Pub Date : 2026-02-01Epub Date: 2026-01-20DOI: 10.1016/j.asej.2026.103993
Jun Zhou, Yajun Duan
Life Cycle Assessment (LCA) often faces challenges such as low data transparency, limited traceability, and poor data sharing among entities. To address these issues, this study develops a blockchain based life cycle information management system that adopts a front end and back end separation architecture and integrates on chain and off chain collaborative storage. The system supports version tracking, auditing, and secure data sharing through unique identifiers, permission control, and smart contracts. Using bogie frame as a case study, it was validated under real data scenarios, showing zero error rates and high consistency in integrity and transparency. Performance tests indicated average response times of 30–60 ms and on chain delays of about 2.8 s, with stable operation at medium scale. The proposed framework enhances transparency, reliability, and efficiency, providing a scalable digital solution for integrating blockchain with LCA in sustainable manufacturing.
{"title":"Blockchain-enabled product life cycle assessment information management system","authors":"Jun Zhou, Yajun Duan","doi":"10.1016/j.asej.2026.103993","DOIUrl":"10.1016/j.asej.2026.103993","url":null,"abstract":"<div><div>Life Cycle Assessment (LCA) often faces challenges such as low data transparency, limited traceability, and poor data sharing among entities. To address these issues, this study develops a blockchain based life cycle information management system that adopts a front end and back end separation architecture and integrates on chain and off chain collaborative storage. The system supports version tracking, auditing, and secure data sharing through unique identifiers, permission control, and smart contracts. Using bogie frame as a case study, it was validated under real data scenarios, showing zero error rates and high consistency in integrity and transparency. Performance tests indicated average response times of 30–60 ms and on chain delays of about 2.8 s, with stable operation at medium scale. The proposed framework enhances transparency, reliability, and efficiency, providing a scalable digital solution for integrating blockchain with LCA in sustainable manufacturing.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103993"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023809","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}
Crest settlement is a key indicator of seismic deformation in rockfill dams. However, the absence of a dedicated seismic settlement database and the incomplete recording of key parameters hinder systematic assessments of seismic damage. To address these limitations, this study develops a comprehensive database documenting crest settlement and associated dam damage. A novel Data–Physics Hybrid-Driven (DPHD) imputation method is introduced to reconstruct missing parameters, and its accuracy is rigorously validated. Single-factor analyses elucidate the mechanisms governing crest settlement, whereas grey relational analysis identifies the dominant influencing factors—namely, dam resistance, seismic-acceleration intensity, near-field effects, and epicentral distance. Based on the database and analytical results, seismic-settlement control standards corresponding to different damage levels are further proposed. The results demonstrate that the DPHD method effectively resolves data gaps, and the derived settlement standards provide practical guidance for seismic design and settlement-mitigation strategies in rockfill dams.
{"title":"Safety analysis of rockfill dams based on crest seismic settlement with intelligent parameter imputation and grey relational analysis","authors":"Zhou Zheng , Jinjuan Li , Shixin Zhang , Mingcong Lv","doi":"10.1016/j.asej.2026.104006","DOIUrl":"10.1016/j.asej.2026.104006","url":null,"abstract":"<div><div>Crest settlement is a key indicator of seismic deformation in rockfill dams. However, the absence of a dedicated seismic settlement database and the incomplete recording of key parameters hinder systematic assessments of seismic damage. To address these limitations, this study develops a comprehensive database documenting crest settlement and associated dam damage. A novel Data–Physics Hybrid-Driven (DPHD) imputation method is introduced to reconstruct missing parameters, and its accuracy is rigorously validated. Single-factor analyses elucidate the mechanisms governing crest settlement, whereas grey relational analysis identifies the dominant influencing factors—namely, dam resistance, seismic-acceleration intensity, near-field effects, and epicentral distance. Based on the database and analytical results, seismic-settlement control standards corresponding to different damage levels are further proposed. The results demonstrate that the DPHD method effectively resolves data gaps, and the derived settlement standards provide practical guidance for seismic design and settlement-mitigation strategies in rockfill dams.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 104006"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023813","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 : 2026-02-01Epub Date: 2026-01-23DOI: 10.1016/j.asej.2026.103984
Abdulaziz S. Alaboodi , Vijipriya Jeyamani , Subbarayan Sivasankaran , Hany R. Ammar , Shahad A. Bin Shuqayr
Pollution of airborne dust particle poses serious challenges to the environment and human health, and the operational reliability of precision measurement laboratories, particularly in regions characterized by harsh climatic conditions. Accurate prediction of dust particle concentrations remains challenging due to complex nonlinear interactions among meteorological factors and the limited availability of fully labeled environmental datasets. Therefore, there is a critical need for advanced and robust modeling approaches that can improve the prediction accuracy while handling data uncertainty and sparsity. In this article, several regression analysis (models) and label propagation approaches have been applied and examined for predicting the airborne dust particle concentrations within the national measurement & calibration center (NMCC), saudi standards metrology and quality organization (SASO), Riyadh, Saudi Arabia. Six different models, namely, random forest regression (RFR), K-nearest neighbours regression (KNNR), cosine similarity-based label propagation regression (CS_LPR), adaptive fuzzy entropy-based label propagation regression (AFE_LPR), random forest-based label propagation (RF_LPR), and KNN-based label propagation (KNN_LPR) models were developed for forecasting the airborne dust particle levels and investigated the performance of each model. The airborne dust particles were experimentally counted using an advanced particle measurement technique by considering various factors, namely, air quality, environmental temperature, humidity, wind speed, and rainfall. The performance of the developed models was checked using different metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2, adjusted R2, mean absolute scaled error (MASE), and Huber loss. The results obtained from each model demonstrate that the label propagation models (CS_LPR, AFE_LPR, RF_LPR, and KNN_LPR) have exhibited well fitted one, and achieved excellent accuracy on the test data (R2 > 0.98) due to effective learning of the training data, including noise and specific patterns present in the dataset. The finding obtained through this research work emphasize that the label propagation methods can effectively address the prediction challenges in environmental monitoring tasks. This paper addresses the comparative performance and features of each approach in airborne dust particle prediction.
{"title":"Comparative analysis of regression and enhanced label propagation approaches for predicting airborne dust particle levels in environmental data","authors":"Abdulaziz S. Alaboodi , Vijipriya Jeyamani , Subbarayan Sivasankaran , Hany R. Ammar , Shahad A. Bin Shuqayr","doi":"10.1016/j.asej.2026.103984","DOIUrl":"10.1016/j.asej.2026.103984","url":null,"abstract":"<div><div>Pollution of airborne dust particle poses serious challenges to the environment and human health, and the operational reliability of precision measurement laboratories, particularly in regions characterized by harsh climatic conditions. Accurate prediction of dust particle concentrations remains challenging due to complex nonlinear interactions among meteorological factors and the limited availability of fully labeled environmental datasets. Therefore, there is a critical need for advanced and robust modeling approaches that can improve the prediction accuracy while handling data uncertainty and sparsity. In this article, several regression analysis (models) and label propagation approaches have been applied and examined for predicting the airborne dust particle concentrations within the national measurement & calibration center (NMCC), saudi standards metrology and quality organization (SASO), Riyadh, Saudi Arabia. Six different models, namely, random forest regression (RFR), K-nearest neighbours regression (KNNR), cosine similarity-based label propagation regression (CS_LPR), adaptive fuzzy entropy-based label propagation regression (AFE_LPR), random forest-based label propagation (RF_LPR), and KNN-based label propagation (KNN_LPR) models were developed for forecasting the airborne dust particle levels and investigated the performance of each model. The airborne dust particles were experimentally counted using an advanced particle measurement technique by considering various factors, namely, air quality, environmental temperature, humidity, wind speed, and rainfall. The performance of the developed models was checked using different metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R<sup>2</sup>, adjusted R<sup>2</sup>, mean absolute scaled error (MASE), and Huber loss. The results obtained from each model demonstrate that the label propagation models (CS_LPR, AFE_LPR, RF_LPR, and KNN_LPR) have exhibited well fitted one, and achieved excellent accuracy on the test data (R<sup>2</sup> > 0.98) due to effective learning of the training data, including noise and specific patterns present in the dataset. The finding obtained through this research work emphasize that the label propagation methods can effectively address the prediction challenges in environmental monitoring tasks. This paper addresses the comparative performance and features of each approach in airborne dust particle prediction.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103984"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023815","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 : 2026-02-01Epub Date: 2026-01-20DOI: 10.1016/j.asej.2025.103926
Etaf Alshawarbeh , I. Elbatal , Ehab M. Almetwally , Sule Omeiza Bashiru , Ibrahim Hassan Alkhairy , Lamis M. Alamoudi , Eslam Hussam , Ahmed M. Gemeay
Modern datasets often exhibit high skewness and non-monotonic hazard rate patterns. These features reveal a gap in many traditional distributions, which struggle to model such behavior accurately. This study introduces the inverse power Akshaya distribution (IPAkD) to address this limitation. The IPAkD is developed using the inverse power transformation and provides greater flexibility for modeling right-skewed data. It has closed-form probability density function and cumulative distribution function expressions, and its hazard rate can capture an upside-down bathtub-shaped pattern. Key properties such as moments, extropy, and order statistics are also presented. The model parameters were estimated using several methods. The IPAkD was applied to seven right-skewed real datasets and compared with twelve existing models using twelve evaluation measures. The findings show that the IPAkD offers a better fit and stronger practical performance, filling an important gap in modeling complex right-skewed datasets.
{"title":"Fitting right-skewed mechanical, medical, and geological data sets by a novel statistical model","authors":"Etaf Alshawarbeh , I. Elbatal , Ehab M. Almetwally , Sule Omeiza Bashiru , Ibrahim Hassan Alkhairy , Lamis M. Alamoudi , Eslam Hussam , Ahmed M. Gemeay","doi":"10.1016/j.asej.2025.103926","DOIUrl":"10.1016/j.asej.2025.103926","url":null,"abstract":"<div><div>Modern datasets often exhibit high skewness and non-monotonic hazard rate patterns. These features reveal a gap in many traditional distributions, which struggle to model such behavior accurately. This study introduces the inverse power Akshaya distribution (IPAkD) to address this limitation. The IPAkD is developed using the inverse power transformation and provides greater flexibility for modeling right-skewed data. It has closed-form probability density function and cumulative distribution function expressions, and its hazard rate can capture an upside-down bathtub-shaped pattern. Key properties such as moments, extropy, and order statistics are also presented. The model parameters were estimated using several methods. The IPAkD was applied to seven right-skewed real datasets and compared with twelve existing models using twelve evaluation measures. The findings show that the IPAkD offers a better fit and stronger practical performance, filling an important gap in modeling complex right-skewed datasets.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103926"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023358","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 : 2026-02-01Epub Date: 2026-01-08DOI: 10.1016/j.asej.2025.103918
Jingsong Duan , Zaiyi Pu
Intercity corporate networks have an important role in increasing enterprise efficiency and competitiveness. Therefore, they act as an engine for economic development and regional cooperation. The ability to predict and model them appropriately will lay the foundation for decision-making and optimization of the distribution of resources in such a way that effective communication across corporations is ensured. This paper presents an approach to the predictive modeling of a support vector regression to simulate the intercity corporation network. Precise data prediction and transmission across the network are very important for any simulated model to be deployed. Apart from optimization methods, the incorporation of meta-heuristic algorithms elevates the accuracy and speed of the forecast. This research investigates six optimization methods and their hybridization with SVR, with a critical investigation and comparison in terms of statistical performance metrics. It can be observed from the results that both the Manta-Ray Optimizer and Battle Royale Optimizer result in good performances with low error rates and high values of R and R2. In this regard, the Manta-Ray Optimizer is chosen as the final optimizer for the proposed hybrid algorithm since it had an R2 value of 0.9430 in the test data, followed by the Salp Swarm Optimization Algorithm with an R2 value of 0.9410, and the Battle Royale Optimizer with the lowest R2 value of 0.9320 observed for the test data.
{"title":"Integrating support vector regression and metaheuristic algorithms for novel prediction in simulating intercity corporation networks","authors":"Jingsong Duan , Zaiyi Pu","doi":"10.1016/j.asej.2025.103918","DOIUrl":"10.1016/j.asej.2025.103918","url":null,"abstract":"<div><div>Intercity corporate networks have an important role in increasing enterprise efficiency and competitiveness. Therefore, they act as an engine for economic development and regional cooperation. The ability to predict and model them appropriately will lay the foundation for decision-making and optimization of the distribution of resources in such a way that effective communication across corporations is ensured. This paper presents an approach to the predictive modeling of a support vector regression to simulate the intercity corporation network. Precise data prediction and transmission across the network are very important for any simulated model to be deployed. Apart from optimization methods, the incorporation of <em>meta</em>-heuristic algorithms elevates the accuracy and speed of the forecast. This research investigates six optimization methods and their hybridization with SVR, with a critical investigation and comparison in terms of statistical performance metrics. It can be observed from the results that both the Manta-Ray Optimizer and Battle Royale Optimizer result in good performances with low error rates and high values of R and R<sup>2</sup>. In this regard, the Manta-Ray Optimizer is chosen as the final optimizer for the proposed hybrid algorithm since it had an R<sup>2</sup> value of 0.9430 in the test data, followed by the Salp Swarm Optimization Algorithm with an R<sup>2</sup> value of 0.9410, and the Battle Royale Optimizer with the lowest R<sup>2</sup> value of 0.9320 observed for the test data.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103918"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928427","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 : 2026-02-01Epub Date: 2026-01-14DOI: 10.1016/j.asej.2025.103966
Hao Su , Ling Yin , Chaochao Qiu , Lijuan Zhang , Weicheng Lin , Xinyong Mao
High-precision machine tools are vital in modern manufacturing, yet their accuracy is often degraded by thermal errors. Traditional models lack cross-machine generalization and rely heavily on large labeled data. This paper proposes a thermal error modeling approach combining an encoder–decoder temporal convolutional network (ED-TCN) with representation subspace distance (RSD) transfer learning for cross-machine prediction. The encoder–decoder structure captures multi-scale features via dilated causal convolutions and residual blocks, enhancing long-term dependency modeling. The RSD-based domain adaptation reduces inter-machine distribution discrepancies while preserving feature scales. Through semi-supervised transfer learning, high-precision prediction is achieved using only 20% of labeled target data, greatly reducing collection costs. Experimental results on two different machine tools under three operating conditions demonstrate outstanding performance, achieving an R2 of 99.5%, an RMSE of 1.201 µm, and an MAE of 1.008 µm, thereby confirming the practicality and robustness of the proposed method.
{"title":"A novel transfer learning model based on ED-TCN and RSD domain adaptation for thermal error prediction of multiple machine tools","authors":"Hao Su , Ling Yin , Chaochao Qiu , Lijuan Zhang , Weicheng Lin , Xinyong Mao","doi":"10.1016/j.asej.2025.103966","DOIUrl":"10.1016/j.asej.2025.103966","url":null,"abstract":"<div><div>High-precision machine tools are vital in modern manufacturing, yet their accuracy is often degraded by thermal errors. Traditional models lack cross-machine generalization and rely heavily on large labeled data. This paper proposes a thermal error modeling approach combining an encoder–decoder temporal convolutional network (ED-TCN) with representation subspace distance (RSD) transfer learning for cross-machine prediction. The encoder–decoder structure captures multi-scale features via dilated causal convolutions and residual blocks, enhancing long-term dependency modeling. The RSD-based domain adaptation reduces inter-machine distribution discrepancies while preserving feature scales. Through semi-supervised transfer learning, high-precision prediction is achieved using only 20% of labeled target data, greatly reducing collection costs. Experimental results on two different machine tools under three operating conditions demonstrate outstanding performance, achieving an R<sup>2</sup> of 99.5%, an RMSE of 1.201 µm, and an MAE of 1.008 µm, thereby confirming the practicality and robustness of the proposed method.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103966"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979662","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 : 2026-02-01Epub Date: 2026-01-13DOI: 10.1016/j.asej.2025.103893
Umair Hussan , Sotdhipong Phichaisawat , Huaizhi Wang , Muhammad Ahsan Ayub , Muhammad Saqib Ali
The integration of intermittent renewable energy sources introduces significant operational uncertainties, challenging the economic efficiency and reliability of power systems. Centralized energy management strategies for multi-microgrid (MMG) networks face critical limitations in scalability, data privacy, and resilience to single-point failures. This study presents a scalable, privacy-preserving, and decentralized energy management framework for cooperative MMG networks to enhance operational efficiency and resilience. To achieve this, a novel consensus-based decentralized optimization algorithm is proposed, utilizing the Alternating Direction Method of Multipliers (ADMM), which decomposes the global optimal energy management problem into local subproblems that can be solved independently by each microgrid (MG). The method enables real-time coordination through iterative updates of local variables, consensus on power exchanges, and minimal information sharing—only power flows and dual variables between neighboring MGs. Simulation results on a modified 33-bus system with three interconnected MGs demonstrate that the proposed framework effectively balances supply and demand, optimizes energy storage utilization, and facilitates peer-to-peer energy trading, achieving lower operational costs and faster convergence compared to conventional ADMM, dual decomposition, consensus gradient, and proximal message passing methods. The proposed ADMM-based consensus framework provides a robust, scalable, and economically efficient solution for decentralized energy management in cooperative MMG systems.
{"title":"A novel consensus-based decentralized framework for optimal energy management in cooperative multi-microgrid networks using ADMM","authors":"Umair Hussan , Sotdhipong Phichaisawat , Huaizhi Wang , Muhammad Ahsan Ayub , Muhammad Saqib Ali","doi":"10.1016/j.asej.2025.103893","DOIUrl":"10.1016/j.asej.2025.103893","url":null,"abstract":"<div><div>The integration of intermittent renewable energy sources introduces significant operational uncertainties, challenging the economic efficiency and reliability of power systems. Centralized energy management strategies for multi-microgrid (MMG) networks face critical limitations in scalability, data privacy, and resilience to single-point failures. This study presents a scalable, privacy-preserving, and decentralized energy management framework for cooperative MMG networks to enhance operational efficiency and resilience. To achieve this, a novel consensus-based decentralized optimization algorithm is proposed, utilizing the Alternating Direction Method of Multipliers (ADMM), which decomposes the global optimal energy management problem into local subproblems that can be solved independently by each microgrid (MG). The method enables real-time coordination through iterative updates of local variables, consensus on power exchanges, and minimal information sharing—only power flows and dual variables between neighboring MGs. Simulation results on a modified 33-bus system with three interconnected MGs demonstrate that the proposed framework effectively balances supply and demand, optimizes energy storage utilization, and facilitates peer-to-peer energy trading, achieving lower operational costs and faster convergence compared to conventional ADMM, dual decomposition, consensus gradient, and proximal message passing methods. The proposed ADMM-based consensus framework provides a robust, scalable, and economically efficient solution for decentralized energy management in cooperative MMG systems.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103893"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979666","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 : 2026-02-01Epub Date: 2026-01-12DOI: 10.1016/j.asej.2025.103923
Liguo Han , Feitao Dong , Hengfei Xiao , Fei Ding , Lijuan Zhao , Peng Li , Chuanzong Li , Yue Zhou
To ensure accurate identification and control of coal and gangue during top-coal caving mining, this study proposes a multimodal information fusion method integrating vibration data, infrared images, and RGB images. The vibration signals were transformed into time–frequency spectrograms using the Continuous Wavelet Transform (CWT), and a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was employed for data augmentation to mitigate sample scarcity. Comparative experiments between early and late fusion strategies revealed that the late fusion approach based on the ResNet architecture yielded superior performance. With the optimal combination of vibration spectrograms (ResNet-18), infrared images (ResNet-50), and RGB images (ResNet-18), the model achieved a favorable balance between high accuracy and computational efficiency. Finally, a multi-domain co-simulation control system was developed for verification, demonstrating an average response time below 0.66 s under various rock-mixing ratio conditions. The proposed framework offers an effective technical solution for high-efficiency, clean coal production.
{"title":"Research on coal gangue identification based on multimodal fusion and multidomain collaborative simulation","authors":"Liguo Han , Feitao Dong , Hengfei Xiao , Fei Ding , Lijuan Zhao , Peng Li , Chuanzong Li , Yue Zhou","doi":"10.1016/j.asej.2025.103923","DOIUrl":"10.1016/j.asej.2025.103923","url":null,"abstract":"<div><div>To ensure accurate identification and control of coal and gangue during top-coal caving mining, this study proposes a multimodal information fusion method integrating vibration data, infrared images, and RGB images. The vibration signals were transformed into time–frequency spectrograms using the Continuous Wavelet Transform (CWT), and a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was employed for data augmentation to mitigate sample scarcity. Comparative experiments between early and late fusion strategies revealed that the late fusion approach based on the ResNet architecture yielded superior performance. With the optimal combination of vibration spectrograms (ResNet-18), infrared images (ResNet-50), and RGB images (ResNet-18), the model achieved a favorable balance between high accuracy and computational efficiency. Finally, a multi-domain co-simulation control system was developed for verification, demonstrating an average response time below 0.66 s under various rock-mixing ratio conditions. The proposed framework offers an effective technical solution for high-efficiency, clean coal production.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103923"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979667","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 : 2026-02-01Epub Date: 2026-01-22DOI: 10.1016/j.asej.2026.103994
Jiafu Su , Hongyu Liu , Yijun Chen , Lianxin Jiang , Na Zhang
This paper proposes FUCOMSort, a novel consensus-based multi-criteria sorting method that utilizes the Full COnsistency Method (FUCOM) to categorize alternatives into predefined categories. By reducing pairwise comparisons, it significantly improves sorting efficiency and classification model consistency. Specifically, the LPHFSs Dombi Weighted Average (LPHFDWA) operator is introduced to effectively aggregate expert information. In addition, a redefined hybrid centrality measure, combining trust relationships with K-core decomposition, is proposed to evaluate their substantial impact on the classification outcomes. A consensus-reaching process is further constructed using trust networks and regret theory, addressing conflicts in expert evaluations. FUCOM is extended to the LPHFSs environment, incorporating both subjective and objective weights through linear equations. The proposed FUCOMSort method reduces pairwise comparisons to just n-1, thereby substantially improving efficiency in large-scale multi-criteria sorting problems. To validate the proposed method, we applied it to the classification of green patent values and conducted sensitivity analysis and comparative analysis. The analysis results demonstrate that the method possesses strong robustness and effectiveness.
{"title":"A novel multi-criteria sorting method based on the linguistic polyhedral hesitant fuzzy consensus-reaching model","authors":"Jiafu Su , Hongyu Liu , Yijun Chen , Lianxin Jiang , Na Zhang","doi":"10.1016/j.asej.2026.103994","DOIUrl":"10.1016/j.asej.2026.103994","url":null,"abstract":"<div><div>This paper proposes FUCOMSort, a novel consensus-based multi-criteria sorting method that utilizes the Full COnsistency Method (FUCOM) to categorize alternatives into predefined categories. By reducing pairwise comparisons, it significantly improves sorting efficiency and classification model consistency. Specifically, the LPHFSs Dombi Weighted Average (LPHFDWA) operator is introduced to effectively aggregate expert information. In addition, a redefined hybrid centrality measure, combining trust relationships with K-core decomposition, is proposed to evaluate their substantial impact on the classification outcomes. A consensus-reaching process is further constructed using trust networks and regret theory, addressing conflicts in expert evaluations. FUCOM is extended to the LPHFSs environment, incorporating both subjective and objective weights through linear equations. The proposed FUCOMSort method reduces pairwise comparisons to just n-1, thereby substantially improving efficiency in large-scale multi-criteria sorting problems. To validate the proposed method, we applied it to the classification of green patent values and conducted sensitivity analysis and comparative analysis. The analysis results demonstrate that the method possesses strong robustness and effectiveness.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 2","pages":"Article 103994"},"PeriodicalIF":5.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023810","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 : 2026-02-01Epub Date: 2026-01-10DOI: 10.1016/j.asej.2025.103969
Ali Asghar , Khuram Ali Khan , Atiqe Ur Rahman , Marwan Ali Albahar , Asma Ibrahim Aleidi
Identifying a specific disease, especially based on common symptoms, can be challenging due to uncertainty and ambiguity, making similarity measures crucial for accurate diagnosis. Therefore, in this article, a novel structure, the similarity measures of a novel mathematical structure called complex picture fuzzy soft sets (cPFSS), is formulated. Firstly, two definitions of similarity measures for cPFSS are established: one based on the membership, non-membership, and neutral functions associated with the set, and the second one based on the distance measures of cPFSS. After that, an algorithm is suggested that is based on the proposed measures between cPFSS, and it is then applied to disease diagnosis. The computational complexity of the proposed algorithm shows that it takes a constant time, even in the case of a large dataset. Additionally, the outcomes show that one patient has the highest similarity with Tuberculosis, while another patient most closely matches Hepatitis.
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