Daniel Šolc^1,*, René Ivančák¹, Juraj Gazda¹, Eva Chovancová¹, Eugen Šlapák¹, Gabriel Bugár²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088224 - 15 September 2026

Abstract Autonomous mobile robots increasingly rely on three tightly coupled capabilities: low-latency wireless connectivity, edge-side compute acceleration, and simulation-based pre-training of perception and control models. Each has been studied extensively in isolation, but their joint deployment on a single platform remains rare. This paper presents an integrated framework combining a private 5G Stand-Alone (5G SA) access network, a Multi-Access Edge Computing (MEC) layer with adaptive offloading, and a digital-twin training pipeline in NVIDIA Omniverse Isaac Sim. It is realised on the Yahboom ROSMASTER R2 with an NVIDIA Jetson Orin NX, a Quectel RM530N-GL 5G modem in… More >

Shengxuan Dong, Xiongwei Li^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086535 - 15 September 2026

Abstract To address the conflict between navigation performance and safety constraints in safe reinforcement learning, this paper proposes Dual Stream-Analytic Hierarchy Process-Safe Soft Actor (DS-AHP-SAC), a safe soft actor-critic algorithm based on a dual-stream Q-network and dynamic Analytic Hierarchy Process (AHP) stratified experience replay. The algorithm achieves a balance between reward maximization and constraint satisfaction through three synergistic designs: (1) decoupling the Q-network into independent navigation and safety value streams to eliminate gradient interference at the Critic level and mitigate gradient competition at the Actor level; (2) constructing a three-criterion dynamic sampling strategy based on AHP, More >

Weiwei Li^*, Li Zhao, Chengshan Li, Wenjie Geng

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085768 - 15 September 2026

Abstract In multimodal recommendation, sparse implicit feedback can lead to noisy collaborative graphs, while long-tail and cold-start items often lack reliable collaborative signals. Textual and visual features provide useful item-side information, but they may also be incomplete or inconsistent across modalities. These issues make robust user and item representation learning difficult. To address them, we propose Diffusion Denoising and Generative Enhancement for Multimodal Recommendation (DDGEM). DDGEM first applies node-wise diffusion denoising in the latent collaborative space to reduce unreliable user–item signals. It then uses relational diffusion to reconstruct adaptive item–item relations instead of relying on fixed More >

Baoping Tian, Zhuxiao Wang^*, Jiahao Xue, Hong Wang, Ying Zhang, Yun Ju

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085661 - 15 September 2026

Abstract Deep reinforcement learning (DRL) has gained significant attention as an essential technology for constructing intelligent agents capable of handling high-dimensional visual observations in complex control environments. With the rapid development of knowledge transfer paradigms, reincarnating reinforcement learning (RRL) has emerged as a promising approach to accelerate policy convergence and alleviate the inefficiency of traditional tabula rasa training by reusing pre-trained teacher policies. However, existing RRL approaches primarily focus on improving knowledge transfer efficiency, while how student networks adaptively regulate and selectively utilize inherited representations during the teacher–student transition remains underexplored. As a result, student agents… More >

Nidhika Chauhan^1,*, Navneet Kaur¹, Jawad Khan², Younhyun Jung², Haleem Farman³, Ahmed Sedik^3,4, Sohaib Bin Altaf Khattak³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084063 - 15 September 2026

Abstract The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates… More >

Fei Bu¹, Zheng Wang^2,3,*, Yong Pan⁴, Zhaomin Wu¹, Yuchen Liang¹, Zhongshan Zhu⁴, Tengfei Tu⁵

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083956 - 15 September 2026

Abstract Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge,… More >

Yu-Chi Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083187 - 15 September 2026

Abstract Quantum logic reversible synthesis is a fundamental operation in quantum computing. One of the most challenging issues in this field resides in navigating the immense search space to synthesize the most compact circuit configurations, which are critical for realizing reliable, noise-free, and error-free quantum computing systems. To address this challenge, this study proposes a novel hypercube-encoded quantum-inspired optimization framework to formulate the synthesis task as a trajectory-finding process. This structure-informed domain knowledge transformation delivers exceptional search direction guidance, moving away from blind, black-box exploration. Specifically, by mapping the reversible functions onto the hypercube architecture, the… More >

Jumpei Hatakeyama^1,2,*, Takashi Fukue², Hidemi Shirakawa¹, Yasuhiro Sugimoto²

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.086397 - 31 August 2026

Abstract This paper describes a pin fin spacing designed to maximize forced convection heat transfer performance by combining pulsating flow with a pin fin array. With the recent miniaturization and high-density packaging of electronic equipment, thermal management has become a critical issue due to the increasing heat generation density. To address this challenge, we have focused on pulsating flow, commonly observed in blood circulation systems, and investigated its potential to enhance heat transfer. Previous studies have shown that pulsating flow enhances the overall heat transfer around heating elements and ribs by supplying low-temperature coolant to the… More >

Jialei Jiang¹, Jiayuan Luo^2,3,*, Yan Su¹, Weiqiang Xiao¹, Jiajun Lu⁴, Haodong Liu⁵, Yuqi Huang²

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.083123 - 31 August 2026

Abstract Quantitative measurement of the high-speed gas-liquid-solid multiphase jets generated during the transient discharge of high-pressure vessels remains a significant challenge. Traditional Particle Image Velocimetry (PIV) techniques often fail in these scenarios due to the intense self-luminous interference, high transient velocities, and the absence of pre-seeded tracer particles. To address these issues, this paper proposes a robust non-intrusive measurement scheme integrating an adaptive image enhancement strategy with an improved cross-correlation algorithm. First, a preprocessing framework combining Contrast Limited Adaptive Histogram Equalization (CLAHE) and frequency-domain high-pass filtering is developed to suppress background noise and reconstruct fluid textures… More >

Zeliang Liang^1,2, Jia Tan^1,*, Jiachao She², Fengnian Wang², Yi Wang², Donghao Li³, Rugang Duan³, Haotian Chu⁴

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.078050 - 31 August 2026

Abstract Significant wettability differences between proppants and coal matrices in deep coal reservoirs limit gas–water mass transfer, a key factor for coalbed methane recovery. This study develops a hybrid wettability fracture model using in-situ data, coupling Navier–Stokes equations with a phase-field method to simulate multi-scale gas–water flow. A random wettability mapping technique captures spatial heterogeneity. The critical capillary number (Ca)-balancing capillary and viscous forces-serves as the key threshold governing flow pathways. At low Ca, capillary forces dominate, causing liquid film aggregation and gas blockage; at high Ca, viscous forces break films and open pathways. Wettability gradients drive asynchronous More >