Xiaozhe Guo¹, Suyan Long², Ziyu Yue², Yifan Wang², Guanting Yin¹, Yuyang Wang¹, Zhaoyuan Wu^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069820 - 27 December 2025

Abstract The increasing complexity of China’s electricity market creates substantial challenges for settlement automation, data consistency, and operational scalability. Existing provincial settlement systems are fragmented, lack a unified data structure, and depend heavily on manual intervention to process high-frequency and retroactive transactions. To address these limitations, a graph-based unified settlement framework is proposed to enhance automation, flexibility, and adaptability in electricity market settlements. A flexible attribute-graph model is employed to represent heterogeneous multi-market data, enabling standardized integration, rapid querying, and seamless adaptation to evolving business requirements. An extensible operator library is designed to support configurable settlement… More >

Haixia Wang, Huan Zhang, Xiuling Wang, Xule Xin, Zhiguo Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069340 - 10 November 2025

Abstract Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision, with applications ranging from crowd counting to various other object counting tasks. To address this, we propose HUANNet (High-Resolution Unified Attention Network), a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework, while optimizing computational distribution across parallel branches. HUANNet introduces three core modules: the High-Resolution Attention Module (HRAM), which enhances feature extraction by optimizing multi-resolution feature fusion; the Unified Multi-Scale Attention Module (UMAM), which integrates spatial, channel, and More >

Zhe Liu^1,2,3,*, Sijia Zhu⁴, Yulong Huang^1,*, Tapan Senapati^5,6,7, Xiangyu Li⁸, Wulfran Fendzi Mbasso⁹, Himanshu Dhumras¹⁰, Mehdi Hosseinzadeh^11,12,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2157-2188, 2025, DOI:10.32604/cmes.2025.072352 - 26 November 2025

Abstract Uncertainty and ambiguity are pervasive in real-world intelligent systems, necessitating advanced mathematical frameworks for effective modeling and analysis. Fermatean fuzzy sets (FFSs), as a recent extension of classical fuzzy theory, provide enhanced flexibility for representing complex uncertainty. In this paper, we propose a unified parametric divergence operator for FFSs, which comprehensively captures the interplay among membership, non-membership, and hesitation degrees. The proposed operator is rigorously analyzed with respect to key mathematical properties, including non-negativity, non-degeneracy, and symmetry. Notably, several well-known divergence operators, such as Jensen-Shannon divergence, Hellinger distance, and χ²-divergence, are shown to be special cases More >

Yuheng Cao, Chunyu Zhang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-1, 2025, DOI:10.32604/icces.2025.010692

Abstract A unified high-order damaged elasticity theory is proposed for quasi-brittle fracture problems by incorporating higher-order gradients for both strain and damage fields. The single scale parameter is defined by the size of the representative volume element (RVE). It formulates the degraded strain energy density to capture size effects and localized damage initiation/propagation with a damage criterion grounded in experimental observations. The structural deformation is solved by using the principle of minimum potential energy with the Augmented Lagrangian Method (ALM) enforcing damage evolution constraints. This simplifies the equilibrium equations, enabling efficient numerical solutions via the Galerkin More >

Saad Sadiq¹, Kashif Sultan¹, Muhammad Sheraz², Teong Chee Chuah^2,*, Muhammad Usman Hashmi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3945-3963, 2025, DOI:10.32604/cmc.2025.067538 - 23 September 2025

Abstract Vehicle recognition plays a vital role in intelligent transportation systems, law enforcement, access control, and security operations—domains that are becoming increasingly dynamic and complex. Despite advancements, most existing solutions remain siloed, addressing individual tasks such as vehicle make and model recognition (VMMR), automatic number plate recognition (ANPR), and color classification separately. This fragmented approach limits real-world efficiency, leading to slower processing, reduced accuracy, and increased operational costs, particularly in traffic monitoring and surveillance scenarios. To address these limitations, we present a unified framework that consolidates all three recognition tasks into a single, lightweight system. The More >

Xiuchuan Cheng¹, Meiling Wu¹, Xu Feng¹, Zhiguo Wang², Guisong Liu², Ye Li^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3527-3549, 2025, DOI:10.32604/cmc.2025.067264 - 23 September 2025

Abstract The requirement for precise detection and recognition of target pedestrians in unprocessed real-world imagery drives the formulation of person search as an integrated technological framework that unifies pedestrian detection and person re-identification (Re-ID). However, the inherent discrepancy between the optimization objectives of coarse-grained localization in pedestrian detection and fine-grained discriminative learning in Re-ID, combined with the substantial performance degradation of Re-ID during joint training caused by the Faster R-CNN-based branch, collectively constitutes a critical bottleneck for person search. In this work, we propose a cascaded person search model (SeqXt) based on SeqNet and ConvNeXt that… More >

Geoffry Mutiso^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025

Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

Kuncham Sreenivasa Rao¹, Rajitha Kotoju², B. Ramana Reddy³, Taher Al-Shehari⁴, Nasser A. Alsadhan⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1899-1916, 2025, DOI:10.32604/cmc.2025.064728 - 29 August 2025

Abstract Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things (SCADA-IIoT) systems against intruders has become essential since industrial control systems now oversee critical infrastructure, and cyber attackers more frequently target these systems. Due to their connection of physical assets with digital networks, SCADA-IIoT systems face substantial risks from multiple attack types, including Distributed Denial of Service (DDoS), spoofing, and more advanced intrusion methods. Previous research in this field faces challenges due to insufficient solutions, as current intrusion detection systems lack the necessary accuracy, scalability, and adaptability needed for IIoT environments. This paper introduces CyberFortis, a… More >

Chunming Tang^*, Yu Wang

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5115-5128, 2025, DOI:10.32604/cmc.2025.062328 - 19 May 2025

Abstract Image dehazing aims to generate clear images critical for subsequent visual tasks. CNNs have made significant progress in the field of image dehazing. However, due to the inherent limitations of convolution operations, it is challenging to effectively model global context and long-range spatial dependencies effectively. Although the Transformer can address this issue, it faces the challenge of excessive computational requirements. Therefore, we propose the FS-MSFormer network, an asymmetric encoder-decoder architecture that combines the advantages of CNNs and Transformers to improve dehazing performance. Specifically, the encoding process employs two branches for multi-scale feature extraction. One branch… More >

Jiakang Sun^1,2, Ke Chen^1,2, Xinyang He^1,2, Xu Liu^1,2, Ke Li^1,2, Cheng Peng^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 219-238, 2025, DOI:10.32604/cmc.2025.059745 - 26 March 2025

Abstract With the advancements in parameter-efficient transfer learning techniques, it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions. However, applying this technique to multimodal knowledge transfer introduces a significant challenge: ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task adaptation. This paper introduces UniTrans, a framework aimed at facilitating efficient knowledge transfer across multiple modalities. UniTrans leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter overhead. To further enhance modality alignment, we introduce two key components: the Multimodal More >