bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluación del pensamiento probabilístico en futuros profesores de matemática en Educación Secundaria
Authors:   Universidad Católica del Maule, Chile, REYES-ASTORGA Miguel , DIAZ-LEVICOY Danilo ,   Universidad Católica del Maule, Chile, RODRIGUEZ-ALVEA Francisco
Published: February 2025

The objective of this study is to evaluate the interpretation of tasks related to probabilistic thinking of future teachers of Mathematics in Secondary Education. A qualitative and descriptive-level methodology was employed. The sample consisted of 26 future mathematics teachers who responded to a questionnaire composed of questions drawn from previous research. The study reveals an incomplete and inexperienced probabilistic thinking on the part of future Secondary Mathematics teachers, who tend to give greater relevance in their answers to non-probabilistic factors.
Enhancing Security in Wireless Sensor Networks: A Machine Learning-based DoS Attack Detection
Authors: Al Sukkar Ghadeer , Al-Sharaeh Saleh
Published: February 2025

The Internet of Things (IoT) is based on Wireless Sensor Networks (WSNs), which are essential for many applications. Denial of Service (DoS) attacks are a major risk for WSNs due to their open architecture and limited resources. This paper investigates how different Machine Learning (ML) methods can be used to identify DoS attacks and mitigate their effects. The predictions from several models were combined using the ensemble method to increase overall accuracy, while explainable Artificial Intelligence (AI) techniques were also deployed to enhance transparency and understanding. To compare the performance of both hard and soft ensemble methods, the WSN Dataset (WSN-DS) and the WSN Blackhole, Flooding, and Selective Forwarding (WSN-BFSF) dataset were utilized. The ensemble techniques aggregated predictions from multiple models to improve overall accuracy, while both showed high accuracy for both datasets. With an accuracy of 98.12%, the soft ensemble technique slightly outperformed the hard ensemble technique for the WSN-DS dataset, which had an accuracy of 97.97%. For the WSN-BFSF dataset, the hard ensemble technique achieved an accuracy of 99.967%, while the soft ensemble technique achieved an excellent accuracy of 100%.
Development of a Green Corrosion Inhibitor from Lophatherum Gracile Extract for Steel Protection
Authors: Haris Muhammad Imran , Agung . , Anjas Muhammad , Houston Geraldy Juniarto Billy , Qadry Nurul , Fakhruddin .
Published: February 2025

Corrosion constitutes a significant challenge in the context of infrastructure development, with particular implications for steel materials. One common method for preventing corrosion is the application of inhibitor materials. Inhibitors are classified into two main categories: organic inhibitors and inorganic inhibitors. Inorganic inhibitors are costly and may have adverse environmental effects. Consequently, organic inhibitors that are cost-effective and environmentally benign were developed. One plant that has the potential to be used as an organic inhibitor is Lophatherum gracile B. (Lophatherum gracile Brogn), due to its antioxidant compounds that can prevent corrosion. The objective of this research is to analyze the effect of the Lophatherum gracile B. extract inhibitor on the corrosion rate and its inhibition efficiency on reinforcing steel. The weight loss method was employed to determine the corrosion rate in a 3% sodium chloride (NaCl) medium, with concentration variations of 0%, 2%, and 4% over a duration of 24, 72, and 96 hours. The findings indicated that the lowest corrosion rate was observed at the 4% concentration, while the highest rate was noted at the 0% concentration. The inhibition efficiency of the Lophatherum gracile B. extract was determined to be greater than 66%. The qualitative analysis of the macro photo material structure indicated that the steel surface treated with Lophatherum gracile B. extract exhibited a reduced level of corrosion in comparison to the control sample. Furthermore, the tensile strength testing demonstrated that the decline in the tensile strength of steel could be attenuated through the use of inhibitors. These findings suggest that the Lophatherum gracile B. extract is an effective inhibitor material for reinforcing steel.
A Fog Computing and Blockchain-based Anonymous Authentication Scheme to Enhance Security in VANET Environments
Authors: Alzaidi Zahraa Sh. , Yassin Ali A. , Abduljabbar Zaid Ameen , Nyangaresi Vincent Omollo
Published: February 2025

Authentication of vehicles and users, integrity of exchanged messages, and privacy preservation are essential features in VANETs. VANETs are used to collect information on road conditions, vehicle location and speed, and traffic congestion data. The open exchange of information within VANETs poses serious security threats. Furthermore, existing schemes have higher communication and computational costs, making them incompatible with resource-constrained VANET applications. This study proposes a multifactor authentication and privacy-preserving security scheme for VANETs based on blockchain and fog computing to meet all these requirements. The proposed scheme uses fingerprints and Quick Response (QR) codes as a multifactor to authenticate vehicle users and fog-cloud computing techniques to reduce the computational burden on RSUs and improve service quality and resilience. Additionally, the scheme synchronizes a consistent ledger across all RSUs using blockchain technology to store and distribute vehicle authentication statuses. Through a thorough comparison with relevant current protocols, the scheme shows a much-reduced computing expense and communication burden in situations with high vehicle density within a timeframe of 6.3846 ms and 544 bytes for communication costs. In addition, the proposed scheme demonstrates a successful balance between efficacy and complexity, protecting confidentiality, anonymous authentication, and ensuring integrity and conditional tracking. Formal and informal security analysis showed that the proposed scheme is more reliable, practical, and secure against many hostile attacks, such as modification attacks, 51% attacks, Sybil attacks, and MITM attacks.
Building Information Modelling (BIM) as an Efficient Solution to Middle Eastern Construction Project Delays
Authors: Almayyahi Saba Jabbar Kadhuim , Ameen Raed Fawzi Mohammed , Al-Busaltan Shakir
Published: February 2025

The construction and building industry is considered one of the essential players in the economic sector that contributes significantly to creating jobs and generating financial resources. This study provides a review of the construction industry's capability available in the Middle Eastern countries, the development process, and its importance for the economic growth. It analyzes what causes construction projects to go over budget and behind schedule in an attempt to draw conclusions that can be applied to a broader body of research. Delays are costly in terms of time, money, quality, and safety. Some adverse outcomes of project delays are disputes between employer and contractor, lower revenue and productivity, and incomplete projects. This research investigates the reasons behind the construction setbacks in the Middle East and what can be done to rectify the situation. Moreover, it examines the Building Information Modeling (BIM) as a tool to reduce the construction delays. The BIM permits the digital construction of facilities before their physical structure, which aids in time management, reduces risks, improves security, and resolves problems.
Charpy Impact Test Result Comparison on Reinforcing Materials used in Continuous Filament 3D Printing
Authors: Molnar Balazs , Magai Robert
Published: February 2025

With the growing industrial demand for materials that can withstand dynamic loads, composite 3D printing, particularly utilizing continuous fiber reinforcements, presents a promising solution. This study investigates the toughness of three fiber-reinforced materials, namely carbon fiber, Kelvar, and fiberglass, by conducting Charpy impact tests. The results reveal that fiber-reinforced 3D materials significantly outperform standard 3D printed components, with fiberglass showing the highest toughness. These findings demonstrate that fiber-reinforced 3D printed materials offer a viable alternative for applications requiring high toughness and dynamic resistance.
Usage of Internet of Things in Iraqi Higher Education: An Extension of Information System Success Model
Authors: Hashim Hayder Salah , Bin Yusoff Yunus , Bin Hassan Zainuddin
Published: February 2025

The usage of Internet of things (IoT) in higher education is still emerging especially in developing countries. The purpose of this study is to examine the information and the system and service quality on the Usage of IoT (UIoT) among students and academic staff and non-academic staff. The study, based on Information System Success model (ISS), proposes that Information Quality (IQ), System Quality (SYSQ), and Service Quality (SQ) have a positive impact on UIoT. The research further proposes that IoT awareness acts as a moderator. The data were collected with a use of a questionnaire. Stratified random sampling was used and the data collected from a sample of 423 participants completed a process of validation and pilot testing. The data analysis was conducted using Smart PLS 4. The findings of the study indicate that SQ, IQ, and SYSQ do have positive effects on UIoT. IoT awareness moderated the effect of IQ only on UIoT. To increase the UIoT, it is advised to focus on enhancing the awareness about the IoT and provide reliable information.
An Enhanced Convolutional Neural Network (CNN) based P-EDR Mechanism for Diagnosis of Diabetic Retinopathy (DR) using Machine Learning
Authors: Hussain Munawar , Ahmed Hassan A. , Babar Muhammad Zeeshan , Ali Arshad , Shahzad H. M. , Rehman Saif ur , Khan Hamayun , Alshahrani Abdulaziz M.
Published: February 2025

This study focuses on Diabetic Retinopathy (DR), a disease caused by diabetes that affects the retina of the eye and eventually leads to blindness. Diabetes development progresses to retinopathy and must be addressed at an early stage for effective treatment. Currently, DR is classified as Non-Proliferative DR (NPDR) and Proliferative DR (PDR). This study proposes an Enhanced DR (P-EDR) method based on CNN using a high-resolution dataset benchmark of retinal images. Initially, the data were preprocessed by normalization, augmentation, and resizing to improve image quality and feature extraction. Evaluation was based on accuracy, specificity, sensitivity, and AUC-ROC. The proposed CNN-based P-EDR outperformed advanced ML strategies such as Support Vector Machine (SVM), Random Forest (RF), Probabilistic Neural network (PNN), and Gradient Boosting Machine (GBM) that were executed and compared to diagnose and classify DR. The proposed P-EDR extracts features such as a hemorrhage of the NPDR retina image to identify the disease using image processing for classification. P-EDR provides significant features from images in detection and classification, making it a successful model for diagnosing DR with improved accuracy of 93%, sensitivity of 92%, specificity of 94%, and AUC-ROC of 0.97%. These results highlight the potential of a P-EDR-based machine learning model to support ophthalmologists with the early and precise detection of DR, eventually helping with appropriate treatment and prevention of vision loss.
Effective Diabetes Prediction using an IoT-based Integrated Ensemble Machine Learning Framework
Authors: Rastogi Rashi , Bansal Mamta , Kumar Naveen , Singla Sanjay , Singla Priti , Jaswal Ram Avtar
Published: February 2025

Diabetes, a prevalent chronic disease, affects a significant global population. Identifying and being aware of key variables promptly may substantially enhance results for both patients and public health efforts. Systematic methods such as monitoring diabetic patients allow the collection of extensive data from diabetic patients. When it comes to keeping track of a patient's health, IoT sensors, such as those used in diabetic patient monitoring systems, are invaluable. Blood glucose levels, body temperature, and location of a diabetic patient can be tracked and recorded through a monitoring device. In addition to monitoring patients, these data can be classified using Machine Learning (ML) methods. This study applies three ML models to three different diabetes datasets and analyzes their performance. According to the results, the fine-tuned random forest model achieved higher accuracy, i.e., 89%, 90%, and 99%.
Mutual Information-based Feature Selection Strategy for Speech Emotion Recognition using Machine Learning Algorithms Combined with the Voting Rules Method
Authors: Roubhi Hamza , Hacine Gharbi Abdenour , Rouabah Khaled , Ravier Philippe
Published: February 2025

This study proposes a new approach to Speech Emotion Recognition (SER) that combines a Mutual Information (MI)-based feature selection strategy with simple machine learning classifiers such as K-Nearest Neighbor (KNN), Gaussian Mixture Model (GMM), and Support Vector Machine (SVM), along with a voting rule method. The main contributions of this approach are twofold. First, it significantly reduces the complexity of the SER system by addressing the curse of dimensionality by integrating a focused feature selection process, resulting in considerable savings in both computational time and memory usage. Second, it enhances classification accuracy by using selected features, demonstrating their effectiveness in improving the overall performance of the SER system. Experiments carried out on the EMODB dataset, using various feature descriptors, including Mel-frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), and Linear Prediction Cepstral Coefficients (LPCC), showed that the best performance was achieved by GMM, with an accuracy of 85.27% using 39 MFCC features, compared to an accuracy of 82.55% using a high-dimensional vector with 111 features. Furthermore, applying the Joint Mutual Information (JMI) selection technique to extracted MFCC features reduces the vector size by 23.07% while improving the accuracy to 86.82%. These results highlight the effectiveness of combining the feature selection process with machine learning algorithms and the voting rules method for the SER task.
Application of the RUSLE Modeling Tool for Quantification of Soil Erosion towards Sustainable Planning: The Case of Kosi River Basin, Bihar, India
Authors: Kumar Rakesh , Singh Jagdish
Published: February 2025

Soil erosion is a pressing global issue, affecting approximately 2.6 billion people across over 100 countries. It occurs from natural processes and human activities such as intensive agriculture and deforestation. In India, the National Bureau of Soil Survey and Land Use Planning estimates that around 146.8 million hectares of soil have been degraded. Preliminary analysis indicates an average soil erosion rate of 16.4 tons per hectare per year, leading to an annual loss of 5.3 billion tons nationwide. The Kosi River, which frequently shifts its course, exacerbates soil erosion issues in Northern Bihar. This study employs the Revised Universal Soil Equation (RUSLE) to estimate soil loss in the Kosi Basin, covering an area of 1,370,873.485 hectares, utilizing a 30-year rainfall dataset from the Indian Meteorological Department (IMD). Furthermore, various remote sensing data reveal that 0.20% of the area is at very high risk, while 65.88% is classified as having low to shallow risk for soil erosion. These results intend to guide regional planning and land use management in Bihar, emphasizing the importance of the soil erosion prevention model for effective environmental management.
Detecting Acute Lymphocytic Leukemia in Individual Blood Cell Smear Images
Authors: Baluabid Ruba , Alnasri Hadeel , Alowaybidi Rafaa , Hafiz Rawan , Alsini Areej , Alharbi Manal
Published: February 2025

Acute Lymphocytic Leukemia (ALL) is a form of blood cancer that mainly affects lymphocytes and white blood cells. The severity of this cancer varies and progresses quickly, requiring immediate and intensive treatment and making a quick and accurate diagnosis essential. This study presents a diagnostic model for the diagnosis of ALL using deep learning. YOLOv8 achieved 95% accuracy when trained on the C-NMC dataset and 94% when trained on the ALL-IDB2 dataset while maintaining generalization. YOLOv8 outperformed other models such as SVM, ResNet-50, a hybrid model that integrates ResNet-50 with the SVM classifier, and DenseNet121. YOLOv8, with its strong architecture, can efficiently extract intricate patterns from medical imaging data and diagnose ALL. The proposed model can potentially reduce pathologist workloads and improve patient diagnosis. This research contributes to the field by providing a reliable tool for automated leukemia detection, paving the way for further advances in medical image analysis.
Predictive Maintenance Algorithm of a 6DOF Robotic Arm using Gradient Boosting Regression
Authors: Villaverde Louie , Maneetham Dechrit , Crisnapati Padma Nyoman
Published: February 2025

The objective of this study is to develop a predictive maintenance algorithm for the ABB IRB 4600, a 6-axis robotic arm, using digital simulations. A variety of tests were conducted using SolidWorks, including calculations pertaining to stress, strain, fatigue, and heat. The simulations included an analysis of the materials used in the construction of the robotic arm, which are gray cast iron and aluminum alloy. The robotic arm was tested in three positions—picking, raised, and placing—with loads of 100 kg, 200 kg, and 300 kg, respectively. The findings indicated that elevated stress, strain, and displacement levels diminish the robot's operational lifespan and accelerate its deterioration over time. The placing position was found to experience the greatest stress, displacement, and strain. The fatigue test also demonstrated that after 10 million cycles, the arm had accumulated damage. The gradient boosting regression algorithm was selected as the Machine Learning (ML) algorithm for the study following a comparison of the performance of various ML regression models. This finding underscores the significance of predictive maintenance in preventing breakdowns and extending the robot's lifespan.
Explainable Machine Learning Algorithms to Predict Cardiovascular Strokes
Authors: Tasnim Afia Fairooz , Rahman Rukshanda , Prabha Mani , Hossain Md. Azad , Nilima Sadia Islam , Mahmud Md Abdullah Al , Erdei Timotei Istvan
Published: February 2025

Cardiovascular disease has been more common throughout the past several decades. Cardiovascular disease detection methods use machine learning algorithms to assess data and provide accurate cardiac diagnosis. An accurate and comprehensive assessment of cardiovascular risk is essential to improve cardiovascular protection and reduce the frequency and severity of heart attacks and strokes. This paper proposes a machine learning-based autonomous strategy for the diagnosis of cardiovascular disease. Some preprocessing methods were applied to improve the results and accuracy. Finally, lazy prediction was used to find the best model by applying a neural network and two ensemble models. The best accuracy of 99% was obtained with the HistGradientBoosting (ensemble) classifier, which obtained respectable results with a higher accuracy rate. This model can enhance the ability to predict cardiovascular disease with better accuracy.
Advancing Healthcare: A Comprehensive Review and Future Outlook of IoT Innovations
Authors: Khatoon Rabeya , Akter Jahanara , Kamruzzaman Md. , Rahman Rukshanda , Tasnim Afia Fairooz , Nilima Sadia Islam , Erdei Timotei Istvan
Published: February 2025

Rapid innovation leading to better patient outcomes have been driven by recent breakthroughs in the Internet of Things (IoT), which have drastically changed the healthcare sector. In order to highlight the importance of IoT in healthcare applications, this article presents a user-friendly and integrated approach for bibliometric analysis. Traditional bibliometric methods often rely solely on Web of Science (WoS) or Scopus, limiting the scope of analysis. To address this issue, the proposed approach uses the R program Bibliometrix to combine data from seven databases, namely Scopus, WoS, IEEE, ACM Digital Library, PubMed, Science Direct, and Google Scholar (GS). After having developed an inclusion/exclusion criterion, 2,990 journal papers published between 2011 and 2022 were subjected to a thorough literature review and bibliometric analysis. This study demonstrates that the healthcare industry is highly interested in the IoT, as well as the rapid growth of research into IoT healthcare applications, blockchain, Artificial Intelligence (AI), 5G telecoms, and data analytics. Authentication methods, fog computing, cloud-IoT integration, cognitive smart healthcare, and other essential topics are further examined by employing co-citation network analysis. In addition to illuminating potential avenues for further investigation, these results provide academics with a comprehensive picture of where IoT research in healthcare is standing at the moment. The output of the conducted analysis shows that there has been a dramatic uptick in publishing since 2017, with most of the articles appearing in prestigious journals related to computer science. By integrating data from multiple databases, the proposed methodology represents a significant advancement in bibliometric analysis, enabling a more comprehensive exploration of IoT's impact on healthcare, and facilitating a deeper understanding of the emerging trends and critical themes in this rapidly evolving field.
Efficient Liver Segmentation using Advanced 3D-DCNN Algorithm on CT Images
Authors: Subha S. , Kumaran U.
Published: February 2025

According to the latest global cancer statistics for 2022, liver cancer ranks as the ninth most common disease in women. Segmenting the liver and distinguishing it from tumors within it pose a significant challenge due to the complex nature of liver imaging. Common imaging methods such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), and Ultrasound (US) are employed to distinguish liver tissue from liver tumors after collecting a sample. Attempting to partition the liver and tumor based on grayscale shades or shapes in abdominal CT images is not ideal because of the overlapping intensity levels and the variability in the location and shape of soft tissues. To address this issue, this study introduces an effective method for liver image segmentation using a 3D deep Convolutional Neural Network (3D-DCNN). The process involves several stages. First, liver images undergo preprocessing to enhance image quality, including median filtering, adaptive filtering, and converting them to grayscale. The feature extraction phase focuses on extracting four sets of features, such as the Local Binary Pattern (LBP) and the Gray-Level Co-occurrence Matrix (GLCM). Additionally, an Iterative Region Growing (IRG) technique is developed to improve the Dice Similarity Coefficient (DSC) prediction by enhancing the quality of the input images obtained from segmented images. This method enables the segmentation of the liver in abdominal CT image volumes and can subsequently be used to segment liver tumor images to evaluate the performance of the proposed 3D-DLNN approach. This method was implemented in MATLAB, and its performance was evaluated using various metrics. In experimental analysis, the proposed technique outperformed other methods, including Jaccard with JISTS-FCM, Fuzzy C-Means (FCM), and FCM with Cluster Size Adjustment (FCM-CSA).
Effects of Rubber Particle Size and Substitution Rate on the Behavior of Eco-Friendly Rubberized Concrete
Authors: Najeeb AlMousawi Sayed , Alsalameen Hussain , Khalaf Jasem , Abdelsamie Omar , Bohaimd Abdulrahman , Saliba Jacqueline
Published: February 2025

One of the world's largest tire graveyards is located in the Al-Salmi area of Kuwait, where over 42 million discarded waste rubber tires have been accumulated over a time period of 17 years. This study aims to develop sustainable, cost-effective building materials for the construction industry, utilizing waste rubber as a partial substitute for fine and coarse aggregates in concrete mixtures. Three types of untreated rubber particles were used: powder rubber (P) with a diameter between 0.4 and 0.6 mm, crumb rubber (CR) with a diameter between 0.6 and 2 mm, and 2.6 and 3.5 mm respectively, and rubber chips (CH) with a diameter ranging between 2 and 18 mm. Fine aggregates were replaced by P and CR, while coarse aggregates were replaced by CH, at a substitution rate of 10 and 20% by volume. The impact of rubber particles on workability was assessed on fresh rubberized concrete, while the compressive strength was evaluated at 7, 14, and 28 days. Microstructural analysis using Scanning Electron Microscopy (SEM) was also conducted to collate the macroscopic behavior with internal structural changes. The results showed that increasing the rubber content and particle size led to reductions in workability and compressive strength. Large rubber particles, particularly chips, caused gaps and microcracks in the matrix, exhibiting poor adhesion at the Interfacial Transition Zone (ITZ). These findings demonstrate the potential of rubberized concrete as an eco-friendly alternative, with optimization needed for practical applications.
Leveraging Community-based Approaches for Enhancing Resource Allocation in Fog Computing Environment
Authors: Ghalwah Alasef M. , Al-Sultany Ghaidaa A.
Published: February 2025

Efficient resource allocation in fog computing environments is essential to address the increasing demand for high-performance and adaptable network services. Traditional methods lack granular differentiation based on traffic characteristics often resulting in suboptimal bandwidth utilization and elevated latency. To enhance network efficiency, this study applies a community-based resource allocation approach leveraging the Louvain algorithm to dynamically cluster network nodes with similar traffic demands. By forming communities based on bandwidth and latency needs, this approach enables a targeted resource distribution, aligning each community with optimized pathways that address specific requirements. The results indicate notable performance gains, including a 14% increase in bandwidth utilization affecting the download and a reduction in latency by an average of 23% for time-sensitive applications. These improvements highlight the effectiveness of the proposed approach in managing diverse network demands, improving data flow stability, and enhancing the overall performance of fog computing infrastructures. These findings underscore the potential for community-based resource allocation to support scalable, adaptable, and secure resource management, positioning it as a viable solution to meet the complex needs of IoT and other distributed network systems.
Integration of Deep Learning with Fox Optimization Algorithm for Early Detection and Classification of Tomato Leaf and Fruit Diseases
Authors: Sundaramoorthi K. , Kamarasan Mari
Published: February 2025

Tomato is a common vegetable crop extensively cultivated in the farming lands in India. The hot climate of India is perfect for its development, but particular weather conditions along with many other aspects affect the growing of tomato plants. Apart from these natural disasters and weather conditions, plant diseases consist a major issue in crop production. Precisely classifying leaf and fruit diseases in tomato plants is a vital step toward computerizing processes. Traditional disease detection models for tomato crops often fall short in predictability. To address this, Machine Learning (ML) and Deep Learning (DL) models have been developed, presenting advanced classification capabilities and the ability to manage the vast variability in agricultural data that conventional computer vision models struggle with. This work presents an Integration of DL with Fox Optimization Algorithm (FOA) for the Recognition and Classification of Tomato Leaf and Fruit Diseases (IDLFOA-DCTLFD). The major objective of the proposed IDLFOA-DCTLFD model is to enhance the detection and classification outcomes of tomato leaf and fruit diseases. At the initial stage, the Median Filter (MF) model is used for pre-processing and the Efficient Channel Attention-SqueezeNet (ECA-SqueezeNet) model is employed for feature extraction. For the hyperparameter tuning process, the proposed IDLFOA-DCTLFD technique implements the FOA. Finally, a Wasserstein Generative Adversarial Network (WGAN) is utilized for the detection of tomato leaf and fruit diseases. The IDLFOA-DCTLFD method is experimentally examined in a tomato leaf and fruit dataset. The experimental validation of the IDLFOA-DCTLFD methodology portrayed a superior accuracy value of 98.02%, surpassing the existing techniques.
Multi-Objective Optimization of a Two-Stage Helical Gearbox with Two Gear Sets in First Stage to Reduce Volume and Enhance Efficiency using the EAMR Technique
Authors: Hung Tran Quoc , Binh Vu Duc , Thanh Dinh Van , Tung Lưu Anh , Tuan Nguyen Khac
Published: February 2025

This study presents the results of a work employing the Evaluation by an Area-based Method of Ranking (EAMR) methodology to address the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox comprising two gear sets in the initial stage. The objective of this study is to identify the most critical design parameters for minimizing the volume of the gearbox while optimizing its efficiency. In this study, three key design parameters were selected for analysis: the wheel face width coefficients Xba for the first and second stages, as well as the gear ratio of the first stage u1. Furthermore, the EAMR technique was employed to address the Multi-Criteria Decision-Making (MCDM) challenge, with the entropy method used to ascertain the weight criterion for resolving the MOOP. The study's findings offer valuable insights into the optimal values for three primary design parameters, which are essential for the development of a two-stage helical gearbox with two gear sets in the initial stage.
Investigation of the Radar Cross-Section and its Optimization Potential for ADAS Tests
Authors: Magai Robert , Molnar Balazs , Simon Norbert , Pekk Leticia
Published: February 2025

The objective of this study is to examine the Radar Cross Section (RCS) of instruments designed for Autonomous Driving Systems (ADAS) testing, with the intention of comparing the results to those of actual human subjects. The RCS values of both dummy and platform objects were documented at varying distances and positions, with the objective of ascertaining the extent to which dummies can serve as substitutes for human values in vehicle radar sensing tests. The findings, substantiated by graphical representations and statistical analyses (e.g., Pearson and Spearman correlation), reveal a moderately strong positive correlation between the RCS and human values, which is statistically significant. The outcomes of the tests demonstrate that the developed instruments can substitute for real human radar cross-section values within the range of 5-15 m. However, as the distance increases, larger deviations are observed. These discrepancies underscore the necessity for a refinement of the dummy design in future ADAS tests, ensuring that distance-sensitive tests accurately reflect real human measurements.
Assessing Factors Impacting Electric Vehicle Adoption in Saudi Arabia: Insights on Willingness to Pay, Environmental Awareness, and Perceived Risk
Authors: Toukabri Maher , Boutaleb Brahim
Published: February 2025

As Saudi Arabia seeks to transition toward sustainable energy, the adoption of Electric Vehicles (EVs) is a key component in reducing carbon emissions and combating climate change. This study explores the factors driving EV adoption, focusing on Willingness To Pay (WTP), Environmental Awareness (EA), Perceived Risks (PR), and Product Attributes (PA). Using a structured survey distributed to 365 respondents, the obtained data were analyzed through the SPSS 27 software, employing regression analysis and factor analysis. The results reveal that WTP and EA are significant predictors of Perceived Value (PV), which, in turn, positively influences consumers’ intention to purchase EVs. Conversely, PR negatively impacts Purchase Intention (PI), though these risks are mitigated by favorable PA. The findings highlight a gap between consumer interest in EVs and the existing infrastructure, suggesting that addressing these concerns is crucial for widespread EV adoption in Saudi Arabia. These insights provide actionable recommendations for policymakers and businesses aiming to enhance consumer confidence and facilitate the growth of the EV market in the region.
Application of the PSI Method in Selecting Sustainable Energy Development Technologies
Authors: Dua Tran Van
Published: February 2025

The development of renewable energy is not only an urgent solution for addressing climate change but also a driving force for sustainable economic growth. The transition to clean, inexhaustible energy sources not only helps to reduce greenhouse gas emissions and protect the environment but also ensures national energy security, creates employment opportunities, and enhances the quality of life for individuals. Presently, various technologies exist for sustainable energy development, each characterized by multiple criteria, complicating the evaluation of their performance. This study presents a straightforward method for identifying the best option among eight sustainable energy development alternatives: hydropower, geothermal, biomass, wind, solar, concentrated solar power, coal technology, and oil-fired power plants, each of which is characterized by 17 distinct criteria. The simple method utilized is the Preference Selection Index (PSI) method, which eliminates the need for criteria weighting. This absence of criteria weight calculation in the PSI method distinguishes it from other ranking techniques that typically require such calculations. Therefore, the PSI method significantly simplifies the comparison of the available options compared to other ranking methods, as it bypasses the need for criteria weight calculations. The optimal option identified through the PSI method was also compared with the optimal option identified using 6 other methods: Multi Atributive Ideal Real Com parative Analysis (MAIRCA), Evaluation Based on Distance from Average Solution (EDAS), COmplex PRroportional ASsessment (COPRAPS), Multiobjective Optimization On the basis of Ratio Analysis (MOORA), Proximity Indexed Value (PIV), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Notably, all employed methods consistently identified geothermal energy as the optimal choice.
Model-Free Swing-Up and Balance Control of a Rotary Inverted Pendulum using the TD3 Algorithm: Simulation and Experiments
Authors: Ho Trong-Nguyen , Nguyen Van-Dong-Hai
Published: February 2025

The Rotary Inverted Pendulum (RIP) system is a highly nonlinear and under-actuated mechanical system, which presents significant challenges for traditional control techniques. In recent years, Reinforcement Learning (RL) has emerged as a prominent nonlinear control technique, demonstrating efficacy in regulating systems exhibiting intricate dynamics and pronounced nonlinearity. This research presents a novel approach to the swing-up and balance control of the RIP system, employing a RL algorithm, Twin Delayed (TD3) Deep Deterministic Policy Gradient (DDPG), obviating the necessity for a predefined mathematical model. The physical model of the RIP was designed in SolidWorks and subsequently transferred to MATLAB Simscape and Simulink for the purpose of training the RL agent. The system was successfully trained to perform both swing-up and balance control using a single algorithm for both tasks, representing a significant innovation that eliminates the need for two or more separate algorithms. Additionally, the trained agent was successfully deployed onto an experimental model, with the results demonstrating the feasibility and effectiveness of the model-free TD3 approach in controlling under-actuated mechanical systems with complex dynamics, such as the RIP. Furthermore, the results highlight the sim-to-real transfer capability of this method.
Evaluating the Impact of Aggregate Size and Sediment Type on Clogging and Permeability of Pervious Concrete
Authors: Prahara Eduardi , Suwondo Riza , Christopher Christopher
Published: February 2025

Pervious Concrete (PC) is increasingly being used in sustainable drainage systems owing to its ability to manage stormwater. However, sediment accumulation can clog pores and reduce their permeability. This study investigates the effect of various sediment types, sand, clay, gravel, and oil, on the permeability of pervious concrete samples with three different aggregate size ranges: 12.5–19.1 mm, 9.5–12.5 mm, and 4.75–9.5 mm. The concrete samples were subjected to sediment loads ranging from 0 g to 100 g, and the permeability was measured after each sediment addition. The objective was to assess the impact of sediment type and quantity on permeability reduction, and to evaluate the role of aggregate size in resisting clogging. The results demonstrate that larger aggregates maintained higher permeability and were less affected by sediment accumulation, whereas smaller aggregates experienced significant clogging and rapid permeability loss. Oil had the least impact on the permeability, whereas gravel and sand caused the greatest reduction. Permeability stabilisation occurred after sediment accumulation reached 60–70 g for all samples. These findings highlight the importance of aggregate size selection in pervious concrete designs to enhance long-term performance and resistance to clogging. Larger aggregates from 12.5 to 19.1 mm were shown to be the most effective in maintaining permeability, even under sediment load.
Sustainable Design of Raft Foundations: Analyzing Embodied Carbon and Cost Impacts
Authors: Suwondo Riza , Vincensia Natalia , Juliastuti Juliastuti , Razak Habibie
Published: February 2025

The construction industry is a significant contributor to global carbon emissions, necessitating the adoption of sustainable design practices. This study investigates the embodied carbon and cost implications of raft foundations, focusing on the effects of different concrete grades, K300, K400, and K500, and slab thicknesses. A comprehensive methodology, guided by BS EN 15978, was employed to assess the carbon emissions across the product, construction, and end-of-life stages. Additionally, a cost analysis was conducted, reflecting typical construction expenses relevant to the Indonesian context. The findings revealed that increasing the concrete grade consistently leads to higher embodied carbon and costs, with K300 demonstrating the lowest values across all thicknesses. Moreover, thicker slabs exacerbate both the environmental and financial impacts, highlighting the trade-offs inherent in material selection and design choices. The study concludes that a strategic balance between structural requirements, cost efficiency, and environmental sustainability can be achieved by utilizing lower-grade concrete, where high strength is not essential. These insights contribute to the discourse on sustainable construction practices, advocating for informed decision-making in raft foundation design to minimize the carbon footprint while maintaining economic viability.
Seismic Fragility Assessment of Base-Isolated Nuclear Power Plant Structures
Authors: Nguyen Trong-Ha , Nguyen Duy-Duan
Published: February 2025

Base isolators constitute solutions for improving the seismic performance of civil and nuclear engineering structures. This paper evaluates the seismic fragility of based-isolated nuclear power plant structures using the proposed fragility curves. A finite element model of the structures is developed deploying SAP2000, a structural analysis program. For constructing fragility curves, a set of ground motions is employed to perform nonlinear time-history analyses associated with Incremental Dynamic Analyses (IDA). Three Damage States (DS) are defined based on the shear deformation of base isolators. Finally, the maximum likelihood estimation technique generates a set of fragility curves for DS. Additionally, a comparison of fragility curves between IDA and Cloud Analysis (CA) is presented.
Challenges and Opportunities for Building Information Modeling in Facility Management: A Case Study from Egypt
Authors: Ezz Mohamed Salah
Published: February 2025

The objective of this study is to evaluate the obstacles encountered when using Building Information Modeling (BIM) in Facility Management (FM) within the context of Egypt. The research methodology employs a case study approach, using a single case study to investigate the phenomenon of interest. A comprehensive literature review was conducted, resulting in the identification of 42 challenges to BIM usage in FM. These challenges were classified into five primary groups and formed the basis for a five-point Likert scale questionnaire, which was utilized to collect insights from FM professionals in Egypt. The survey participants included facilities and maintenance managers, as well as BIM employees. The data collected were also analyzed deploying the Impact Effect Index (EI) method. Furthermore, the EI findings indicated that the primary difficulties were the integration of building system design with BIM, the establishment of handover requirements, the integration specifications between the FM and BIM, and securing accurate and reliable data. The category with the highest EI was challenges related to BIM implementation in FM. The research identifies the significant challenges affecting BIM adoption in FM in Egypt, thereby promoting the development of BIM implementation strategies. Consequently, the findings hold practical importance for various stakeholders within the construction sector in Egypt.
Effect of Powdered Nano-Local Banded Iron Formation (BIF) Rock on Some Mechanical Properties of Cementitious Concrete
Authors: Osman Khaled M. , Elyamany Magdy A. , Elfakharany Maged E. , Mostafa Sayed S.
Published: February 2025

Banded Iron Formation (BIF) rocks are a significant source of iron ore, and they can also be used in the production of cementitious materials. However, the BIFs of the Egyptian Eastern Desert (ED) are not currently employed in steel iron manufacturing due to their elevated silica content and the technical challenges and high cost associated with extracting iron ore from them. Furthermore, the incorporation of nano-sized particles of BIF into cementitious mortar may impart specific characteristics that could enhance mechanical strength and durability, or even contribute to sustainability. This study examines the impact of nano- and powder-based materials derived from locally sourced BIF rocks on the properties of cementitious concrete when used as partial replacements for Ordinary Portland Cement (OPC). A comprehensive evaluation was conducted on concrete mixtures with varying cement replacement ratios, 1, 2, 3, and 4%, to assess the impact on key mechanical properties at different curing ages, 7, 28, and 90 days. The concrete samples exhibited significant enhancements in mechanical properties at all curing periods. The 2% Nano-BIF replacement yielded the most notable increase. Furthermore, X-Ray Diffraction (XRD) and Transmission Electron Microscopy (TEM) analysis demonstrated that the Interfacial Transition Zone (ITZ) between the cement paste and aggregates exhibited a robust compacted bond, indicating that local nano-BIFs have the potential to serve as an effective additive for enhancing the mechanical properties of cementitious concrete.
Behavior and Strength Prediction of Concrete Beams reinforced with GFRP Bars and subjected to High Temperature
Authors: Saeed Maan Hatam , Al-Ahmed Ali Hussein Ali
Published: February 2025

The behavior and strength prediction of concrete beams reinforced with Glass Fiber-Reinforced Polymer (GFRP) bars at high-temperature conditions are examined in this work. Twelve beams burnt at 500°C and 700°C were reviewed as part of the experimental methods, and were contrasted with four more unburned beams. The parameters chosen in this study consist of the type of main bar material, protection type against fire, concrete cover thickness, and the burning temperature. The experimental results indicate that the stiffness of all samples diminishes with rising burning temperatures. This is attributable to the degradation of concrete during the fire being exposed to, leading to an increase in beam deflection under the same load. The plastering of 1 cm was better than the fire-resistant dye as a form of protection against burning, while All beams experienced flexural failure.
Solving Multi-Criteria Shortest Path by Optimization with Morphological Filters
Authors: Kateb Hachemi Amar Amar , Tahraoui Mohammed Amin , Belmadani Abderrahim
Published: February 2025

The challenge of determining the shortest path within a multimodal transportation network involves identifying the most efficient travel route while considering various interconnected modes of transportation, such as roads, railways, and public transit. This problem becomes increasingly complex when numerous criteria and modes are involved, complicating the decision-making process. This study proposes a novel approach to computing the shortest path in multimodal networks, focusing on four modes of transportation: metro, trams, buses, and taxis. The optimization criteria include distance, travel time, and monetary cost. The proposed method utilizes a new metaheuristic called Optimization by Morphological Filters (OMF), inspired by image processing techniques. This approach was compared with the Genetic Algorithms (GA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Experiments were carried out using graph models of multimodal transport networks that closely resemble real-world scenarios varying in size. Furthermore, the proposed method was evaluated using a real network from the city of Lyon, France. The results demonstrate that the OMF approach performs well in terms of convergence to optimal solutions and computation time.
Mechanical Strength Optimization of HDPE Biocomposite with Water Hyacinth Fiber Reinforcement using a Dispersing Agent
Authors: Dewi Kusuma , Raharjo Wijang Wisnu , Kusharjanta Bambang
Published: February 2025

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