bluuebunny/crossref_metadata_2025 huggingface dataset:
The Flexural Behavior of One-Way Concrete Bubbled Slabs Reinforced by GFRP-Bars with Embedded Steel I-Sections
Authors: Abdulkhaliq Mohannad , Al-Ahmed Ali Hussein
Published: August 2024

This study examines the behavior of polymer bubbled deck slab systems, one-way concrete slabs with polymer sphere voids reinforced with Glass Fiber-Reinforced Polymer (GFRP) rebars, and embedded I-shaped steel beams. Six one-way structural concrete slabs (2600 mm long, 600 mm wide, 150 mm deep) were tested and directly supported under two points bending. Five Bubbled Slabs (BS), one of which was un-strengthened, were compared to the reference Solid Slab (SS) without polymer spheres. Each slab had 95 polymer sphere voids of 90 mm diameter and 15.48% self-weight decrease. Several parameters, including specimen type (SS or BS) and internal strengthening, were optimized using steel I-shapes in two distinct forms (2 and 4 pcs. of steel I-sections). Channel Shear Connectors (CSCs) and bent-up steel bars (10 mm in diameter) were implemented to increase shear resistance, with the 4I-section form having a cross-sectional area equivalent to the 2I-section form. In contrast to the SS, the BS exhibited a wider range of deformations during the same loading stage, with ultimate load capacity decreasing by 30% and deflection occurring at a greater ratio of approximately 18% to 85%. Additionally, the embedded steel I-shapes improved specimen performance compared to BS and SS. This occurred by reducing deflection at a service load by 60% and 49%, eliminating cracks, improving ultimate load capacity by 85% and 30%, and enhancing flexural stiffness by 102% and 71%, respectively, at the ultimate loading stage. CSC increased ultimate load by 13% to 22% and deflection by 8% to 15%, compared to specimens without CSC.
Evaluating Surface Water Quality of Euphrates River in Al-Najaf Al-Ashraf, Iraq with Water Quality Index (WQI)
Authors: Al Sharifi Suhair Razzaq , Zwain Hanadi H. , Hasan Zinah K.
Published: August 2024

The present study illustrates the rapid pollution of Euphrates River, inwards Al-Najaf Al-Ashraf governance (Al-Kufa River) in Iraq, which is one of the most important rivers in the region. The river faces formidable pressure due to encroachments, discharge of untreated domestic and industrial waste, drainage water from cultivated orchards, and dumping of solid waste. This study was conducted to assess the temporary and locative organic pollution in the region. Water Quality Index (WQI) of the Al-Kufa river was determined according to the Canadian Council of Ministers of Environment (CCME) method by calculating seven parameters (water temperature, DO, COD, EC, TDS, turbidity, and pH) in two sampling stations. Surface water samples were collected in a period of four months (September to December) in 2023. WQI level, correlation analysis between parameters, and Iraqi and CCME drinking water specification standards were employed to classify the surface water status. The results show that the ecological condition can be classified as marginal with WQI= 49 and poor with WQI= 42 in stations 1 and 2, respectively.
The Effect of Technological Innovation and Knowledge Management Process on Organisational Agility: A Systematic Literature Review
Authors: Alkaabi Saleh Mohammed Yousef Obaid , Sohaimi Nor Suzylah Binti , Bin Yatiban Aminurraasyid
Published: August 2024

Organizational agility has become essential and its importance has increased after COVID-19. There are inconsistent findings regarding the factors that affect organizational agility. This study focuses on the effect of technological innovation and the knowledge management process by reviewing the literature related to these variables. Three databases, Scopus, Web of Science (WoS), and Google Scholar, were used using certain search keywords, and a total of 30 articles were identified between 2010 and 2022 and reviewed. The findings showed that the number of articles has increased sharply during and after the COVID-19 pandemic. However, the use of theories to explain organizational agility is still emerging, with the resource-based view, the dynamic capability, and the knowledge-based view being the most used theories. The sample size is increasing to meet the structural equation modeling requirements. The effect of technological innovation and the knowledge management process is positive in most studies. More studies are needed to examine organizational agility as a dependent variable in different countries, contexts, and industries. In addition, future studies should examine other moderating variables in this context.
Analysis of Rooftop Solar Power Development in Northwest Vietnam using the Analytic Hierarchy Process
Authors: Le Ngo Phuong , Giap Giap Ngoc , Khanh Nguyen Binh , Trung Bui Tien , Phap Vu Minh , Phi Dai Hung
Published: August 2024

Traditional energy sources are gradually being exhausted, which causes many negative effects on the environment and contributes to the climate change. Countries around the world, including Vietnam, are focusing on developing renewable energy sources, such as solar power, to combat climate change and create a foundation for sustainable development. The development of solar power, particularly rooftop solar power, is being encouraged in various provinces and cities in Vietnam. The Northwest is a mountainous region in the Northern part of Vietnam with considerable solar energy potential. However, developing rooftop solar power projects requires balancing economic, technical, and environmental goals. Currently, there is no comprehensive research in Vietnam that fully evaluates the sustainable development goals for rooftop solar power in the Northwest region. This paper focuses on identifying the factors that influence the decision to install and use rooftop solar power in the Northwest region of Vietnam with the support of the Analytic Hierarchy Process (AHP) method and Expert Choice software. This impact ranges to varying degrees. The most significant influencing factor is the solar energy development policy, with a priority of 36.1%, while the social factor has the lowest priority value of 7.7%. The primary factor affecting people's decisions is the future solar power development policy, with a weight value of 24.2.
A Review of the Surface Roughness Prediction Methods in Finishing Machining
Authors: Trinh Van-Long
Published: August 2024

The desired Surface Roughness (SR) can be achieved via general machining methods by using a cutting tool to remove a material layer on the workpiece surface. Cutting Parameters (CP), cutting tool properties, and workpiece properties must be considered. The finishing machining methods that can be applied to produce the desired SR are turning, milling, grinding, boring, and polishing. The technological parameters must be tightly combined in the Machining Process (MP). The CP selection presents some issues regarding time, cost, and practical skill when considering different cutting methods, cutting tools, and workpiece materials. SR predicting methods of machined parts have the advantages of shortening the time of CP selection, reducing machining cost, and bringing the desired SR. This paper reviews the recent methods followed in predicting the SR of the MPs. The SR prediction methods will bring many benefits for MP, such as improved SR, reduced cost, improved cutting conditions, and enhanced quality.
Enhanced Chaos Game Optimization for Multilevel Image Thresholding through Fitness Distance Balance Mechanism
Authors: Ben Miled Achraf , Elhossiny Mohammed Ahmed , Elghazawy Marwa Anwar Ibrahim , Mahmoud Ashraf F. A. , Abdalla Faroug A.
Published: August 2024

This study proposes a method to enhance the Chaos Game Optimization (CGO) algorithm for efficient multilevel image thresholding by incorporating a fitness distance balance mechanism. Multilevel thresholding is essential for detailed image segmentation in digital image processing, particularly in environments with complex image characteristics. This improved CGO algorithm adopts a hybrid metaheuristic framework that effectively addresses the challenges of premature convergence and the exploration-exploitation balance, typical of traditional thresholding methods. By integrating mechanisms that balance fitness and spatial diversity, the proposed algorithm achieves improved segmentation accuracy and computational efficiency. This approach was validated through extensive experiments on benchmark datasets, comparing favorably against existing state-of-the-art methods.
Leukemia Diagnosis using Machine Learning Classifiers based on MRMR Feature Selection
Authors: Hameed Sipan M. , Ahmed Walat A. , Othman Masood A.
Published: August 2024

Early and accurate diagnosis of leukemia is crucial for effective treatment. Machine Learning (ML) offers promising tools for leukemia diagnosis classification, but the required high-dimensional datasets pose challenges. This study explores the effectiveness of ML algorithms for leukemia disease classification and investigates the impact of feature selection with the Minimum Redundancy Maximum Relevance (MRMR ) technique. MRMR was implemented to select informative features and evaluate four ML algorithms (Naïve Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs)) using feature subsets with varying levels of relevance based on MRMR scores. Our results demonstrate that MRMR effectively reduced dimensionality while maintaining and even improving classification accuracy. KNN and SVM achieved the highest accuracy (100% for 67, 30, and 24 feature subsets), suggesting the benefit of focusing on highly relevant features. NB exhibited consistent accuracy across all feature sets.
Evaluating Nine Machine Learning Algorithms for GaN HEMT Small Signal Behavioral Modeling through K-fold Cross-Validation
Authors: Ahmad Neda , Nath Vandana
Published: August 2024

This paper presents an investigation into the modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) using multiple Machine Learning (ML) algorithms. Despite the documented use of various ML techniques, a thorough comparison and performance analysis under different operating conditions were lacking. This study fills this gap by conducting a rigorous evaluation of nine ML models using TCAD-generated data of Pseudomorphic AlGaN/InGaN/GaN HEMT. The research focuses on Small Signal Behavioral Modeling and examines regression techniques such as Multiple Linear Regression (MLR), Multivariate Linear Regression (MVLR), Ridge Regression (L2), Lasso Regression (L1), Elastic Net Regression (ENR), Decision Trees (DT), Random Forest (RF), Gradient Boosting Regression(GBR), and Support Vector Regression (SVR). These methods use biases, frequency, and device geometry as independent variables, with S-parameters being the dependent variables. K-fold cross-validation was employed to ensure model reliability and accuracy across diverse operating conditions. Results reveal that the RF, coupled with 10-fold cross-validation, exhibits superior performance giving 99.7% accurate results, with a Mean Squared Error (MSE) of 4.6375×10-5, and a coefficient of determination (R2) of 0.9977. Conversely, SVR, L1, and ENR fall short of expectations. This study underscores the significance of methodological advancements in ML-based GaN HEMT modeling and provides valuable insights for future research in this domain.
Design and Implementation of an IoT-Integrated Smart Locker System utilizing Facial Recognition Technology
Authors: Alzhrani Abdulrahman A. , Balfaqih Mohammed , Alsenani Fadi , Alharthi Mohemmed , Alshehri Ali , Balfagih Zain
Published: August 2024

The Internet of Things (IoT) has been widely employed in the development of smart locker systems over the last decade. However, some of these systems are based on authentication methods which lack flexibility. Such systems did not consider the possibility that an authentication method could be unavailable for different reasons, namely access card loss, camera or mice break, etc. Moreover, such systems do not consider dual-authentication methods that enhance security. This paper aims to develop a smart locker system that considers several authentication methods including dual authentication (phone number and One Time Password (OTP)), fingerprint, face recognition, and emergency code utilizing IoT technology. Dual authentication method is the considered base authentication method. The system has been fabricated and evaluated taking into account different scenarios including monitoring door status, ensuring access for authorized users, and denying access to unauthorized users.
Enhancing the Structural Integrity and Performance of an Agricultural Robot with Caterpillar Tracks: A Comprehensive Deformation Analysis
Authors: Kappagantula Sivayazi , Mannayee Giriraj , Veerendra Arigela Satya , Dutta Soham , Flah Aymen
Published: August 2024

The robustness and longevity of agricultural robots, specifically those utilizing caterpillar tracks for coconut harvesting, are based on understanding their strain, stress, and load thresholds. This study delves into the deformation characteristics of caterpillar track systems, pinpointing critical structural vulnerabilities and potential points of failure. Through a meticulous analysis of the maximum allowed strain and stress thresholds, this study unravels crucial insights to enhance performance and reliability in coconut field operations. Leveraging the power of ANSYS for structural analysis and simulation under varied constraints, this study aims to fortify the structural integrity of agricultural robots. By offering valuable insights and solutions, this study paves the way for advancements in agricultural robotics technology, ensuring that these machines can endure rigorous tasks while maintaining peak functionality.
Stance Detection in Hinglish Data using the BART-large-MNLI Integration Model
Authors: Giddaluru Somasekhar , Maturi Sreerama Murty , Ooruchintala Obulesu , Munirathnam Mahendra
Published: August 2024

Real-time stance detection can be used in a wide range of applications like debate analysis, sentiment analysis, system feedback, etc. This study focuses on stance detection in political speeches, discerning whether the speaker is in favor, against, neutral, or lacking any stance on a given topic. The problem with this type of speeches dwells in the modification of the existing methods of stance detection to allow for the fine distinctions of Hinglish, a language mixture blending Hindi and English, as conveyed in human-edited texts. The proposed method utilizes the Bidirectional Auto-Regressive Transformers Multi-Genre Natural Language Inference (BART-large-MNLI) model with zero-Shot, few-Shot and N-Shot learning approaches. The proposed model is compared with the existing models of stance detection on Hinglish texts. For the pre-trained BART-based models, a limited number of labeled examples are utilized to determine the labels of test instances. For the other models, the train-test split method is adopted to get accurate results. The results indicate that the model surpasses the previous models.
A Privacy Recommending Data Processing Model for Internet of Vehicles (IoV) Services
Authors: Alqarni Ali
Published: August 2024

The Internet of Vehicles (IoV) faces security challenges in maintaining privacy due to the presence of open networks and diverse services. Ensuring privacy is essential in transportation networks to provide users with a long-lasting driving, navigation, and communication experience. In this paper, the proposed Privacy Recommending Data Processing Model (PRDPM) is deployed to handle the huge amount of data accumulated in this field. The proposed model adopts data processing techniques that are dependent on user demand and are influenced by either neighboring entities or service providers. The various application requirements are analyzed to minimize the potential privacy consequences. The data from various intervals are utilized to validate the parameters in the operational plane. Thus, data balancing is performed using plane differentiation to prevent privacy leaks in either of the vehicular services. This is useful for neighbors and infrastructures across various applications/users.
The Influence of Printing Materials on Shrinkage Characterization in Metal 3D Printing using Material Extrusion Technology
Authors: Tran Thi Van Nga , Long Dang Cao , Van Cuong Nguyen
Published: August 2024

This study investigates the shrinkage characteristics of various materials in metal 3D printing using Material Extrusion (ME) technology. The materials examined include 17-4PH Stainless Steel V1 and V2, Inconel 625, H13 Tool Steel V1, and A2 Tool Steel. Experiments reveal that shrinkage rates vary significantly among these materials, with 17-4PH Stainless Steel V1 exhibiting the lowest average shrinkage rate of 16.2%, while Inconel 625 shows the highest average shrinkage rate of 24.5%. These findings are critical for improving dimensional accuracy in metal 3D printing. Additionally, results demonstrate that print orientation affects shrinkage. The analysis of product accuracy reveals inconsistencies between printed dimensions and design specifications, likely influenced by printing parameters. The conclusion underscores the importance of selecting appropriate printing materials and optimizing parameters to ensure dimensional accuracy in 3D printed products.
Comparative Assessment of Fraudulent Financial Transactions using the Machine Learning Algorithms Decision Tree, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, and Random Forest
Authors: Manorom Paiboon , Detthamrong Umawadee , Chansanam Wirapong
Published: August 2024

Today, fast-paced technology plays an important role in financial transactions, especially in payment-related digital habits. As fraud is a major concern in online payments, many machine-learning approaches have been proposed to detect and prevent fraudulent payment transactions. This study aimed to evaluate Decision Tree, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, and Random Forest in detecting fraudulent payment transactions. The results show that Random Forest, K-Nearest Neighbor, Decision Tree, and Logistic regression achieved total accuracy rates exceeding 99%. However, such impressive results do not necessarily indicate satisfactory performance. The results highlight the need to detect fraudulent transactions and investigate specific improvements to effectively manage and minimize unexpected financial transaction fraud.
Planning Optimization of a Standalone Photovoltaic/Diesel/Battery Energy System for a Gold Mining Location in Mauritania
Authors: Salihi Kebbada , Taha Mohammed Qasim , Oubeidi Abdou , Ndongo Mamoudou , Jabrallah Sadok Ben , Heiba Bamba El
Published: August 2024

The greenhouse gas emissions resulting from the excessive use of Diesel Generators (DGs) in mining locations pose a threat to the environment and the macroeconomic sustainability of this industry. This paper aims to decrease or eliminate the use of DG units in gold mining areas to increase access to more clean Renewable Energy Sources (RESs) such as Photovoltaic (PV) systems. In order to evaluate PV potential at small-scale gold mining sites in Mauritania, ArcGIS software is utilized to analyze Chagatt gold mining location as a case study. The techno-economic viability of a PV/DG/battery Hybrid Energy System (HES) was examined and discussed. For yearly modeling, the PVsyst and HOMER Pro were employed to assess the performance of the ideal size of HES in terms of installation and energy costs. The findings indicate that Mauritania's gold mining locations are most suitable for PV energy harvesting. As a result, this industry may rely on clean PV energy.
Detection of QR Code-based Cyberattacks using a Lightweight Deep Learning Model
Authors: Sarkhi Mousa , Mishra Shailendra
Published: August 2024

Traditional intrusion detection systems rely on known patterns and irregularities. This study proposes an approach to reinforce security measures on QR codes used for marketing and identification. The former investigates the use of a lightweight Deep Learning (DL) model to detect cyberattacks embedded in QR codes. A model that classifies QR codes into three categories: normal, phishing, and malware, is proposed. The model achieves high precision and F1 scores for normal and phishing codes (Class 0 and 1), indicating accurate identification. However, the model's recall for malware (Class 2) is lower, suggesting potential missed detections in this category. This stresses the need for further exploration of techniques to improve the detection of malware QR codes. Despite the particular limitation, the overall accuracy of the model remains impressive at 99%, demonstrating its effectiveness in distinguishing normal and phishing codes from potentially malicious ones.
Effects of High Loss on Ignition Fly Ash as a Partial Replacement for Sand on the Properties of Mortars
Authors: Thuy Nguyen Thi Bich , Tangtermsirikul Somnuk , Mien Tran Van , Kiet Bui Anh
Published: August 2024

This study investigates the effects of fly ash with a high unburned carbon content as a partial sand replacement material on the properties of mortars. Fly ash was added to the mortar in four different ratios: 0, 10, 30, and 50% by volume. This study examined two series of mortar mix proportions, one having a controlled water-to-cement ratio and the other having a controlled flow. The fresh properties and compressive strength of the mortars were investigated. The experimental results showed that the compressive strength increased with increasing fly ash-to-aggregate ratio (FA/A). However, the increase in FA/A resulted in higher water requirements, lower flow, and longer setting times. The calcium hydroxide content and total porosity of the mortars were also examined to support the results of the compressive strength tests. Based on the results, the FA/A ratio had a significant impact on the fresh properties and compressive strength of the mortars. To ensure fresh properties and compressive strength of mortars, a fly ash-to-sand ratio of up to 30% is recommended.
Early Glaucoma Detection using LSTM-CNN integrated with Multi Class SVM
Authors: Vuppu Vijaya Madhavi , Kumari P. Lalitha Surya
Published: August 2024

Glaucoma, a progressive eye disease, is a major public concern on health due to its gradual onset and the possibility of irreversible vision loss. Early glaucoma detection is critical because it allows for timely intervention and management, lowering the risk of severe visual impairment. To address this pressing need, we present a comprehensive glaucoma detection methodology that focuses on image processing techniques and machine learning models. The initialization and preprocessing of retinal fundus images obtained from the DRIVE database is the first step in our approach. These images are resized to a standard size, grayscaled, and blurred with Gaussian blur to ensure consistency and noise reduction. Our methodology is built around feature extraction and modeling. We harness the power of deep learning, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), which we integrate seamlessly with multi-class Support Vector Machines (SVMs). This synergy enables our Deep Flex SVM-MC model to capture intricate data patterns during training while also demonstrating exceptional adaptability in multi-class classification tasks. The proposed model has a glaucoma detection accuracy of 97.2%, an exceptional sensitivity of 97.53%, indicating its proficiency in correctly identifying glaucoma cases, and a specificity of 96.4%.
Optimizing Edge AI for Tomato Leaf Disease Identification
Authors: Gatla Anitha , Reddy S. R. V. Prasad , Mandru Deenababu , Thouti Swapna , Kavitha J. , Souissi Ahmed Saad Eddine , Veerendra A. S. , Srividya R. , Flah Aymen
Published: August 2024

This study addresses the critical challenge of real-time identification of tomato leaf diseases using edge computing. Traditional plant disease detection methods rely on centralized cloud-based solutions that suffer from latency issues and require substantial bandwidth, making them less viable for real-time applications in remote or bandwidth-constrained environments. In response to these limitations, this study proposes an on-the-edge processing framework employing Convolutional Neural Networks (CNNs) to identify tomato diseases. This approach brings computation closer to the data source, reducing latency and conserving bandwidth. This study evaluates various pre-trained models, including MobileNetV2, InceptionV3, ResNet50, and VGG19 against a custom CNN, training and validating them on a comprehensive dataset of tomato leaf images. MobileNetV2 demonstrated exceptional performance, achieving an accuracy of 98.99%. The results highlight the potential of edge AI to revolutionize disease detection in agricultural settings, offering a scalable, efficient, and responsive solution that can be integrated into broader smart farming systems. This approach not only improves disease detection accuracy but can also provide actionable insights and timely alerts to farmers, ultimately contributing to increased crop yields and food security.
Enhancing the Load-bearing Capacity of a Damaged Hydraulic Structure Rehabilitated by Underwater Concreting
Authors: Thi Bach Phuong Ho , Nguyen Viet Duc , Vuong Quoc Vu , Dang Hoang Minh
Published: August 2024

Due to the severe environment, damages often occur early on hydraulic structures, even in parts submerged in water. This paper presents a study on how the load-bearing capacity of the hydraulic structure is enhanced, once it has been rehabilitated by underwater concreting. A column has been considered as the hydraulic structure. The damaged part of the column was submerged in water, hence, it was repaired by the underwater concrete. For this, the tremie casting method was taken into account. The compression test results pointed out that the damaged column after rehabilitation had a similar load-bearing capacity to the undamaged column, while the not rehabilitated column had reduced load-bearing capacity by 20%. The failure mode of the damaged column after the rehabilitation presented longitudinal cracks along the body of the column, which is similar to that occurred in the undamaged column. Besides, there was no sign of delamination between the protective layer and the existing concrete of the damaged column, proving the feasibility of the selected casting method and concrete mixture for rehabilitation.
ANN-weighted Distance Grey Wolf Optimizer for NOx Emission Optimization in Coal Fired Boilers of a Thermal Power Plant
Authors: Malik Mahmad Raphiyoddin Shaphiyoddin , Priya Viswambaran Saraswati
Published: August 2024

This research work suggests the application of predictive modeling for the Nitrogen Oxide (NOx) emissions from the 210 MW pulverized boiler that burns coal. In order to lower the NOx emissions in the flue gas, it is necessary to optimize various operational parameters during combustion, including oxygen in flue gas, various damper opening positions, air-distribution system, nozzle tilt, and the temperature of the flue gas outlet. Information gathered from variable parametric field tests was used to create an Artificial Neural Network (ANN) model. The model estimated NOx emissions based on the parameters of coal combustion. The ANN model was put to the test under full load conditions and the results of its predictions were compared to the actual values. The trained ANN with its biases and weights in the form of arithmetical equations was given as a fitness function to the weighted distance Grey Wolf Optimizer (GWO) to improve operating conditions for decreased NOx emissions.
Performance Evaluation of Asphalt Mixtures with Rediset LQ-1200 Additive
Authors: Nguyen Thanh Len , Le Van Phuc , Nguyen Quang Phuc , Dang Gia Bao , Nguyen Minh Hong Nhan
Published: August 2024

One of the primary causes of premature pavement deterioration in Asphalt Concrete (AC) mixtures is the breaking of the adhesive bond between the aggregate and the binder. The purpose of this study is to assess the performance characteristics of AC mixture utilizing the Rediset LQ-1200 additive. In order to achieve this goal, tests such as the Marshall Stability (MS), Marshall Stability Ratio (MSR), Indirect Tensile (IDT), indirect tensile cracking, and Wheel Tracking (WT) were deployed to evaluate the mechanical properties of AC mixtures. The findings demonstrated a considerable improvement in the physical parameters of the AC mixture with the Rediset LQ-1200 additive compared to the base AC mixture. The MS and MSR were improved by about 28% and 13%, respectively, while the AC mixture's resistance to rutting and cracking was efficiently increased.
Feasibility Analysis of Wind Power Plant in South East Region, Vietnam
Authors: Truong Nguyen Tuong An , Khanh Nguyen Binh , Giap Luong Ngoc , Trung Bui Tien , Le Ngo Phuong , Vinh Tran The
Published: August 2024

The wind power market is expanding quickly and the cost of wind power equipment is decreasing, making wind power technology a key player in the world's energy transition. Assessing wind potential and selecting the right wind turbine site are crucial parameters for developing a wind farm. Vietnam focuses on onshore and nearshore wind power projects due to its promising wind power potential and supportive policies. However, Vietnam has diverse climate characteristics and wind patterns. Therefore, initial basic research is necessary to evaluate the feasibility of investing in wind power projects. This study examines the technical feasibility of a typical wind power project in the Southeast region of Vietnam using Wind Atlas Analysis and Application Program (WAsP) software. The results indicate that the wind turbine's type and installation location significantly affect wind power plants' efficiency. The total power output of the wind power project after factoring with losses at a rate of 17%, is 304,149 MWh.
Comparative Analysis of an Apartment Building using Seismic Codes NBC 105:1994 and NBC 105:2020 (A Case Study)
Authors: Malla Suraj , Alagirisamy Mukil , Dangol Purushotam , Giri Om Prakash
Published: August 2024

The present study undertakes a comparative analytical examination of seismic analysis standards in Nepal, focusing on NBC 105:1994 and the updated NBC 105:2020, encompassing both the Ultimate Limit State (ULS) and Serviceability Limit State (SLS). Employing a regular Reinforced Concrete (RC) apartment building in Pokhara as a case study, the geometric and sectional configurations of structural elements are intentionally kept consistent for comparison. The analysis involves creating a 3D model using ETABS version 19, encompassing linear static, Equivalent Static (ES), and linear dynamic Response Spectrum (RS) analyses, followed by nonlinear static (pushover) analysis. The results highlight substantial differences between the two codes. Base shear from NBC 105:2020 is notably higher, being 28.59% ULS and 22.74% SLS greater than NBC 105:1994. The scale factor for combined response design values is significantly lower in both X and Y directions for NBC 105:2020. Story shear is extended by 33% ES and 37% RS with NBC 105:2020 compared to NBC 105:1994. Maximum design displacement and Inter-Story Drift (ISD) are markedly higher with NBC 105:2020, indicating its more severe seismic parameters. This study emphasized the enhanced seismic resilience provided by NBC 105:2020, particularly evident in increased base shear, reduced design scale factors, and higher values for story shear, displacement, and ISD. These findings contribute valuable insights into the seismic design improvements introduced in Nepal's seismic codes after the Gorkha Earthquake in 2015.
A Novel Method for the Estimation of the Elastic Modulus of Ultra-High Performance Concrete using Vibration Data
Authors: Nguyen Huong Duong , Khatir Samir , Nguyen Quoc Bao
Published: August 2024

The elastic modulus of concrete is one of the most important parameters in the analysis and design of concrete structures. However, determining the elastic modulus in civil structures using core-drilled samples is time-consuming and labor-intensive. Additionally, the elastic modulus of Ultra-High Performance Concrete (UHPC) varies significantly depending on its composition. This paper proposes an improved, non-destructive application to identify the elastic modulus of UHPC materials in in-service structures. The elastic modulus is estimated through calibration between a numerical model and experimental UHPC plate vibration test results, using frequency and mode shapes. This calibration involves solving an inverse problem using optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search, and the YUKI algorithm. Updating the plate characteristics is made possible by the development of numerous iterations, where each iteration updates the elastic modulus, thickness, and width values in the term to find the best solution. The highest accuracies compared to experimental data natural frequency values were found in models updated by GA, PSO, YUKI, and Cuckoo algorithms, with errors of 10.77%, 6.58%, 6.87%, and 6.87%, respectively. An experimental sample was tested to determine the elastic modulus of the UHPC, and the proposed application showed a 0.55% error compared to the experimental value. Thus, the estimated elastic modulus value is highly accurate.
FEM Structural Analysis for Ship's Beam Modification: A Case Study
Authors: Popa Adrian , Manea Mihaela-Greti , Ristea-Komornicki Marian-Valentin
Published: August 2024

Classification is mandatory for all seagoing ships engaged in international trade worldwide, with regulations that require the strength of the hull construction to be confirmed. Upon completion of the ship's construction or during the execution of repair works in shipyards (reconversion works, modification of panels, replacement of equipment, etc.), the strength structure of the ship may be affected and the classification society must certify that it meets the requirements. Replacing old equipment sometimes implies some variations in overall dimensions, which may impose some changes (adjustments) on the layout of structural panel elements. This study applied the Finite Element Method (FEM) considering the von Mises theory in a case study of a beam modification with the support of the ANSYS software. This study used the reverse engineering method to investigate an already implemented solution.
Evaluation Strength of Materials of the Compressor Wheel and Engine Power in the Turbocharger
Authors: Danh Tran Huu , Ky Le Hong , Anh Pham Hoang , Tam Dang Thanh , Hiep Nguyen Hoang
Published: August 2024

This paper presents the research results on the strength of materials and power of the Toyota 3C engine when changing the structure and number of blades of the compressor wheel in the turbocharger. 3D models of different compressor wheels were created using reverse engineering and then simulated in the ANSYS environment with turbine shaft rotation speeds of 10,000, 15,000, and 20,000 rpm, respectively, to examine the strength of the compression wheel materials. To evaluate engine power, compressor wheels were machined on a 5-axis CNC milling machine. The MP 100S specialized test bed was used to perform experiments and compare engine power when using the original and alternative compressor wheels of the CT9 turbocharger. The compressor wheels were made of aluminum alloy, with a structure and number of blades selected to ensure durability when working. The CT9 turbocharger has a four-pair blade compressor wheel that consistently delivers higher engine power than in other cases.
The Effect of Construction Joints on the Behavior of Reinforced Concrete Deep Beams
Authors: Kadhum Saba Basim , Al-Zuhairi Alaa Hussein
Published: August 2024

The main objective of the present research is to conduct a thorough investigation into the impact of construction joints on the structural performance of reinforced concrete deep beams. This study involves a series of experimental tests and the use of advanced numerical analysis techniques to gain a deeper understanding of the behavior of these beams in the presence of construction joints. The experimental component incorporates analysis findings from both previous and current research. Specifically, six reinforced concrete deep beam specimens featuring horizontal and inclined construction joints were utilized as simply being supported with two-point loading. The test findings indicate that the presence of a horizontal construction joint located below, at, or above the mid-height of the beam can lead to reductions in the ultimate load capacity by 9%, 11%, and 1%, respectively. The numerical part of the study focused on creating detailed models of the deep beam specimens with construction joints using the ABAQUS software. The proposed model showed a good agreement with the experimental tests, with estimations not exceeding 7% for the load-carrying capacity.  This reduction becomes more significant when the concrete compressive strength is high, necessitating the use of bonding agents and additional reinforcement techniques to mitigate the impact of construction joints on the structural integrity.
Overcoming the Limitations of the RAPS Method by identifying Alternative Data Normalization Methods
Authors: Thien Nguyen Van , Dung Hoang Tien , Trung Do Duc
Published: August 2024

This study proposes a new approach to improve the performance of the Ranking Alternatives by Perimeter Similarity (RAPS) method in Multi-Criteria Decision-Making (MCDM). RAPS has attracted attention but encounters difficulties when handling zero values in the decision matrix. This study suggests using alternative data normalization methods and assesses their suitability when combined with RAPS in various scenarios. The results identified three additional normalization methods that are appropriate for integration with RAPS. These findings provide a theoretical basis and specific guidelines for selecting data normalization methods when applying RAPS in MCDM.
Improved Quality Parameter Estimation of Photovoltaic System Models based on SAO Algorithm
Authors: Attafi Rim , Zitouni Naoufal , Dashtdar Masoud , Flah Aymen , Elnaggar Mohamed F. , Kanan Mohammad
Published: August 2024

Solar energy provides one of the most favorable options regarding the transition to clean energy sources. The parameters of a photovoltaic (PV) system play determine its performance under various scenarios. The PV model parameter estimation is an example of nonlinear planning. This study proposes a novel use of the established Smell Agent Optimizer (SAO) algorithm to anticipate the undefined parameters of the PV model's single-diode and two-diode equivalent circuits. This study aims to create a precise PV model that can accurately characterize its performance under changing operational conditions. The desired objective function is defined as the square of the mean squared error between the model's current-voltage curve and the measured curve.
Investigating Efficient Thermal Distribution in a House Room by combining Statistics with Computational Fluid Dynamics
Authors: Martins Jairo Aparecido , Siqueira Adriano Francisco , Romao Estaner Claro
Published: August 2024

The study of energy sources is an open subject due to constraints on the current energy global production versus the current and future energy demands. From the consumption perspective, houses pull considerable energy from the electrical grid. With that being said, this paper investigates the theoretical thermal distribution of the heat in the basement of a house and measures the theoretical temperatures throughout different points at the same height by using statistics and numerical simulation. The numerical simulation, such as Computational Fluid Dynamic Analysis by COMSOLTM combined with Statistics by MiniTabTM was utilized to determine the most economical settings for the variables in the heating system evaluation. It is understood that thermal comfort for householders is achieved when the heat is evenly distributed in the room. To have a more realistic model set-up, the air flow in the room was considered as a turbulent model. The studied variables were intake airflow, positioning of the vents (intakes), airflow temperature, and external temperature. The results showed the significance of the variables. The latter were ranked from the highest to the lowest as: external temperature, airflow velocity, inlet location, and temperature input, while the highest interaction was found between the external temperature and air inlet velocity. This study comes up with a superior understanding of the system and generates an efficient setting for the variables for energy-saving purposes.
Contributory Factors related to the Tensile Strength of Hot Mix Asphalt Concrete
Authors: Oleiwi Mina M. , Albayati Amjad K.
Published: August 2024

Tensile strength is a critical property of Hot Mix Asphalt (HMA) pavements and is closely related to distresses such as fatigue cracking. This study aims to evaluate methods for assessing fatigue cracking in Asphalt Concrete (AC) mixes. In order to achieve optimum density at different binder contents, the mixes were compressed using a gyratory compactor. Tensile strength was assessed using the Indirect Tensile (IDT) and Semi-Circular Bend (SCB) tests. The results showed that the tensile strength measured by the SCB test was consistently higher than that measured by the IDT test at 25 °C. In addition, the SCB test showed a stronger correlation between increasing binder content and tensile strength. For binder contents ranging from 4.2% to 5.2%, the IDT test results increased from 541% to 678.7%, while the SCB test results increased from 630.3% to 743.7%. These results suggest that the SCB test provides a more accurate representation of the tensile strength of AC mixes than the IDT test.
Application of Synthetic Data on Object Detection Tasks
Authors: Nguyen Huu Long , Le Duc Toan , Hoang Hong Hai
Published: August 2024

Object detection is a computer vision task that identifies and locates one or more effective targets from image or video data. The accuracy of object detection heavily depends on the size and the diversity of the utilized dataset. However, preparing and labeling an adequate dataset to guarantee a high level of reliability can be time-consuming and labor-intensive, because the process of building data requires manually setting up the environment and capturing the dataset while keeping its variety in scenarios. There have been several efforts on object detection that take a long time to prepare the input data for training the models. To deal with this problem, synthetic data have emerged as a potential for the replacement of real-world data in data preparation for model training. In this paper, we provide a technique that can generate an enormous synthetic dataset with little human labor. Concretely, we have simulated the environment by applying the pyBullet library and capturing various types of input images. In order to examine its performance on the training model, we integrated a YOLOv5 object detection model to investigate the dataset. The output of the conducted model was deployed in a simulation robot system to examine its potential. YOLOv5 can reach a high accuracy of object detection at 93.1% mAP when solely training on our generated data. Our research provides a novelistic method to facilitate the understanding of data generation process in preparing datasets for deep learning models.
Enhancing e-Commerce Strategies: A Deep Learning Framework for Customer Behavior Prediction
Authors: Al-Otaibi Yasser D.
Published: August 2024

Today, the use of artificial intelligence (AI) to enhance the processes of online shopping is crucial for e-commerce as it uses the past purchasing behavior of customer-automated processes. Nevertheless, predicting or understanding customers’ buying behavior remains a major challenge. This research work attempts to put forward a new approach by utilizing Deep Learning (DL) models to identify whether a customer will buy or not depending on his age and salary. By employing lightweight dense layers in the DL architecture, the model is trained with the use of publicly available datasets and has great accuracy and performance metrics. This predictive model offers valuable lessons for e-commerce because the recommendation and marketing personalization methods it deploys can be integrated into the business to yield improved experience and performance for customers and users.
Design of a Compact Circular Microstrip Patch Antenna for 5G Applications
Authors: Djouimaa Awatef , Bencherif Karima
Published: August 2024

This paper presents the design and analysis of a compact circular microstrip patch antenna for 5G millimeter wave technology applications. The dimensions of the proposed patch are 5.959 mm × 5.959 mm × 1.400 mm. The antenna exhibits a resonant frequency of 28 GHz, a return loss of -45 dB, a bandwidth of 1.7638 GHz, and a gain of 0.1573. In order to meet the requirements of 5G applications at 28 GHz, a compact, high-gain, and large bandwidth antenna is necessary. The principal objective of this study is to enhance the performance of the antenna parameters, thereby achieving an optimal balance between size, gain, and bandwidth.  A notable improvement in bandwidth and gain is achieved. The design, analysis, and optimization processes were conducted using the High Frequency Surface Structure (HFSS) software, which employs the Finite Element Method (FEM) numerical method.
The Influence of Capillary Geometry on the Stiffness of Hydrostatic Bearings in Medium-Sized Cylindrical Grinding Machines: A Simulation Analysis
Authors: Nguyen Manh-Toan , Tran The-Hung , Le Duc-Do , Tran Duc-Toan , Bui Duy-Thinh , Tuan Anh Bui
Published: August 2024

One of the most crucial parameters in the field of machine tool engineering is the stiffness of the machine tool spindle, as it has a direct impact on the precision and accuracy of machining. This paper presents a research on the effects of geometric parameters of capillary tubes, including diameter (dc) and length (lc), on the stiffness of hydrostatic bearings in machine tools. The oil pump pressure in this study was set at a range from 3 MPa to 5 MPa. The simulation results demonstrate the relationship between the capillary geometric parameters, pressure, and the stiffness of the hydrostatic spindle unit. These findings indicate a direct relationship between stiffness and pump pressure, with increases in pressure also raising lubricant temperature, which may impair cooling. The shape of the capillary directly affects the ratio of oil chamber pressure to pump pressure, which in turn affects hardness. The results of the study indicate that for spindles in machine tools, a dc ranging from 0.3 mm to 0.6 mm and a (lc/dc) ratio of between 20 and 100 are feasible based on the given stiffness requirement. It is possible to select an appropriate set of capillary parameters to achieve the best stiffness with specific requirements under the working conditions of hydrostatic bearings based on the results of the simulation analysis.
Optimization of Wheel Dressing Technological Parameters when Grinding Hardox 500 Steel
Authors: Danh Tran Huu , Ky Le Hong
Published: August 2024

This paper presents the research results on the influence of wheel dressing technological parameters on minimum Surface Roughness (SR) and maximum Material Removal Rate (MRR) of Hardox 500 steel. The Box-Behnken planning method with 4 factors was used, including rough wheel dressing depth ar, the number of rough wheel dressing times nr, fine wheel dressing depth af, and the number of fine wheel dressing times nf. The impact of these factors on the objectives of the grinding process was evaluated. Furthermore, an optimal grinding mode was proposed to increase MRR while minimizing SR. The proposed model can be used in industry and further research can investigate the grinding of workpiece materials with high-alloy or high-speed steel.