alexandria engineering journal最新文献

{"title":"Efficient entropy estimation for inverted exponentiated Pareto distribution using ranked set sampling: A comparative study","authors":"Amal S. Hassan ,&nbsp;Tmader Alballa ,&nbsp;Etaf Alshawarbeh ,&nbsp;Rehab Alsultan ,&nbsp;Said G. Nassr ,&nbsp;Rokaya Elmorsy Mohamed","doi":"10.1016/j.aej.2026.01.018","DOIUrl":"10.1016/j.aej.2026.01.018","url":null,"abstract":"<div><div>Entropy, a key concept in information theory, measures the degree of unpredictability or uncertainty present in a random variable or system. It plays a vital role across various disciplines, including communication theory, thermodynamics, and statistical mechanics. On the other hand, Ranked Set Sampling (RSS) provides an effective approach to mitigating the challenges associated with costly or complex measurement procedures. Given the wide-ranging applications of the inverted exponentiated Pareto distribution, this study investigates the estimation of its parameters and various entropy measures, encompassing Havrda and Charvát, Tsallis, Rényi, and Arimoto. We examine the performance of these estimators under both RSS and simple random sampling (SRS) frameworks.To tackle this task, seven classical estimation techniques are employed: maximum product spacing, least squares, Kolmogorov, Anderson-Darling, weighted least squares, maximum likelihood, and Cramér-von Mises. Using an equal number of measured units, simulation studies evaluates the performance of estimators derived from SRS and RSS, considering both perfect and imperfect ranking scenarios. Three evaluation criteria are adopted for comparison: relative efficiency, mean squared error, and absolute bias. In assessing the estimated quality of RSS and SRS, the Kolmogorov technique appears beneficial in most cases, based on numerical results. In terms of estimation accuracy, RSS consistently performs better than SRS, regardless of whether the ranking is perfect or imperfect. Additionally, compared to imperfect ranking method, perfect ranking produces estimates that are more accurate. The advantage of the RSS design over the SRS design is further supported by real data results that indicate the tensile strength measures in GPA carbon fibers.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 242-269"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036717","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}

{"title":"AI-driven optimization techniques for smart sustainable manufacturing in Industry 5.0 ecosystem: A comprehensive review","authors":"Sita Rani ,&nbsp;Ramesh Karnati ,&nbsp;Vivek Patel ,&nbsp;M.K. Ranganathaswamy ,&nbsp;Prakhar Tomar ,&nbsp;Aman Kataria ,&nbsp;Amrindra Pal","doi":"10.1016/j.aej.2026.01.016","DOIUrl":"10.1016/j.aej.2026.01.016","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) driven optimization techniques is transforming smart manufacturing in the industry 5.0 landscape leading to sustainable industrial processes. This review comprehensively explores AI-driven optimization methods that enhance efficiency, resilience, and sustainability in modern manufacturing ecosystems. It highlights the role of various AI - based algorithms in optimizing production processes, energy consumption, and supply chains. Along with this, it also presents the significance of AI-driven manufacturing in improving secure production by facilitating real-time monitoring, anomaly detection, and predictive maintenance. In this work, the authors also examine how AI contributes to human-centric manufacturing, addressing challenges such as resource utilization, waste reduction, and adaptive decision-making. Key advancements, limitations, and future research directions are analyzed to provide a holistic view of AI’s transformative potential. The findings underscore the necessity of AI-driven optimization for achieving sustainable, efficient, and flexible manufacturing processes in Industry 5.0. This work serves as a significant reference for researchers, industry professionals, and policymakers seeking to leverage AI for sustainable industrial advancements. This paper presents the comprehensive synthesis of AI-driven optimization techniques represented for the emerging Industry 5.0 model, prioritizing smart sustainable manufacturing. Unlike prior reviews, it systematically compares traditional and AI-based approaches, highlights the transformative synergy of advanced technologies like AI, IoT, digital twins, and blockchain for real-time, human-centric manufacturing, and details hybrid optimization methods integrating AI algorithms. This review uniquely maps the integration of these innovations with sustainability, adaptability, and mass personalization, presenting a roadmap to help industries employ intelligent, data-driven, and eco-friendly optimization solutions for future-ready manufacturing.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 133-158"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036730","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}

{"title":"Autonomous aerial pipeline detection and tracking using YOLOv8 and real-time control algorithms","authors":"Ibrahim Akinjobi Aromoye ,&nbsp;Lo Hai Hiung ,&nbsp;Patrick Sebastian ,&nbsp;Abdullateef Oluwagbemiga Balogun ,&nbsp;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}

{"title":"Deep neural network-integrated finite-time fault-tolerant control for upper limb rehabilitation robots under actuator constraints","authors":"Fuad E. Alsaadi ,&nbsp;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}

{"title":"From words to proverbs: Evaluating LLMs’ linguistic and cultural competence in Saudi dialects with Absher","authors":"Renad Al-Monef ,&nbsp;Hassan Alhuzali ,&nbsp;Nora Alturayeif ,&nbsp;Ashwag Alasmari","doi":"10.1016/j.aej.2025.12.066","DOIUrl":"10.1016/j.aej.2025.12.066","url":null,"abstract":"<div><div>As large language models (LLMs) become increasingly central to Arabic NLP applications, their effectiveness in linguistically diverse settings, particularly regions with rich dialectal variation such as Saudi Arabia, remains underexplored. Existing evaluation paradigms tend to prioritize high-resource languages or Modern Standard Arabic (MSA), overlooking regional linguistic and cultural specificities. This leads to performance limitations and cultural biases in real-world deployments. To address this gap, we introduce <span>Absher</span>, the first comprehensive and fine-grained benchmark designed to assess the understanding of LLMs regarding Saudi dialects and their embedded cultural nuances. <span>Absher</span> consists of over 18,000 multiple choice questions derived from a curated dataset of dialectal words, phrases, and proverbs sourced from five major Saudi regions. The benchmark spans six task categories: Meaning, True/False, Fill-in-the-Blank, Contextual Usage, Cultural Interpretation, and Location Recognition, enabling multifaceted evaluation across both linguistic and cultural dimensions. We perform zero-shot evaluations on six state-of-the-art open LLMs: ALLaM, LLaMA, Jais, Mistral, Qwen, and AceGPT. Our results reveal substantial performance variability across dialects and question types. Qwen achieved the highest overall accuracy, excelling in word-level questions (63%), while ALLaM outperformed others in the interpretation of proverbs (48% accuracy). All models struggled with content from underrepresented dialects, particularly Southern and Eastern variants, and with context-free True/False questions, highlighting weaknesses in dialect grounding and binary reasoning. These findings demonstrate the need for dialect-aware training and culturally aligned evaluation. We position <span>Absher</span> as a critical step toward more equitable and effective LLMs development for real-world Arabic applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 25-41"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993580","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}

{"title":"A power network anomaly alarm denoising method based on a hybrid LSTM-attention model","authors":"Mingfei Zeng,&nbsp;Yuting Lian","doi":"10.1016/j.aej.2026.01.006","DOIUrl":"10.1016/j.aej.2026.01.006","url":null,"abstract":"<div><div>Power grid fault alarms play a crucial role in minimizing system damage and service disruptions. However, existing deep learning approaches frequently neglect the topological structure and physical characteristics of power system data, leading to suboptimal fault identification performance. To address this limitation, this paper proposes PhysLSTM-Attn, a novel physics-informed deep learning method for power network anomaly alarm denoising. The model incorporates Kirchhoff’s Laws directly within the feature embedding layer, ensuring that learned representations adhere to fundamental circuit conservation principles. A topology-aware bidirectional LSTM encoder captures both temporal dependencies and spatial relationships by integrating graph-Laplacian-based positional encodings into its gating mechanism. In addition, an electrical-distance-enhanced multi-head attention mechanism computes attention weights based on electrical coupling strength rather than semantic similarity, providing a more accurate reflection of device interactions. A multi-hop graph convolutional network further models cascading fault propagation across multiple electrical distances, while a confidence calibration module supplies reliability estimates to support decision-making. Comprehensive experiments on the East China Power Grid Alarm Dataset and the IEEE 118-Node Extended Dataset demonstrate accuracy improvements of 8.32 % and 7.43 % over the LSTM-Attention baseline, respectively.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 76-88"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036709","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}

{"title":"Advances in localization techniques and algorithms for UWSNs: A comprehensive review of challenges, opportunities, future directions, and comparative analysis","authors":"Zahid Ullah Khan ,&nbsp;Aman Muhammad ,&nbsp;Javed Khan ,&nbsp;Sajid Ullah Khan ,&nbsp;Irshad Ahmed Abbasi ,&nbsp;Hassan Nazeer Chaudhry ,&nbsp;Nazik Alturki ,&nbsp;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}

{"title":"DST-AttG: Network attack chain inference and situational forecasting with dynamic spatiotemporal attention coupled graph network","authors":"XiaoLe Sun ,&nbsp;ChuanPing Hu","doi":"10.1016/j.aej.2026.01.026","DOIUrl":"10.1016/j.aej.2026.01.026","url":null,"abstract":"<div><div>In response to the current challenges in network threat detection, such as complex attack features, incomplete attack chain inference, and insufficient situational forecasting accuracy, this paper proposes the DST-AttG model, which integrates dynamic spatiotemporal attention and graph convolutional networks for network threat node classification, attack chain inference, and situational forecasting tasks. The model uses dynamic spatiotemporal attention to capture the temporal evolution patterns and spatial correlation features of attack behaviors, combines the graph convolution layer to model network node topologies, leverages the temporal prediction module to predict attack trends, and employs the closed-loop feedback mechanism to dynamically correct prediction biases and optimize robustness. Experiments on the CTU-13 and UNSW-NB15 publicly available datasets demonstrate that the DST-AttG model outperforms comparison models in all tasks, while improving training and inference efficiency by 25%–50% compared to the second-best models, achieving a balance between detection accuracy and operational efficiency. Ablation experiments confirm the critical role of dynamic spatiotemporal attention and graph convolution layers in achieving the model’s high performance. The study shows that the DST-AttG model provides an effective solution for threat identification, tracing, and forecasting in network security, offering valuable technical references for the intelligent upgrade of defense systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"138 ","pages":"Pages 114-127"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186275","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}

{"title":"Fiber reinforced cementitious matrix bond behavior on masonry substrate","authors":"Jin Lu ,&nbsp;Xiaofan Liao","doi":"10.1016/j.aej.2026.01.008","DOIUrl":"10.1016/j.aej.2026.01.008","url":null,"abstract":"<div><div>In this study, the Multi Pier MP method, previously presented for Fiber Reinforced Polymer (FRP) delamination on masonry substrates, is adapted for the Fiber Reinforced Cementitious Matrix (FRCM) single lap shear behavior. Based on this method, an assemblage of vertical (piers) and diagonal (braces) truss members forms a two-dimensional truss structure, representing the entire composite system, including fibers, matrix, and masonry substrate. Piers and braces carry the normal and shear stresses of the system, respectively. With the help of the materials' axial stress-strain curve, the non-linear behavior of truss members was identified. All possible failure modes, such as rupture, fiber slippage, and matrix shear damage, were considered. Validation on four experimental FRCM reinforced masonry pillars demonstrated that the model can estimate the composite's single lap shear behavior and their failure mechanisms with acceptable accuracy. Additionally, all internal stresses and displacement profiles were obtained across the reinforcements for both elastic and plastic ranges up to failure. Despite its accuracy and comprehensiveness, minimal time was required to implement this method in any commercial code, only needing to address non-linearity in unidirectional elements.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"137 ","pages":"Pages 225-241"},"PeriodicalIF":6.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036710","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}

{"title":"An edge-available defect detection And Localization Flow Model","authors":"Yueyang Sui,&nbsp;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}