Ain Shams Engineering Journal最新文献

{"title":"Enhanced Detection of Diabetic Retinopathy using GAI-Net: Deep Learning Model based on GAN with Autoencoder","authors":"Chilukuri Rajitha,&nbsp;P Praveen,&nbsp;K Rajchandar","doi":"10.1016/j.asej.2026.104050","DOIUrl":"10.1016/j.asej.2026.104050","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR) is a serious illness associated with diabetes and is one of the leading causes of universal visual impairment. Early and proper diagnosis is crucial to prevent permanent eye damage. The proposed paper introduces a new hybrid deep learning framework, GAI-Net, that uses Generative Adversarial Networks (GANs), Autoencoders, and InceptionV3 to detect DR in five levels of severity. The model addressed multiple issues in DR detection, including small datasets and class imbalance, through quality synthetic images and discriminative hierarchical features. Generative component increases data variation, and the discriminative modules increases the representation of the features and accuracy in recognition. To compare the performance of the proposed GAI-Net, a comparative analysis was undertaken with advanced models, including traditional GANs, DCGAN, Attention-GAN, EfficientNet GAN, and InceptionV3 through the use of Kaggle dataset images are included. The obtained results indicate that GAI-Net outperforms all other comparative models, achieving a detection accuracy of 0.99 and an F1-score of 0.98, which is a high level of effectiveness. The methodology shows the strengths of combining the generative and discriminative deep learning methods in medical image analysis. GAI-Net is able to diagnose diabetic retinopathy with a scalable, accurate, and entirely automatic model by constructing balanced datasets and improving the encoding of features. The current research paper explains the potential use of hybrid architectures in computer-aided diagnosis, as it helps strengthen the development of the next-generation tools to find diabetic retinopathy with extensive medical uses.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104050"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190941","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 Multi-Criteria decision framework for sustainable rainwater harvesting site selection based on livelihood Capital factors","authors":"Abdiqani Muse Hassan ,&nbsp;Abdikarim Hassan Hussein","doi":"10.1016/j.asej.2026.104066","DOIUrl":"10.1016/j.asej.2026.104066","url":null,"abstract":"<div><div>Water scarcity is intensifying globally, increasing the need for sustainable and locally adaptable water solutions. Rainwater harvesting systems (RHS) are promoted as a practical response, yet poor site selection often limits success. This study integrates the Sustainable Livelihood Framework (SLF) into the RHS site selection to assess how social, economic, and technical factors interact. A hybrid multi-criteria decision-making approach was applied, combining the Fuzzy Delphi Method to validate determinants, the Fuzzy Analytic Hierarchy Process to prioritize them, and Fuzzy DEMATEL to map interdependencies. Out of 43 initial determinants, 26 were validated. Results indicate that socio-economic livelihood capitals are the most influential, while natural factors like rainfall remain indispensable enabling conditions. Key determinants include rainfall, leadership support, community participation, water equity, and subsidies, whereas education, credit access, technical skills, energy supply, and maintenance costs drive implementation. The study offers a holistic framework for inclusive, resilient, and context-responsive water resource planning.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104066"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422178","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":"Bim-driven scheduling optimization for in-situ production and yard-stock integration of precast concrete members","authors":"Jeeyoung Lim ,&nbsp;Sunkuk Kim","doi":"10.1016/j.asej.2026.104022","DOIUrl":"10.1016/j.asej.2026.104022","url":null,"abstract":"<div><div>The construction industry faces persistent challenges of inefficiency and fragmentation. In precast concrete (PC) projects, in-situ production and yard-stock management require precise coordination of production cycles, storage layouts, and installation schedules, yet existing studies have rarely addressed their integrated optimization. This study proposes a BIM-driven scheduling optimization framework that incorporates probabilistic simulation (Oracle Crystal Ball) into BIM-based 4D modeling. The framework was applied to steel-reinforced precast concrete components in a large-scale logistics project, with the aim of minimizing project duration and improving yard-stock efficiency under site-specific constraints. Simulation results demonstrated a strong positive correlation between lead-time and project duration (+0.58), identifying lead-time control as a critical scheduling variable. A negative correlation between duration and yard-stock area (–0.52) confirmed that timely mold utilization improves spatial efficiency, while crane deployment showed minimal impact. The optimized schedule achieved 6.3 months, reducing the original 8-month plan by 10% and surpassing the client’s contractual timeline by 75%. This research contributes by introducing one of the first BIM-based frameworks that integrates production, storage, and installation into a unified scheduling model, providing both practical decision support and a foundation for future objective optimization.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104022"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422247","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":"Microstructural and mechanical response of fly ash–bentonite modified fiber-reinforced concrete under coupled sulfate attack and wet–dry cycles","authors":"Nan Zhao ,&nbsp;Chao Liu ,&nbsp;Chao Zhu ,&nbsp;Sheliang Wang ,&nbsp;Haijun He","doi":"10.1016/j.asej.2026.104000","DOIUrl":"10.1016/j.asej.2026.104000","url":null,"abstract":"<div><div>Concrete infrastructures located in coastal areas are frequently subjected to coupled sulfate corrosion and wet–dry cycling (WDC). The durability behavior of concrete incorporating bentonite and fly ash (FA) was evaluated under the simultaneous influence of WDC. Concrete containing nine mixtures of different amounts of bentonite and FA were exposed to 150 WDC in Na₂SO₄(aq), MgSO₄(aq) and water, respectively. Physical and mechanical properties, for instance, compressive strength along with the relative dynamic modulus of elasticity (RDEM) were tested to assess the degree of deterioration of the concrete during WDC. In addition, the concrete stress–strain characteristics were analyzed to determine the linkage among modulus of elasticity, peak stress, and peak strain in different sulfate solutions and the frequency of WDC. Furthermore, environmental SEM and industrial computed tomography (CT) were employed to examine the product composition and pore structure evolution at various erosion phases. Results show that concrete incorporating fly ash and bentonite exhibited up to a 33.2% increase in compressive strength in Na₂SO₄ and a 61.5 MPa peak strength in water during WDC. However, RDEM decreased by up to 53.5% in high–fly ash mixtures after 150 cycles. CT analysis further revealed that pores larger than 0.1 mm³ increased by 120.11% in the control group, whereas the addition of bentonite and fly ash reduced macropore volume by 49.21%, demonstrating their synergistic role in refining the pore structure and enhancing durability. The peak stress followed the same increase–decrease pattern in sulfate environments but continued to rise in water with more WDC cycles. This study introduces a novel multiscale evaluation of fiber-reinforced concrete incorporating bentonite and fly ash under coupled sulfate attack and wet–dry cycling, a combination rarely addressed in previous research. By integrating mechanical testing, stress–strain modeling, SEM, and 3D CT pore analysis, the work reveals the synergistic roles of bentonite and fly ash in refining pore structure and governing deterioration mechanisms. These findings provide new insights for designing durable concrete for coastal environments.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104000"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057589","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":"Surface modification and functionalization of biogenic apatite via direct and silane-assisted vinylimidazole polymerization: Comparative evaluation of structural and surface properties","authors":"Bayram KIZILKAYA","doi":"10.1016/j.asej.2026.104068","DOIUrl":"10.1016/j.asej.2026.104068","url":null,"abstract":"<div><div>In this study, waste products such as fish bones (H) known as biocompatible natural apatite sources were performed to develop sustainable, recycled and environmentally friendly materials. The study investigated the polymerization of 1-vinylimidazole (VIM) monomer on direct and silane-assisted waste fish bone particle surfaces. In the first method (HP₅), direct polymerization of VIM was performed on the fish bone particles. In the second method (HS₃P₅), the apatite surface was firstly silanized with 3-(Methacryloyloxy)propyl-trimethoxy silane (MPS) and then polymerized with VIM. Elemental analysis results showed that the number of monomers bound to the surface was calculated as 302 µmol/g for HP5 and 714 µmol/g for HS3P5, using nitrogen as the indicator element for VIM. Zeta potential shifted from −20.40 to −35.91 mV and conductivity from 3.03 × 10⁻² to 7.67 × 10⁻² S/cm. This study proposes an eco-friendly and sustainable material design by reusing biological waste and employing non-toxic, biocompatible polymers.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104068"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422175","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":"LiCPV: hybrid deep learning for PV fault detection","authors":"Samar Elbedwehy ,&nbsp;M. Turki-Hadj Alouane ,&nbsp;Esraa Hassan ,&nbsp;Zahraa Tarek","doi":"10.1016/j.asej.2026.104041","DOIUrl":"10.1016/j.asej.2026.104041","url":null,"abstract":"<div><div>Solar photovoltaic (PV) systems are becoming more and more important for producing sustainable energy, but cell-level flaws—which are frequently detected by electroluminescence (EL) imaging—have a major impact on their dependability. Although deep learning models for PV defect classification have been used in earlier research, the majority of these studies rely on single architectures, small datasets, and traditional training techniques, which limits detection accuracy and generalization. This work introduces a novel hybrid learning framework that systematically combines transformer-based and convolution-based architectures—PiT-base and ConvNeXt—to exploit their complementary feature extraction capabilities for PV EL defect classification. Unlike prior approaches, the proposed method is further distinguished by the first comprehensive evaluation of the Lion optimizer in this application domain, demonstrating its superior stability and convergence behavior compared to traditional optimizers. The framework is evaluated under both offline and online learning scenarios, providing new insights into training and deployment performance that are largely unexplored in existing literature. Extensive experiments are conducted on two benchmark datasets—ELPV and PVELAD—thereby extending validation beyond the commonly used ELPV dataset. The proposed approach achieves accuracies of 82.45% (offline) and 81.94% (online) on ELPV, and 86.96% (offline) and 86.74% (online) on PVELAD, significantly outperforming previously reported state-of-the-art results.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104041"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422177","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 renewable energy integration for EV charging via model-based reinforcement learning","authors":"Shahr Alshahr ,&nbsp;Ahmed Alshahir ,&nbsp;Hammad Alnuman ,&nbsp;Meshari D. Alanazi ,&nbsp;Amr Yousef ,&nbsp;Ghulam Abbas","doi":"10.1016/j.asej.2026.104040","DOIUrl":"10.1016/j.asej.2026.104040","url":null,"abstract":"<div><div>The rapid emergence of Electric Vehicles (EVs) presents both opportunities and challenges for sustainable transportation, particularly in integrating renewable energy sources into charging infrastructure while maintaining grid stability. Current EV charging systems exhibit inefficient energy utilization, peak demand stress, and suboptimal integration of intermittent renewable energy sources, primarily due to their reliance on conventional grid power and the lack of intelligent distribution mechanisms. Hence, the research introduces Smart Renewable Optimization with Vehicle-aware Reinforcement Learning (SRO-VRL), a model-based RL approach that makes real-time, adaptive decisions on energy distribution. This model dynamically optimizes EV charging schedules by prioritizing renewable energy sources, EV demand, and grid constraints to ensure efficient charging. The model begins by collecting real-time data from EV charging stations, grid load measurements, and renewable energy sources. The approach relies on collecting real-time data from private and public EV charging stations connected to local distribution grids at the community/microgrid level in Europe. This infrastructure includes mixed residential chargers (Type-2/Type-3 AC) and community charging stations. Its hybrid renewable energy mix, which is supported by battery energy storage, primarily comes from solar energy. The local utility system supplies additional power. Grid data show low-voltage feeders that are constrained by capacity and pricing. In comparison to benchmark reinforcement learning and rule-based strategies, the results demonstrate the effectiveness and scalability of the proposed SRO-VRL framework for community-scale microgrids and regional smart grids, improving renewable energy utilization by approximately 4–5%, lowering peak grid load by approximately 7–8%, and reducing overall EV charging costs by approximately 5–6%. System states are then formulated to encompass EV state-of-charge (SoC), predicted renewable supply, and grid constraints, while charging power allocations constitute the action space. In comparison to benchmark reinforcement learning and rule-based strategies, the results demonstrate the effectiveness and scalability of the proposed SRO-VRL framework for community-scale microgrids and regional smart grids, improving renewable energy utilization by approximately 4–5%, reducing peak grid load by approximately 7–8%, and lowering overall EV charging costs by approximately 5–6%.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104040"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190846","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":"Photonic spin hall shift manipulation at the graphene atomic medium","authors":"Qaisar Khan ,&nbsp;Meraj Ali Khan ,&nbsp;Ibrahim Al-Dayel ,&nbsp;Majid Khan","doi":"10.1016/j.asej.2026.103990","DOIUrl":"10.1016/j.asej.2026.103990","url":null,"abstract":"<div><div>This study reports the manipulation of photonic spin hall shift at the graphene medium that lies between two mirrors. The incoming probe light engages with a cavity filled with four levels graphene medium. The spin hall shift of the photons is tuned to positive or negative values, depending on the properties of the driving fields. The maximum spin hall shift which lies in <span><math><mo>−</mo><mn>50</mn><mi>λ</mi><mo>≤</mo><mi>S</mi><msubsup><mi>h</mi><mrow><mi>p</mi></mrow><mrow><mi>L</mi><mo>,</mo><mi>R</mi></mrow></msubsup><mo>≤</mo><mn>50</mn><mi>λ</mi></math></span> is a function of incidence angle and independent of control field Rabi frequency. The minimum spin hall shift lies in the range <span><math><mo>±</mo><mn>17.79</mn><mi>λ</mi><mo>≤</mo><mi>S</mi><msubsup><mi>h</mi><mrow><mi>p</mi></mrow><mrow><mi>L</mi><mo>,</mo><mi>R</mi></mrow></msubsup><mo>≤</mo><mo>±</mo><mn>17.83</mn><mi>λ</mi></math></span> against the control field Rabi frequency (<span><math><mo>|</mo><msub><mi>R</mi><mrow><mn>1</mn></mrow></msub><mo>|</mo></math></span>= 0 G and 20 G). These findings have significant applications in areas such as sensing technology, quantum computing and optical communication.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 103990"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057587","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":"An intelligent VMD-WTC-GRU hybrid framework with uncertainty quantification for forecasting extreme flood events in semi-arid regions","authors":"Rahele Ramezani ,&nbsp;Abolhassan Gheiby ,&nbsp;Hossein Malakooti ,&nbsp;Ommolbanin Bazrafshan","doi":"10.1016/j.asej.2025.103960","DOIUrl":"10.1016/j.asej.2025.103960","url":null,"abstract":"<div><div>Flood prediction in data-scarce semi-arid regions presents significant challenges. This study develops an innovative artificial intelligence (AI) framework for the Jamash watershed, utilizing comprehensive daily data (2000–2023) of meteorological, hydrological, and remote sensing variables. We evaluated two feature selection methods—Random Forest (RF) and Wavelet Transform Coherence (WTC)—and employed Variational Mode Decomposition (VMD) to process non-stationary time series. Twelve modeling scenarios compared Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, both standalone and hybrid forms, with AI-based uncertainty assessment. Results demonstrated the superior performance of the hybrid VMD-WTC-GRU model, achieving a determination coefficient of 0.83 during testing. Uncertainty analysis confirmed this model as both the most accurate and reliable, exhibiting the narrowest uncertainty band (32.26) with satisfactory confidence probability coverage (0.95). This integrated AI framework effectively overcomes challenges of limited data and hydrological complexity in flood forecasting.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 103960"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191327","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 monocular omnidirectional vision-based method for structured light strip extraction and robot target localization under complex interference","authors":"Zihan Zhang,&nbsp;Qihui Guo,&nbsp;Ivan Kholodilin,&nbsp;Maksim A. Grigorev","doi":"10.1016/j.asej.2025.103959","DOIUrl":"10.1016/j.asej.2025.103959","url":null,"abstract":"<div><div>A method for structured light extraction and depth reconstruction, tailored to monocular omnidirectional vision systems, is proposed in this study to improve localization accuracy under wide field-of-view conditions with multiple objects and complex interferences such as reflections and occlusions. This method incorporates a multi-threshold fusion adjustment mechanism and introduces new algorithms for structured light clustering and discontinuity repair, aiming to improve the accuracy of centerline extraction. By integrating a neural network algorithm, the position of an object in the robot coordinate system can be accurately estimated from a single monocular omnidirectional image snapshot. The experimental results demonstrate that, compared with conventional extraction methods, the proposed method reduces the depth reconstruction error by 69.18 % in interference environments. By integrating the algorithm into the robotic system, multi-object recognition and localization were successfully achieved using a monocular camera. This provides a reference for the application of monocular omnidirectional vision in robotic systems.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103959"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928429","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}