bluuebunny/crossref_metadata_2025 huggingface dataset:
The Cyclical Effects on Labor Force Participation: A Study of the Saudi Labor Market using ARDL Techniques
Authors: Alzyadat Jumah Ahmad
Published: February 2025

This article assesses the cyclical effects on Labor Force Participation (LFP) in the Saudi labor market. The Discouraged Worker Effect (DWF) and Added Worker Effect (AWF) for men and women LFP rates were examined by applying the Autoregressive Distributed Lag (ARDL) method. The results showed the existence of AWF in the long run in the case of women. They also confirmed that the women's LFP rate is countercyclical to economic activity and procyclical to unemployment and inflation rates. However, there is no evidence of the impact of the unemployment rate and economic cycles on women's decision to participate in the labor force in the short run. Moreover, the results support the invariance unemployment hypothesis for men's decision to participate in the labor force. This means that these factors do not affect men's decision to join the workforce, but other factors make them look for work to increase income. This study also suggests that women can be motivated to enter the labor market if their chances of finding work are improved through an active economic policy that creates jobs for both men and women.
Verification of the Finite Element Model of a Moving Load Passing Over a Single Irregular Suspended Load in the Dynamic Analysis of a Beam System
Authors: Van Tran Thi Thuy , Gridnev S. Yu. , Ravodin I. V.
Published: February 2025

This article compares the variants of dynamic models of mobile load to describe the joint oscillations of span structures and vehicles on road bridges, taking into account the irregularities of the road surface. Using a known solution to the beam system oscillation problem, when the sprung load moves through a single unevenness, the joint modeling application of an inert mobile load and a span structure in the LS-Dyna FE complex using contacts is considered. The proposed method eliminates the need to use special plugins to describe the car dynamics and allows considering the the separation of the wheel from the road surface. At the same time, the use of contacts to create dynamic models of vehicles in the FEM is complicated by the lack of a verified way to account for road surface irregularities. In bridge calculations, spatial modeling of an elastic pavement layer with irregularities leads to the fact that the rigidity of the span structure varies in length depending on the micro profile. An effective way to solve this problem is to use solids with orthotropic material properties to describe the geometry of irregularities. Due to the unequal mechanical properties of the material along and across the beam, the layer with irregularities adequately transfers the load from the vehicle model to the supporting structures while not affecting the rigidity of the span structure. A good coincidence of the results of solving the dynamic problem by the proposed method in LS-Dyna with the results obtained by other authors in the SAP2000 program shows the possibility of using contacts for the dynamic calculation of bridge structures considering the irregularities of the road surface.
An Innovative Approach on Recycle Foam Concrete as a Sustainable Alternative with the addition of Nano Titanium Dioxide TiO2 on the Properties of Foam Concrete
Authors: Zuhier Baraa , Al-Mulali Mohammed Zuhear
Published: February 2025

Sustainability and construction waste recycling have become crucial topics today in response to the growing environmental challenges and the increasing accumulation of waste. Therefore, it is essential to explore innovative solutions that improve the sustainability of concrete mixes. An effective approach is the use of Lightweight Foamed Concrete (LFC), a revolutionary new material that is considered a viable solution for the reduction of the weight of conventional concrete. This research focuses on the study of the effect of replacing 50% of virgin sand by volume with Recycled Foam Concrete (RFC) waste crushed at four gradation levels with aggregate sizes between 12.5-9.5 mm, 9.5-4.75 mm, 4.75-2.36 mm, and 2.36-1.18 mm, and the effect of adding 0.5% Nano titanium dioxide TiO2 by weight of cement. The water-to-cement and cement-to-aggregate ratio were maintained at 0.45 and 1:1.3, respectively. Nanoparticles are incorporated into Foam Concrete (FC) to enhance its strength, due to their beneficial properties, such as their small particle size and high reactivity. The results conclude on the optimal sizes of RFC with the addition of Nano TiO2 for use in FC mixes that enhance compressive strength and increase carbonation compared to traditional FC mixes.
Processing used Aluminium Production Granular Filters to Produce Concrete
Authors: Bykov Petr O. , Kuandykov Almaz B. , Kaliyeva Ainagul B. , Siemens Eduard
Published: February 2025

This article presents the findings of experimental studies on the involvement in the processing of spent filters from ash and slag waste used in the refining of primary aluminum as a filler for concrete production. The processing of spent granular filters was conducted in three stages. The first stage involved the preliminary processing of filter grains to remove aluminum scrap. The second stage entailed the metallurgical processing of separated aluminum scrap through remelting in an induction crucible furnace and subsequent refining. The third stage focused on the production of a concrete mixture comprising crushed spent filter grains, quartz sand, bauxite sludge, screenings of crushed rocks with a fraction of 20 mm–30 mm, and Portland cement. This mixture was used to create samples of building products. The test results indicate that the tensile strength of the concrete samples for building products ranges from 20.89 MPa to 37.75 MPa, depending on the Portland cement content. This strength corresponds to that of heavy concrete.
Optimization of Stock Predictions on Indonesia Stock Exchange: A New Hybrid Deep Learning Method
Authors: Dwiandiyanta Bernadectus Yudi , Hartanto Rudy , Ferdiana Ridi
Published: February 2025

This study presents a new method for predicting stock prices on the Indonesia Stock Exchange (IDX) using a hybrid deep learning model. The proposed model combines historical price data, consisting of open, high, low, and close values, with technical indicators such as Moving Average (MA), Simple Moving Average (SMA), and Exponential Moving Average (EMA). The proposed model offered improved accuracy and efficiency using distinct architectures of Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) to process the datasets. The results showed that the proposed hybrid model significantly outperformed traditional single-architecture models in terms of R2, achieving 0.98 for the BBCA stock, surpassing models using only RNN or GRU. In addition, comparable improvements were observed with additional equities, such as the PT. Bank Mandiri Tbk (BMRI) and PT. Bank Negara Indonesia Tbk (BBNI) stocks, achieving an R2 of 0.99, demonstrating the proficiency of the proposed model in capturing the complex dynamics of the stock market. The results demonstrated the significant potential of combining historical data and technical indicators into the modeling procedure to predict stock prices. This process can benefit investors and economic forecasters in the stock market. The results could be further expanded by classifying datasets and investigating different sets of models to improve the performance of financial forecasting.
Exploring the Adverse Impact of Smartphone Use on Young Individuals' Self-Esteem: A Structural Equation Modeling Approach based on Five Temperaments
Authors: Snegha J. , Sudha M.
Published: February 2025

Excessive Smartphone Use (ESU) has emerged as a major social concern, with widespread reliance on smartphones and the Internet resulting in various detrimental effects. This research aimed to analyze the primary psychological factors that affect ESU among individuals using Structural Equation Modeling (SEM). Smartphone addiction levels were evaluated using the Smartphone Addiction Scale, along with five different temperamental traits, namely, Depressive, Cyclothymic, Hyperthymic, Anxious, and Irritable, using the Affective Temperament Measure. Self-esteem was examined using the Rosenberg self-esteem scale. Of 376 participants aged 16 to 23, 88.4% showed signs of ESU, with certain temperamental traits being more prevalent. Negative impacts were especially noticeable among women. Self-esteem levels were found to be 15.7% high, 23.8% moderate, and 60.5% low. The results emphasize the harmful effects of excessive smartphone use on emotional and cognitive well-being, particularly in those with lower self-esteem. This study highlights the need to understand these relationships and create strategies to mitigate the negative effects of smartphone overuse.
An AI-Driven Framework for Optimizing Business Intelligence across Organizational Hierarchies
Authors: Thaher Mohammed Shaban
Published: February 2025

In today's global trade landscape, Artificial Intelligence (AI) significantly enhances productivity and transforms business processes across sectors. This research investigates the role of business intelligence in improving service delivery within corporate entities. By applying Deming's methodology, strategies to optimize decision-making processes are identified, and hidden insights are revealed through advanced data analysis techniques. A Data Flow Diagram (DFD) illustrates the development stages and system implementation, offering practical guidance for general managers. The findings provide actionable insights that enhance efficiency and decision-making in organizational contexts.
Powertrain Design and Modeling for a Fuel Cell Hybrid ElectricVehicle
Authors: Dhieb Youssef , Ayadi Walid , Yaich Mohamed , Ghariani Moez
Published: February 2025

The objective of this study was to develop a Fuel Cell Hybrid Electric Vehicle (FCHEV) powertrain with the aim of enhancing battery usage autonomy. The vehicle, which participated in the Eco-Marathon competition as a prototype, incorporates batteries, a Direct Current (DC) electric motor, and a Proton Exchange Membrane (PEM) fuel cell. The design permits the operation of the fuel cell to be conducted in a more efficacious and fuel-efficient manner. The study employs the MATLAB-Advisor software to construct powertrain models that are then validated in laboratory settings. These models are subsequently compared with the performance of the actual FCHEV prototype and adapted for use in automotive applications. The FCHEV power model calculates instantaneous energy consumption using input variables, such as vehicle speed, acceleration, and road gradient. Furthermore, Real Cycle drive was carried out to improve the trade-off between energy consumption, fuel cells, battery State of Charge (SOC) dynamics, and battery power smoothness, while ensuring that all essential limitations were met. The addition of a fuel cell to an electric car model enhances its range by 250%, significantly improving its adoption and usage.
Factors Shaping the Net Asset Value of Saudi Real Estate Investment Trusts
Authors: Hechmi Soumaya
Published: February 2025

This study investigates the impact of the dividend per unit, loan-to-value ratio (LTV), operating performance, and liquidity on the financial performance of Saudi Real Estate Investment Trusts (REITs), as measured by Net Asset Value (NAV). The study examines a sample of 17 listed Saudi REITs over a five-year period, 2019-2023, resulting in 85 observations. Through multiple regression analysis, the results demonstrate a significant positive relationship between NAV and dividend per unit, operating performance, liquidity, and size as a control variable. In contrast, a negative relationship is found between LTV and NAV. These findings provide important insights for investors and portfolio managers, enhancing their understanding of the key factors that drive the financial performance of Saudi REITs.
Finite Element Analysis of CM247LC Superalloy for Gas Turbine Blade Application
Authors: Chavan Tejan , Khedkar Nitin
Published: February 2025

The objective of this article is to conduct a comparative analysis of the various materials used in the production of gas turbine blades. The materials under investigation include CM247LC, Nimonic 80A, and Inconel 738. The selected blade materials are required to demonstrate exceptional resistance to high temperatures and corrosion. It is determined that the most appropriate material for the construction of a gas turbine blade is a nickel-based superalloy. For the purposes of Finite Element Analysis (FEA), the aforementioned materials are defined as nickel-based superalloys. A comprehensive analysis of these materials was conducted using the ANSYS 2024 R2 student edition and a combination of structural and vibrational analyses was carried out. The deformation observed in CM247LC and Nimonic 80A exhibited nearly identical values of 0.965 mm and 0.884 mm, respectively. The results of the vibrational analysis indicated that all materials successfully circumvented the natural frequency as well as the operational natural frequency of 50 Hz, thereby ensuring the safe operation of the gas turbine blade. The findings demonstrated that the CM247LC satisfied both criteria for material selection, making it the most suitable material for gas turbine blade applications when compared to alternative materials. This is due to its comparatively lower deformation despite experiencing a greater magnitude of centrifugal force.
A Deep Learning with Metaheuristic Optimization-Driven Breast Cancer Segmentation and Classification Model using Mammogram Imaging
Authors: Sreevani M. , Latha R.
Published: February 2025

Cancer is the second leading cause of death globally, with Breast Cancer (BC) accounting for 20% of the new diagnoses, making it a major cause of morbidity and mortality. Mammography is effective for BC detection, but lesion interpretation is challenging, prompting the development of Computer-Aided Diagnosis (CAD) systems to assist in lesion classification and detection. Machine Learning (ML) and Deep Learning (DL) models are widely used in disease diagnosis. Therefore, this study presents an Optimized Graph Convolutional Recurrent Neural Network based Segmentation for Breast Cancer Recognition and Classification (OGCRNN-SBCRC) technique. In the preparation phase, images and masks are annotated and then classified as benign or malignant. To achieve this, the Wiener Filter (WF)-based noise removal and log transform-based contrast enhancement are used for preprocessing. The OGCRNN-SBCRC technique utilizes the UNet++ method for segmentation and the RMSProp optimizer for parameter tuning. In addition, the OGCRNN-SBCRC technique employs the ConvNeXtTiny Convolution Neural Network (CNN) approach for feature extraction. For BC classification and detection, the Graph Convolutional Recurrent Neural Network (GCRNN) model is used. Finally, the Aquila Optimizer (AO) model is employed for the hyperparameter tuning of the GCRNN approach. The simulation analysis of the OGCRNN-SBCRC methodology, using the BC image dataset, demonstrated superior performance with an accuracy of 99.65%, surpassing existing models.
Improving Intrusion Detecction Systems by using Deep Learning Methods on Time Series Data
Authors: Mohammed Asma Ahmed A.
Published: February 2025

Intrusion Detection Systems (IDSs) are the cornerstone of cybersecurity, monitoring network traffic to find abnormal suspicious activities. Traditional IDSs usually face challenges in adapting to the cyber threats that evolve day by day, leading to very high false positive rates and missed detections. This study focuses on enhancing the performance of an IDS system by integrating deep learning techniques with time series data. The efficiency of RNN, CNN, and LSTM networks was evaluated in detecting intrusions in real-time. The experimental results showed that hybrid models, especially the CNN+RNN+LSTM combination, performed best with a 0.86 F1 score, 0.92 precision, and 0.79 recall, indicating that hybrid deep learning methods can improve detection accuracy while reducing false alarms, opening a resilient future for cybersecurity.
Geotechnical Properties of Soil-Lightweight Aggregate Mixtures
Authors: Aljboori Wurood , Al-Soud Madhat Shakir , Ali Asma Mahdi
Published: February 2025

This study investigates the stabilization and strengthening of clayey soils using several Lightweight Aggregates (LWAs), including Lightweight Expanded Clay Aggregate (LECA), Ponza (Pumice), and Thermostone. LWAs were incorporated into the soil to evaluate their impact on critical geotechnical parameters, such as compaction, consolidation, Unconfined Compressive Strength (UCS), and shear strength. The results revealed that incorporating LWAs effectively reduces the soil density, increases the void ratios, and enhances certain soil properties. LECA demonstrated the most substantial impact in decreasing soil density and increasing porosity, achieving a maximum density reduction of 60% and a void ratio increase of 75% at a 60% addition. While LWAs enhanced the internal friction angle by up to 90% -with Thermostone showing the highest increase at a 60% addition- cohesion diminished across all concentrations. The UCS peaked with a 94% increase at a 10% LECA addition but decreased with higher LWA percentages due to the porous nature of the additives disrupting the soil matrix. Optimal performance was observed with LWA concentrations between 10% and 30%, balancing improved strength and soil integrity. These findings suggest that LWAs can effectively stabilize and strengthen clayey soils, particularly in applications requiring reduced weight and enhanced shear strength, provided that the mixing ratios are carefully calibrated to align with project-specific requirements.
Deep Learning-Driven Ontology Learning: A Systematic Mapping Study
Authors: Amalki Asma , Tatane Khalid , Bouzit Ali
Published: February 2025

Today, ontologies are the widely accepted framework for managing knowledge in a manner that supports sharing, reuse, and automatic interpretation. Ontologies are fundamental to various Artificial Intelligence (AI) applications, including smart information retrieval, knowledge management, and contextual organization. However, the rapid growth of data in various domains has made ontology acquisition and enrichment, time-consuming, labor-intensive, and expensive. Consequently, there is a need for automated methods for this task, commonly referred to as ontology learning. Deep learning models have made significant advancements in this field, as they can extract concepts from vast corpora and infer semantic relationships from wide-ranging datasets. This paper aims to explore and synthesize existing research on the application of deep learning techniques to ontology learning. To achieve this, a Systematic Mapping Study (SMS) was conducted, encompassing 2765 papers published between 2015 and September 2024, from which 47 research papers were selected for review and analysis. The studies were systematically categorized according to eight refined criteria: publication year, type of contribution, empirical study design, type of data used, deep learning techniques implemented, domain of application, focused ontology learning tasks, and evaluation metrics and benchmarks.
Project Delivery System Selection using the AHP Multi-Criteria Decision Making Method
Authors: Mahmood Buroog Basheer , Shadhar Alaa Kharbat , Husain Ihsan Ali , Mahjoob Ahmed Mohammed Raoof
Published: February 2025

This study aims to select the most suitable project delivery method for the implementation of construction projects in Iraq. The descriptive analytical approach was used to determine the importance of the criteria considered in this study, namely cost, project duration, and quality, according to owners, contractors, and consultants. A field study was used utilizing questionnaire survey and interviews to determine the degree of importance of these criteria. Then, data regarding 28 projects for the period from 2022 to 2024 were was collected to measure the performance of these criteria. Univariate data analyses were performed to assess the performance of Integrated Project Delivery (IPD) along with the deriving priority scales based on the Analytic Hierarchy Process (AHP) theory. The results of the AHP indicate that the IPD system ranked first with a preference rate of 34.5%.
Nuclear Energy Future in Mitigating Climate Change and Achieving Sustainable Development
Authors: Abd El-Azeem Samah Abdullah
Published: February 2025

The growing global population and rising developmental demands have led to increased electricity consumption, primarily from fossil fuel-powered stations, contributing to climate change and necessitating urgent sustainable development. This situation, exacerbated by global warming and its adverse environmental impacts, highlights the escalating demand for energy. As the awareness of climate change has intensified, nuclear energy has gained renewed attention as a key player in mitigating its effects. With global energy consumption being expected to rise substantially, expanding nuclear power plants has become a priority. Nuclear energy presents an opportunity to reduce reliance on fossil fuels while providing a clean and reliable source of electricity. This study investigates the role of nuclear energy in electricity generation, emphasizing its potential as a low-carbon energy source. It underscores the critical need to advance technologies, foster innovation, and enhance safety protocols to ensure energy security. The current study also advocates for increased funding and investment, the establishment and enforcement of effective policies and regulatory frameworks, and the development of human resources and infrastructure. Additionally, it recommends that governments prioritize research into innovative nuclear technologies, collaborate internationally, and accelerate the development and deployment of nuclear energy to support sustainable development.
Q_YOLOv5m: A Quantization-based Approach for Accelerating Object Detection on Embedded Platforms
Authors: Alshammry Nizal , Saidani Taoufik , Albalawi Nasser S. , Alenezi Sami Mohammed , Alhamazani Fahd , Alshammari Sami Aziz , Aleinzi Mohammed , Alanazi Abdulaziz , Elsayed Mahmoud Salaheldin
Published: February 2025

The deployment of deep learning models on resource-constrained embedded platforms presents significant challenges due to limited computational power, memory, and energy efficiency. To address this issue, this study proposes a novel quantization method tailored to accelerate object detection using a quantized version of the YOLOv5m model, called Q_YOLOv5m. This method reduces the model's computational complexity and memory footprint, allowing for faster inference and lower power consumption, making it ideal for real-time applications on embedded systems. This approach incorporates advanced weight and activation quantization techniques to balance performance with accuracy, dynamically adjusting precision based on hardware capabilities. The efficacy of Q_YOLOv5m was confirmed, exhibiting substantial enhancements in inference speed and a reduction in model size with negligible loss in object detection accuracy. The findings underscore the capability of Q_YOLOv5m for edge applications, including autonomous vehicles, intelligent surveillance, and IoT-based monitoring systems.
Impact of Change Orders on Cost Overruns and Delays in Large-Scale Construction Projects
Authors: Ramadhan Jatiaryo Sidiq , Waty Mega
Published: February 2025

This study investigates the impact of Change Orders (CO) on construction project performance, focusing on cost overruns and project delays. Partial least square structural equation modeling was used to analyze the relationships between key causal factors, including design changes, planning errors, and project outcomes. Data were collected from 127 construction practitioners involved in large-scale projects managed by PT XYZ, a leading Indonesian contractor. The analysis identifies that design changes contribute to 56.5% of cost overruns and 40% of project delays, while planning errors account for 34.5% of cost overruns and 23.1% of delays. These findings highlight the critical importance of improving project planning accuracy and enhancing design management processes to reduce the adverse effects of CO. Structured protocols for managing CO, better coordination among stakeholders, and adopting advanced technologies are recommended to minimize their effect. These insights are particularly relevant for large-scale projects where CO frequently disrupt budgets and timelines. By addressing these issues, project managers can enhance overall performance and reduce risks associated with cost and time escallations. This research provides practical strategies applicable to various construction contexts, supporting more efficient project delivery and better management of CO.
Enhancing Semantic Search Precision through the CBOW Algorithm in the Semantic Web
Authors: Mahmoud Ashraf F. A. , Mohammed Zakariya M. S. , Ben Ammar Mohamed , Satty Ali , Abdalla Faroug A. , Khamis Gamal Saad Mohamed , Salih Mohyaldein , Mohamed Abdelnasser Saber
Published: February 2025

The Semantic Web enhances data interoperability and enables intelligent information retrieval through structured data representation. However, challenges remain in achieving high precision in semantic search. This paper uses the Continuous Bag of Words (CBOW) model to enhance semantic search precision. By generating rich word embeddings, CBOW enables a better understanding of contextual relationships among terms within semantic queries. Our approach has been evaluated using the websites intended to be used as a sample for testing the efficiency of semantic information retrieval, demonstrating significant improvements in search precision compared to traditional methods. The findings indicate that integrating CBOW into semantic search frameworks can lead to more relevant and accurate search results, paving the way for future advancements in Semantic Web technologies.
Experimental Investigation of Concrete-filled Steel Tube Beams with Transverse Openings
Authors: Abdulridha Ali Mohammed , Al Zaidee Salah R.
Published: February 2025

Modern building construction requires numerous pipes and ducts for services like air conditioning and electricity, often accommodated by web openings in beams. This study investigates the structural performance of Concrete-Filled Steel Tube (CFST) beams with transverse openings, which can affect their load-carrying capacity and behavior. Eight CFST beams and four Hollow Steel Tube (HST) beams were tested under two concentrated loads, including four CFST and two HST beams with transverse openings. The present research examines how openings impact load capacity, failure modes, ductility, strain, and Energy Absorption (EA) across varying cross-sections and Depth-to-thickness (D/t) ratios. The results show that transverse openings significantly affect CFST beams more than HST beams. The load-carrying capacity of CFST beams was reduced by up to 18.6%, while HST beams exhibited reductions of only up to 3.77%. Ductility and EA followed similar trends, with CFST beams experiencing reductions of up to 20% in ductility and 30.7% in EA. The HST beams showed relatively minor decreases of 2.54% in ductility and 14.1% in EA. The failure of CFST beams with openings was characterized by steel rupture through the openings. The effect of openings increased with higher D/t ratios. Despite the reductions caused by the openings, the overall enhancement in all studied aspects provided by the concrete filling in CFST beams with transverse openings remained significant.
Control of a Doubly Fed Induction Generator for Variable Speed Wind Energy Conversion Systems using Fuzzy Controllers optimized with a Genetic Algorithm
Authors: Guediri Mourad , Touil Slimane , Hettiri Messaoud , Guediri Abdelhafid , Ikhlef Nabil , Hocine Bouchekhou , Guediri Abdelkarim
Published: February 2025

This paper presents a comprehensive study of a wind turbine system operating under variable wind conditions, utilizing a Doubly Fed Induction Generator (DFIG) connected to the grid. The DFIG is controlled via a rotor-side transducer, allowing for independent regulation of the conductors to manage both active and reactive power flows effectively. The control strategy focuses on generating reference voltages for the rotor to ensure that active and reactive power align with the desired targets, optimizing the tracking of the maximum power point to maximize electrical output. The research analyzes the system's dynamic performance under fluctuating wind conditions, emphasizing control strategies for managing active and reactive energy. A notable innovation is the integration of fuzzy logic and genetic algorithm into the control strategy for the wind turbine's switching mechanism, which enhances system performance and efficiency. Simulation results demonstrate that this approach provides higher efficiency, improved performance, and greater stability compared to the traditional Proportional-Integral (PI) controllers. Advanced artificial intelligence methods, such as fuzzy genetic algorithm control, were employed and the proposed system's effectiveness was validated with Matlab/Simulink simulations.
Multi-Objective Optimization of the Turning Process using the Probability Method
Authors: Dua Tran Van , Thinh Hoang Xuan , Bao Nguyen Chi , Duc Duong Van , Hoang Tran Minh
Published: February 2025

This research aims to determine the optimal values of the cutting parameters when solving the turning process multi-objective problem. Three cutting parameters are considered in this study: spindle speed (nw), feed rate (f), and depth of cut (ap). A turning experiment series was conducted on a conventional lathe, with nine experiments having been designed according to the Taguchi experimental design matrix. In each experiment, the values of the three parameters changed and the material Removal Rate (Q) was measured. The Probability method was used to solve the multi-objective optimization problem. The Method based on the Removal Effects of Criteria (MEREC) technique was employed to calculate the weights of the criteria. The results of the optimization problem using the Probability method were also compared with those obtained using other methods, including the Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vlsekriterijumska optimizacijaI KOmpromisno Resenje (VIKOR), Multi-Atributive Ideal-Real Comparative Analysis (MAIRCA), Evaluation by an Area-based Method for Ranking (EAMR), Complex Proportional Assessment (COPRAS), Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), Proximity Indexed Value (PIV), and Combined Compromise Solution (COCOSO). All the methods converged on the same unique solution to the multi-objective optimization problem. The optimal values for the parameters were: nw = 1350 rev/min, corresponding to a feed rate of 0.13 mm/rev, and a depth of cut of 0.4 mm. When machining with these optimal cutting parameters, the resulting values for Ra, RE, and Q were 1.057 µm, 0.03 mm, and 13225.68 mm²/min, respectively.
A Deep Ensemble Gene Selection and Attention-guided Classification Framework for Robust Cancer Diagnosis from Microarray Data
Authors: Haddou Bouazza Sara
Published: February 2025

Microarray technology has enabled unprecedented insight into cancer diagnosis through large-scale gene expression analysis. However, the high dimensionality and complexity of microarray datasets pose significant challenges, as only a small subset of genes is typically informative, with the remainder introducing noise and complicating classification. Traditional gene selection methods, including filter, wrapper, and hybrid techniques, have achieved promising results but often fail to capture complex gene interactions, suffer from computational inefficiencies, or lack interpretability. This study presents DEGS-AGC (Deep Ensemble Gene Selection and Attention-Guided Classification), a novel integrated framework for gene selection and classification. DEGS-AGC is designed to address these limitations through two primary components: Deep Ensemble Gene Selection (DEGS), which leverages ensemble learning with Random Forest, XGBoost, and Deep Neural Networks to select relevant genes while reducing redundancy via sparse autoencoders, and Attention-Guided Classification (AGC), where an attention mechanism dynamically assigns weights to genes to improve interpretability and classification precision. The DEGS-AGC framework was evaluated against traditional methods, using consistent classification models for robust comparisons. Evaluation metrics demonstrated the potential of DEGS-AGC as an effective tool for high-dimensional biomedical data analysis. The results highlighted the ability of DEGS-AGC to offer accurate, interpretable, and computationally feasible solutions for cancer diagnosis, advancing the development of data-driven personalized approaches in healthcare.
Enhancing Ad Hoc Network Security using Palm Vein Biometric Features
Authors: Mohamed Abdelnasser , Salama Ahmed , Ismail Amr
Published: February 2025

This study proposes an innovative approach to securing ad hoc networks through palm vein biometric authentication, addressing critical security vulnerabilities in decentralized wireless communications. The research introduces an Adaptive Fusion Biometric Key Generation (AFBKG) framework that seamlessly integrates palm vein biometric features with state-of-the-art cryptographic protocols. The methodology implements a comprehensive six-stage process, incorporating Near-Infrared (NIR) imaging at 850 nm wavelength, advanced image preprocessing techniques, and deep learning-based feature extraction using a fine-tuned Convolutional Neural Network (CNN), culminating in a robust 512-dimensional feature vector. A rigorous performance evaluation was conducted, which demonstrated exceptional results, achieving 98% authentication accuracy with a 0.1% False Acceptance Rate (FAR) and 95% spoofing resistance. The AFBKG algorithm significantly outperforms traditional security methods, demonstrating 95% authentication strength and 92% resistance to Man-in-the-Middle (MITM) attacks while maintaining minimal key management complexity (15%). The system's superior scalability (90%) and computational efficiency (10% overhead) compared to conventional biometric approaches are noteworthy. These findings establish palm vein biometric authentication as a cutting-edge solution for enhancing ad hoc network security, offering substantial improvements over traditional password-based systems and alternative biometric methods.
A Study on the Use of Necuron-Type Polymer Plastics utilized for Sliding Bearing Manufacturing
Authors: Nae Ion , Zisopol Dragos Gabriel , Romanet Mirela , Bogdan-Roth Mihai , Ramadan Ibrahim
Published: February 2025

This paper presents research on the characteristics of the thermoplastic materials used in the manufacture of sliding bearings. Two types of materials from the category of thermosetting polyurethanes with the commercial names Necuron 1050 and Necuron 1300 were investigated. Tests were conducted to determine their physical and mechanical characteristics, such as density, hardness, tensile strength, compressive strength, and longitudinal modulus of elasticity. The results obtained during the study attest to the quality of the materials in the thermosetting polyurethane category in terms of hardness, tear resistance, and compression resistance. The materials in the category of thermosetting polyurethanes stand out as the right choice for applications that require mechanical strength, durability, and resistance to environmental factors.
Phytochemical Analysis and Antioxidant Activity Assessment of Methanolic Extract from Jasmine Flowers
Authors: Al-Bagawi Amal H. , Rassem Hesham H. , Khamidun Mohd H. , Alanazi Tahani Y. A. , Masood Najat , Rajeh Sahar Y. , Magam Sami M. , Adam Anbia
Published: February 2025

This study offers a comprehensive analysis of the phytochemical composition and antioxidant properties of the methanol extracts from Jasmine flowers. Employing a combination of advanced techniques, including Gas-Chromatography Mass-Spectrometry (GC-MS), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and antioxidant activity assays, the research uncovered key insights into the bioactive potential of Jasmine. The GC-MS analysis identified nine distinct compounds, including major constituents, such as 2-Phenylthiolane (44.12%), Cyclohexene, 3-ethenyl- (25.88%), Acetaldehyde (12.70%), and N-Methylallylamine (10.31%) among others. The FTIR spectra revealed significant functional groups, including O-H and C-C stretches, suggesting the presence of phenolic compounds. The SEM imaging highlighted the morphological changes in the Jasmine flower powder, showing expanded oil glands post-pre-treatment, which enhanced the oil extraction process. The methanol extract exhibited a strong antioxidant activity, as evidenced by the DPPH radical scavenging assay. These findings position Jasmine flowers as a promising natural source of phytochemicals, particularly antioxidants, with potential for further pharmacological and industrial applications. Future studies could focus on isolating and evaluating additional bioactive compounds for their therapeutic potential.
Optimizing Fly Ash and Cement Ratios for Immobilizing Lead and Arsenic in Liquid Hazardous Waste
Authors: Irawan Diki Surya , Alham Muhammad , Sari Deffi Ayu Puspito , Fadiilah Dessy
Published: February 2025

Improper handling of hazardous and toxic industrial wastes can directly impact human health and the environment. To overcome this problem, the PT Prasadha Pamunah Limbah Industri (PPLI) provides industrial waste processing services including Stabilization and Solidification (S/S) treatment using cement and fly ash mixtures. This research evaluated the efficiency of S/S in reducing the heavy metal content of lead (Pb) and arsenic (As) in liquid hazardous waste, using waste, fly ash, and cement ratios of 1:1:1, 1.5:1:0.5, and 2:0.5:0.5, tested with the Toxicity Characteristic Leaching Procedure (TCLP). The results demonstrated that the 1:1:1 and 1.5:1:0.5 ratios effectively reduced the Pb and As content, achieving reduction efficiencies of 99.50% and 99.34% for the 1:1:1 ratio, and 99.36% and 99.19% for the 1.5:1:0.5 ratio, respectively. However, the 2:0.5:0.5 ratio produced a brittle mixture prone to leachate formation and it is not recommended. According to the TCLP results, the 1:1:1 and 1.5:1:0.5 ratios met the environmental quality standards outlined in Government Regulation No. 22 of 2021 on Environmental Protection and Management. The statistical analysis using an independent T-test indicated no significant difference in the reduction efficiency between the two effective ratios, suggesting that either can be reliably used. This study addresses existing research gaps by applying the S/S method to liquid hazardous waste, an area previously focused on soil applications. Besides, it demonstrates that high immobilization rates exceeding 99% can also be achieved for Pb and As using cement and fly ash. This study provides a novel perspective on S/S applications, enhancing the efficiency and applicability of hazardous waste treatment processes.
Enhancing Traffic Counting in Rainy Conditions: A Deep Learning Super Sampling and Multi-ROI Pixel Area Approach
Authors: Warni Elly , Alimuddin A. Ais Prayogi , Salam A. Ejah Umraeni , Fachri Moch , Rizal H. Muhammad
Published: February 2025

In Intelligent Transportation Systems (ITS), adaptive traffic control relies heavily on precise, real-time traffic data. Controllers use information such as vehicle count, vehicle density, traffic congestion, and intersection wait times to optimize traffic flow and improve efficiency. Traffic cameras collect and process this data, but environmental factors like rain can degrade the performance of data retrieval systems. We propose a vehicle detection method that integrates pixel area analysis with Deep Learning Super Sampling (DLSS) to enhance performance under rainy conditions. Our method achieved an accuracy of 80.95% under rainy conditions, outperforming traditional methods, and performing comparably to specialized methods such as DCGAN (93.57%) and DarkNet53 (87.54%). However, under extreme conditions such as thunderstorms, the method's accuracy dropped to 36.58%, highlighting the need for further improvements. These results, evaluated using the AAU RainSnow Traffic Surveillance Dataset, demonstrate that our method improves traffic data collection in diverse and challenging weather conditions while identifying areas for future research.
Classification using Enhanced Spectral and Spatial Transformer with Grasshopper Optimization
Authors: Niharika Pilligundla , Chandre Shanker
Published: February 2025

Hyperspectral image (HSI) classification plays a crucial role in remote sensing, allowing the identification of various land cover types. Traditionally, Convolutional Neural Networks (CNNs) have been widely used for this purpose. However, they often face challenges related to high training parameter requirements and limited capacity for feature extraction, affecting their overall effectiveness. To overcome these challenges, this study proposes a novel approach integrating the Enhanced Deep Spectral and Spatial Transformer (EDSST) with Grasshopper Optimization (GHO). EDSST leverages transformer architecture to perform advanced spectral and spatial feature extraction, effectively mitigating the limitations of CNNs. This method improves feature abstraction and classification performance by reducing the number of training parameters while implementing a self-focusing mechanism. This approach incorporates a Classification Head (CH) with an orthogonal softmax activation function to accurately classify hyperspectral images. The proposed method was rigorously evaluated using the Salinas dataset, a benchmark in HSI classification research. The results show substantial improvements over existing techniques, achieving an accuracy of 99.5472%, precision of 99.5574%, recall of 99.5267%, and an F score of 99.6145%. These findings not only demonstrate the effectiveness of the proposed method in HSI classification but also highlight its efficiency and robustness, offering a promising solution for future applications in remote sensing and environmental monitoring.
Evaluating the Impact of Weighting Methods on the Stability of Scores for Alternatives in Multi-Criteria Decision-Making Problems
Authors: Linh Nguyen Thi Dieu , Son Nguyen Hong , Thao Dang Xuan
Published: February 2025

Criteria weights play a crucial role in Multi-Criteria Decision Making (MCDM) problems when selecting the best alternative from a set of options. This study aims to compare three objective weighting methods: MEthod based on the Removal Effects of Criteria (MEREC), Entropy, and Symmetry Point of Criterion (SPC). These methods were applied to a case study involving the ranking of eight sustainable energy development alternatives, each characterized by seventeen criteria. Four representative MCDM methods, the Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Proximity Indexed Value (PIV), and Root Assessment Method (RAM), were also deployed. The results revealed that the Entropy method provided the most stable and consistent performance, followed by the MEREC method, with the SPC method showing the least stability.
Repair and Strengthening of Cantilever Continuous Bridges using External Prestressed Cables: The Case Study of the Tan De Bridge in Vietnam
Authors: Nguyen Dac Duc , Tran Viet Hung
Published: February 2025

Continuous cantilever bridges utilizing cross-sectional box girders have been widely used worldwide, as well as in Vietnam, with outstanding advantages, such as the ability to cover large spans, good torsional resistance, high stability, and low maintenance costs. The arrangement of the prestressed cables in the span can use all internal cables (cables arranged in a box cross-section) or a combination of internal wires and external cables (cables arranged in a box cavity). The arrangement of the external cables helps reduce the cross-sectional area and self-weight of the span, leading to savings in foundation costs and an increase in the ability to exceed the span. However, the external cables degrade and become damaged. Therefore, repair and strengthening solutions are required to ensure the longevity of the project. This study focused on researching solutions to restore and enhance span structures using external prestressed cables through specific projects implemented in Vietnam.
Application of Multi Criteria Decision Making Methods for the Determination of the Best Dressing Factors for Surface Grinding Hardox 500
Authors: Bao Le Duc , Binh Vu Duc , Thanh Dinh Van , Tu Nguyen Thanh , Tung Luu Anh
Published: February 2025

This study applies Multi-Criteria Decision-Making (MCDM) methods to identify the optimal dressing parameters for the surface grinding of Hardox 500 steel. The investigation focuses on three key objectives: Surface Roughness (SR), Material Removal Rate (MRR), and Wheel lifespan (Lw). Five dressing variables were considered: non-feeding dressing (nn), fine dressing depth (df), fine dressing times (nf), rough dressing depth (dr), and rough dressing times (nr). Three MCDM methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Simple Additive Weighting (SAW), and Evaluation based on Distance from Average Solution (EDAS)—were employed to solve the MCDM problem. Additionally, the Entropy technique was used to determine the criterion weights. A total of 16 experimental runs were conducted based on the L16 (44 x 21) design configuration. The analysis identified Option 7 as the optimal dressing mode, characterized by the input parameters: dr = 0.02 mm, nr = 3 times, df = 0.05 mm, nf = 3 times, and nn = 0. To validate the consistency of rankings obtained from the three MCDM methods, the Spearman’s rank correlation coefficient (R) was employed. The results demonstrated a strong correlation among the rankings, confirming the reliability of the proposed approach. These findings provide a robust framework for optimizing surface grinding parameters to enhance performance and productivity.
Enhancing Network Access Control using Multi-Modal Biometric Authentication Framework
Authors: Mohammed Abdelnasser , Salama Ahmed , Shebka Nasser , Ismail Amr
Published: February 2025

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