Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >

Hongyu Zhao^1,2, Shengsheng Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068426 - 10 November 2025

Abstract Driven by advancements in mobile internet technology, images have become a crucial data medium. Ensuring the security of image information during transmission has thus emerged as an urgent challenge. This study proposes a novel image encryption algorithm specifically designed for grayscale image security. This research introduces a new Cantor diagonal matrix permutation method. The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix, where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice (CML). The high initial value sensitivity of the… More >

Mubashir Ali^1,2, Jingzhen Li¹, Zedong Nie^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4153-4177, 2025, DOI:10.32604/cmes.2025.073048 - 23 December 2025

Abstract Diabetes imposes a substantial burden on global healthcare systems. Worldwide, nearly half of individuals with diabetes remain undiagnosed, while conventional diagnostic techniques are often invasive, painful, and expensive. In this study, we propose a noninvasive approach for diabetes detection using photoplethysmography (PPG), which is widely integrated into modern wearable devices. First, we derived velocity plethysmography (VPG) and acceleration plethysmography (APG) signals from PPG to construct multi-channel waveform representations. Second, we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal, statistical, and nonlinear features with recursive feature elimination and deep feature extraction using… More >

Hong Zhang^1,2,#, Ying Zhu^3,#, Xingyu Li^3,#, Zhonghua Liu^1,2, Guoliang Li^1,2, Zhaomiao Lin^1,2, Yongxiang Qiu^1,2, Yongqing Xu^1,2, Shimin Lyu³, Jiyang Wang³, Sixin Qiu^1,2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2479-2498, 2025, DOI:10.32604/phyton.2025.064850 - 29 August 2025

Abstract Vascular wilt caused by Fusarium oxysporum f. sp. batatas (Fob) is a devastating disease threatening global sweet potato production. To elucidate Fob’s pathogenicity mechanisms and inform effective control strategies, we generated a green fluorescent protein (GFP)-tagged Fob strain to track infection dynamics in sweet potato susceptible cultivar Xinzhonghua and resistant cultivar Xiangshu75-55, respectively. Through cytological observation, we found in the susceptible Xinzhonghua, Fob predominantly colonized stem villi, injured root growth points, and directly invaded vascular bundles through stem wounds. Spore germination peaked at 2–3 h post-inoculation (hpi), followed by cyclical mycelial expansion and sporulation within vascular tissues… More >

Almustafa Abd Elkader Ayek¹, Bilel Zerouali^2,*, Ankur Srivastava³, Mohannad Ali Loho^4,5, Nadjem Bailek^6,7, Celso Augusto Guimarães Santos^8,9

Revue Internationale de Géomatique, Vol.34, pp. 669-689, 2025, DOI:10.32604/rig.2025.067137 - 19 August 2025

Abstract This study investigates the spatial and temporal dynamics of key air pollutants—nitrogen dioxide (NO₂), carbon monoxide (CO), methane (CH₄), formaldehyde (HCHO), and the ultraviolet aerosol index (UVAI)—over the period 2019–2024. Utilizing high-resolution remote sensing data from the Sentinel-5 Precursor satellite and its TROPOspheric Monitoring Instrument (TROPOMI) processed via Google Earth Engine (GEE), pollutant concentrations were analyzed, with spatial visualizations produced using ArcGIS Pro. The results reveal that urban and industrial hotspots—notably in Damascus, Aleppo, Homs, and Hama—exhibit elevated NO₂ and CO levels, strongly correlated with population density, traffic, and industrial emissions. Temporal trends indicate significant pollutant fluctuations More > Graphic Abstract

Rongsen Wu¹, Jie Xu¹, Yuhang Zhang¹, Changming Zhao^2,*, Yiweng Xie³, Zelei Wu¹, Yunji Li², Jinhong Guo⁴, Shiyang Tang^5,6

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4961-4978, 2025, DOI:10.32604/cmc.2025.061396 - 19 May 2025

Abstract With the rapid development of artificial intelligence and Internet of Things technologies, video action recognition technology is widely applied in various scenarios, such as personal life and industrial production. However, while enjoying the convenience brought by this technology, it is crucial to effectively protect the privacy of users’ video data. Therefore, this paper proposes a video action recognition method based on personalized federated learning and spatiotemporal features. Under the framework of federated learning, a video action recognition method leveraging spatiotemporal features is designed. For the local spatiotemporal features of the video, a new differential information… More >

Shaodong Zou¹, Di Wu¹, Jianhou Gan^1,2,*, Juxiang Zhou^1,2, Jiatian Mei^1,2

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1101-1116, 2025, DOI:10.32604/cmc.2025.061376 - 26 March 2025

Abstract The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos, providing a foundation for realizing intelligent and accurate teaching. However, the complex nature of the classroom environment has added challenges and difficulties in the process of student action recognition. In this research article, with regard to the circumstances where students are prone to be occluded and classroom computing resources are restricted in real classroom scenarios, a lightweight multi-modal fusion action recognition approach is put forward. This proposed method is capable of enhancing the… More >

Dongdong Zhang, Chunping Wang, Huiying Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4319-4338, 2025, DOI:10.32604/cmc.2025.060653 - 06 March 2025

Abstract Video camouflaged object detection (VCOD) has become a fundamental task in computer vision that has attracted significant attention in recent years. Unlike image camouflaged object detection (ICOD), VCOD not only requires spatial cues but also needs motion cues. Thus, effectively utilizing spatiotemporal information is crucial for generating accurate segmentation results. Current VCOD methods, which typically focus on exploring motion representation, often ineffectively integrate spatial and motion features, leading to poor performance in diverse scenarios. To address these issues, we design a novel spatiotemporal network with an encoder-decoder structure. During the encoding stage, an adjacent space-time More >

Bin Sun¹, Yinuo Wang¹, Tao Shen^1,*, Lu Zhang¹, Renkang Geng²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4219-4236, 2025, DOI:10.32604/cmc.2025.060567 - 06 March 2025

Abstract Traffic datasets exhibit complex spatiotemporal characteristics, including significant fluctuations in traffic volume and intricate periodical patterns, which pose substantial challenges for the accurate forecasting and effective management of traffic conditions. Traditional forecasting models often struggle to adequately capture these complexities, leading to suboptimal predictive performance. While neural networks excel at modeling intricate and nonlinear data structures, they are also highly susceptible to overfitting, resulting in inefficient use of computational resources and decreased model generalization. This paper introduces a novel heuristic feature extraction method that synergistically combines the strengths of non-neural network algorithms with neural networks… More >

Shumin Li¹, Qifang Luo^1,2,*, Yongquan Zhou^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1955-1994, 2025, DOI:10.32604/cmes.2025.059738 - 27 January 2025

Abstract Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research. However, the current research on wireless sensor network deployment problems uses overly simplistic models, and there is a significant gap between the research results and actual wireless sensor networks. Some scholars have now modeled data fusion networks to make them more suitable for practical applications. This paper will explore the deployment problem of a stochastic data fusion wireless sensor network (SDFWSN), a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in… More >