The fractional differential equation is one of the important tools to realize the importance of the fractional calculus. Also, neutrosophic set provides a more comprehensive framework for handling uncertainty by truth, indeterminacy, and falsity membership function. This study aims to develop fractional differential equation in neutrosophic uncertain environment. A rigorous mathematical theorem has been formulated and proven, which establishes the existence and uniqueness of the solution to the initial-valued neutrosophic fractional differential equation (NFDE). The weak and strong characteristics of the solutions to NFDEs within the context of the Caputo fractional derivative framework is presented. A non-homogeneous linear NFDE is manifested by taking two types of neutrosophic fractional derivative in Caputo’s sense. An economic lot-sizing inventory model of deteriorating items with green level and stock-level dependent demand is presented as an application of the proposed theory by taking various inventory related parameters as neutrosophic numbers. Several cases of the proposed problems are also presented. The result observed that the fractional order model in a neutrosophic environment yields significantly better results compared to integer order models in the neutrosophic environment, as well as integer or fractional order models in a crisp environment.
{"title":"Existence and uniqueness of solutions to neutrosophic fractional differential equations and their implications for inventory controls model","authors":"Rakibul Haque , Mostafijur Rahaman , Loredana Ciurdariu , Adel Fahad Alrasheedi , Sankar Prasad Mondal","doi":"10.1016/j.asej.2025.103862","DOIUrl":"10.1016/j.asej.2025.103862","url":null,"abstract":"<div><div>The fractional differential equation is one of the important tools to realize the importance of the fractional calculus. Also, neutrosophic set provides a more comprehensive framework for handling uncertainty by truth, indeterminacy, and falsity membership function. This study aims to develop fractional differential equation in neutrosophic uncertain environment. A rigorous mathematical theorem has been formulated and proven, which establishes the existence and uniqueness of the solution to the initial-valued neutrosophic fractional differential equation (NFDE). The weak and strong characteristics of the solutions to NFDEs within the context of the Caputo fractional derivative framework is presented. A non-homogeneous linear NFDE is manifested by taking two types of neutrosophic fractional derivative in Caputo’s sense. An economic lot-sizing inventory model of deteriorating items with green level and stock-level dependent demand is presented as an application of the proposed theory by taking various inventory related parameters as neutrosophic numbers. Several cases of the proposed problems are also presented. The result observed that the fractional order model in a neutrosophic environment yields significantly better results compared to integer order models in the neutrosophic environment, as well as integer or fractional order models in a crisp environment.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103862"},"PeriodicalIF":5.9,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520605","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}
Pub Date : 2025-11-11DOI: 10.1016/j.asej.2025.103858
Murat Yavuz , İbrahim Türkoğlu
Marble is one of the most widely preferred natural stones in various sectors such as construction, decoration and art, due to its aesthetic structure, durability and wide range of colors. This widespread usage has elevated marble beyond merely being a decorative material, turning it into a raw material with significant economic value. However, maintaining this economic value requires more than just high-quality physical and chemical properties; it is also crucial that the product images provided by manufacturers are clear, informative, and visually appealing. In this context, applying super-resolution (SR) methods to enhance marble imagery represents an innovative step toward bridging the gap between visual quality and automated digital evaluation.
Nevertheless, marble images used in production and marketing processes are often of insufficient quality due to factors such as low resolution, blurriness or inadequate lighting. Poor visual quality reduces competitiveness, particularly in digital sales and promotional activities.
This study aims to reconstruct low quality marble images using Super Resolution methods to enhance image quality. Images of Elazığ Cherry Marble were used, forming a dataset comprising 2.551 image patches derived from 370 labeled slabs previously utilized in quality classification tasks. This ensured sufficient data diversity for model training and evaluation. Various super-resolution models, including GAN-based (e.g., ESRGAN) and attention driven (e.g., RSMAN) architectures, were employed. After SR reconstruction, the success of the enhancement was evaluated using image quality assessment metrics, namely PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). The results demonstrated that images subjected to super-resolution were of higher quality and exhibited significantly improved visual clarity compared to the original low-resolution images.
Moreover, classification tests conducted using the enhanced images achieved an accuracy rate of 96,4%. This indicates a positive impact not only on image quality but also on model performance. Consequently, it is concluded that this approach enhances customer satisfaction and strengthens competitive advantage within the marble industry. Overall, the integration of SR techniques not only improves visual perception but also enhances the functional performance of automated marble classification systems, leading to higher competitiveness and customer trust within the digital marketplace.
Although this study focused on Elazığ Cherry Marble, the proposed SR classification framework is inherently material-agnostic. It can be transferred to other natural stones or textured materials (e.g., granite, travertine, wood, textiles) with minimal domain-specific retraining, indicating strong potential for broader industrial adaptation.
{"title":"Enhancing marble image classification performance via super-resolution-assisted image improvement","authors":"Murat Yavuz , İbrahim Türkoğlu","doi":"10.1016/j.asej.2025.103858","DOIUrl":"10.1016/j.asej.2025.103858","url":null,"abstract":"<div><div>Marble is one of the most widely preferred natural stones in various sectors such as construction, decoration and art, due to its aesthetic structure, durability and wide range of colors. This widespread usage has elevated marble beyond merely being a decorative material, turning it into a raw material with significant economic value. However, maintaining this economic value requires more than just high-quality physical and chemical properties; it is also crucial that the product images provided by manufacturers are clear, informative, and visually appealing. In this context, applying super-resolution (SR) methods to enhance marble imagery represents an innovative step toward bridging the gap between visual quality and automated digital evaluation.</div><div>Nevertheless, marble images used in production and marketing processes are often of insufficient quality due to factors such as low resolution, blurriness or inadequate lighting. Poor visual quality reduces competitiveness, particularly in digital sales and promotional activities.</div><div>This study aims to reconstruct low quality marble images using Super Resolution methods to enhance image quality. Images of Elazığ Cherry Marble were used, forming a dataset comprising 2.551 image patches derived from 370 labeled slabs previously utilized in quality classification tasks. This ensured sufficient data diversity for model training and evaluation. Various super-resolution models, including GAN-based (e.g., ESRGAN) and attention driven (e.g., RSMAN) architectures, were employed. After SR reconstruction, the success of the enhancement was evaluated using image quality assessment metrics, namely PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). The results demonstrated that images subjected to super-resolution were of higher quality and exhibited significantly improved visual clarity compared to the original low-resolution images.</div><div>Moreover, classification tests conducted using the enhanced images achieved an accuracy rate of 96,4%. This indicates a positive impact not only on image quality but also on model performance. Consequently, it is concluded that this approach enhances customer satisfaction and strengthens competitive advantage within the marble industry. Overall, the integration of SR techniques not only improves visual perception but also enhances the functional performance of automated marble classification systems, leading to higher competitiveness and customer trust within the digital marketplace.</div><div>Although this study focused on Elazığ Cherry Marble, the proposed SR classification framework is inherently material-agnostic. It can be transferred to other natural stones or textured materials (e.g., granite, travertine, wood, textiles) with minimal domain-specific retraining, indicating strong potential for broader industrial adaptation.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103858"},"PeriodicalIF":5.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520643","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}
Pub Date : 2025-11-11DOI: 10.1016/j.asej.2025.103854
Md Munir Hayet Khan , Deiaaldeen Khaleel , Faidhalrahman Khaleel , Basheer Al-Hadeethi , Jumaa Awad Al-Somaydaii , Haitham Abdulmohsin Afan , Ali AbdulJabbar Alfahad , Alaa H. AbdUlameer , Cengiz Duran Atis , Ela Bahşude Görür Avşaroğlu
This study investigates various machine learning models, namely multi-layer perceptron (MLP) and generalized regression neural network (GRNN), for predicting the mechanical properties of high compressive strength geopolymer mortars. Both classification (MLPC and GRNNC) and regression (MLPR and GRNNC) based models, with MLP architectures comprising 1 and 2 hidden layers, are developed. Furthermore, three optimization algorithms, namely Levenberg–Marquardt (LM), momentum (M), and resilient backpropagation (R), are utilized. The models’ inputs are alkali concentrations, heat-curing temperatures, and curing periods. The results showed that the classification-based MLP with one hidden layer and resilient optimizer (MLPC-1-R) outperformed the other models by recording lower prediction deviations and high prediction accuracy. On the other hand, the regression-based models showed promising results and less sensitivity to the optimization type, unlike the classification-based ones. Finally, the resilient backpropagation (R) optimizer tends to provide consistent and high performance for both classification and regression-based models.
{"title":"Neural network-based prediction of mechanical properties in high-strength fly ash-based geopolymer mortars: a comparative analysis of model architectures and optimizers","authors":"Md Munir Hayet Khan , Deiaaldeen Khaleel , Faidhalrahman Khaleel , Basheer Al-Hadeethi , Jumaa Awad Al-Somaydaii , Haitham Abdulmohsin Afan , Ali AbdulJabbar Alfahad , Alaa H. AbdUlameer , Cengiz Duran Atis , Ela Bahşude Görür Avşaroğlu","doi":"10.1016/j.asej.2025.103854","DOIUrl":"10.1016/j.asej.2025.103854","url":null,"abstract":"<div><div>This study investigates various machine learning models, namely multi-layer perceptron (MLP) and generalized regression neural network (GRNN), for predicting the mechanical properties of high compressive strength geopolymer mortars. Both classification (MLPC and GRNNC) and regression (MLPR and GRNNC) based models, with MLP architectures comprising 1 and 2 hidden layers, are developed. Furthermore, three optimization algorithms, namely Levenberg–Marquardt (LM), momentum (M), and resilient backpropagation (R), are utilized. The models’ inputs are alkali concentrations, heat-curing temperatures, and curing periods. The results showed that the classification-based MLP with one hidden layer and resilient optimizer (MLPC-1-R) outperformed the other models by recording lower prediction deviations and high prediction accuracy. On the other hand, the regression-based models showed promising results and less sensitivity to the optimization type, unlike the classification-based ones. Finally, the resilient backpropagation (R) optimizer tends to provide consistent and high performance for both classification and regression-based models.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103854"},"PeriodicalIF":5.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520601","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}
Pub Date : 2025-11-11DOI: 10.1016/j.asej.2025.103792
Anthony Limi , K. Rangarajan , Imen Ali Kallel , Yassine Saoudi
This paper presents a novel inventory model for non-instantaneously deteriorating items with demand dependent on the selling price. The model strategically utilizes the cost-free storage period offered by ports as a temporary buffer before transferring goods to owned warehouses, enabling cost-effective inventory management within a two-warehouse distribution system. Unlike traditional supply chains that depend solely on rented or owned storage, the proposed approach minimizes holding costs and improves resource utilization by taking advantage of the port’s free storage window. Additionally, the model incorporates investments in energy-efficient green technologies to reduce carbon emissions during transportation between the port, warehouse, and industry. Numerical experiments confirm the model’s ability to significantly reduce total inventory costs, while sensitivity analysis highlights its robustness under varying selling prices. The inclusion of green technology further enhances environmental sustainability. Implemented in MATLAB R2024a, the model provides valuable insights for managing inventory efficiently in price-sensitive and environmentally regulated supply chains.
{"title":"Optimizing inventory management with strategic utilization of port free storage within the chemical industry","authors":"Anthony Limi , K. Rangarajan , Imen Ali Kallel , Yassine Saoudi","doi":"10.1016/j.asej.2025.103792","DOIUrl":"10.1016/j.asej.2025.103792","url":null,"abstract":"<div><div>This paper presents a novel inventory model for non-instantaneously deteriorating items with demand dependent on the selling price. The model strategically utilizes the cost-free storage period offered by ports as a temporary buffer before transferring goods to owned warehouses, enabling cost-effective inventory management within a two-warehouse distribution system. Unlike traditional supply chains that depend solely on rented or owned storage, the proposed approach minimizes holding costs and improves resource utilization by taking advantage of the port’s free storage window. Additionally, the model incorporates investments in energy-efficient green technologies to reduce carbon emissions during transportation between the port, warehouse, and industry. Numerical experiments confirm the model’s ability to significantly reduce total inventory costs, while sensitivity analysis highlights its robustness under varying selling prices. The inclusion of green technology further enhances environmental sustainability. Implemented in MATLAB R2024a, the model provides valuable insights for managing inventory efficiently in price-sensitive and environmentally regulated supply chains.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103792"},"PeriodicalIF":5.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520604","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}
Pub Date : 2025-11-11DOI: 10.1016/j.asej.2025.103860
Avishek Adhikari , Mohan Dhakal , Manoj Paudel , Rabindra Paudel , Santosh G. Chhetri , Tek Raj Gyawali
Nepal faces the dual challenge of rapid infrastructure growth and aligning with UN Sustainable Development Goals. Relying on imported chemical admixtures hinders sustainable construction. This study evaluates solid molasses (SM), natural molasses (NM), and a chemical superplasticizer (SP) as admixtures in M20 concrete. SM, a sugar industry by-product, and NM, produced by boiling sugarcane juice, were tested against SP using a control mix (w/c = 0.48; cement:sand:coarse aggregate = 1:1.44:3.02). Admixtures were added in varying dosages by cement weight to assess effects on workability, strength, density, and water absorption. Optimal contents were 0.080 % (SM), 0.075 % (NM), and 0.600 % (SP). SM delivered superior performance across all properties, indicating it as the most effective and sustainable admixture. Its use can enhance concrete quality, reduce reliance on imports, and promote waste valorization, making it a viable solution for sustainable infrastructure in Nepal and similar developing nations.
{"title":"Evaluation of solid molasses on the physical and mechanical properties of concrete","authors":"Avishek Adhikari , Mohan Dhakal , Manoj Paudel , Rabindra Paudel , Santosh G. Chhetri , Tek Raj Gyawali","doi":"10.1016/j.asej.2025.103860","DOIUrl":"10.1016/j.asej.2025.103860","url":null,"abstract":"<div><div>Nepal faces the dual challenge of rapid infrastructure growth and aligning with UN Sustainable Development Goals. Relying on imported chemical admixtures hinders sustainable construction. This study evaluates solid molasses (SM), natural molasses (NM), and a chemical superplasticizer (SP) as admixtures in M20 concrete. SM, a sugar industry by-product, and NM, produced by boiling sugarcane juice, were tested against SP using a control mix (w/c = 0.48; cement:sand:coarse aggregate = 1:1.44:3.02). Admixtures were added in varying dosages by cement weight to assess effects on workability, strength, density, and water absorption. Optimal contents were 0.080 % (SM), 0.075 % (NM), and 0.600 % (SP). SM delivered superior performance across all properties, indicating it as the most effective and sustainable admixture. Its use can enhance concrete quality, reduce reliance on imports, and promote waste valorization, making it a viable solution for sustainable infrastructure in Nepal and similar developing nations.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103860"},"PeriodicalIF":5.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520639","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}
Pub Date : 2025-11-11DOI: 10.1016/j.asej.2025.103857
Meshari D. Alanazi
The navigation of autonomous amphibious aerial vehicles (AAVs) relies on temporal and spatial data, including precise location, distance, and information about obstacles, to ensure effective routing. The high volatility of these data and the fact that they are time-sensitive both contribute to increased navigation difficulty. A Multi-Granular Graph (MGG) Attention-Fusion Algorithm (AFA) has been proposed as a solution to this problem. The purpose of this algorithm is to optimize navigational mode, routing selection, and decision-making techniques by utilizing temporal data. Through the use of node–edge graph learning, the method achieves data fusion by utilizing external parameters to reduce complexity. This eliminates the requirement for substantial local data storage. The suggested method identifies optimal routes with less impediments and reduced errors by recognizing global data through maximal fusion. This allows for the identification of optimal routes. The results of experimental verification, which utilized variations in distance, velocity, and motor speed, demonstrate a 13.26% enhancement in route precision, a 12.93% reduction in complexity, and a 12.77% decrease in route error. Complex interplay between sensor inputs, ambient conditions, and vehicle dynamics make the navigation problem intrinsically nonlinear. The suggested MGG-AFA approach uses data fusion and hierarchical attention-based graph learning to capture these nonlinear interactions.
{"title":"Adaptive multi-granularity graph attention fusion algorithm for autonomous amphibious aerial vehicle route optimization","authors":"Meshari D. Alanazi","doi":"10.1016/j.asej.2025.103857","DOIUrl":"10.1016/j.asej.2025.103857","url":null,"abstract":"<div><div>The navigation of autonomous amphibious aerial vehicles (AAVs) relies on temporal and spatial data, including precise location, distance, and information about obstacles, to ensure effective routing. The high volatility of these data and the fact that they are time-sensitive both contribute to increased navigation difficulty. A Multi-Granular Graph (MGG) Attention-Fusion Algorithm (AFA) has been proposed as a solution to this problem. The purpose of this algorithm is to optimize navigational mode, routing selection, and decision-making techniques by utilizing temporal data. Through the use of node–edge graph learning, the method achieves data fusion by utilizing external parameters to reduce complexity. This eliminates the requirement for substantial local data storage. The suggested method identifies optimal routes with less impediments and reduced errors by recognizing global data through maximal fusion. This allows for the identification of optimal routes. The results of experimental verification, which utilized variations in distance, velocity, and motor speed, demonstrate a 13.26% enhancement in route precision, a 12.93% reduction in complexity, and a 12.77% decrease in route error. Complex interplay between sensor inputs, ambient conditions, and vehicle dynamics make the navigation problem intrinsically nonlinear. The suggested MGG-AFA approach uses data fusion and hierarchical attention-based graph learning to capture these nonlinear interactions.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103857"},"PeriodicalIF":5.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520640","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}
Pub Date : 2025-11-10DOI: 10.1016/j.asej.2025.103840
Fangfang Ye , Jinming Wang , Cao Shuhua , Zhou Dong , Yuliang Sun , Ting Wang , Amr Yousef , Ezzeddine Touti
Imagery from unmanned aircraft is utilized in this study to present a deep learning framework for the aim of identifying anomalies that has been optimized with the Dragonflies Optimisation Algorithm (DL-DOA) for crowd surveillance in locations with high density. The suggested technique attains a 98.7% F1-score, a 98.5% ROC-AUC, and a low rate of error (MAE 0.03) when assessed using the VisDrone dataset. The suggested approach is proven to be more accurate and resilient than the approaches that are currently in use, as seen by this. The application of sophisticated preprocessing and occluded management in conjunction with YOLO and the Generational adversarial Occlusion Networks (GAON) can potentially result in efficient noise reduction (83–91%) and successful recognition in the presence of partial occlusion. The effectiveness of DL-DOA’s models is enhanced by adaptive hyperparameter adjustment, which maintains a near real-time inference rate of 0.18 s per frame. Despite the fact that there are limitations in extreme weather and extremely dense crowds, this method demonstrates promise for changing, large-scale monitoring situations. Parallel processing will be the focus of future endeavors in order to improve both adaptability and efficiency.
本研究利用无人驾驶飞机的图像来提供一个深度学习框架，目的是识别异常，该异常已通过蜻蜓优化算法（DL-DOA）进行优化，用于高密度地点的人群监视。当使用VisDrone数据集进行评估时，建议的技术达到98.7%的f1得分，98.5%的ROC-AUC和低错误率（MAE 0.03）。由此可见，所建议的方法已被证明比目前使用的方法更准确、更有弹性。结合YOLO和代际对抗遮挡网络（GAON），应用复杂的预处理和遮挡管理可以有效地降低噪声（83-91%），并在存在部分遮挡的情况下成功识别。DL-DOA模型通过自适应超参数调整增强了模型的有效性，保持了0.18 s /帧的近实时推理率。尽管在极端天气和极其密集的人群中存在局限性，但这种方法表明，它有望用于不断变化的大规模监测情况。为了提高适应性和效率，并行处理将是未来工作的重点。
{"title":"Intelligent unmanned aerial vehicles surveillance for detecting abnormalities in crowds under occlusion and multi target conditions","authors":"Fangfang Ye , Jinming Wang , Cao Shuhua , Zhou Dong , Yuliang Sun , Ting Wang , Amr Yousef , Ezzeddine Touti","doi":"10.1016/j.asej.2025.103840","DOIUrl":"10.1016/j.asej.2025.103840","url":null,"abstract":"<div><div>Imagery from unmanned aircraft is utilized in this study to present a deep learning framework for the aim of identifying anomalies that has been optimized with the Dragonflies Optimisation Algorithm (DL-DOA) for crowd surveillance in locations with high density. The suggested technique attains a 98.7% F1-score, a 98.5% ROC-AUC, and a low rate of error (MAE 0.03) when assessed using the VisDrone dataset. The suggested approach is proven to be more accurate and resilient than the approaches that are currently in use, as seen by this. The application of sophisticated preprocessing and occluded management in conjunction with YOLO and the Generational adversarial Occlusion Networks (GAON) can potentially result in efficient noise reduction (83–91%) and successful recognition in the presence of partial occlusion. The effectiveness of DL-DOA’s models is enhanced by adaptive hyperparameter adjustment, which maintains a near real-time inference rate of 0.18 s per frame. Despite the fact that there are limitations in extreme weather and extremely dense crowds, this method demonstrates promise for changing, large-scale monitoring situations. Parallel processing will be the focus of future endeavors in order to improve both adaptability and efficiency.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103840"},"PeriodicalIF":5.9,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519769","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}
Pub Date : 2025-11-10DOI: 10.1016/j.asej.2025.103778
B. Padmavathi , G. Ramya , M. Shunmugathammal , Roopa Muralidhar , Hab.Eng. Jerzy RyszardSzymański , Marta Żurek-Mortka , Mithileysh Sathiyanarayanan
Emerging battery-constrained IoT devices face significant communication challenges due to frequent power interruptions and limited time synchronization. Traditional low-power wireless protocols are limited by intermittent energy availability and lack of precise timing. While the IoT offers many advantages, it also poses significant challenges including energy constraints, which may lead to a limited lifespan of IoT devices. To overcome these issues, a novel DUal RadiO COMmunication (DUROCOM) protocol has been proposed. It provides dependable communication between a receiver and several devices by synchronizing them using two low-power wake-up radios. The Reptile Search Algorithm is used for protocol optimization to adjust the data rate. The efficacy of the framework is evaluated using metrics namely throughput, energy consumption, power control efficiency, and latency. The DUROCOM technique improves the energy efficiency by 7.91%, 43.57%, and 66.44% and Network Lifetime by 4.37%, 10.88%, and 18.51% better than the existing Quantum-SSA-Markov Model, HDS, and BaMbI approaches.
{"title":"DUROCOM: energy efficient dual radio communication protocol for battery constrained IoT networks","authors":"B. Padmavathi , G. Ramya , M. Shunmugathammal , Roopa Muralidhar , Hab.Eng. Jerzy RyszardSzymański , Marta Żurek-Mortka , Mithileysh Sathiyanarayanan","doi":"10.1016/j.asej.2025.103778","DOIUrl":"10.1016/j.asej.2025.103778","url":null,"abstract":"<div><div>Emerging battery-constrained IoT devices face significant communication challenges due to frequent power interruptions and limited time synchronization. Traditional low-power wireless protocols are limited by intermittent energy availability and lack of precise timing. While the IoT offers many advantages, it also poses significant challenges including energy constraints, which may lead to a limited lifespan of IoT devices.<!--> <!-->To overcome these issues, a novel DUal RadiO COMmunication (DUROCOM) protocol has been proposed. It provides dependable communication between a receiver and several devices by synchronizing them using two low-power wake-up radios. The Reptile Search Algorithm is used for protocol optimization to adjust the data rate. The efficacy of the framework is evaluated using metrics namely throughput, energy consumption, power control efficiency, and latency. The DUROCOM technique improves the energy efficiency by 7.91%, 43.57%, and 66.44% and Network Lifetime by 4.37%, 10.88%, and 18.51% better than the existing Quantum-SSA-Markov Model, HDS, and BaMbI approaches.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103778"},"PeriodicalIF":5.9,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520638","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}
Pub Date : 2025-11-10DOI: 10.1016/j.asej.2025.103834
Pedram Jazayeri , Hamid R. Safavi , Mohammad Saleh Ebrahimi , Alireza Rahmatpanah , Mohammad Nazemizadeh
This study proposes a two-phase framework for the smart management of Water Distribution Networks (WDNs). In the first phase, daily water demand in Najaf Abad, Isfahan, Iran, is predicted using Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Random Forest (RF), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. Unlike previous studies focusing mainly on climatic factors, this research improves accuracy by integrating social and cultural features derived from the Persian calendar, including a day index and holiday continuity, to reflect demand patterns during official and religious holidays as the first novelty. The second phase optimizes pump operation through a Mixed-Integer Non-Linear Programming (MINLP) model to minimize energy, repair, and maintenance costs. The optimization incorporates predicted demand, seasonal energy tariffs, water elevation, and pump switching intervals. This framework can provide global or operator-preferred local solutions as the second novelty and can be implemented in real time using existing telemetry systems without extra costs. Finally, the obtained results highlight LSTM achieved the greatest improvement in MSE (72.1%), while RF performed best in R2 (16%) and PCC (7.6%). Application during hot and cold periods reduced pumping energy costs by up to 9.7% (from $46.3 to $41.8) and 15.8% (from $36.6 to $30.8), and reduced pump switches by 64.9% (from 74 times to 26 times) and 75.7% (from 90 times to 22 times), respectively. Using TOPSIS, optimal pumping intervals of 12, 8, and 6 h were identified. The framework offers an efficient and practical solution for improving WDN performance.
{"title":"Bridging prediction and optimization: A unified MINLP, traditional machine learning and deep learning framework for water distribution networks management","authors":"Pedram Jazayeri , Hamid R. Safavi , Mohammad Saleh Ebrahimi , Alireza Rahmatpanah , Mohammad Nazemizadeh","doi":"10.1016/j.asej.2025.103834","DOIUrl":"10.1016/j.asej.2025.103834","url":null,"abstract":"<div><div>This study proposes a two-phase framework for the smart management of Water Distribution Networks (WDNs). In the first phase, daily water demand in Najaf Abad, Isfahan, Iran, is predicted using Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Random Forest (RF), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. Unlike previous studies focusing mainly on climatic factors, this research improves accuracy by integrating social and cultural features derived from the Persian calendar, including a day index and holiday continuity, to reflect demand patterns during official and religious holidays as the first novelty. The second phase optimizes pump operation through a Mixed-Integer Non-Linear Programming (MINLP) model to minimize energy, repair, and maintenance costs. The optimization incorporates predicted demand, seasonal energy tariffs, water elevation, and pump switching intervals. This framework can provide global or operator-preferred local solutions as the second novelty and can be implemented in real time using existing telemetry systems without extra costs. Finally, the obtained results highlight LSTM achieved the greatest improvement in <em>MSE</em> (72.1%), while RF performed best in <em>R<sup>2</sup></em> (16%) and <em>PCC</em> (7.6%). Application during hot and cold periods reduced pumping energy costs by up to 9.7% (from $46.3 to $41.8) and 15.8% (from $36.6 to $30.8), and reduced pump switches by 64.9% (from 74 times to 26 times) and 75.7% (from 90 times to 22 times), respectively. Using TOPSIS, optimal pumping intervals of 12, 8, and 6 h were identified. The framework offers an efficient and practical solution for improving WDN performance.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103834"},"PeriodicalIF":5.9,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520606","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}
Pub Date : 2025-11-08DOI: 10.1016/j.asej.2025.103842
Jizhen Du
Environmental concerns have increasingly drawn attention to electric vehicles (EVs) over recent decades. The setting up of fast-charging stations requires careful consideration of details, including the precise location and size of the charging stations. Additionally, it is essential to persuade individuals to fund the installation of outlets for charging and create favorable prerequisites for investors to profit from their investments. In this article, the problem of planning fast-charging stations is represented as a nonlinear programming using integers. In this model, the target functions of the allocation business and the charging station owner are evaluated independently. The position and dimensions of charging stations, as well as the cost of electricity exchange between the distribution company and the charging station, are determined in such a way that the goal is accomplished of both entities is enhanced. In this approach, the theory of queues and traffic assignment models according to user equilibrium are employed to ascertain the magnitude of charging stations. In this paper, an improved Particle Swarm Optimization algorithm with variable coefficients is defined, which enhances local search capability and provides superior exploration ability.
{"title":"Optimizing the deployment of fast-charging stations for electric vehicles: A mixed-integer nonlinear programming approach with particle swarm optimization","authors":"Jizhen Du","doi":"10.1016/j.asej.2025.103842","DOIUrl":"10.1016/j.asej.2025.103842","url":null,"abstract":"<div><div>Environmental concerns have increasingly drawn attention to electric vehicles (EVs) over recent decades. The setting up of fast-charging stations requires careful consideration of details, including the precise location and size of the charging stations. Additionally, it is essential to persuade individuals to fund the installation of outlets for charging and create favorable prerequisites for investors to profit from their investments. In this article, the problem of planning fast-charging stations is represented as a nonlinear programming using integers. In this model, the target functions of the allocation business and the charging station owner are evaluated independently. The position and dimensions of charging stations, as well as the cost of electricity exchange between the distribution company and the charging station, are determined in such a way that the goal is accomplished of both entities is enhanced. In this approach, the theory of queues and traffic assignment models according to user equilibrium are employed to ascertain the magnitude of charging stations. In this paper, an improved Particle Swarm Optimization algorithm with variable coefficients is defined, which enhances local search capability and provides superior exploration ability.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103842"},"PeriodicalIF":5.9,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467441","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}
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