Ain Shams Engineering Journal最新文献

{"title":"Laplace Adomian decomposition method for integro differential equations on time scale","authors":"Shafiq Hussain,&nbsp;Feroz Khan","doi":"10.1016/j.asej.2025.103271","DOIUrl":"10.1016/j.asej.2025.103271","url":null,"abstract":"<div><div>The aim of this work is to probe the Laplace Adomian decomposition method (LADM) for some certain linear and non-linear integro-differential equations on an arbitrary time scales. Although, several researchers have treated integro-differential equations (linear/nonlinear) utilizing different techniques, but most of them were based on classical calculus. In particular, <span><span>[10]</span></span> have catered the integro-differential equations on time scale using Adomian decomposition method (ADM). Whereas <span><span>[28]</span></span> have entertained the initial value problems with ADM on time scales. However, there exist no piece of work in literature that addressed integro-differential equations using LADM on time scales. Hence, in this work, that gap is covered. Moreover, the proposed method on time scale is effective in the sense that it mitigates the integration steps which otherwise come while solving with ADM. Lastly, examples and solutions in Sections <span><span>3</span></span> and <span><span>4</span></span> that exactly match those found by ADM are used to validate the suggested approach.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103271"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178377","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}

{"title":"A framework for enhancing safety planning in oil and gas construction projects","authors":"Abdullah Alsehaimi ,&nbsp;Ahsan Waqar ,&nbsp;Dorin Radu ,&nbsp;Ciprian Bratu ,&nbsp;Hamad Almujibah ,&nbsp;Omrane Benjeddou","doi":"10.1016/j.asej.2024.103257","DOIUrl":"10.1016/j.asej.2024.103257","url":null,"abstract":"<div><div>The construction and building industry is a major driver of economic growth; however, safety performance remains a key challenge. This research aimed to develop an effective framework to improve safety planning for oil and gas construction projects. A mixed methods approach was utilized, including a questionnaire survey of 167 construction practitioners and interviews with project engineers. Results showed materials/equipment factors as most significant, with “damaged equipment” having the highest RII of 0.821. The second highest factor was “poor equipment condition” (RII = 0.814), followed by “limited material transportation protections” (RII = 0.804). Among management factors, “performance pressure” scored highest (RII = 0.871), while “insufficient staffing” was lowest (RII = 0.775). For worker factors, “poor communication” ranked first (RII = 0.818) and “lack of knowledge” last (RII = 0.779). In terms of site factors, “accidents from carelessness” was predominant (RII = 0.811) but “limited workspace” was least considerable (RII = 0.768). Further statistical analysis using Spearman’s correlation coefficient showed that the perspectives of contractors, consultants, and safety/health officers had positive and significant correlations in the range of 0.35 to 0.45. This research provides empirical evidence on the most influential safety factors in oil and gas projects and proposes a practical framework for contractors and managers to improve safety planning and performance. The framework offers a proactive risk-based approach to reducing accidents and protecting workers in this hazardous industry.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103257"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177836","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}

{"title":"Detection of community emotions through Sound: An Investigation using the FF-Orbital Chaos-Based feature extraction model","authors":"Li Xu ,&nbsp;Arif Metehan Yildiz ,&nbsp;Ilknur Tuncer ,&nbsp;Fatih Ozyurt ,&nbsp;Sengul Dogan ,&nbsp;Turker Tuncer","doi":"10.1016/j.asej.2024.103248","DOIUrl":"10.1016/j.asej.2024.103248","url":null,"abstract":"<div><div>Community Emotion Detection (CED) is important to public safety and sound forensics. It enables the analysis of collective emotions in dynamic environments such as public events, protests, and emergencies.</div><div>This research presents a new, cost-effective, automatic, and self-organized classification model for CED. The major objective of this research is to show the effectiveness of signal processing in the CED research area. The largest known CED sound dataset was constructed for this study. This dataset was curated to investigate the presented automated CED model’s classification capability. The curated CED sound dataset consists of 5,051 three-second overlapping sound samples categorized as negative, neutral, and positive emotions. This dataset increases the model’s generalizability by reflecting diverse acoustic and emotional scenarios.</div><div>A self-organized feature extraction function, called the Forward-Forward Pattern-Based Feature Generator (FF-Orbital), is introduced. The FF-Orbital autonomously selects the most suitable pattern from six predefined patterns. This eliminates the need for manual feature engineering. Additionally, a multi-level feature extraction method is enabled by integrating the Unbalanced Tree Multilevel Discrete Wavelet Transform (UTMDWT). This method generates frequency bands that provide extract spatial and frequency-domain features. Iterative Neighborhood Component Analysis (INCA) has selected the most informative features. INCA is a self-organized feature selector. Classification is then performed using a Bayesian-optimized Support Vector Machine (SVM).</div><div>Tests were conducted using 10-fold cross-validation. The model achieved a classification accuracy of 98.81%. These results demonstrate the usability of the CED model and its effectiveness in digital forensics, public safety, and community-level sentiment analysis.</div><div>This work makes a significant contribution to the CED research domain by providing a feature-engineering-based alternative to resource-intensive deep learning models. The results show that the proposed model is valuable for signal processing and sound forensics.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103248"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177837","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}

{"title":"Fractional Moore-Gibson-Thomson model for mass diffusion and thermal dynamics: Application to an infinite viscoelastic medium with a cylindrical cavity","authors":"Yazeed Alhassan","doi":"10.1016/j.asej.2025.103276","DOIUrl":"10.1016/j.asej.2025.103276","url":null,"abstract":"<div><div>This article presented an innovative fractional model that integrated mass diffusion and thermal diffusion equations within thermo-viscoelastic Kelvin-Voigt materials, providing a deeper understanding of material behavior. The model employed the Atangana-Baleanu-Caputo (ABC) fractional derivative in conjunction with the Moore-Gibson-Thomson (MGT) equation, effectively capturing non-local and memory-dependent processes that were often overlooked in traditional models. By incorporating these advanced concepts, the model offered a more accurate representation of material responses, particularly in scenarios involving complex thermal and mass diffusion effects. The model utilized advanced mathematical techniques, including Laplace transforms and Mathematica, to solve the resulting complex differential equations, ensuring computational efficiency and accuracy. Validation of the model was conducted through comparisons with previous studies, demonstrating its improvements over existing approaches, and confirming its practical applicability. The article also provided graphical results and analysis, emphasizing the significant effects of heat transfer, viscoelasticity, and mass diffusion on material performance. These contributions were crucial for advancing engineering applications, especially in systems where traditional models fell short, such as in microelectronics, aerospace, and energy systems. The novelty of this work lay in its ability to address the limitations of conventional models by incorporating memory-dependent effects and non-local interactions, which significantly enhanced the prediction and design of materials under complex loading conditions.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103276"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178046","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}

{"title":"Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets","authors":"Viet Anh Truong ,&nbsp;Ngoc Sang Dinh ,&nbsp;Thanh Long Duong ,&nbsp;Ngoc Thien Le ,&nbsp;Cong Dinh Truong ,&nbsp;Linh Tung Nguyen","doi":"10.1016/j.asej.2025.103285","DOIUrl":"10.1016/j.asej.2025.103285","url":null,"abstract":"<div><div>Past research has predominantly focused on utilizing meta-heuristic algorithms to optimize neural network structures, while the exploration of deep learning in optimization has remained relatively limited. The proposed hybrid approach seeks to enhance wind power bidding strategies, improving profitability by predicting optimal output power for day-ahead electricity markets. This method integrates Long Short-Term Memory (LSTM) with Particle Swarm Optimization (PSO), leveraging LSTM’s ability to predict the active movement tendencies of particles for more efficient and faster optimization. Experiments conducted on the IEEE 30-bus power system show that the LSTM-PSO hybrid outperforms mathematical models and standalone PSO algorithms. It also delivers an optimal wind power bidding strategy, yielding peak annual revenue, while recommending a 16 % reduction in bidding output power variance in models that integrate wind power with thermal power and energy storage systems (ESS). Ultimately, this approach fosters confidence in wind energy investment, contributing to sustainable development.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103285"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178415","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}

{"title":"Report on optimisation for efficient dynamic task distribution in drone swarms using QRDPSO algorithm","authors":"Giuseppe Converso ,&nbsp;Duaa Mehiar ,&nbsp;Alexander Rukovich ,&nbsp;Rashit Brzhanov","doi":"10.1016/j.asej.2024.103237","DOIUrl":"10.1016/j.asej.2024.103237","url":null,"abstract":"<div><div>The primary aim was to develop a Quantum Robot Darwinian Particle Swarm Optimisation (QRDPSO) algorithm and assess its performance against the conventional RDPSO. Using MATLAB-based mathematical modelling, QRDPSO was evaluated for its efficiency in dynamic task distribution and inter-drone communication stability. The results demonstrate that QRDPSO finds optimal solutions 16.3% faster than RDPSO, with performance improvements as the swarm size increases. Specifically, when the number of drones was increased from 5 to 20, the number of iterations required for QRDPSO changed from 384 to 189. However, for RDPSO, the number of iterations changed from 439 to 242. Additionally, QRDPSO showed a 27.1% reduction in drone loss rates, outperforming RDPSO in terms of maintaining operational resources, especially in larger swarms. These findings have practical implications, as QRDPSO’s efficiency and stability can support extensive drone applications requiring synchronised, reliable swarm behaviour.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103237"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177019","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}

{"title":"A simulation of integrated gasification, energy generation, and GIS from solid waste in Jazan Municipalities, KSA (Part 1: Gasification)","authors":"Mohamed Hassan ,&nbsp;Salih Eltahir Elmarud Ali ,&nbsp;Mahmoud Abdelrahim Abdelgiom","doi":"10.1016/j.asej.2025.103282","DOIUrl":"10.1016/j.asej.2025.103282","url":null,"abstract":"<div><div>This paper aims to use GIS software to choose the energy plant’s ideal location based on side selection criteria and to build a simulation using Aspen Plus software to estimate the gas production. The outcome of this spatial analysis using GIS will typically encompass a prioritized list of potential sites, accompanied by visual maps that illustrate suitable locations, constraints, and accessibility. The simulation was validated using experimental and simulation data from the literature on plasma gasification. The results of this study showed good matching with the literature data. After the simulation was validated, it was used to study the case of Jazan region MSW. The results showed that the gas composition is 12.93 % H₂, 8.68 % CO₂, 3.08 % N₂, 0.08 % H₂S, and 31.11 % CO, with a temperature of 1210 °C. This gas was transferred to energy production simulation to generate energy, which will be discussed in part 2 of this paper.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103282"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178416","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}

{"title":"Pentagonal fuzzy fractional three-stage sustainable transportation network problem incorporating extension principle","authors":"B. Baranidharan ,&nbsp;Gunji Bala Murali ,&nbsp;Zechun Cao ,&nbsp;G.S. Mahapatra","doi":"10.1016/j.asej.2025.103261","DOIUrl":"10.1016/j.asej.2025.103261","url":null,"abstract":"<div><div>This study presents a framework for addressing the fuzzy fractional three-stage transportation network (FFTSTN) in the context of sustainable conveyance. The proposed model considers the transportation costs and profits involved in moving goods from manufacturers to storage godowns, storage godowns to distributors, and distributors to consumers. The primary aim of this study is to mitigate transportation expenses and assess transportation's environmental viability through reducing emissions. A generalized pentagonal fuzzy number (GPFN) is employed to address the uncertainty of parameters of the three-stage transportation network. The study established a methodology to explore solutions for the FFTSTN model without de-fuzzifying GPFNs. Additionally, it explored the economic sustainability and future transportation prospects using the extension principle to solve the proposed FFTSTN system. This solution approach considers the nonlinearity of the objective function by utilizing the Charnes-Cooper transformation technique for the problem. A numerical example shows applicability, validity, and comparison between the compromise solutions.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103261"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178049","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}

{"title":"Development of Fe-reinforced PLA-based composite filament for 3D printing: Process parameters, mechanical and microstructural characterization","authors":"Melisa Memiş,&nbsp;Dilşad Akgümüş Gök","doi":"10.1016/j.asej.2025.103279","DOIUrl":"10.1016/j.asej.2025.103279","url":null,"abstract":"<div><div>The most used raw materials in 3D printers, commonly known as Fused Deposition Modeling (FDM), are filaments. These filaments are obtained by going through heating, injection, cooling, winding and coiling stages in the filament extruder machine. FDM technology, which is used especially for prototype production purposes, is expected to be used more widely due to its increased mass production capacity, recyclability, environmental friendliness and waste reduction. In this study, before the production of composite filament, preliminary tests were carried out with polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) and polyethylene terephthalate glycol (PET-G) polymer granules in a laboratory type filament extruder machine, and the process parameters were determined for each polymer filament. Since the optimum process parameters (temperature, injection rate, extrusion rate, and winding rate) were obtained in PLA, composite filament was produced by reinforcing 5% iron (Fe) powder into the PLA matrix in the same extruder machine. The produced filaments were subjected to tensile, hardness, FTIR, surface roughness and SEM-EDS analyses. Analysis has shown that Fe-reinforced PLA-based composite filament increases the hardness of pure PLA filament by 21.15%, tensile strength by 49.60% and increases the surface roughness by 4 times. As a result, it was determined that 5%Fe powder added to PLA improved the mechanical properties but negatively affected the surface roughness.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103279"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178413","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}

{"title":"Multi-Objective optimization in the construction of steel-concrete composite columns with carbon emission considerations: Pareto front development and decision-making","authors":"Zhengjie Zhan ,&nbsp;Pan Xia ,&nbsp;Dongtao Xia ,&nbsp;Yan Hu","doi":"10.1016/j.asej.2025.103283","DOIUrl":"10.1016/j.asej.2025.103283","url":null,"abstract":"<div><div>This study tackles the multi-objective optimization (MOP) challenges in constructing steel–concrete columns by introducing a novel TCQE model that considers time, cost, quality, and carbon emissions. Employing relative deviation theory with dynamic weighting, the model normalizes MOP and applies an improved ant colony algorithm (IACA) to generate optimized solutions. Empirical research validates the model’s efficiency and applicability. Furthermore, a decision-making framework based on AHP-TOPSIS is proposed, demonstrating superior weight distribution and fairness in the decision process. Compared to the ideal point method and VIKOR, the proposed approach consistently identifies optimal solutions, affirming its scientific validity and effectiveness. The findings suggest broad application prospects in practical construction projects and provide valuable insights for construction management research, highlighting the theoretical and practical significance of the model and framework.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 2","pages":"Article 103283"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178045","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}