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 >

Samira Rastbod¹, Mehdi Jahangiri^2,*, Behrang Moradi¹, Haleh Nazari¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070089 - 27 December 2025

Abstract Curtain wall systems have evolved from aesthetic façade elements into multifunctional building envelopes that actively contribute to energy efficiency and climate responsiveness. This review presents a comprehensive examination of curtain walls from an energy-engineering perspective, highlighting their structural typologies (Stick and Unitized), material configurations, and integration with smart technologies such as electrochromic glazing, parametric design algorithms, and Building Management Systems (BMS). The study explores the thermal, acoustic, and solar performance of curtain walls across various climatic zones, supported by comparative analyses and iconic case studies including Apple Park, Burj Khalifa, and Milad Tower. Key challenges—including… More > Graphic Abstract

Jin Lin^1,*, Bin Yu², Chao Chen¹, Jiezhen Cai¹, Yifan Wu², Cunping Wang³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069310 - 27 December 2025

Abstract With the increasing integration of renewable energy, microgrids are increasingly facing stability challenges, primarily due to the lack of inherent inertia in inverter-dominated systems, which is traditionally provided by synchronous generators. To address this critical issue, Virtual Synchronous Generator (VSG) technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators. To enhance the operational efficiency of virtual synchronous generators (VSGs), this study employs small-signal modeling analysis, root locus methods, and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency… More > Graphic Abstract

Yuang Chen^1,2, Yong Li^1,*, Fang Lin^1,2, Shuhan Lv^1,2, Jiaze Jiang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070990 - 09 December 2025

Abstract Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet, this paper proposes a novel lightweight neural network model called ResghostNet. This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks, which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations. Specifically, ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow, and designs a weight self-attention mechanism combined with SE blocks to enhance feature More >

Muhammad Waqar Khan^1,*, Adnan Ahmed Siddiqui¹, Syed Sajjad Hussain Rizvi²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070858 - 09 December 2025

Abstract The multi-objective optimization problems, especially in constrained environments such as power distribution planning, demand robust strategies for discovering effective solutions. This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization (MCCMO) Algorithm, termed Adaptive Diversity Preservation (ADP). This enhancement is primarily focused on the improvement of constraint handling strategies, local search integration, hybrid selection approaches, and adaptive parameter control. The improved variant was experimented on with the RWMOP50 power distribution system planning benchmark. As per the findings, the improved variant outperformed the original MCCMO across the eleven performance metrics, particularly in terms… More >

Longyue Li¹, Guoqing Zhang¹, Bo Cao¹, Shuqi Wang², Ye Tian^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.070622 - 09 December 2025

Abstract Modern battlefields exhibit high dynamism, where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values, leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command, where static weights cannot reflect the operational value decay or surge of key indicators. To address this issue, this study proposes a dynamic adaptive weighting method for evaluation indicators based on G1-CRITIC-PIVW. First, the G1 (Sequential Relationship Analysis Method) subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via… More >

Abu Tayab^1,*, Yanwen Li¹, Ahmad Syed², Ghanshyam G. Tejani^3,4,*, Doaa Sami Khafaga⁵, El-Sayed M. El-kenawy⁶, Amel Ali Alhussan⁷, Marwa M. Eid^8,9

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-27, 2026, DOI:10.32604/cmc.2025.070583 - 09 December 2025

Abstract Autonomous connected vehicles (ACV) involve advanced control strategies to effectively balance safety, efficiency, energy consumption, and passenger comfort. This research introduces a deep reinforcement learning (DRL)-based car-following (CF) framework employing the Deep Deterministic Policy Gradient (DDPG) algorithm, which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning. Utilizing real-world driving data from the highD dataset, the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios. The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control (MPC-ACC) controller. Results show that the… More >

Yuqiang Wu^1,2, Zhao Ji¹, Guanqi You¹, Zihan Zhang¹, Chaoping Lu³, Huanliang Xu¹, Zhaoyu Zhai^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.070414 - 09 December 2025

Abstract Understanding fish movement trajectories in aquaculture is essential for practical applications, such as disease warning, feeding optimization, and breeding management. These trajectories reveal key information about the fish’s behavior, health, and environmental adaptability. However, when multi-object tracking (MOT) algorithms are applied to the high-density aquaculture environment, occlusion and overlapping among fish may result in missed detections, false detections, and identity switching problems, which limit the tracking accuracy. To address these issues, this paper proposes FishTracker, a MOT algorithm, by utilizing a Tracking-by-Detection framework. First, the neck part of the YOLOv8 model is enhanced by introducing… More >

Wei Liu^1,*, Ruiyang Wang¹, Guangwei Liu²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070328 - 09 December 2025

Abstract Q-learning is a classical reinforcement learning method with broad applicability. It can respond effectively to environmental changes and provide flexible strategies, making it suitable for solving robot path-planning problems. However, Q-learning faces challenges in search and update efficiency. To address these issues, we propose an improved Q-learning (IQL) algorithm. We use an enhanced Ant Colony Optimization (ACO) algorithm to optimize Q-table initialization. We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma. The IQL algorithm is extensively tested in three grid environments of different scales. The results validate the… 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 >