A novel crossover model for monkeypox disease that incorporates -Caputo fractional derivatives is presented here, where we use a simple nonstandard kernel function . We can be obtained the Caputo and Caputo–Katugampola derivatives as special cases from the proposed derivative. The crossover dynamics model defines four alternative models: fractal fractional order, fractional order, variable order, and fractional stochastic derivatives driven by fractional Brownian motion over four time intervals. The -nonstandard finite difference method is designed to solve fractal fractional order, fractional order, and variable order mathematical models. Also, the nonstandard modified Euler Maruyama method is used to study the fractional stochastic model. A comparison between -nonstandard finite difference method and -standard finite difference method is presented. Moreover, numerous numerical tests and comparisons with real data were conducted to validate the methods’ efficacy and support the theoretical conclusions.
{"title":"A new crossover dynamics mathematical model of monkeypox disease based on fractional differential equations and the Ψ-Caputo derivative: Numerical treatments","authors":"N.H. Sweilam , S.M. Al-Mekhlafi , W.S. Abdel Kareem , G. Alqurishi","doi":"10.1016/j.aej.2024.10.019","DOIUrl":"10.1016/j.aej.2024.10.019","url":null,"abstract":"<div><div>A novel crossover model for monkeypox disease that incorporates <span><math><mi>Ψ</mi></math></span>-Caputo fractional derivatives is presented here, where we use a simple nonstandard kernel function <span><math><mrow><mi>Ψ</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>. We can be obtained the Caputo and Caputo–Katugampola derivatives as special cases from the proposed derivative. The crossover dynamics model defines four alternative models: fractal fractional order, fractional order, variable order, and fractional stochastic derivatives driven by fractional Brownian motion over four time intervals. The <span><math><mi>Ψ</mi></math></span>-nonstandard finite difference method is designed to solve fractal fractional order, fractional order, and variable order mathematical models. Also, the nonstandard modified Euler Maruyama method is used to study the fractional stochastic model. A comparison between <span><math><mi>Ψ</mi></math></span>-nonstandard finite difference method and <span><math><mi>Ψ</mi></math></span>-standard finite difference method is presented. Moreover, numerous numerical tests and comparisons with real data were conducted to validate the methods’ efficacy and support the theoretical conclusions.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 181-193"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.aej.2024.10.033
Le Sun , Shunqi Liu , Ghulam Muhammad
The diversity of sources and uneven distribution of medical data contributes to the statistical heterogeneity within the Medical Internet of Things (MIoT) networks. In this context, comprehensive analysis of patient data is imperative to provide more precise diagnoses and treatment strategies, rendering personalized medical treatment indispensable. Moreover, the transmission of medical data over networks raises concerns regarding data privacy, necessitating thorough consideration. To address these challenges, we propose FedWFC, a federated learning method that combines a novel importance weight with fuzzy k-means clustering to effectively handle the heterogeneous medical data in MIoT networks. Firstly, we utilize fuzzy k-means for clustering and partitioning local model parameters from MIoT devices, enabling centralized updates of multiple global models based on these clusters. This cluster-centric approach streamlines personalized updates for local models. Secondly, the introduction of the new importance weight allows us to tighten the optimization error bound for global model updates. Experiments show that FedWFC improves the macro F1 score by 4.24% and the micro accuracy by 4.99% compared with existing methods, highlighting its effectiveness in MIoT data processing.
{"title":"FedWFC: Federated learning with weighted fuzzy clustering for handling heterogeneous data in MIoT networks","authors":"Le Sun , Shunqi Liu , Ghulam Muhammad","doi":"10.1016/j.aej.2024.10.033","DOIUrl":"10.1016/j.aej.2024.10.033","url":null,"abstract":"<div><div>The diversity of sources and uneven distribution of medical data contributes to the statistical heterogeneity within the Medical Internet of Things (MIoT) networks. In this context, comprehensive analysis of patient data is imperative to provide more precise diagnoses and treatment strategies, rendering personalized medical treatment indispensable. Moreover, the transmission of medical data over networks raises concerns regarding data privacy, necessitating thorough consideration. To address these challenges, we propose FedWFC, a federated learning method that combines a novel importance weight with fuzzy k-means clustering to effectively handle the heterogeneous medical data in MIoT networks. Firstly, we utilize fuzzy k-means for clustering and partitioning local model parameters from MIoT devices, enabling centralized updates of multiple global models based on these clusters. This cluster-centric approach streamlines personalized updates for local models. Secondly, the introduction of the new importance weight allows us to tighten the optimization error bound for global model updates. Experiments show that FedWFC improves the macro F1 score by 4.24% and the micro accuracy by 4.99% compared with existing methods, highlighting its effectiveness in MIoT data processing.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 194-202"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.aej.2024.10.055
Dandan Zhang, Yafei Song, Qian Xiang, Yang Wang
Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficult for the traditional identification methods to effectively capture the sample feature information operated by the confusion method only based on the delayed database information. The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. Through these investigations, our goal is to construct a detection system that is both more precise and efficient, capable of addressing the ever-evolving threats to cybersecurity. Pursuing research in this direction is not only vital for enhancing network security defenses and safeguarding user data, but it will also foster the advancement of related state-of-the-art technologies and further mitigate the economic and societal repercussions of malware attacks. In light of this issue, this paper proposes the Image-based Malware Classification with Multi-scale Kernels (IMCMK), a Convolutional Neural Network (CNN) architecture using multi-scale convolution kernels mixing action to improve malware variants detection capabilities. First, we propose the Multi-scale Kernels (MK) block combining deep large kernel convolution and standard small kernel convolution with shortcuts to improve the accuracy. Furthermore, we propose Multi-scale Kernel Fusion (MKF) to reduce the number of parameters that come with the large kernels. The improved Squeeze-and-Excitation (SE) block can obtain the correlation between different channels to further increase the model performance. Experimental results show that IMCMK outperforms the state-of-the-art methods in malware family classification accuracy, which has achieved 99.25 %.
快速准确地识别未知恶意软件及其变种是有效防范恶意攻击的前提和基础。然而，随着恶意软件变种的爆炸式增长，人工更新样本数据库的效率越来越低。传统的识别方法仅基于延迟的数据库信息，难以有效捕捉混淆法操作的样本特征信息。恶意软件检测方向的研究致力于克服传统检测方法的局限性，深入探讨数据可视化、机器学习、混合检测等前沿技术在恶意软件检测领域的应用。通过这些研究，我们的目标是构建一个更精确、更高效的检测系统，以应对不断变化的网络安全威胁。朝着这个方向开展研究不仅对加强网络安全防御和保护用户数据至关重要，而且还能促进相关先进技术的发展，进一步减轻恶意软件攻击对经济和社会造成的影响。有鉴于此，本文提出了基于图像的多尺度内核恶意软件分类（Image-based Malware Classification with Multi-scale Kernels，IMCMK），这是一种使用多尺度卷积内核混合作用的卷积神经网络（CNN）架构，旨在提高恶意软件变种的检测能力。首先，我们提出了多尺度内核（MK）区块，将深度大内核卷积和标准小内核卷积与捷径相结合，以提高准确性。此外，我们还提出了多尺度内核融合（MKF），以减少大内核带来的参数数量。改进的挤压激励（SE）块可以获得不同通道之间的相关性，从而进一步提高模型性能。实验结果表明，IMCMK 的恶意软件族分类准确率超过了最先进的方法，达到了 99.25%。
{"title":"IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification","authors":"Dandan Zhang, Yafei Song, Qian Xiang, Yang Wang","doi":"10.1016/j.aej.2024.10.055","DOIUrl":"10.1016/j.aej.2024.10.055","url":null,"abstract":"<div><div>Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficult for the traditional identification methods to effectively capture the sample feature information operated by the confusion method only based on the delayed database information. The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. Through these investigations, our goal is to construct a detection system that is both more precise and efficient, capable of addressing the ever-evolving threats to cybersecurity. Pursuing research in this direction is not only vital for enhancing network security defenses and safeguarding user data, but it will also foster the advancement of related state-of-the-art technologies and further mitigate the economic and societal repercussions of malware attacks. In light of this issue, this paper proposes the Image-based Malware Classification with Multi-scale Kernels (IMCMK), a Convolutional Neural Network (CNN) architecture using multi-scale convolution kernels mixing action to improve malware variants detection capabilities. First, we propose the Multi-scale Kernels (MK) block combining deep large kernel convolution and standard small kernel convolution with shortcuts to improve the accuracy. Furthermore, we propose Multi-scale Kernel Fusion (MKF) to reduce the number of parameters that come with the large kernels. The improved Squeeze-and-Excitation (SE) block can obtain the correlation between different channels to further increase the model performance. Experimental results show that IMCMK outperforms the state-of-the-art methods in malware family classification accuracy, which has achieved 99.25 %.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 203-220"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and timely traffic flow predictions are essential for effective traffic management and congestion reduction. However, most traditional prediction methods often fail to capture the complex dynamics and correlations within traffic flows due to insufficient processing of spatiotemporal data. Specifically, these methods struggle to integrate and analyze the multi-layered spatial and temporal interactions inherent in traffic data, leading to suboptimal prediction accuracy and robustness. To address this limitation, this paper presents a Multi-Graph Structures and Hypergraph Convolutional Network (MGHCN) that combines diverse graphs and hypergraphs. The MGHCN simplifies the predictive framework by integrating key components that improve its robustness and accuracy. One of the most critical components is the dual hypergraph structure, which captures edge correlations by converting traditional graph edges into hypergraph nodes. To better capture the spatiotemporal correlation of traffic data, a Graph Convolutional Network (GCN) is employed to analyze these hypergraphs in depth. Finally, a novel adjacency matrix and a dynamic graph module are used to accurately simulate interactions between spatiotemporal features, thereby enhancing the accuracy and robustness of predictions. Experimental validation on four distinct real-world traffic datasets shows that MGHCN outperforms existing state-of-the-art traffic prediction methods.
{"title":"MGHCN: Multi-graph structures and hypergraph convolutional networks for traffic flow prediction","authors":"Xuanxuan Fan , Kaiyuan Qi , Dong Wu , Haonan Xie , Zhijian Qu , Chongguang Ren","doi":"10.1016/j.aej.2024.10.022","DOIUrl":"10.1016/j.aej.2024.10.022","url":null,"abstract":"<div><div>Accurate and timely traffic flow predictions are essential for effective traffic management and congestion reduction. However, most traditional prediction methods often fail to capture the complex dynamics and correlations within traffic flows due to insufficient processing of spatiotemporal data. Specifically, these methods struggle to integrate and analyze the multi-layered spatial and temporal interactions inherent in traffic data, leading to suboptimal prediction accuracy and robustness. To address this limitation, this paper presents a Multi-Graph Structures and Hypergraph Convolutional Network (MGHCN) that combines diverse graphs and hypergraphs. The MGHCN simplifies the predictive framework by integrating key components that improve its robustness and accuracy. One of the most critical components is the dual hypergraph structure, which captures edge correlations by converting traditional graph edges into hypergraph nodes. To better capture the spatiotemporal correlation of traffic data, a Graph Convolutional Network (GCN) is employed to analyze these hypergraphs in depth. Finally, a novel adjacency matrix and a dynamic graph module are used to accurately simulate interactions between spatiotemporal features, thereby enhancing the accuracy and robustness of predictions. Experimental validation on four distinct real-world traffic datasets shows that MGHCN outperforms existing state-of-the-art traffic prediction methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 221-237"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.aej.2024.10.056
Jafar Hasnain, Nomana Abid
The velocity slippage with combined effects of buoyancy force plays an important role in the extraction of crude oil since it reduces the pressure drop along the pipelines and well-bore. Due to this, it becomes difficult to push the oil out of the reservoir. So, the main goal is to analyse the velocity slip and no-slip on two immiscible viscous fluids flowing in a curved channel with viscous dissipation. To the best of the author's knowledge, there is no study given on two non-miscible laminar Newtonian layers of viscous-viscous fluids. The curved channel is divided into two regions and both regions are occupied with viscous fluids. The flow is caused by the constant pressure gradient and buoyancy force. The analytical solutions of highly nonlinear mathematical equations are obtained using perturbation series by taking . The impact of the curvature ratio parameter, velocity slips, Reynolds number, buoyancy force, viscosity ratio parameter, and height ratio parameter on heat and mass transfer are examined and presented through graphs. Moreover, the behaviour of shear stresses, skin drag and Nusselt number on both walls of the channel is analyzed and presented through bar charts. It is concluded that the velocities of the immiscible fluids augment by increasing the curvature parameter whereas the temperatures and shear stresses fall. It is also observed that the shear stress at the lower wall is more significant with a rise in curvature parameter in the case when velocity slippage is not considered as compared to velocity slippage.
{"title":"Analytical study on flow of two non-miscible laminar layers of Newtonian fluids in a curved channel with wall slippage","authors":"Jafar Hasnain, Nomana Abid","doi":"10.1016/j.aej.2024.10.056","DOIUrl":"10.1016/j.aej.2024.10.056","url":null,"abstract":"<div><div>The velocity slippage with combined effects of buoyancy force plays an important role in the extraction of crude oil since it reduces the pressure drop along the pipelines and well-bore. Due to this, it becomes difficult to push the oil out of the reservoir. So, the main goal is to analyse the velocity slip and no-slip on two immiscible viscous fluids flowing in a curved channel with viscous dissipation. To the best of the author's knowledge, there is no study given on two non-miscible laminar Newtonian layers of viscous-viscous fluids. The curved channel is divided into two regions and both regions are occupied with viscous fluids. The flow is caused by the constant pressure gradient and buoyancy force. The analytical solutions of highly nonlinear mathematical equations are obtained using perturbation series by taking <span><math><mrow><mi>ε</mi><mrow><mo>(</mo><mo>=</mo><mrow><mi>Pr</mi><mi>E</mi><mi>c</mi></mrow><mo>)</mo></mrow><mo>≪</mo><mn>1</mn></mrow></math></span>. The impact of the curvature ratio parameter, velocity slips, Reynolds number, buoyancy force, viscosity ratio parameter, and height ratio parameter on heat and mass transfer are examined and presented through graphs. Moreover, the behaviour of shear stresses, skin drag and Nusselt number on both walls of the channel is analyzed and presented through bar charts. It is concluded that the velocities of the immiscible fluids augment by increasing the curvature parameter whereas the temperatures and shear stresses fall. It is also observed that the shear stress at the lower wall is more significant with a rise in curvature parameter in the case when velocity slippage is not considered as compared to velocity slippage.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 238-248"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.aej.2024.09.031
Burenbatu , Yuanmeng Liu , Tianyi Lyu
Real-time lower limb movement resistance monitoring is critical for various applications in clinical and sports settings, such as rehabilitation and athletic training. Current methods often face limitations in accuracy, computational efficiency, and generalizability, which hinder their practical implementation. To address these challenges, we propose a novel Mobile Multi-Task Learning Network (MMTL-Net) that integrates MobileNetV3 for efficient feature extraction and employs multi-task learning to simultaneously predict resistance levels and recognize activities. The advantages of MMTL-Net include enhanced accuracy, reduced latency, and improved computational efficiency, making it highly suitable for real-time applications. Experimental results demonstrate that MMTL-Net significantly outperforms existing models on the UCI Human Activity Recognition and Wireless Sensor Data Mining Activity Prediction datasets, achieving a lower Force Error Rate (FER) of 6.8% and a higher Resistance Prediction Accuracy (RPA) of 91.2%. Additionally, the model shows a Real-time Responsiveness (RTR) of 12 ms and a Throughput (TP) of 33 frames per second. These findings underscore the model’s robustness and effectiveness in diverse real-world scenarios. The proposed framework not only advances the state-of-the-art in resistance monitoring but also paves the way for more efficient and accurate systems in clinical and sports applications. In real-world settings, the practical implications of MMTL-Net include its potential to enhance patient outcomes in rehabilitation and improve athletic performance through precise, real-time monitoring and feedback.
{"title":"Real-time monitoring of lower limb movement resistance based on deep learning","authors":"Burenbatu , Yuanmeng Liu , Tianyi Lyu","doi":"10.1016/j.aej.2024.09.031","DOIUrl":"10.1016/j.aej.2024.09.031","url":null,"abstract":"<div><div>Real-time lower limb movement resistance monitoring is critical for various applications in clinical and sports settings, such as rehabilitation and athletic training. Current methods often face limitations in accuracy, computational efficiency, and generalizability, which hinder their practical implementation. To address these challenges, we propose a novel Mobile Multi-Task Learning Network (MMTL-Net) that integrates MobileNetV3 for efficient feature extraction and employs multi-task learning to simultaneously predict resistance levels and recognize activities. The advantages of MMTL-Net include enhanced accuracy, reduced latency, and improved computational efficiency, making it highly suitable for real-time applications. Experimental results demonstrate that MMTL-Net significantly outperforms existing models on the UCI Human Activity Recognition and Wireless Sensor Data Mining Activity Prediction datasets, achieving a lower Force Error Rate (FER) of 6.8% and a higher Resistance Prediction Accuracy (RPA) of 91.2%. Additionally, the model shows a Real-time Responsiveness (RTR) of 12 ms and a Throughput (TP) of 33 frames per second. These findings underscore the model’s robustness and effectiveness in diverse real-world scenarios. The proposed framework not only advances the state-of-the-art in resistance monitoring but also paves the way for more efficient and accurate systems in clinical and sports applications. In real-world settings, the practical implications of MMTL-Net include its potential to enhance patient outcomes in rehabilitation and improve athletic performance through precise, real-time monitoring and feedback.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 136-147"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.aej.2024.10.003
Marouan Kouki , Saira Shukat , Ikram Ullah , Mohammad Mahtab Alam , Ali Hasan Ali
Transport of heat in combustion engines, burners and consumption of energy via nuclear explosions is remarkably effected by magnetize nanofluid and radiation. Present attempt is relevant to the current Engineering applications; as design of heat exchangers, systems of renewable energy, and Nanotechnology. Therefore, main concern of the study is explored the radiative flux in Micropolar nanofluid flow under the Lorentz force and gravity modulation. The impacts of cross diffusion is also included in flow field. The mathematical model governing the flow are transformed into ODEs via similarity variables. The Keller box approach is utilized for numerical outcomes. A comprehensive analysis of the physical parameters is carried out, and numerical outcomes are displayed in graphical and tabular form. Obtained outcomes are compared with results that have already been published and found a good match. It has been found that temperature profile and concentration profile have a direct relation against Soret and Dufour respectively. Temperature profile and concentration profile has a direct relation against Dufour and Soret effects. Thermal field grows by enhancing radiation, Brownian motion thermophoresis parameter. Furthermore, the skin friction.increases as the inclination factor grows up, but Nusselt and Sherwood numbers decline.
{"title":"Keller-box based computational investigation of magnetized gravity-driven Micropolar nanofluid flow past an exponentially contracting surface with cross diffusion effect and engineering applications","authors":"Marouan Kouki , Saira Shukat , Ikram Ullah , Mohammad Mahtab Alam , Ali Hasan Ali","doi":"10.1016/j.aej.2024.10.003","DOIUrl":"10.1016/j.aej.2024.10.003","url":null,"abstract":"<div><div>Transport of heat in combustion engines, burners and consumption of energy via nuclear explosions is remarkably effected by magnetize nanofluid and radiation. Present attempt is relevant to the current Engineering applications; as design of heat exchangers, systems of renewable energy, and Nanotechnology. Therefore, main concern of the study is explored the radiative flux in Micropolar nanofluid flow under the Lorentz force and gravity modulation. The impacts of cross diffusion is also included in flow field. The mathematical model governing the flow are transformed into ODEs via similarity variables. The Keller box approach is utilized for numerical outcomes. A comprehensive analysis of the physical parameters is carried out, and numerical outcomes are displayed in graphical and tabular form. Obtained outcomes are compared with results that have already been published and found a good match. It has been found that temperature profile and concentration profile have a direct relation against Soret and Dufour respectively. Temperature profile and concentration profile has a direct relation against Dufour and Soret effects. Thermal field grows by enhancing radiation, Brownian motion <span><math><mtext>and</mtext></math></span> thermophoresis parameter. Furthermore, the skin friction.increases as the inclination factor grows up, but Nusselt and Sherwood numbers decline.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 160-170"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.aej.2024.10.051
Jinwei Fan, Zhuang Li, Ri Pan, Kun Sun, Kai Chen
Accurate volumetric error model is the basis for accuracy design. In this paper, a universal model for volumetric error prediction considering tolerance is proposed. Firstly, geometric error parameters are obtained by analysing the motion forms of key components. Secondly, the map between geometric error and tolerance is developed using Fourier function. Subsequently, the volumetric error prediction model (VEPM) is established based on key component tolerance. The model was applied to guide the development of machine tools. Finally, model validation experiments are carried out with two configurations of machine tools. The results show that, for the horizontal grinder, the predicted values for ±45° diagonal errors are 0–2.7 μm and 0–4.5 μm, which are consistent with the measured average values of 0.03–2.33 μm and −0.10–5.46 μm, respectively. Moreover, the predicted and measured values for +45° diagonal error of the vertical grinder are −15.0–0 μm and −15.07–0 μm, respectively. The experimental results illustrate the VEPM is effective and universal. The model has the potential to be applied to the design and development of machine tools.
{"title":"Volumetric error modeling and prediction for machine tools based on key component tolerance","authors":"Jinwei Fan, Zhuang Li, Ri Pan, Kun Sun, Kai Chen","doi":"10.1016/j.aej.2024.10.051","DOIUrl":"10.1016/j.aej.2024.10.051","url":null,"abstract":"<div><div>Accurate volumetric error model is the basis for accuracy design. In this paper, a universal model for volumetric error prediction considering tolerance is proposed. Firstly, geometric error parameters are obtained by analysing the motion forms of key components. Secondly, the map between geometric error and tolerance is developed using Fourier function. Subsequently, the volumetric error prediction model (VEPM) is established based on key component tolerance. The model was applied to guide the development of machine tools. Finally, model validation experiments are carried out with two configurations of machine tools. The results show that, for the horizontal grinder, the predicted values for ±45° diagonal errors are 0–2.7 μm and 0–4.5 μm, which are consistent with the measured average values of 0.03–2.33 μm and −0.10–5.46 μm, respectively. Moreover, the predicted and measured values for +45° diagonal error of the vertical grinder are −15.0–0 μm and −15.07–0 μm, respectively. The experimental results illustrate the VEPM is effective and universal. The model has the potential to be applied to the design and development of machine tools.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 171-180"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.aej.2024.10.053
Amal K. Alkhalifa , Nuha Alruwais , Wahida Mansouri , Munya A. Arasi , Mohammed Alliheedi , Fouad Shoie Alallah , Alaa O. Khadidos , Abdulrhman Alshareef
The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.
{"title":"Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment","authors":"Amal K. Alkhalifa , Nuha Alruwais , Wahida Mansouri , Munya A. Arasi , Mohammed Alliheedi , Fouad Shoie Alallah , Alaa O. Khadidos , Abdulrhman Alshareef","doi":"10.1016/j.aej.2024.10.053","DOIUrl":"10.1016/j.aej.2024.10.053","url":null,"abstract":"<div><div>The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 148-159"},"PeriodicalIF":6.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces a novel analytical and numerical framework for determining the damped and undamped frequencies of elastically restrained Euler–Bernoulli (EB) and shear beams (SB) supported by two-parameter (visco-Winkler) and three-parameter (visco-Pasternak) viscoelastic foundations (VF). The scientific novelty lies in extending the classical separation of variables approach and coupling it with eigenvalue-based dispersion relations to derive an innovative spatial matrix formulation for displacements, slopes, and their derivatives. This method provides enhanced accuracy and robustness, especially in modeling complex vibrational behavior in the presence of damping and shear effects, a challenge often encountered in conventional studies. The research further integrates the Galerkin finite element method (GFEM) to offer a shear locking-free solution, demonstrating convergence to exact results, and thereby addressing critical limitations in previous methods. Additionally, the study introduces the application of state-space formulations combined with the Runge–Kutta method (RK4) to precisely analyze the response of damped systems, which adds significant value in exploring complex beam dynamics. Through a comprehensive comparison of analytical and finite element methods (FEM), the findings are validated and visualized under varying damping conditions, providing practical insights for the design and optimization of structures with viscoelastic supports. The contributions of this work include not only a deeper understanding of the interaction between damping, foundation stiffness, and structural dynamics but also the development of a versatile and scalable approach that broadens the applicability of beam models in advanced engineering applications.
{"title":"Exploring damped and undamped frequencies in beam structures with viscoelastic supports using GFEM and state-space formulation","authors":"Gulnaz Kanwal , Hani Alahmadi , Rab Nawaz , Tayyab Nawaz","doi":"10.1016/j.aej.2024.09.112","DOIUrl":"10.1016/j.aej.2024.09.112","url":null,"abstract":"<div><div>This study introduces a novel analytical and numerical framework for determining the damped and undamped frequencies of elastically restrained Euler–Bernoulli (EB) and shear beams (SB) supported by two-parameter (visco-Winkler) and three-parameter (visco-Pasternak) viscoelastic foundations (VF). The scientific novelty lies in extending the classical separation of variables approach and coupling it with eigenvalue-based dispersion relations to derive an innovative spatial matrix formulation for displacements, slopes, and their derivatives. This method provides enhanced accuracy and robustness, especially in modeling complex vibrational behavior in the presence of damping and shear effects, a challenge often encountered in conventional studies. The research further integrates the Galerkin finite element method (GFEM) to offer a shear locking-free solution, demonstrating convergence to exact results, and thereby addressing critical limitations in previous methods. Additionally, the study introduces the application of state-space formulations combined with the Runge–Kutta method (RK4) to precisely analyze the response of damped systems, which adds significant value in exploring complex beam dynamics. Through a comprehensive comparison of analytical and finite element methods (FEM), the findings are validated and visualized under varying damping conditions, providing practical insights for the design and optimization of structures with viscoelastic supports. The contributions of this work include not only a deeper understanding of the interaction between damping, foundation stiffness, and structural dynamics but also the development of a versatile and scalable approach that broadens the applicability of beam models in advanced engineering applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"111 ","pages":"Pages 58-77"},"PeriodicalIF":6.2,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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