Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*, Changheun Oh⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077510 - 09 April 2026

Abstract The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for… More >

Suman Sukhavasi, Thinagaran Perumal^*, Norwati Mustapha, Razali Yaakob

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075613 - 12 March 2026

Abstract The Internet of Things (IoT) ecosystem is inherently heterogeneous, comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange. However, as the number of service requests grows, existing approaches suffer from increased discovery time and degraded Quality of Service (QoS). Moreover, the massive data generated by heterogeneous IoT devices often contain redundancy and noise, posing challenges to efficient data management. To address these issues, this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management. The architecture employs Modified-Ordered Points to Identify the Clustering Structure (M-OPTICS)… More >

Jinwei Mao^1,2, Wang Luo^1,2,*, Jiangtao Xu³, Daohua Zhu³, Wei Liang³, Zhechen Huang³, Bao Feng^1,2, Shuang Yang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.072505 - 12 March 2026

Abstract With the rapid development of power Internet of Things (IoT) scenarios such as smart factories and smart homes, numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency. Multi-access edge computing technology deploys cloud computing capabilities at the network edge; constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability. Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,… More >

Chun-Chieh Hu^1,*, Shuhan Zheng^1,2, Youjia Lin^1,2

International Journal of Mental Health Promotion, Vol.28, No.2, 2026, DOI:10.32604/ijmhp.2025.073744 - 27 February 2026

Abstract Objectives: In recent years, mental health has emerged as a pressing public health concern in China, driven by mounting societal pressures and fast-paced urban lifestyles. Physical activity, a well-established means of enhancing psychological well-being, has received growing scholarly and policy attention. This study uses panel data from the 2020 and 2022 waves of the China Family Panel Studies (CFPS) to examine the impact of exercise frequency on mental health (with indicators such as CESD-8 depression scores) among college students and young employees, thereby providing empirical support for targeted mental health interventions. Methods: This study examines the… More >

Adnan Ashique¹, Nehad Ali Shah¹, Usman Afzal¹, Yazen Alawaideh², Sohaib Abdal³, Jae Dong Chung^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.073292 - 26 February 2026

Abstract There is a need for accurate prediction of heat and mass transfer in aerodynamically designed, non-Newtonian nanofluids across aerodynamically designed, high-flux biomedical micro-devices for thermal management and reactive coating processes, but existing work is not uncharacteristically remiss regarding viscoelasticity, radiative heating, viscous dissipation, and homogeneous–heterogeneous reactions within a single scheme that is calibrated. This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation, thermal radiation, and homogeneous-heterogeneous reactions. The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary… More >

Borja Bordel Sánchez^*, Ramón Alcarria, Tomás Robles

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.072603 - 26 February 2026

Abstract In this paper, we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks. This system enables end nodes to select the optimum time and scheme to transmit private data safely. In 6G dynamic heterogeneous infrastructures, unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy. Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service (QoS). As the transport network is built of ad hoc nodes, there is no guarantee about their trustworthiness or behavior, and transversal functionalities are delegated to the extreme nodes. However, More >

Huihui Zhu¹, Fei Tang^2,*, Chunhua Jin³, Ping Wang¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073550 - 10 February 2026

Abstract The ubiquitous adoption of mobile devices as essential platforms for sensitive data transmission has heightened the demand for secure client-server communication. Although various authentication and key agreement protocols have been developed, current approaches are constrained by homogeneous cryptosystem frameworks, namely public key infrastructure (PKI), identity-based cryptography (IBC), or certificateless cryptography (CLC), each presenting limitations in client-server architectures. Specifically, PKI incurs certificate management overhead, IBC introduces key escrow risks, and CLC encounters cross-system interoperability challenges. To overcome these shortcomings, this study introduces a heterogeneous signcryption-based authentication and key agreement protocol that synergistically integrates IBC for client More >

Fankun Meng^1,2,3, Yuyang Liu^1,2,*, Xiaohua Liu⁴, Chenlong Duan^1,2, Yuhui Zhou^1,2,3

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.075865 - 06 February 2026

Abstract Carbonate gas reservoirs are often characterized by strong heterogeneity, complex inter-well connectivity, extensive edge or bottom water, and unbalanced production, challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior. To address these issues within a unified, data-driven framework, this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx, and is applicable to a wide range of reservoir types. The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support. The governing equations are solved using a Newton–Raphson scheme, while particle More > Graphic Abstract

Zhuofan Li¹, Daniel H. Pak², James S. Duncan², Liang Liang³, Minliang Liu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073757 - 29 January 2026

Abstract Patient-specific finite element analysis (FEA) is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo. However, inverse material property identification using FEA, which requires iteratively solving nonlinear hyperelasticity problems, is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians. In this study, we present an inverse material parameter identification strategy that integrates deep neural networks (DNNs) with FEA, namely inverse DNN-FEA. In this framework, a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution, which aims to reduce susceptibility to local optima… More >

Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >