FA) content, sodium silicate solution/solid binder ratio, curing temperature, curing time, fiber volume fraction, fiber length (l) and diameter (d), as well as fiber tensile strength, were considered as input factors. In contrast, flexural strength was the output parameter. The effectiveness of ResNet was evaluated by three statistical factors, correlation coefficient (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). ResNet validation revealed the effectiveness of predictive methods with 94.5%, 0.292 MPa, and 4.068% for R², RMSE, and MAPE, respectively. The suggested models may be used as standard mixtures for geopolymer concrete reinforced with steel fibers.

Performance Evaluation of Red Clay Soils stabilized with Bluegum Sawdust Ash and Sisal Fiber as Low-Volume Road Sub-base Materials

Wanangwa Chinkhuntha — 2024-12-02

The engineering properties of Red Clay Soils (RCS) in tropical regions are frequently inadequate for road construction due to a number of factors, including high compressibility, high creep rates, high plasticity, low strength, and swelling potential. This research project examines the potential of stabilizing RCS using Bluegum Sawdust Ash (BSDA) and Sisal Fiber (SF) to develop a cost-effective and environmentally sustainable material for use in low-volume roadways. Tests were conducted on both unstabilized and stabilized soil samples to evaluate a range of physical properties, including Atterberg limits, compaction, Unconfined Compressive Strength (UCS), and California Bearing Ratio (CBR). BSDA was introduced in increments from 2% to 10% at 2% intervals, with 6% of it being optimal. This resulted in a reduction in the Plasticity Index (PI) from 20.78% to 10.90% and a significant increase in both the UCS and the CBR values. The addition of SF resulted in further enhancement of stabilization, with an increase in the soaked CBR to 28.12% and UCS to 736.011 kN/m³. This triphasic approach, which combines RCS, BSDA, and SF, offers a sustainable and economical solution for the construction of road subbases in civil engineering.

An Assessment of Strength Characteristics of Transparent Concrete in Pavement

Manish Pratap Singh — 2024-12-02

This study explores the potential of transparent concrete for use in roadway applications, focusing on its ability to transmit light through the incorporation of Plastic Optical Fibres (POFs) within the concrete matrix and evaluating both its mechanical strength and its light transmission capabilities. This material can be applied to various road elements, such as surface illumination, signage, roadblocks, medians, curbs, and areas with changing geometry. The light transmission performance was evaluated using voltage, light sources, and Light-Dependent Resistors (LDRs). A comparative analysis was performed between samples with varying POF content and spacing and traditional concrete. The findings reveal that compressive and flexural strengths remain largely unaffected by the inclusion of POFs up to 1.5% by weight, beyond which a decrease in strength is observed. Additionally, the light transmission efficiency improves significantly with an increase in the POF volume ratios.

AMPS-1D Simulation of P3HT Solar Cells: Impact of HOMO-LUMO Offset, Thickness, Temperature, and Optical Bandgap on Performance

Hadab Al-Otaibi — 2024-12-02

This study employed the AMPS-1D software to investigate the relationship between the open-circuit voltage (V_oc) and the energy difference between the Highest Occupied Molecular Orbital (HOMO) of the donor and the Lowest Unoccupied Molecular Orbital (LUMO) of the acceptor in P3HT:PCBM bulk heterojunction organic solar cells. The findings indicate a correlation between V_oc and the HOMO-LUMO offset up to 1.1 eV, after which V_oc remains constant. This behavior is further elucidated using a theorem based on the quasi-Fermi level, which predicts a V_oc of 0.64 V, in good agreement with our simulation result of 0.68 V. The Power Conversion Efficiency (PCE) of the solar cell was studied with respect to the active layer thickness, demonstrating an increase in PCE up to 0.40 μm followed by a decrease, yielding a maximum PCE of 5.023%, consistent with the literature. The effect of temperature on PCE was also examined, demonstrating an increase in PCE with decreasing temperature in the range of 150–320 K, with a performance of 6.371% at 150 K. Furthermore, the impact of the optical bandgap on PCE was explored, showing that the PCE increased with a decrease in the optical bandgap of the P3HT:PCBM solar cell, reaching 9.94% when the optical bandgap was 1.5 eV. These findings provide valuable insights into the optimization of the performance of organic solar cells by manipulating key parameters, such as the HOMO-LUMO offset, active layer thickness, temperature, and optical bandgap.

Optimal Index Selection using Optimized Deep Deterministic Policy Gradient for NoSQL Database

V. Sumalatha — 2024-12-02

As big data technology has developed, so have complex applications that require increasing resources. The need for high-performance reading and writing increases the usage of NoSQL (MongoDB) databases. As the number of queries in a given amount of time negatively affects the performance of the database, an automated index selection strategy should be used to improve the database performance. This study proposes an Optimized Deep Deterministic Policy Gradient (ODDPG) to select the optimal index. The Adaptive Crocodile Optimization Algorithm (ACOA) is used to improve DDPG's decision-making performance. The ACOA algorithm is used to receive the best action sequences of a DQN. Simulation results showed that the proposed method achieved better results than the existing DDPG model by 2.3% in Average Time Of Query (ATQ) executed, 10% in Query Per Hour (QPH), and 11% in throughput.

A Blockchain Semantic-based Approach for Secure and Traceable Agri-Food Supply Chain

Boubakeur Annane — 2024-12-02

Ensuring food security is crucial for maintaining food quality and enhancing consumer services by guaranteeing both safety and satisfaction. However, traditional methods to ensure food security are often susceptible to various forms of fraud and require significant processing overhead, making them inefficient for the evolving demands of modern food supply chains. To address these shortcomings, blockchain technology has emerged as a robust and efficient solution to enhance food security. This paper presents a novel lightweight blockchain-based signature mechanism designed for the rapid detection of food fraud. It also includes a domain-specific ontology to serve as a structured knowledge model, allowing systematic analysis and detection of different types of fraud within the food supply chain. This approach uses smart contracts built on lightweight blockchain technology to initiate and manage transactions related to food fraud. Then, semantic rules are applied to detect and identify fraudulent activities. Once fraud is detected, associated transactions are encrypted and tracked, ensuring visibility and traceability among consortium members. Experimental results based on large-scale transaction data demonstrated ~7.5× speed improvement over iterative search algorithms while maintaining high transaction traceability and significantly reducing storage costs.

A Pilot Test of Dehydration of Tiver dredged Mud using Vacuum Consolidation with Different Vertical Drains

Huy Dong Phan — 2024-12-02

The combination of vacuum preloading and vertical drains provides an effective approach to the usage of dredged slurry for reclamation. However, the efficacy of this approach in improving soil quality is often impeded by clogging, a prevalent issue that frequently arises in the vicinity of Prefabricated Vertical Drains (PVD) during vacuum preloading. In addition to numerous efforts aimed at improving the performance of vacuum systems, this study conducts a pilot test to explore innovations in vertical drain types, including PVD, Filter Pipes (FP), and Sand Drains (SD). The monitoring data indicate that the use of SD is advantageous in mitigating the effects of clogging and enhancing the performance of vacuum consolidation techniques. Specifically, after 160 days of vacuum operation, the volumetric strains of dredged mud reached approximately 30.2%, 27.1%, and 23.1% for SD, FP, and PVD, respectively. The treated dredged mud exhibited disparate enhancements in physical properties and strength, reflecting the differential impacts of clogging phenomena among the cases of vertical drains.

The Effect of Formal Specifications and Working Conditions on the Resistance and Vibration of the NACA 4415 Aircraft Wing Model

Ali S. Jaafar — 2024-12-02

Due to their role in airplane performance, wings receive much attention considering their strength enhancement and vibration reduction. Many parameters have been considered and various materials have been used to achieve these objectives. The current work studies numerically the effect of the number of ribs and the angle of attack, on strength, response, and natural frequency at various speed values, using the ANSYS 2021 R1 solver. The adopting material is the AA 7075 T6 aluminum alloy with 71.7 GPa modulus of elasticity, 503 MPa tensile yield strength, 2810 kg/m³ density, and 0.33 Poisson’s ratio. Results show that when the velocity is increased by 30%, a corresponding elevation of 11% can be seen in vibrational distortion. Regarding the angle of attack, it was noted that doubling its value leads to a 28% reduction in vibration-induced deformation. This phenomenon occurs as a result of the alteration in the pressure distribution on the wing caused by the change in the angle of attack.

Monte Carlo Modeling and Simulation of Electron Dynamics in Low Temperature Methane Gas

Abdelatif Gadoum — 2024-12-02

This study examines the collisions of electrons with methane molecules to determine the cross-sections required for calculating electron transport coefficients in methane gas. Employing Monte Carlo Simulations in MATLAB, critical transport characteristics, including electron mobility and diffusion coefficients, were computed. These simulated coefficients are subsequently compared to experimental data to validate the accuracy of the current study’s findings. This comprehensive approach ensures the precision of the performed calculations and their alignment with empirical evidence, thereby enhancing the understanding of the complex interactions and dynamics between electrons and methane molecules in this system.

Assessing the Acceptance for Implementing Artificial Intelligence Technologies in the Governmental Sector

Ramiz Assaf — 2024-12-02

Artificial Intelligence (AI) has been recently implemented in various advanced government applications, including security, transportation, and healthcare. The wide variety of AI applications raised the issue of adoption difficulties in governmental usage, which is what this study investigates. More specifically, the present study examines the relationship between personnel perceptions and organizational, technological, and environmental factors that affect the AI acceptance and adoption in the governmental sector. To this end, a conceptual framework integrating the Technology Acceptance Model (TAM) with the Technology Organization Environment (TOE) is proposed and evaluated, where a survey for collecting relevant data from 179 employees working in four Palestinian ministries was utilized. The Partial Least Squares-Structural Equation Modeling (PLS-SEM) analysis of data using Smart PSL 4.1.0.8 revealed a significant association between TAM constructs and AI acceptance and adoption. Specifically, the relationships between the TOE variables and TAM's Perceived Usefulness (PU) or Perceived Ease Of Use (PEOU) were significant, except for the legal framework and organizational readiness relationship with PEOU. Besides the analytical investigation, this paper contributes practical insights into AI implementation in the government sector emerging from personnel perspectives. Theoretically, the study analyzes the validity of the conceptual model and thoroughly investigates its constructs and factors, hence suggesting that the governmental ministries focus on the linkage between institutional factors and individual AI perceptions for the latter’seffective acceptance and adoption.

A New Approach to the Quality Determination of Used Palm Cooking Oil using Supervised Learning based on Electronic Sensors

Lilik Anifah — 2024-12-02

As discarding used palm oil in nature is very dangerous, a processing mechanism is needed to utilize it according to needs. This utilization depends on the palm oil used, so the sorting process becomes important. This study proposes a new classification approach for the quality of used palm oil using Self-Organizing Map (SOM), Linear Vector Quantization (LVQ), and K-means, based on electronic sensors. This study included hardware design, software development, data collection, and training and testing processes. Based on the experimental results, the proposed system performed well using 13 parameters consisting of e-nose data, color, viscosity, and turbidity. The accuracy of SOM was 91.11%, LVQ achieved 95.56%, and K-Means obtained an accuracy of 98.89%. This system can be used as a decision support system in the automatic recognition of used palm oil to classify its quality.

A Systematic Literature Review on Automatic Sexism Detection in Social Media

Wang Lei — 2024-12-02

Sexist content has become increasingly prevalent on social media platforms, underscoring the critical need for the development of efficient Automatic Sexism Detection methods. Previous literature reviews have not encompassed the new advancements in Automatic Sexism Detection observed over the past three years. Hence, the present study conducted a Systematic Literature Review (SLR) that examined 48 primary studies published between 2014 and 17th Sept. 2024, retrieved from six bibliographic databases. This paper aims to present a comprehensive literature review on Automatic Sexism Detection, encompassing the datasets, preprocessing techniques, feature extraction methods, text representations, classification approaches, and evaluation models employed in Automatic Sexism Detection research. The paper includes a discussion of the findings, limitations, and future research directions of the chosen articles. Additionally, it provides an overview of the conclusions drawn from the conducted research. The performed analysis reveals a lack of corpus beyond the English and Spanish language encountered in datasets, with most of the latter being annotated for either misogyny or non-misogyny. Common preprocessing techniques analyzed in the current study include lowercase conversion, text removal, tokenization, stemming, and rewriting. Discrete representations, such as TF-IDF, N-grams, and BoW, are frequently utilized, while distributed representations, like Bert and GloVe, are prominent. Bert is the predominant classification model utilized while combining lexical features can enhance the results in the majority of the discussed scenarios. Accuracy (A) and F1 score (F1) are the most widely deployed evaluation metrics in this field.

A Potential Pozzolanic Material consisting of Rice Straw Ash and Fly Ash for Geopolymer Mortar Production-based Cementitious System

A. Arwin Amiruddin — 2024-12-02

Rice straw waste, among other issues, is a significant source of air pollution and methane emissions from biological decomposition. This study examines the use of Rice Straw Ash (RSA), when combined with Fly Ash (FA) and Laterite Soil (LS), as a pozzolan in cementitious systems. The study's purpose is to examine the microstructure and compressive strength of a geopolymer mortar composed of FA, RSA, and LS. The RSA is activated with sodium hydroxide (NaOH), an alkaline activator, with concentrations of 6, 12, and 15 M NaOH. After air and water curing for 3, 7, and 28 days, the compression strength of the geopolymer mortar was tested. To determine the dominant compound of the pozzolan reactions that were generated in cementitious systems, Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD) were employed. When geopolymer mortar is cured in air and water, its compressive strength increases with age. This is due to the fact that RSA, FA, and LS have the ability to form iron oxide (Fe₃O₄) in the amorphous phase and have a strong bond with alumina (Al₂O₃) and silica (SiO₂). The material's fineness affects its compressive strength as well. This study intends to replace cement in mortar and concrete utilizing environmentally friendly materials. Furthermore, the creation of geopolymer material usually requires the use of oven heat to enhance the geopolymerization procedure. However, this study shows that this method does not require oven heat.

Multi-Objective Optimization of Finishing Milling of C45 Steel using Factorial Design

Pham Ngoc Linh — 2024-12-02

This study presents an innovative approach to optimizing the milling process of C45 steel using a factorial design of experiments. The impact of key technological parameters, including cutting speed (V_c), feed per tooth (f_z), depth of cut (a_p), and lubrication conditions (dry and flood), on surface roughness (R_a) and Material Removal Rate (MRR) was thoroughly analyzed. Through the application of Analysis of Variance (ANOVA), significant factors and their interactions were identified, with f_z and lubrication conditions showing the most substantial influence on R_a. The interaction between f_z and lubrication condition was particularly notable, highlighting the importance of these parameters in achieving optimal surface quality. Multi-objective optimization was conducted using the desirability function method to balance the objectives of minimizing R_a and maximizing MRR. The optimal V_c and f_z conditions under flood lubrication were found 200 m/min and 0.3 mm/tooth, respectively, achieving a desirability index of 0.801. Under dry lubrication, the optimal conditions were 200 m/min and 0.3 mm/tooth, respectively, with a desirability index of 0.803. These results demonstrate that both lubrication conditions can be effectively optimized to enhance machining performance. The findings provide a comprehensive framework for improving the milling process of C45 steel, contributing valuable insights into the effects of cutting parameters and lubrication conditions on surface quality and MRR.

A Research on Two-Stage Facial Occlusion Recognition Algorithm based on CNN

Wang Zhe — 2024-12-02

In recent years, pattern recognition has garnered widespread attention, especially in the domain of face recognition. Traditional face recognition methods have certain limitations in unconstrained environments due to factors such as lighting, facial expressions, and poses. Deep learning can be used to address these challenges. This paper proposes a comprehensive approach to face occlusion recognition based on a two-stage Convolutional Neural Network (CNN). Face verification aims at verifying whether two face images belong to the same individual, and it is a more fundamental task compared to face recognition. The process of face recognition essentially involves multiple instances of face verification, sequentially validating different individuals to ultimately determine the corresponding individual for each face. The primary steps in this research include facial detection, image preprocessing, facial landmark localization, facial landmark extraction, feature matching recognition, and 2D image-assisted 3D face reconstruction. A novel two-stage CNN was designed for facial detection and alignment. The first stage of the network is dedicated to the search for facial windows and regressing vector boundaries. The second stage utilizes 2D images to assist in 3D face reconstruction and perform secondary recognition for cases not identified in the first stage. This method demonstrated excellent performance in handling facial occlusions, achieving high accuracy on datasets such as AFW and FDDB. On the test dataset, face recognition accuracy reached 97.3%, surpassing the original network accuracy of 89.1%. This method outperforms traditional algorithms and general CNN approaches. This study achieved efficient face validation and further handling of unrecognized situations, contributing to the enhancement of face recognition system performance.

Feature Selection using Improved Nomadic People Optimizer in Intrusion Detection

Zinah Sattar Jabbar Aboud — 2024-12-02

Intrusion Detection (ID) in network communication and Wireless Sensor Networks (WSN) is a big challenge that has grown with the rapid development of these technologies. Various types of intrusion attacks may occur to the transferred data of such networks and various ID methods and algorithms have been proposed. One powerful tool used in this field is Machine Learning (ML), which has achieved satisfied detection results. However, these results with the available ID datasets can be further improved. This paper proposes an accurate approach for ID in the network and WSN using ML methods including chaotic map, Nomadic People Optimizer (NPO), and Support Vector Machine (SVM). The proposed approach has five main stages which are: data collection, pre-processing, feature selection, classification, and evaluation. An improved version of NPO based on chaotic map and Cauchy mutation called CNPO is proposed. The proposed scheme uses chaotic maps to initialize the population and Cauchy mutation for solution distribution. Besides, the proposed fitness function based on SVM is proposed. The CNPO is employed for the feature selection task. The proposed approach was evaluated in two datasets, NSL-KDD, and WSN-DS, with accuracy of 99.97% and 99.99, respectively.

Implementing Blockchain for Enhancing Security and Authentication in Iraqi E-Government Services

Huda Kamil Abdali — 2024-12-02

E-Government is used to provide various services to citizens via an online portal and is currently available in many countries. Current e-government technology is supported by an extensive, centrally controlled database and a collection of applications linked to it through web interfaces. However, e-government depends too much on centralization. E-government services store sensitive data about citizens, making them particularly vulnerable to cyberattacks, data breaches, and access control. Therefore, alternative techniques should be developed to protect sensitive data and ensure secure storage in e-government platforms. This study proposes a safe and distributed electronic system for e-government based on blockchain technology to protect sensitive data from breaches. This system uses advanced encryption methods, including Lightweight Encryption Device (LED) and Elliptic-Curve Cryptography (ECC), to protect transmitted data. The proposed system employs a two-layer encryption approach to secure user data. The first layer utilizes the LED algorithm with a randomly generated key, and the second employs the ECC algorithm with a public key obtained from the blockchain server to enhance user data security and privacy. The proposed system allows data to be disseminated across many networks, retrieves and synchronizes data in case of unauthorized changes, and restores them to their original form. Experimental results showed that the proposed system takes an average of 0.05 seconds to complete the login process for five successful login attempts, confirming the effectiveness of the proposed approach in the execution of login procedures. The effectiveness of this system in resisting different attack types was verified through formal and informal security analyses and simulations based on the Scyther tool.

The Impact of Green Marketing Mix Practices on Customer's Purchase Intention of Electric Vehicles in Palestine

Yahya Saleh — 2024-12-02

This research aims to investigate the impact of a green marketing mix, involving green promotion and green pricing support practices, on the consumer’s intention to purchase Electric Vehicles (EVs) in Palestine, with the mediation role of Green Perceived Value (GPV). A quantitative method was used in this research, where relevant data were collected from a random sample via a structured distributed questionnaire which was answered by 53 respondents. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used for the analysis. The model revealed that the green promotion and green price support practices, as well as GPV have a positive impact on customer intention to purchase EVs. Moreover, the results confirmed that GPV partially mediates the relation between green marketing mix practices and customer intention to purchase EVs. The results of this research present a guideline for marketing decision makers in automobile dealerships to improve customer EV purchase intention.

Multi-Objective Optimization of a Two-stage Helical Gearbox with Double Gears in the First Stage using MARCOS

Le Duc Bao — 2024-12-02

This study demonstrates the solution of the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox with double gears at the first stage, following the MARCOS methodology. The goal of this work is to identify the most effective essential design factors to reduce the bottom area of the gearbox while maximizing its efficiency, which constituted a significant novel finding. For this purpose, three crucial design parameters were selected, the first stage gear ratio and the wheel face width (Xba) coefficients for the first and second stage. Furthermore, the Multi-Criteria Decision Making (MCDM) issue was chosen to be handled by the MARCOS method, and the weight criterion for solving the MOOP was determined by the MEREC method. The drawn conclusions are useful in developing a two-stage helical gearbox with double gears at the first stage by helping to identify the ideal values for the three important design parameters.

Determinants of Consumer’s Adoption of Latest Version Smartphones: An Empirical Study of Saudi Consumers

Brahim Boutaleb — 2024-12-02

This empirical study investigates the determinants of the adoption of the latest version of smartphones among Saudi Arabian consumers. Through a quantitative approach and data collected from 377 participants, the research explores factors, such as personal characteristics, social influences, product features, price considerations, social media impact, online purchasing behavior, and the role of advertisements. Descriptive and inferential analyses provide insights into the sample population's demographic profiles and occupational patterns. The ANOVA test evaluated the relationship between various factors and consumer adoption of the latest smartphones. The results indicated no significant difference in the adoption behavior based on the personal characteristics, influence from family and friends, or product features. However, data for other hypotheses were missing, impeding a comprehensive analysis. While the test provided useful understanding, further examination and interpretation are necessary to draw conclusive insights into the factors influencing smartphone adoption. This study contributes valuable knowledge into the complex dynamics of smartphone adoption in Saudi Arabia, offering targeted strategies to manufacturers, marketers, and policymakers, and aiming to enhance market penetration and consumer engagement.

A Customized CNN Architecture with CLAHE for Multi-Stage Diabetic Retinopathy Classification

Songgrod Phimphisan — 2024-12-02

This paper presents a customized Convolutional Neural Network (CNN) architecture for multi-stage detection of Diabetic Retinopathy (DR), a leading cause of vision impairment and blindness. The proposed model incorporates advanced image enhancement techniques, particularly Contrast Limited Adaptive Histogram Equalization (CLAHE), to improve the visibility of critical retinal features associated with DR. By integrating CLAHE with a finely tuned CNN, the proposed approach significantly enhances accuracy and robustness, allowing for more precise detection across various stages of DR. The proposed model was evaluated against several state-of-the-art techniques, with the customized CNN alone achieving an overall accuracy of 97.69%. The addition of CLAHE further boosts the performance, achieving an accuracy of 99.69%, underscoring the effectiveness of combining CLAHE with CNN for automated DR detection. This approach provides an efficient, scalable, and highly accurate solution for early and multistage DR detection, which is crucial for timely intervention and prevention of vision loss.

The Potential of Biomethane produced from Waste Landfill to Supplement Renewable Energy in Saudi Arabia

Muhammad Muhitur Rahman — 2024-12-02

This study investigates the potential of biomethane from waste landfills in five major cities of Saudi Arabia (Riyadh, Jeddah, Makkah, Madina, and Dammam) using the Landfill Gas Emissions (LandGEM) model to estimate methane emissions from 2015 to 2115. The research assesses the cumulative methane emissions, projected to reach nearly 25.5 billion m³ by 2115, and quantifies the electricity generation potential from this biomethane, peaking at 1,299 GWh annually. Sensitivity analysis of key parameters, including the methane producing rate (k) and potential methane producing capacity (L₀), indicates that L₀ has a more impact on methane output. These findings highlight the importance of methane capture and landfill management strategies to enhance the renewable energy capacity of Saudi Arabia. Policy implications are discussed, highlighting the opportunity for biomethane to supplement the country’s energy mix in alignment with its Vision 2030 goals and commitment to reducing greenhouse gas emissions through the Global Methane Pledge.

Superpixel-based C-SVC for Brain Tissue Classification in MRI Scans

Afaf Tareef — 2024-12-02

Accurate identification of brain tissue is an ill-posed problem due to the inhomogeneous intensity and the extremely complicated and irregular border between endocrine tissues. This study introduces a superpixel-based approach to brain tissue classification in MRI scans. The proposed approach starts with image smoothing and feature highlighting, followed by image splitting based on the SLIC superpixel and merging strategy. Then, distinct superpixel-based appearance and boundary features are extracted and refined by minimizing redundancy and maximizing relevance technique before sending to the C-support vector classifier. Finally, a refinement step is adopted based on morphological characteristics and the distance regularized level set evolution model to modify the matter contour. The proposed approach was evaluated and compared with ten existing algorithms using the publicly accessible IBSR dataset. The experimental results show the better efficiency of the proposed approach in delimiting the contour of each matter than the other approaches in the literature.

An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength

Muataz I. Ali — 2024-12-02

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperature exerted the most significant influence at 100%, while sample dimensions had a minimal impact at 17.9%. In addition, the mathematical model closest to the proposed was the Bazli model, because the latter depends on two variables (thickness and temperature). The ANN accurately predicted the residual tensile strength of GFRP at elevated temperatures, achieving a correlation coefficient of 97.3% and a determination coefficient of 94.3%.

Comparative Performance Analysis of Gas Turbine with and without Intercooler using Natural Gas and Hydrogen Fuels

Asad Ali Sodhro — 2024-12-02

The Gas Turbine (GT) represents one of the most significant technological advancements of the early 20th century. A limited number of studies have explored the significance of intercooling in improving GT efficiency. Specifically, the comparative performance of GT utilizing Natural Gas (NG) and hydrogen fuel, with and without intercoolers, remains largely unexplored. In this study, design point and off-design performance models for a three-shaft GT were developed using commercial software. During the model development process, the intercooler was considered, as the GT was originally designed with an intercooler. The intercooler was subsequently deactivated to simulate the GT's performance with NG and without an intercooler. Following this analysis, the fuel type was switched to hydrogen to investigate the performance of the GT with and without an intercooler. The results indicate that the inclusion of an intercooler increases the power output from 75,176.8 kW to 99,000.2 kW for NG and from 75,012.2 kW to 99,001.6 kW for hydrogen. However, the thermal efficiency marginally decreases from 45.5% to 45.14% for NG and from 45.9% to 45.52% for hydrogen. These findings demonstrate that the intercooler enhances power output but results in a minor drop in efficiency. Furthermore, hydrogen consistently exhibits superior thermal efficiency and fuel consumption compared to NG in both scenarios.

Air Conditioning Energy Efficiency and Thermal Comfort in Hotel Buildings in Hot and Humid Tropical Climates

Nasrullah Nasrullah — 2024-12-02

The objective of this research is to examine energy consumption efficiency strategies with respect to the type of dwelling unit, cooling load, and thermal comfort. This can be evaluated through the study of the hotel building air system, with the aim of ensuring the thermal comfort of its occupants. This research employs a quantitative methodology, using observation, measurement, and experimental techniques. The case study is situated within the context of hotels in Makassar City, South Sulawesi Province, Indonesia. The subject of this study is the Swissbel Hotel building, located in the coastal area of Makassar City. The data are analyzed in order to determine the Energy Consumption Intensity (IKE), Room Energy Intensity (REI), and occupancy rate based on the heat load inside and outside the building. The data are then processed using a parametric statistical approach. Subsequently, the building envelope is analyzed through the calculation of the Overall Thermal Transfer Value (OTTV) in accordance with the standards set out in SNI 6390: 2011. The cooling load and a thermal comfort assessment, in line with the Predicted Mean Vote (PMV) index, Predicted Percentage of Dissatisfaction (PPD) index, and effective temperature according to the ASHRAE 55-2022 standard, are then calculated, and a simulation is performed to determine the most efficient strategy for the use of air conditioning energy. The findings indicated that alterations in the dimensions of the facade (sun shading) resulted in a 16% reduction in the cooling load, with the incorporation of horizontal and vertical shading sizes of 4 m × 2.5 m, square glass openings, and 8 mm heat-absorbing glass. This led to a PMV value of 0.40, a PPD of 8%, and an effective temperature value of 26.8°C. The modification resulted in a percentage gain of 20.08%, at 10-60% occupancy load with a cooling load reduction rate of 1,912,071 kW and a thermal comfort index within the comfortable range.

Mobility Prediction Algorithms for Handover Management in Heterogeneous LiFi and RF Networks: An Ensemble Approach

Jaafaru Sanusi — 2024-12-02

Light Fidelity (LiFi) is a communication technology that operates in the Visible Light (VL) region, using light as a medium to enable ultra-high-speed communication. The spectrum occupied by LiFi does not overlap with the Radio Frequency (RF) spectrum. Thus, they can be used in a hybrid manner to enhance the Quality of Service (QoS) for users. However, in a heterogeneous LiFi and RF network, users experience constant handovers due to the small coverage area of the LiFi and their frequent movement. This study proposes an intelligent handover scheme, where the network parameters of the users are used to train four machine learning models, namely an Artificial Neural Network (ANN), an Adaptive Neurofuzzy Inference System (ANFIS), a Support Vector Machine (SVM), and a Regression Tree (RT), to predict the mobility of the users, so that the central network can have a priori mobility information to ensure seamless connectivity. Furthermore, the performance of the standalone models was enhanced by integrating ensemble learning techniques such as the Simple Averaging Ensemble (SAE), Weighted Averaging Ensemble (WAE), and a Meta-Learning Ensemble (MLE). The results show that the ensemble algorithms improved prediction performance, with an average error decrease of 44.40%, 53.53%, and 61.03% for SAE, WAE, and MLE, respectively, which further demonstrated the effectiveness and robustness of using ensemble algorithms to predict user mobility.

The Effect of CoCoSo Method on the Ranks of Alternatives: A Case Study of Copper Electrical Wire Selection

Hoang Xuan Thinh — 2024-12-02

When using MCDM (Multi-Criteria Decision-Making) methods to rank alternatives, decision makers’ opinions have a huge influence on the ranking results. The decision makers’ opinions can vary depending on the chosen MCDM method, data normalization method, and weighting method. For some MCDM methods, during the application process, users also need to choose the value of a certain coefficient (called the user coefficient). Obviously, the value of the user coefficient depends on users’ opinions, and of course, these opinions can affect the ranking of the alternatives. In this article, the effects of users’ opinions on the ranks of the alternatives when using the CoCoSo (Combined Compromise Solution) method are investigated. Users’ opinions (including the weighting criteria method and the user coefficient) are considered the input of the investigation process. Organizing the investigation of the effects of these two parameters on the ranks of alternatives was applied to the case of copper electrical wire selection. The results show that the users’ opinions have little effect on the ranks of alternatives. This result confirms CoCoSo's outstanding advantage.

A Study on the Influence of Silicon Content on Wear and Mechanical Properties of Cast Nickel-Aluminum Bronze

Chawanan Thongyothee — 2024-12-02

Although aluminum bronze exhibits considerable wear and mechanical properties, nickel-aluminum bronze displays superior performance. A potential issue is the resistance to wear, particularly whether the addition of silicon (Si) enhances anti-wear properties. Samples with varying silicon content (0.2%, 0.4%, and 0.6%) were prepared in three conditions for the purpose of investigating the effects of silicon on wear and mechanical properties. The materials were forged and cast in accordance with the specifications set forth in JIS H 5120 for the production of specimens. The wear and mechanical properties were evaluated through a series of tests, including tensile strength, hardness, impact, and ball-on-disc wear tests. The results demonstrated that, in comparison to ASTM B148-52 or the precursor AlBC-3, as observed in nickel-aluminum bronze, the addition of silicon during the melting of the substrate resulted in a transformation of the precipitation of the kappa phase to α+κ. The intermetallic compounds were introduced to enhance the formation of pearlite. This resulted in an increase in the tensile strength and hardness of the metal, but a simultaneous decline in its ability to absorb impact. Furthermore, the wear test results indicated that the surface friction coefficient between the nickel-aluminum-bronze disc and the SUS304 stainless steel ball was associated with the amount of silicon added and the hardness. In other words, the workpiece friction coefficient would decrease with increasing hardness due to the rising silicon content. Additionally, it was observed that no adhesive wear was evident between the stainless-steel balls on the nickel-aluminum-bronze disc.

Minimizing IoT Security Deployment Costs using the Dominating Set Approach

Samir Balbal — 2024-12-02

The rise of the Internet of Things (IoT) has generated significant interest by enabling connectivity across various objects, ranging from the smallest devices to large-scale systems. Despite its benefits, IoT poses considerable security challenges due to the many interconnected devices that collect and transmit sensitive data across networks. Therefore, ensuring robust data protection and preventing unauthorized access or misuse are essential concerns. To address this issue, strategically placing security services within IoT networks is vital for safeguarding both devices and data. One promising strategy for optimizing this placement is the use of the dominating set concept derived from graph theory, which helps in the efficient allocation of security resources. This study presents an IoT network as a simple weighted graph, considering device capabilities while focusing on adopting the dominating set concept to enhance the placement of security services in IoT networks. To achieve this, an enhanced greedy heuristic is proposed for efficiently generating the dominating set. The effectiveness and performance of the proposed approach are evaluated through a comparative analysis combined with existing methods in the recent literature.

Categories and Factors of Cost Overrun in Construction Projects: A Systematic Review

Omar Afana — 2024-12-02

Cost overruns represent a significant challenge in construction project management and often compromise project success. This study addresses gaps in previous research, particularly the lack of a unified classification of cost overrun factors. The aim is to provide a comprehensive, unbiased, and structured synthesis of existing research on the factors contributing to cost overruns in construction projects. It involves identifying, evaluating, and categorizing studies to answer predefined research questions related to cost overruns across various geographical contexts, project types, stakeholder perspectives, and project lifecycle phases. Through a Systematic Literature Review (SLR), the current study identifies and categorizes 99 factors into 10 distinct categories: 1) Execution, Resource, and Project Management Factors, 2) Design Factors, 3) Contractor Factors, 4) Consultant Factors, 5) Client Factors, 6) Financial Management Factors, 7) Bidding and Cost Estimation Factors 8) Contracts, Legal, and Regulatory Factors, 9) External Risks, Technology, and Sustainability Factors, and 10) Defects Liability Period (DLP) Operations and Maintenance Factors. Additionally, the present research examines both advanced and traditional methodologies for mitigating these overruns, emphasizing accurate cost estimation, risk management, and the use of advanced technologies, like Building Information Modeling (BIM), alongside strong financial and contract management. This paper synthesizes results from different global contexts to establish a solid foundation for future academic research and industry practices aimed at alleviating cost overruns in construction projects. It also promotes the development of customized frameworks that are specific to a country, a lifecycle phase, or a combination of conditions.

Monocular Camera Calibration based on Genetic Simulated Annealing Algorithms

Hafsa Khrouch — 2024-12-02

This study presents a nonlinear camera calibration approach based on combining genetic and simulated annealing algorithms. This is a global optimization technique, which combines simulated annealing with genetic algorithms to find the optimal camera's intrinsic and extrinsic parameters. Since this matter is considered an optimization problem by several studies, a novel hybrid approach was developed and studied based on two powerful nature-inspired techniques to find the intrinsic and extrinsic parameters of the camera. Numerous experiments were conducted to evaluate the efficiency of the proposed approach. The results demonstrate that the proposed hybrid approach is robust, reliable, and accurate.

Vessel Detection in Satellite Images using Deep Learning

Darshana Sankhe — 2024-12-02

Maritime surveillance and monitoring have emerged as crucial components, serving various purposes such as security, environmental protection, and economic activities. This paper focuses on utilizing Synthetic Aperture Radar (SAR) satellite imagery to detect and track vessels in maritime regions. SAR technology provides notable advantages in imaging capabilities, enabling effective vessel detection under diverse weather conditions and during both day and night. Deep learning (DL) models are trained employing annotated SAR images, including multiple vessel patterns, sizes, and orientations. The enhancement of model generalization and robustness is accomplished by applying transfer learning techniques and data augmentation strategies, ensuring reliable detection performance across different environmental conditions and vessel types. By leveraging SAR imagery, this paper aims to contribute to enhanced maritime situational awareness, enabling timely identification of small vessels, including those involved in illegal fishing, smuggling, or other illicit activities. The results of this research hold promise for bolstering maritime security, aiding search and rescue operations, and facilitating effective regulation of maritime traffic.

The Impact of Change Orders on the Waste Materials of Large-Scale Projects

Mega Waty — 2024-12-02

Change orders (CO) are formal agreements that alter, add to, or modify the work specified in a contract document. These changes often necessitate adjustments to the project scope, potentially requiring contract modification. Generally, CO have been identified as a significant contributor to Wast Materials (WM) in road improvement projects, as outlined in waste management recommendations. The impact of waste management on project success is substantial, as materials constitute a critical component of construction, accounting for approximately 40-60% of the total project cost. This research aimed to determine the impact of CO on project waste management, with a specific focus on large-scale projects. To achieve this objective, a 4-stage decision-making process was adopted using the Delphi method to design and distribute questionnaires. The results identified 2 variables, comprising 21 indicators, that contribute to waste management in road construction projects. Additionally, 3 primary impacts of CO were recorded, affecting costs, quality, and implementation time. Further analysis using Exploratory Factor Analysis (EFA) and Smart PLS 4.0 revealed that CO had a significant impact on 2 variables: procurement and material handling, as well as implementation and material planning. These variables, consisting of 21 indicators, accounted for 69% of the observed effects, with a prediction accuracy rate of 67.7% regarding the impact of changes in construction work.

Α Combined Metaheuristic Optimization Technique for Optimal Site and Scaling of PVDG System in a Radial Distribution Network

Mansoor Alturki — 2024-12-02

Although integrating Renewable Energy Resources (RERs) into distribution systems offers benefits such as clean energy and free availability, it also introduces challenges, such as Inverse Power Flow (IPF) issues. This study proposes an efficient approach to address these issues by optimizing the placement and sizing of Photovoltaic Distributed Generation (PVDG) systems in Radial Distribution Networks (RDNs). The proposed strategy involves selecting the optimal PVDG location using the Loss Sensitivity Factor (LSF) and determining the optimal PVDG size with the Artificial Bee Colony (ABC) algorithm. This method aims to minimize active power losses and enhance the voltage profile in the investigated system. The performance of the ABC algorithm was evaluated against other optimization methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The effectiveness of the proposed strategy was tested and validated on τηε IEEE 15-Βus and IEEE 85-Βus RDNs. The results obtained show that the ABC algorithm outperformed the other methods in reducing power losses and improving voltage profiles.

Reducing the Environmental Impact of Asphalt Emulsion Production from Petroleum Bitumen utilizing Buton Island Natural Asphalt in Various Scenarios

. Israil — 2024-12-02

By utilizing Buton asphalt as the solid component in the creation of emulsified asphalt, a substantial amount of petroleum bitumen, which is a finite energy resource, can be reduced. Additionally, the utilization of natural mining materials can be decreased, hence lowering the carbon footprint and impact of the emulsified asphalt-producing sector. This research assesses different approaches to mitigate the environmental consequences of manufacturing emulsified asphalt using Buton asphalt as a substitute for petroleum bitumen in the solid phase. Asbuton Indonesia is an asphalt emulsion that employs solid raw materials, particularly the Extracted Bitumen from Buton Rock Asphalt (EBBRA). The solvents in the mixture consist of kerosene, an emulsifier, hydrochloric acid (HC1), calcium chloride (CaCl), and water. The research process involved the EBBRA using a Socklet tool, followed by the production of emulsion asphalt. Subsequently, quality tests were conducted on the emulsion asphalt in the laboratory, and the results of these tests were analyzed to determine the value of the emulsion asphalt quality. The study's findings confirm the suitability of natural asphalt from Buton Island, Indonesia, as a primary ingredient for emulsified asphalt. This involves extracting bitumen from the minerals found in the asphalt. The test results indicate that the E3 sample has a solid phase content of 57.4% EBBRA and 5% kerosene, which aligns with the criteria set by ASTM and SNI-Indonesia. The liquid phase contains an emulsifier at a concentration of 1%, HC1 at a concentration of 0.5%, CaCl at a concentration of 0.1%, and water at a concentration of 36%. This study encompassed five different scenarios for making asphalt emulsion, with each of them utilizing Buton asphalt as the solid phase in variable proportions. Laboratory testing results demonstrate that including Buton asphalt in the production of asphalt emulsion mixtures can yield advantages for the construction industry, waste management sector, and the environment.

The Effect of Tuned Mass Damper Mass Ratio on Wind Turbine Vibration Mitigation

Waleed Dirbas — 2024-12-02

This paper examines the efficacy of Tuned Mass Dampers (TMDs) in mitigating vibration in wind turbines under diverse excitation force conditions. The impact of TMD on the response of a wind turbine exposed to sinusoidal and random wind forces, at varying mass ratios μ_m: 0.02, 0.05, 0.10, and 0.20, was assessed through the use of a MATLAB SIMULINK model. The findings indicate that TMDs markedly attenuate vibration when subjected to sinusoidal forces, particularly at higher TMD mass ratios. In contrast, the reduction in vibration level in the presence of random wind forces is relatively modest, becoming more pronounced at higher TMD mass ratios. In addition, the internal forces generated by incorporating the TMD into the system were calculated for different mass ratio values. It was noted that these forces increased in proportion to the mass ratio, although they remained within reasonable limits. However, an increase in the TMD mass ratio has been observed to result in a corresponding increase in these forces. This underscores the importance of meticulous mass ratio selection for the optimal functioning of TMD systems. It suggests that dealing with complex, broadband excitation may entail inherent limitations. The findings of this study may prove valuable in enhancing the understanding of the stability and lifetime of wind turbines under dynamic wind conditions.

Optimized Multi-Level Security for Content Contribution and Retrieval in Online Social Networks using a Content Visualization Mechanism

S. Nasira Tabassum — 2024-12-02

Online social networks have become an integral part of modern communication, providing platforms for users to share personal information, media, and opinions. However, these platforms face significant challenges in preserving user privacy while ensuring efficient data retrieval and maintaining data integrity. Existing privacy preservation methods, such as PPK-MEANS, CFCAF, and CLDPP, are limited in their ability to handle the growing complexity and scale of user data, often leading to inefficiencies such as high Content Retrieval Time (CRT), increased Information Loss (IL), and compromised data accuracy. These inefficiencies are crucial to address, as they can degrade the user experience by causing delays, compromising data integrity, and limiting system scalability. High CRT frustrates users, while increased IL reduces data accuracy, undermining trust and system reliability. The primary issue addressed in this study is the need for an advanced privacy-preserving mechanism that can provide multilevel security while maintaining optimal system performance. To overcome these limitations, the Layered Secure Online Collaborative Verification (LSOCV) algorithm is proposed, designed to offer a scalable solution with tiered privacy controls based on user requirements. LSOCV enhances Privacy Retrieval Accuracy (PRA), significantly reduces CRT, and minimizes IL. The experimental results show that LSOCV achieved a PRA of 91.97%, reduced CRT to 7ms, and decreased IL by up to 8% for 500KB files, outperforming existing approaches. This method provides robust privacy protection and efficient data handling on social networks, with the potential for future application in big data environments, such as Hadoop, to ensure scalable, secure, and efficient privacy-preserving solutions.

Advancements and Challenges: A Comprehensive Review of GAN-based Models for the Mitigation of Small Dataset and Texture Sticking Issues in Fake License Plate Recognition

Dhuha Habeeb — 2024-12-02

This review paper critically examines the recent advancements in refining Generative Adversarial Networks (GANs) to address the challenges posed by small datasets and the persisting issue of texture sticking in the domain of fake license plate recognition. Recognizing the limitations posed by insufficient data, the survey begins with an exploration of various GAN architectures, including pix2pix_GAN, CycleGAN, and SRGAN, that have been employed to synthesize diverse and realistic license plate images. Notable achievements include high accuracy in License Plate Character Recognition (LPCR), advancements in generating new format license plates, and improvements in license plate detection using YOLO. The second focal point of this review centers on mitigating the texture sticking problem, a crucial concern in GAN-generated content. Recent enhancements, such as the integration of StyleGAN2-ADA and StyleGAN3, aim to address challenges related to texture dynamics during video generation. Additionally, adaptive data augmentation mechanisms have been introduced to stabilize GAN training, particularly when confronted with limited datasets. The synthesis of these findings provides a comprehensive overview of the evolving landscape in mitigating challenges associated with small datasets and texture sticking in fake license plate recognition. The review not only underscores the progress made but also identifies emerging trends and areas for future exploration. These insights are vital for researchers, practitioners, and policymakers aiming to bolster the effectiveness and reliability of GAN-based models in the critical domain of license plate recognition.

Capsule-based and TCN-based Approaches for Spoofing Detection in Voice Biometry

Kirill Borodin — 2024-12-02

Nowadays, deep neural networks are in a phase of rapid development. Simultaneously, the field of biometric forgery is also advancing. Systems that can successfully pass face verification systems are emerging and continuously improving deepfake videos and voice messages are created. These developments can have a negative impact on a person’s reputation or cause serious security breaches. This paper proposes an approach for spoofing detection in voice biometrics using the ASVspoof2019 LA dataset The model is trained and validated on subsets representing one type of attack, and evaluated on a subset containing more advanced types of spoofing attacks, demonstrating the model’s ability to generalize to more complex attack scenarios. Two models, capsule-based and TCN-based, are proposed, noted as ResCapsGuard and Res2TCNGuard, respectively. ResCapsGuard achieved an Equal Error Rate (EER) value of 2.27, while Res2TCNGuard reached an EER value of 1.49. Notebooks with our models are available in repositories in github. Due to the fact that a random part is cut out of the audio, the results may vary.

Geometric Optimization of the Five-Point Double-Toggle System for the Clamping Unit of an Injection Molding Machine with the Response Surface Method

Thien Bao Ho — 2024-12-02

Injection molding is one of the most used processes for the manufacture of plastic products with high-volume capacity. The five-point double toggle mechanism is frequently employed in the clamping unit to augment the force generated by a linear actuator, thereby achieving the requisite clamping force. This is a crucial aspect that determines the productivity and quality of the finished product in the injection molding process. Nevertheless, the geometrical synthesis of this type of mechanism has not been sufficiently addressed with regard to its integration into the mold design and the opening/closing stroke. This paper presents a novel approach for analyzing and evaluating the impact of parameters pertaining to mechanism posture on its force amplification, employing the Taguchi method. The relationship between force amplification ratio and the most influential parameters is simplified with the Face-Centered Central Composite Design (FCCCD) method, thereby allowing the optimal posture of the mechanism at the mold-closing stage to be determined. With these optimal parameters, once the mold height and desired opening/closing stroke have been selected, the dimensions of the links in the mechanism can be calculated. The results demonstrate that there are numerous combinations of these parameters that can yield a high force amplification ratio, thus providing the designer with a range of options for the design of a clamping unit. The proposed method can be employed at the initial stage of the design process to obtain a preliminary design, thus preparing for the dynamic analysis or further optimization problems of the mechanism.

A Hybrid CNN-RNN Model for Automated Recognition of Kannada Characters in Ancient Inscriptions

B. K. Rajithkumar — 2024-12-02

This study presents a novel approach for the automated recognition of Kannada characters in ancient inscriptions using a hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) model. The unique features of stone inscriptions, such as erosion, uneven surfaces, and varying font styles, pose significant challenges to traditional character recognition systems. The proposed hybrid model leverages the strengths of CNNs for feature extraction and RNNs for sequence prediction, enabling robust recognition of complex and degraded characters. The proposed model was trained and tested on a curated dataset of annotated Kannada inscriptions, achieving an impressive accuracy of 95%. This high accuracy demonstrates the model's effectiveness in deciphering ancient scripts, which is critical for the preservation and study of historical texts. The results highlight the potential of deep learning techniques in advancing the field of epigraphy and cultural heritage preservation.

Investigation of Concrete Paving Block Characteristics and Performance across Different Shapes and Thicknesses

Tommy Iduwin — 2024-12-02

Concrete Paving Blocks (CPBs) are generally used in pavement structures. Quite often there are differences in the test result characteristics of cube and block-shaped samples. This research aims to analyze the characteristics of differences in height by considering 60 samples from five different factories. The sample dimensions are 200 mm length and 100 mm width with varying heights of 60 mm, 80 mm, and 100 mm. The tests include water absorption, compressive strength, flexural strength, tensile splitting strength, skid resistance, and abrasion resistance. The results of the mortar content show no significant difference between the five sources. According to the findings, the weight loss value depends on strength, and tensile splitting strength is influenced by density, with higher density producing higher tensile splitting strength. It was also found that the compressive strength test method is more realistic when using cube-shaped samples, while beam-shaped samples are more suitable for identifying bending characteristics. Finally, it was shown that the flexural strength value is influenced by density.

Development of a Deep Learning-based Arabic Speech Recognition System for Automatons

Abdulrahman Alahmadi — 2024-12-02

The latest developments in voice recognition have achieved amazing results that are on par with those of human transcribers. However, this significant efficiency may not apply to all languages, nor Arabic. Arabic is the native language of 22 countries and is spoken by approximately 400 million individuals. Verbal difficulties have become a growing problem in recent decades, especially among children, and data samples on Arabic phonetic recognition are limited. For Arabic pronunciation, Artificial Intelligence (AI) techniques show encouraging results. Some devices, such as the Servox Digital Electro-Larynx (EL), can produce voice for such individuals. This study presents a Deep Learning-based Arabic speech recognition system for automatons to recognize captured sounds from the Servox Digital EL. The proposed system employs an autoencoder using a mix of Long-Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. The proposed approach has three main stages: de-noising, feature extraction, and Arabic pronunciation. The experimental findings demonstrate that the proposed model was 95.31% accurate for Arabic speech recognition. The evaluation shows that the use of GRU in both the encoding and decoding structures improves efficiency. The proposed model had a Word Error Rate (WER) of 4.69%. The test results demonstrate that the proposed model can be used to create a real-time application to recognize commonly spoken Arabic words.

An Ensemble Forecasting Method based on optimized LSTM and GRU for Temperature and Humidity Forecasting

Maryam Saleem — 2024-12-02

Temperature and humidity predictions play a crucial role in various sectors such as energy management, agriculture, and climate science. Accurate forecasting of these meteorological parameters is essential for optimizing crop yields, managing energy consumption, and effectively mitigating the impact of climate change. In this context, this paper proposes an enhanced ensemble forecasting method for day-ahead temperature and humidity predictions. The proposed method integrates a Long Short-Term Memory (LSTM) network, a Gated Recurrent Unit (GRU), Particle Swarm Optimization (PSO) and Bayesian Model Averaging (BMA). PSO is employed to optimize the parameters of the LSTM and GRU, thereby improving forecasting accuracy. The method is implemented using Python 3.10 with TensorFlow. Additionally, the proposed approach is compared with ensemble-1, LSTM, and GRU models to demonstrate its effectiveness. The simulation results confirm the superior performance of the proposed method over existing competitive approaches.

Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks

K. Nirmal — 2024-12-02

A Wireless Sensor Network (WSN) encompasses interconnected Sensor Nodes (SNs) that interact wirelessly to collect and transfer data. Security in the context of WNS refers to protocols and measures implemented for the overall functionality of the network, along with protecting the availability, confidentiality, and integrity of data against tampering, unauthorized access, and other possible security risks. An Intrusion Detection System (IDS) utilizing Deep Learning (DL) and Feature Selection (FS) leverages advanced methods to enhance effectiveness in the detection of malicious activities in a network by enhancing relevant data features and leveraging the power of Deep Neural Networks (DNNs). This study presents a Dynamic Arithmetic Optimization Algorithm within a DL-based IDS (DAOADL-IDS) in WSNs. The purpose of DAOADL-IDS is to recognize and classify intrusions in a WSN using a metaheuristic algorithm and DL models. To accomplish this, the DAOADL-IDS technique utilizes a Z-score data normalization approach to resize the input dataset in a compatible format. In addition, DAOADL-IDS employs a DAOA-based FS (DAOA-FS) model to select an optimum set of features. A Stacked Deep Belief Network (SDBN) model is employed for the Intrusion Detection (ID) process. The hyperparameter selection of the SDBN model is accomplished using the Bird Swarm Algorithm (BSA). A wide experimental analysis of the proposed DAOADL-IDS method was performed on a benchmark dataset. The performance validation of the DAOADL-IDS technique showed an accuracy of 99.68%, demonstrating superior performance over existing techniques under various measures.

A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System

Shaimaa Shukri Abd. Alhalim — 2024-12-02

This paper examines the formulation and implementation of a neuro-controller for the excitation system of synchronous generators in a Single-Machine Infinite Bus (SMIB) power system. The SMIB model is employed as a fundamental model of a power system, thereby facilitating the assessment and comparison of disparate control strategies with the objective of enhancing system stability. The goal of this study is to enhance the stability of the SMIB power system through the implementation of an Artificial Neural Network (ANN) neuro-controller, providing a comparison of its performance to that of a Power System Stabilizer (PSS) and a Proportional-Integral-Derivative (PID) controller. The proposed neuro-controller will be integrated into the generator's excitation system and will be designed to regulate the excitation voltage in response to fluctuations in the system's operational parameters. To this end, an ANN is calibrated to account for the singularity of the generator's excitation level and terminal voltage. The Levenberg-Marquardt algorithm is employed to ascertain the optimal weight coefficients for the ANN. To assess the performance of the neuro-controller, simulations were conducted using MATLAB/Simulink. The simulations encompass a comprehensive range of operational scenarios, including diverse disturbances and alterations in the reference voltage level. Subsequently, the neuro-controller's outputs are evaluated in comparison to the PSS and PID controllers, as these are the prevailing controllers used to enhance voltage regulation and transient stability in power systems. This paper presents the results of an analysis of the neuro-controller's impact on the system's robustness, voltage variation amplitude, and generator dynamic performance during faults. Simulation results demonstrate that the application of an ANN-based neuro-controller yields superior outcomes in voltage regulation and transient stability compared to the conventional controllers PSS and PID. Furthermore, the neuro-controller is distinguished by accelerated response times and enhanced precision in voltage level regulation. The neuro-controller represents a superior approach to the control of a power system, particularly in the context of SMIB, which would ultimately result in enhanced performance and stability.

A Robust Control Strategy for Effective Field-Oriented Control of PMSMs

Thanh-Lam Le — 2024-12-02

Field-Oriented Control (FOC) is widely recognized as a standard framework for Permanent Magnet Synchronous Motor (PMSM) drives. Linear control techniques are commonly employed in designing controllers for this strategy. However, traditional control methods often exhibit performance limitations and reduced robustness, particularly under harsh operating conditions, which makes the FOC structure less appealing and less effective. To address and overcome these challenges, this study proposes a Second-order Non-singular Terminal Sliding (SNTS) mode approach to achieve fast, accurate, and robust tracking for the FOC control structure applied to PMSM drives. The SNTS method combines the benefits of non-singular terminal sliding mode and second-order control laws. This approach ensures rapid and precise tracking while minimizing steady-state errors by using a nonlinear terminal sliding mode surface instead of a linear one. Furthermore, the system state transitions smoothly along the sliding mode surface with continuous functions, which reduces chattering around the sliding surface. The second-order control law incorporated into this method helps mitigate chattering and achieve fast convergence. The Lyapunov stability theory is employed to verify the stability of the SNTS technique designed for the PMSM system. Simulation and experimental validation on a hardware platform confirm the effectiveness and superiority of the proposed SNTS method, demonstrating its capability to enhance the performance of speed controllers for PMSM drives.

Optimizing Performance in Mango Plant Leaf Disease Classification through Advanced Machine Learning Techniques

Sarika Khandelwal — 2024-12-01

Leaf diseases pose a significant threat to the productivity and quality of mango crops, necessitating effective detection and management strategies. This study presents an automated system for the detection of mango leaf diseases using machine learning techniques. Using image processing methods to extract relevant features from leaf images, various machine learning models were trained to accurately classify common mango leaf diseases. This approach involved using a comprehensive dataset of diseased and healthy mango leaves, preprocessing images, and extracting features such as color, texture, and shape. Features were extracted using MobileNetV2 and EfficientNetV2. Feature fusion was performed using a dense layer. Principal component analysis was used to reduce dimensionality. These reduced features were then fed to a support vector classifier to classify the mango leaf images. Eight different classes were considered, including the seven most common diseases in mango leaves and one class for healthy ones. The proposed model achieved a remarkable accuracy of 99.83 %. These results demonstrate that machine learning models can achieve high accuracy in the early detection of mango leaf diseases. Implementing this system in agricultural practices can significantly help farmers in timely disease management, reducing crop losses, and improving mango production.

Investigation of the Brittleness and Sensitivity of the Gypseous Sand Improved by Nano-clay

Mustafa Jamal Abrahim — 2024-12-02

This study examines the brittleness index and sensitivity ratio of two gypseous sand soils under saturation conditions improved by nano-clay. The soil samples were obtained from the cities of Al-Najaf and Tikrit containing 29% and 55% gypsum, respectively. The tests were performed on remolded specimens in a direct shear box. The soil specimens were examined mainly under saturated conditions for both different soil and nano-clay contents (0, 2, 5, and 7 %) under three normal stress levels: 25, 50, and 100 kPa. Additional tests were performed under dry soil conditions for comparison. The calculations of the brittleness index and sensitivity ratio of the saturated soil specimens were dependent on the newly suggested definitions of the peak values of the shear stress. The t_P is the peak value in the dry condition, whereas the t_R is the peak value in the saturation condition. The results emphasize that the values of the brittleness index and sensitivity ratio require more attention to the possibility that the soil is brittle owing to increased gypsum dissolution and the demolition of the soil structure. The brittleness index and sensitivity ratio increased with increasing gypsum content and decreased with increasing nano-clay content and average stress levels. The optimum percentage of nano-clay for both soil specimens was found to be 5%.

Assessment of Kalimantan Coal Ash Properties and Potential as Sustainable Construction Material

Irfan Prasetia — 2024-12-02

This study analyzes the physical and chemical characteristics of Fly Ash and Bottom Ash (FABA) from Kalimantan's coal-fired power plants to assess their effectiveness and potential as sustainable construction materials. The characterization was conducted through a series of tests following ASTM C311 standards, while the mechanical properties of mortars incorporating Fly Ash (FA) were evaluated following ASTM C618 standards. The results indicate that all FA samples meet the ASTM C618 Fineness requirements, with particle sizes below 45 μm, confirming their suitability as pozzolanic materials. In contrast, Bottom Ash (BA) samples do not meet the Fineness standard but still show potential for use in other construction applications. Chemical analysis utilizing X-ray Fluorescence (XRF) further supports the potential use of FABA from Kalimantan, revealing a high content of reactive oxides, SiO₂, Al₂O₃, Fe₂O₃, which justify the classification as Class F for PLTU Asam-Asam (AA) and PLTU Bengkayang (BK), and the classification as Class C for PLTU Pulang Pisau (PP). X-Ray Diffraction (XRD) analysis also validates the high silica (SiO₂) content and low levels of deleterious compounds. Additionally, the mechanical properties confirm the effectiveness of FA from PLTU AA with a Strength Activity Index (SAI) value exceeding 100%. The findings of this research provide strong evidence for the potential of Kalimantan FABA as a sustainable material while contributing to enhanced compressive strength and durability in concrete application.

A Comparative Analysis of Machine Learning Techniques for URL Phishing Detection

Adel Ataih Albishri — 2024-12-02

The growing threat of URL phishing attacks raises the need for advanced detection systems to protect digital environments. This paper explores the effectiveness of various machine learning models in classifying URLs as phishing or benign, focusing on the random forest model. Using ensemble learning, the random forest demonstrated superior accuracy and reliability compared to traditional methods, achieving consistent performance with accuracy rates between 99.93% and 99.98%. The model's performance was evaluated daily over eight days, highlighting its robustness in handling real-world scenarios. This study utilized GridSearchCV to optimize model hyperparameters, enhancing model robustness and minimizing overfitting. Future research directions include advanced feature engineering, deep learning techniques, and multimodal data integration to further improve phishing detection systems.

Comparison of Multiple Regression and Model Averaging Model-Building Approach for Missing Data with Multiple Imputation

Mohd Asrul Affendi Abdullah — 2024-12-02

Model construction is of significant importance for the extraction of information from datasets and the prediction of responses based on predictor variables. The objective of this study is to compare the Multiple Regression (MR) and model averaging approaches in the context of missing data and to validate the effectiveness of the Multiple Imputation (MI) method used to address missing data issues. A comparison was performed between the results obtained from the multiple-imputed data and those derived from the Complete Case (CC) data, using a diabetes dataset from Hospital Besar Alor Setar. Prior to the application of MI and model building, k-fold cross-validation was employed to partition the dataset, resulting in 90% of the data lacking complete covariates for training and 10% of the data comprising complete covariates for testing. Subsequently, MI was applied to the 90% training dataset. Model M115, derived from the multiple-imputed data, was identified as the optimal model for MR. In the model averaging approach, two models were identified as optimal: Model 1 (without interaction variables) and Model 2 (with interaction variables). The first one, exhibited the lowest values of Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). These results indicate that model averaging, specifically Model 1, is the superior model-building approach for this study, demonstrating improved performance compared to MR and validating the effectiveness of the MI method.

Eye Movement Classification using Feature Engineering and Ensemble Machine Learning

Hassanein Riyadh Mahmood — 2024-12-02

This paper explores the classification of gaze direction using electrooculography (EOG) signals, integrating signal processing, deep learning, and ensemble learning techniques to enhance accuracy and reliability. A complex technique is proposed in which several feature types are derived from EOG data. Spectral properties generated from power spectral density analysis augment basic statistical characteristics such as mean and standard deviation, revealing the frequency content of the signal. Skewness, kurtosis, and cross-channel correlations are also used to represent intricate nonlinear dynamics and inter-channel interactions. These characteristics are then reformatted into a two-dimensional array imitating picture data, enabling the use of the pre-trained ResNet50 model to extract deep and high-level characteristics. Using these deep features, an ensemble of bagging-trained decision trees classifies gaze directions, lowering model variance and increasing prediction accuracy. The results show that the ensemble deep learning model obtained outstanding performance metrics, with accuracy and sensitivity ratings exceeding 97% and F1-score of 98%. These results not only confirm the effectiveness of the proposed approach in managing challenging EOG signal classification tasks but also imply important consequences for the improvement of Human-Computer Interaction (HCI) systems, especially in assistive technologies where accurate gaze tracking is fundamental.

Enhanced Diagnosis of Lung Cancer through an Ensemble Learning Model leveraging an Adaptive Optimization Algorithm

Lassaad Ben Ammar — 2024-12-02

Early and accurate diagnosis of lung cancer is crucial to improving patient outcomes and survival rates. Machine and deep learning models have emerged as promising tools to improve the accuracy and efficiency of disease diagnosis. However, achieving optimal diagnostic performance remains a challenging task in medical research. This study integrates ensemble learning techniques with an adaptive optimization algorithm to enhance the accuracy of lung cancer diagnosis. By combining the predictive potential of multiple base classifiers, the ensemble-learning model improves overall performance and mitigates the weaknesses of individual classifiers. Additionally, the adaptive optimization algorithm dynamically adjusts the model parameters to optimize the classification performance. The effectiveness of the approach was evaluated using a comprehensive dataset that includes lung cancer images. Rigorous evaluation and comparison with state-of-the-art models showed that the proposed method achieved superior diagnostic performance, reaching an overall accuracy of 99%.

Retinal Image Augmentation using Composed GANs

Manal Alghamdi — 2024-12-02

Medical image analysis faces a significant challenge in the scarcity of annotated data, which is crucial for developing generalizable Deep Learning (DL) models that require extensive training data. Consequently, the field of medical image generation has garnered substantial interest and potential for further exploration. Besides widely employed data augmentation techniques, such as rotation, reflection, and scaling, Generative Adversarial Networks (GANs) have demonstrated the ability to effectively leverage additional information from datasets by generating synthetic samples from real images. In the context of retinal image synthesis, an image-to-image translation approach is frequently adopted to generate retinal images from available vessel maps, which can be scarce and resource-intensive to obtain. Deviating from prior work reliant on pre-existing vessel maps, this study proposes a learning-based model that is independent of vessel maps, utilizing Progressive Growing GAN (PGGAN) to generate vascular networks from random noise. The visual and quantitative evaluations conducted suggest that the majority of the images generated by the proposed model are substantially distinct from the training set while maintaining a high proportion of true image quality, underscoring the model's potential as a powerful tool for data augmentation.

Reinforced Concrete Columns with Treated Recycled Concrete Aggregate: An Experimental and Theoretical Study

Hussam N. Badri — 2024-12-02

Six Reinforced Concrete (RC) columns composed of Treated Recycled Concrete Aggregate (TRCA) and Natural Aggregate (NA) were subjected to experimental and theoretical analyses to ascertain their axial compressive behavior. The method of soaking recyclable aggregate in a NAOH solution was then employed to treat it. The TRCA was subjected to replacement ratios of 20%, 40%, 60%, 80%, and 100% relative to the total weight of NA. The dimensions of the column were 700 mm, 150 mm, and 150 mm, respectively. The column was reinforced with steel of varying diameters. The transverse reinforcement was 6 mm in diameter, whereas the longitudinal reinforcement was 8 mm in diameter. To examine the axial compressive behavior of the columns, the final load values obtained from the static tests were revealed. The measured axial capacity of the columns was then compared with the theoretical values derived from the ACI codes. The incorporation of TRCA contents was observed to enhance the columns' axial capacity, as evidenced by the experimental results. However, the computed theoretical values were found to be more conservative than the experimental observations. This suggests that there is no risk involved in using TRCA and NA-TRCA columns in construction.

A Manual Disinfection Enhancement Method utilizing a Pulsed-Xenon Ultraviolet Device in Accordance with the Effect on the Contamination Levels of Vancomycin-Resistant Enterococci (VRE) and Methicillin-Resistant Staphylococcus Aureus (MRSA)

Saeed Hussein Alhmoud — 2024-12-02

In hospitals, Ultraviolet (UV) disinfection lowers the rates of nosocomial infections; surface decontamination systems using Pulsed Xenon Ultraviolet light (PPX-UV) may be useful in lowering the microbiological load. This study aims to evaluate and compare Methicillin-Resistant Staphylococcus aureus (MRSA) and Vancomycin Resistant Enterococci (VRE) using manual plus PPX-UV disinfection technology versus standard manual room cleaning. Samples of high-touch surfaces from 20 rooms were taken both before and after both group the manual cleaning alone and the manual plus PPX-UV. Post-cleaning results showed a notable reduction in colony counts for both VRE (99%) and MRSA (98%) when comparing manual cleaning to manual plus PPX-UV treatment. The manual method showed higher colony counts for both bacteria compared to the manual plus PPX-UV method, with statistically significant differences in incidence rate ratios observed (p < .05). The study findings demonstrate that while manual cleaning methods can reduce microbial load, the manual plus PPX-UV method is notably more effective in achieving lower colony counts post-cleaning. This study underscores the importance of employing effective disinfection strategies in healthcare environments.

Integrating Six Sigma Principles towards Multi-Objective Production Planning for Enhanced Quality in the Al Kharj Manufacturing Sector

Mohammed Alqahtani — 2024-12-02

Effective production planning and creating high-quality items are two of the most critical factors in establishing long-term success in the manufacturing business. This study presents Goal Programming (GP), a Multi-Objective Optimization technique for decision-making issues, incorporating Six Sigma concepts to tackle the industrial sector's complex production planning problems. The aim is to provide a systematic framework for decision-making, ensuring a comprehensive approach to quality engineering in manufacturing processes. The current study entails considerable practical implications regarding the fridge industry in specific practical ways. Production planning was enhanced by evaluating market demand, manufacturing costs, and sales data using LINGO. Deploying Six Sigma to find the limits on demand, helped reduce production costs while making additional revenue from sales. Sensitivity analysis revealed that by the following year, the firm is expected to have decreased manufacturing costs by 13.13% to minimize expenses, while it is also anticipated to have reduced sales by 1.12% to maximize revenue. The GP technique demonstrated that the Al Kharj fridge industry could optimize sales revenue and costs. The suggestions provided based on these findings are actionable and have the potential to be effectively implemented in the industrial sector.

Prediction of Myocardial Infarction Complications using Gradient Boosting

Gamal Saad Mohamed Khamis — 2024-12-02

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, representing a significant public health challenge. Myocardial Infarction (MI), a severe manifestation of CVDs, contributes substantially to these fatalities. Machine learning holds great promise for predicting MI. This study explores the potential of Gradient Boosting (GB) techniques for this purpose, explicitly focusing on CatBoost, LightGBM, XGBoost, and XGBoost Random Forest. The study leverages GB's embedded feature selection, missing-value handling, and hyperparameter tuning capabilities. Performance was evaluated using multiple metrics: Area Under the Curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews Correlation Coefficient (MCC). A probabilistic comparison matrix was used to assess the relative performance of the GB models. The results demonstrate the superiority of CatBoost, achieving a classification accuracy of 94.9%, an AUC of 0.992, a recall of 94.9%, and an MCC of 0.82. The probabilistic comparison further confirms CatBoost's superior performance. These findings contribute to MI prediction, highlighting the predictive potential of the CatBoost algorithm and ultimately aiding the fight against MI to achieve better patient outcomes.

Automatic Bug Triaging Process: An Enhanced Machine Learning Approach through Large Language Models

Deepshikha Chhabra — 2024-12-02

Bug resolution and maintenance are the most critical phases of the software development life cycle. The traditional bug triaging concept refers to the manual assignment of bugs to the appropriate developer after reading the bug details from the bug tracker and further resolving it. The advent of machine learning algorithms provides various solutions for automated bug triaging. Machine learning algorithms can be used to classify bugs and assign each to a developer. Reducing manual efforts optimizes bug-triaging by utilizing manpower in other software development processes. Furthermore, machine learning Large Language Models (LLMs) can be used to take advantage of their natural language processing features and capabilities. This study proposes a machine learning-based embed chain LLM approach for automatic bug triaging. This approach is used to automatically classify bug reports. Based on the results, the appropriate developer is recommended. In addition, the proposed approach is used to automatically predict the priority of bug reports. This paper also discusses the strengths and challenges of the proposed approach.

A Fine Tuned-based Framework to Predict Salesforce Data using Machine Learning in Business Analytics

Naveen Kumar — 2024-12-02

Sales forecasting is one of the critical areas in business analytics where business organizations aim to enhance efficiency and, therefore, revenues. An excellent example of a CRM program is Salesforce, which produces massive amounts of sales data that are essential for forecasting and decision-making. Data analysis involves the use of complex and effective tools for its processing. This study proposes a framework based on the following classification algorithms: Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF). The proposed framework follows a fine-tuned approach to improve the prediction of sales data. Regarding the fine-tuning of these algorithms, it was observed that specific changes were required within the hyperparameters to better relate to the inherent patterns and other factors that exist in the sales data. The optimization process was very crucial in improving the performance of the model. The proposed framework was used on a sales dataset and evaluated in terms of accuracy, precision, data loss, and F1 score. Fine-tuned algorithms had higher accuracy and lower data loss.

Terminal Synergetic Control with the Dragonfly Algorithm for Zoonotic Visceral Leishmaniasis Eradication

Tinnakorn Kumsaen — 2024-12-02

Visceral Leishmaniasis (VL) is a prevalent vector-borne disease that affects both human and animal populations in subtropical and tropical regions, contributing to a substantial mortality rate. Establishing efficient control policies is crucial to eradicating the VL epidemic. The VL epidemic system, containing reservoirs, vectors, and human populations, can be accurately modeled through differential equations. Managing the VL epidemic under multiple control policies can be considered a high-order nonlinear feedback control challenge. This study explores the application of Terminal Synergetic Control (TSC) to eradicate Zoonotic Visceral Leishmaniasis (ZVL). Notably, Synergetic Control (SC) is one of the suitable feedback control methods for manipulating high-order nonlinear systems, providing practical control inputs because of their chattering-free behavior. Additionally, the convergence properties of the control system can be enhanced through terminal attraction. Optimization of control parameters within the system is achieved through the integration of control mechanisms by the Dragonfly Algorithm (DA). The results demonstrate that the multiple control policies synthesized by the TSC method effectively regulate subpopulations in alignment with the specified control objectives. Furthermore, the enhanced convergence rate achieved by the TSC method, in comparison to the SC method, serves as evidence of TSC's effectiveness in guiding the dynamics of ZVL epidemic eradication. This research underscores the potential of the TSC method, utilizing optimal control parameters provided by the DA, to achieve targeted outcomes with improved convergence properties.

Flexural Behavior of Zero Coarse Aggregate Concrete Beams reinforced with Glass Fibers: An Experimental Comparative Study

Fadya S. Klak — 2024-12-02

In recent decades, engineers have focused on finding solutions to reduce the weight of concrete structures. Undoubtedly, the coarse aggregate weight in concrete is important. This study examined the flexural behavior of zero coarse aggregate concrete with Glass Fiber (GF) added to the steel reinforcement. Also, normal-weight fine aggregate was substituted with autoclaved aerated concrete (Thermostone) by 50% and 75% by weight. The process involved comparison of the test results of two groups. The first group comprised normal reinforcement Lightweight Aggregate Concrete (LWAC), while the second group comprised fiber-reinforced LWAC and a specimen of Lightweight (LW) mortar. Fiber addition boosts energy absorption and slows down the rapid development of crack formation. GFs by 1.5% of concrete weight were added. The results revealed a decrease in the failure load of beams reinforced with GF compared to those reinforced with steel bars. The decrease amounted to 54%, 50%, and 59% for aggregate replacement percentages of 0%, 50%, and 75%, respectively. Replacing steel reinforcement with GF reduced the ultimate load by almost half. All beams with steel reinforcement experienced flexural failure, while the beams with GF reinforcement underwent shear failure.

Involving Infrastructure as a Latent Variable in Active Transportation Mode Choice: The Case Study of Baghdad City

Azaldeen Ali Abdulhussein — 2024-12-02

Infrastructural developments have been implemented with the objective of promoting active transportation as a sustainable transportation mode, including the construction of cycling and walking routes and the improvement of existing infrastructure. The impact of this factor on ridership has been a subject of research, and the incorporation of this variable into mode choice models has also been contemplated. Nevertheless, road users’ perception regarding the utilization of these infrastructures has yet to be investigated. Accordingly, this research aims to examine the role of infrastructure provision for walking and cycling as a Latent Variable (LV) in the transport mode choice of high school students in Baghdad City, where this subject has not been previously addressed. A self-designed questionnaire was employed to collect the requisite data. The Integrated Choice and Latent Variable (ICLV) models have been designed and the results demonstrate that the time and cost of trips have a significant impact on the choice of transportation mode. Furthermore, the provision of walking and cycling infrastructure has a notable effect on the choice of walking and cycling for transport. The sub-models indicate that the provision of cycling fences and pedestrian bridges are the most significant variables. Consequently, it is recommended that these results be considered in the provision and improvement of cycling and walking infrastructures to promote active transportation.

Long-Term Settlement Prediction for Over- Consolidated Soft Clay under Low Embankment

Ngoc Thang Nguyen — 2024-12-02

The long-term post-construction settlement of an embankment laid on a deep, soft soil foundation can give rise to a series of safety concerns and significant structural damage. The settlement is primarily attributed to the creep deformation of the soft soil following the removal of the surcharge load and the impact of traffic loads on the soft soil. In this study, a plan strain triaxial test was conducted to investigate the deformation of an undisturbed soft clay specimen subjected to static and cyclic loading. The results demonstrate that the volume creep and vertical creep are associated with the overconsolidation state of the soft soil. The Over-Consolidated Ratio (OCR_q) of shear stress, can be used as a parameter to describe the state of overconsolidation of soft soil under spherical stress. Based on the vertical creep coefficient and considering the influence of stress history on the stress state of soft soil, a two-dimensional long-term settlement model under cyclic loading has been proposed.

A Priority based Self-Organised MAC Protocol for Real Time Wireless Sensor Network Applications

Archana R. Raut — 2024-12-02

Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable and time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of ML techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large numbers of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML centered priority-based self-organized MAC (ML-MAC) protocol to provide a priority-based transmission system to ensure the timely delivery of critical data packets. In this research, depending upon the predictions of the ML model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm outperforms the existing I-MAC protocol.

An Experimental Study on Industrial Concete Pile Foundation in Soft Soil: Comparison of Monolithic and Pile with Welded Joints

Rustam Effendi — 2024-12-02

This study presents an experimental investigation into the industrial foundation piles. The research carried out employed both destructive and non-destructive testing methods to evaluate the concrete compressive strength and flexural strength capacity of the piles under monotonic loading. The Destructive Test (DT) involves a 28-day cylinder concrete compressive test, while the Non-Destructive Test (NDT) utilizes the Ultrasonic Pulse Velocity (UPV) and Rebound Hammer (RH) tests. The flexural strength test is conducted using a loading frame, and the results are compared to the GeoPIV-RG, which measures the displacement during the test. The experimental investigations provide insights into the behavior of pile joints when subjected to monotonic load in soft and loose soil. The results indicate a significant difference between the compressive strength obtained from the DT and NDT, with a ratio of 0.64-0.74. Furthermore, the failure occurred at the joints, rather than the welded area, with the ratio of the initial stiffness of the piles with joints to the monolithic pile being 0.15 for zig-zag welded and 0.30 for circular welded, and reaching an average value of 0.225. According to the GeoPIV-RG result, the displacement is similar to the flexural strength test result.

Determining the Best Design Factors of a Two-stage Helical Gearbox with Two Gear Sets in the First Stage to Increase Efficiency and Reduce Volume using the SAW Method

Van Thanh Dinh — 2024-12-02

This study describes the outcomes of employing the Simple Additive Weighting (SAW) approach to address the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox with two gear sets at the first stage. Its objective is to determine the key design variables that can reduce the volume of the gearbox while simultaneously maximizing its efficiency. For this investigation, three key design parameters were selected, namely the coefficients of the wheel face width of the first and second stages (X_ba1 and X_ba2), and the gear ratio of the first stage u₁. In addition, the SAW technique was deployed to deal with the problem of Multi-Criteria Decision Making (MCDM), while the Method based on the Removal Effects of Criteria (MEREC) was employed to determine the weight criterion for addressing the MOOP. The obtained results are valuable for defining the optimal values for three primary design factors, which are essential for the development of a two-stage helical gearbox with two gear sets at the first stage.

A Deep Learning Approach to Plastic Bottle Waste Detection on the Water Surface using YOLOv6 and YOLOv7

Naufal Laksana Kirana — 2024-12-02

Deep learning is a branch of machine learning with many layers, such as the You Only Look Once (YOLO) method. From various versions of YOLO, YOLOv6 and YOLOv7 are considered more prominent because they achieve high Mean Average Precision (mAP) values. Both versions of YOLO have been implemented into various problems, especially in the waste detection problem. Plastic bottle waste is one of the most common types of waste that pollutes Indonesian waters. This study aims to solve this problem by helping to sort waste in surface waters by applying YOLOv6 and YOLOv7. FloW-Img was used, obtained on request from the Orcaboat website. The dataset consists of 500,000 bottle objects in 2,000 images. The YOLOv6 and YOLOv7 models were evaluated using mAP and running time. The results show that YOLOv6 and YOLOv7 can handle bottle waste detection well, with mAP values of 0.873 and 0.512, respectively. In addition, YOLOv6 (4.21 m/s) has a higher detection speed than YOLOv7 (13.7 m/s). However, in tests with images that do not have bottle objects, YOLOv7 provides better detection accuracy and consistency results, making it more suitable for real-world applications that demand high accuracy in environments with much visual noise.

Reliable Vehicular Ad Hoc Networks for Intelligent Transportation Systems based on the Snake Optimization Algorithm

Hanadi Al-Maliki — 2024-12-02

Vehicular Ad Hoc Networks (VANETs) represent an environment in which mobility exceeds the normal values, topology changes rapidly, and safety constraints are too high. The fundamental problem with VANETs is making transmission, acceptance, and sending out of messages between vehicles as timely, reliable, and secure as possible. The current study aims to address these challenges by applying the Snake Optimization Algorithm (SOA), enhancing network protocol efficiency, performance, and robustness. In this work, a comprehensive examination of the effects of optimal SOA on VANET protocols is provided over networks with different node sizes of 100, 250, and 500. End-to-end delay, path delivery overhead, and average number of hops improved after the utilization of SOA in all the considered networl configurations.

Utilizing Explainable AI and Biosensors for Clinical Diagnosis of Infectious Vector-Borne Diseases

Thavavel Vaiyapuri — 2024-12-02

Infectious Diseases (ID) are a significant global threat due to their epidemic nature and substantial impact on mortality rates. COVID-19 has proven this assertion by wreaking havoc on human wellness and healthcare resources. This has underscored the need for early ID diagnosis to restrict the spread and protect human lives. Recently, Artificial Intelligence (AI)-assisted biosensors have shown great potential to assist physicians in making decisions to minimize mortality rates. However, their adoption in clinical practice is still in its infancy, primarily due to the challenges faced by physicians to interpret decisions derived from these black-box systems. The objective of this study is to earn the trust of physicians to promote their acceptance and widespread adoption in healthcare. Against this backdrop, this research is a pioneering effort to investigate not only the diagnostic accuracy of several Machine Learning (ML) algorithms for ID but more specifically how to leverage the benefits of Shapley values to provide valuable insights regarding the contribution of clinical features for early ID diagnosis. This analysis examines four ML algorithms that stem from different theories, such as Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Support Vector Classifier (SVC), and Multilayer Perceptron (MLP). The visual analysis results presented for local and global interpretation facilitate the observation of the marginal impact of each clinical feature on a patient-by-patient basis. Therefore, the results of this study are expected to aid practitioners in better evaluating the diagnostic decisions of the ML models developed and boost the use of AI-assisted biosensors for ID diagnoses.

Deterioration Analysis of Pavement Structures Incorporating Polymer-Modified Asphalt

Van Bich Nguyen — 2024-12-02

It is well-established that structural pavement deterioration is largely influenced by the frequency and magnitude of wheel loads. Furthermore, Polymer-Modified Asphalt (PMA) has gained a widespread application in pavement engineering, as the incorporation of polymers enhances the mechanical properties and improves the overall performance of the pavement, particularly with regard to fatigue resistance. In this study, an experimental program was conducted, comprising the Beam Wheel Tracker Fatigue Test (BWTFT), the Indirect Tensile Stiffness Modulus (ITSM) test, and the Indirect Tensile Fatigue Test (ITFT), on three types of asphalt mixtures: one conventional and two polymer-modified asphalt mixtures, under various conditions. Three distinct pavement structure scenarios were assumed/created/built to perform a deterioration analysis. Subsequently, an iterative approach was developed utilizing the average stiffness reduction and stiffness modulus data obtained from the laboratory results. The findings indicated that this method allows for a more accurate simulation of pavement behavior, confirming that strain levels fluctuate throughout the lifespan of the pavement. Furthermore, the study concluded that the use of PMA provides significantly greater benefits when a deterioration analysis is conducted, compared to traditional approaches.

Label Propagation Algorithm for Face Clustering using Shared Nearest Neighbor Similarity

Gao Yousheng — 2024-12-02

Facial image datasets are particularly vulnerable to challenges such as lighting variations and occlusion, which can complicate data classification. Semi-supervised learning, using a limited amount of labeled facial data, offers a solution by enhancing face classification accuracy while reducing manual labeling efforts. The Label Propagation Algorithm (LPA) is a commonly used semi-supervised algorithm that employs Radial Basis Function (RBF) to measure similarities between data nodes. However, RBF struggles to capture complex nonlinear relationships in facial data. To address this, an improved LPA is proposed that integrates Shared Nearest Neighbor (SNN) to enhance the correlation measurement between facial data and RBF. Three known datasets were considered: FERET, Yale, and ORL. The experiments showed that in the case of insufficient label samples, the accuracy reached 89.76%, 92.46%, and 81.48%, respectively. The proposed LPA enhances clustering robustness by introducing 128 dimensional facial features and more complex similarity measurement. The parameter of similarity measurement can be adjusted based on the characteristics of different datasets to achieve better clustering results. The improved LPA achieved better performance and face clustering effectiveness by enhancing robustness and adaptability.

Enhanced Deep Learning Techniques for Real-Time Speech Emotion Recognition in Multilingual Contexts

Donia Y. Badawood — 2024-12-02

Emotion recognition from speech is crucial for advancing human-computer interactions, enabling more natural and empathetic communication. This study proposes a novel Speech Emotion Recognition (SER) framework that integrates Convolutional Neural Networks (CNNs) and transformer-based architectures to capture local and contextual speech features. The model demonstrates strong classification performance, particularly for prominent emotions such as anger, sadness, and happiness. However, challenges persist in detecting less frequent emotions, such as surprise and calm, highlighting areas for improvement. The limitations of current datasets, such as limited linguistic diversity, are discussed. The findings underscore the model's robustness and identify avenues for future enhancement, such as incorporating more diverse datasets and employing techniques such as transfer learning. Future work will explore multimodal approaches and real-time implementation on edge devices to improve the system's adaptability in real-world scenarios.

Integrating Wetlands as Nature-Based Solutions for Sustainable Built Environments

Anushri Barman — 2024-12-02

Wetlands are ecosystems that can provide numerous services critical for sustainable development, especially in urban areas, by ensuring environmental stability. The wetlands receive increasing recognition as Nature-Based Solutions (NBSs) to environmental challenges. This review synthesizes the numerous roles of wetlands as NBSs for promoting sustainability in both rural and urban environments and highlights the potential contributions of multiple wetland services and benefits towards sustainable built environments. The review methodology involved an article search from various databases with the utilization of specific keywords in an organized framework to understand the contribution of wetlands as NBSs. The articles were reviewed to provide a comprehensive analysis of the existing research on the associated topics, focusing on specific sub titles and pre-selected themes. The findings of this review identify various parameters through which wetlands contribute to sustainable built environments, including ecological resilience, storm water management, climate adaptation, biodiversity enhancement, recreational opportunities, pollution control, and cultural values. The review also encompasses case studies of different types of wetland features such as riparian buffer zones, retention ponds, reed beds, bio swales, rain gardens, constructed wetlands, etc. in the urban environment and their contribution as NBSs. These contributions are discussed in terms of integration in urban development planning in different segments. Future work recommendations consist of a holistic integration of wetlands into urban planning and design considerations to promote more resilient, healthy, and sustainable built environments for present and future generations.

A Study on the Tensile Behavior of Specimens Manufactured by FDM from Recycled PETG in the Context of the Circular Economy Transition

Dragos Gabriel Zisopol — 2024-12-02

This article presents the results of a study on the influence of 3D printing by Fused Deposition Modeling (FDM) parameters on the tensile behavior of parts made from Everfil recycled Polyethylene Terephthalate Glycol (rPETG). For this study, 27 rPETG tensile specimens with 100% recycled material were manufactured using an Anycubic 4 Max Pro 2.0 3D printer and by varying the printing parameters: height of the deposited layer in one pass, L_h, and filling percentage, I_d. The L_h was set to 0.10, 0.15, and 0.20 mm and the I_d was set to 50, 75, and 100 %. The two variable parameters, I_d and L_h, influenced the tensile characteristics of the rPETG specimens: maximum breaking strength, percent elongation at break, and modulus of elasticity. The ultimate breaking strength and modulus of elasticity of the rPETG specimens were most influenced by I_d, whereas the percentage elongation at break was mostly affected by L_h. The optimized FDM parameters for the fabrication of rPETG tensile specimens were found to be L_h = 0.20 mm and I_d = 100%.

AI-Driven Energy Efficiency Optimizations in mHealth Applications: A Comprehensive Review on User Behavior Prediction and System Performance

Abdullah Almasri — 2024-12-02

Recently, mHealth applications have gained immense popularity, revolutionizing healthcare management for chronic diseases and fitness tracking. However, continuous data processing and transmission increase the strain on battery life. This study examines AI and machine learning-based techniques to reduce energy consumption in mHealth applications without compromising functionality. Adaptive sampling, task scheduling, and predictive user behavior modeling were implemented, significantly reducing power consumption and extending battery life. Challenges such as data privacy and model generalization in deploying these AI technologies are also addressed, along with future research and broader adoption.

Geospatial Modeling of Aeolian Dynamics in the Algerian Steppe from Zahrez Chergui to Hodna

Abdelmalek Rerboudj — 2024-12-02

Assessing the hazards associated with aeolian geomorphological processes requires a fundamental understanding of their spatial distribution. These phenomena often have detrimental impacts on the environment, economy, and society. This problem is prevalent in the Algerian steppe, encompassing the Zahrez, Chergui, and Hodna regions. This study proposes a research method for developing more accurate and simpler indices to evaluate the extent and directionality of sand migration. Specifically, it examines surface characteristics, such as altitude, slope, and slope exposure. However, some tools used for spatial modeling of wind dynamics necessitate corrections to account for the effects of topography and surface features on wind, which for this study are implemented using spatial techniques. The results are incorporated into the model developed by Fryberger, which requires wind data and a Digital Surface Model (DSM) to estimate the factors included in this model. The findings indicate that the average potential quantity of sand movement is 64 t m^-1 yr^-1 over the entire study area, with 37.3% of the region experiencing severe deflation of 140 t m^-1 yr^-1. This result can be utilized to enhance the understanding of the direction and magnitude of sand movement in any region.

Multi-Step Wind Speed Forecasting by Secondary Decomposition Algorithm and LSTM

Ari Shawkat Tahir — 2024-12-02

Enhancing the reliability of wind speed forecasting is vital for efficient wind power generation. Given the wind's stochastic nature, preprocessing is crucial to obtain a clean wind speed series. This study introduces an innovative wind speed prediction model that integrates Variational Mode Decomposition (VMD), Symplectic Geometry Mode Decomposition (SGMD), and Long Short-Term Memory (LSTM). The model begins with VMD dividing the series into low- and high-frequency parts, then the SGMD further analyzes the high-frequency segment, and LSTM predicts results based on these components. Collaborative use of VMD and SGMD enables thorough decomposition of intricate wind speed data, while LSTM boosts the model's ability to capture patterns and dependencies. This hybrid model addresses the challenges posed by wind power uncertainty, aiming to efficiently integrate wind energy into power systems. The proposed hybrid model was compared to some benchmark models and outperformed them, reducing MAPE by 58% and RMSE by 31% for Dataset 1, and improving MAPE by 14% and RMSE by 36% for Dataset 2. The results confirm the competitive strength of the proposed strategy. Furthermore, the suggested two-stage decomposition technique demonstrates suitability for the examination of nonlinear characteristics in wind speed patterns.

A Study of the Influence of Additives of Nanostructured Functional Ceramics in the Coating of Welding Electrodes on their Welding and Technological Properties

Rustam Saidov — 2024-12-02

The present work aims to study the influence of Nanostructured Functional Ceramics Photocatalysts (PNFC) under the brand name ZB-2, obtained using a synthesis method deploying pulsed radiation activation technology on welding and technological properties. This method of obtaining ceramic material allows the latter to be produced on an industrial scale. Therefore, it can replace the technology for producing PNFC under the influence of concentrated solar radiation at the Big Solar Furnace (BSF) of the Institute of Materials Science, Academy of Sciences of the Republic of Uzbekistan. The ZB-2 ceramic material has shown its effectiveness when used as an additive in the coating charge of the MR-3 welding electrode. Thus, the breaking arc length of the MR-3 welding electrode is increased up to 2% with the addition of ZB-2 to the coating charge. With additions of more than 2%, the breaking arc length decreases. The additive ZB-2 has the same effect on the diameter of the deposited point of the MP-3 welding electrode. When its content in the coating is up to 2%, the diameter of the deposited point increases, and a further increase in the additive content reduces this indicator. Adding up to 1% ZB-2 into the coating composition has a beneficial effect on the size of the peak at the end of the electrode. When its content exceeds 1%, the latter decreases. Also, an increase in the content of the PNFC additive in the coating of the MR-3 electrode reduces the value of the melting coefficient and increases the value of the deposition coefficient, which contributes to a sharp reduction in losses due to waste and spattering up to 53% when the additive content in the coating mass is up to 8%.

Finite Element Simulation and Experimental Analysis of the Thermo-Mechanical Properties of Dissimilar S275 and 316L Austenite Stainless Steels using the RFW Process

Francois Bayock Njock — 2024-12-02

This research examines the effect of thermomechanical and microstructural constituents on welding of AISI 316L (austenite stainless steel) and S275 steel. A Finite Element Model (FEM) was constructed using ANSYS 19.1, and an experimental study was conducted using the Rotary Friction Welding (RFW) process. It was determined that there is a genuine correlation between the simulation FEM and the experimental procedure with regard to the thermal profile and ultimate yield strength, particularly when a welding speed of 2,000 rev/min is employed. At that speed, the higher temperature recorded and calculated was 1,450 ^oC. The discrepancy between the numerical FEM and the experimental temperature profile for the peak temperature calculation was determined to be 2.78%. The mechanical analysis was conducted through tensile force calculations and experiments, the results of which indicated an estimated error of 12%. The calculated error for the ultimate yield strength of the various samples is less than 6% for tensile strength. Upon tensile testing, failure occurred in the S275 sample. The microstructure exhibited increases in Cr and Ni of 1.2% and 1.01%, respectively, in comparison to the base metal of 316L stainless steel.

Promoting Robo-Advisor Adoption among B40 in Malaysia through Advisory Transparency and UTAUT Models

Aishah Nadhirah Ahmad Nazmi — 2024-12-02

The B40 is generally considered to have disadvantages in financial literacy and monetary resources, which often prevents them from making sound investment decisions. Therefore, based on recent advances in Artificial Intelligence (AI) and financial technologies, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the notion of advisory transparency as a mediator, this study investigates factors influencing the intention to adopt financial robo-advisors among the B40 in Malaysia. The 217 responses collected using self-administered bilingual questionnaires were analyzed using Structural Equation Modeling (SEM). The results show that advisory transparency plays a significant role in mediating performance expectancy, facilitating conditions and effort expectancy to robo-advisor adoption intention. Specifically, the results imply that better advisory transparency, performance, and facilitating conditions of robo-advisor usage with minimal effort can, directly and indirectly, promote the intention of robo-advisor adoption. Consistent with the characteristics of B40, who are typically risk-averse and lack digital finance literacy, the findings suggest that more emphasis should be placed on the transparency of the robo-advisory process and digital financial education to promote robo-advisor adoption among the B40. This study fills a gap by integrating advisory transparency into the UTAUT model and providing insight into how advisory transparency interacts with UTAUT factors in promoting robo-advisor adoption. The results of this study can be a reference for policymakers, particularly in devising social welfare and educational policies to eradicate poverty in the country.

Application of Artificial Intelligence in the Identification of Banana Bunch Top Virus (BBTV) in Mozambique

Abel Simango — 2024-12-02

Agricultural production faces many challenges, such as disease and pest infestation, which can lead to severe crop loss and environmental impacts due to the excessive use of chemicals. Artificial intelligence has become a key technique to solve different agricultural-related challenges. The main objective of this study was to train and validate artificial intelligence algorithms for the detection of Banana Bunchy Top Virus (BBTV) in banana crops. Approximately 2,500 images of healthy and BBTV-infected leaves were collected, stratified according to the stage of plant development, and used to calibrate and validate an artificial intelligence algorithm for the detection of BBTV. Pre-trained models such as VGG 16, ResNet50, and InceptionV3 were tested. The ResNet50 model achieved a training accuracy of 99.56% and validation precision, recall, and F1 score of 96.53%, 94.94%, and 95.73%, respectively, outperforming the other models in detecting BBTV-infected plants.

Design of Deep Learning Techniques for PCBs Defect Detecting System based on YOLOv10

Sumarin Ruengrote — 2024-12-02

As Printed Circuit Boards (PCBs) are critical components in electronic products, their quality inspection is crucial. This study focuses on quality inspection to detect PCB defects using deep learning techniques. Traditional widely used quality control methods are time-consuming, labor-intensive, and prone to human errors, making the manufacturing process inefficient. This study proposes a deep-learning approach using YOLOv10. Through the incorporation of architectural improvements such as CSPNet and PANet that improve feature extraction and fusion, as well as a dual assignments mechanism that increases localization accuracy, YOLOv10 offers significant improvements over earlier versions, such as YOLOv5 and YOLOv8, and Faster R-CNN models. These innovations allow YOLOv10 to deliver superior performance in terms of both speed and precision. The experiments used a custom dataset consisting of 1,260 PCB samples collected from the industry. The dataset was partitioned into 80% for model training and 20% for testing. The model was trained for 100 epochs with a batch size of 32 to evaluate its performance in identifying various PCB defects. YOLOv10, with its optimized architecture, fully utilized its capabilities while requiring less computational power than YOLOv5 and YOLOv8, especially in resource-constrained environments. Despite resource constraints, YOLOv10 achieved high accuracy, with a precision of at least 96% and a recall of 97%, surpassing earlier YOLO models and Faster R-CNN. It also achieved 99% mAP and more than 96% F1 score. These improvements in speed and accuracy make YOLOv10 a highly efficient solution for automated PCB inspection, reducing manual effort and offering fast and accurate classification adaptable to various applications.

Comparative Assessment of Hash Functions in Securing Encrypted Images

Ghayth Al-Asad — 2024-12-02

Different encryption methods have been developed to securely transmit confidential images over the Internet and combat the increasing cybercrime. Many of these methods use hash functions to enhance encryption strength. Due to the lack of a comprehensive evaluation of how different hash functions affect image encryption, this study presents a comparative analysis of the performance of various hash functions as encryption keys and analyzes their security, speed, and efficiency. The source image is first processed as a series of bytes. The bytes are divided into byte vectors, each with a length that matches the length of the hash value of a specified hash function. An XOR operation is performed between the hash value bytes and the associated byte vector. The bytes are reordered in each vector according to the ascending order of the associated hash value. Several metrics, such as Normalized Mean Absolute Error (NMAE), Peak Signal to Noise Ratio (PSNR), entropy, key size, and hash time, were used to evaluate the performance of different hash functions in image encryption. The results showed a clear variation in using various hash functions in terms of security, speed, and efficiency. With NMAE>72%, PSNR<6.62 dB, and Entropy>7.999 bpp, the use of the SHA family and MD5 is recommended in applications that need to achieve a high level of distortion in encrypted images. To resist brute-force attacks on the key, Blake2b, SHA512, and Whirlpool are the best choices with a key size of 512 bits. The Tiger is the fastest hash function, requiring the least average time of 0.372 seconds to complete the encryption process, making it the best choice for real-time applications. These findings help to choose the appropriate hash function in developing cryptographic techniques for a particular area.

Effect of Elevated Temperature on Microstructure and Mechanical Properties of Hot-Rolled Steel

Ali Malik Saadoon — 2024-12-02

The mechanical properties and microstructure of hot-rolled steel are critical in determining its performance in industrial applications, particularly when exposed to elevated temperatures. This study examines the effects of varying temperatures and soaking times on these properties through a series of controlled experiments. The primary objective was to optimize the key response parameters, including tensile strength, yield strength, and elongation, by analyzing the influence of temperature and time. A full factorial design approach was used, applying the desirability function theory to explore all possible combinations and identify optimal processing conditions. The experimental results showed that the soaking time played a critical role, significantly influencing the mechanical properties with an impact ratio of 62%. The microstructural analysis displayed that higher temperatures and longer soaking times resulted in the formation of coarser ferrite and pearlite grains, contributing to a decrease in strength and an increase in ductility. The optimum process condition - 650 °C for 60 min - produced the highest values for tensile strength (400.32 MPa), elongation (36.78%) and yield strength (288.52 MPa). The study also highlighted the temperature-dependent nature of the mechanical behavior of hot-rolled steel. While tensile strength and yield strength initially increase with temperature, prolonged exposure, particularly at 600 °C and 750 °C, results in significant grain coarsening and a corresponding degradation of these properties. Conversely, elongation improves at moderate temperatures (150 °C to 300 °C) but decreases with prolonged exposure, especially at higher temperatures. These findings underscore the importance of precise control of thermal processing parameters to optimize the mechanical properties of hot-rolled steel. The findings offer significant insights that can be leveraged to optimize material performance in industrial applications, where thermal exposure is a critical consideration.

A Νew Heuristic Optimization Approach to the Single Hoist Cyclic Scheduling Problem

Aymen El Amraoui — 2024-12-02

This paper introduces an innovative heuristic optimization approach, referred to as Optimization Approach-Single Hoist Cyclic Scheduling Problem (OA-SHCSP), which aims to minimize the cycle time of the Single Hoist Scheduling Problem (SHCSP). The effectiveness of this proposed heuristic is compared with a previously established heuristic, the Earliest Starting Time (EST). The comparison results reveal that the proposed OA-SHCSP heuristic consistently outperforms the EST heuristic in minimizing cycle time, particularly when more than two products are produced simultaneously. Moreover, as the number of part tasks soaked during a cycle increases, OA-SHCSP demonstrates significantly improved computational efficiency over the EST heuristic. The reduction in average cycle time achieved by OA-SHCSP ranges from 28.73% to 60.29%, underscoring its effectiveness and potential for application in high-volume production environments.

A Study of the Optimization of FDM Parameters for the Manufacture of Compression Specimens from Recycled PETG in the Context of the Transition to the Circular Economy

Dragos Gabriel Zisopol — 2024-12-02

The current paper presents the results of a research on the optimization of Fused Deposition Modeling (FDM) parameters, namely the height of the deposited layer in one pass, L_h, and the filling percentage, Id, with the purpose of manufacturing compression specimens from recycled Polyethylene Terephthalate Glycol (rPETG), and thus, aiming the transition to circular economy. A total of 45 compression specimens were manufactured from rPETG on the Anycubic 4Max Pro 3D printer with variable parameters L_h = 0.10 mm, 0.15 mm, 0.20 mm, and Id = 50%, 75%, 100%. All 45 specimens were tested in compression on the Barrus White 20 kN universal testing machine. The considered variable parameters influence the Compressive Strength (CS) of the specimens, with Id being the parameter with overwhelming influence.

Neurospectral Computation for the Resonant Characteristics of an Equilateral Triangular Patch Antenna on Suspended Substrates

Ahmed Mahamdi — 2024-12-02

Modeling and design of an equilateral triangular patch antenna on suspended and single substrate are accomplished in this paper. The spectral domain approach is important due to its accuracy, but has a high computational cost. On the other hand, the Artificial Neural Networks (ANNs) have recently become a fast and flexible vehicle for modeling and designing microwave antennas. This paper introduces electromagnetic knowledge combined with ANNs to compute the resonant frequency of the fundamental and higher order modes and to eliminate the difficulties of handling the singularity points encountered in the numerical evaluation of integrals. The resonant frequency results obtained from the neural model are in very good agreement with the experimental and theoretical results available in the literature.

Enhancing Elevator Ride Quality through Vector Control Techniques and S-Curve Profiles

Ali Abdulkareem Ali — 2024-12-02

This study examines motor drive techniques, including Field-Oriented Control (FOC), sensorless FOC, and Direct Torque Control (DTC), to improve elevator ride quality by reducing jerk-sudden changes in acceleration that cause discomfort. A 200 cm tall prototype elevator system was developed, using S-curve velocity profiles alongside the considered control strategies. The system includes a TMS320F28379D DSP-controlled induction motor, sensors, and an encoder to assess performance. Results show that FOC with S-curve profiles reduces jerk by 72–73%, significantly improving comfort compared to the standard trapezoidal profile. Sensorless FOC reduces jerk by 68–71%, providing a cost-effective option, though it faces challenges during downward motion under load. DTC, reduces jerk by 65–68% and results in less smooth travel, especially during downward movement. In comparison, the trapezoidal velocity profile produced higher jerk levels and less ride comfort. This study emphasizes the critical role of control technique selection in enhancing elevator comfort and efficiency.

An Efficient Model for Lung Cancer Detection through the Integration of Genetic Algorithm and Machine Learning

Abdulaziz A. Alsulami — 2024-12-02

Prompt lung cancer detection is essential for patient health. Deep Learning (DL) models have been intensively used for lung cancer screening, as they provide high accuracy in diagnoses. However, DL models require significant computational power, which may not be accessible in all settings. Conventional Machine Learning (ML) models may not produce high prediction accuracy, especially with large data. This study uses a Genetic Algorithm (GA) approach to select optimal features from lung cancer images and reduce their dimensionality. This allows conventional ML models to achieve a high prediction accuracy when classifying medical images while using lower computational power compared with DL models. The proposed model integrates GA along with ML for lung cancer detection. The experimental results show that using GA with a feed-forward neural network classifier achieved high performance, reaching 99.70% classification accuracy.

Optimizing Switching Activity using LFSR-Driven Logic for VLSI Circuits

Α. Swetha Priya — 2024-12-02

In Very-Large-Scale-Integration (VLSI) designs, thorough testing is indispensable for identifying the structural defects of the chip. Timely detection and correcting serious defects are pivotal in preventing faulty chips from reaching customers and avoiding failures. In scan designs, the toggling activity is a critical factor due to the defects between lower cells and metal layers, exacerbated by diverse Process, Voltage, and Temperature (PVT) conditions. These defects significantly impact design testability, quality, and reliability, necessitating meticulous testing to ensure that chips meet the desired specifications. Effectively managing power consumption in the current VLSI landscape is crucial amid the ongoing energy crisis. Balancing the need for Low-Power (LP) with the complexity of integrating transistors onto a single silicon substrate poses significant challenges. As chip densities increase, power dissipation during testing surges, adversely affecting durability, performance, cost, and reliability. Engineers are racing to optimize test power usage, employing advanced Design for Test (DFT) techniques to incorporate efficient power management into Silicon-on-Chip (SoC) designs. Linear Feedback Shift Registers (LFSRs) are phenomenal in addressing DFT parameters like power, and performance, and for better pseudo-random pattern generation. Hence, this paper proposes a groundbreaking approach to power reduction techniques deploying the LFSR architecture, and thus challenging conventional scan-based testing methods. The proposed LFSR architecture is meticulously designed and rigorously tested using Cadence DFT Modus solution on ISCAS’89 benchmarking circuits. Coverage was unequivocally evaluated for Quality of Results (QoR) metrics, such as fault coverage, memory usage, pattern counts, switching activity, fault testing, and runtime. The specific evaluation clearly proved the superiority of the LFSR-based approach over the scan-based architecture. Adopting the novel LFSR architecture resulted in a ~5.6X reduction in toggling activity accompanied by a substantial ~10K pattern reduction and a runtime of nearly ~1.5 hours. Notably, the test power was reduced to 50% showcasing superior efficiency. This approach emerges as the ideal solution for industrial designs providing the best QoR for power, performance, and area. This innovative methodology marks a significant leap toward an energy-efficient and cost-effective VLSI circuit especially for stacked 2.5-3D ICs and chiplets poised to revolutionize chip manufacturing.

Electroless Synthesis of Cobalt Nanowires in Magnetic Field and their Characterization by Resonant Magnetometry Methods

Tatiana Gegechkori — 2024-12-02

In this paper, a simple and effective low-temperature electroless chemical method that provides the synthesis of cobalt micro- and nanowires due to the processes of self-organization of magnetic cobalt nanoparticles under the influence of a magnetic field, using the technology of chemical synthesis of magnetic nanoparticles and nanowires is proposed. Cobalt nanoparticles have magnetic dipole moments. An external magnetic field forces them to be oriented parallel to it. Dipole-dipole interactions between magnetic nanoparticles lead to attraction between cobalt nanoparticles leading to their self-organization into nanowires, reducing their total energy. The resulting smaller nanoparticles fill the gaps between the ordered nanoparticles, leading to the formation of smooth cobalt nanowires. The magnetic and structural properties of the synthesized and commercial nanowires polarized by a magnetic field in the epoxy matrix were studied using the resonant radio-frequency magnetometry and electron microscopy methods. These methods are of interest for optimizing the coercive force of cobalt nanowires with a view to their possible use in creating permanent magnets that do not use rare earth elements, as well as in information processing devices and sensors.

Optimizing Hepatitis C Virus Inhibitor Identification with LightGBM and Tree-structured Parzen Estimator Sampling

Teuku Rizky Noviandy — 2024-12-02

Identifying potent inhibitors against the Hepatitis C Virus (HCV) is crucial due to the continuous emergence of drug-resistant strains. Traditional drug discovery methods, including high-throughput screening, are often resource-intensive and time-consuming. Machine Learning (ML) approaches, particularly Quantitative Structure-Activity Relationship modeling, have been increasingly adopted to address this. This study utilized LightGBM, an efficient gradient-boosting framework, to predict the activity of potential HCV inhibitors. Additionally, the Tree-structured Parzen Estimator (TPE) was employed for hyperparameter optimization to enhance model performance. The optimized LightGBM-TPE model outperformed other ML models, including standard LightGBM, XGBoost, Random Forest, K-Nearest Neighbors, and Support Vector Machines, achieving an accuracy of 86.27%, a precision of 85.47%, a recall of 87.50%, a specificity of 85.03%, and an F1-score of 86.47%. Feature importance analysis identified critical molecular descriptors contributing to the model's predictive power. The results underscore the potential of advanced ML techniques and robust optimization methods to accelerate drug discovery, particularly for challenging targets such as HCV.

A Quantum Encryption Algorithm based on the Rail Fence Mechanism to Provide Data Integrity

Arshad Ali — 2024-12-02

The rapid development of quantum computer technology poses an increasing threat to conventional encryption algorithms, and accordingly, more advanced security practices need to be developed. The current paper presents an innovative quantum cryptographic mechanism that combines classical encryption techniques with quantum principles such as superposition, entanglement, and uncertainty to enhance data security in digital communication. The proposed scheme, defined as Enhanced Quantum Key Distribution (EQKD), demonstrates superior performance in key metrics, including Quantum Bit Error Rate (QBER), fidelity, key distribution rate, and resilience to eavesdropping. In particular, EQKD achieves lower QBER and higher fidelity over longer distances while also enhancing key generation efficiency and increasing the probability of detecting eavesdropping attempt. These findings highlight the effectiveness of EQKD in improving the security and reliability of quantum cryptographic systems.

A Hybrid Machine Learning Model for Market Clustering

Rendra Gustriansyah — 2024-12-02

Market clustering is increasingly important for companies to understand consumer shopping behavior in the context of complex data. This study aims to develop a hybrid model that integrates Principal Component Analysis (PCA) and k-medoids to enhance market clustering based on consumer shopping patterns. The methods used include data preprocessing, PCA application for dimensionality reduction, and clustering using k-medoids. The quality of the clusters is evaluated with various validity indices. The results show that the hybrid model produces clusters with better quality compared to the single k-medoids method, as seen from the Calinski-Harabasz Index (CHI), theSilhouette Width (SW), and the Davies-Bouldin (DB) index. The implications of these findings emphasize the importance of adopting hybrid methods in marketing strategies to improve understanding of consumer behavior dynamics and allow companies to adjust their marketing strategies more effectively. This study provides a strong foundation for further development in clustering analysis across various industry sectors and highlights the potential for innovative techniques to address dynamic market challenges.

Analysis of the Effects of Temperature and Treatment Duration on the Resistance of Expansive Soil Improved with Lime in Baghdad, Iraq

Amenah Adnan Shakir Al-Mohammedi — 2024-12-02

Multiple studies have revealed the challenge of constructing infrastructure on expansive soils, including pipelines, roads, or buildings. This predicament stems from the uneven moisture distribution inherent to the specific soil type. Numerous methods, involving the addition of chemicals, have been employed to enhance the properties of clayey soils. This study introduces lime as a cation exchange material and demonstrates its capacity to improve the load-bearing characteristics, rendering it a more favorable option for engineering construction purposes. Lime's reaction with clay minerals and water produces calcium hydroxide, which subsequently reacts with the silica and alumina in the clay to form new compounds that promote stability. Additionally, lime helps reduce the soil's water-holding ability, thereby decreasing its swelling potential. This research will focus on evaluating the influence of temperature and treatment duration on the osmotic pressure behavior of chemically treated expansive clayey soils using lime. The swell meter test was utilized to develop lime-clay samples containing 7% lime by dry weight. These samples were then subjected to compression at temperatures ranging from 20 °C to 40 °C over a period of up to 28 days. The findings indicate that the pozzolanic reaction results in higher compressive strengths when tested at the upper limits of the temperature range in laboratory experiments. Therefore, the combined effects of temperature and curing duration play a positive role in improving the compressive strength of expansive soils.

Assessment of Building Nondeterministic Dynamic Structural Behavior considering the Effect of Geometric Nonlinearity and Aerodynamic Damping

Jean Carlos Mota Silva — 2024-12-02

The objective of this research is to evaluate the dynamic structural response of tall buildings subjected to wind loads, taking into account the influence of geometric nonlinearity and aerodynamic damping. The project focuses on a steel-concrete composite structure with 48 floors and a height of 172.8 m, examining its response to wind non-deterministic dynamic actions. The building finite element model was developed based on the Finite Element Method (FEM), using the ANSYS computational program, and considering the soil-structure interaction effect, with the objective of obtaining a realistic representation of the dynamic behavior. The building dynamic response was obtained based on the displacement and acceleration values, determined with the consideration of a wind velocity range between 5 m/s (18 km/h) and 45 m/s (162 km/h). The findings of this study indicate that when the effect of geometric nonlinearity was incorporated into the analysis, the dynamic response of the investigated building exhibited notable discrepancies. The maximum differences observed in the horizontal translational displacements and accelerations were 30% and 45%, respectively. In contrast, the inclusion of aerodynamic damping had a negligible impact on the structural dynamic response, with maximum differences of 5% for displacements and 10% for accelerations.

Enhancing Healthcare Monitoring: A Deep Learning Approach to Human Activity Recognition using Wearable Sensors

Sami Aziz Alshammari — 2024-12-02

Wearable devices and deep learning methods for Human Activity Recognition (HAR) have attracted a lot of interest because they could change healthcare monitoring. This study presents a CNN-LSTM model to accurately and reliably detect human movements from smartphone sensor data. The proposed model takes advantage of both the strengths of Long Short-Term Memory (LSTM) networks for modeling time and Convolutional Neural Networks (CNNs) for extracting features from space. This enables determining how the input data change over time and space. This study examines whether this method can work and is practical in real-life healthcare settings, focused on uses such as watching patients from distance, caring for the elderly, and therapy. The proposed model was evaluated on publicly accessible standard datasets. Various architectural configurations and hyperparameters were examined to determine their performance. The proposed CNN-LSTM model performed well and has great potential for practical use in activity tracking and environment understanding systems.

Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting

Thanh-Ngoc Tran — 2024-12-02

Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum load data of Queensland, Australia, are used for the research. The analysis of the results indicates that the accuracy of the XGBoost model significantly depends on the values of its hyperparameters. Using default hyperparameter values may lead to substantial errors in load forecasts, while selecting appropriate values for the GA to determine the optimal hyperparameters for the XGBoost model can significantly improve its accuracy.

A New Hybrid Energy Storage System for Electric Vehicle Drive System

S. V. Pratap Simha Reddy — 2024-12-02

In this paper, a new Hybrid Energy Storage System (HESS) for Electric Vehicle (EV) drive systems is proposed to increase their battery lifespan, with the potential to meet peak power demands without heavily straining the batteries. The developed feedback control circuit works as a controller to maintain the voltage of the Supercapacitor (SC) at a value higher than the battery voltage during the high acceleration periods of the driving cycle, creating a relatively constant load profile for the battery. The battery is not used to directly harvest energy from the regenerative braking, being, thus, isolated from frequent charges during high acceleration/deceleration periods, which increases its lifespan. The simulation results demonstrate HESS's effectiveness in significantly reducing battery discharge/charge rates compared to a standalone system.

An Adaptive Neural Fuzzy Inference System for the Estimation of the Atmospheric Corrosion Rate of Steel

Trong-Ha Nguyen — 2024-12-02

This paper aims to develop a practical Adaptive Neural Fuzzy Inference System (ANFIS) for estimating carbon steel's Atmospheric Corrosion Rate (ACR). The ANFIS model is developed using 125 datasets. The input variables of the ANFIS model include average Temperature (T), average Relative Humidity (RH), total Rainfall (Rf), Time of Wetness (TOW), and average Chloride Ion (Cl^-). The output variable of the Machine Learning (ML) model is the ACR value. The results of the proposed model are compared to those of the literature. The comparisons reveal that the ANFIS model established in this study outperforms the existing equations in predicting ACR. Furthermore, a Graphical User Interface (GUI) is developed for practical use in predicting the ACR of carbon steel.

Autofocus Vision System Enhancement for UAVs via Autoencoder Generative Algorithm

Anwer Ahmed — 2024-12-02

The Autofocus (AF) technology has become well-known over the past four decades. When attached to a camera, it eliminates the need to manually focus by giving the viewer a perfectly focused image in a matter of seconds. Modern AF systems are needed to achieve high-resolution images with optimal focus, and AF has become very important for many fields, possessing advantages such as high efficiency and autonomously interacting with Fenvironmental conditions. The proposed AF vision system for Unmanned Aerial Vehicle (UAV) navigation uses an autoencoder technique to extract important features from images. The system's function is to monitor and control the focus of a camera mounted to a drone. On an AF dataset, the proposed autoencoder model exhibited an amazing 95% F-measure and 90% accuracy, so it can be considered a robust option for achieving precision and clarity in varying conditions since it can effectively identify features.

An Ensemble Kernelized-based Approach for Precise Emotion Recognition in Depressed People

Bidyutlata Sahoo — 2024-12-02

As the COVID-19 pandemic created serious challenges for mental health worldwide, with a noticeable increase in depression cases, it has become important to quickly and accurately assess emotional states. Facial expression recognition technology is a key tool for this task. To address this need, this study proposes a new approach to emotion recognition using the Ensemble Kernelized Learning System (EKLS). Nonverbal cues, such as facial expressions, are crucial in showing emotional states. This study uses the Extended Cohn-Kanade (CK+) dataset, which was enhanced with images and videos from the COVID-19 era related to depression. Each of these images and videos is manually labeled with the corresponding emotions, creating a strong dataset for training and testing the proposed model. Facial feature detection techniques were used along with key facial measurements to aid in emotion recognition. EKLS is a flexible machine-learning framework that combines different techniques, including Support Vector Machines (SVMs), Self-Organizing Maps (SOMs), kernel methods, Random Forest (RF), and Gradient Boosting (GB). The ensemble model was thoroughly trained and fine-tuned to ensure high accuracy and consistency. EKLS is a powerful tool for real-time emotion recognition in both images and videos, achieving an impressive accuracy of 99.82%. This study offers a practical and effective approach to emotion recognition and makes a significant contribution to the field.

Application of the Multi-Criteria Decision Method to Find the Best Input Factors for Electrical Discharge Machining 90CrSi Tool Steel using Graphite Electrodes

Thi Phuong Thao Le — 2024-12-02

This paper examines the optimization of the Electrical Discharge Machining (EDM) process when machining cylindrical parts of 90CrSi tool steel using various graphite electrodes. A Multi-Criteria Decision Making (MCDM) approach, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), and Multi-Attributive Border Approximation Area Comparison (MABAC) was utilized to identify the optimal input factors that would achieve three machining objectives: minimizing Surface Roughness (SR) and Electrode Wear Rate (EWR) and maximizing Material Removal Rate (MRR). Criteria weights were calculated using the Method based on the Removal Effects of Criteria (MEREC). Additionally, three types of graphite electrodes (HK0, HK15, and HK20) and five process factors, such as Servo Voltage (SV), Input Current (IP), pulse on time (T_on), pulse off time (T_off), and Types of Graphite (TOG) were tested with experiments structured using a Taguchi L18 design and Minitab R19 software. The results indicate that the optimal EDM input parameters are as follows: IP = 9.5 A, SV = 5 V, T_on = 8 µs, T_off = 8 µs, with the HK20 electrode balancing SR, EWR and MRR for enhanced machining performance.

Research on the Estimation of the Flexural Capacity of EPS Lightweight Concrete Panels

Sy-Dan Dao — 2024-12-02

Expanded Polystyrene (EPS) lightweight concrete possesses numerous special characteristics, including exceptionally low weight, good sound insulation, effective heat insulation, great fire resistance, and low water absorption. Consequently, it has garnered significant attention for research and practical applications in the construction industry, particularly in the development of soundproofing and thermal insulation components, as well as lightweight elements to reduce the superstructure weight, such as external wall panels, floor slabs, roof panels, and base materials. The primary objective of this study is to analyze and estimate the flexural capacity of EPS lightweight concrete panels through both experimental and numerical methods. Furthermore, the influence of the longitudinal reinforcement ratio, strength, and thickness of EPS lightweight aggregate concrete panels on their flexural capacity is thoroughly investigated and evaluated. The findings demonstrate that the Abaqus software, with an appropriate material model for EPS lightweight aggregate concrete, can reliably predict the flexural capacity of EPS lightweight concrete panels reinforced with longitudinal rebars.

Nature-Based Solutions for Flood Mitigation in Metropolitan Areas

. Juliastuti — 2024-12-02

Flooding is a globally common problem in metropolitan areas including Jakarta, Indonesia. The increased intensity and frequency of rainfall caused by climate change and rapid urbanization have raised the risk of flooding in urban areas. One solution is to implement polders to mitigate flooding in coastal metropolitan areas. Regrettably, the current polder system is inadequate for managing flooding due to rapid land-use changes and regional expansion. This study analyzes flood control in the Jakarta region using the East Sunter Polder System, which experienced flooding in both 1990 and 2020 despite the implementation of the polder system. The polder system, consisting of four catchment areas—Petukangan, KBN 1/Sukapura, KBN 2, and Kebantenan—faces drainage challenges exacerbated by rainfall. To mitigate flood risks, Nature-Based Solutions (NBSs) have been implemented, including retention ponds and long storage systems. Hydrological and hydraulic analyses were conducted using HEC-HMS and HEC-RAS, and ArcGIS was employed for floodplain integration. This study underscores the significance of incorporating NBSs in urban flood management, demonstrating how they enhance resilience and mitigate flood risks. By integrating NBSs into the urban planning framework, the findings suggest that flood risk management can be significantly improved, leading to better preparation and long-term sustainability for managing natural hazards.

Performance of Steel Beams reinforced by CFRP Sheets with Fire-Retardant Coating

Majid M. Kharnoob — 2024-12-02

The objective of this study was to determine the physical, mechanical, and chemical aspects of bonding and priming Fire-Retardant Coatings (FRCs) to steel surfaces before and after fire testing. Carbon Fiber Reinforced Polymers (CFRP) have been used to strengthen steel components. Certain types of polymer systems have demonstrated high fire retardancy without additives, while others require additives to achieve optimum fire resistance. A wide range of compounds are available to enhance the fire retardant properties of these materials, characterized by their chemical nature and behavior (such as halogenated, metal complex, silicon-based, and phosphorus additives) and mode of action (either condensed or gas-phase active systems).This article provides a comprehensive overview of fire retardant additives for CFRP used in various large-scale applications, including the aerospace, automotive, railway, electronics, and civil engineering industries, as well as their fire retardant mechanisms at the microscopic, macroscopic, and nanoscale levels. In addition to fire retardant properties, this study also discusses the effects of additives on other material parameters and coatings, such as glass transition temperature, mechanical performance, and FRP processability. The primary focus is on thermoset systems, with a brief mention of thermoplastics according to the matrix compounds relevant to the FRP market size. Test results show that the direct velocity ultrasonic value varied between 3.016 and 3.618 km/s, giving an estimated compressive strength of 15.974 MPa. The standard deviation was 0.533 km/s with a relative standard deviation of 16.407%.

Enhancing the Mechanical and Fire-Resistant Properties of GFRP Composite using Boric Acid and Sodium Silicate Fillers

Tri Wibawa — 2024-12-02

This study investigates the effects of Boric Acid (BA) , H₃BO₃, and Sodium Silicate (SS), Na₂SiO₃, as single fillers on the mechanical and fire-resistant properties of Glass Fiber Reinforced Polymer (GFRP) composites. Various compositions of BA and SS were incorporated into the GFRP matrix, and the resulting composites were analyzed using X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Thermo-Gravimetric Analysis (TGA), and Differential Thermal Analysis (DTA). The results demonstrate that BA significantly enhances the amorphous structure and mechanical strength of GFRP composites, with optimal performance at 10% BA content. In contrast, SS improves thermal stability but reduces mechanical strength at higher concentrations due to agglomeration. Fire resistance testing revealed that both fillers increase the ignition time and decrease the burning rate, with BA exhibiting superior performance. These findings suggest that BA is a more effective filler for improving the mechanical and fire-resistant properties of GFRP composites, while SS can serve as a complementary additive to enhance thermal stability.

Automatic Step Size Selection of the PO MPPT Algorithm to Improve Wind Power Generation

Andi Nur Putri — 2024-12-02

Perturb and Observe (P&O) is a commonly used algorithm for Maximum Power Point Tracking (MPPT) in wind turbines. MPPT plays a critical role in enhancing wind turbine efficiency by dynamically adjusting operating parameters to adapt to fluctuating wind conditions. Although P&O is favored for its simplicity and adaptability, its performance is hindered by step size selection issues, which lead to inefficiency, oscillations, and slow convergence. To overcome these limitations, this research proposes a modified P&O algorithm that automates step size selection based on divided sectors of wind speed and normalized power in region two. Additionally, an integration of the pitch-angle control from region three was employed to maintain the optimal power output under variable wind conditions. The proposed approach reduces tracking time, minimizes perturbation errors, and ensures a stable power output. The proposed modifications enhance the efficiency and reliability of Wind Energy Conversion Systems (WECS) by addressing the shortcomings of the conventional P&O methods.

IoT Traffic Parameter Classification based on Optimized BPSO for Enabling Green Wireless Networks

Yasser Fouad — 2024-12-02

The rapid expansion of artificial intelligence (AI) integrated with the Internet of Things (IoT) has fueled the development of various smart devices, particularly for smart city applications. However, the heterogeneity of these devices necessitates a robust communication network capable of maintaining a consistent traffic flow. This paper employs Machine Learning (ML) models to classify continuously received network parameters from diverse IoT devices, identifying necessary adjustments to enhance network performance. Key network traffic parameters, such as packet data, are transmitted through gateways via specialized tools. Six different ML techniques with default parameters were used: Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Stochastic Gradient Descent Classifiers (SGDC), to classify the traffic of the environment (IoT / non IoT). The models' performance was evaluated in a real-time smart laboratory environment comprising 38 IoT devices from various vendors with the following metrics: Accuracy, F1-score, Recall and Precision. The RF model achieved the highest Accuracy of 95.6%. Also the Binary Particle Swarm Optimizer (BPSO) was used across the RF. The results demonstrated that the BPSO-RF with hyperparameter optimization enhanced the Accuracy from 95.6% to 99.4%.

HCLNet: A Hybrid CNN-LSTM Model for Enhanced Healthcare Product Classification for Recommendation

B. Ramakantha Reddy — 2024-12-02

The rapid growth of healthcare commodities requires automated categorization. Effective categorization solutions save time and money and can provide precise recommendations. Poorly annotated data, inconsistency of product category, and imbalanced datasets impede machine learning algorithms in current systems. CNNs work well with large datasets but struggle with the classification of healthcare products due to class imbalance and lack of labeled data. This study presents a Hybrid CNN-LSTM (HCLNet) model to improve the classification of healthcare products and resolve these issues. This method enhances classification using CNN feature extraction and LSTM sequential pattern recognition. HCLNet can better handle class imbalance and limited labeled data with a comprehensive data preprocessing pipeline, including selection, transformation, and filtering. The hybrid design overcomes CNN constraints and captures product feature temporal connections using LSTM layers. HCLNet was compared with ResNet, GoogleNet, and AlexNet, surpassing them. HCLNet classified complex and imbalanced datasets with 96.25% accuracy, 96.60% precision, and 96.05% recall. The proposed method can improve the classification of healthcare products to obtain accurate automated product recommendations.

Evaluation of Disaster Risk and Mitigation Strategies for Post-Disaster Permanent Housing in the Palu Koro Fault Area

Andi Asnudin — 2024-12-02

The Palu Koro Fault in Sulawesi, Indonesia, an area with very high seismic activity, with a historical record of large earthquakes, including the devastating event on September 28, 2018. This earthquake, accompanied by a tsunami and liquefaction, caused significant damage to infrastructure and residential areas in Palu City, Donggala Regency, and Sigi Regency. A future similar event needs to be studied based on technical aspects related to disaster vulnerability criteria. The SNI 03-1733 (2004) establishes the disaster risk criteria in residential areas, such as landslides, floods, and earthquakes. BNPB has also created a disaster-prone map in Indonesia. However, studies on safe housing emerging from various disasters, such as earthquakes, tsunamis, liquefaction, and other risks, specifically being in the path of planes or near high voltage currents, are still limited. This research aims to assess disaster risk in post-disaster permanent residential relocation using the AS/NZS ISO 31000 risk management framework, which includes vulnerability conditions, evaluation of technical aspects, disaster risk analysis, and development of recommendations, combining quantitative and qualitative approaches. Data were analyzed using a scale-based method, with descriptive statistics to calculate frequency, averages, and percentage of the risk categories at each location. Qualitative analysis produces narratives regarding the impact of risks on community safety and residential infrastructure. The current study results show that high-risk factors, including earthquakes, floods, and landslides, require immediate mitigation. Additionally, immediate action should be taken for risks categorized as unacceptable, involving building strengthening, drainage system improvement, and soil stabilization, to reduce the risk of liquefaction. Concerning moderate risks, which belong to the undesirable category, they also require further treatment to minimize the impact of future disasters. The current study also emphasizes the importance of community survivors' participation in the relocation and disaster preparation process. This underlines the need for an integrated approach to disaster risk management to strengthen the resilience of communities and infrastructure in disaster-prone areas.

A Low-Profile Reconfigurable Wide Band BPF with RF-MEMS Switches for 5G/ Satellite Applications

Surendra Babu Velagaleti — 2024-12-02

A low-profile wide-band tunable semi-circular cavity BPF is designed and analyzed in this work. RF-MEMS switches were utilized on either side of the 50 Ω microstrip transmission lines to provide reconfigurability. BPF tunability is achieved when the two switches travel from upstate to downstate in an electrostatically activated shunt capacitive shunt type RF-MEMS switch. The switch has a capacitance ratio of 10 and operates at a transition time of 30 μS with an actuation voltage of 6.5 V to move it downward. This is suitable for 5G wireless communication applications (n77, n78, and n79) as well as C-band applications. Return loss of -22 dB is obtained at 3.7 GHz when the switch is in the ON state, while reflection co-efficient of -32 dB is obtained at 6.7 GHz when the switch is in the OFF state. When both switches are ON or OFF, a bandpass filter provides a 3GHz frequency shift. The frequency range where BPF is intended to operate, which is adjustable for various purposes, is 1–10 GHz. The characteristics of the switch were investigated by simulating its design in COMSOL Multiphysics and the outcomes were contrasted with theoretical computations. The adjustable properties of the BPF have been observed and shown using the HFSS v 13 tool.

Physical and Mechanical Properties of Abaca Fiber Reinforced Polymer Composites for Sustainable Structural Application

. Fakhruddin — 2024-12-02

This study evaluates the physical and mechanical properties of Abaca Fiber Reinforced Polymer (AbFRP) laminates for sustainable structural application. The research examines the impact of alkali treatment on the weight and diameter of the abaca fibers, as well as the tensile strength of both individual fibers and AbFRP laminates. The results indicate that the application of 0.5 wt% NaOH treatment reduced the weight and diameter of abaca fibers by 5.7% and 4.2%, respectively. Alkali treatment enhanced the tensile strength of single fibers, increasing it from 977.8 MPa to 1978.6 MPa, while the tensile strength of AbFRP laminates decreased from 78.1 MPa to 67.3 MPa. In terms of elastic modulus, the untreated and treated AbFRP laminates exhibited values of 14.9 GPa and 18.5 GPa, respectively. These findings demonstrate that AbFRP laminates possess notable mechanical characteristics, making them suitable for implementation for structural application.

Simulation of a New Well SAGD Configuration based on the example of an Oil Field in Kazakhstan

Alexandr Logvinenko — 2024-12-02

Steam-Assisted Gravity Drainage (SAGD) method is recognized as one of the most effective methods for the recovery of heavy oil and natural bitumen. This technology has received several modifications throughout its history designed to improve it. One of the promising modifications is the Single Well-SAGD (SW-SAGD), which allows significantly reducing the CAPEX for drilling a well. However, this method has several disadvantages such as steam breakthrough into the production part and the uneven development of the steam chamber along the well. This article presents the concept of a Single-Well Cyclic SAGD (SWC-SAGD), which allows preventing the breakthrough of the injected steam into the production section and the uniform development of the steam chamber along the well. The comparison analysis of the developed modification of SWC-SAGD was carried out using the classical method of 3D hydrodynamic modeling of both options using the example of one of the fields of the Republic of Kazakhstan. The results of the work show the efficiency of the proposed technology in terms of field oil total production.

Determination of the Ultimate Bearing Capacity of a Single Barrette Wall using FEA and Cubic Nonlinear Regression

Truong Xuan Dang — 2024-12-02

This study analyzes the mechanical behavior of barrette walls under various load levels, a critical issue in the design and construction of structures subjected to large loads. The primary objective of the research is to determine the nonlinear relationship between load and settlement of barrette walls, as well as to assess the maximum load-bearing capacity of the walls under diverse loading conditions. The finite element analysis method was employed to simulate the detailed interaction between the barrette wall and the soil, combined with cubic and linear regression analysis techniques to establish the model of the relationship between load and settlement displacement. The research results reveal a nonlinear relationship between load and settlement of the wall, with an inflection point occurring at a load level of approximately 12,000 kN, where the change in settlement becomes more pronounced. The cubic regression equation achieved a coefficient of determination R² = 0.999, demonstrating the high accuracy of the model. The maximum load-bearing capacity of the barrette wall was determined to be 15,745.59 kN, providing a clear scientific basis for evaluating the load-bearing capacity of structures. The conclusions from this study affirm the importance of using finite element simulations in soil mechanics analysis and the design of structures subjected to large loads. The achieved results not only enhance understanding of the behavior of Barrette walls but also contribute to the development of new technical solutions and design methods, with the potential for wide application in the construction and geotechnical engineering sectors.

Assessing Awareness of Building Information Modeling (BIM) among AEC Professionals in Nepal

Moti Shrestha — 2024-12-02

The advent of Building Information Modeling (BIM) has led to a revolutionary transformation in the construction sector on a global scale. The implementation of BIM has resulted in a multitude of benefits, including enhanced project outcomes, cost savings, and improved collaboration. However, in Nepal, the adoption of BIM in the Architecture, Engineering, and Construction (AEC) industry presents a distinctive set of challenges and opportunities. A survey of architects and engineers in both construction and academia indicates a moderate level of awareness of BIM, with 59% of respondents reporting familiarity with the technology. Of these, 69% employ BIM primarily during the design phase, thereby underscoring its utility in project planning. Nevertheless, practical applications remain constrained, as a considerable proportion of users have less than two years of experience with BIM. Despite its potential, 41% of professionals remain unaware of BIM, indicating a significant knowledge gap. This suggests the need for increased education and training to fully leverage BIM's benefits. While the current focus is on design, broader integration across all project stages is essential to maximize its impact. Expanding awareness and developing skills in BIM will be key to advancing its implementation in Nepal's AEC sector, driving efficiency and innovation in construction practices.

Structural Performance of Sisal Fiber Mat Retrofits for Post-Fire Damaged Reinforced Concrete Beams

Odarkor Diody Nah — 2024-12-02

Concrete experiences degradation in mechanical properties when exposed to high temperatures, leading to spalling, disintegration, and surface damage. Research shows a 26-40% reduction in structure strength under fire. While strengthening and restoring existing structures is a practical solution, a more sustainable approach is needed. This study evaluated the performance of sisal fiber mat retrofits for post-fire damaged beams and investigated natural fiber retrofits as a sustainable solution for fire-damaged structures, addressing challenges like elevated temperatures and moisture sensitivity to restore safety, stability, and functionality. The physical, chemical, and mechanical properties of the constituent materials used to fabricate the reinforced concrete beams and retrofitted material were characterized. The mechanical properties of 150×225×650 mm reinforced concrete beams exposed to 800 °C for 1 hour were assessed. The load-carrying capacity of the concrete beam was determined after it had been repaired with one or two layers of sisal fiber mat. The results indicated that the load-carrying capacity of concrete reinforced beams exposed to fire was reduced by 13.03%. However, the use of two layers of sisal fiber mat retrofits in the beams restored the load-carrying capacity by 33.86% and improved ductility by 43.56%. These findings demonstrate the feasibility of using sisal fiber mat retrofits to repair fire-damaged reinforced concrete beams, as the fiber mat enhances the load-carrying capacity by providing additional tensile strength to the structure.

A Framework for Smart City Traffic Management utilizing BDA and IoT

Jayalakshmi Nagalapuram — 2024-12-02

This paper explores a new approach to traffic flow optimization in smart cities, harnessing the combined power of Big Data Analytics (BDA) and the Internet of Things (IoT). The system utilizes a citywide network of connected sensors to acquire live traffic information, including vehicle speeds, density, and congestion points. These data are thereafter processed applying some top-notch BDA algorithms to identify traffic anomalies and forecast congestion levels, and generate actionable insights. By analyzing this information, the system can dynamically adjust traffic signals, recommend alternative routes, and improve traffic efficiency in real-time. The system's adaptive learning capabilities allow it to continuously enhance its predictions based on new data, ensuring its effectiveness in managing evolving traffic patterns. This intelligent traffic management solution promises to significantly reduce congestion, ameliorate overall mobility and road safety, and contribute to a more sustainable city environment.

SWB I-Shaped Microstrip Patch Antenna with Extended Ground Plane Structure for 5G and beyond 5G Applications

Sanaa Iriqat — 2024-12-02

This study presents an I-shaped antenna with an extended ground plane structure, which plays a pivotal role in markedly enhancing the antenna's performance in terms of bandwidth, gain, and efficiency across a multitude of 5G mm-wave bands, including 28 GHz, 39 GHz, 41 GHz, 60 GHz, 73 GHz, and others. The proposed antenna exhibits Super Wideband (SWB) characteristics, with a frequency range extending from 25.5 GHz to beyond. It also demonstrates a peak gain of 10.75 dBi and a maximum radiation efficiency of 88%. The compact dimensions of the design, measuring 7 × 10.6 × 1.52 mm³, facilitate the attainment of high gain (10.75 dBi), SWB characteristics (25.5–80 GHz), and high radiation efficiency (<88%), rendering it a promising contender for prospective 5G and B5G applications.

Enhanced Intrusion Detection in Software-Defined Networking using Advanced Feature Selection: The EMRMR Approach

Raed Basfar — 2024-12-02

Most traditional IP networks face serious security and management challenges due to their rapid increase in complexity. SDN resolves these issues by the separation of control and data planes, hence enabling programmability for centralized management with flexibility. On the other hand, its centralized architecture makes SDN very prone to DDoS attacks, hence necessitating the use of advanced and efficient IDSs. This study focuses on improving IDS performance in SDN environments through the integration of deep learning techniques and novel feature selection methods. This study presents an Enhanced Maximum Relevance Minimum Redundancy (EMRMR) approach that incorporates a Mutual Information Feature Selection (MIFS) strategy and a new Contextual Redundancy Coefficient Upweighting (CRCU) strategy to optimize feature selection for early attack detection. Experiments on the inSDN dataset showed that EMRMR achieved better precision, recall, F1-score, and accuracy compared to the state-of-the-art approaches, especially when fewer features are selected. These results highlight the efficiency of the proposed EMRMR approach in the selection of relevant features with minimal computational overhead, which enhances the real-time capability for IDS in SDN environments.

Digital Voting with Blockchain using Interplanetary File System and Practical Byzantine Fault Tolerance

Giddaluru Somasekhar — 2024-12-02

Traditional voting schemes are often overwhelmed by problems such as deception, influence, and incompetence, which can be resolved by applying blockchain technology with transparency, decentralization, and immutability. This study proposes a safe and indisputable digital voting system with blockchain technology to maintain the integrity of the voting procedure. The reliability and privacy of the voting procedure are upheld with distributed ledger technology and cryptographic techniques. The essence of the proposed method is the immutability of the blockchain ledger, which ensures a tamper-proof record of each cast vote, promoting transparency and offering a way of audit for free verification. The proposed method employs cryptographic protocols to protect individual votes while preserving complete transparency and verifiability of the voting procedure. The InterPlanetary Filesystem (IPFS) is applied to ensure data integrity. Moreover, the practical Byzantine Fault Tolerance (pBFT) consensus algorithm is utilized to remove glitches in distributed settings. The proposed approach provides a decentralized platform where voters can cast their votes from anywhere without difficulty using an internet connection, eliminating the need for physical ballot papers and polling stations. Using immutable ledger and cryptographic security aspects in blockchain, the reliability of the voting procedure can be protected while maintaining voter anonymity and confidentiality. Finally, it is shown that the proposed scheme outweighs other existing approaches.

Swin Transformer with Enhanced Dropout and Layer-wise Unfreezing for Facial Expression Recognition in Mental Health Detection

Mujiyanto Mujiyanto — 2024-12-02

This study presents an improved Facial Expression Recognition (FER) model using Swin transformers for enhanced performance in detecting mental health through facial emotion analysis. In addition, some techniques involving better dropout and layer-wise unfreezing were implemented to reduce model overfitting. This study evaluates the proposed models on benchmark datasets such as FER2013 and CK+ and real-time Genius HR data. Model A has no dropout layer, Model B has focal loss, and Model C has enhanced dropout and layer-wise unfreezing. Model C was the best among all proposed models, achieving test accuracies of 71.23% on FER2013 and 78.65% on CK+. Weighted cross-entropy loss and image augmentation were used to handle class imbalance. Based on Model C emotion predictions, a scoring mechanism was designed to analyze employees' mental health for the next 30 days. The higher the score, the higher the risk of mental health. This study demonstrates a practical version of the Swin transformer in FER models for detecting and early mental health intervention.

A Hybrid Metaheuristic Aware Enhanced Deep Learning Approach for Software Effort Estimation

Mahesh Bbadana — 2024-12-02

Software Effort Estimating (SEE) is a fundamental task in all software development lifecycles and procedures. Therefore, when deciding how to anticipate effort in a variety of project types, the comparative assessment of effort prediction methods has emerged as a standard strategy. Unfortunately, these studies include a range of sample techniques and error metrics, making a comparison with other work challenging. To overcome these drawbacks, this study proposes a deep learning model to effectively estimate software effort. The estimation is mainly focused on minimizing the cost and time consumption. The input data is taken from the dataset and preprocessing is performed to remove the noise content. Then the required features are extracted using the preprocessed data with the help of the simple and higher-order statistical features. A novel Modified Chaotic Enriched Jaya with Moth Flame Optimization (MCEJMO) algorithm is introduced for feature selection to enhance SEE accuracy. The estimation is performed using Multilayer Long Short-Term Memory (M-LSTM). The proposed method achieved a Mean Square Error (MSE) of 0.2825 for dataset 1 and 0.2285 for dataset 2.