Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.036
Khaled A. Hafez , Ahmed T. Ahmed , Mohamed M. Helal
This research evaluates the computational resource requirements for CFD simulation parameters in predicting ship resistance, using the Volume of Fluid (VOF) method with the ISIS-CFD solver on a scaled 57,000-ton deadweight (DWT), single-screw bulk carrier, Oceanbeauty. The paper explores the effects of various simulation parameters such as the non-dimensional distance to the wall of the nearest cell center (y+), near wall treatment, turbulence model, time step (), and discretization scheme, across a velocity range () from to and a corresponding Froude number range () from to . The study employs an unstructured hexahedral grid, coupled with Wall Function (WF) and Wall Resolved (WR) approaches, and conducts a grid independence analysis to assess numerical uncertainty of the CFD simulations, validating hull resistance predictions against EFD data and ensuring compliance with relevant International Towing Tank Conference (ITTC) guidelines. The key findings highlight the significant influence of turbulence model choice and near-wall treatment (WF or WR) on prediction accuracy, underscoring the importance of an integrated approach to simulation requirements, flow characteristics, accuracy standards, and computational resources for reliable numerical results. Finally, based on Oceanbeauty’s CFD resistance prediction, the generalization of the results to diverse hull forms, with different design parameters, is presented and discussed.
{"title":"The influence of simulation parameters on bulk carrier resistance: A comparative analysis of computational and experimental fluid dynamics (CFD/EFD)","authors":"Khaled A. Hafez , Ahmed T. Ahmed , Mohamed M. Helal","doi":"10.1016/j.aej.2026.01.036","DOIUrl":"10.1016/j.aej.2026.01.036","url":null,"abstract":"<div><div>This research evaluates the computational resource requirements for CFD simulation parameters in predicting ship resistance, using the <strong><u>V</u></strong>olume <strong><u>o</u></strong>f <strong><u>F</u></strong>luid (VOF) method with the ISIS-CFD solver on a scaled 57,000-ton deadweight (DWT), single-screw bulk carrier, Oceanbeauty. The paper explores the effects of various simulation parameters such as the non-dimensional distance to the wall of the nearest cell center (y<sup>+</sup>), near wall treatment, turbulence model, time step (<span><math><mrow><mi>Δ</mi><mi>t</mi></mrow></math></span>), and discretization scheme, across a velocity range (<span><math><msub><mrow><mi>V</mi></mrow><mrow><mi>m</mi></mrow></msub></math></span>) from <span><math><mn>1.018</mn></math></span> to <span><math><mrow><mn>1.503</mn><mspace></mspace><mrow><mrow><mi>m</mi></mrow><mo>/</mo><mrow><mi>s</mi></mrow></mrow></mrow></math></span> and a corresponding Froude number range (<span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>n</mi></mrow></msub></math></span>) from <span><math><mn>0.126</mn></math></span> to <span><math><mn>0.186</mn></math></span>. The study employs an unstructured hexahedral grid, coupled with <strong><u>W</u></strong>all <strong><u>F</u></strong>unction (WF) and <strong><u>W</u></strong>all <strong><u>R</u></strong>esolved (WR) approaches, and conducts a grid independence analysis to assess numerical uncertainty of the CFD simulations, validating hull resistance predictions against EFD data and ensuring compliance with relevant International Towing Tank Conference (ITTC) guidelines. The key findings highlight the significant influence of turbulence model choice and near-wall treatment (WF or WR) on prediction accuracy, underscoring the importance of an integrated approach to simulation requirements, flow characteristics, accuracy standards, and computational resources for reliable numerical results. Finally, based on Oceanbeauty’s CFD resistance prediction, the generalization of the results to diverse hull forms, with different design parameters, is presented and discussed.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"138 ","pages":"Pages 1-20"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.044
Ibrahim Akinjobi Aromoye , Lo Hai Hiung , Patrick Sebastian , Abdullateef Oluwagbemiga Balogun , Lukman Shehu Ayinla
The oil and gas industry relies heavily on extensive pipeline networks, necessitating regular inspections and maintenance to ensure structural integrity and prevent failures. Traditional inspection methods, including manual visual checks and high-sensitivity sensors, are often labour-intensive, prone to human error, and inefficient in hazardous environments. Drone-based inspections have emerged as a promising alternative; however, most existing systems still depend on skilled operators, limiting scalability and autonomy. To address these, this study introduces a novel autonomous aerial pipeline monitoring system that leverages advanced computer vision techniques. The system employs a Tello drone with an onboard camera and integrates three core algorithms: pipeline detection, pipeline following, and altitude control. These algorithms were optimised for real-time performance and stability. The object detection model, trained using YOLOv8s, achieved approximately 71 % accuracy under standard conditions. Further experiments involving data preprocessing, augmentation, and model training configurations demonstrated that a 90/5/5 split with 100 training epochs produced the highest precision of 94 %. During real-time pipeline tracking, the system achieved a mean squared error (MSE) of 0.0023 m², indicating high-precision navigation. In addition, the altitude control algorithm attained a MAE of 0.0044 m, effectively minimising altitude fluctuations. Compared to existing drone-based inspection systems, the proposed approach demonstrated superior accuracy, achieving 97.4 % mAP compared with 72 % in current solutions, and reducing tracking MSE from 0.0111 m² to 0.0023 m². These results highlight the system’s capacity to enhance autonomy, reduce reliance on human operators, and improve safety in hazardous environments, advancing the state of the art in autonomous pipeline monitoring.
{"title":"Autonomous aerial pipeline detection and tracking using YOLOv8 and real-time control algorithms","authors":"Ibrahim Akinjobi Aromoye , Lo Hai Hiung , Patrick Sebastian , Abdullateef Oluwagbemiga Balogun , Lukman Shehu Ayinla","doi":"10.1016/j.aej.2026.01.044","DOIUrl":"10.1016/j.aej.2026.01.044","url":null,"abstract":"<div><div>The oil and gas industry relies heavily on extensive pipeline networks, necessitating regular inspections and maintenance to ensure structural integrity and prevent failures. Traditional inspection methods, including manual visual checks and high-sensitivity sensors, are often labour-intensive, prone to human error, and inefficient in hazardous environments. Drone-based inspections have emerged as a promising alternative; however, most existing systems still depend on skilled operators, limiting scalability and autonomy. To address these, this study introduces a novel autonomous aerial pipeline monitoring system that leverages advanced computer vision techniques. The system employs a Tello drone with an onboard camera and integrates three core algorithms: pipeline detection, pipeline following, and altitude control. These algorithms were optimised for real-time performance and stability. The object detection model, trained using YOLOv8s, achieved approximately 71 % accuracy under standard conditions. Further experiments involving data preprocessing, augmentation, and model training configurations demonstrated that a 90/5/5 split with 100 training epochs produced the highest precision of 94 %. During real-time pipeline tracking, the system achieved a mean squared error (MSE) of 0.0023 m², indicating high-precision navigation. In addition, the altitude control algorithm attained a MAE of 0.0044 m, effectively minimising altitude fluctuations. Compared to existing drone-based inspection systems, the proposed approach demonstrated superior accuracy, achieving 97.4 % mAP compared with 72 % in current solutions, and reducing tracking MSE from 0.0111 m² to 0.0023 m². These results highlight the system’s capacity to enhance autonomy, reduce reliance on human operators, and improve safety in hazardous environments, advancing the state of the art in autonomous pipeline monitoring.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 424-442"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.033
Fuad E. Alsaadi , Njud S. Alharbi
This paper introduces a hybrid fault-tolerant control framework for nonlinear upper-limb rehabilitation robots subject to actuator saturation and time-varying uncertainties. The approach combines a deep neural network (DNN)–based state-space model to capture nonlinear rehabilitation dynamics, a finite-time disturbance observer to address unmodeled effects and actuator degradation, and a finite-time sliding-mode controller that enforces actuator limits. Established finite-time Lyapunov tools are used to guarantee convergence in the presence of modeling errors, faults, and input constraints. Simulation studies under ideal, input-constrained, and actuator-fault conditions show substantial improvements in tracking accuracy, up to 58 % faster convergence, and smoother, more energy-efficient control inputs compared to PID and classical SMC baselines. The use of fixed-size matrix–vector computations supports real-time execution on embedded platforms. This framework effectively integrates data-driven modeling with robust finite-time control, providing a practical and reliable solution for human-in-the-loop rehabilitation systems.
{"title":"Deep neural network-integrated finite-time fault-tolerant control for upper limb rehabilitation robots under actuator constraints","authors":"Fuad E. Alsaadi , Njud S. Alharbi","doi":"10.1016/j.aej.2026.01.033","DOIUrl":"10.1016/j.aej.2026.01.033","url":null,"abstract":"<div><div>This paper introduces a hybrid fault-tolerant control framework for nonlinear upper-limb rehabilitation robots subject to actuator saturation and time-varying uncertainties. The approach combines a deep neural network (DNN)–based state-space model to capture nonlinear rehabilitation dynamics, a finite-time disturbance observer to address unmodeled effects and actuator degradation, and a finite-time sliding-mode controller that enforces actuator limits. Established finite-time Lyapunov tools are used to guarantee convergence in the presence of modeling errors, faults, and input constraints. Simulation studies under ideal, input-constrained, and actuator-fault conditions show substantial improvements in tracking accuracy, up to 58 % faster convergence, and smoother, more energy-efficient control inputs compared to PID and classical SMC baselines. The use of fixed-size matrix–vector computations supports real-time execution on embedded platforms. This framework effectively integrates data-driven modeling with robust finite-time control, providing a practical and reliable solution for human-in-the-loop rehabilitation systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 329-343"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.039
Zahid Ullah Khan , Aman Muhammad , Javed Khan , Sajid Ullah Khan , Irshad Ahmed Abbasi , Hassan Nazeer Chaudhry , Nazik Alturki , Sultan Alanazi
Underwater Wireless Sensor Networks (UWSNs) play a crucial role in diverse applications, including environmental monitoring, underwater exploration, aquatic life research, and military surveillance. Accurate localization of sensor receiver (Rx) nodes is essential for ensuring precise data collection and maintaining network reliability. This research offers a comprehensive examination of the challenges and advancements in UWSNs localization techniques, addressing the complexities of achieving accurate localization and presenting mathematical solutions for each issue. Furthermore, the paper introduces an innovative classification framework for localization techniques, dividing them into two primary categories: centralized and distributed approaches. Each category is further segmented into estimation based and prediction-based techniques, providing a structured perspective to improve the understanding of various localization methods in UWSNs. Additionally, localization algorithms are classified into two major types range free and range-based methods. The study provides an in-depth discussion of their core principles and real-world applications. It also reviews recent advancements in localization algorithms and techniques for UWSNs, highlighting cutting edge methods and their contribution in improving localization accuracy and efficiency. Moreover, mathematical and simulation-based analyses are employed to assess key localization algorithms, such as Centroid, Distance Vector Hop (DV-Hop), and Approximate Point in Triangle (APIT). A comparative evaluation of these algorithms is conducted using multiple performance metrics, offering valuable insights into their strengths and limitations. Lastly, the study explores future research directions and potential opportunities, emphasizing key areas for further innovation and development in UWSN localization. By providing a comprehensive analysis of existing localization approaches, this research lays the groundwork for future advancements in the aforesaid field, ultimately aiming to enhance the performance and reliability of UWSNs across various underwater applications.
{"title":"Advances in localization techniques and algorithms for UWSNs: A comprehensive review of challenges, opportunities, future directions, and comparative analysis","authors":"Zahid Ullah Khan , Aman Muhammad , Javed Khan , Sajid Ullah Khan , Irshad Ahmed Abbasi , Hassan Nazeer Chaudhry , Nazik Alturki , Sultan Alanazi","doi":"10.1016/j.aej.2026.01.039","DOIUrl":"10.1016/j.aej.2026.01.039","url":null,"abstract":"<div><div>Underwater Wireless Sensor Networks (UWSNs) play a crucial role in diverse applications, including environmental monitoring, underwater exploration, aquatic life research, and military surveillance. Accurate localization of sensor receiver (Rx) nodes is essential for ensuring precise data collection and maintaining network reliability. This research offers a comprehensive examination of the challenges and advancements in UWSNs localization techniques, addressing the complexities of achieving accurate localization and presenting mathematical solutions for each issue. Furthermore, the paper introduces an innovative classification framework for localization techniques, dividing them into two primary categories: centralized and distributed approaches. Each category is further segmented into estimation based and prediction-based techniques, providing a structured perspective to improve the understanding of various localization methods in UWSNs. Additionally, localization algorithms are classified into two major types range free and range-based methods. The study provides an in-depth discussion of their core principles and real-world applications. It also reviews recent advancements in localization algorithms and techniques for UWSNs, highlighting cutting edge methods and their contribution in improving localization accuracy and efficiency. Moreover, mathematical and simulation-based analyses are employed to assess key localization algorithms, such as Centroid, Distance Vector Hop (DV-Hop), and Approximate Point in Triangle (APIT). A comparative evaluation of these algorithms is conducted using multiple performance metrics, offering valuable insights into their strengths and limitations. Lastly, the study explores future research directions and potential opportunities, emphasizing key areas for further innovation and development in UWSN localization. By providing a comprehensive analysis of existing localization approaches, this research lays the groundwork for future advancements in the aforesaid field, ultimately aiming to enhance the performance and reliability of UWSNs across various underwater applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 468-504"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Medium- and small-span beam bridges are critical components of modern transportation networks. Studying their degradation patterns based on long-term inspection data is crucial for making informed maintenance and repair decisions. To accurately predict the technical condition of medium and small-span bridges, this study proposes a bridge degradation prediction model based on a hybrid machine learning algorithm integrating Recursive Feature Elimination (RFE), Seagull Optimization Algorithm (SSOA), Genetic Algorithm (GA), and Natural Gradient Boosting (NGBoost). First, a comprehensive database of bridge technical conditions was constructed using 12 years of inspection data from 600 bridges in a specific province. RFE was employed to select 10 key factors, including bridge age, traffic volume, and bearing ratings, to optimize the model's input dimensions. Subsequently, the SSOA-GA method was used to optimize the hyperparameters of the NGBoost model, improving the prediction accuracy and generalization capabilities. To further improve the model’s interpretability, SHAP analysis was conducted, revealing the critical influence of factors like bridge age, traffic volume, and bearing ratings on bridge technical conditions. The results indicate that the proposed model demonstrates excellent performance in prediction accuracy and generalization, achieving an R² value of 0.975 and an RMSE of 0.115. It effectively captures the nonlinear relationships between bridge conditions and multiple influencing factors. Moreover, with the help of SHAP analysis, the relative contributions of input factors were quantified, confirming the critical influence of bridge age, traffic volume, and bearing ratings on bridge technical conditions. This significantly enhances the model's interpretability and practicality, providing a scientific basis for formulating bridge maintenance and repair strategies.
{"title":"Predicting the condition of small and medium-span bridges using hybrid machine learning","authors":"Luo-meng Zhang , Dong Liang , Hai-bin Huang , Yao-zong Hu , Jin-song Zhang","doi":"10.1016/j.aej.2025.12.056","DOIUrl":"10.1016/j.aej.2025.12.056","url":null,"abstract":"<div><div>Medium- and small-span beam bridges are critical components of modern transportation networks. Studying their degradation patterns based on long-term inspection data is crucial for making informed maintenance and repair decisions. To accurately predict the technical condition of medium and small-span bridges, this study proposes a bridge degradation prediction model based on a hybrid machine learning algorithm integrating Recursive Feature Elimination (RFE), Seagull Optimization Algorithm (SSOA), Genetic Algorithm (GA), and Natural Gradient Boosting (NGBoost). First, a comprehensive database of bridge technical conditions was constructed using 12 years of inspection data from 600 bridges in a specific province. RFE was employed to select 10 key factors, including bridge age, traffic volume, and bearing ratings, to optimize the model's input dimensions. Subsequently, the SSOA-GA method was used to optimize the hyperparameters of the NGBoost model, improving the prediction accuracy and generalization capabilities. To further improve the model’s interpretability, SHAP analysis was conducted, revealing the critical influence of factors like bridge age, traffic volume, and bearing ratings on bridge technical conditions. The results indicate that the proposed model demonstrates excellent performance in prediction accuracy and generalization, achieving an R² value of 0.975 and an RMSE of 0.115. It effectively captures the nonlinear relationships between bridge conditions and multiple influencing factors. Moreover, with the help of SHAP analysis, the relative contributions of input factors were quantified, confirming the critical influence of bridge age, traffic volume, and bearing ratings on bridge technical conditions. This significantly enhances the model's interpretability and practicality, providing a scientific basis for formulating bridge maintenance and repair strategies.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 270-287"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.028
Yueyang Sui, Anluo Yi
Unsupervised defect detection aims to identify and localize unpredictable defects in industrial manufacturing processes caused by uncontrollable factors. Flow-based unsupervised models have recently attracted considerable attention from the research community. However, existing methods generally suffer from limited sensitivity to fine edge structures in images, making it difficult to effectively capture boundary information of defective regions, as well as excessive redundancy in feature representations, which degrades both discriminative power and computational efficiency. To address these limitations, we propose an Edge-Aware Defect Detection and Localization Flow model (EADFlow). EADFlow integrates a Frequency Domain Edge-Aware Module to enhance the modeling of high-frequency edge information and introduces a Focused Local and Global Attention Module to reduce feature redundancy and strengthen feature representation capability. Experimental results show that EADFlow achieves state-of-the-art performance across multiple industrial defect detection benchmarks significantly outperforming existing advanced methods.
无监督缺陷检测旨在识别和定位工业制造过程中由不可控因素引起的不可预测缺陷。基于流的无监督模型最近引起了研究界的广泛关注。然而，现有方法对图像精细边缘结构的敏感性有限，难以有效捕获缺陷区域的边界信息，特征表示冗余度过高，降低了判别能力和计算效率。为了解决这些限制，我们提出了一个边缘感知缺陷检测和定位流模型（EADFlow）。EADFlow集成了频域边缘感知模块（Frequency Domain edge - aware Module），增强了高频边缘信息的建模能力；引入了聚焦局部和全局关注模块（Focused Local and Global Attention Module），减少了特征冗余，增强了特征表示能力。实验结果表明，EADFlow在多个工业缺陷检测基准测试中达到了最先进的性能，显著优于现有的先进方法。
{"title":"An edge-available defect detection And Localization Flow Model","authors":"Yueyang Sui, Anluo Yi","doi":"10.1016/j.aej.2026.01.028","DOIUrl":"10.1016/j.aej.2026.01.028","url":null,"abstract":"<div><div>Unsupervised defect detection aims to identify and localize unpredictable defects in industrial manufacturing processes caused by uncontrollable factors. Flow-based unsupervised models have recently attracted considerable attention from the research community. However, existing methods generally suffer from limited sensitivity to fine edge structures in images, making it difficult to effectively capture boundary information of defective regions, as well as excessive redundancy in feature representations, which degrades both discriminative power and computational efficiency. To address these limitations, we propose an Edge-Aware Defect Detection and Localization Flow model (EADFlow). EADFlow integrates a Frequency Domain Edge-Aware Module to enhance the modeling of high-frequency edge information and introduces a Focused Local and Global Attention Module to reduce feature redundancy and strengthen feature representation capability. Experimental results show that EADFlow achieves state-of-the-art performance across multiple industrial defect detection benchmarks significantly outperforming existing advanced methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 288-298"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.043
Jing Zhang, Tianming Yang, Qiang Guo, Zhiqiang Yang, Jingfang Wang
With the proliferation of IoT devices and the widespread adoption of 5G technology, UAV-assisted edge computing (UAVAEC) architecture is rapidly expanding. However, the openness of wireless communication links renders the network vulnerable to malicious attacks, making security a significant concern in UAVAEC scenarios. We propose a blockchain-based approach for resource provisioning in UAVAEC architecture, ensuring secure network communication. To solve the service response latency of IoT terminals rendered by blockchain consensus process, this article adopts an offline strategy when recording the resource allocation process and crucially introduces an improved DPoS consensus mechanism. This mechanism aims to make the blockchain lightweight, and make the entire system run more efficiently in the UAVAEC environment. To strike a balance between efficiency and decentralization, we have designed an improved transaction authentication procedure centered around the competition for accounting rights. Extensive simulation experiments confirm the performance of our proposed algorithm. Compared to benchmark algorithms, it achieves higher throughput, lower block generation delay, and greater utility for candidate nodes.
{"title":"A resource provision method for UAV-assisted edge computing based on improved DPoS consensus mechanism","authors":"Jing Zhang, Tianming Yang, Qiang Guo, Zhiqiang Yang, Jingfang Wang","doi":"10.1016/j.aej.2026.01.043","DOIUrl":"10.1016/j.aej.2026.01.043","url":null,"abstract":"<div><div>With the proliferation of IoT devices and the widespread adoption of 5G technology, UAV-assisted edge computing (UAVAEC) architecture is rapidly expanding. However, the openness of wireless communication links renders the network vulnerable to malicious attacks, making security a significant concern in UAVAEC scenarios. We propose a blockchain-based approach for resource provisioning in UAVAEC architecture, ensuring secure network communication. To solve the service response latency of IoT terminals rendered by blockchain consensus process, this article adopts an offline strategy when recording the resource allocation process and crucially introduces an improved DPoS consensus mechanism. This mechanism aims to make the blockchain lightweight, and make the entire system run more efficiently in the UAVAEC environment. To strike a balance between efficiency and decentralization, we have designed an improved transaction authentication procedure centered around the competition for accounting rights. Extensive simulation experiments confirm the performance of our proposed algorithm. Compared to benchmark algorithms, it achieves higher throughput, lower block generation delay, and greater utility for candidate nodes.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 386-400"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.034
Ali Akbar Firoozi , Ali Asghar Firoozi , Taoufik Saidani
This study rigorously evaluates the critical role of civil engineering in advancing sustainable water management, a cornerstone for achieving global environmental sustainability and resource conservation objectives. Amid increasing water demands, exacerbated by climate change and rapid urbanization, the imperative to incorporate innovative practices within water management systems has intensified. This manuscript offers a comprehensive analysis of cutting-edge technological advancements and methodologies currently transforming water supply, wastewater treatment, and stormwater management. It highlights the significant impact of these innovations in meeting sustainability goals and confronts the complex challenges that obstruct their broader adoption. Additionally, the paper outlines strategic approaches to surmount financial, technological, regulatory, and societal obstacles, thereby promoting a sustainable, efficient, and equitable water resource management paradigm. Through the integration of theoretical frameworks with empirical case studies, this research aims to ignite further academic exploration, policy refinement, and practical applications that uphold the principles of sustainability within the field of civil engineering.
{"title":"Advancing sustainable water management: The pivotal role of civil engineering in navigating environmental and urban challenges","authors":"Ali Akbar Firoozi , Ali Asghar Firoozi , Taoufik Saidani","doi":"10.1016/j.aej.2026.01.034","DOIUrl":"10.1016/j.aej.2026.01.034","url":null,"abstract":"<div><div>This study rigorously evaluates the critical role of civil engineering in advancing sustainable water management, a cornerstone for achieving global environmental sustainability and resource conservation objectives. Amid increasing water demands, exacerbated by climate change and rapid urbanization, the imperative to incorporate innovative practices within water management systems has intensified. This manuscript offers a comprehensive analysis of cutting-edge technological advancements and methodologies currently transforming water supply, wastewater treatment, and stormwater management. It highlights the significant impact of these innovations in meeting sustainability goals and confronts the complex challenges that obstruct their broader adoption. Additionally, the paper outlines strategic approaches to surmount financial, technological, regulatory, and societal obstacles, thereby promoting a sustainable, efficient, and equitable water resource management paradigm. Through the integration of theoretical frameworks with empirical case studies, this research aims to ignite further academic exploration, policy refinement, and practical applications that uphold the principles of sustainability within the field of civil engineering.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 360-385"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.035
Shi Luo , Xiaoyue Chen , Sheng Zheng , Yuxin Zhao
To address the challenges of coexisting defects at multiple scales and the tendency for small defects to be missed in LCD defect detection, this paper proposes a novel detection algorithm. The method first designs a Multi-Differential Fusion Module (MDFM), which enhances sensitivity to small defects (especially dot defects) by integrating multiple differential sensing strategies. Second, a multi-branch fusion efficient feature pyramid network (MFEFPN) is constructed. Leveraging a multi-branch structure and efficient fusion mechanisms, this network effectively mitigates information loss and feature interference issues inherent in traditional feature pyramid networks. To further balance accuracy and computational efficiency, we designed an Adaptive Shared Lightweight Detection Head (ASLD), which maintains excellent detection accuracy while significantly reducing the number of parameters and computational complexity (GFLOPs) through a parameter-sharing mechanism. Additionally, geometric constraint terms are incorporated into the loss function to further enhance the localization capability of defect boundaries. Experimental results show that the proposed MDMB-YOLO achieves an accuracy of 85.2%, with a 4.4% improvement in accuracy, a 3.3% improvement in recall rate, a 2.8% improvement in mAP50, and a 0.9% improvement in mAP50-95 compared to the baseline model. The number of parameters and GFLOPs were reduced by 23.3% and 8%, respectively, compared to the baseline model, indicating that this approach offers both accuracy and efficiency advantages in LCD defect detection tasks. The dataset used in this study has been publicly released, and we encourage its use for related research in accordance with the platform’s terms at:https://aistudio.baidu.com/dataset/detail/358247/settings.
{"title":"MDMB-YOLO: A liquid crystal display defect detection method using Multi-Differential Fusion and multi-branch feature pyramid","authors":"Shi Luo , Xiaoyue Chen , Sheng Zheng , Yuxin Zhao","doi":"10.1016/j.aej.2026.01.035","DOIUrl":"10.1016/j.aej.2026.01.035","url":null,"abstract":"<div><div>To address the challenges of coexisting defects at multiple scales and the tendency for small defects to be missed in LCD defect detection, this paper proposes a novel detection algorithm. The method first designs a Multi-Differential Fusion Module (MDFM), which enhances sensitivity to small defects (especially dot defects) by integrating multiple differential sensing strategies. Second, a multi-branch fusion efficient feature pyramid network (MFEFPN) is constructed. Leveraging a multi-branch structure and efficient fusion mechanisms, this network effectively mitigates information loss and feature interference issues inherent in traditional feature pyramid networks. To further balance accuracy and computational efficiency, we designed an Adaptive Shared Lightweight Detection Head (ASLD), which maintains excellent detection accuracy while significantly reducing the number of parameters and computational complexity (GFLOPs) through a parameter-sharing mechanism. Additionally, geometric constraint terms are incorporated into the loss function to further enhance the localization capability of defect boundaries. Experimental results show that the proposed MDMB-YOLO achieves an accuracy of 85.2%, with a 4.4% improvement in accuracy, a 3.3% improvement in recall rate, a 2.8% improvement in mAP50, and a 0.9% improvement in mAP50-95 compared to the baseline model. The number of parameters and GFLOPs were reduced by 23.3% and 8%, respectively, compared to the baseline model, indicating that this approach offers both accuracy and efficiency advantages in LCD defect detection tasks. The dataset used in this study has been publicly released, and we encourage its use for related research in accordance with the platform’s terms at:<span><span>https://aistudio.baidu.com/dataset/detail/358247/settings</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 521-536"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.aej.2026.01.011
Jing Wang , Qiang Liang , Yan Li , Yong Wang
Hot rolling of 5A06 aluminum alloy is crucial for manufacturing large structural components in the aerospace and shipbuilding industries. Accurate prediction of the alloy’s hot flow stress is essential for optimizing the rolling process and ensuring product quality. Hot compression experiments were conducted to obtain the experimental data using the Gleeble-3500 in this study. Research was performed at several temperature levels for deformation (specifically 573 K, 623 K, etc., up to 773 K), utilizing diverse strain rates (from 0.01 s−1 to 10 s−1, increasing by 10 times each), concluding with a peak deformation percentage of 60 %. It established Johnson-Cook (J-C), Zerilli-Armstrong (Z-A), and Gene Expression Programming (GEP) constitutive models, evaluating accuracy via statistics and DEFORM-2D finite element simulation (FES). Statistics showed GEP was best: R²= 0.98 (J-C:0.96, Z-A:0.95), lowest RMSE= 11.41(J-C:26.51, Z-A=30.43), MAE= 8.62(J-C:18.83, Z-A: 15.86), AARE= 9.47 % (J-C: 31.40 %, Z-A: 11.64 %). FES further confirmed the GEP model’s superiority, as it exhibited the smallest load deviation from experimental values. Furthermore, deformation stress predictions of non-experimental conditions were conducted under two sets of conditions: a strain rate of 0.1 s−1 with deformation temperatures of 593 K and 643 K, and a deformation temperature of 573 K with strain rates of 0.05 s−1, 0.5 s−1, and 2.5 s−1. The results show that the stress curves at 593 K and 643 K align with the trend of experimental curves at 573 K, 623 K, and 673 K and lie within their corresponding intervals; similarly, the stress curves at 0.05 s−1, 0.5 s−1, and 2.5 s−1 conform to the trend of experimental curves at 0.01 s−1, 0.1 s−1, and 1 s−1, with their positions following the rule that higher strain rates lead to greater deformation stresses. The GEP model can not only effectively predict the flow stress under experimental conditions but also forecast the flow stress under non-experimental deformation conditions, thus providing a valuable tool for the numerical simulation and process optimization of the hot rolling process of 5A06 aluminum alloy.
{"title":"A gene expression programming-enabled prediction for hot flow stress of 5A06 aluminum alloy used in large structures","authors":"Jing Wang , Qiang Liang , Yan Li , Yong Wang","doi":"10.1016/j.aej.2026.01.011","DOIUrl":"10.1016/j.aej.2026.01.011","url":null,"abstract":"<div><div>Hot rolling of 5A06 aluminum alloy is crucial for manufacturing large structural components in the aerospace and shipbuilding industries. Accurate prediction of the alloy’s hot flow stress is essential for optimizing the rolling process and ensuring product quality. Hot compression experiments were conducted to obtain the experimental data using the Gleeble-3500 in this study. Research was performed at several temperature levels for deformation (specifically 573 K, 623 K, etc., up to 773 K), utilizing diverse strain rates (from 0.01 s<sup>−1</sup> to 10 s<sup>−1</sup>, increasing by 10 times each), concluding with a peak deformation percentage of 60 %. It established Johnson-Cook (J-C), Zerilli-Armstrong (Z-A), and Gene Expression Programming (GEP) constitutive models, evaluating accuracy via statistics and DEFORM-2D finite element simulation (FES). Statistics showed GEP was best: R²= 0.98 (J-C:0.96, Z-A:0.95), lowest RMSE= 11.41(J-C:26.51, Z-A=30.43), MAE= 8.62(J-C:18.83, Z-A: 15.86), AARE= 9.47 % (J-C: 31.40 %, Z-A: 11.64 %). FES further confirmed the GEP model’s superiority, as it exhibited the smallest load deviation from experimental values. Furthermore, deformation stress predictions of non-experimental conditions were conducted under two sets of conditions: a strain rate of 0.1 s<sup>−1</sup> with deformation temperatures of 593 K and 643 K, and a deformation temperature of 573 K with strain rates of 0.05 s<sup>−1</sup>, 0.5 s<sup>−1</sup>, and 2.5 s<sup>−1</sup>. The results show that the stress curves at 593 K and 643 K align with the trend of experimental curves at 573 K, 623 K, and 673 K and lie within their corresponding intervals; similarly, the stress curves at 0.05 s<sup>−1</sup>, 0.5 s<sup>−1</sup>, and 2.5 s<sup>−1</sup> conform to the trend of experimental curves at 0.01 s<sup>−1</sup>, 0.1 s<sup>−1</sup>, and 1 s<sup>−1</sup>, with their positions following the rule that higher strain rates lead to greater deformation stresses. The GEP model can not only effectively predict the flow stress under experimental conditions but also forecast the flow stress under non-experimental deformation conditions, thus providing a valuable tool for the numerical simulation and process optimization of the hot rolling process of 5A06 aluminum alloy.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 537-551"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号