Guangyu Huo, Chang Su, Xiaoyu Zhang^*, Xiaohui Cui, Lizhong Zhang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.072147 - 09 December 2025

Abstract Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space More >

Yali Cao¹, Weijian Hu^1,2, Lingfang Li^1,*, Minchao Li¹, Meng Xu², Ke Han²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069373 - 09 December 2025

Abstract Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems (ITS), playing a pivotal role in mitigating congestion, enhancing route optimization, and improving the utilization efficiency of roadway infrastructure. However, existing methods struggle in complex traffic scenarios due to static spatio-temporal embedding, restricted multi-scale temporal modeling, and weak representation of local spatial interactions. This study proposes Bi-STAT+, an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions: (1) an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations; (2) frequency-domain analysis in the temporal dimension for… More >

Zitong Zhao¹, Zixuan Zhang², Zhenxing Niu^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069752 - 10 November 2025

Abstract Reliable traffic flow prediction is crucial for mitigating urban congestion. This paper proposes Attention-based spatiotemporal Interactive Dynamic Graph Convolutional Network (AIDGCN), a novel architecture integrating Interactive Dynamic Graph Convolution Network (IDGCN) with Temporal Multi-Head Trend-Aware Attention. Its core innovation lies in IDGCN, which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs, and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data. For 15- and 60-min forecasting on METR-LA, AIDGCN achieves MAEs of 0.75% and 0.39%, and RMSEs More >

Jiqiang Xia¹, Qi Zhan¹, Le Tian^1,2,3,*, Yuxiang Hu^1,2,3, Jianhua Peng⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069533 - 10 November 2025

Abstract The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency, high-throughput communication, necessitating frequent and flexible updates to network routing configurations. However, maintaining consistent forwarding states during these updates is challenging, particularly when rerouting multiple flows simultaneously. Existing approaches pay little attention to multi-flow update, where improper update sequences across data plane nodes may construct deadlock dependencies. Moreover, these methods typically involve excessive control-data plane interactions, incurring significant resource overhead and performance degradation. This paper presents P4LoF, an efficient loop-free update approach that enables the controller to reroute multiple flows through More >

Amina Mahreen¹, Fateh Mebarek-Oudina^2,3,4,*, Amna Ashfaq¹, Jawad Raza¹, Sami Ullah Khan⁵, Hanumesh Vaidya⁶

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2829-2853, 2025, DOI:10.32604/fdmp.2025.072492 - 01 December 2025

Abstract Understanding the complex interaction between heat and mass transfer in non-Newtonian microflows is essential for the development and optimization of efficient microfluidic and thermal management systems. This study investigates the magnetohydrodynamic (MHD) thermosolutal convection of a Casson fluid within an inclined, porous microchannel subjected to convective boundary conditions. The nonlinear, coupled equations governing momentum, energy, and species transport are solved numerically using the MATLAB bvp4c solver, ensuring high numerical accuracy and stability. To identify the dominant parameters influencing flow behavior and to optimize transport performance, a comprehensive hybrid optimization framework—combining a modified Taguchi design, Grey… More >

Yuxin Wang¹, Youjiang Wang², Jieping Wang², Chao Zhang^1,*, Fanguang Meng³, Linhua Zhang¹, Yongxing Song^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2761-2777, 2025, DOI:10.32604/fdmp.2025.072236 - 01 December 2025

Abstract High-pressure water jet technology has emerged as a highly effective method for removing industrial-scale deposits from pipelines, offering a clean, efficient, and environmentally sustainable alternative to conventional mechanical or chemical cleaning techniques. Among the many parameters influencing its performance, the geometry of the nozzle plays a decisive role in governing jet coherence, impact pressure distribution, and overall cleaning efficiency. In this study, a comprehensive numerical and experimental investigation is conducted to elucidate the influence of nozzle geometry on the behavior of high-pressure water jets. Using Computational Fluid Dynamics (CFD) simulations based on the Volume of… More >

Zhenzhen Shen^1,2, Kang Yang², Dengfeng Wei², Quansheng Liang², Zhenpeng Ma², Hong Wang², Keyu Wang², Chunwei Zhang², Xiaohu Yang^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2741-2760, 2025, DOI:10.32604/fdmp.2025.070395 - 01 December 2025

Abstract The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media. This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic modeling with deep learning. Porous media structures were generated using the Quartet Structure Generation Set (QSGS) method, and a mesoscopic-scale seepage calculation model was applied to compute flow rates. These results were then used to train a deep learning model for rapid prediction. The analysis shows that larger average pore diameters lead to higher internal flow velocities and mass flow More >

Ratan Kumar Das^1,*, Abhijit Date¹, Harun Chowdhury¹, Hamed Hassan²

Energy Engineering, Vol.122, No.12, pp. 4815-4835, 2025, DOI:10.32604/ee.2025.071727 - 27 November 2025

Abstract Bangladesh has achieved notable progress in expanding electricity access nationwide. Nonetheless, remote and topographically challenging regions such as the Chittagong Hill Tracts (CHT) continue to face coverage gaps due to grid extension difficulties. This research investigates the technical feasibility of micro-hydro power (MHP) systems as viable off-grid solutions for rural electrification in CHT. Field surveys conducted across various sites assessed available head and flow rates using GPS-based elevation measurements and portable flow meters. Seasonal fluctuations were factored into the analysis to ensure year-round operational viability. The study involved estimating power output, selecting appropriate turbine types… More > Graphic Abstract

Faiza Benabdallah¹, Kaouther Ghachem¹, Walid Hassen², Haythem Baya², Hind Albalawi³, Lioua Kolsi^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1839-1861, 2025, DOI:10.32604/cmes.2025.071678 - 26 November 2025

Abstract Current developments in magnetohydrodynamic (MHD) convection and nanofluid engineering technology have have greatly enhanced heat transfer performance in process systems, particularly through the use of carbon nanotube (CNT)–based fluids that offer exceptional thermal conductivity. Despite extensive research on MHD natural convection in enclosures, the combined effects of complex obstacle geometries, magnetic fields, and CNT nanofluids in three-dimensional configurations remain insufficiently explored. This research investigates MHD natural convection of carbon nanotube (CNT)-water nanofluid within a three-dimensional cavity. The study considers an inclined cross-shaped hot obstacle, a configuration not extensively explored in previous works. The work aims… More >

Jian Xia^1,2, Xianzhong Hu¹, Yan Li¹, Wei Zhang^3,*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1489-1506, 2025, DOI:10.32604/sdhm.2025.065997 - 17 November 2025

Abstract Incorporating microencapsulated phase change materials (MPCM) into mortar enhances building thermal energy storage for energy savings but severely degrades compressive strength by replacing sand and creating pores. This study innovatively addresses this critical limitation by introducing nano-silicon (NS) as a modifier to fill pores and promote hydration in MPCM mortar. Twenty-five mixes with varying NS content from 0 to 4 weight percent and different MPCM contents were comprehensively tested for flowability, compressive strength, thermal conductivity, thermal energy storage via Differential Scanning Calorimetry, and microstructure via Scanning Electron Microscopy. Key quantitative results showed MPCM reduced mortar… More >