Ain Shams Engineering Journal最新文献

{"title":"Enhancing lake water level forecasting with attention-based LSTM: a data-driven approach to hydrology and tourism dynamics","authors":"Máté Chapon ,&nbsp;Serkan Ozdemir","doi":"10.1016/j.asej.2025.103723","DOIUrl":"10.1016/j.asej.2025.103723","url":null,"abstract":"<div><div>In recent decades, freshwater lakes in the Northern Hemisphere have faced significant challenges, including severe water shortages and increased stormwater discharges. As a result, accurate forecasting of lake water levels has become essential for effective water resource management, flood mitigation, and ecological sustainability—all of which are interconnected with dynamics in tourism within freshwater basins. This study evaluates the performance of an Attention-based Long Short-Term Memory (LSTM) model compared to a standard LSTM for predicting lake water levels over 5-day and 30-day intervals, utilizing five different input combinations at one of Hungary’s popular tourist destinations Lake Velence. The results demonstrate that the Attention-based LSTM consistently outperforms the standard LSTM, particularly in long-term forecasting, as it effectively captures relevant temporal dependencies and reduces error accumulation. Additionally, a Pearson correlation analysis was performed to examine the relationship between guest nights and environmental factors, including lake water level, precipitation, temperature, and evapotranspiration. The findings reveal a strong correlation between guest nights and both temperature and evapotranspiration, while the associations with lake water level and precipitation are relatively weak. This indicates that climate conditions, rather than hydrological variations, primarily drive visitor numbers. The study highlights the importance of integrating advanced machine learning models in hydrological forecasting and tourism planning, providing valuable insights for sustainable water management and climate-adaptive tourism strategies.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103723"},"PeriodicalIF":5.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921451","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":"Optimal operation of electric vehicle charging stations with solar and wind energy systems in distribution network using JAYA algorithm","authors":"Kushal Manohar Jagtap ,&nbsp;Farhad Ilahi Bakhsh ,&nbsp;Ramya Kuppusamy ,&nbsp;Yuvaraja Teekaraman","doi":"10.1016/j.asej.2025.103712","DOIUrl":"10.1016/j.asej.2025.103712","url":null,"abstract":"<div><div>The inadequate additional burden of electric vehicle charging stations (EVCSs) and renewable energy resources (RERs) affects the techno-economic operations of distribution networks (DNs) negatively. This paper proposes a two-stage multi-objective optimization model for the secure and economic operations of DNs through integration of EVCSs and RERs. In stage 1, the power demands of EVCSs equipped with Level 1 (AC), Level 2 (AC), and Level 3 (DC) supply equipment are determined. This stage aims to ensure system stability and security while optimizing the energy fed to EVCSs to meet the power demand of EVs during charging. By following the network in stage 1, RERs in stage 2, especially solar and wind energy systems, are integrated with a focus on maximizing economic benefits. Simultaneously, the cost of power purchases from the grid by DN operators is minimized without affecting the stability and security of the network. Hourly fluctuations of electricity prices provided by the electricity market and contract prices of solar and wind energy systems are also taken into account in this stage to analyze their dynamic effect on the network performance. In both stages, voltage stability and voltage deviation tools are utilized to identify the most appropriate/suitable locations for EVCSs as well as solar and wind energy resources. The paper employs a single weight tree structure approach to handle the multi-objective optimization, as opposed to the traditional approach of converting multi-objectives into a single objective by using multiple weights. The proposed method is tested on 69-bus DN and optimum results are obtained by using the Modified JAYA algorithm. Obtained results are compared with those of traditional JAYA, Particle Swarm optimization and Differential Evolution algorithms.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103712"},"PeriodicalIF":5.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925436","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":"Analysis of the impact of damage rate on the performance of orange fruit harvesting robot","authors":"Sadaf Zeeshan ,&nbsp;Tauseef Aized ,&nbsp;Fahid Riaz","doi":"10.1016/j.asej.2025.103735","DOIUrl":"10.1016/j.asej.2025.103735","url":null,"abstract":"<div><div>Reducing damage rates is paramount for optimizing the efficiency of fruit harvesting robots and advancing their journey towards commercial viability. Despite the crucial role that damage rates play in determining fruit quality and marketability, there is a notable lack of comprehensive and in-depth studies analyzing this aspect, especially within the context of fruit harvesting robots. Most research tends to prioritize metrics such as success rate and accuracy of fruit picking, leaving the examination of damage rates relatively overlooked. This study fills this gap by conducting a thorough examination of the factors contributing to damage rates in fruit harvesting robots, including the causes of damage, the types and sizes of bruises incurred, and the impact of occlusion, illumination conditions, and end effector orientation. Additionally, the research investigates strategies for minimizing damage rates, offering insights into optimizing fruit harvesting techniques to reduce potential damage. Occlusion, illumination, and gripper angle were found to significantly influence fruit damage. Specifically, a 10 % increase in occlusion raised damage by 1.18 %, a 100 Lumen/m² increase in illumination reduced damage by 10.5 %, and deviation from the optimal 90° gripper angle increased damage by 1.8 % per 10° shift. Overall, proper fruit orientation reduced damage by 40 %, minimal occlusion by 36 %, and optimal illumination by 25 %. A multiple linear regression model explained the variance in damage rate (R2 = 0.924) and achieved a low RMSE of 1.85 %, demonstrating high predictive accuracy and validating the model’s reliability in quantifying the influence of harvesting parameters. By investigating these aspects and exploring strategies for minimizing damage, the study aims to advance fruit harvesting robotics and contribute to the successful commercialization of this technology.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103735"},"PeriodicalIF":5.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925438","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":"Security risk assessment of internet of things health devices using DREAD and STRIDE models","authors":"Buhang Zhai ,&nbsp;Oluwatobi Noah Akande ,&nbsp;Saurabh Agarwal ,&nbsp;Wooguil Pak","doi":"10.1016/j.asej.2025.103721","DOIUrl":"10.1016/j.asej.2025.103721","url":null,"abstract":"<div><div>A high volume of IoT devices used in healthcare is not regulated for security, which can allow attacks to occur that endanger healthcare organizations based on the value of patient data. Such devices are primarily implemented in a way that prioritizes usability and cost. Security is rarely prioritized due to a lack of universal security standards. The threats are constantly evolving, and strengthening device security has become a high-priority task. We conducted a qualitative and quantitative risk assessment of the twenty-three top IoT-based health devices using the qualitative STRIDE model for threat identification, and the quantitative DREAD model for threat prioritization. Specific countermeasures are proposed for each risk, which, if properly implemented, can considerably reduce vulnerabilities. We also present a prototype web platform for interactive, user-friendly risk assessment and security awareness in healthcare IoT, designed to enable improved protection for patients from the inefficient provision of security through unsafe technologies.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103721"},"PeriodicalIF":5.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916304","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 and computational analysis of circular eccentric venturi meters: Discharge coefficients and flow regimes","authors":"M.S. Karthik ,&nbsp;P.Siva Kota Reddy ,&nbsp;Tapankumar Trivedi ,&nbsp;Asha Rajiv ,&nbsp;Madhu Chennabasappa ,&nbsp;Fehmi Gamaoun","doi":"10.1016/j.asej.2025.103727","DOIUrl":"10.1016/j.asej.2025.103727","url":null,"abstract":"<div><div>In this present study, experimental and numerical analysis on circular eccentric Venturi meters were carried out. An experimental discharge coefficient of 0.969 was determined for a beta ratio of 0.5 and 6.25 mm eccentricity. A computational fluid dynamics simulation investigated the behavior of the meter at different Reynolds numbers and eccentric heights. The results agreed with literature data and validated the proposed computational methodology. It was observed that increasing the eccentric distance had no significant impact on the discharge coefficient. The coefficient exhibited a strong correlation with the flow regime, decreasing from 0.979 to 0.186 as the Reynolds number decreased from 100,000 to 1. However, the coefficient decreased significantly as the Reynolds number decreased. The estimated combined standard uncertainty in the discharge coefficient was ± 2.6 %, with an expanded uncertainty of ± 5.2 % at a 95 % confidence level. These findings could improve industrial flow measurements through a better eccentric Venturi meter design.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103727"},"PeriodicalIF":5.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908922","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":"Mechanisms of road microclimate in arid regions and their spatial components: A case study of Tuanshan village","authors":"Likai Lin ,&nbsp;Yan Gui","doi":"10.1016/j.asej.2025.103731","DOIUrl":"10.1016/j.asej.2025.103731","url":null,"abstract":"<div><div>Roads serve as critical transportation corridors within settlements and represent the most frequently utilized public spaces. The regulation and enhancement of the microclimate environment along these roads significantly influence outdoor activity comfort, particularly in arid regions where temperature modulation is essential. This study employs field investigations and on-site measurements to conduct a comparative analysis of various road types in Tuanshan Village, aiming to identify the key spatial elements that impact the microclimate conditions of roads in arid environments. The study reveals that (1) the spatial characteristics surrounding roads are intricately linked to their microclimate environment; (2) generally, roads situated near water bodies and beneath tree canopies achieve optimal microclimatic conditions; (3) vegetation plays a crucial role in mitigating solar radiation during the regulation of the microclimate; and (4) alleys and water features positively contribute to enhancing the wind environment along roadways.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103731"},"PeriodicalIF":5.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903286","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":"Energy arbitrage using second-life electric vehicle battery: A feasibility study based on CU-BEMS data","authors":"Aree Wangsupphaphol ,&nbsp;Sotdhipong Phichaisawat ,&nbsp;Awang Jusoh","doi":"10.1016/j.asej.2025.103740","DOIUrl":"10.1016/j.asej.2025.103740","url":null,"abstract":"<div><div>This study explores energy storage from repurposed EV batteries. For storage sizing, 18 months of actual load profiles from a building energy management system are employed to avoid the shortcomings of a general representative load profile. Ambient temperature is used to evaluate battery performance throughout the project. Our battery degradation models have a higher R-squared than previous studies, making our study unique. Battery deterioration is analyzed using off-peak energy arbitrage and solar energy arbitrage, which use full and half cycles per day and offer different yields. Economic evaluation uses net present value and IRR. The study indicated electricity prices drive return period. The sensitivity analysis shows that off-peak energy arbitrage is not practicable for the 10-year project, but a battery cost of $50 to $60 per kWh and operational temperatures below 35 °C would make solar energy arbitrage profitable.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103740"},"PeriodicalIF":5.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908923","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":"3D-MobiBrainNet: Multi-class Alzheimer’s disease classification using 3D brain magnetic resonance imaging","authors":"Zia-ur Rehman ,&nbsp;Mohd Khalid Awang ,&nbsp;Ghulam Ali ,&nbsp;Muhammad Hamza ,&nbsp;Tariq Ali ,&nbsp;Muhammad Ayaz ,&nbsp;Mohammad Hijji","doi":"10.1016/j.asej.2025.103714","DOIUrl":"10.1016/j.asej.2025.103714","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is the predominant form of dementia for which no curative treatment currently exists. The accelerated aging progression necessitates precise detection of initial AD for effective patient management and illness delay. Earlier research generally used two-dimensional (2D) imaging, which used a single slice that caused loss of spatial information. Most of the previous techniques concentrated on binary classification; however, they encountered difficulties. Which ultimately leads to more parameters and higher computational costs. Compared to binary classification, little work has been done with multi-class classification with 3D images, but that research had low accuracies. To address these limitations, this research proposes 3D-MobiBrainNet, a novel deep learning framework designed to enhance the multi-class classification of AD by leveraging 3D MRI and multi-plane feature fusion. The model processes volumetric data across the axial, coronal, and sagittal planes, ensuring a more comprehensive understanding of brain abnormalities. This method comprised three main steps: (i) Plane-specific extraction of features employs a bottleneck block which comprises depth-wise separable convolutions for every MRI plane to optimize feature extraction and reduce computation complexities; (ii) feature enhancement and selection utilized a feature recalibration strategy to emphasizes important characteristics and a ReLU6 (Rectified Linear Unit) activation function to improve computing efficiency; and (iii) 3D feature integration and classification combine features from each of the three planes into a unified 3D space of features. Experimental results demonstrate that 3D-MobiBrainNet achieves state-of-the-art classification performance using Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset with an accuracy of 97.33 %, recall of 97.33 %, F1-score of 97.33 %, and an area under the curve (AUC) of 99.92 %. Another metric under evaluation was the model’s parameters. Compared to other implemented techniques, the proposed model had fewer parameters (34,145,099), enhancing its prediction performance and requiring fewer processing resources and memory. Additionally, the five-fold cross-validation method was used to check the model’s ability to work well on unseen data and make sure it does not over fit. The results were promising, with a 90.162 % success rate, which showed the good generalizability performance of the model.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103714"},"PeriodicalIF":5.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908476","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":"Hybrid augmentation for multi-channel deep learning in guava leaf disease detection","authors":"Osman Güler ,&nbsp;Taha Etem ,&nbsp;Mustafa Teke","doi":"10.1016/j.asej.2025.103716","DOIUrl":"10.1016/j.asej.2025.103716","url":null,"abstract":"<div><div>Guava (Psidium guajava) faces significant threats from leaf diseases that compromise its yield and quality. Traditional diagnostic methods that rely on manual inspection are inefficient and subjective, necessitating automated solutions. This study introduces a robust ensemble deep learning framework for guava leaf disease classification by combining hybrid data augmentation with advanced architectures to address the variability in environmental conditions and imaging. A dataset of 2,063 guava leaf images in five categories was expanded using traditional geometric augmentation and synthetic GAN-generated images. Seven state-of-the-art deep learning models, and Vision Transformer B16) were evaluated, with InceptionV3 (92.50% accuracy on GAN data) and ResNet50 (93.12% accuracy on augmented data) selected for their complementary strengths. A multi-channel model fused their features, achieving a 97.50% test accuracy, 0.975 F1-score, and 0.9934 AUC. The results demonstrate the viability of integrating CNNs with transformer architectures under unified augmentation strategies, thereby offering a scalable solution for real-time field deployment.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103716"},"PeriodicalIF":5.9,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903131","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":"Deep learning and optimization-based feature selection for fetal health classification using CTG data","authors":"Turgay Kaya ,&nbsp;Duygu Kaya ,&nbsp;Fatmanur Atar","doi":"10.1016/j.asej.2025.103698","DOIUrl":"10.1016/j.asej.2025.103698","url":null,"abstract":"<div><div>This study introduces a DL and metaheuristic optimization-based framework for fetal health assessment using cardiotocography (CTG) signals to mitigate maternal and neonatal mortality. One-dimensional CTG signals were transformed into 2D representations, and deep feature extraction was performed using AlexNet. Feature vectors FC6, FC7, and their combination were subjected to optimization via Whale Optimization Algorithm (WOA + DL) and War Strategy Optimization (WSO + DL), utilizing updated fitness functions tailored for feature selection. Experimental results with SVM classifiers demonstrated superior performance with FC6 (89.98 %) and WSO + DL (90.17 %). FC6 exhibited strong discriminative capacity, while FC7 contained semantically richer features. The concatenated FC6 + FC7 vector increased feature diversity. WSO + DL achieved optimal balance across classification accuracy, feature subset size, convergence rate, and overall performance metrics. The integration of DL and metaheuristic algorithms effectively isolated informative feature subsets, improving training efficiency, minimizing redundant/noisy data, reducing overfitting risk, and enhancing classification accuracy. Optimization method selection proved critical to overall model performance.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103698"},"PeriodicalIF":5.9,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895476","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}