Ain Shams Engineering Journal最新文献

{"title":"Interpretable fault diagnosis of coal-mine conveyor-belt idler bearings using multi-source fusion diagrams and mobile residual soft threshold–MobileViT","authors":"Wansheng Chen ,&nbsp;Jiaxing Shen ,&nbsp;Hu Zhu ,&nbsp;Ping Xu","doi":"10.1016/j.asej.2025.103777","DOIUrl":"10.1016/j.asej.2025.103777","url":null,"abstract":"<div><div>The fault diagnosis of conveyor belt idler bearings plays a crucial role in coal mine safety. In practical, inevitable challenges such as high-noise interference and small-sample conditions. Existing methods exhibit inadequate performance under the above conditions. This paper proposes a technique for fault diagnosis that integrates multisource fusion diagrams with a lightweight MRST-MobileViT model. To enhance fault feature representation, GADF, MDF, RP and CWT were employed to convert vibration signals into multi-source fusion diagram with richer fault information. A mobile residual soft thresholding module is incorporated into the MobileViT architecture to enhance the feature extraction capability. Verification through public datasets and self-constructed test data demonstrate that this method has a better diagnostic performance under high-noise and small-sample conditions. Furthermore, Grad-CAM heatmap analysis was performed to validate the interpretability of the model. This study provides an effective and reliable solution for the fault diagnosis of idler bearings in coal mines.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103777"},"PeriodicalIF":5.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pattern selection and parameter optimization design of reaction-diffusion information propagation model","authors":"Yingzi He ,&nbsp;Shuling Shen ,&nbsp;Linhe Zhu","doi":"10.1016/j.asej.2025.103764","DOIUrl":"10.1016/j.asej.2025.103764","url":null,"abstract":"<div><div>In modern society, with the rapid development of the Internet, the speed of information dissemination greatly increases. Information security has a profound impact on national and social safety. Therefore, the study of information dissemination is essential. Based on the traditional SIR(Susceptible-Infected-Recovered) model, a three-dimensional information transmission model is proposed. To understand the distribution of populations near the system’s equilibrium point, we combine image features and the definition of Turing instability. We derive the necessary conditions for Turing instability. To investigate Turing pattern morphology, amplitude equations are derived using the method of multiple scales. For continuous systems, optimal-control and parameter-identification frameworks are formulated. Numerical simulations assess parameter sensitivity. In numerical simulations, we analyze the sensitivity of different parameters in the continuous system. With the remaining parameters fixed, varying one parameter. This reveals how each parameter shapes the Turing patterns. This confirms that the model has strong robustness. Next, visualize the amplitude equations and identify the Turing patterns across parameter ranges. For effective control of information dissemination, an optimal-control–based parameter identification study applies three algorithms. These algorithms include the BB algorithm (Barzilai-Borwein algorithm), PG algorithm (Projected Gradient algorithm), and BFGS algorithm (Broyden-Fletcher-Goldfarb-Shanno algorithm). The convergence of the algorithms is analyzed, and their strengths and limitations are compared. The results show that the choice of algorithm significantly impacts the iteration outcomes. The BB algorithm works well for simple problems with few parameters. The PG algorithm is highly stable when dealing with fewer parameters but may face difficulties with more complex issues. On the other hand, the BFGS algorithm shows excellent robustness and achieves good convergence in multidimensional parameter spaces and complex problems. To validate the model’s applicability based on the data of COVID-19 tweets posted on Twitter in Indonesia, the least squares method and random walk fitting model are used, combined with the Data-Enhanced Residual Gradient Adjustment (DERGA) method for prediction. Thus, in real-life situations, we can control information dissemination by adjusting parameters like the decay rate and propagation rate through government measures and media interventions. It is important to achieve the target values of these parameters in order to control the spread of information, which is crucial for today’s information management.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103764"},"PeriodicalIF":5.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strength performance and leachability of cement-stabilised copper tailings as land reclamation geomaterial for sustainable copper tailings disposal","authors":"Marsheal Fisonga ,&nbsp;Yun Jiang ,&nbsp;Tingting Deng ,&nbsp;Yongfeng Deng ,&nbsp;Fei Wang ,&nbsp;Cryton Phiri ,&nbsp;Junjun Ni ,&nbsp;Qi Feng ,&nbsp;Muhammad Sani Bello","doi":"10.1016/j.asej.2025.103769","DOIUrl":"10.1016/j.asej.2025.103769","url":null,"abstract":"<div><div>This study focuses on the ultimate utilisation of copper tailings as a sustainable land reclamation geomaterial, simultaneously addressing the adverse environmental impacts that they cause and the scarcity of land resources. Unlike the limited approach of using copper tailings as partial replacements for natural fine aggregates in concrete, land reclamation demands far larger volumes of geomaterial. The primary objectives were to determine the optimal replacement ratio of natural fine aggregate with copper tailings, assess the development of mechanical and microstructural strength, and evaluate the leachability of heavy metals. The study also explored the enhancement of tensile strength through the incorporation of basalt fibre. Using Excel Solver, the optimal replacement ratios were determined for tailings from four different sources. Mechanical performance of cement-stabilised Dexing copper tailings was tested in compression, tension, and flexure, while microstructural properties were analysed using X-ray Diffraction and Scanning Electron Microscopy. Results showed optimal replacement rates of about 9 % for Dexing, Kakosa, and Mindola tailings, and 15 % for Muntimpa tailings. The 28-day compressive strength of Dexing tailings treated with 15 % cement exceeded the required 1.2 MPa for stockpile engineering applications. Microstructural analysis confirmed the formation of strength-contributing compounds, including calcium hydroxide, ettringite, and calcium silicate hydrate. Additionally, incorporating 0.5 % of 9 mm basalt fibres notably improved tensile strength. Toxicity characteristic leachability tests confirmed that heavy metal concentrations remained within safe limits for both drinking water and seawater (Category III). Therefore, this study concludes that using 100 % copper tailings in land reclamation offers a more sustainable solution than their limited use in concrete applications.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103769"},"PeriodicalIF":5.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient nitrogen recovery from domestic wastewater through struvite precipitation: Optimizing process parameters and characterization analysis","authors":"Raghda Hamdi ,&nbsp;Hassimi Abu Hasan ,&nbsp;Mohd Hafizuddin Muhamad ,&nbsp;Nur Aisyah Nazairi","doi":"10.1016/j.asej.2025.103760","DOIUrl":"10.1016/j.asej.2025.103760","url":null,"abstract":"<div><div>Recovery of nitrogen from domestic wastewater through struvite precipitation enables the circular economy with revenue generation to treatment plants. Struvite formation factors (pH, magnesium mass, and reagent type) were investigated in the present study. Struvite is formed via a reaction involving NH₄⁺-N, Mg, and P. Magnesium sources (MgCl₂, MgSO₄, MgO), pH (5–11), and magnesium mass (0.5–1.25 g) were identified using response surface methodology (RSM). The optimal condition was pH 8.53 with 1.13 g MgO, resulting in 97.0 % NH₃-N removal and 2.66 g struvite production. The quality of struvite was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), indicating an irregular rod-shaped morphology, 12.2 nm crystalline size, and 98.9 % crystallinity. The findings highlight the necessity to enhance nitrogen recovery efficiency for large-scale wastewater treatment considering the future potential of struvite.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103760"},"PeriodicalIF":5.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust open-set partial discharge diagnosis based on hybrid supervised contrastive learning and SVM framework","authors":"H.P.D.Shiran Madhuranga,&nbsp;Wong Jee Keen Raymond,&nbsp;Hazlee Azil Illias,&nbsp;Nurulafiqah Nadzirah Binti Mansor","doi":"10.1016/j.asej.2025.103762","DOIUrl":"10.1016/j.asej.2025.103762","url":null,"abstract":"<div><div>Automated partial discharge (PD) diagnosis using machine learning models is useful for high-voltage equipment (HVE) insulation condition monitoring. However, without a mechanism to identify unknown PD classes (defined as new classes not present in the training data), models will misclassify unknown classes as one of the known classes. To address this, a novel hybrid open-set recognition (OSR) framework based on Supervised Contrastive Learning (SupCon) is proposed to address a previously unexplored direction in the PD diagnosis domain. The framework integrates discriminative representation learning with both unified and per-class rejection strategies using one-class classification, enabling effective separation of known and unknown PD classes. The main contribution is the synergistic integration of SupCon for constructing structured latent spaces, SVM for precise closed-set classification, and dual OCSVMs for adaptive unknown rejection, together forming a unified pipeline that achieves both fine-grained discrimination and robust unknown detection. To evaluate the effectiveness of the proposed framework, comprehensive experiments are conducted across 30 OSR tasks, covering 12 PD classes from three types of high-voltage equipment under varying openness levels. The proposed framework is benchmarked against five state-of-the-art approaches, including ArcFace, GAN-Flow, a convolutional neural network (CNN), Autoencoder, and Vision Transformer. Experimental results demonstrate that the proposed framework achieved the best performance, with a mean normalized accuracy of 97.66 % and a Youden’s index of 0.953, confirming its robustness, generalization capability, and potential to advance open-set PD diagnostic methodologies.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103762"},"PeriodicalIF":5.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patented AI tool and method for evaluating building quality - Analysis of indoor environment and human comfort a case study","authors":"Meqdad Hamdan Hasan ,&nbsp;Othman S. Alshamrani ,&nbsp;Emhiedy S. Gharaibeh","doi":"10.1016/j.asej.2025.103756","DOIUrl":"10.1016/j.asej.2025.103756","url":null,"abstract":"<div><div>Ensuring indoor environmental quality and occupant comfort in buildings is critical to enhancing productivity and well-being, yet existing assessment methods often fail to integrate objective measurements (collected by sensors on an autonomous robot, representing measurable environmental parameters such as air quality and temperature) with subjective feedback (gathered via online surveys). This study addresses this gap by developing an autonomous tool for evaluating the quality of buildings and building systems. The objective is to compare the effectiveness of Bayesian Belief Networks, a novel artificial intelligence-based approach, with a classical Linear Additive Method that incorporates Analytic Hierarchy Process and Multi-Attribute Utility Theory. Data collection is achieved using an autonomous robot for objective measurements and Bluetooth-guided occupant surveys for subjective feedback. Two Bayesian Belief network models and one Linear Additive Method model were developed and evaluated using data from an educational building in the Eastern Province of Saudi Arabia. Results show that while the Bayesian Belief Networks algorithm requires more computational time, it effectively handles complexities in hybrid data and provides more reliable predictions for indoor environmental quality. The comparison revealed that one Bayesian Belief Networks closely matches Linear Additive Method results with a correlation coefficient of 0.92, while the other did not converge, highlighting challenges in model optimization. This novel framework offers significant potential for scalable and accurate building quality assessments across diverse building types and climates.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103756"},"PeriodicalIF":5.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Food safety risk analysis utilising K-lexicographic-max product of neutrosophic graph","authors":"M. Kaviyarasu ,&nbsp;M. Rajeshwari ,&nbsp;Nasreen Kausar ,&nbsp;Dragan Pamucar ,&nbsp;Vladimir Simic ,&nbsp;Nasser El-Kanj","doi":"10.1016/j.asej.2025.103761","DOIUrl":"10.1016/j.asej.2025.103761","url":null,"abstract":"<div><div>In this study, we introduce the concept of the <span><math><mi>K</mi></math></span>-Lexicographic Max Product (<span><math><mi>K</mi><mo>−</mo><mi>LMP</mi></math></span>) of neutrosophic graphs and explore its associated degree structure to enhance decision-making frameworks in food safety applications related to risk assessment, including freshness, contamination, and spoilage. Neutrosophic graphs, capable of handling indeterminacy, inconsistency, and incompleteness, provide a flexible mathematical foundation for modelling complex systems. By incorporating the <span><math><mi>K</mi><mo>−</mo><mi>LMP</mi></math></span> into neutrosophic graphs, we offer a novel approach to comparing and ranking food safety scenarios where multiple attributes and uncertain information coexist. We present example graphs and theorems related to <span><math><mi>K</mi><mo>−</mo><mi>LMP</mi></math></span> and further define the <span><math><mi>K</mi></math></span>-Lexicographic degree to quantify node significance within the context of neutrosophic graphs. To validate the practical utility of this approach, a food safety analysis is implemented, demonstrating how the model identifies critical control points and supports more robust, transparent decision-making under uncertainty. This work contributes to the advancement of neutrosophic graph theory and its interdisciplinary application in food quality and safety management.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103761"},"PeriodicalIF":5.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dip-DARK: A smart and innovative classifier for enhanced intrusion detection and security in heterogeneous IoT networks","authors":"Mani V.R. ,&nbsp;Vivekanandan P.","doi":"10.1016/j.asej.2025.103692","DOIUrl":"10.1016/j.asej.2025.103692","url":null,"abstract":"<div><div>Presently, significant research works focused on the design and development of security methods for protecting Heterogeneous Internet of Things (HetIoT) networks. Yet, the conventional approaches suffer with the problems of high processing time, lower accuracy, increased system designing complexity, and reduced efficiency. Therefore, in the proposed study, a novel and unique framework known as Dip-DARK—Dipper Throated Optimization integrated Deep Activation based Runge Kutta Classifier—is developed to safeguard the HetIoT network from potentially dangerous intrusions. Some of the well-known and most recent intrusion datasets, including as CIC-DDoS 2019, ToN-IoT, Edge-IIoT, and In-SDN, have been used for system development and validation. The proposed model is validated and tested by using these datasets. Then, to effectively shrink the dataset, the most important features are best selected using the Dipper Throated Optimization (DipTO) model, an intelligent optimization method. As a result, the Deep Activation based Runge Kutta (DARK) classifier was able to precisely predict the type of intrusion using the set of optimized features. Additionally, using a variety of performance measures, the proposed Dip-DARK model’s intrusion detection findings are evaluated and contrasted with current state-of-the-art model methodologies.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 11","pages":"Article 103692"},"PeriodicalIF":5.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical design and motion planning of a centipede-inspired robot","authors":"Ziqiang Zhang ,&nbsp;Xiaoshuo Liu ,&nbsp;Yiming Wang ,&nbsp;Zongtian Liu ,&nbsp;Jinnong Liao","doi":"10.1016/j.asej.2025.103754","DOIUrl":"10.1016/j.asej.2025.103754","url":null,"abstract":"<div><div>Motion planning for hyper-redundant robots in complex terrains has always been a research hotspot in robotics. The main difficulty faced by hyper-redundant robots lies in motion planning on complex terrains such as steps. This study took the centipede as the simulation object and developed a centipede-inspired robot (CeRob). The CeRob consists of 7 independent modules, where each inter-module joint provides two independently controllable DOFs (pitch and yaw). Each module is symmetrically equipped with two single degree-of-freedom (DOF) cam-linkage leg mechanisms, which provide good reliability while meeting trajectory requirements. Subsequently, a front-planning and rear-following motion planning method was proposed. By combining the established objective function and constraints for motion planning of the front joints and achieving smooth following of subsequent motions through the joints, the motion planning efficiency of the robot was improved. The robot designed in this study has broad application prospects in fields such as post-disaster rescue, planetary exploration, and military reconnaissance.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103754"},"PeriodicalIF":5.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic control mechanism and engineering practice of full-profile support in karst roadways under dual-wing mining disturbance","authors":"Bin Li ,&nbsp;Shoukun Chen ,&nbsp;Youlin Xu ,&nbsp;Bo Zhou ,&nbsp;Zhisong Chen ,&nbsp;Jitao Zhang","doi":"10.1016/j.asej.2025.103767","DOIUrl":"10.1016/j.asej.2025.103767","url":null,"abstract":"<div><div>With increasing mining depth and complex methods, roadways face severe geological challenges like intense deformation and soft rock layers. Support becomes especially difficult in karst regions, which feature fractures and cavities. This study analyzes mining methods, rock behavior, simulations, and field data to identify deformation causes, proving that combined high stress, gas, temperature, and mining disturbances compromise traditional support safety. Alternatively, an innovative full-profile spatial reinforcement technology is proposed, which effectively stabilizes weak, deformable roadways, enhancing long-term performance. The proposed integration of active and passive supports realized in the case study of Songhe Dongyi Mine, Guizhou, China, increased resistance, boosted rock self-bearing capacity, reduced deformation by 38.5 % and stress concentration factor more than twice (from 2.8 to 1.3), controlled floor heave (&lt;50 mm), and improved operation safety. The integrated approach was very effective in geologically complex mining environments, providing theoretically substantiated innovations and practical applications for karst terrain engineering.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103767"},"PeriodicalIF":5.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096579","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}