Sk. A. Shezan^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.073418 - 27 December 2025

Abstract Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization. However, the high penetration of intermittent renewable sources often causes frequency deviations, voltage fluctuations, and poor reactive power coordination, posing serious challenges to grid stability. Conventional Interconnection Flow Controllers (IFCs) primarily regulate active power flow and fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks. To overcome these limitations, this study proposes an enhanced Interconnection Flow Controller (e-IFC) that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller (IRFC) within a unified adaptive… More >

Yechun Xin¹, Xinyuan Zhao¹, Dong Ding², Shuyu Chen², Chuanjie Wang², Tuo Wang^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069748 - 27 December 2025

Abstract To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current (HVDC) links and multi-infeed DC systems in load-center regions, this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter (MMC-CLCC) HVDC transmission system and its corresponding control strategy. First, the system topology is constructed, and a submodule configuration method for the MMC—combining full-bridge submodules (FBSMs) and half-bridge submodules (HBSMs)—is proposed to enable direct power flow reversal. Second, a hierarchical control strategy is introduced, including MMC voltage control, CLCC current control, and a coordination mechanism, along with the derivation of… More >

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 >

Tianen Liu^1,2, Kun Dai^1,2, Shiju Ren^1,2, Chuanxiang Zhang^1,2, Xiaoling Tang^3,*, Jinghong Hu^3,*, Yidong Cai³, Jun Lu³

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 2981-2995, 2025, DOI:10.32604/fdmp.2025.073859 - 31 December 2025

Abstract Many mature onshore oilfields have entered a high-water-cut stage, with reservoir recovery approaching economic limits. Converting these depleted or nearly depleted reservoirs into underground gas storage (UGS) facilities offers an efficient way to leverage their substantial storage potential. During cyclic gas injection and withdrawal, however, the reservoir experiences complex three-phase flow and repeated stress fluctuations, which can induce rock fatigue, inelastic deformation, and ultimately sand production. This study uses controlled physical experiments to simulate sand production in reservoir rocks subjected to alternating gas injection and production under three-phase conditions. After preparing oil-water-saturated cores through waterflooding,… More > Graphic Abstract

Sheng Lei^1,2,3, Guanglong Sheng^1,2,3,*, Hui Zhao^1,2,3

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 3011-3031, 2025, DOI:10.32604/fdmp.2025.073802 - 31 December 2025

Abstract This study investigates the unsteady flow characteristics of shale oil reservoirs during the depletion development process, with a particular focus on production behavior following fracturing and shut-in stages. Shale reservoirs exhibit distinctive production patterns that differ from traditional oil reservoirs, as their inflow performance does not conform to the classic steady-state relationship. Instead, production is governed by unsteady-state flow behavior, and the combined effects of the wellbore and choke cause the inflow performance curve to evolve dynamically over time. To address these challenges, this study introduces the concept of a “Dynamic IPR curve” and develops… More >

Zhengqing Zhang^1,2,3,*, Xiaojun Yuan², Hui Wu²

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 2927-2943, 2025, DOI:10.32604/fdmp.2025.071115 - 31 December 2025

Abstract Frost accumulation on the evaporator fins of air source heat pumps (ASHPs) severely degrades heat transfer performance and overall system efficiency. To address this, the present study employs computational fluid dynamics (CFD) to investigate how fin spacing influences frosting behavior, emphasizing the coupled evolution of frost thickness, density, airflow, and temperature distribution within fin channels. Results reveal that fin spacing is a key parameter governing both the extent and rate of frost growth. Wider fin spacing enhances frost accumulation, with a final frost mass of 6.41 g at 12 mm, about 71.8% higher than at More >

Xiaoqin Zheng¹, Yuqing Wan¹, Qian Zhang², Liqin He², Shihao Tang¹, Qiguo Zhuang², Lihua Wang^2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3879-3892, 2025, DOI:10.32604/phyton.2025.073229 - 29 December 2025

Abstract To investigate endogenous hormone changes in “Hongyang” kiwifruit from overwintering buds to floral morphogenesis (bell-shaped flowering stage), systematic observations were conducted during the undifferentiated stage, axillary bud differentiation stage, and floral morphogenesis stage from late November 2023 to early April 2024. Paraffin sectioning was employed to examine floral bud morphology, while LC-MS targeted metabolomics quantified changes in 15 endogenous hormones across 8 classes. Results indicated floral bud differentiation commenced from late January to early February and concluded by mid-April, spanning approximately 70 days. Approximately 33 days after axillary bud initiation marked the axillary bud primordium… More >