bluuebunny/crossref_metadata_2025 huggingface dataset:
A Priority based Self-Organised MAC Protocol for Real Time Wireless Sensor Network Applications
Authors: Raut Archana R. , Khandait Sunanda , Theng Dipti
Published: December 2024

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.
Comparing Several Orifice Flange Shapes for Hydrodynamic Cavitation Treatment and COD Reduction in Textile Wastewater
Authors: Gajbhiye Pratima , Shah Vishal Kumar U. , Thakker Miral R. , Kumar Satish , Bongale Arunkumar , Dave Darshana
Published: December 2024

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.
Evaluating Large Language Models' Proficiency in Answering Arabic GAT Exam Questions
Authors: Alahmadi Mohammad D. , Alharbi Mohammed , Tayeb Ahmad , Alshangiti Moayad
Published: December 2024

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.
Monitoring of a Low-Order Even Radial Vibrational Circumferential Mode in a Round Hollow Cylinder
Authors: Ouis Djamel , Gramez Abdelghani
Published: December 2024

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.
Autofocus Vision System Enhancement for UAVs via Autoencoder Generative Algorithm
Authors: Ahmed Anwer , Farhan Rabah Nori
Published: December 2024

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.
A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System
Authors: Alhalim Shaimaa Shukri Abd. , Bahloul Wissem , Chtourou Mohamed , Derbel Nabil
Published: December 2024

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.
An Intelligent Optimazitation Method for Evacuation Route Planning in the Occurrence of Natural Disasters
Authors: Nirwana Hafsah , Firgiawan Wawan , Cokrowibowo Sugiarto , Zainuddin Zahir
Published: December 2024

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.
The Effect of Formal Specifications and Working Conditions on the Resistance and Vibration of the NACA 4415 Aircraft Wing Model
Authors: Jaafar Ali S. , Hmoad Nassear R.
Published: December 2024

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.
Reducing the Environmental Impact of Asphalt Emulsion Production from Petroleum Bitumen utilizing Buton Island Natural Asphalt in Various Scenarios
Authors: Israil . , Tumpu M. , Al Makassari Nursafanah Dzakiyah
Published: December 2024

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.
An Ensemble Approach to Improve the Performance of Real Time Data Stream Classification
Authors: Joshi Dhara , Shukla Madhu
Published: December 2024

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.
A High Gain Dual Band Hexagonal Metamaterial Inspired Antenna for 5G Applications
Authors: Talari Sneha , Sekhar P. Chandra
Published: December 2024

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 εr 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.
Analysis of Permanent Magnet Demagnetization during the Starting Process of a Line-start Permanent Magnet Synchronous Motor
Authors: Anh Tuan Le , Bien Thuy Trinh , Xuan Cuong Ngo , Anh Tuan Do , Do Nhu Y.
Published: December 2024

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.
Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data
Authors: Dorji Karma , Jittanon Sorawut , Thanarak Prapita , Mensin Pornthip , Termritthikun Chakkrit
Published: December 2024

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.
A Deep Learning Approach to Plastic Bottle Waste Detection on the Water Surface using YOLOv6 and YOLOv7
Authors: Kirana Naufal Laksana , Kurnianingtyas Diva , Indriati .
Published: December 2024

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.
Label Propagation Algorithm for Face Clustering using Shared Nearest Neighbor Similarity
Authors: Yousheng Gao , Hamzah Raseeda , Abdul Rahim Siti Khatijah Nor , Aminuddin Raihah , Li Ang
Published: December 2024

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.
Leveraging Convolutional Neural Network (CNN)-based Auto Encoders for Enhanced Anomaly Detection in High-Dimensional Datasets
Authors: Javed M. Aetsam , Anjum Madiha , Ahmed Hassan A. , Ali Arshad , Shahzad H. M. , Khan Hamayun , Alshahrani Abdulaziz M.
Published: December 2024

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.
A Pilot Test of Dehydration of Tiver dredged Mud using Vacuum Consolidation with Different Vertical Drains
Authors: Phan Huy Dong , Phan Nguyen Binh
Published: December 2024

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.
DeepYoga: Enhancing Practice with a Real-Time Yoga Pose Recognition System
Authors: Bansal Roli , Sharma Richa , Jain Priyanshi , Arora Rahul , Pal Sourabh , Vishal .
Published: December 2024

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.
Prediction of Higher Education Student Dropout based on Regularized Regression Models
Authors: Bouihi Bouchra , Bousselham Abdelmajid , Aoula Essaadia , Ennibras Fatna , Deraoui Adel
Published: December 2024

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%.
Voting Strategies for Arabic Named Entity Recognition using Annotation Schemes
Authors: Belhajem Ikram
Published: December 2024

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.
Use of the SWAT Model to Simulate the Hydrological Response to LULC in a Binational Basin between Ecuador and Peru
Authors: Murillo Robinson Fabricio Pena , Lavado Casimiro Waldo , Pachac Huerta Yenica Cirila , Zapana Quispe Melania , Guevara-Freire Deysi
Published: December 2024

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.
Integrating FUCA, SRP, and OPARA Methods to Assess Faculty's Scientific Research Capacity
Authors: Vo Thi Nhu Uyen
Published: December 2024

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.
Mechanical Properties of Recycled Aggregate Concrete with Industrial Waste Ash
Authors: Weesakul Uruya , Htun Thant Paing , Ejaz Ali , Thansirichaisree Phromphat , Hussain Qudeer
Published: December 2024

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.
Modeling of the Stress-Strain Quality of Hydroentangled Nonwoven Fabrics
Authors: Ridene Sana , Sayeb Soumaya , Helali Houda , Ben Hassen Mohamed , Jaradat Sameer Y. , Assaf Ramiz , Barham Ahmad S. , Kanan Mohammad
Published: December 2024

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.
Z-MSP: Zonal-Max Stable Protocol for Wireless Sensor Networks
Authors: Harbouche Ahmed , Djabour Djamal , Saiah Amine
Published: December 2024

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.
Monte Carlo Modeling and Simulation of Electron Dynamics in Low Temperature Methane Gas
Authors: Gadoum Abdelatif , Benyoucef Djilali
Published: December 2024

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.
Minimizing IoT Security Deployment Costs using the Dominating Set Approach
Authors: Balbal Samir , Bouamama Salim
Published: December 2024

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.
An Experimental Investigation of Slab-Column Connection Strengthened with Steel Collar under Eccentric Load
Authors: Ali Hanaa Abdulbaset , Al-Sherrawi Mohannad H.
Published: December 2024

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.
Sensitivity Analysis of a Stator Current-based MRAS Estimator for Sensorless Induction Motor Drives
Authors: Zaky Mohamed S. , Metwaly Mohamed K.
Published: December 2024

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.
Dynamic Arithmetic Optimization Algorithm with Deep Learning-based Intrusion Detection System in Wireless Sensor Networks
Authors: Nirmal K. , Murugan S.
Published: December 2024

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.
Assessment of Building Nondeterministic Dynamic Structural Behavior considering the Effect of Geometric Nonlinearity and Aerodynamic Damping
Authors: Mota Silva Jean Carlos , Santos da Silva Jose Guilherme
Published: December 2024

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 the Scalability of Blockchain Networks using a Data Partitioning Technique
Authors: Lathamani Basavaiah , Kundur Niranjan C. , Swamy Chaya J. , Hanumanthaiah Pavana Kumari , Dhulavvagol Praveen M. , Anil Bellary Chiterki
Published: December 2024

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.
The Effect of Digital Leadership Styles on Sustainable Performance: A Systematic Literature Review
Authors: Alghfeli Hadef Sultan Mutaeb Bin Dhaein , Binti Sohaimi Nor Suzylah , Binti Abdullah Chik Norlaila
Published: December 2024

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.
Finite Element Simulation and Experimental Analysis of the Thermo-Mechanical Properties of Dissimilar S275 and 316L Austenite Stainless Steels using the RFW Process
Authors: Njock Francois Bayock , Kesse Martins , Yebga Maxime , Eyike Eric Ndjem , Nlend Ruben
Published: December 2024

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.