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 >

Qin Hu, Hongshan Kong^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.069078 - 10 November 2025

Abstract To address the issues of frequent identity switches (IDs) and degraded identification accuracy in multi object tracking (MOT) under complex occlusion scenarios, this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling. By constructing a joint tracking model centered on “intra-class independent tracking + cross-category dynamic binding”, designing a multi-modal matching metric with spatio-temporal and appearance constraints, and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy, this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion, cross-camera tracking, and crowded environments. Experiments… More >

Lijuan Zhang¹, Yu Zhou², Jiawei Tian^3,*, Fupei Guo⁴, Xiang Zhang⁴, Bo Yang^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3949-3968, 2025, DOI:10.32604/cmes.2025.071869 - 23 December 2025

Abstract In endoscopic surgery, the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences. To address these challenges, we propose a novel Hybrid Triangular Matching (HTM) modeling framework for soft tissue feature tracking. Specifically, HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm. By leveraging barycentric coordinate theory, More >

Ashna Abduljabbar Haji^*, Ranj S. Abdullah^*

Energy Engineering, Vol.122, No.12, pp. 5111-5127, 2025, DOI:10.32604/ee.2025.073367 - 27 November 2025

Abstract The hybrid photovoltaic solar-assisted heat pump are primarily used to generate electricity and provide thermal energy for heating applications. This study investigates the performance enhancement of a hybrid Photovoltaic Thermal Solar-Assisted Heat Pump (PV/T-SAHP) system integrated with a solar tracking mechanism. The system was simulated using TRNSYS to evaluate its monthly electrical output and coefficient of performance (COP) of the heat pump system over a year. The results showed a significant improvement in energy generation and efficiency compared to a conventional PV/T system without SAHP system. Overall, the solar tracking configuration of the PV/T-SAHP generated… More > Graphic Abstract

Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >

Jahfar Khan Said Baz¹, Peng Zhang^2,3,*, Mian Muhammad Kamal⁴, Heba G. Mohamed⁵, Muhammad Sheraz⁶, Teong Chee Chuah^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 947-969, 2025, DOI:10.32604/cmes.2025.069956 - 30 October 2025

Abstract Multiple Object Tracking (MOT) is essential for applications such as autonomous driving, surveillance, and analytics; However, challenges such as occlusion, low-resolution imaging, and identity switches remain persistent. We propose HAMOT, a hierarchical adaptive multi-object tracker that solves these challenges with a novel, unified framework. Unlike previous methods that rely on isolated components, HAMOT incorporates a Swin Transformer-based Adaptive Enhancement (STAE) module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions. The hierarchical Dynamic Graph Neural Network with Temporal Attention (DGNN-TA) models both short- and long-term associations, and the Adaptive Unscented Kalman Filter… More >

Yukun Yang^*, Jun He, Wenfeng Chen, Zhi Li, Kun Chen

Energy Engineering, Vol.122, No.11, pp. 4653-4678, 2025, DOI:10.32604/ee.2025.068199 - 27 October 2025

Abstract To address the issues of unclear carbon responsibility attribution, insufficient renewable energy absorption, and simplistic carbon trading mechanisms in integrated energy systems, this paper proposes an electric-heat-hydrogen integrated energy system (EHH-IES) optimal scheduling model considering carbon emission stream (CES) and wind-solar accommodation. First, the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate, branch carbon intensity, and node carbon potential, realizing accurate tracking of carbon flow in the process of multi-energy coupling. Second, a stepped carbon pricing mechanism is established to… More >

Muhammad Asif Khan¹, Dildar Hussain², Bhuyan Kaibalya Prasad³, Irfan Ullah⁴, Inayat Khan⁵, Jawad Khan^6,*, Yeong Hyeon Gu^2,*, Pavlos Kefalas⁷

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5451-5468, 2025, DOI:10.32604/cmc.2025.069134 - 23 October 2025

Abstract Dialogue State Tracking (DST) is a critical component of task-oriented spoken dialogue systems (SDS), tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values. Recent advances leverage Large Language Models (LLMs) with prompt-based tuning to improve tracking accuracy and efficiency. However, these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts, without explicitly modeling the complex dependencies between slots and values. In this work, we propose PUGG, a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer… More >

Sahbi Boubaker^1,2,*, Salim Hadj Said³, Souad Kamel¹, Habib Dimassi³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5703-5721, 2025, DOI:10.32604/cmc.2025.069047 - 23 October 2025

Abstract Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear, time-varying dynamics of human-robot interaction, limited sensor availability, and unknown external disturbances. This study proposes a novel control strategy that combines flatness-based control with two cascaded observers: a high-gain observer to estimate unmeasured joint velocities, and a nonlinear disturbance observer to reconstruct external torque disturbances in real time. These estimates are integrated into the control law to enable robust, state-feedback-based trajectory tracking. The approach is validated through simulation scenarios involving partial state measurements and abrupt external torque perturbations, reflecting realistic rehabilitation conditions. Results confirm More >

Sheeba Razzaq^1,*, Majid Iqbal Khan²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5377-5398, 2025, DOI:10.32604/cmc.2025.068539 - 23 October 2025

Abstract Multi-Object Tracking (MOT) represents a fundamental but computationally demanding task in computer vision, with particular challenges arising in occluded and densely populated environments. While contemporary tracking systems have demonstrated considerable progress, persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment. This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features. Proposed framework employs: (1) a Height Modulated and Scale Adaptive Spatial Intersection-over-Union (HMSIoU) metric for improved spatial correspondence estimation across variable object scales and partial occlusions; (2) a feature More >