alexandria engineering journal最新文献

{"title":"Quantum neural network empowered spectral efficiency analysis in IRS-assisted communication systems","authors":"Md Habibur Rahman ,&nbsp;Md Abdul Aziz ,&nbsp;Rana Tabassum ,&nbsp;Mohammad Abrar Shakil Sejan ,&nbsp;Myung-Sun Baek ,&nbsp;Hyoung-Kyu Song","doi":"10.1016/j.aej.2025.08.032","DOIUrl":"10.1016/j.aej.2025.08.032","url":null,"abstract":"<div><div>The rapid increase in data consumption by mobile users has created a demand for innovative architectures to address this growing need. Concurrently, classical neural network-based intelligent reflecting surfaces (IRS) have gained traction in 6G networks due to their capacity to enhance spectral efficiency (SE). However, relying solely on classical computing for SE calculations imposes limitations on computational rates, stemming from the exponential increase in complexity. In response to this challenge, this paper proposes the use of quantum convolutional neural networks (QCNNs) to improve the SE of communication systems facilitated by IRS configurations. To optimize IRS phase shifts, we first apply singular value decomposition to the received IRS-optimized data. The resulting information is then incorporated into the quantum data and subsequently processed by the parameterized quantum circuit. A dense layer is introduced after the proposed model to boost training performance. The proposed QCNN model demonstrates a significant enhancement in SE while maintaining lower complexity, leveraging the capabilities of qubits in the current era of quantum computing.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"128 ","pages":"Pages 1168-1176"},"PeriodicalIF":6.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912083","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":"M2-MADRL: A multi-mode routing optimization strategy based on MADRL for Edge Ad Hoc Networks","authors":"Jirui Li ,&nbsp;Cong Zheng ,&nbsp;GuoZhi Li ,&nbsp;Haihua Zhu ,&nbsp;Zigang Chen ,&nbsp;Jie Yuan","doi":"10.1016/j.aej.2025.08.021","DOIUrl":"10.1016/j.aej.2025.08.021","url":null,"abstract":"<div><div>This paper addresses the challenges of dynamic routing in Edge Ad Hoc Networks (EANETs), such as high mobility, complex topologies, and diverse service requirements, by proposing M2-MADRL, a Multi-Mode routing framework based on Multi-Agent Deep Reinforcement Learning. The study focuses on three key aspects: first, designing a multi-mode routing architecture that enables nodes to dynamically switch between routing protocols (e.g., AODV, DSDV) and adaptive parameter adjustments, adapting to varying network scales and mobility levels. Second, integrating an enhanced MADRL model based on the Centralized Training with Decentralized Execution (CTDE) framework, which uses local value networks for individual node optimization and a global value network to improve overall network performance, resolving conflicts between centralized control and EANETs’ distributed nature. Third, introducing a double-layer Semantic Self-Attention Mechanism (SSAM) to help agents accurately interpret heterogeneous network states and context-aware actions, accelerating convergence and enhancing decision-making precision. Experiments show M2-MADRL outperforms MADDPG, DE-MADDPG, and RTHop in Packet Delivery Rate (PDR), Effective Throughput (ET), and Average Transmission Delay (ATD), demonstrating superior adaptability in EANETs. For example, in 150-node networks, it averagely demonstrates 11.9% higher PDR, 23.0% higher ET, and 44.6% lower ATD than the classic MADDPG method.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1165-1184"},"PeriodicalIF":6.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913327","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":"Innovative TiVC/dsDNA biosensor platform for enhanced detection of anastrozole; Experiment and DFT approaches","authors":"Yi Xu ,&nbsp;Si-wei Wang ,&nbsp;Hasan Bagheri ,&nbsp;Chengshui Chen ,&nbsp;Ying Zhou","doi":"10.1016/j.aej.2025.08.027","DOIUrl":"10.1016/j.aej.2025.08.027","url":null,"abstract":"<div><div>Biosensors are at the core of the quick and precise identification of chemotherapeutic drugs in biological fluids. Various groups of researchers have therefore spent their time designing new biosensor devices for identifying cancer biomarkers, as well as tracking chemotherapy medications and associated analytes. This research introduces a novel bio-electrochemical sensor designed for the precise monitoring of Anastrozole, an anticancer drug for breast cancer, lung cancer treatment and an agent with anti-proliferative effects in human prostate cancer cell lines, in aqueous solution. The TiVC MXene catalyst was synthesized using an etching process and characterized through techniques such as TEM, BET, XRD, MAP, FESEM, EDS and CV. The salmon ds-DNA acting as the guanine source element for interaction reaction and TiVC MXene serving as the primary conductive layer decorated at surface of screen-printed electrode (SPE) for fabrication of Anastrozole biosensor. The presence of TiVC MXene catalyst improved the biosensor's conductivity by approximately 1.55 times, enabling trace analysis of Anastrozole. The current relative to guanine bases in ds-DNA structure was selected as a key indicator to track the intercalation of Anastrozole. This method demonstrated a detection limit of 9.0 nM, we were able to detect Anastrozole over a concentration range of 0.03–100 µM due to a notable decrease in the guanine signal. Additionally, the hypothesis that anastrozole interacts with DNA receptors through intercalation was validated by density functional theory (DFT) simulations. This improves our comprehension of the kinetics involved in this interaction and is in good agreement with our experimental findings.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1146-1152"},"PeriodicalIF":6.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896735","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":"EDCEW-LLM: Error detection and correction in English writing: A large language model-based approach","authors":"Jin Wang","doi":"10.1016/j.aej.2025.08.005","DOIUrl":"10.1016/j.aej.2025.08.005","url":null,"abstract":"<div><div>This study investigates the use of large language models (LLMs) for error detection and correction in English writing (EDCEW), focusing on the challenges of overcorrection and pedagogical feedback. We explore the relationship between targeted prompting strategies, transformer model architectures, and the error profiles of English language learners across CEFR proficiency levels (A, B, and C). We evaluated LLM through zero-shot, few-shot and fine-tuning approaches, showing that overcorrection is more common in texts from advanced learners, while lower proficiency levels show unique patterns of errors. To address this issue, we introduce a two-stage pipeline: first, a fine-tuned LLM leverages sequence-to-sequence modeling to perform precise token-level error detection and correction, achieving competitive F1 scores across multilingual datasets (English, German, Chinese); second, a prompted LLM (GPT 3.5) generates rule-based, natural language explanations for each identified error, enhancing the educational values. We present comprehensive benchmark results on standard WEDC datasets (BEA-2019, JFLEG), supported by human evaluations, that show the effectiveness of the proposed approach in producing accurate corrections and linguistically informative error explanations. The proposed model improves correction accuracy and facilitates deeper linguistic comprehension through explicit, pedagogically sound feedback, addressing a significant limitation of existing automated GEC tools. Moreover, this research advances LLM-driven WEDC by providing a robust framework that balances correction precision with pedagogical utility. Qualitative results further highlight the model’s ability to address diverse error types, such as Subject+Verb agreement, redundant comparisons, and incorrect preposition usage, with contextually appropriate corrections.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1153-1164"},"PeriodicalIF":6.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896736","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":"Integrated communication and location monitoring system for vehicle monitoring via smartphone calls","authors":"Fahd N. Al-Wesabi ,&nbsp;Amnah Alshahrani ,&nbsp;Rakan Alanazi ,&nbsp;Mohammed Mujib Alshahrani ,&nbsp;Shaymaa Sorour ,&nbsp;Asmaa Mansour Alghamdi ,&nbsp;Malak Zayed Alamri ,&nbsp;Sultan Alanazi","doi":"10.1016/j.aej.2025.08.035","DOIUrl":"10.1016/j.aej.2025.08.035","url":null,"abstract":"<div><div>Real-time tracking and monitoring of vehicles has become increasingly important for ensuring security, optimizing logistics, and enhancing personal convenience. Current vehicle tracking systems often rely on expensive, complex hardware or continuous data transmission, creating barriers to widespread adoption. A streamlined and cost-effective model is proposed to address this gap, combining a smartphone application with a microcontroller to simplify vehicle monitoring. Prior research has established the reliability of Global Positioning System (GPS) /Global System for Mobile Communication (GSM) modules for location tracking, but has underutilized the potential of combining embedded sensors with machine learning analytics. When the user initiates a missed call or SMS to the embedded device, the system retrieves the vehicle’s geographic coordinates and transmits a Google Maps link to the user's smartphone. Previous tracking solutions typically required constant connectivity or specialized tracking platforms; the proposed approach offers an on-demand, low-maintenance alternative. The proposed work advances the field by introducing a novel architecture where an OBD-II connected hardware module collaborates with smartphone and cloud components through optimized data protocols. The system was evaluated through controlled tests and real-world deployments with 25 vehicles over six months. Prototype testing demonstrated &lt; 5 m GPS accuracy and &lt; 3-second response time across various network conditions, with field trials showing 90 %+ user satisfaction in fleet management scenarios. This work contributes to transportation technology by proving the feasibility of hybrid tracking architectures. Future work will explore blockchain applications for data security and expanded IoT sensor integration, potentially transforming how vehicle-monitoring systems balance cost, accuracy, and functionality.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"128 ","pages":"Pages 1159-1167"},"PeriodicalIF":6.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903829","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":"WAScope: Detecting privacy data leakage with web application-specific API confusion","authors":"Yu Nie ,&nbsp;Jianming Fu ,&nbsp;Xinghang Lv ,&nbsp;Chao Li ,&nbsp;Shixiong Yang ,&nbsp;Guojun Peng","doi":"10.1016/j.aej.2025.08.006","DOIUrl":"10.1016/j.aej.2025.08.006","url":null,"abstract":"<div><div>The number of web applications deployed on the internet has exceeded one billion, accumulating vast amounts of user privacy data. The compromise of such data may lead to severe consequences. While existing research has primarily focused on data exfiltration through system APIs, the security risks posed by application-specific APIs have been largely overlooked. These APIs directly manage the collection, processing, and transmission of sensitive user data, making them critical attack surfaces. This study systematically investigates privacy data leakage caused by unauthorized access through web application-specific APIs. We presented <em>WAScope (Web Application-specific API Scope)</em>, a dynamic analysis tool that detects privacy leakage by combining API confusion techniques with a customized privacy dictionary. We conducted experiments on 100 real-world web applications using <em>WAScope. The tool identified 15,593 privacy-aware API data flows across 76 applications, among which 2,757 APIs were manually confirmed to expose sensitive data due to improper access controls.</em> Manual validation further validated the findings, revealing a 9% false positive rate. We reported these vulnerabilities to the China National Vulnerability Database (CNVD), receiving 10 official CNVD-IDs that demonstrate the effectiveness of <em>WAScope</em>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"128 ","pages":"Pages 1145-1158"},"PeriodicalIF":6.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896487","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":"Numerical study on fluidization behaviors and heat transfer characteristics of carbonized slag in a fluidized bed with liquid addition","authors":"Yan Zhao ,&nbsp;Bo Liu","doi":"10.1016/j.aej.2025.08.026","DOIUrl":"10.1016/j.aej.2025.08.026","url":null,"abstract":"<div><div>Fluidized bed technology is pivotal in chemical engineering, metallurgy, and energy, facilitating efficient heat and mass transfer. This research investigates the particle dynamics and thermal transport of carbonized slag in a fluidized bed with liquid addition, a process involving complex gas-liquid-solid interactions under varying operating conditions. A two-dimensional computational fluid dynamics model is developed, incorporating three interphase resistance models and an evaporation model. Simulation results show good agreement with experimental data regarding fluidization indices and particle motion trajectories, validating the reliability of the simulation methodology. The findings show a bimodal solid volume fraction distribution at a bed height of 45 mm, with high concentration in the transition region. The particle dynamics dominate structural evolution, causing fluctuations at gas-solid interfaces. Increased gas velocity reduces temperature drops by 12–16 %, while higher temperatures enhance evaporation efficiency by 47 %. The non-monotonic thermal variations occur with increasing height-to-diameter ratios due to coupled phase-change and convective heat transfer. These findings offer valuable insight for the theoretical optimization of fluidized bed reactors and the fluidization operation processes of carbonized slag.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"128 ","pages":"Pages 1134-1144"},"PeriodicalIF":6.8,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893110","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":"Progressive feature enrichment and ensemble learning for enhanced rice seed purity classification","authors":"Minh-Dung Le,&nbsp;Van-Giap Le,&nbsp;Thi-Thu-Hong Phan","doi":"10.1016/j.aej.2025.08.019","DOIUrl":"10.1016/j.aej.2025.08.019","url":null,"abstract":"<div><div>This study introduces an effective method for classifying rice seed purity varieties, addressing the challenge of identifying mixed seeds in real-world scenarios. Initially, conventional feature extraction methods such as Gray Level Co-occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and GIST are evaluated. However, these methods prove insufficient due to their inability to fully capture the complex characteristics of rice seeds. To address this, we propose a refined feature engineering strategy, optimizing domain-specific morphological, color, and textural features of rice seeds. Through an iterative refinement process, the feature set is systematically expanded. Initially, it comprises 18 basic features. Subsequently, we integrate color features, increasing the total to 22. Continued optimization with GLCM-based texture features leads to an expansion to 36 features, and finally, the set reaches 52 features, encompassing advanced morphological, color, and texture attributes. Notably, we also employ ensemble methods, particularly stacking, which combines the predictions of multiple models to create a final prediction. Experimental results demonstrate a significant improvement in classification accuracy compared to conventional extraction methods. Specifically, stacking boosts accuracy to 97.58%. These findings underscore the superiority of customized feature design over generic extractors, providing a robust solution for practical rice seed purity identification.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1131-1145"},"PeriodicalIF":6.8,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888639","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 secure access method in English education network based on edge computing","authors":"Yu Bi ,&nbsp;Tian Su","doi":"10.1016/j.aej.2025.07.033","DOIUrl":"10.1016/j.aej.2025.07.033","url":null,"abstract":"<div><div>With the rapid progress of edge computing, authentication key negotiation protocols, as one of the key means to ensure secure communication, have attracted widespread attention. However, current authentication key negotiation protocols designed for mobile edge computing are often difficult to balance security and performance requirements. In order to solve this challenge, a secure access method is designed for an English education network environment oriented to edge computing. Firstly, this scheme adopts a lightweight physical unclonable function (PUF) as the trust guarantee for entities in the English education network and generates authentication credentials based on the identities of the terminal equipment and the server. Secondly, formal analysis by ROM and non-formal analysis verify that this scheme has strong security properties. Finally, communication consumption, computation consumption and memory usage are compared using to verify that this scheme demonstrates significant advantages in terms of efficiency.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"128 ","pages":"Pages 1125-1133"},"PeriodicalIF":6.8,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888717","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":"Highly Robust South Fork Zumbro River Flow Forecasting Based on Deep Temporal Modeling","authors":"Yingjun Sun ,&nbsp;Peixia Wang ,&nbsp;Chong Wang ,&nbsp;Xiaodong Wang ,&nbsp;Hailin Feng ,&nbsp;Wei Wang ,&nbsp;Kai Fang","doi":"10.1016/j.aej.2025.08.020","DOIUrl":"10.1016/j.aej.2025.08.020","url":null,"abstract":"<div><div>River flow forecasting plays a vital role in water resource management and ecological conservation. Accurate flow forecasting enables decision makers to allocate resources efficiently, implement early flood prevention measures, and protect ecosystems. However, environmental noise interferes with forecasting, reducing the precision and reliability of decision making. To address this, we propose a Highly Robust River Flow Forecasting Model (HRRFFM). The model comprises two components: data preprocessing and deep temporal modeling. Data preprocessing involves interpolation, environmental noise simulation, and Wiener filtering to improve data quality and model robustness. The deep temporal modeling integrates Bidirectional Long Short-Term Memory (BILSTM) networks and Transformer architecture to capture river flow dynamics. BILSTM captures bidirectional features, enhancing the model’s capacity to learn complex flow sequences. Transformer utilizes self-attention and multi-head attention mechanisms to model global dependencies and amplify subtle time-series variations, significantly improving feature extraction efficiency and forecasting accuracy. In this paper, Gaussian noise is employed to simulate environmental disturbances. The model’s performance is validated through ablation studies across varying noise levels and forecast horizons. At the noise intensity of <span><math><mi>σ</mi></math></span> = 0.05, for the three-hour-ahead predictions, HRRFFM outperforms baseline models with average improvements of 11.56%, 13.59%, and 4.98% in RMSE, MAE, and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, respectively.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1122-1130"},"PeriodicalIF":6.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886270","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}