Ain Shams Engineering Journal最新文献

{"title":"Error evaluation and stability analysis of numerical schemes in multiple sclerosis modeling","authors":"Zeeshan Afzal ,&nbsp;Mansoor Alshehri","doi":"10.1016/j.asej.2025.103551","DOIUrl":"10.1016/j.asej.2025.103551","url":null,"abstract":"<div><div>This study presents a mathematical model for Multiple Sclerosis (MS) analyzed using the finite difference method and the fourth-order Runge-Kutta (RK4) method. A comparative error analysis shows strong agreement between both methods, with the finite difference approach closely aligning with RK4, as indicated by low Mean Squared Error (MSE), Mean Absolute Error (MAE), and maximum absolute error values. While RK4 provides superior accuracy and stability for long-term simulations, the finite difference method remains a practical and computationally efficient alternative. These findings underscore the potential of mathematical modeling combined with numerical techniques to enhance the understanding of MS dynamics and support the development of optimized intervention strategies. Future research will concentrate on refining the model through the integration of additional biological parameters and clinical data to improve its predictive accuracy.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103551"},"PeriodicalIF":6.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313649","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 novel Buck-Boost Modified Series Forward (BBMSF) converter for enhanced efficiency in hybrid renewable energy systems","authors":"K. Karthikeyan ,&nbsp;P. Umasankar","doi":"10.1016/j.asej.2025.103557","DOIUrl":"10.1016/j.asej.2025.103557","url":null,"abstract":"<div><div>A novel Buck-Boost Modified Series Forward (BBMSF) converter is proposed in this research work to enhance the efficiency and reliability of Hybrid Renewable Energy Systems (HRES) integrating solar photovoltaic panels, vertical-axis wind turbines, and battery storage. The proposed converter dynamically switches between buck and boost modes, ensuring consistent voltage regulation and optimal energy transfer under varying environmental and load conditions. Unlike traditional designs, the BBMSF architecture employs an auto-transformer configuration to reduce magnetic losses and simplify control mechanisms. A simulation-based evaluation was conducted using different wind turbine configurations—Darrieus and Savonius—highlighting the superior performance of the Darrieus-based system, which achieved a steady-state error of 0.67 and reduced switching losses of 7.63%. Sensitivity analyses were further performed to assess the converter’s response to fluctuations in load, solar irradiance, and battery state of charge, demonstrating its robust adaptability and high conversion efficiency. The results confirm the effectiveness of the BBMSF converter in maintaining stable energy output and minimizing losses, making it a promising solution for real-world renewable energy integration.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103557"},"PeriodicalIF":6.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322376","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":"Production of Ti/HA functionally graded material implants using powder metallurgy technique for reduction of the effect of chemical pollution","authors":"Saeed Daneshmand ,&nbsp;Mohammad Heydari Vini ,&nbsp;Ali Basem ,&nbsp;Narinderjit Singh Sawaran Singh ,&nbsp;Muntadher Abed Hussein ,&nbsp;Soheil Salahshour ,&nbsp;Ali Mokhtari","doi":"10.1016/j.asej.2025.103573","DOIUrl":"10.1016/j.asej.2025.103573","url":null,"abstract":"<div><div>Nowadays, there is a growing need to use functionally gradient materials for use in biomedical applications. This requirement is particularly significant to the effect of implant application and gradient structure. The powder metallurgy technique was used in this study to fabricate titanium/hydroxyapatite and other FGM implants with the concentration changed gradually in the longitudinal direction of the cylindrical shape, to optimize both mechanical and biocompatibility properties or alter bio reactivity in each region. High-frequency induction heating, three-spark plasma sintering, and electric furnace heating techniques were implemented to sinter the materials. During the fabrication of titanium/hydroxyapatite functionally gradient materials and due to the stress relaxation in the implanted region of bone, Brinell hardness decreased gradually from the Ti part to the HA part. The results show that the tissue reaction occurred in a gradient in response to the gradient structure of FGM, which implies the possibility of controlling the tissue response via the gradient function of FGM.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103573"},"PeriodicalIF":6.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313647","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":"Eccentricity based graph parameters of subdivision-vertex-vertex (edge-edge) join products","authors":"Anam Shahzad ,&nbsp;Muhammad Kamran Jamil ,&nbsp;Adel Fahad Alrasheedi ,&nbsp;Muhammad Waheed ,&nbsp;Aisha Javed","doi":"10.1016/j.asej.2025.103549","DOIUrl":"10.1016/j.asej.2025.103549","url":null,"abstract":"<div><div>Effective utilization of graph products are essential for the development of complex networking systems. The graph product can produce a diverse array of fundamental graphs as an application. A topological index, often referred to as a graph parameter, is an expression that allocates a numerical value to a particular chemical structure, proving beneficial for predictive modeling. Lu et al. <span><span>[1]</span></span> constructed two new variations of join (sum) graphs and examined the adjacency, Laplacian, and signless Laplacian characteristic polynomials of these variants. In this article, we discussed and investigated some distance and degree based topological properties of these two variations, known as subdivision-vertex-vertex join (SVVJ) and subdivision-edge-edge join (SEEJ) graphs. Moreover, we computed the total eccentricity, eccentric connectivity, connective eccentric and three versions of eccentricity based Zagreb indices.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103549"},"PeriodicalIF":6.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313648","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":"Hierarchical auto-associative polynomial CNN for cloud scheduling with privacy optimization using white shark","authors":"Arulkumar V. ,&nbsp;Sini Anna Alex ,&nbsp;Suresh Kumar Kanaparthi ,&nbsp;Durga Devi K","doi":"10.1016/j.asej.2025.103546","DOIUrl":"10.1016/j.asej.2025.103546","url":null,"abstract":"<div><div>In this research a novel Privacy Oriented White Shark Encompassed hierarchical auto-associative polynomial Convolutional Neural NetwoRk (POWER) framework for task scheduling has been proposed. Initially, the Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN) for scheduling the healthcare task by considering the parameters. The HAP-CNN has been optimized using White Shark Optimization (WSO) for enhancing the accuracy in generating the schedule. The proposed task scheduling model is calculated based on several characteristics, including task migration, reaction time, transmission time, makespan, and cost. Recall, specificity, accuracy, precision, and F1 score were utilized to assess the proposed method’s efficacy. With the suggested model, 99.32% classification accuracy was attained. The proposed model enhanced the total accuracy by 2.29%, 1.07% and 7.37% better than Task Scheduling utilizing a multi-objective grey wolf optimizer (TSMGWO), Prioritized Sorted Task-Based Allocation (PSTBA), and Large-Scale Industrial Internet of Things asynchronous Advantage Actor Critic system (LsiA3CS) respectively.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103546"},"PeriodicalIF":6.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313646","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":"First-principles study of Cs2AgXBr6 (X = In, Bi): A hierarchy of functional performance for renewable energy applications","authors":"Ghiyas Anwar ,&nbsp;Zafar Iqbal ,&nbsp;Muhammad Majid Gulzar ,&nbsp;Ali Arishi","doi":"10.1016/j.asej.2025.103537","DOIUrl":"10.1016/j.asej.2025.103537","url":null,"abstract":"<div><div>In recent years, lead-free double halide perovskites have attracted significant attention due to their potential in renewable energy technologies. To explore their suitability for eco-friendly applications, we have investigated the structural, elastic, electronic, optical, and photocatalytic properties of Cs₂AgXBr₆ (X = In, Bi) double perovskites. These characteristics were calculated using the WIEN2k code with the full-potential linearized augmented plane-wave (FP-LAPW) method based on density functional theory (DFT). Using the strongly constrained and appropriately normed (SCAN) meta-GGA functional, we calculated lattice constants of 11.0501 Å and 11.2998 Å and band gaps of 0.208 eV and 1.277 eV for Cs₂AgInBr₆ and Cs₂AgBiBr₆, respectively. By joining the Tran–Blaha modified Becke–Johnson (TB-mBJ) potential with the SCAN functional, we obtained improved band gap values of 1.796 eV (X = In) and 2.304 eV (X = Bi), showing better agreement with experimental data. The structural stability of both compounds is evaluated using modified Goldschmidt and octahedral tolerance factors. The elastic constants, obtained using the SCAN functional, suggest that the materials are mechanically stable and ductile. Both functionals are employed to study optical properties by calculating relevant optical parameters such as absorption coefficient, <span><math><mrow><mi>α</mi><mfenced><mrow><mi>ω</mi></mrow></mfenced><mo>,</mo></mrow></math></span> energy loss function, <span><math><mrow><mi>L</mi><mfenced><mrow><mi>ω</mi></mrow></mfenced></mrow></math></span>, extinction coefficient,<span><math><mrow><mi>k</mi><mfenced><mrow><mi>ω</mi></mrow></mfenced></mrow></math></span>, and optical conductivity, <span><math><mrow><mi>σ</mi><mfenced><mrow><mi>ω</mi></mrow></mfenced></mrow></math></span>. Subsequently, the potential of these materials for photocatalytic hydrogen production via overall water splitting is systematically examined. Our study demonstrates that the SCAN and TB-mBJ functionals offer reliable results while significantly reducing computational cost compared to resource-intensive hybrid DFT methods.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 9","pages":"Article 103537"},"PeriodicalIF":6.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307938","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":"Development of an outranking-oriented multiple-criteria decision model within q-rung orthopair fuzzy contexts","authors":"Jing Ye ,&nbsp;Ting-Yu Chen","doi":"10.1016/j.asej.2025.103508","DOIUrl":"10.1016/j.asej.2025.103508","url":null,"abstract":"<div><div>In the construction industry, sustainable supplier selection poses a complex decision-making challenge, marked by uncertainty and vagueness. To address this, an innovative ELimination Et Choice Translating REality (ELECTRE) I, II, and III framework within the q-rung orthopair fuzzy set (q-ROFS) environment is proposed. This approach introduces a novel q-ROFS score function that integrates membership, non-membership, and hesitation degrees, offering enhanced differentiation compared to existing functions. The methods simplify traditional ELECTRE outranking relations, reduce computational complexity, and provide both partial and complete rankings while maintaining logical consistency. The results demonstrate the practicality and efficiency of the suggested methodology. Furthermore, sensitivity and comparative analyses confirm the robustness of the developed approaches. This research enhances the q-ROFS ELECTRE methodology by enabling more nuanced preference expression and a streamlined decision process, thus advancing fuzzy decision-making. It also provides a reliable resource for decision-makers in the construction industry and beyond, improving operational efficiency and sustainability.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 9","pages":"Article 103508"},"PeriodicalIF":6.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312835","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":"Explainable AI-driven evaluation of plant protein rheology using tree-based and Gaussian process machine learning models","authors":"Mustafa Tahsin Yilmaz ,&nbsp;Salman Badurayq ,&nbsp;Kemal Polat ,&nbsp;Ahmad H. Milyani ,&nbsp;Abdulaziz S. Alkabaa ,&nbsp;Osman Gul ,&nbsp;Furkan Turker Saricaoglu","doi":"10.1016/j.asej.2025.103565","DOIUrl":"10.1016/j.asej.2025.103565","url":null,"abstract":"<div><div>In this study, we conducted a comparative analysis of the explainability of Decision Tree Regressor (DTR) and Gaussian Process Regressor (GPR) models in predicting the shear stress and viscosity of sesame protein isolate (SPI) systems, employing explainable machine learning (EML) techniques to elucidate complex, nonlinear relationships among processing parameters. SPI samples were processed across pressure levels ranging from 0 to 100 MPa and ion concentration (IC) values from 0 to 200 mM. DTR model accurately predicted shear stress (<em>R</em>² = 0.999), while a GPR model achieved high performance for viscosity prediction (<em>R</em>² = 0.9925). Formally, the modeling task is framed as learning a predicting mapping function <span><math><mrow><mi>f</mi><mo>:</mo><msup><mrow><mi>R</mi></mrow><mi>p</mi></msup><mo>→</mo><mi>R</mi></mrow></math></span>, where <span><math><mrow><mi>x</mi><mo>∈</mo><msup><mrow><mi>R</mi></mrow><mi>p</mi></msup></mrow></math></span> denotes the vector of predictors (pressure, IC, shear rate) and <span><math><mrow><mi>y</mi><mo>∈</mo><mi>R</mi></mrow></math></span> is the target variable (shear stress or viscosity), by minimizing a loss function such as mean squared error. Interpretation of model predictions using SHapley Additive exPlanations (SHAP), permutation importance, and partial dependence analysis revealed that pressure and IC are the most influential factors affecting shear stress and viscosity, with pressure inducing protein conformational changes that impact rheological properties. The shear rate exhibited a lesser direct impact within the systems examined. Partial Dependence Plots (PDPs) from the DTR model revealed strong, nearly linear positive relationships between pressure and shear stress, while the GPR model depicted more nuanced responses, highlighting the models’ differing sensitivities. Variance-Based Sensitivity Indices (VBSIs) further quantified these influences, with pressure and IC showing higher sensitivity scores in the DTR model compared to the GPR model. Permutation importance and SHAP interaction analyses corroborated these results, emphasizing the dominant role of pressure and IC, both independently and interactively, in determining shear stress. In contrast, viscosity predictions were influenced by more distributed and subtle interactions among all features. Employing explainable machine learning techniques enables a comprehensive understanding of feature relevance in complex, nonlinear rheological systems, facilitating the elucidation of viscosity development in sesame protein systems through rheological indices. This approach ensures no bias toward formulation composition and applied pressure, offering valuable insights for optimizing formulation and processing conditions in food applications to enhance the functional properties of SPI-based products.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 9","pages":"Article 103565"},"PeriodicalIF":6.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307939","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":"Experimental investigations of strengthened beam with co-cured carbon FRP and mussel shell-modified epoxy","authors":"Syahrin Azhar ,&nbsp;Sugiman Sugiman ,&nbsp;Zaim Omar ,&nbsp;Hilton Ahmad","doi":"10.1016/j.asej.2025.103563","DOIUrl":"10.1016/j.asej.2025.103563","url":null,"abstract":"<div><div>Traditional strengthening materials, such as carbon fibre reinforced polymer (CFRP) plates bonded with synthetic fillers, often rely on non-renewable resources and may have limited environmental compatibility. Thus, mussel shells with a high content of calcium carbonate particles act as rigid particles and alternatives to synthetic filler counterparts added in epoxy resin, enhancing the mechanical properties of filled epoxy. This paper aims to investigate the improvement of flexural resistance by incorporating mussel shell powder as a bio-filler of epoxy resin (hereafter referred to as mussel shell-modified epoxy, MME), which was then used as the bonding agents with CFRP sheets via co-cured technique as a beam strengthening method. A four-point bending test was conducted to investigate four parametric studies, i.e., CFRP bonded lengths (Series A), mussel shell powder volume fraction (Series B) and different percentages of pre-load applied on concrete beams with different bonding agent types, i.e., MME (Series C) and neat epoxy resin (Series D). A 7.5% volume fraction of MME and the most extended CFRP sheet enhanced the ultimate load with deflection (associated with concrete ductility) of the strengthened plain beams by 108% and 58%, respectively, compared to the control beam. Interestingly, up to 66% improvement was observed in Series C by applying pre-load at 60% of the control beam’s ultimate load, comparing beams strengthened with MME to those using neat epoxy (Series D). In this test, pre-loading was applied by subjecting the concrete beams to 60% of their ultimate capacity using a Universal Testing Machine (UTM) before applying CFRP sheets, ensuring consistent initial stress conditions for reliable comparison. Hence, MME is a viable bio-filler incorporated in epoxy resin to enhance co-cured CFRP as a strengthening material.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103563"},"PeriodicalIF":6.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307453","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":"Understanding chemical ecotoxicity through interpretable ensemble learning","authors":"Manish Kumar Tripathi ,&nbsp;Sneha Pandey ,&nbsp;Anoop Kumar Tiwari ,&nbsp;Kottakkaran Sooppy Nisar ,&nbsp;Abhigyan Nath","doi":"10.1016/j.asej.2025.103574","DOIUrl":"10.1016/j.asej.2025.103574","url":null,"abstract":"<div><div>Characterization factors are used in life cycle assessments to translate the quantity of chemicals and other pollutants produced during a product’s life cycle into the standard unit of an impact category, such as ecotoxicity. The chemical ecotoxicity (HC50) evaluation is an expedient method to determine the harmful effects of these chemicals on ecosystems. Ecotoxicity parameters for a wide variety of compounds are therefore a genuine and significant concern. The current work employed an optimized XGBoost model for real value prediction of HC50 values. Further the optimized prediction model is analysed using a series of model agnostic explainable learning approaches. The best optimized model achieved an acceptable R² = 0.684 with mse = 0.587. Further, the best optimized model is subjected to various global and local explainable algorithms, resulting in turning the black box model into a white box model, resulting in interpretation of how the decisions are being made by the learned model.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 9","pages":"Article 103574"},"PeriodicalIF":6.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307940","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}