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  • Open Access

    REVIEW

    Empowering Underground Utility Tunnel Operation and Maintenance with Data Intelligence: Risk Factors, Prospects, and Challenges

    Jie Zou1,2, Ping Wu2,*, Jianwei Chen3, Weijie Fan2, Yidong Xu2

    Structural Durability & Health Monitoring, Vol.19, No.3, pp. 441-471, 2025, DOI:10.32604/sdhm.2024.058864 - 03 April 2025

    Abstract As an essential part of the urban infrastructure, underground utility tunnels have a long service life, complex structural performance evolution and dynamic changes both inside and outside the tunnel. These combined factors result in a wide variety of disaster risks during the operation and maintenance phase, which make risk management and control particularly challenging. This work first reviews three common representative disaster factors during the operation and maintenance period: settlement, earthquakes, and explosions. It summarizes the causes of disasters, key technologies, and research methods. Then, it delves into the research on the intelligent operation and More >

  • Open Access

    ARTICLE

    An Explainable Autoencoder-Based Feature Extraction Combined with CNN-LSTM-PSO Model for Improved Predictive Maintenance

    Ishaani Priyadarshini*

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 635-659, 2025, DOI:10.32604/cmc.2025.061062 - 26 March 2025

    Abstract Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries. However, traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions. This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations, such as transparency, fairness, and explainability, in artificial intelligence driven decision-making. The framework employs an Autoencoder for feature reduction, a Convolutional Neural Network for pattern recognition, and a Long Short-Term Memory network for temporal analysis.… More >

  • Open Access

    EDITORIAL

    Guest Editorial Special Issue on Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems

    Jipu Li1, Haidong Shao2,*, Yun Kong3, Zhuyun Chen4

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3609-3613, 2025, DOI:10.32604/cmc.2024.062183 - 17 February 2025

    Abstract This article has no abstract. More >

  • Open Access

    REVIEW

    Patterns in Heuristic Optimization Algorithms: A Comprehensive Analysis

    Robertas Damasevicius*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1493-1538, 2025, DOI:10.32604/cmc.2024.057431 - 17 February 2025

    Abstract Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering, economics, and computer science. These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions. While heuristic optimization algorithms vary in their specific details, they often exhibit common patterns that are essential to their effectiveness. This paper aims to analyze and explore common patterns in heuristic optimization algorithms. Through a comprehensive review of the literature, we identify the patterns that are commonly observed in these algorithms, including… More >

  • Open Access

    ARTICLE

    KHSRP promotes cancer stem cell maintenance, tumorigenesis, and suppresses anti-tumor immunity in gastric cancer

    YARU DU1,2,#, ZHIHUI PEI1,3,#, SHUQIN HU4, CHUANWEN LIAO1, SHUHAO LIU1,*

    Oncology Research, Vol.33, No.2, pp. 309-325, 2025, DOI:10.32604/or.2024.058273 - 16 January 2025

    Abstract Objectives: KH-type splicing regulatory protein (KHSRP) is an RNA-binding protein involved in several cellular processes, including nuclear splicing, mRNA localization, and cytoplasmic degradation. While KHSRP’s role has been studied in other cancers, its specific involvement in gastric cancer remains poorly understood. This study aims to explore KHSRP expression in gastric cancer and its potential effects on tumor progression and immune response. Methods: KHSRP expression in gastric cancer tissues and normal tissues was analyzed using data from The Cancer Genome Atlas (TCGA) database. The correlation between KHSRP expression, patient survival, and immune response was also assessed.… More >

  • Open Access

    ARTICLE

    Malfunction Diagnosis of the GTCC System under All Operating Conditions Based on Exergy Analysis

    Xinwei Wang1,2,*, Ming Li1, Hankun Bing1, Dongxing Zhang1, Yuanshu Zhang1

    Energy Engineering, Vol.121, No.12, pp. 3875-3898, 2024, DOI:10.32604/ee.2024.056237 - 22 November 2024

    Abstract After long-term operation, the performance of components in the GTCC system deteriorates and requires timely maintenance. Due to the inability to directly measure the degree of component malfunction, it is necessary to use advanced exergy analysis diagnosis methods to characterize the components’ health condition (degree of malfunction) through operation data of the GTCC system. The dissipative temperature is used to describe the degree of malfunction of different components in the GTCC system, and an advanced exergy analysis diagnostic method is used to establish a database of overall operating condition component malfunctions in the GTCC system.… More >

  • Open Access

    ARTICLE

    Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

    Yizhuo Zhu1, Shaosi He2, Deming Lei2,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 843-866, 2024, DOI:10.32604/cmc.2024.054473 - 15 October 2024

    Abstract Unrelated parallel machine scheduling problem (UPMSP) is a typical scheduling one and UPMSP with various real-life constraints such as additional resources has been widely studied; however, UPMSP with additional resources, maintenance, and energy-related objectives is seldom investigated. The Artificial Bee Colony (ABC) algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources, among other factors. In this study, an energy-efficient UPMSP with additional resources and maintenance is considered. A dynamical artificial bee colony (DABC) algorithm is presented to minimize makespan and total energy consumption… More >

  • Open Access

    REVIEW

    Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance: A Review

    Md Naeem Hossain1, Md Mustafizur Rahman1,2,*, Devarajan Ramasamy1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 951-996, 2024, DOI:10.32604/cmes.2024.056022 - 27 September 2024

    Abstract Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically. Hence, there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns. Due to vehicles’ increasingly complex and autonomous nature, there is a growing urgency to investigate novel diagnosis methodologies for improving safety, reliability, and maintainability. While Artificial Intelligence (AI) has provided a great opportunity in this area, a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis (VFD)… More > Graphic Abstract

    Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance: A Review

  • Open Access

    ARTICLE

    A Two-Stage Scenario-Based Robust Optimization Model and a Column-Row Generation Method for Integrated Aircraft Maintenance-Routing and Crew Rostering

    Khalilallah Memarzadeh1, Hamed Kazemipoor1,*, Mohammad Fallah1, Babak Farhang Moghaddam2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1275-1304, 2024, DOI:10.32604/cmes.2024.050306 - 27 September 2024

    Abstract Motivated by a critical issue of airline planning process, this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions. Following the route network scheme and generated flight timetables, aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management. This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints, rules, and regulations. Considering multiple locations for airline maintenance and crew bases, we solve the problem of integrated… More > Graphic Abstract

    A Two-Stage Scenario-Based Robust Optimization Model and a Column-Row Generation Method for Integrated Aircraft Maintenance-Routing and Crew Rostering

  • Open Access

    ARTICLE

    Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

    V. G. Saranya*, S. Karthik

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 127-150, 2024, DOI:10.32604/cmes.2024.053825 - 20 August 2024

    Abstract Wireless Sensor Networks (WSNs) are a collection of sensor nodes distributed in space and connected through wireless communication. The sensor nodes gather and store data about the real world around them. However, the nodes that are dependent on batteries will ultimately suffer an energy loss with time, which affects the lifetime of the network. This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability. The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization (MFOA-EACO), where the Mayfly Optimization Algorithm (MFOA) is used to… More >

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