bluuebunny/crossref_metadata_2025 huggingface dataset:
A Parametric Study of GFRP Composite Beams with Encased I-Section using 3D Finite Element Modeling
Authors: Bahlol Fahad M. , Al-Ahmed Ali Hussein Ali
Published: February 2025

Glass Fiber Reinforced Polymer (GFRP) materials play a crucial role in the construction industry due to their lightweight properties, corrosion resistance, and high strength. Furthermore, the GFRP reinforcement ratio is a significant factor in the strength design philosophy that governs the design of flexible members. This study presents a parametric investigation of the performance of concrete composite beams reinforced and encased with pultruded GFRP. This study investigates the effect of concrete compressive strength and GFRP reinforcement ratio on the structural behavior of composite beams with encased GFRP sections under static loads. To achieve this objective, five simply supported models were numerically simulated using the Abaqus software. The reference model comprised normal concrete with a 30 MPa compressive strength, 0.42% GFRP longitudinal reinforcing ratio, and transverse steel rebars, with the GFRP I-section encased in the center of the cross-section. The other models maintained similar properties and geometries but varied in reinforcement ratio (0.85% and 1.2%) and compressive strength (25 MPa and 20 MPa). The results showed that increasing the reinforcement ratio in composite beams with encased GFRP sections improved the ultimate capacity by approximately 29% and 41% for 0.85% and 1.2% ratios, respectively, compared to the reference beam. Conversely, reducing compressive strength below 30 MPa decreased maximum load by about 16% and 23% for 25 MPa and 20 MPa values, respectively, in relation to the reference beam.
Analysis of Critical Success Factors of Agile Software Projects based on the Fuzzy Delphi Method
Authors: Zhang Fuye , Abdullah Nur Atiqah Sia , Mohd Rosli Marshima
Published: February 2025

Agile software development initiatives have gained widespread recognition both domestically and internationally, particularly in the Chinese software industry. However, traditional enterprises often face challenges, such as inadequate project management and lower success rates, which can be attributed to a limited understanding of agile methodologies and effective implementation of agile practices. To address these challenges and identify the Critical Success Factors (CSFs) in agile software projects, an extensive literature review was conducted. As a result, a CSFs model for agile projects in China was constructed. The aim of this study is to evaluate the CSFs model using the Fuzzy Delphi Method (FDM). The research involved 30 authoritative experts from the Chinese agile software development industry and academia, each with more than 10 years of relevant industry knowledge and experience. The FDM was applied to collect data through questionnaires and verify theoretical success factors and dimensions in three rounds of the survey. Finally, a total of 28 factors were analyzed and ranked to develop an optimized CSFs model that has a significant impact on agile software development in China. The research findings provide a feasible set of CSFs for the effective implementation of agile software projects in China. This CSFs model also offers valuable insights for the broader adoption of agile practices in China, with the potential to greatly improve the success rate of agile software development and implementation.
Implementation of a Deep Learning ANN-based Algorithm utilizing the IEEE 34 Bus Test System to Investigate the Effects of Distributed Generation on Fault Diagnosis in Distribution Networks
Authors: Deng Parach Daniel , Irungu George K. , Munda Josiah
Published: February 2025

Electrical distribution systems are undergoing significant modifications since the application of new technologies. New possibilities for automated, dependable, and efficient electrical power grids have been made possible by the technological advancement. While new technologies might improve electrical network performance and offer creative solutions to future network difficulties, they can also have unintended consequences that need to be carefully studied and considered. A recent technological advancement that enhances power grid performance is Distributed Generation (DG). While DG unit integration has measurable benefits for electrical grids, its significant effects on power network protection systems create many questions and difficulties about the proper way to identify and isolate distribution network faults. The DLANN-based approach looks into the ways the integration of DGs affects fault identification and location. This method involves two steps: first, three-phase currents are constantly analyzed for detection, and Discrete Wavelet Transform (DWT) is utilized to extract the currents' features. The second step is classification employing Artificial Neural Networks (ANNs) to pinpoint the defective stages. Counting the shorted phases will reveal the sort of short circuit. The MATLAB programming environment is utilized in the development of the fault identification and classification technique. The fault type (one, two, or three phases), fault resistance, fault location bus, fault distance, and the DG type (upstream or downstream) are all considered. The methodology is used on a modified IEEE 34-bus test system, and four scenarios, one with combined DGs units, one with IBDGs, one with SBDGs, and one without DGs, are modeled. As per the simulation results, 100% fault detection and classification accuracy were obtained, whereas the average fault location accuracy attained without DGs, with IBDGs, SBDGs and combined DGs for selected nodes were 99.94%, 99.91%, 99.86%, and 99.88%, respectively
Optimal CNN Model for Obstructive Sleep Apnea Detection using Particle Swarm Optimization
Authors: Tran Thanh-Huong , Nguyen Phuong Anh , Ngoc Le Anh , Tran Duc-Tan , Pham Minh Trien
Published: February 2025

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder with significant health risks. It is characterized by the narrowing of the upper airway during sleep, leading to vibrations in the airway structures and the production of snoring sounds. Recently, Convolutional Neural Networks (CNNs) have been leveraged to extract meaningful features from snoring sound data, enabling early and accurate detection of OSA. The effectiveness of these neural network optimizations depends on the starting values of the model, the gradient algorithm used, and the complexity of the problem. This study introduces an improved Particle Swarm Optimization (PSO) strategy that linearly adjusts the learning rate coefficient to enhance accuracy and convergence speed. Our approach was evaluated on a collected and pre-processed dataset based on the PSG-Audio database. Experimental results demonstrate that our method significantly outperforms the conventional optimization algorithm and existing PSO techniques, achieving a remarkable accuracy of 99.1%. These findings confirm the potential of our optimized model for OSA detection.
Improving Residential Complex Project Performance using Information and Communication Technology supported by the DEXi Method
Authors: Abdulhussain Maytham Basim , Burhan Abbas Mohammed
Published: February 2025

A complex project involves a significant level of risk, uncertainty, and complexity due to several variables, including the project's size, duration, scope, and interdependencies. The use of Information and Communication Technology (ICT) leads to increasingly better and more sustainable results in the progress of complex tasks. Specialized Information Technology (IT) software packages are available on the market to meet the specific needs of the construction industry. The main objective of this research is to identify the factors that have the greatest impact on complex projects and to explore the utilization of ICT applications to improve productivity. The first step of the research methodology is to assess the effectiveness of ICT in construction. The second step is to specify the responses of the participants in complex projects. The final step involves employing a verified computational methodology to identify the parameters that influence the effectiveness of ICT use in the construction sector. This study utilized survey data, expert comments, in-depth interviews, and exploratory research to assess the impact of management styles on ICT performance metrics. The research also drew on previous work in the construction project area to enhance its findings. According to the results of the Relative Importance Index (RII), the most significant component in the use of ICT was the technical calculation based on time. The analysis showed that improving the efficiency of subcontractors and suppliers' coordination yielded a rank value of 0.869. The special computational software called Decision Expert (DEXi) method is also used to facilitates decision making based on specific criteria. Ultimately, this study concludes that ICTs are crucial for improving the efficiency and effectiveness of sustainable project implementation in all aspects.
Determination of Best Input Factors in Powder-Mixed Electrical Discharge Machining 90CrSi Steel using Multi-Criteria Decision Making Methods
Authors: Nguyen Van Tung , Dinh Van Thanh , Phan Dang Phong , Vu Duc Binh , Vu Ngoc Pi
Published: February 2025

This article outlines the results of a Multi-Criteria Decision Making (MCDM) analysis conducted on the Powder-Mixed Electrical Discharge Machining (PMEDM) process for cylindrical parts fabricated from 90CrSi tool steel, using graphite electrodes. The study aims to identify the optimal input factors to simultaneously minimize Surface Roughness (SR) and Electrode Wear Rate (EWR), while maximizing Material Removal Speed (MRS). Five input factors were selected: powder concentration (CP), pulse-on time (Ton), pulse-off time (Toff), pulse current (IP), and servo voltage (SV). Experimental data were generated using the Taguchi method with an L18 design. The optimization process was performed using the Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Evaluation by an Area-based Method of Ranking (EAMR) methods. Criteria weights were calculated utilizing the Entropy and the Multi-Expert Ranking Evaluation with Compensation (MEREC) techniques. The analysis identified the best PMEDM input factor, providing an optimal solution for enhancing the efficiency of machining cylindrically shaped components.
Αn Experimental and Numerical Study on the Drying of Celery (Apium Graveolens L.) Growing in Southern Tunisia
Authors: Nasfi Nadia , Romdhane Mehrez
Published: February 2025

The present work experimentally investigates the curves of the drying kinetics of celery leaves (Apium Graveolens L.) in a drying convective oven. These curves were determined at 50°C, 60°C, and 70°C. For the fitting of the experimental results, the Lewis, Handerson, and Pabis, Page, Midilli &Kucuk, Logarithmic, Modified Page, Wang, and Singh and Two Terms models were used. The Midilli & Kucuk model provided the best fit for the experimental results. The effective water diffusion coefficient (Deff) varied from 3.65×10-10 m2/s to 7.29×10-10 m2/s in the considered temperature range. The higher temperature gave a higher effective water diffusion coefficient and Drying Rate (DR). The activation energy calculated using an exponential expression based on the Arrhenius equation was 31.72 kJ/mol. The Characteristic Drying Curve (CDC) was also determined as a three-degree polynomial.
The Impact of Construction Activities on the Stability of Highway Slopes
Authors: Baziz Fouad , Bahloul Ouassilla , Baziz Nafissa
Published: February 2025

This study concerns the case of a landslide that occurred at kilometer point PK 208 of the East-West Highway in the northeastern part of Algeria, located in the eastern region of Constantine Province. The primary objective is to understand the behavior and mechanisms of this complex landslide phenomenon. The phenomenon's relationship to various factors that influence this activity, including permanent causes, such as challenging terrain, the geological nature, and the evolving characteristics of the soil under the influence of various climatic conditions or human activities, was investigated. The study examined the impact of the East-West Highway construction at PK 208 on the stability of slopes composed of clayey and marly soils. Field investigations were conducted, and various studies on landslides were analyzed. A monitoring system was employed to track subsurface and surface movements, as well as changes in the groundwater table level. Additionally, numerical modeling using PLAXIS software was performed to evaluate the impact of construction activities, particularly the rise in the groundwater table, on slope stability. The obtained results demonstrated that the position of the groundwater table plays a crucial role in the stability of these structures, underscoring the importance of considering local hydrogeological conditions in the planning and execution of such projects. It is concluded that the complexity of such phenomena in slopes with similar geological, geomorphological, hydrogeological, and geotechnical characteristics is a significant issue that requires particular attention during the planning and execution of such projects.
Assessing the Influence of Brick Powder as Filler in Asphalt Hot Mixes
Authors: Hussein Zainab M. , Tayh Sady A. , Jasim Abbas F.
Published: February 2025

This study investigated the efficacy of utilizing waste Brick Powder (BP) as a partial or complete replacement for the filler in Hot Asphalt Mixes (HAM). BP was used to substitute Portland Cement (PC) in varying proportions: 25%, 50%, 75%, and 100%. The mixes were evaluated based on Marshall properties, Indirect Tensile Strength (ITS), and Tensile Strength Ratio (TSR). The findings revealed increased Marshall stability, stiffness, and ITS in the mixes containing BP. The flow decreased for HAM containing BP, particularly for those with a complete replacement of cement having utilized BP as the filler, indicating an improved ability of the HAM to withstand loads. The tests conducted at 25, 40, and 60 °C showed that the ITS increased steadily with an increased BP proportion, which is beneficial for rutting resistance, as high service temperatures influence rutting, and high ITS corresponds to a longer rutting life of the asphalt mix. The effect of improving the tensile strength at 60 °C was higher than at 25 °C and 40 °C. Additionally, the BP mixes demonstrated greater resilience to moisture effects compared to the reference mix. The use of BP as an alternative filler for PC did not significantly impact the volumetric properties of the HAM. It was determined that BP could be successfully added to the HAM at a 100% replacement rate as a filler, with an ideal asphalt content of 5.6%. The results suggest that the processing and management of waste bricks can be a sustainable development strategy.
A Context-Enhanced Model for Fake News Detection
Authors: Beseiso Majdi , Al-Zahrani Saleh
Published: February 2025

News published on social networks has a notable impact on changing people's perceptions on various topics. However, all news available on social media may not be genuine and might come from unverified sources. The prevalence of fake news is an inevitable concern that needs to be addressed effectively. This study presents an ensemble algorithm to improve fake news detection tools. Long-Short-Term-Memory (LSTM) and an ensemble of LSTM and Convolutional Neural Networks (CNN) were used. The proposed model used bidirectional LSTM layers and CNN convolutional 2D layers with kernel sizes of 2, 3, and 4 for 2-gram, 3-gram, and 4-gram tokens. The results obtained show an accuracy of 96.7% and 97.3% on a fake news dataset using the LSTM model and CNN-LSTM model, respectively, significantly improved from the maximum accuracy of 94.88% reported in a previous study. Embedding layers yielded significant improvements when paired with extended word sequences and pre-trained embedding vectors. Diverse tokenization methods with and without pre-trained embedding layers were also considered. The ensemble model achieved a 10.03% improvement in predictive accuracy on the Liar dataset, compared to the 6.08% improvement reported in a previous study using the same dataset.
Cognitive Fish Swarm Optimization for Multi-Objective Routing in IoT-based Wireless Sensor Networks utilized in Greenhouse Agriculture
Authors: Deepalakshmi D. , Pushpa B.
Published: February 2025

This research presents the working mechanism of Cognitive Fish Swarm Optimization (CFSO) for multi-objective routing and channel selection in Internet of Things (IoT)-based Wireless Sensor Networks (IWSNs). CFSO is inspired by the collective intelligence and cooperation observed in fish swarms. The model involves three main components: perception, cognition, and behavior. Each fish in the swarm perceives the network conditions by gathering information from its surrounding environment, including signal strength, channel availability, and network congestion. The fish then utilizes its cognitive abilities to evaluate different routing paths and channel options based on specific objectives, namely energy efficiency, packet delivery ratio, and delay. This evaluation process involves analyzing historical information and utilizing heuristics to create notified results. Each fish adapts its behavior by adjusting its movement pattern and selecting optimal routing paths and channels. This adaptive behavior is critical for achieving reliable and efficient data transmission in IWSNs. The fish swarm balances exploration and exploitation strategies to search for optimal solutions comprehensively. Exploration allows for discovering new paths and channels, while exploitation focuses on refining the best-known solutions. The efficiency of the CFSO method in enhancing data transmission efficiency in greenhouse agriculture applications was validated through extensive simulations in the NS-3 network simulation framework. The findings suggest that the CFSO method is a promising technique for addressing routing and channel selection challenges in IWSN by leveraging the collective intelligence of fish swarms. The CFSO model portrayed a superior throughput and Network Lifetime (NLT) values of 71.34% and 77.20%, respectively, significantly outpacing SSEER and CRP across overall node counts.
Equilibrium Moisture Effects in Silica Gel Adsorption/Desorption
Authors: Musa Amir Abubaker , Nciri Rached , Nasri Faouzi
Published: February 2025

This research involves a numerical analysis focused on examining how the initial moisture content and equilibrium moisture content affect a Regular Density (RD) silica gel-packed bed, in terms of the composite mass and heat transfer dynamics during the adsorption and desorption processes. The detailed interaction between the air and the desiccant material is thoroughly represented through the development of the governing drying kinetics, mass conservation, and energy conservation equations, thereby establishing a solid mathematical foundation to clarify these essential processes. All numerical simulations are meticulously executed using the Finite Volume Element method, allowing for a detailed analysis of the mass and heat transfer phenomena within the RD silica gel-packed bed. The utilization of this advanced computational technique enables a deeper understanding of the complex interactions between the moisture content and transfer process efficiency in the desiccant system. This research culminates in the development of correlations that serve as predictive tools. An accurate estimation is facilitated by both the removal/addition of humidity from/to the air and the maximum enthalpy released/absorbed by the RD silica gel medium throughout the adsorption and desorption phases. These correlations, rooted in the equilibrium and initial moisture content of the RD silica gel medium, provide valuable insights into optimizing the performance and efficiency of desiccant systems in various operating conditions. There are intricate relationships between moisture content and transfer processes. This study contributes to advancing the understanding of desiccant systems, paving the way for more efficient and sustainable air treatment technologies.
Shear Capacity of Self-Compacting Concrete Beams provided by External Steel Plate using Z Stirrups
Authors: Kasim Douaa , Alsaraj Wissam
Published: February 2025

The stirrups are the primary factor that resists the shear forces, while other parameters like aggregates and dowels contribute less than 7% of the shear strength. Shear connectors transmit the shear forces in composite beams between steel parts and concrete, and these connectors can be utilized as vertical legs to assist the stirrups in their performance. The results indicate that decreasing the shear span to effective depth (a/d) ratio from 3 to 2.5 led to a 24% increase in the crack load and a 16% increase in the maximum load value. Additionally, the shear connectors have a significant effect on increasing the shear capacity, and this effect becomes more pronounced as their lengths increase, effectively replacing the function of the stirrups. Furthermore, the change in the shape of the stirrups does not significantly affect the shear capacity, and the decrease in endurance can be compensated by reducing the distance between the stirrups. It was also observed that the horizontal parts of the stirrups may have a limited and negligible effect, suggesting that the stirrups could potentially be replaced by I-shaped alternatives.
Hydro-Forming of U-Shaped Parts with Branches
Authors: Trinh Mai Thi , Nguyen Trung Dac , Pham Quoc Tuan , Pham Anh Ngoc , Dinh Duy Van
Published: February 2025

Tube parts formed by hydroforming technology have many outstanding advantages, such as high mechanical properties, fast forming time, and the ability to shape complex parts. U-shaped tube parts with branches are applied in various fields, such as the automotive, electronics, and medical industries. The current challenges in hydroforming technology for manufacturing these parts lie in controlling and determining suitable process parameters to ensure the highest product quality. Through numerical simulations and theoretical calculations, with the input material being a copper tube made from CDA110, this study has investigated the parameters of die cavity fluid pressure and punch displacement. Based on these results, a regression equation for the relationship between fluid pressure and punch displacement was established, which allows for determining the appropriate ranges for fluid pressure and punch displacement to successfully hydroform U-shaped parts with branches. The research results aim to assist engineers in reducing the time required to identify suitable process parameters for hydroforming similar-shaped parts.
Hybrid Multi-Criteria Decision Making Methods: Combination of Preference Selection Index Method with Faire Un Choix Adèquat, Root Assessment Method, and Proximity Indexed Value
Authors: Mai Nguyen Trong
Published: February 2025

This study presents an investigation into the hybridization of Multiple Criteria Decision Making (MCDM) methods. The Preference Selection Index (PSI) method is used in two distinct ways: first, for its traditional purpose of ranking alternatives, and second, to calculate criteria weights. These criteria weights are utilized to rank the alternatives provided by other MCDM methods, including the Faire Un Choix Adéquat (FUCA), Root Assessment Method (RAM), and Proximity Indexed Value (PIV), resulting in the creation of three hybrid models: FUCA-PSI, RAM-PSI, and PIV-PSI. The effectiveness of these hybrid approaches is tested by ranking 20 Vietnamese cities based on their digital transformation efforts. The results demonstrate that the hybrid approaches produce a highly correlated ranking, as evidenced by the Spearman rank correlation coefficient found among these methods, with the lowest being 0.8571. Both the PSI method and the three hybrid models identified the same top alternative, confirming the reliability and accuracy of the rankings.
Vibroarthrographic Signal Classification for Knee Joint Disorder Detection using Tunable Q-factor Wavelet Transform based on Entropy Measures
Authors: Basavaraju Krishna Sundeep , Kumar T. Kishore , Reddy K. Ashoka
Published: February 2025

Research on Vibroarthrographic (VAG) signals presents a promising means for the early diagnosis of knee joint disorders. However, the classification problem for these signals faces serious issues due to their complex and dynamic nature. This study proposes a novel method for decomposing and analyzing VAG signals based on a Tunable Q-factor Wavelet Transform (TQWT) and entropy-based measures. TQWT is used to preprocess and decompose VAG signals recorded during knee motion into subbands. Different entropy metrics, such as approximate entropy, sample entropy, fuzzy entropy, slope entropy, and so on, were computed over different subbands of the signal to capture significant signal features. Effective features were selected using Recursive Feature Elimination (RFE) and then classified using ensemble classifiers such as XGBoost, Ensemble Random Forest (ERF), and RF-logistic regression. The classification accuracy of the proposed sample entropy method was 87.64% and had 90% sensitivity, 86.36% specificity, and 0.88 AUC-ROC. These results demonstrate the ability of the TQWT-based approach to discriminate knee joint abnormalities. Future work will explore performance scaling with larger datasets and apply it to other joint disorders.
A Deep Learning CNN-GRU-RNN Model for Sustainable Development Prediction in Al-Kharj City
Authors: Aljuaydi Fahad , Zidan Mohammed , Elshewey Ahmed M.
Published: February 2025

This study introduces an advanced Deep Learning (DL) framework, the Convolutional Neural Network-Gated Recurrent Unit-Recurrent Neural Network (CNN-GRU-RNN). This model is engineered to forecast climate dynamics extending to the year 2050, with a particular focus on four pivotal scenarios: temperature, air temperature dew point, visibility distance, and atmospheric sea level pressure, specifically in Al-Kharj City, Saudi Arabia. To address the data imbalance problem, the Synthetic Minority Over-Sampling Technique was employed for Regression along with the Gaussian Noise (SMOGN). The efficacy of the CNN-GRU-RNN model was benchmarked against five regression models: the Decision Tree Regressor (DTR), the Random Forest Regressor (RFR), the Extra Trees Regressor (ETR), the Bayesian Ridge Regressor (BRR), and the K-Nearest Neighbors Regressor (KNNR). The models were evaluated using five distinct metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). The experimental outcomes demonstrated the superiority of the CNN-GRU-RNN model, which surpassed the traditional regression models across all four scenarios.
Introduction to Predictive Maintenance Application using Machine Learning: The Case of the Injection System of a Diesel Engine
Authors: Znaidi Zineb , Ech-Chhibat Moulay El Houssine , Khiat Azeddine , El Khiate Mounir , Samri Hassan , Ait El Maalem Laila
Published: February 2025

Diesel engines are crucially important in various fields, particularly in the automotive sector, as they ensure a reliable supply of mechanical energy. However, injection system failures, which are among the most recurrent failures, can lead to performance deterioration and increased pollutant emissions and maintenance costs. Therefore, adopting an effective maintenance strategy to analyze and predict such failures would significantly improve the efficiency of these engines. Based on collected data from engines by reliable sensors, the application of predictive maintenance coupled with a machine learning model allows effective prediction of failures for optimal appropriate maintenance. This study presents an approach to diagnosing the injection system of automotive diesel engines using a test bench based on data from temperature sensors installed on engine cylinders. These temperature data exhibit unusual variations in the event of an injection system failure. The Random Forest (RF) algorithm was employed to analyze these data and establish a clear relationship between cylinder temperatures and failure. The proposed model can detect failures associated with the injection system. Performance evaluation, particularly after parameter tuning, underscores the model's efficacy, achieving an accuracy exceeding 97%.
Investigating the Impact of EDM Parameters on Surface Roughness and Electrode Wear Rate in 7024 Aluminum Alloy
Authors: Ghazi Safaa Kadhim , Abdullah Mostafa Adel , Abdulridha Hind Hadi
Published: February 2025

Electrical Discharge Machining (EDM) is a significant process in the industry for machining hard metals. It is a time-consuming and costly method that requires a high level of expertise to operate effectively. EDM is considered one of the unconventional operating processes due to its unique characteristics and the challenges associated with its implementation. One of the challenges facing researchers is determining the optimal parameters for achieving high surface quality while minimizing equipment consumption and associated costs. In addition to being classified as one of the operating processes, EDM processes are also classified as electro-thermal processes that affect surface quality. In this study, the influence of EDM parameters on surface roughness and electrode wear rate when machining aluminum alloy type 7024 was investigated. A total of 27 experiments were conducted to evaluate the impact of three parameters at three levels. The parameters under investigation include current, pulse on time, and pulse off time. Subsequent analysis of the results by variance analysis revealed that the most influential parameter for both surface roughness and electrode wear rate is electric current, with a rate of influence of 74%. The results were then subjected to further analysis using variable effect graphs to identify the optimal variables for achieving the best results. Finally, neural networks were employed to predict the results, with an accuracy of up to 99%.
The Delumping Method as a Key Factor in obtaining a characterized Hydrocarbon Fluid using the Example of Kazakhstani Oil
Authors: Khussain Bolatbek , Baibekova Ayaulym , Sass Alexandr , Brodskiy Alexandr , Zhurinov Murat , Kenessary Abzal , Tyulebayeva Ranida , Logvinenko Alexandr , Abishev Daniyar , Ismailova Jamilyam
Published: February 2025

The composition of hydrocarbon systems plays an essential role in ensuring reliability in field operation processes, the design of oil onshore facilities, and the availability of reliable information about the PVT properties of systems. In this article, experimental and numerical methods are used to obtain data on the component composition of the extracted oil products and their PVT properties. These methods help obtaining a detailed composition and phase state of the hydrocarbon system. The numerical delumping method is utilized in this article. It is worth noting that all calculations were made on the basis of data on the component composition of Kazakhstani oil samples. The results and comparative evaluation show that the proposed delumping mix-method has excellent consistency with the data obtained with experiments and with the Nichita’s analytical approach. In the future, the results on the detailed composition can improve and link the compositional modeling of the reservoir with the modeling of surface structures.
Investigation of the Spatiotemporal Distribution of PM10, PM2.5, and PM1 from Motor Vehicles in Roadside Environments
Authors: Bachtiar Vera Surtia , Purnawan . , Afrianita Reri , Aziz Rizki , Ramadhanil .
Published: February 2025

This study examines the spatial and temporal distribution of PM10, PM2.5, and PM1 concentrations in Padang City, Indonesia, focusing on the impact of motor vehicle emissions. Measurements were conducted at distances ranging from 5 m to 100 m from major roadways and at different times of the day to evaluate the effects of traffic patterns and meteorological conditions on air quality. The findings revealed that Particulate Matter (PM) concentrations are significantly higher near roads, with PM10 peaking at over 55 μg/m³ in the afternoon at 5 m from the roadway. Similarly, PM2.5 and PM1 also reach the maximum levels of 45 μg/m³ and 35 μg/m³, respectively, during peak traffic hours. While meteorological factors, such as temperature, wind speed, relative humidity, and pressure, exhibit weak correlations with the PM levels, traffic volume emerges as the primary contributor to air pollution. These results underscore the need for effective traffic management and emission reduction strategies to mitigate pollution and protect public health. The current study’s recommendations include enhancing roadside air quality monitoring, and conducting further research on seasonal variations and the specific contributions of different vehicle types to PM pollution dynamics.
Locating the Source of Information in Social Networks using Critical Nodes
Authors: Mouley Karima , Tahraoui Mohammed Amin
Published: February 2025

Locating the information source within social networks is crucial to understand information propagation. The source can be detected based on specific nodes known as observation nodes, and identifying them is a critical challenge that can significantly affect the accuracy of identification. To address this issue, this study proposes a novel source detection approach based on the Susceptible-Infected (SI) model and the Critical Node Problem (CNP). CNP involves identifying a subset of nodes within a graph whose removal results in the maximum reduction of a given connectivity metric, thereby isolating significant areas within the graph. A heuristic algorithm was developed, grounded in the maximal independent set for general graphs to solve the CNP, allowing the identification of the most crucial observation nodes that enhance the accuracy and using the data recorded from them to estimate the localization of the source. Experimental evaluations on various real-world networks showed that the proposed approach achieved a source detection accuracy of up to 89%, outperforming existing methods. These results demonstrate the robustness of the proposed approach, highlighting its potential to significantly improve accuracy in network-based source localization tasks across multiple applications.
Dynamic Association Mining Techniques for the Faster Extraction of High Utility Itemsets from Incremental Databases
Authors: Meruva Subba Reddy , Venkateswarlu Bondu
Published: February 2025

Financial and market analysis applications require the mining of strong-utility itemsets. Finding frequent itemsets with high utility patterns is vital for such wide applications. Recent utility-based mining methods were successfully used in the current study to identify high value itemsets from static datasets. Stream databases or incremental databases update the itemsets at regular intervals (schedulers). Incremental Mining-based High Utility Itemset (IM-HUI) algorithms improve the methodologies based on High Utility Itemset (HUI) methods. The proposed technique refines the itemset values and updates the HUIs based on incremental schedulers. It reduces the time and space while mining HUIs over dynamic databases. The efficacy of the proposed work is compared experimentally to that of existing mining techniques on benchmark datasets.
Moar: A Swimmer Motion Swimming Style Identification Model using Deep Learning
Authors: Al-Majnoni Atheer , Al-Sahli Jumana , Al-Ahmady Dana , Al-Mutairi Amani , Alsini Areej , Alharbi Manal
Published: February 2025

Athletes in various sports, such as swimming, are increasingly using motion capture to identify and optimize their movement techniques. However, traditional motion capture systems tend to be expensive and limited. Computer vision-based methods have emerged as alternatives to identify four swimming styles: freestyle, backstroke, breaststroke, and butterfly. However, previous models did not identify flaws in swimmer movement. A significant challenge is the lack of labeled swimming video datasets that indicate these flaws. To overcome this challenge, this study collected and labeled a dataset of swimmer flaws and integrated them with the publicly available dataset SwimXYZ. Then, YOLO models were trained on the generated data. The YOLOv8s model demonstrated an impressive mean average precision (mAP@0.50) of 98% in the detection of swimming style and 95% in the simultaneous detection of swimming style and the identification of incorrect movements. This model can be used in real-time applications to help swimmers evaluate and improve the accuracy of their techniques.
Automated Glaucoma Detection Techniques: A Literature Review
Authors: Abdulsalam Wisal Hashim , Ali Rasha H. , Jadooa Sawsan H. , Hussein Samera Shams
Published: February 2025

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing on ML and DL techniques were selected. The best performance metrics obtained using ML recorded in the reviewed papers, were for the SVM, which achieved accuracies of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, DRISHTI-GS, and sjchoi86-HRF databases, respectively, employing the REFUGE-trained model, while when deploying the ACRIMA-trained model, it attained accuracies of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36%, in the same databases, respectively. The best performance metrics obtained utilizing DL recorded in the reviewed papers, were for the lightweight CNN, with an accuracy of 99.67% in the Diabetic Retinopathy (DR) and 96.5% in the Glaucoma (GL) databases. In the context of non-healthy screening, CNN achieved an accuracy of 99.03% when distinguishing between GL and DR cases. Finally, the best performance metrics were obtained using ensemble learning methods, which achieved an accuracy of 100%, specificity of 100%, and sensitivity of 100%. The current review offers valuable insights for clinicians and summarizes the recent techniques used by the ML and DL for glaucoma detection, including algorithms, databases, and evaluation criteria.
Sustainable Development of an Optimized Design Model for Groundwater Purification Units: A Solution for Irrigation Use in Rural Communities
Authors: Al-Rashed Wael S.
Published: February 2025

Groundwater is an essential resource for both irrigation and drinking water, particularly in arid and semi-arid regions where it often serves as the only dependable source. However, its quality is increasinlgy threatened by factors such as urbanization, population growth, and the overuse of chemical fertilizers in agriculture. hese challenges are particularly acute in Saudi Arabia, where groundwater quality deterioration poses significant obstacles to sustainable water use. This study proposes an optimized design for groundwater purification units aimed at improving water quality for irrigation. The proposed systems integrate coagulation with advanced purification methods, including nanofiltration or sand filtration, to effectively remove contaminants and enhance groundwater suitability for agricultural use. Nanofiltration excels in removing dissolved salts, organic molecules, and microorganisms, while sand filtration offers an economical solution for reducing suspended solids and turbidity By addressing critical water quality challenges, the model ensures more sustainable agricultural practices and a cleaner water supply for local communities. This research underscores the need for effective water management and purification strategies to safeguard groundwater as a reliable and safe resource for future generations, especially in regions like Saudi Arabia that face severe water scarcity and pollution pressures.
Load Frequency Control Design for Complex Power Systems Implementing Integral Single-Phase Sliding Mode Control
Authors: Tran Anh-Tuan , Huynh Van Van , Tran Thinh Lam-The
Published: February 2025

This paper proposes a Decentralized Integral Single-phase Sliding Mode Control (DISSMC) for Load Frequency Control (LFC) in Complex Power Systems with Multi-Source generation (CPSMS), integrating reheat, hydro, gas, and wind turbines. A generalized structure is employed to model Multi-Area Linked Power Systems (MALPS), providing a realistic representation of diverse power plants. The proposed method formulates an integral Sliding Surface (SS) to mitigate the chattering phenomena and ensures finite-time stability through a continuous control law. Additionally, a single-phase technique eliminates the reaching phase, ensuring immediate trajectory control. The MATLAB/Simulink simulations validate the DISSMC's effectiveness in stabilizing frequency fluctuations and managing load variations across three interconnected regions, each hosting different power plant types. The results highlight the proposed controller's robustness and adaptability to dynamic load and renewable energy source fluctuations.
Improving the Engineering Properties of Highly Expansive Soil by adding Psyllium Seed Biogel
Authors: Al-Mousawi Sondos Kareem , Fadhil Shaimaa Hasan
Published: February 2025

This study investigates the use of environmentally sustainable materials, particularly biopolymers, to enhance the engineering properties of expansive soils. Psyllium Seed (PS) biogel, added in four percentages, 0.4%, 0.8%, 1.2%, and 1.6% by dry weight of soil, was evaluated as a biopolymer additive for highly expansive soil. A series of tests were conducted on treated and untreated soil samples. The results revealed a slight decrease in the Atterberg limits with 1.6% PS biogel, where the liquid limit (LL) reached 75.3%, the plastic limit (PL) dropped to 32.65%, and the plasticity index (PI) was reduced to 42.65%. The swelling potential decreased significantly by 76% at 1.6% PS biogel. Compressibility improved with a 52.3% reduction in the compression index (Cc) and a 96% reduction in the recompression index (Cr) at 1.6% PS biogel content. The Unconfined Compressive Strength (UCS) increased, with the best improvement being observed at 0.8% PS biogel (81.6%), and continued enhancement with longer curing periods. Elasticity also improved, with the strain at failure increasing by 83.75% at 1.2% PS biogel. The SEM analysis confirmed that 0.8% PS biogel rearranged the clay particles and reduced voids, leading to enhanced UCS and reduced swelling. These findings highlight the PS biogel as an environmentally sustainable and effective material for improving the engineering properties of expansive soils.
A Numerical Study of Concrete Composite Circular Columns encased with GFRP I-Section using the Finite Element Method
Authors: Allawi Abbas , Ahmed Hiba Shihab , Hindi Riyadh
Published: February 2025

This paper presents ABAQUS simulations of fully encased composite columns, aiming to examine the behavior of a composite column system under different load conditions, namely concentric, eccentric with 25 mm eccentricity, and flexural loading. The numerical results are validated with the experimental results obtained for columns subjected to static loads. A new loading condition with a 50 mm eccentricity is simulated to obtain additional data points for constructing the interaction diagram of load-moment curves, in an attempt to investigate the load-moment behavior for a reference column with a steel I-section and a column with a GFRP I-section. The result comparison shows that the experimental data align closely with the simulation results regarding the ultimate strength, deformation, and failure modes, thereby validating the accuracy of the considered models. On the other hand, the numerical results of the column specimens under 50 mm eccentric load demonstrated that, in that case, the ultimate load of the columns decreased. The capacity of the reference column, a column with steel I-section, and a column with GFRP I-section decreased to 67%, 63%, and 64%, respectively compared with the columns tested under concentric load. The analytical investigation predicted the load-carrying capacity and bending moment capacity of the specimens with good accuracy. Based on the experimental curves, and the high strength found in the specimens that use the steel I- and GFRP I-sections, a good agreement between the numerical simulation and the experimental results was noticed.
Deep Learning-assisted Automatic Modulation Classification using Spectrograms
Authors: Ouamna Hamza , Kharbouche Anass , Madini Zhour , Zouine Younes
Published: February 2025

With the increasing demand for reliable and efficient V2X (Vehicle-to-Everything) communications in cognitive radio environments, spectrum sharing becomes imperative. In this context, accurate modulation classification serves as a fundamental component for efficient spectrum sensing and allocation. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) trained on spectrograms of BPSK and QPSK modulation schemes for automatic modulation classification in V2X scenarios. Experimental results demonstrated the effectiveness of the proposed CNN-based framework in accurately classifying modulation schemes in V2X communications.
Assessing the Effects of Libyan Iron Slag on Self-Compacting Concrete Characteristics
Authors: Altwair Nurdeen Mohamed , Abuzgaia Ali Gomaa , Alsharif Abdualhamid Mohamed , Sryh Lamen Saleh , Abdulsalam Saleh Elmahdi Ali , Swalem Khalid Ashur
Published: February 2025

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