alexandria engineering journal最新文献

{"title":"Artificial neural network algorithm for time dependent radiative Casson fluid flow with couple stresses through a microchannel","authors":"Pradeep Kumar ,&nbsp;Felicita Almeida ,&nbsp;Qasem Al-Mdallal","doi":"10.1016/j.aej.2025.04.027","DOIUrl":"10.1016/j.aej.2025.04.027","url":null,"abstract":"<div><div>Artificial neural network due to its versatile applications is used in various domains. It helps in analysing large datasets which might be difficult to accomplish by conventional models. They help in modelling and analysing complex fluid flow problems and when properly trained they help in predicting the flow structures. Thus, this study focuses on constructing an artificial neural network design to solve mathematical problem of Casson fluid flow in the presence of non-linear radiation and a magnetic field. The study focuses on the flow that changes with time in a microchannel, resulting in partial differential equations that are computed with the help of finite difference approach. The occurrence of irreversibility in the medium is analysed in relation to the flow, and a neural network model is developed. The numerical results indicate that the irreversibility produced in the medium increases as the radiation parameter and temperature difference parameter increase. The mean squared error values achieved for all the scenarios fall within the range of <span><math><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo><mn>12</mn></mrow></msup></math></span> to <span><math><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo><mn>8</mn></mrow></msup></math></span>, indicating the successful interpretation of the neural network model constructed in tight correlation with the target data. Gradient descent was performed within the range of <span><math><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo><mn>8</mn></mrow></msup></math></span>, and the error histograms have the lowest values within the range of <span><math><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo><mn>8</mn></mrow></msup></math></span> to <span><math><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo><mn>6</mn></mrow></msup></math></span>. The regression analysis and plotfit demonstrate a high degree of concordance between the data points for training, testing, and validation, with an approximate correlation coefficient <span><math><mrow><mo>≈</mo><mn>1</mn></mrow></math></span>. An investigation of absolute error conducted for various parameters reveals that the errors fall within the range of <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></math></span> to <span><math><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></math></span>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 167-184"},"PeriodicalIF":6.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834631","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":"Big data grace: Implementations of the feature engineering and data science algorithms for environmental protection law","authors":"Wenyue Wu ,&nbsp;Yiming Zhao","doi":"10.1016/j.aej.2025.03.121","DOIUrl":"10.1016/j.aej.2025.03.121","url":null,"abstract":"<div><div>This study is intended to predict CO2 emissions using a set of features. With this aim, three machine learning (ML) algorithms have been used, namely, support vector regression (SVR), Long Short Term Memory (LSTM), and multilayer perceptron (MLP). First of all, correlation analysis was performed which revealed a low level of multicollinearity among the set of features. Hereafter, moving towards the modeling and compared the ML models, the findings showed that SVR (Linear) is the most reliable one, showing superiority to the rest by having the least Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values. On the contrary, the Dynamic LSTM model demonstrated the worst performance across all evaluated metrics. Specifically, it showed the highest values for MSE, RMSE, MAE, and MAPE. Static LSTM and SVR (RBF) models performed moderately, with Static LSTM marginally outperforming SVR (RBF) on the evaluation metrics like MAE and MSE. This will provide insight into guiding policy decisions in the future regarding strategies on environmental management and demographics development. This study highlights ML’s role in environmental monitoring, aiding policymakers with data-driven strategies to reduce CO2 emissions and shape sustainable policies.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 256-264"},"PeriodicalIF":6.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834814","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":"Dynamic cluster-based cooperative cache management at the network edges in NDN-based Internet of Things","authors":"Muhammad Ali Naeem ,&nbsp;Ali Kashif Bashir ,&nbsp;Yahui Meng","doi":"10.1016/j.aej.2025.03.131","DOIUrl":"10.1016/j.aej.2025.03.131","url":null,"abstract":"<div><div>The Named Data Networking (NDN) has been postulated as a new paradigm for the Internet of Things (IoT). It utilizes its data-centric networking model where content is acquired by name instead of position. Meanwhile, in NDN networks, caching is included in the nodes for temporary storing or user request responses. However, nodes in IoT networks have small cache storage compared to the huge amount of content transmitted, which poses challenges in efficiency. This work introduces a new caching policy for NDN-IoT systems called Dynamic Clustering-based Cooperative Caching (DCCC). DCCC determines and stores the content hitting rates concerning the user desires, dynamic threshold, and dissemination of cooperation with inter-node adaptive clusters. In detail, the results from growing numbers of simulations show that DCCC achieves better outcomes than prior caching schemes in average cache hits per node, hops reduction per node, and the delay of content delivery.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 297-310"},"PeriodicalIF":6.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834686","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":"Inference on the triple modular redundancy system based on exponential distribution under type-I hybrid step-stress model with type-II censoring","authors":"Laila A. Al-Essa ,&nbsp;Atef F. Hashem ,&nbsp;Tmader Alballa ,&nbsp;Alaa H. Abdel-Hamid","doi":"10.1016/j.aej.2025.03.096","DOIUrl":"10.1016/j.aej.2025.03.096","url":null,"abstract":"<div><div>This research introduces a novel hybrid model for the step-stress accelerated life test in which the stress transitions from a low level to a high level following a certain number of failures or a pre-determined time, whichever comes first. The stress is applied to the triple modular redundancy system, in which each device has a lifetime subjecting to the exponential distribution. This hybrid model significantly reduces testing time under a designated stress level. The test study also examines a simple step-stress testing scenario, concluding based on the type-II censoring scheme. Stress elevation follows the tampered hazard rate model. To estimate the reliability of the triple modular redundancy system and its mean time to failure, point and interval estimates for relevant parameters are determined using maximum likelihood and maximum product of spacings methods. Additionally, optimal timing for raising applied stress is explored, through two different approaches. The effectiveness of estimation methods is examined through the analysis of real-world data and Monte Carlo simulations. While both estimation methods yield convergent results, the maximum likelihood method proves to be more accurate, especially in the case of small sample sizes.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 185-197"},"PeriodicalIF":6.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834632","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":"Investigation of hydraulic transient flow and dynamic response performance of axial pump based on novel vibration signals characteristics and numerical analyses","authors":"Ahmed Ramadhan Al-Obaidi ,&nbsp;Anas Alwatban","doi":"10.1016/j.aej.2025.04.012","DOIUrl":"10.1016/j.aej.2025.04.012","url":null,"abstract":"<div><div>Simulation studies were conducted to investigate structure of internal flow and pulsation in pressure properties of pumps under different flow circumstances. An empirical model for shear-stress transport turbulence was also used to investigate how tip leakage vortices affect turbulent flows. Experiments were conducted in order to validate CFD results for accuracy and reliability. The study found that the vortex at the tip of the internal flow affects the pressure difference between blade surfaces, thereby altering the clearance flow and tip leakage vortex. Additionally, the study observed unsteadiness near the surface and extra suction due to secondary flow besides vortices tip leakage upstream of suction wall. Depending on the inlet pressure, axial flow pumps have different pulsation characteristics. Pulsation amplitudes near the impeller are high because blade passage frequency is high. In small flow conditions, clearance backflow causes significant pressure fluctuations at blade tip of axial pumps. The pump frequency and amplitude decrease as flow increases. Simulations model tests showed that the amplitude pressure of impeller pump is significantly larger under low flow conditions, where there is substantial backflow clearance owing to pump having a large clearance.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 104-126"},"PeriodicalIF":6.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834815","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":"Harnessing deep learning to analyze climate change impacts on crop production","authors":"Amena Mahmoud ,&nbsp;Khursheed Aurangzeb ,&nbsp;Musaed Alhussein ,&nbsp;Manal Sobhy Ali Elbelkasy","doi":"10.1016/j.aej.2025.04.008","DOIUrl":"10.1016/j.aej.2025.04.008","url":null,"abstract":"<div><div>Agricultural practices in Africa uplift the economy and sustain livelihoods. However, in recent years numerous issues like climate change, reduced productivity, narrowing resources, and increased prevalence have emerged. In order to combat these obstacles, we devised a plan that integrates IoT, AI ML, and geospatial technology. By analyzing 45 industrial reports and peer-reviewed journals we found out that the use of advanced technology makes resource optimization, irrigation management, and disease detection significantly easier. Our findings reveal some astonishing facts like the efficiency of CNN neural networks in terms of disease detection which stood at an impressive 92 %, then there are neural networks that achieve an accuracy of 88.9 % in predicting the yield of crops. Lastly, RL has managed to attain a water-saving efficiency of an impressive 25.4 %. Despite these advancements, the adoption rate in Africa is still low, this can be attributed to poor infrastructure, lack of funds, and absence of professional knowledge. In order to counter these shortcomings, we suggest political term initiatives, initiatives aimed at enhancing expertise and knowledge, and affordable IOT implementation. In addition to identifying the socio-economic and infrastructural barriers to technology adoption, this essay also offers suggestions that advocate for facilitating sustainable agricultural practices in Africa. So that the identified gaps are bridged, the research aids in enhancing climate change resilience for sustainable growth in the agriculture industry of the continent.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 67-82"},"PeriodicalIF":6.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829945","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":"Machine learning-based fatigue lifetime prediction of structural steels","authors":"Konstantinos Arvanitis ,&nbsp;Pantelis Nikolakopoulos ,&nbsp;Dimitrios Pavlou ,&nbsp;Mina Farmanbar","doi":"10.1016/j.aej.2025.04.014","DOIUrl":"10.1016/j.aej.2025.04.014","url":null,"abstract":"<div><div>Fatigue of materials stands as a prevalent cause of mechanical structure failures, which often occur suddenly, unpredictably, and catastrophically. Accurately predicting the fatigue lifespan of materials is crucial, especially given the potential for fatigue failure to occur within a short design life. While traditional methodologies based on S-N curve models remain prevalent in industry, there is a contemporary shift towards employing Artificial Intelligence and Machine Learning techniques to significantly refine the accuracy of fatigue lifetime predictions. In this study, a dataset containing experimental data from various structural steels is used. Through preprocessing and feature selection, four techniques are explored: Polynomial Regression, Support Vector Regression (SVR), XGB Regression and Artificial Neural Network (ANN), aiming to identify the most effective algorithm. The implementation of these methodologies for fatigue lifetime prediction yields substantial outcomes. All models exhibit satisfactory performance, with XGB Regression demonstrating superior effectiveness. Furthermore, Polynomial Regression provides highly satisfactory results, almost identical to the Artificial Neural Network. Notably, it requires significantly less computational power, making it a practical alternative in cases of restricted computational resources or limited implementation time. Overall, the proposed methodology effectively leverages material preprocessing details, mechanical properties and experimental conditions to provide accurate predictions of the remaining fatigue lifespan of structural steels.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 55-66"},"PeriodicalIF":6.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829944","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":"Investigation of double-diffusive mixed convective flow of water-based Brinkman-type hybrid nanofluid utilizing a fractal fractional approach","authors":"Ahmed M. Galal ,&nbsp;Ali Raza ,&nbsp;Umair Khan ,&nbsp;Aurang Zaib ,&nbsp;Anuar Ishak ,&nbsp;Hami Gündoğdu ,&nbsp;B. Alshahrani ,&nbsp;Mona Mahmoud","doi":"10.1016/j.aej.2025.04.024","DOIUrl":"10.1016/j.aej.2025.04.024","url":null,"abstract":"<div><div>Nanofluids play a crucial role in enhancing the thermal performance of various engineering and industrial applications, particularly in manufacturing and chemical processes. Similarly, porous materials are essential in chemical engineering and plasma physics, contributing to advancements in heat and mass transfer. However, the study of Brinkman-type fluid flow in a porous channel under varying thermal and mass flux conditions remains largely unexplored. This research develops a computational model to analyze the unsteady flow of Brinkman-type hybrid nanofluids within a porous channel confined by two plates. The model incorporates fractional derivatives to offer a more generalized perspective on thermal and mass flux behavior under the influence of an inclined magnetic field. Two hybrid nanofluids, with water and kerosene oil as base fluids mixed with Cu and TiO₂ nanoparticles, are examined. The fractional fractal derivatives (FFD) approach is utilized to extend the governing equations for velocity and thermal flux. These equations are transformed into non-dimensional forms and solved using the Laplace transform method, with the Stehfest and Tzou techniques applied for inversion. The parametric analysis reveals that increasing the Brinkman-type restriction significantly influences fluid velocity, enabling better control over flow behavior. The fractional derivative approach provides deeper insights into the interaction between thermal and mass fluxes in hybrid nanofluids. This study contributes valuable knowledge for optimizing heat and mass transfer in various engineering and industrial applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 90-103"},"PeriodicalIF":6.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829947","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":"FNN-BiLSTM-Attention-DA: A hybrid fuzzy neural network and BiLSTM with multi-sensor information fusion for water quality monitoring and warning","authors":"Dong Liu ,&nbsp;Xiaolong Cheng","doi":"10.1016/j.aej.2025.04.011","DOIUrl":"10.1016/j.aej.2025.04.011","url":null,"abstract":"<div><div>To conduct water quality anomaly alerts and set water quality alarm thresholds, a difference analysis (DA) model dependent on the FNN-BiLSTM-Attention mechanism is proposed in this study. The model efficiently lessens the impact of outliers and values that are missing in the statistical sample data on the predicted values, increasing the preciseness of the water condition alarms while accounting for the effects of seasonal and hydrological cycles on data changes. Five water quality indicators were used to describe the input data, which FNN first analyzed to extract the data's geographical properties. The time series features were then obtained by feeding the prior outputs into the forward and backward LSTM layers, respectively, via the BiLSTM layer. The FNN-BiLSTM-Attention model has the best MAE and MAPE on all water quality measures, according to the experimental data, and it has the lowest average MAE and MAPE on the water quality indicator dataset (YRB dataset), which is 0.174 and 6.32 %, respectively. Also, it has the highest average correlation coefficient of 0.936. In addition, the performance of the model was further validated on another proposed wastewater treatment plant dataset (WTPD dataset) in order to verify the generalization performance of the model.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"124 ","pages":"Pages 624-639"},"PeriodicalIF":6.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829330","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 normalized time-fractional Korteweg–de Vries equation","authors":"Hyun Geun Lee ,&nbsp;Soobin Kwak ,&nbsp;Jyoti ,&nbsp;Yunjae Nam ,&nbsp;Junseok Kim","doi":"10.1016/j.aej.2025.03.137","DOIUrl":"10.1016/j.aej.2025.03.137","url":null,"abstract":"<div><div>A novel normalized time-fractional Korteweg–de Vries (KdV) equation is presented to investigate the effects of fractional time derivatives on nonlinear wave dynamics. The classical KdV model is extended by incorporating a fractional-order derivative, which captures memory and inherited properties in the evolution of soliton-like structures. Computational studies of the equation’s nonlinear dynamics use a numerical scheme designed for the fractional temporal dimension. Simulations show that as the fractional parameter <span><math><mi>α</mi></math></span> decreases from 1 (the classical case) to smaller values, soliton dynamics change significantly. The soliton amplitude decreases, and its width increases. These changes are interpreted as dispersive or dissipative effects introduced by the fractional time component. At lower values of <span><math><mi>α</mi></math></span>, the soliton becomes broader and flatter, and its propagation is slowed. At intermediate values of <span><math><mi>α</mi></math></span>, multiple peaks and broader waveforms are observed, which implies more complex nonlinear interactions under fractional time evolution. The importance of fractional time derivatives in modifying the behavior of soliton solutions is highlighted, which demonstrates their potential in modeling physical systems where memory effects play a crucial role. The computational results provide insights into fractional partial differential equations and create new opportunities for future research in nonlinear wave propagation under fractional dynamics.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 83-89"},"PeriodicalIF":6.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829946","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}