Pub Date : 2026-01-01DOI: 10.1016/j.asej.2025.103942
Xing Li , Huan Hao , Lingying Chen , Fuqiang Zhao , Yuhui Liu
This study proposes a GNN-based multi-scale heat transfer path optimization method for magnetic-thermal coupling simulation of wound conductors. Key parameters like magnetic vector potential, flux density, and temperature distribution are identified. An adaptive graph network integrates these parameters to build a 3D conductor model. A multi-scale spatio-temporal graph convolution module captures heat transfer path characteristics, while the GraphSAGE algorithm aggregates thermal resistance and electromagnetic loss data from adjacent nodes to train the GNN.The trained GNN outputs optimized multi-scale heat transfer path results, including temperature distribution and magnetic field loss. Experiments show the method effectively simulates magnetic-thermal coupling, with ohmic losses in low-voltage and high-voltage windings at ∼600 W and ∼300 W, respectively, and peak eddy current losses reaching ∼1600 W and ∼1700 W. Temperatures mainly range between 320–340 K (low-voltage) and 300–320 K (high-voltage). The method’s optimization reduces magnetic losses and material usage.
{"title":"Multi-scale heat transfer path optimization in magnetic-thermal coupling simulation of winding conductors using graph neural networks","authors":"Xing Li , Huan Hao , Lingying Chen , Fuqiang Zhao , Yuhui Liu","doi":"10.1016/j.asej.2025.103942","DOIUrl":"10.1016/j.asej.2025.103942","url":null,"abstract":"<div><div>This study proposes a GNN-based multi-scale heat transfer path optimization method for magnetic-thermal coupling simulation of wound conductors. Key parameters like magnetic vector potential, flux density, and temperature distribution are identified. An adaptive graph network integrates these parameters to build a 3D conductor model. A multi-scale spatio-temporal graph convolution module captures heat transfer path characteristics, while the GraphSAGE algorithm aggregates thermal resistance and electromagnetic loss data from adjacent nodes to train the GNN.The trained GNN outputs optimized multi-scale heat transfer path results, including temperature distribution and magnetic field loss. Experiments show the method effectively simulates magnetic-thermal coupling, with ohmic losses in low-voltage and high-voltage windings at ∼600 W and ∼300 W, respectively, and peak eddy current losses reaching ∼1600 W and ∼1700 W. Temperatures mainly range between 320–340 K (low-voltage) and 300–320 K (high-voltage). The method’s optimization reduces magnetic losses and material usage.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103942"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883456","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103954
Mohammed Alghamdi, Salman Alhifthi, Naif Alsanabani, Khalid Al-Gahtani, Ayman Altuwaim, Abdullah AlSharef
Hospitals are mission-critical facilities where operational integrity is crucial for ensuring patient safety and delivering adequate healthcare. Reactive, fragmented approaches often undermine effective hospital facility management (FM). This study addresses this gap by developing and validating a system dynamics (SD) model to analyze the causal relationships and feedback loops among key performance factors. A multi-phase methodology was employed, integrating expert surveys using the Relative Importance Index (RII), the Analytic Hierarchy Process (AHP), and the DEMATEL technique to structure and quantify the model. The developed SD model was validated through sensitivity analysis. Study findings revealed that cumulative impacts hinder the system, resulting in a significant 26.38% budget overrun over twelve months. The model identifies ’Design Errors’ and ’System and Selection of Materials’ as the most destructive factors, causing severe performance degradation across the system. The implications are significant, providing a strategic blueprint for hospital managers to shift towards proactive interventions by focusing on these high-leverage points.
{"title":"Modeling the interdependencies of critical factors in hospital facility management: a system dynamics framework","authors":"Mohammed Alghamdi, Salman Alhifthi, Naif Alsanabani, Khalid Al-Gahtani, Ayman Altuwaim, Abdullah AlSharef","doi":"10.1016/j.asej.2025.103954","DOIUrl":"10.1016/j.asej.2025.103954","url":null,"abstract":"<div><div>Hospitals are mission-critical facilities where operational integrity is crucial for ensuring patient safety and delivering adequate healthcare. Reactive, fragmented approaches often undermine effective hospital facility management (FM). This study addresses this gap by developing and validating a system dynamics (SD) model to analyze the causal relationships and feedback loops among key performance factors. A multi-phase methodology was employed, integrating expert surveys using the Relative Importance Index (RII), the Analytic Hierarchy Process (AHP), and the DEMATEL technique to structure and quantify the model. The developed SD model was validated through sensitivity analysis. Study findings revealed that cumulative impacts hinder the system, resulting in a significant 26.38% budget overrun over twelve months. The model identifies ’Design Errors’ and ’System and Selection of Materials’ as the most destructive factors, causing severe performance degradation across the system. The implications are significant, providing a strategic blueprint for hospital managers to shift towards proactive interventions by focusing on these high-leverage points.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103954"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883458","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103941
Dengguo Wu , Jian Yang , Wenjie Cai , Wenzhong Xia , Dongfang Jiang , Shengyao Liu , Buting Xu , Haiyang Wang
Air quality in urban residential areas depends on the vehicles, celebrations, carbon emissions, etc. experienced around the day/ year. The effects of carbon emissions on residents’ thermal comfort are more important than control measures, for which measurements are mandatory. This article, therefore, introduces an ENVi-Met-based thermal structure assessment model to identify the air quality affected by carbon emissions. In the thermal structure assessment, the three-dimensional space of the urban Area, including the emitting and emission-conducted regions, is modeled to compute the air quality index (AQI). A stabilized assessment of the mean AQI that distinguishes between the worst and better thermal structures is estimated from continuous assessments. In this process, the change in AQI from the lowest to the highest is the boundary for the structural evaluation. The primary objective of this study is to develop an integrated framework that explains how carbon emissions affect the thermal structure and air quality in urban residential areas. The work employs the ENVI-met microclimate simulation system to model the spatial and temporal distribution of emissions, thereby facilitating a complex representation of pollutant dynamics within three-dimensional urban environments. It subsequently sets high and low AQI limits that reflect differing thermal conditions, making it easier to distinguish areas that are thermally strained from those that are well-ventilated. The model employs convergence-based transfer learning to maintain stable AQI forecasts over time, ensuring predictions remain consistent even as environmental variables change. Lastly, the framework examines thermal comfort by considering the combined effects of emission intensity, vegetation absorption, and microclimatic interactions. The boundary-based change differentiation is validated using converged transfer learning to identify the maximum changes in thermal structures. Learning converges for the AQI differentiation value for stabilization detection. Therefore, this stabilization value is used to train the learning network to maintain boundary consistency across different structural changes. The proposed model achieves an 11.39% high AQI analysis with a maximum Kappa of 12.58% between the convergence and stability for the time/day and stable variants under hot climatic conditions.
{"title":"Effects of green quantity and structure on thermal comfort and air quality of urban residential areas based on ENVl-met model","authors":"Dengguo Wu , Jian Yang , Wenjie Cai , Wenzhong Xia , Dongfang Jiang , Shengyao Liu , Buting Xu , Haiyang Wang","doi":"10.1016/j.asej.2025.103941","DOIUrl":"10.1016/j.asej.2025.103941","url":null,"abstract":"<div><div>Air quality in urban residential areas depends on the vehicles, celebrations, carbon emissions, etc. experienced around the day/ year. The effects of carbon emissions on residents’ thermal comfort are more important than control measures, for which measurements are mandatory. This article, therefore, introduces an ENVi-Met-based thermal structure assessment model to identify the air quality affected by carbon emissions. In the thermal structure assessment, the three-dimensional space of the urban Area, including the emitting and emission-conducted regions, is modeled to compute the air quality index (AQI). A stabilized assessment of the mean AQI that distinguishes between the worst and better thermal structures is estimated from continuous assessments. In this process, the change in AQI from the lowest to the highest is the boundary for the structural evaluation. The primary objective of this study is to develop an integrated framework that explains how carbon emissions affect the thermal structure and air quality in urban residential areas. The work employs the ENVI-met microclimate simulation system to model the spatial and temporal distribution of emissions, thereby facilitating a complex representation of pollutant dynamics within three-dimensional urban environments. It subsequently sets high and low AQI limits that reflect differing thermal conditions, making it easier to distinguish areas that are thermally strained from those that are well-ventilated. The model employs convergence-based transfer learning to maintain stable AQI forecasts over time, ensuring predictions remain consistent even as environmental variables change. Lastly, the framework examines thermal comfort by considering the combined effects of emission intensity, vegetation absorption, and microclimatic interactions. The boundary-based change differentiation is validated using converged transfer learning to identify the maximum changes in thermal structures. Learning converges for the AQI differentiation value for stabilization detection. Therefore, this stabilization value is used to train the learning network to maintain boundary consistency across different structural changes. The proposed model achieves an 11.39% high AQI analysis with a maximum Kappa of 12.58% between the convergence and stability for the time/day and stable variants under hot climatic conditions.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103941"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883552","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103955
Qianxi Li , Wuquan He , Guoqiang Tang , Keyi Zhao , Li Cao , Jun Liu , Wene Wang
To address redundant investment costs, uneven hydraulic distribution, and insufficient operational reliability in conventional irrigation pipe‐network designs, this study establishes a multi-objective optimization model with the objectives of minimizing total investment cost (economy) and minimizing the mean and variance of nodal surplus heads (reliability). Guided by the characteristics of the solution space, we introduce three strategies—weighted intelligent initialization, adaptive variation, and soft‐constraint handling—and propose an enhanced Golden Jackal Optimization (GJO) algorithm tailored for random Rotational Irrigation Group (RIG) division. The superiority of the improved GJO in high‐dimensional, multi‐objective problems is validated through comparative simulations on the ZDT test‐function suite and against the NSGA‐II algorithm. A self‐pressurized drip irrigation project in Xinjiang serves as a case study: the optimized design reduces pipe‐network investment by 12.78 %, decreases the mean nodal surplus head by 11.45 %, and lowers the surplus‐head variance by 16.67 % compared with the original scheme, thereby demonstrating the method’s validity and practicality. Finally, correlation analysis elucidates the trade‐off relationships among pipe‐network investment cost, mean nodal surplus hydraulic head, and surplus‐head variance.
{"title":"A multi-objective optimization design method for self-pressure drip irrigation networks considering economic efficiency and reliability","authors":"Qianxi Li , Wuquan He , Guoqiang Tang , Keyi Zhao , Li Cao , Jun Liu , Wene Wang","doi":"10.1016/j.asej.2025.103955","DOIUrl":"10.1016/j.asej.2025.103955","url":null,"abstract":"<div><div>To address redundant investment costs, uneven hydraulic distribution, and insufficient operational reliability in conventional irrigation pipe‐network designs, this study establishes a multi-objective optimization model with the objectives of minimizing total investment cost (economy) and minimizing the mean and variance of nodal surplus heads (reliability). Guided by the characteristics of the solution space, we introduce three strategies—weighted intelligent initialization, adaptive variation, and soft‐constraint handling—and propose an enhanced Golden Jackal Optimization (GJO) algorithm tailored for random Rotational Irrigation Group (RIG) division. The superiority of the improved GJO in high‐dimensional, multi‐objective problems is validated through comparative simulations on the ZDT test‐function suite and against the NSGA‐II algorithm. A self‐pressurized drip irrigation project in Xinjiang serves as a case study: the optimized design reduces pipe‐network investment by 12.78 %, decreases the mean nodal surplus head by 11.45 %, and lowers the surplus‐head variance by 16.67 % compared with the original scheme, thereby demonstrating the method’s validity and practicality. Finally, correlation analysis elucidates the trade‐off relationships among pipe‐network investment cost, mean nodal surplus hydraulic head, and surplus‐head variance.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103955"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924094","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103944
Ayah-Allah Khalil , Mohamed Fikry , Dina Saadallah
Existing cities in developing countries like Egypt face many challenges, as most development occurs without considering people and are developed-oriented cars. Recognizing the significance of achieving equitable access towards education services that support spatial justice and sustainability, and although many studies have addressed and examined walkability, limited attention has been given to walkable oriented development. The research aims to develop a model to help determine possible street improvement to be walkable towards educational services, taking into account the views of stakeholders. The methodological framework of the East District of Kafr_Elsheikh, Egypt, used a five-stage MCDM-GIS analytical method. The results indicated that only 26% of the study area was walkable. Distance was the most effective indicator, with a weight of 0.67, while Landscape_strips and Facilities had the least effect. This model can be dynamically applied to assist planners and decision-makers in making decisions regarding initiating the development processes and prioritizing interventions.
{"title":"Walkable oriented development modelling approach in developing countries as a sustainable urban planning; Kafr_Elsheikh City, Egypt as a case study","authors":"Ayah-Allah Khalil , Mohamed Fikry , Dina Saadallah","doi":"10.1016/j.asej.2025.103944","DOIUrl":"10.1016/j.asej.2025.103944","url":null,"abstract":"<div><div>Existing cities in developing countries like Egypt face many challenges, as most development occurs without considering people and are developed-oriented cars. Recognizing the significance of achieving equitable access towards education services that support spatial justice and sustainability, and although many studies have addressed and examined walkability, limited attention has been given to walkable oriented development. The research aims to develop a model to help determine possible street improvement to be walkable towards educational services, taking into account the views of stakeholders. The methodological framework of the East District of Kafr_Elsheikh, Egypt, used a five-stage MCDM-GIS analytical method. The results indicated that only 26% of the study area was walkable. Distance was the most effective indicator, with a weight of 0.67, while Landscape_strips and Facilities had the least effect. This model can be dynamically applied to assist planners and decision-makers in making decisions regarding initiating the development processes and prioritizing interventions.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103944"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924095","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103921
Asmaa Abo Bakr Kamel , Nahla A. Belal , Yasser El-Sonbaty
DNA methylation is a vital epigenetic mechanism influencing cell differentiation, disease progression, and therapeutic development. However, accurately detecting CpG methylation sites remains challenging due to their dynamic nature. This, in addition, to the high cost of current experimental techniques that were expanded to cover the whole genome and the low cost experimental techniques with limited coverage. This study aims to develop an efficient, cell type independent model to predict CpG methylation sites using cost-effective and widely available data sources. Sequence features were extracted from whole-genome sequencing (WGS) data and integrated them with features from the Infinium HumanMethylation450 (450 k) array. This is to predict both binary methylation states and continuous beta values at single base resolution on human chromosome 21 (chr21). Eleven machine learning and ensemble methods were evaluated, with random forest (RF) achieving the best performance of 75 % balanced accuracy for classification. The model was further applied to beta value regression and categorized using a novel five-level “EGYPT Methylation Categorization” system, achieving 63.2 % of categorical prediction accuracy. These results demonstrated that integrating inexpensive genomic data with robust machine learning techniques can effectively approximate methylation patterns, offering a scalable and transferable approach for epigenetic analysis and precision medicine.
{"title":"A computational method to predict methylation of CpG sites in Chr21 using sequence features from WGS with 450 K array features","authors":"Asmaa Abo Bakr Kamel , Nahla A. Belal , Yasser El-Sonbaty","doi":"10.1016/j.asej.2025.103921","DOIUrl":"10.1016/j.asej.2025.103921","url":null,"abstract":"<div><div>DNA methylation is a vital epigenetic mechanism influencing cell differentiation, disease progression, and therapeutic development. However, accurately detecting CpG methylation sites remains challenging due to their dynamic nature. This, in addition, to the high cost of current experimental techniques that were expanded to cover the whole genome and the low cost experimental techniques with limited coverage. This study aims to develop an efficient, cell type independent model to predict CpG methylation sites using cost-effective and widely available data sources. Sequence features were extracted from whole-genome sequencing (WGS) data and integrated them with features from the Infinium HumanMethylation450 (450 k) array. This is to predict both binary methylation states and continuous beta values at single base resolution on human chromosome 21 (chr21). Eleven machine learning and ensemble methods were evaluated, with random forest (RF) achieving the best performance of 75 % balanced accuracy for classification. The model was further applied to beta value regression and categorized using a novel five-level “EGYPT Methylation Categorization” system, achieving 63.2 % of categorical prediction accuracy. These results demonstrated that integrating inexpensive genomic data with robust machine learning techniques can effectively approximate methylation patterns, offering a scalable and transferable approach for epigenetic analysis and precision medicine.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103921"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883453","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103957
Mohammed Albekairi , Nasr Rashid , Meshari D. Alanazi , Turki M. Alanazi , Khaled Kaaniche , Amr Yousef , Ghulam Abbas
The tumor microenvironment (TME) is an important factor in cancer development, treatment response, and immune regulation. Segmenting tumor subregions in histopathological images remains a challenge due to heterogeneity in space, morphology, and staining. In this regard, this paper presents a Histology-Guided Deep Learning Framework (HG-DLF) that involves multi-scale feature fusion, dual attention mechanisms, and graph convolutional networks to achieve accurate and robust TME analysis. Using the PanNuke dataset of 7,904 histology image patches across 19 tissue types, HG-DLF successfully segments tumor, immune, stromal, and nuclear structures. The model demonstrates a Dice Similarity Coefficient of 97.8%, an Intersection over Union (IoU) of 98.34%, and a Hausdorff Distance of 1.67—well surpassing baseline models, such as Deep CNNs and Her2Net, by more than 20% in accuracy and over 50% in inference time. The dual attention mechanism facilitates discriminative feature extraction, and the graph-based module leverages spatial context and tissue boundaries. The model demonstrates greater generalizability across folds in k-fold cross-validation and high accuracy despite morphological differences. HG-DLF offers an interpretable, scalable computational pathology solution with applications for estimating tumor heterogeneity, immune infiltration levels, and supporting clinical decision-making in precision oncology.
{"title":"Multi-scale attention-based deep learning framework for tumor microenvironment profiling","authors":"Mohammed Albekairi , Nasr Rashid , Meshari D. Alanazi , Turki M. Alanazi , Khaled Kaaniche , Amr Yousef , Ghulam Abbas","doi":"10.1016/j.asej.2025.103957","DOIUrl":"10.1016/j.asej.2025.103957","url":null,"abstract":"<div><div>The tumor microenvironment (TME) is an important factor in cancer development, treatment response, and immune regulation. Segmenting tumor subregions in histopathological images remains a challenge due to heterogeneity in space, morphology, and staining. In this regard, this paper presents a Histology-Guided Deep Learning Framework (HG-DLF) that involves multi-scale feature fusion, dual attention mechanisms, and graph convolutional networks to achieve accurate and robust TME analysis. Using the PanNuke dataset of 7,904 histology image patches across 19 tissue types, HG-DLF successfully segments tumor, immune, stromal, and nuclear structures. The model demonstrates a Dice Similarity Coefficient of 97.8%, an Intersection over Union (IoU) of 98.34%, and a Hausdorff Distance of 1.67—well surpassing baseline models, such as Deep CNNs and Her2Net, by more than 20% in accuracy and over 50% in inference time. The dual attention mechanism facilitates discriminative feature extraction, and the graph-based module leverages spatial context and tissue boundaries. The model demonstrates greater generalizability across folds in k-fold cross-validation and high accuracy despite morphological differences. HG-DLF offers an interpretable, scalable computational pathology solution with applications for estimating tumor heterogeneity, immune infiltration levels, and supporting clinical decision-making in precision oncology.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103957"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883547","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103936
Shenggang Zhu , Enzhong Wang , Fanfei Zeng
This paper develops an analytical modeling framework for load responsiveness for regional integrated energy systems to capture the coupled dynamics among electricity, thermal, and natural gas networks under dynamic pricing schemes. In contrast to conventional single-carrier demand response models, the proposed load response program includes cross-energy elasticities, inter-temporal coupling, and physical interdependencies among energy carriers using a unified economic-thermodynamic formulation. The behavior of the user is quantified for utility maximization theory and derived closed-form relationships between demand elasticity, price variation, and satisfaction functions. Furthermore, inter-carrier interaction coefficients represent substitution and co-generation effects between electricity and gas consumption. This couples the economic signals and physical system states. The resulting integrated load response model simultaneously yields consistency with both microeconomic rationality and the physics of multi-energy systems. To achieve optimal scheduling under this coupling, a multi-objective optimization framework is defined to minimize total operating cost, environmental impact, and reliability risk. Therefore, a novel entropy- and fuzzy logic-enhanced Multi-Objective Particle Swarm Optimization (EF-MOPSO) algorithm is developed to solve the high-dimensional nonconvex problem. The proposed EF-MOPSO introduces adaptive inertia control, entropy-based diversity metrics, and fuzzy-leader selection to balance convergence speed and solution dispersion. It ensures robust exploration of the Pareto front under stochastic uncertainties. The proposed model applies a time-of-use (TOU) based dynamic pricing structure for electricity and natural gas to stimulate demand-side flexibility. The electricity price is divided into three daily periods: off-peak (01:00–06:00), mid-peak (06:00–10:00 and 14:00–18:00), and peak (10:00–14:00 and 18:00–21:00), with multipliers of 0.75, 1.05, and 1.30, respectively, relative to the base tariff. The natural gas price is set at a baseline of 3.24 CNY/m3 and becomes responsive in the second scenario under a similar TOU pattern. This pricing mechanism encourages users to shift loads from high-cost peak periods to lower-cost off-peak periods, which improves operational flexibility and enables a 10% reduction in total system cost while preserving supply reliability.
{"title":"Offering an extensive multi-objective modeling strategy for optimizing load balancing in regional integrated energy systems","authors":"Shenggang Zhu , Enzhong Wang , Fanfei Zeng","doi":"10.1016/j.asej.2025.103936","DOIUrl":"10.1016/j.asej.2025.103936","url":null,"abstract":"<div><div>This paper develops an analytical modeling framework for load responsiveness for regional integrated energy systems to capture the coupled dynamics among electricity, thermal, and natural gas networks under dynamic pricing schemes. In contrast to conventional single-carrier demand response models, the proposed load response program includes cross-energy elasticities, inter-temporal coupling, and physical interdependencies among energy carriers using a unified economic-thermodynamic formulation. The behavior of the user is quantified for utility maximization theory and derived closed-form relationships between demand elasticity, price variation, and satisfaction functions. Furthermore, inter-carrier interaction coefficients represent substitution and co-generation effects between electricity and gas consumption. This couples the economic signals and physical system states. The resulting integrated load response model simultaneously yields consistency with both microeconomic rationality and the physics of multi-energy systems. To achieve optimal scheduling under this coupling, a multi-objective optimization framework is defined to minimize total operating cost, environmental impact, and reliability risk. Therefore, a novel entropy- and fuzzy logic-enhanced Multi-Objective Particle Swarm Optimization (EF-MOPSO) algorithm is developed to solve the high-dimensional nonconvex problem. The proposed EF-MOPSO introduces adaptive inertia control, entropy-based diversity metrics, and fuzzy-leader selection to balance convergence speed and solution dispersion. It ensures robust exploration of the Pareto front under stochastic uncertainties. The proposed model applies a time-of-use (TOU) based dynamic pricing structure for electricity and natural gas to stimulate demand-side flexibility. The electricity price is divided into three daily periods: off-peak (01:00–06:00), mid-peak (06:00–10:00 and 14:00–18:00), and peak (10:00–14:00 and 18:00–21:00), with multipliers of 0.75, 1.05, and 1.30, respectively, relative to the base tariff. The natural gas price is set at a baseline of 3.24 CNY/m<sup>3</sup> and becomes responsive in the second scenario under a similar TOU pattern. This pricing mechanism encourages users to shift loads from high-cost peak periods to lower-cost off-peak periods, which improves operational flexibility and enables a 10% reduction in total system cost while preserving supply reliability.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103936"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883549","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103947
Nengqi Zhang, Yihang Zhang, Jian Zhang
Multi-Automatic Guided Vehicle (AGVs) are core components of intelligent warehousing and logistics. In current deployments, storage locations are usually fixed, which decouples storage from vehicle scheduling and causes inefficient routing, congestion, and poor space utilization. The resulting coupled storage scheduling problem is NP-hard and challenging for classical optimization and heuristic methods.
This study proposes a deep reinforcement learning framework based on Multi-Task Proximal Policy Optimization (MTPPO) that jointly optimizes dynamic storage allocation and AGV dispatching in an end-to-end manner. The framework decomposes the joint decision into two coordinated subtasks, AGV assignment and storage location selection, and employs a hybrid CNN–GNN architecture to fuse local congestion patterns with global topological relationships in the warehouse. An adaptive action-masking mechanism and a Monte Carlo trajectory-level reward reconstruction scheme are introduced to enforce feasibility constraints and stabilize training.
Simulation studies on multi-AGV warehousing scenarios with varying grid sizes, obstacle densities, and task loads show that MTPPO shortens task completion time by about 10% and significantly reduces waiting-time variance compared with rule-based and metaheuristic baselines. In the storage dimension, the learned policy reduces actual transport and waiting time by an average of 8.7% relative to the best shortest-distance strategy, with gains rising to 11–18% under high-density obstacle layouts. These results demonstrate that jointly learning storage allocation and AGV scheduling yields more efficient, stable, and robust operation than optimizing each component in isolation. Also, the policy can scale to larger layouts and remain robust to moderate temporal noise. The proposed MTPPO framework thus provides a practical and scalable solution for dynamic storage space optimization in multi-AGV systems.
{"title":"Dynamic storage space optimization for multi-AGV systems: a multi-task proximal policy optimization approach","authors":"Nengqi Zhang, Yihang Zhang, Jian Zhang","doi":"10.1016/j.asej.2025.103947","DOIUrl":"10.1016/j.asej.2025.103947","url":null,"abstract":"<div><div>Multi-Automatic Guided Vehicle (AGVs) are core components of intelligent warehousing and logistics. In current deployments, storage locations are usually fixed, which decouples storage from vehicle scheduling and causes inefficient routing, congestion, and poor space utilization. The resulting coupled storage scheduling problem is NP-hard and challenging for classical optimization and heuristic methods.</div><div>This study proposes a deep reinforcement learning framework based on Multi-Task Proximal Policy Optimization (MTPPO) that jointly optimizes dynamic storage allocation and AGV dispatching in an end-to-end manner. The framework decomposes the joint decision into two coordinated subtasks, AGV assignment and storage location selection, and employs a hybrid CNN–GNN architecture to fuse local congestion patterns with global topological relationships in the warehouse. An adaptive action-masking mechanism and a Monte Carlo trajectory-level reward reconstruction scheme are introduced to enforce feasibility constraints and stabilize training.</div><div>Simulation studies on multi-AGV warehousing scenarios with varying grid sizes, obstacle densities, and task loads show that MTPPO shortens task completion time by about 10% and significantly reduces waiting-time variance compared with rule-based and metaheuristic baselines. In the storage dimension, the learned policy reduces actual transport and waiting time by an average of 8.7% relative to the best shortest-distance strategy, with gains rising to 11–18% under high-density obstacle layouts. These results demonstrate that jointly learning storage allocation and AGV scheduling yields more efficient, stable, and robust operation than optimizing each component in isolation. Also, the policy can scale to larger layouts and remain robust to moderate temporal noise. The proposed MTPPO framework thus provides a practical and scalable solution for dynamic storage space optimization in multi-AGV systems.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103947"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883451","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 : 2026-01-01DOI: 10.1016/j.asej.2025.103946
Intisar R. Saleh , B. Rafiei , K. Gharali , B. Sajadi
This study discusses the economic feasibility of investment in renewable energy comparing wind turbines (WT) and photovoltaic (PV) technology. Nine scenarios were compared according to economic indices namely Net Present Value (NPV), Payback Period (PBP), and Internal Rate of Return (IRR) using parameters like power efficiency, capital cost, and electricity tariff. By incorporating a 20 kW battery storage system to support night-time demand, and considering current market rates for investment, operation, and maintenance alongside local and international electricity tariffs, the study concludes that at an international electricity price of $0.10/kWh, wind turbine (WT) systems achieve an NPV of $43,674—approximately 1.5 times higher than that of photovoltaic (PV) systems ($28,764.5). In addition, the IRR for WT and PV were found to be 15.2 % and 16.7 %, respectively, suggesting that both technologies can be financially viable given favourable tariffs. The need for tariff reform, cost reduction, and efficiency enhancement to release renewable energy investment in Iraq is emphasized by these findings.
{"title":"Economic feasibility of solar and wind energy harvesting in Karbala, Iraq","authors":"Intisar R. Saleh , B. Rafiei , K. Gharali , B. Sajadi","doi":"10.1016/j.asej.2025.103946","DOIUrl":"10.1016/j.asej.2025.103946","url":null,"abstract":"<div><div>This study discusses the economic feasibility of investment in renewable energy comparing wind turbines (WT) and photovoltaic (PV) technology. Nine scenarios were compared according to economic indices namely Net Present Value (NPV), Payback Period (PBP), and Internal Rate of Return (IRR) using parameters like power efficiency, capital cost, and electricity tariff. By incorporating a 20 kW battery storage system to support night-time demand, and considering current market rates for investment, operation, and maintenance alongside local and international electricity tariffs, the study concludes that at an international electricity price of $0.10/kWh, wind turbine (WT) systems achieve an NPV of $43,674—approximately 1.5 times higher than that of photovoltaic (PV) systems ($28,764.5). In addition, the IRR for WT and PV were found to be 15.2 % and 16.7 %, respectively, suggesting that both technologies can be financially viable given favourable tariffs. The need for tariff reform, cost reduction, and efficiency enhancement to release renewable energy investment in Iraq is emphasized by these findings.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103946"},"PeriodicalIF":5.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883454","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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