Journal of Engineering Research最新文献

{"title":"Leveraging edge detection techniques to enhance Arabic sign language static-gesture recognition using deep learning","authors":"Wahiba Ismaiel ,&nbsp;Lilia kechiche ,&nbsp;Yassine Aribi ,&nbsp;Omer Salih Dawood Omer ,&nbsp;Walied Merghani","doi":"10.1016/j.jer.2025.09.011","DOIUrl":"10.1016/j.jer.2025.09.011","url":null,"abstract":"<div><div>Recently, there has been a growing interest in developing solutions to address communication barriers for the deaf and hard-of-hearing community. Sign language is the primary language of this group. Computer vision technology is used to process sign language due to its ease of application. Sign language recognition involves the use of technology to bridge communication gaps and enhance accessibility for individuals who use sign language as their primary form of communication. Many researchers have presented various methods to facilitate communication, among others. These methods include sign language recognition techniques, translation between sign and text or audio, and hand gesture identification, among others. We proposed an effective approach to improve the feature extraction process for Arabic sign gesture recognition. Feature extraction is a crucial aspect of deep learning models because it facilitates data processing, improves performance, and helps interpret results. This process also enables models to manage large datasets more efficiently. We presented two deep learning models: the agile convolutional neural network (ASLR_CNN) and ResNet50, to improve the comprehensiveness of the extracted features. These models were combined with the Canny Edge Detector (CED), which identifies the edges of Arabic hand gestures, as well as the complex features extracted from the edges by the proposed models. To evaluate the effectiveness of our methodology, we trained the proposed models on two public datasets: AASL and ArASL. The performance of these models was evaluated using a variety of metrics, including accuracy, precision, recall, F-score, and confusion matrix. The results indicated that both the ASLR_CNN and ResNet50 models achieved high accuracy on the ArASL dataset, reaching 97.14 % and 96.88 %, respectively. In contrast, the accuracy dropped to 89.49 % and 86.12 % for the ASLR_CNN and ResNet50 models, respectively, when using the AASL dataset.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 896-915"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147454235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of geometric dimensioning and tolerancing in sustainable hard drilling using vegetable oil-based MQL","authors":"Amir Rasti ,&nbsp;Ahmad Reza Omiddodman,&nbsp;Mehdi Ustad","doi":"10.1016/j.jer.2025.05.012","DOIUrl":"10.1016/j.jer.2025.05.012","url":null,"abstract":"<div><div>This study investigates the influence of cutting parameters on the hole precision in hard drilling of AISI 4340, with a particular emphasis on vegetable-based minimum quantity lubrication (MQL) as a green machining approach. The effect of key parameters including cutting speed, feed rate, and workpiece hardness was analyzed on the cutting force and hole precision using soybean oil as the cutting fluid. The results showed a stable trend in the time history of cutting forces during the cutting process, with no significant wear or other disruptions, indicating that vegetable oil can provide effective lubrication during hard drilling. The axial cutting force was lower than 8000 N in the studied range of cutting parameters. The findings also revealed that feed rate is the most important factor influencing output characteristics, which deteriorated hole diameter, circularity, and cylindricity tolerances of up to 200 %. This is especially evident with softer materials (30 HRC). Furthermore, workpiece hardness had a minor effect on circularity, but a significant impact on straightness and overall cylindricity up to 56 %. Soybean oil-assisted MQL resulted in tolerances magnitude of less than 60 μm, demonstrating its effectiveness in hard drilling AISI 4340.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 315-323"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147453734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSpace: Multi security namespace management for containers","authors":"Hao Liang ,&nbsp;Gongshan He ,&nbsp;Jinhu Jiang","doi":"10.1016/j.jer.2025.04.007","DOIUrl":"10.1016/j.jer.2025.04.007","url":null,"abstract":"<div><div>Existing system security frameworks like LSM are primarily designed for the operating system kernel and are not applicable to user-level container methods. This is primarily because the system security framework is designed and implemented within the operating system kernel, which is shared across the entire system and cannot fulfill the isolation requirements of containers. The security policies of multiple containers are intermixed in the underlying system kernel, which impacts each other and increases the overhead of monitoring, leading to inefficiencies in cloud servers running numerous containers. Improvements to the security of containers are ad hoc and partial, often lacking support for the universal SELinux mode. In this paper, we introduce MSpace, an enhanced kernel SELinux framework that supports containerized secure spaces with individual security policies and mechanisms. To enhance security, we first introduce an instantiation mechanism for virtual hook tables, which, in conjunction with the container’s namespace, ensures each has its own instantiated hook tables instead of a system-wide one, thereby isolating secure spaces. Subsequently, a security namespace method was devised to support container-specific private security policies. Lastly, to maintain compatibility, only the instantiation and deallocation interfaces were added while preserving the original interfaces unchanged. The results demonstrate that MSpace effectively alleviates the issue of using SELinux for internal containers, with an average reduction of 2.83 % in system call latency compared to using host SELinux directly.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 681-687"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147453809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dispatch under pressure: An investigation into the cognitive load of Kuwait’s emergency responders","authors":"Mohammad Alsager Alzayed ,&nbsp;Nourah Alsardi","doi":"10.1016/j.jer.2025.06.004","DOIUrl":"10.1016/j.jer.2025.06.004","url":null,"abstract":"<div><div>Emergency dispatchers play a pivotal role in ensuring efficient and effective emergency response systems. Reducing emergency dispatcher cognitive workload is crucial for enhancing response efficiency and accuracy, ultimately saving lives by ensuring rapid and precise coordination of critical resources. This study aims to assess the impact of age, work shift, and experience on the cognitive workload experienced by emergency dispatchers in Kuwait. A series of survey questions, structured around the dimensions of the NASA-TLX, were administered to gather quantitative data from 110 emergency dispatchers. In addition to quantitative analysis, qualitative data were obtained through interviews conducted with 11 dispatchers. Analysis of survey responses revealed elevated NASA-TLX scores among the participants, indicating significant cognitive workload. Results indicated that years of experience significantly influenced the cognitive workload experienced by dispatchers. By categorizing responses into the NASA-TLX dimensions, the study identified the most prevalent factors contributing to cognitive workload among emergency dispatchers. This research provides valuable insights into the cognitive demands faced by emergency dispatchers in Kuwait, highlighting the importance of experience in managing workload effectively. The findings contribute to the development of strategies aimed at optimizing the cognitive ergonomics of emergency dispatch operations, ultimately enhancing the efficiency and quality of emergency response systems.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 1039-1048"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147453896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fire swin: Video anomaly detection using a hybrid model with convolutional layers, fire module, and swin transformer","authors":"Abdulrahman Alshalawi,&nbsp;Wadood Abdul,&nbsp;Ghulam Muhammad","doi":"10.1016/j.jer.2025.08.016","DOIUrl":"10.1016/j.jer.2025.08.016","url":null,"abstract":"<div><div>There are more than one billion surveillance cameras installed throughout the world that produce enormous volumes of video data on daily basis. Manual search of anomalous events in such huge amounts of data is laborious and extensively time consuming. In contrast, various computer vision methods have been used, and recently deep learning methods have been extensively investigated. However, video anomaly detection in surveillance videos is a complex and challenging task due to varied backgrounds, capturing positions, context, and unexpected events that require high-level semantic understanding and spatiotemporal reasoning. In this paper, we propose an end-to-end novel hybrid model that contains time distributed convolutional layers with a fire module for spatial feature extraction, compression for efficient processing, and a Swin transformer for self-attention mechanism that can capture long-range dependencies and local details. We applied our method to the UCF crime dataset and CUHK Avenue dataset which contains realistic and varied anomalous events with the standard split ratio of used in both UCF crime and CUHK Avenue datasets for training and testing respectively. We demonstrated with extensive experiments that the proposed model achieved 92.20 % accuracy in detecting various anomalies in UCF Crime, and 94.40 % using CUHK dataset, an increase in performance as compared to existing state-of-the-art methods.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 811-819"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147453898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the quality and service levels of walkways in the state of Kuwait","authors":"Duaij S. AlRukaibi ,&nbsp;Ahmed O. Abdulhadi","doi":"10.1016/j.jer.2025.08.004","DOIUrl":"10.1016/j.jer.2025.08.004","url":null,"abstract":"<div><div>Walkways are easy, safe, convenient, and healthy routes for pedestrians to walk and participate in other activities such as running, jogging, and aerobic exercise. This paper aims to evaluate different walkways by assessing three comfort levels: physical, psychological, and physiological, represented by a comprehensive environmental walkability index (EWI), which provides a grading system from A to F depending on the quality and service levels of the walkways. The scope of this study was conducted across five different walkways in the state of Kuwait. The evaluation of service level gives values at the macro scale for the condition and infrastructure of the walkway, while the evaluation of quality level looks at the micro-scale of the comfort and quality of walkability for pedestrians. Analytic hierarchy process (AHP) is used to estimate the importance weights of each parameter in comfort levels. The air quality index (AQI) was adopted to analyze the overall ambient air quality level and determine the primary responsible pollutant. Different pollutant concentration data were analyzed on an hourly and monthly basis and compared to EPA’s National Ambient Air Quality Standards (NAAQS) to evaluate the air quality situation in the walkways. The results indicate CO, NO₂, SO_2, and O₃ compliance with NAAQS standards and showed PM₁₀ exceeding permitted levels in afternoon hours and summer months. Elevated levels of PM₁₀ concentration were observed in February 2015 because of the dust storm. AQI analysis results show that PM₁₀ is the primary responsible pollutant. The EWI model assessed the selected walkways and assigned grades for the year 2019, revealing that Ahmadi Walkway (AW) has the highest grade (B) among the walkways because of the high level of facilities and walkway surface quality, while Ali Subah Al Salem Walkway (ASW) obtained the lowest grade (D) because of the lack of facilities, low-quality surface type, and high air quality index. This study provides an integrated assessment that delivers environmental walkability index outcomes to enable professionals to improve quality and service levels in the walkways.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 121-137"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147454152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Unraveling the role of sintering temperature on physical, structural and tribological characteristics of ball milled Co28Cr6Mo biomaterial based alloy” [J. Eng. Res. 12 (2024) 571–579]","authors":"Mohammed Farah ,&nbsp;Mamoun Fellah ,&nbsp;Dikra Bouras ,&nbsp;Naouel Hezil ,&nbsp;Abderrachid Becheri ,&nbsp;Barille Regis ,&nbsp;Henda Daoudi ,&nbsp;Alex Montagne ,&nbsp;Tmader Alballa ,&nbsp;Abd El-Wahed Khalifa Hamiden","doi":"10.1016/j.jer.2025.12.010","DOIUrl":"10.1016/j.jer.2025.12.010","url":null,"abstract":"","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 1215-1216"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147454237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid numerical–machine learning framework for heat transfer prediction in TiO₂ nanofluid microtubes","authors":"Muhammed Said Ates ,&nbsp;Ebru Akpinar ,&nbsp;Cagri Kaymak ,&nbsp;Mehmet Das","doi":"10.1016/j.jer.2025.12.007","DOIUrl":"10.1016/j.jer.2025.12.007","url":null,"abstract":"<div><div>Heating and cooling processes using microtubes are crucial for the thermal management of electronic devices. The investigation of such complex conjugate heat transfer systems via traditional numerical methods demands significant computational time and resources. Therefore, the necessity of this study arises from the need for faster, cost-effective, and highly accurate prediction models to accelerate the design and optimization of micro-scale cooling systems. The primary aim of this study is to numerically investigate the thermal performance of a water-based TiO₂ nanofluid flowing through thick-walled microtubes and, crucially, to develop a robust hybrid numerical–machine learning framework capable of offering high predictive accuracy. This study aims to address a gap in the literature. The model considers an infinitely long microtubes with a heated section of length L, where a constant surface heat flux is applied. The governing two-dimensional laminar flow and energy equations, expressed in cylindrical coordinates, were non-dimensionalized, discretized via finite and central difference schemes, and solved numerically. Viscous damping and slip velocity boundary conditions were also incorporated. Results show that conjugate heat transfer effects intensify with increasing wall thickness but diminish with lower wall–fluid interface and Peclet parameters. These parameters influence transient and steady-state behavior differently. The distinctive contribution of this work lies in the data-driven modeling and prediction of the microtube heat transfer parameter (Qwi) using six regression methods: Linear Regression, Random Forest, Support Vector Regression (RBF kernel), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and one-dimensional Convolutional Neural Network (1D-CNN). All models achieved high predictive accuracy (R² &gt; 0.96), with ensemble and kernel-based methods—particularly XGBoost and SVR—producing the best results (R² = 0.9999, RMSE &lt; 0.001). The findings demonstrate that while tree- and kernel-based models outperform the linear baseline, deep learning architectures exhibit strong generalization to nonlinear behaviors, underscoring the potential of AI-driven modeling in nanofluid-based thermal systems.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 659-680"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147453726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient baseline for multi-view 3d human pose estimation","authors":"Guozheng Peng,&nbsp;Lixin Han","doi":"10.1016/j.jer.2025.07.007","DOIUrl":"10.1016/j.jer.2025.07.007","url":null,"abstract":"<div><div>Recent advancements have been made in calculating 3D human pose keypoints from 2D joint locations obtained via a 2D backbone. While these methods demonstrate excellent performance, they demand substantial computing resources. In this work, we propose a baseline method for multi-view 3D human pose estimation using a fully connected neural network to predict 3D keypoint positions. Our approach provides a straightforward framework for fusing 2D poses from multiple camera views and regressing 3D human pose. Extensive experiments demonstrate the effectiveness of our proposed method on Human3.6M, the largest publicly available benchmark for 3D human pose estimation. Furthermore, it is important to note that increasing the number of input camera views does not inherently guarantee improved 3D pose reconstruction accuracy and quality. The optimal number of views and strategic selection of viewpoint combinations are critical factors in achieving precise 3D pose estimation results.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 748-755"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147453727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization of EV battery packs: Lightweight and safety via Pearson-RSM-NSGA-II","authors":"Yuanyi Zhao ,&nbsp;Zhenfei Zhan ,&nbsp;Zhiwei Zhao ,&nbsp;Yangchen Ou ,&nbsp;Jun Wang","doi":"10.1016/j.jer.2025.06.009","DOIUrl":"10.1016/j.jer.2025.06.009","url":null,"abstract":"<div><div>With the rapid development of electric vehicles and energy storage systems, traditional battery pack designs often encounter multi-objective conflicts. Optimizing the battery pack structure to improve safety and reduce weight while ensuring strength, stiffness, and dynamic performance has become a significant challenge in the design process. To address the multi-objective co-optimization challenge of lightweighting and safety in electric vehicle battery packs, this paper proposes a multi-objective optimization framework that integrates the Pearson correlation coefficient, response surface methodology (RSM), and the non-dominated sorting genetic algorithm II (NSGA-II). For the first time, the Pearson correlation coefficient was incorporated into the multi-objective optimization process of battery packs to quantitatively assess the linear relationship between component thickness and performance metrics, thereby overcoming the limitations of conventional parameter screening methods. First, a finite element model of the battery pack shell is established. Based on the simulation software Abaqus/CAE, extrusion and modal simulations are conducted according to the test criteria. Subsequently, the linear correlation between the thickness of quantitative components and performance indices is analyzed using Pearson correlation coefficient to rapidly identify vibration-sensitive and collision-vulnerable components, thereby determining the specific optimization range. A multi-objective optimization model is then established, with mass minimization and modal frequency maximization as the core objectives, by combining the RSM surrogate model and the NSGA-II algorithm. After optimization, the mass of the battery pack is reduced by 1.73 %, and the first-order modal frequency increases from 12.911 Hz to 18.401 Hz, representing a 42.5 % improvement. Compared with conventional optimization methods, the proposed approach achieves a 26.8 % increase in the first-order modal frequency and a 0.2 kg reduction in weight, demonstrating the superiority of the optimization process. Additionally, modal, crash, and crush simulations are performed to validate the optimized battery pack, confirming that its crashworthiness meets safety standards.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"14 1","pages":"Pages 335-346"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147453737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}