Marija Macenić, Tomislav Kurevija^*, Tin Herbst

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.067067 - 27 December 2025

Abstract The increased interest in geothermal energy is evident, along with the exploitation of traditional hydrothermal systems, in the growing research and projects developing around the reuse of already-drilled oil, gas, and exploration wells. The Republic of Croatia has around 4000 wells, however, due to a long period since most of these wells were drilled and completed, there is uncertainty about how many are available for retrofitting as deep-borehole heat exchangers. Nevertheless, as hydrocarbon production decreases, it is expected that the number of wells available for the revitalization and exploitation of geothermal energy will increase. The… More >

Meixi Chu¹, Xinyu Jiang^1,*, Yushu Tao²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071533 - 09 December 2025

Abstract Industrial operators need reliable communication in high-noise, safety-critical environments where speech or touch input is often impractical. Existing gesture systems either miss real-time deadlines on resource-constrained hardware or lose accuracy under occlusion, vibration, and lighting changes. We introduce Industrial EdgeSign, a dual-path framework that combines hardware-aware neural architecture search (NAS) with large multimodal model (LMM) guided semantics to deliver robust, low-latency gesture recognition on edge devices. The searched model uses a truncated ResNet50 front end, a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention, and localized Transformer layers tuned for on-device inference. To reduce… More >

Baixuan Han¹, Yueping Peng^1,*, Zecong Ye², Hexiang Hao¹, Xuekai Zhang¹, Wei Tang¹, Wenchao Kang¹, Qilong Li¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.071071 - 09 December 2025

Abstract Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior… More >

Xinwei Zhu, Jianxun Zhang^*, Huan Zeng

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068578 - 09 December 2025

Abstract Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance… More >

Jiang Mi¹, Zhijian Gan¹, Pengliu Tan^2,*, Xin Chang², Zhi Wang², Haisheng Xie²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.069348 - 10 November 2025

Abstract In response to the challenges in highway pavement distress detection, such as multiple defect categories, difficulties in feature extraction for different damage types, and slow identification speeds, this paper proposes an enhanced pavement crack detection model named Star-YOLO11. This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network. The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency. To enhance the accuracy of pavement crack detection and improve model efficiency, three key modifications to… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >

Yu Tao¹, Ruopeng Yang^1,2, Yongqi Wen^1,*, Yihao Zhong¹, Kaige Jiao¹, Xiaolei Gu^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-17, 2026, DOI:10.32604/cmc.2025.068670 - 10 November 2025

Abstract Since Google introduced the concept of Knowledge Graphs (KGs) in 2012, their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition, extraction, representation, modeling, fusion, computation, and storage. Within this framework, knowledge extraction, as the core component, directly determines KG quality. In military domains, traditional manual curation models face efficiency constraints due to data fragmentation, complex knowledge architectures, and confidentiality protocols. Meanwhile, crowdsourced ontology construction approaches from general domains prove non-transferable, while human-crafted ontologies struggle with generalization deficiencies. To address these challenges, this study proposes an Ontology-Aware LLM Methodology for Military Domain More >

Yongwei Li¹, Kaituo Jiao^2,*, Dongxu Han³, Bo Yu², Xiaoze Du¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3453-3479, 2025, DOI:10.32604/cmes.2025.073239 - 23 December 2025

Abstract The permeability contrast between the Hot Dry Rock (HDR) reservoir and the surrounding formations is a key factor governing fluid loss in Enhanced Geothermal Systems (EGS). This study thus aims to investigate its impact on system performance under varying operating conditions, and a three-dimensional thermo–hydro–mechanical (THM) coupled EGS model is developed based on the geological parameters of the GR1 well in the Qiabuqia region. The coupled processes of fluid flow, heat transfer, and geomechanics within the reservoir under varying reservoir–surrounding rock permeability contrasts, as well as the flow and heat exchange along the wellbores from… More >

Ugur Madran, Saeed Mian Qaisar^*, Duygu Soyoglu

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1969-1992, 2025, DOI:10.32604/cmes.2025.072024 - 26 November 2025

Abstract Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids. It offers substantial benefits across social, environmental, and economic dimensions. To effectively realize these advantages, a fine-grained collection and analysis of smart meter data is essential. However, the high dimensionality and volume of such time-series present significant challenges, including increased computational load, data transmission overhead, latency, and complexity in real-time analysis. This study proposes a novel, computationally efficient framework for feature extraction and selection tailored to smart meter time-series data. The approach begins with an extensive offline analysis, where features are… More >

Yating Xu¹, Mansheng Xiao^1,*, Mengxing Gao¹, Zhenzhen Liu¹, Zeyu Xiao²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1635-1656, 2025, DOI:10.32604/sdhm.2025.068822 - 17 November 2025

Abstract During the operation, maintenance and upkeep of concrete buildings, surface cracks are often regarded as important warning signs of potential damage. Their precise segmentation plays a key role in assessing the health of a building. Traditional manual inspection is subjective, inefficient and has safety hazards. In contrast, current mainstream computer vision–based crack segmentation methods still suffer from missed detections, false detections, and segmentation discontinuities. These problems are particularly evident when dealing with small cracks, complex backgrounds, and blurred boundaries. For this reason, this paper proposes a lightweight building surface crack segmentation method, HL-YOLO, based on… More >