alexandria engineering journal最新文献

{"title":"Numerical modeling of solid particles reduction in water clarifiers using Lattice Boltzmann method","authors":"E. Jaiuan ,&nbsp;Y. Khunatorn","doi":"10.1016/j.aej.2025.05.010","DOIUrl":"10.1016/j.aej.2025.05.010","url":null,"abstract":"<div><div>Suspended solids in water are detrimental to power plant production. Statistical data from the field turbidity monitoring states different solid particle reduction performance between two existing solid contact clarifiers. This study compares their turbidity reduction efficiencies and discusses treatment characteristics of these clarifiers. The clarifiers feature circular basins with a dual layer of baffles and different internal mixing agitation. The first clarifier (<em>CLR47</em>) has the inner baffle as a truncated polygon with a lateral inlet. The second clarifier (<em>CLR89</em>) has a 30-degree tangential inlet attached to a small circular inner baffle. The Lattice Boltzmann Method (LBM) computational method was employed for fluid flow computation. Particles with 13 different diameter sizes ranged from 20 μm to 850 μm were accounted for suspended solid modeling via discrete phase model (DPM). The numerical model presents the overall solid settling efficiency as 46 % and 78 % for <em>CLR47</em> and <em>CLR89</em> respectively. The low recirculation within the mixing region and effluent high contamination of 20 μm size class particles of design <em>CLR89</em> were noticed. While the <em>CLR47</em> model has greater turbidity reduction despite greater maintain mixing efficiency for a higher flocculation rate expectation.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 251-264"},"PeriodicalIF":6.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942269","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":"Irreversibility analysis of magnetized flow of engine oil hybrid nanofluid (Ti6Al4V-ZnO/EO) over a stretching surface","authors":"Syed M. Hussain ,&nbsp;Aftab Ahmed Faridi ,&nbsp;Hijaz Ahmad ,&nbsp;Kashif Ali ,&nbsp;Muhammad Rashid Iqbal ,&nbsp;Wasim Jamshed ,&nbsp;Kamel Guedri ,&nbsp;Abdulrazak H. Almaliki","doi":"10.1016/j.aej.2025.05.023","DOIUrl":"10.1016/j.aej.2025.05.023","url":null,"abstract":"<div><div>This article analyzes the irreversibility of hydro-magnetic dynamics of hybridized nanofluid comprising of titanium alloy <em>(Ti</em>_<em>6</em><em>Al</em>_<em>4</em><em>V)</em> and zinc-oxide <em>(ZnO)</em> submerged into engine oil (used as a fuel) through a porous stretching sheet with multipart effects of viscous dissipation and non-uniform heat source. An induced magnetic field exists in the flow field due to the conducting nature of the hybrid nanofluid. The equations of the flow model are transformed by the similarity transformations and then simulated through a numerical scheme constructed with a formulation of central differences that adopts successive over-relaxation methodology. In this thermodynamic system, entropy and Bejan number profiles corresponding to the prominent parameters are simultaneously analysed for both the nanofluid case and hybrid nanofluid case in the Cartesian coordinates. A comparison table is constructed to validate the numerical results with existing literature. The impacts of various parameters on Nusselt number and skin friction coefficient are graphed with reference to the magnetic field parameter. A significant reduction in irreversibility and a remarkable improvement in thermal characteristics of engine oil are observed due to induced magnetism effects. The engine oil hybrid nanofluid (<em>Ti</em>_<em>6</em><em>Al</em>_<em>4</em><em>V-ZnO/EO</em>) exhibits a 13.89 % higher heat transfer rate over a porous stretching surface compared to the engine oil nanofluid (<em>Ti</em>_<em>6</em><em>Al</em>_<em>4</em><em>V/EO</em>).</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 214-227"},"PeriodicalIF":6.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935246","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":"Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments","authors":"Sarah A. Alzakari ,&nbsp;Mohammed Aljebreen ,&nbsp;Mashael M. Asiri ,&nbsp;Wahida MANSOURI ,&nbsp;Sultan Alahmari ,&nbsp;Mohammed Alqahtani ,&nbsp;Shaymaa Sorour ,&nbsp;Wafi Bedewi","doi":"10.1016/j.aej.2025.05.024","DOIUrl":"10.1016/j.aej.2025.05.024","url":null,"abstract":"<div><div>Smart cities have attracted extensive coverage from multidisciplinary studies, and many artificial intelligence (AI) solutions have been designed. Conversely, cybersecurity has constantly been a crucial issue and is becoming gradually dangerous in smart cities. The attack defence models are inappropriate for perceiving multistep assaults as the recognition rules are restricted, and efficacy is partial due to many false security alarms. Therefore, an innovative solution is immediately required to progress cybersecurity defence ability. Machine learning (ML) methods are commonly employed to recognize numerous attacks because they could help network administrators grab analogous initials to avert intrusion. This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). The foremost intention of this study is to improve cybersecurity detection and classification in smart city environments. To accomplish that, the CCPOA-HDLM method comprises distinct processes such as min-max normalization, improved Salp swarm algorithm (ISSA)-based feature selection, Multi-Channel Convolutional Neural Network - Recurrent Neural Network (MCNN-RNN)-based cybersecurity detection, and crested porcupine optimizer (CPO)-based parameter selection process. Primarily, data normalization utilizing min-max normalization is implemented. Next, the CCPOA-HDLM method utilizes ISSA based feature selection method to select optimum features. The CCPOA-HDLM technique employs a hybrid of the MCNN-RNN model for the cybersecurity detection and classification process. Moreover, the hyperparameter range of the hybrid of DL techniques occurs using the CPO technique. The experimental validation of the CCPOA-HDLM approach is performed on the UNSW-NB15 dataset, and the outcome portrayed a superior accuracy value of 99.04 % over other recent approaches under various measures.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 239-250"},"PeriodicalIF":6.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942270","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":"Temporal Pyramid Alignment and Adaptive Fusion of Event Stream and Image Frame for Keypoint Detection and Tracking in Autonomous Driving","authors":"Peijun Shi,&nbsp;Chee-Onn Chow,&nbsp;Wei Ru Wong","doi":"10.1016/j.aej.2025.04.098","DOIUrl":"10.1016/j.aej.2025.04.098","url":null,"abstract":"<div><div>This paper proposes a method to address the alignment and fusion challenges in multimodal fusion between event and RGB cameras. For multimodal alignment, we adopt the Temporal Pyramid Alignment mechanism to achieve multi-scale temporal synchronization of event streams and RGB frames. For multimodal fusion, we design a module that employs adaptive fusion to dynamically adjust the contribution of each modality based on scene complexity and feature quality. A gating network computes fusion weights by considering both relative modality importance and noise characteristics. A Cross-Modal Feature Compensation module is integrated into the framework to enhance information utilization. Additionally, the framework incorporates a Dynamic Inference Path Selection mechanism, guided by input complexity, to optimize computational resource allocation, along with a dynamic noise suppression mechanism to improve the robustness of feature extraction. Experimental results on the DSEC dataset demonstrate that the proposed method achieves a 36.9% mAP and 40.1% tracking success rate, particularly effective in extreme lighting and fast motion scenarios, surpassing existing approaches by 1.8% mAP and 1.6% SR, while maintaining real-time efficiency at 13.1 FPS. This work provides an important solution for applications in autonomous driving, robotics, and augmented reality, where robust multimodal perception under dynamic conditions is critical.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 228-238"},"PeriodicalIF":6.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935247","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 flexible statistical distribution for capturing complex patterns in industrial data","authors":"Ehab M. Almetwally ,&nbsp;Amal S. Hassan ,&nbsp;Mohamed Kayid ,&nbsp;Arne Johannssen ,&nbsp;Mohammed Elgarhy","doi":"10.1016/j.aej.2025.05.004","DOIUrl":"10.1016/j.aej.2025.05.004","url":null,"abstract":"<div><div>The effective modeling of real-world data requires flexible statistical distributions to accurately capture complex patterns<strong>.</strong> For that purpose, this paper introduces an extension of the XLindley distribution, specifically designed for modeling textile data. The suggested Marshall-Olkin transmuted XLindley distribution (MOTXLD) has additional shape and transmuted parameters, which considerably influence its skewness, kurtosis, and tail behavior. The MOTXLD is versatile and can have right-skewed, uni-modal, or reversed-J-shaped density curves. A comprehensive statistical analysis of the MOTXLD is conducted, including the derivation of key properties. To estimate the model parameters, both frequentist and Bayesian techniques are implemented. The bootstrap approach, the normal approximation method, and Bayesian credible intervals are some of the techniques employed to build confidence intervals. A simulation study is conducted to assess the efficiency of the estimated parameters. According to the outcomes of this study, Bayesian estimates often perform better than frequentist estimates. Bayesian credible intervals generally show a higher coverage probability compared to confidence intervals based on maximum likelihood estimation, implying more reliable interval estimates. The adaptability of the proposed distribution is demonstrated using real datasets from the textile industry sector, highlighting its potential for effective modeling in this domain.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 651-667"},"PeriodicalIF":6.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937255","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":"Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution","authors":"Khalid M. Hosny ,&nbsp;Amr A. Abd El-Mageed ,&nbsp;Amr A. Abohany ,&nbsp;Reda M. Hussein ,&nbsp;Mona Gaffar","doi":"10.1016/j.aej.2025.04.020","DOIUrl":"10.1016/j.aej.2025.04.020","url":null,"abstract":"<div><div>Estimating the optimal parameter values for photovoltaic (PV) models is inherently challenging due to the complex and nonlinear nature of their current–voltage (I–V) characteristic curves. Precise parameter estimation is critical for ensuring the efficient operation of PV systems, as it directly influences energy output and current generation. Traditional methods for addressing this problem often suffer from convergence to local optima and require substantial computational resources, particularly concerning the count of fitness evaluations. To overcome these challenges, this paper presents an enhanced optimization method: the Brown Bear Optimization Algorithm (BBOA) hybridized with Diagonal Linear Uniform Initialization (DLUI) and the Differential Evolution (DE) algorithm, termed BBOA-DLUI-DE. This hybrid approach’s innovative design lies in integrating the DE algorithm to enhance solution diversity, ensuring better exploration and preventing premature convergence. DLUI contributes to a uniformly diverse initial population that supports rapid and robust optimization. This synergy between BBOA, DLUI, and DE addresses the limitations of existing methods by combining efficient global search capabilities with effective local refinement. The proposed BBOA-DLUI-DE method has been rigorously evaluated against state-of-the-art techniques, demonstrating superior performance in finding optimal parameter values for various PV models. Comparative statistical and practical analyses highlight that BBOA-DLUI-DE outperforms traditional methods regarding accuracy and computational efficiency. Furthermore, validation using manufacturing data sheets (MCSM55 and TFST40) confirms the practical applicability and robustness of the proposed method, making it a highly effective tool for estimating PV parameters.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 164-199"},"PeriodicalIF":6.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935244","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":"Integration of graph neural networks and long short-term memory models for advancing heart failure prediction","authors":"Ibrahim Alrashdi,&nbsp;Ahmed I. Taloba","doi":"10.1016/j.aej.2025.05.014","DOIUrl":"10.1016/j.aej.2025.05.014","url":null,"abstract":"<div><div>Heart failure constitutes a chronic disease affecting millions of people worldwide, hence creating an important burden on healthcare infrastructures. Predictive models about the onset or worsening of HF can be instrumental in conducting proper and timely interventions to improve the outcomes of the care of patients with HF. This paper introduces a novel approach to predicting HF, integrating graph neural networks (GNNs) with long short-term memory (LSTM) networks for better prediction accuracy. This hybrid model, GNN-LSTM, applies the advantages of both networks: the complex interdependencies between clinical variables capture clinical relationships; LSTMs can better manage temporal dependencies. The model was tested on a large, highly representative dataset containing diversified clinical variables from HF patients, with 98.9 % predictive accuracy, which outperforms the single models as well as their respective performances by conventional methods like CNN, SMOTE, LSTM-RNN, CNN-LSTM, CNN-GRU, and traditional GNN approaches. Thus, the GNN-LSTM model, developed in Python, produces robust results across cases, irrespective of coronary heart disease co-presence comorbidity. Nonetheless, one of the limitations of the research is that generability is still in the future. This integrated approach has huge promises for improving HF prediction, with early interventions and personalized health strategies that would diminish the burden on patients and healthcare systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 143-163"},"PeriodicalIF":6.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935243","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":"Enhancing accuracy in dynamic pose estimation for sports competitions using HRPose: A hybrid approach integrating SinglePose AI","authors":"Rui Han ,&nbsp;Mingnong Yi ,&nbsp;Wei Feng ,&nbsp;Feng Qi ,&nbsp;Yining Zhou","doi":"10.1016/j.aej.2025.04.062","DOIUrl":"10.1016/j.aej.2025.04.062","url":null,"abstract":"<div><div>Human pose estimation plays a critical role in various applications, such as sports performance evaluation, rehabilitation, and human–computer interaction. Recent advancements in deep learning have significantly improved the accuracy and robustness of human pose estimation models. However, challenges remain in dynamic environments, especially in sports competitions, where high-speed movements, occlusions, and complex backgrounds often hinder accurate estimation. This paper proposes HRPose, a novel approach that combines HRNet for feature extraction and SinglePose AI for precise keypoint localization. It maintains high-resolution feature maps throughout the feature extraction process, enabling the model to capture fine-grained spatial details. SinglePose AI uses these features to generate and refine keypoint heatmaps, achieving accurate pose estimation even in challenging conditions. We evaluate HRPose on benchmark datasets, including the MPII Human Pose and PoseTrack datasets, and compare it with several models. Our results demonstrate that HRPose achieves superior performance in terms of mAP, precision, and robustness. Additionally, we discuss the real-time performance of HRPose and its potential applications in various domains, such as sports, healthcare, and rehabilitation. Future work will focus on improving the model’s robustness to extreme conditions, such as low lighting and motion blur, and exploring its integration with multimodal data for more comprehensive analysis.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 200-213"},"PeriodicalIF":6.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935245","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 comprehensive review on mechanical performance and evaluation of shear panel dampers","authors":"Jun He ,&nbsp;Yongsheng Zhu ,&nbsp;Sidong Feng ,&nbsp;Huiteng Pei ,&nbsp;Weiwei Lin","doi":"10.1016/j.aej.2025.04.081","DOIUrl":"10.1016/j.aej.2025.04.081","url":null,"abstract":"<div><div>Shear panel dampers (SPDs) are garnering increasing attention due to their potential to enhance structural safety by effectively mitigating the adverse effects of earthquake and wind loading. However, the absence of a systematic classification method and standardized evaluation criteria for assessing the performance of various types of SPDs across different aspects complicates the selection of the appropriate damper type for practical applications. This paper categorizes SPDs based on structural and material characteristics, such as web configurations, buckling restraint types, and material compositions, providing a comprehensive literature review and systematically summarizing current progress and findings. Building on this comprehensive literature review, a robust evaluation approach is proposed, employing a multi-attribute weighting method to quantitatively evaluate various SPDs in terms of mechanical performance, structural characteristics, manufacturability, and other critical attributes. The results indicate that the proposed evaluation method yields reliable assessment outcomes, enabling decision-makers to clearly identify the advantages and limitations of each SPD type. This approach provides a convenient and dependable framework for selecting damper types in practical applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 92-115"},"PeriodicalIF":6.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928220","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":"Fatigue performance evaluation of high-strength railway axles subjected to different surface defects","authors":"Hai Zhao ,&nbsp;Rui-Peng Han ,&nbsp;Jie-Wei Gao ,&nbsp;Shun-Peng Zhu ,&nbsp;Jing Han","doi":"10.1016/j.aej.2025.05.011","DOIUrl":"10.1016/j.aej.2025.05.011","url":null,"abstract":"<div><div>Defects are the root causes of fatigue failure of railway axles. The purpose of present paper is to evaluate the fatigue behavior of high-strength 30NiCrMoV12 railway axle subjected to different surface defects, including electric discharging machine (EDM) notch, quasi-static indentation and impact damage. Balls and cubes made of bearing steel were shot by a compressed-gas gun toward specimens to create impact damages. Rotate-bending fatigue tests were conducted and morphologies of defects and fracture surfaces were characterized to correlate degradation of fatigue strength with defect geometry. The results show that the indentation and impact damage by ball imposes little influence on the fatigue strength, while the EDM defect is significantly detrimental. The shape variety of impact damage generated by flying cubes led to a larger scatter in fatigue behavior. A conservative estimation could be obtained by regarding the impact damage as EDM defect with equivalent depth.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"127 ","pages":"Pages 66-74"},"PeriodicalIF":6.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928218","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}