Miguel A. Millán^1,*, Rubén A. Galindo², Fausto Molina-Gómez¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2923-2941, 2025, DOI:10.32604/cmes.2025.069356 - 30 September 2025

Abstract Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes, resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns. This complexity poses significant challenges for slope stability analysis, requiring the development of specialized techniques to address these issues. This research presents a numerical methodology that incorporates spatial variability, nonlinear material characterization, and probabilistic analysis using a Monte Carlo framework to address this issue. The heterogeneous structure is represented by randomly assigning different lithotypes across the slope, while maintaining predefined global proportions. This contrasts with the more common approach… More >

Yiming Guo, Hongyu Ma^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3485-3505, 2025, DOI:10.32604/cmc.2025.067590 - 23 September 2025

Abstract In dynamic 5G network environments, user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching. Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device (D2D) cooperative caching, limiting the reduction of transmission latency. To address this issue, this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning. First, a Transformer-based geolocation prediction model is designed, leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.… More >

Chuan Yuan¹, Chang Liu^2,3, Shijun Chen¹, Weiting Xu^2,3, Jing Gou¹, Ke Xu^2,3, Zhengbo Li^4,*, Youbo Liu⁴

Energy Engineering, Vol.122, No.9, pp. 3573-3593, 2025, DOI:10.32604/ee.2025.067785 - 26 August 2025

Abstract The increasing penetration of second-life battery energy storage systems (SLBESS) in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries. This paper presents a novel model-free adaptive voltage control-embedded dung beetle-inspired heuristic optimization algorithm for optimal SLBESS capacity configuration and power dispatch. To simultaneously address the computational complexity and ensure system stability, this paper develops a comprehensive bilevel optimization framework. At the upper level, a dung beetle optimization algorithm determines the optimal SLBESS capacity configuration by minimizing total lifecycle costs while incorporating… More >

Yuhan Wang, Yimei Meng, Wanqiu Xia, Yusen Liang, Yaru Wang, Peiling Li^*, Lei Fang^*

Oncology Research, Vol.33, No.8, pp. 2141-2159, 2025, DOI:10.32604/or.2025.064367 - 18 July 2025

Abstract Background: Ovarian cancer (OC) is a representative malignancy of the female reproductive system, with a poor prognosis. Long non-coding RNAs (lncRNAs) crucially affect tumor development. This study aimed to identify lncRNAs that potentially participated in OC. Methods: LncRNA expression in cells and tissues was quantified using reverse transcription-quantitative PCR, while fluorescence in situ hybridization determined their cellular localization. Various in vitro assays, together with a mouse xenograft model, were employed to elucidate the function of CYMP antisense RNA 1 (CYMP-AS1) in OC. The molecular mechanisms underlying CYMP-AS1 regulation were investigated through RNA pull-down and immunoprecipitation assays, immunofluorescence… More > Graphic Abstract

Mohammad Tabrez Quasim^1,*, Khair Ul Nisa¹, Mohammad Shahid Husain², Abakar Ibraheem Abdalla Aadam¹, Mohammed Waseequ Sheraz¹, Mohammad Zunnun Khan¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1169-1187, 2025, DOI:10.32604/cmc.2025.062989 - 09 June 2025

Abstract Smart edge computing (SEC) is a novel paradigm for computing that could transfer cloud-based applications to the edge network, supporting computation-intensive services like face detection and natural language processing. A core feature of mobile edge computing, SEC improves user experience and device performance by offloading local activities to edge processors. In this framework, blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers, protecting against potential security threats. Additionally, Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically. IoT applications that require significant resources… More >

Peiying Zhang^1,2,*, Yihong Yu^1,2, Jing Liu³, Chong Lv^1,2, Lizhuang Tan^4,5, Yulin Zhang^6,7,8

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4393-4409, 2025, DOI:10.32604/cmc.2025.064654 - 19 May 2025

Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… More >

Hayder Faeq Alhashimi¹, Mhd Nour Hindia¹, Kaharudin Dimyati^1,*, Effariza Binti Hanafi¹, Feras Zen Alden², Faizan Qamar³, Quang Ngoc Nguyen^4,5,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3585-3622, 2025, DOI:10.32604/cmc.2025.062867 - 19 May 2025

Abstract The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent… More >

Xiaolin Li¹, Xinyue Li¹, Liming Zhou^2,*, Hangran Yang¹, Xiaoqing Yuan¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3821-3841, 2025, DOI:10.32604/cmc.2025.059937 - 06 March 2025

Abstract Magneto-electro-elastic (MEE) materials are widely utilized across various fields due to their multi-field coupling effects. Consequently, investigating the coupling behavior of MEE composite materials is of significant importance. The traditional finite element method (FEM) remains one of the primary approaches for addressing such issues. However, the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision, which inevitably leads to a reduction in computational efficiency. To enhance the computational accuracy and efficiency of the FEM for heterogeneous… More >

Hao Li^1,#, Kuan Shao^1,#, Xin Wang², Mufeng Wang³, Zhenyong Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5387-5405, 2025, DOI:10.32604/cmc.2025.059718 - 06 March 2025

Abstract Due to the development of cloud computing and machine learning, users can upload their data to the cloud for machine learning model training. However, dishonest clouds may infer user data, resulting in user data leakage. Previous schemes have achieved secure outsourced computing, but they suffer from low computational accuracy, difficult-to-handle heterogeneous distribution of data from multiple sources, and high computational cost, which result in extremely poor user experience and expensive cloud computing costs. To address the above problems, we propose a multi-precision, multi-sourced, and multi-key outsourcing neural network training scheme. Firstly, we design a multi-precision More >

Jeta Dobruna^1,2, Zana Limani Fazliu^2,*, Iztok Humar¹, Mojca Volk¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2211-2245, 2025, DOI:10.32604/cmes.2025.059541 - 03 March 2025

Abstract Resource allocation remains a challenging issue in communication networks, and its complexity is continuously increasing with the densification of the networks. With the evolution of new wireless technologies such as Fifth Generation (5G) and Sixth Generation (6G) mobile networks, the service level requirements have become stricter and more heterogeneous depending on the use case. In this paper, we review a large body of literature on various resource allocation schemes that are used in particular in mobile wireless communication networks and compare the proposed schemes in terms of performance indicators as well as techniques used. Our… More >