alexandria engineering journal最新文献

{"title":"A hybrid SORT-CNN algorithm for multi-target real-time recognition of urban traffic surveillance images","authors":"Rulin Liu ,&nbsp;Zhihua Long","doi":"10.1016/j.aej.2026.04.054","DOIUrl":"10.1016/j.aej.2026.04.054","url":null,"abstract":"<div><div>Aiming at the problems of insufficient real-time performance of multi-target detection and poor tracking stability in complex scenes in urban intelligent transportation system, this paper proposes a multi-target real-time recognition and tracking algorithm based on deep learning. Firstly, a target detection model integrating CNN network structure and attention mechanism is constructed, and the detection accuracy of vehicles, pedestrians and other targets is significantly improved by designing a novel SRFPN (SORT-CNN Feature Pyramid Network) module that integrates CNN and Transformer features to enhance multi-scale representation, and by optimizing the loss function with an additional penalty term specifically designed to reduce identity switches during data association. Secondly, the Simple Online and Realtime Tracking (SORT) algorithm framework is built and combined to form the algorithm model, so as to lighten the Re-ID feature extraction module, and finally, the target occlusion and ID switching problems are solved by using the trajectory prediction and data association optimization strategy; Experimental results show that the average detection accuracy of this algorithm is 95%, and the tracking accuracy is 82.4%, ranking first in the comprehensive evaluation index, which has significant advantages over traditional methods. The research results provide an effective solution for real-time target analysis in urban traffic monitoring scenarios, and have practical reference value for the construction of intelligent traffic management system.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 207-219"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147860904","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":"YOLOv8-GSC: An agricultural crop precision detection model integrating lightweight network and attention mechanism","authors":"Wenzheng Dai,&nbsp;Zhanhui Zhang,&nbsp;Zhixin Zhang,&nbsp;Huashuo Zhu","doi":"10.1016/j.aej.2026.04.027","DOIUrl":"10.1016/j.aej.2026.04.027","url":null,"abstract":"<div><div>Visual perception and agricultural target recognition are core to advancing agricultural automation, yet existing CNN-based detection models face critical accuracy-efficiency trade-offs in scenarios with multi-scale targets and cluttered backgrounds. To address this, we propose YOLOv8-GSC, an improved model integrating YOLOv8 with three key modules in a synergistic manner—distinct from simple module superposition. GhostNet reduces redundant computations for model lightweighting; Spatial and Channel Reconstruction Convolution (SCConv) enhances fine-grained feature extraction for small targets like crop lesions; and Polarized Multi-Scale Attention (PMSA) enables multi-scale attention to capture targets of varying sizes. Experiments on PlantVillage and Agriculture-Vision datasets demonstrate that YOLOv8-GSC outperforms comparative models, achieving mean Average Precision at 50% IoU (mAP50) of 77.6% and 74.05%, and mean Average Precision at 75% IoU (mAP75) of 49.8% and 47.63%, respectively. It maintains high inference speed, realizing an optimal balance between accuracy and efficiency, and effectively tackles key agricultural detection challenges, providing reliable technical support for crop disease monitoring and precision farming.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 63-76"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147806756","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 critical review of microwave and conventional pyrolysis technologies for biomass and plastic waste conversion","authors":"Anuradha K,&nbsp;Ranjeet Kumar Mishra","doi":"10.1016/j.aej.2026.04.046","DOIUrl":"10.1016/j.aej.2026.04.046","url":null,"abstract":"<div><div>This comprehensive review examines the current state and future potential of microwave and conventional pyrolysis, emphasising the significance of process parameters in facilitating sustainable resource recovery from biomass and plastics. A detailed assessment of process variables, including temperature, residence time, and feedstock composition, is essential to this evaluation and to determining the yields and quality of pyrolysis products. The review highlights the intricate interactions among these factors and their critical role in achieving optimal resource recovery. The major focus of the review is the comparison of conventional pyrolysis methods with the microwave-assisted techniques. The well-established processes of traditional pyrolysis are contrasted with the rapid, accurate heating capabilities of microwave technology. This comparison demonstrates the potential of microwave-assisted pyrolysis to enhance energy efficiency and resource recovery. The discussion focuses on identifying current knowledge gaps and defining key opportunities for future research, with particular attention to integrating biomass and plastic pyrolysis into sustainable resource recovery approaches. The review contributes to the ongoing discourse on advancing pyrolysis technologies to support a more sustainable, circular economy by synthesising current knowledge and projecting future opportunities. The current review highlights the potential of conventional and microwave-assisted pyrolysis for sustainable resource recovery in a clear, detailed manner.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 103-136"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147806759","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":"Bifurcation analysis for a COVID-19 cell model with passive antibody treatment control strategy","authors":"Muhammad Imran ,&nbsp;Brett McKinney ,&nbsp;Saira Batool ,&nbsp;Azhar Iqbal Kashif Butt","doi":"10.1016/j.aej.2026.04.048","DOIUrl":"10.1016/j.aej.2026.04.048","url":null,"abstract":"<div><div>We present a mathematical model that describes the cellular dynamics of COVID-19 infection and antibody response. We improve upon existing mathematical models by adding detailed evidence-based assumptions and proving key features of solutions, such as the existence of unique, positive, and bounded solutions. Our susceptible–exposed–infected–virus particle–antibody (SEIVA) model divides the cell population into three compartments: susceptible <span><math><mrow><mo>(</mo><mi>S</mi><mo>)</mo></mrow></math></span>, latent/exposed (<span><math><mi>E</mi></math></span>), and infectious (<span><math><mi>I</mi></math></span>). In addition, it incorporates virus particles (<span><math><mi>V</mi></math></span>) and antibody compartments (<span><math><mi>A</mi></math></span>) with neutralizing interactions. We identify both the disease-free and the endemic equilibrium states, and we use the next-generation matrix approach to determine that the most important measure for understanding the severity of the infection is the basic reproduction number <span><math><mrow><mo>(</mo><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>)</mo></mrow></math></span>. Using the normalized sensitivity index technique, we identify the pivotal parameters that influence <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. We establish the conditions for the stability of the proposed model at the aforementioned equilibrium points. We also analyze bifurcation and chaos in the dynamics of the proposed COVID-19 cell model to the infection rate and determine its critical value. We further extend this model with an additional control parameter that represents passive antibody treatment and examine the model’s responsiveness to various control strategies. Overall, this work presents a comprehensive modeling approach for analyzing and controlling the disease at the cellular level, combining advanced mathematical techniques with practical insights for disease management.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 326-343"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147861323","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":"Materials engineering design of radiative cooling sports textiles and their emerging applications in outdoor athletics","authors":"Xiangkui Kong,&nbsp;Cuihong Fan","doi":"10.1016/j.aej.2026.04.036","DOIUrl":"10.1016/j.aej.2026.04.036","url":null,"abstract":"<div><div>The escalating frequency of extreme heat events poses significant thermoregulatory challenges and health risks to individuals engaged in outdoor athletics. Conventional sports textiles, primarily designed for moisture management, offer limited capacity for thermal dissipation, often exacerbating heat stress. Passive daytime radiative cooling (PDRC) has emerged as a disruptive technology, enabling heat dissipation into the cold outer space through the atmospheric transparency window (8–13 µm) without energy consumption. This review provides a comprehensive analysis of the materials engineering design principles, fabrication strategies, and performance of radiative cooling textiles tailored for sports applications. We critically examine three primary material platforms: (1) intrinsically infrared-transparent polymers engineered with nanoporous structures, such as polyethylene; (2) photonic metamaterials and metasurfaces with spectrally-selective properties; and (3) polymer composites and coatings incorporating Mie-scattering nanoparticles. For each category, we analyze the fundamental mechanisms, manufacturing scalability, durability, and wearability, drawing on a wide array of recent literature. A significant portion of this review is dedicated to the scholarly debate surrounding the practical efficacy of these textiles in real-world athletic scenarios. We scrutinize the controversies regarding performance under varying conditions of humidity, convective airflow, sweat accumulation, and non-ideal radiative environments typical of sports arenas. Furthermore, we highlight the profound lack of dedicated clinical trials and field studies measuring core physiological parameters and performance metrics in athletes. The review also addresses the critical challenges of economic viability and lifecycle environmental impacts, which remain major barriers to mass-market adoption. Finally, we outline future research directions, emphasizing the need for multifunctional, environmentally sustainable designs and rigorous human-centric performance validation to bridge the gap from laboratory novelty to practical athletic apparel.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"144 ","pages":"Pages 117-128"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147804837","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":"Intelligent and secure mobility management for 6g networks: A systematic review of AI-driven and physical-layer solutions","authors":"Tarek Ali,&nbsp;Mohammed Al-Khalidi,&nbsp;Ali Kashif Bashir","doi":"10.1016/j.aej.2026.04.024","DOIUrl":"10.1016/j.aej.2026.04.024","url":null,"abstract":"<div><div>The development of Sixth-Generation (6G) wireless networks introduces new security and mobility management challenges due to dense deployments, heterogeneous multi-access systems, extensive reliance on Artificial Intelligence (AI), and the integration of non-terrestrial networks. This work investigates intelligent and secure mobility management for 6G and beyond through a systematic literature review. Following the PRISMA guidelines, 301 relevant studies published between 2010 and 2024 were identified across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. The review evaluates current AI methods, including Deep Learning (DL), Reinforcement Learning (RL), and federated learning, with respect to their use in mobility prediction, handover optimization, resource allocation, and energy-efficient network operation. It also examines Physical-Layer Security (PLS) mechanisms that complement traditional upper-layer cryptographic techniques to mitigate jamming, eavesdropping, and adversarial attacks while controlling computational overhead. The analyzed studies are systematically organized by linking performance targets such as Quality of Service (QoS), Quality of Experience (QoE), latency, reliability, and scalability to five key 6G enabling technologies/functions: reconfigurable intelligent surfaces, Millimeter-Wave (mmWave) communications, massive Multiple-Input Multiple-Output (MIMO) systems, mobile edge computing, and AI-assisted channel estimation, which provides the Channel State Information (CSI) required by secure beamforming, Intelligent Reflecting Surface (IRS) control, and mobility-aware PLS. The findings show that existing systems remain limited in their ability to guarantee secure and dependable connectivity under high-speed and extreme-mobility conditions. The review highlights three critical open challenges: protecting AI model training against physical and digital attacks, ensuring that PLS schemes operate without disrupting network control, and maintaining robust mobility management under stringent performance constraints. Based on these insights, the paper proposes design principles to guide future research on AI-based, security-focused mobility management frameworks that satisfy 6G performance requirements.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 253-286"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147860837","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":"MCiANT: A Monte Carlo inspired auction mechanism for health data NFT trading","authors":"Bhabani Sankar Samantray,&nbsp;K. Hemant Kumar Reddy","doi":"10.1016/j.aej.2026.04.039","DOIUrl":"10.1016/j.aej.2026.04.039","url":null,"abstract":"<div><div>Auctions play a vital role in modern commerce by offering a transparent, structured, and competitive method for trading goods, services, and data. However, traditional in-person auctions are limited in terms of accessibility, convenience, security, and efficiency. However existing online auction platforms, while addressing some of these limitations, still face challenges such as limited transparency, centralized control, and insufficient security and privacy protections. Moreover these issues become extremely critical in case of sensitive applications like healthcare, defense and finance. To address these challenges, this article proposes a three-phase trading framework. In the first phase, data generation, anonymization, and storage are performed. In the second phase, an ensemble learning-based price forecasting approach is employed to estimate the asking and bidding prices, which depend on the volume and type of data. Finally, in the third phase, a Monte Carlo-inspired auction-based Non-Fungible Token (NFT) trading mechanism (MCiANT) is incorporated to enable efficient trading between buyers and sellers. The efficacy of the proposed MCiANT framework is compared with three distinct auction algorithms: the Vickrey auction, the Markov-Inspired Stationary Distribution Auction (MISD), and the Two-Phase English–Dutch Hybrid Auction (TPEDHA). The results demonstrate that the MCiANT framework significantly outperforms the others, achieving success-rate improvements of 5%, 5%, and 1% over the Vickrey, MISD, and TPEDHA auctions, respectively. Furthermore, the proposed framework is evaluated using health data by measuring anonymization time, encryption time, InterPlanetary File System (IPFS) upload time, and I/O performance.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"144 ","pages":"Pages 158-174"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147804820","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":"Truthful two-stage auctions for task offloading in the cooperation of UAVs and HAPs","authors":"Xi Liu ,&nbsp;Jun Liu ,&nbsp;Chunyan Zhu","doi":"10.1016/j.aej.2026.04.041","DOIUrl":"10.1016/j.aej.2026.04.041","url":null,"abstract":"<div><div>Uncrewed aerial vehicles (UAVs) face significant energy-consumption constraints, limiting their capacity to deliver continuous computing services to ground users (GUs). High-altitude platforms (HAPs) can remain airborne for extended periods and provide significant computing power to GUs. However, offloading tasks to HAPs requires using UAV relay services. This paper investigates the task offloading problem within cooperative UAV-HAP systems. Two computing modes are examined: HAP-based computation and UAV-based computation. In the HAP-based mode, tasks are offloaded to HAPs through UAV relays, while in the UAV-based mode, tasks are directly offloaded to UAVs. A two-stage auction framework is proposed, consisting of a HAP resource auction followed by a UAV resource auction. The first stage allocates HAP resources, and the second stage allocates UAV resources. A two-stage auction mechanism is introduced to maximize social welfare. The proposed mechanism first allocates HAP resources, then UAV resources. We show that the proposed mechanism is truthful in a two-stage auction. The mechanism also achieves voluntary participation, individual rationality, consumer sovereignty, budget balance, and computational efficiency. The approximations of the allocation algorithms are also analyzed. Extensive simulation experiments and comprehensive comparisons with alternative algorithms demonstrate the superior performance of the proposed mechanism.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"144 ","pages":"Pages 143-157"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147804836","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":"Hybrid quantum transfer learning for supervised UAV aerial image classification","authors":"Sayed Abdel-Khalek ,&nbsp;Mariam Algarni ,&nbsp;Suliman A. Alsuhibany ,&nbsp;Mahmoud Abdel-Aty","doi":"10.1016/j.aej.2026.04.034","DOIUrl":"10.1016/j.aej.2026.04.034","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) enable high-resolution remote-sensing for land-cover and object mapping, yet conventional deep learning (DL) models often remain sensitive to scale, viewpoint, and illumination variability in UAV imagery. This paper presents QTL-SAIC, a quantum transfer learning framework for supervised aerial image classification that integrates classical feature learning with quantum-enhanced decision making. A pre-trained Capsule Network (CapsNet) extracts compact, discriminative features, which are classified using a variational quantum neural network (QNN) within a hybrid quantum–classical pipeline. The quantum module is fully specified (qubit number, encoding, circuit architecture, measurements, optimization, and execution backend with shot settings) to support reproducibility. Hyperparameters are optimized via the Gannet Optimization Algorithm (GOA), and evaluation follows stratified splits, repeated multi-seed trials and statistical reporting, with class imbalance addressed through cost-sensitive and/or balanced sampling strategies. Results indicate that QTL-SAIC yields competitive and stable performance relative to resource-matched classical baselines. Although no formal quantum advantage is claimed, ablations on circuit depth and noise sensitivity delineate the conditions under which the quantum component contributes to predictive performance.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 137-153"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147860839","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 combined double chaotic maps and subswarm based enhanced particle swarm optimization for global optimization and engineering design problems","authors":"Fevzeddin Ülker,&nbsp;Selcuk Emiroglu,&nbsp;Yılmaz Uyaroglu","doi":"10.1016/j.aej.2026.03.043","DOIUrl":"10.1016/j.aej.2026.03.043","url":null,"abstract":"<div><div>In this study, newly introduced a double chaotic maps based enhanced Particle Swarm Optimization (DCMEPSO) algorithm with two subswarm is presented to obtain a global optimum solution. In this context, Squared Sine Logistic (SSL) map for one of subswarm and Modular Integrated Logistic Exponential (MILE) map for other subswarm are presented to enrich population diversity and enhance global search capabilities. In addition, our method introduces different position updating mechanisms to facilitate the escape from possible local optima and speed search processes in achieving global optimum. The performance of the presented optimization algorithm has been investigated under eight well-known benchmark functions, 3 of which are unimodal and 5 of which are multimodal. We consider four optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), and Salp Swarm Algorithm (SSA), as competitive algorithms to assess the performance of DCMEPSO with experimental studies such as well-known functions of different sizes, and engineering design problems. The superiority of the proposed algorithm from other compared algorithms is demonstrated with Wilcoxon rank sum test to possess the definitive assessment. The proposed algorithm can easily tackle with high-dimensional complex problems. From extensive test results, mean rank analysis over 8 benchmark functions shows that the proposed method achieves 60.45% and 50.0% mean-rank-based improvements in the 30D and 50D cases, respectively, compared to the closest competitor. The Matlab code of DCMEPSO is available at: <span><span>https://github.com/fevzeddinulker/DCMEPSO/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"142 ","pages":"Pages 91-113"},"PeriodicalIF":6.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147614068","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}