Sun Park^*, JongWon Kim

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072865 - 12 January 2026

Abstract In real-world autonomous driving tests, unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur. Conducting actual test drives under various weather conditions may also lead to dangerous situations. Furthermore, autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS. Driving simulators, which replicate driving conditions nearly identical to those in the real world, can drastically reduce the time and cost required for market entry validation; consequently, they have become widely used. In this paper, we design a virtual driving test environment capable of More >

Yong Li, Yuxuan Chen^*, Jiahui He, Guowei He, Chenxi Dai, Jingjing Tong, Wenting Lei

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069257 - 27 December 2025

Abstract Ensuring reliable power supply in urban distribution networks is a complex and critical task. To address the increased demand during extreme scenarios, this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants (VPPs). The proposed strategy improves system flexibility and responsiveness by optimizing the power adjustment of flexible resources. In the proposed strategy, the Gaussian Process Regression (GPR) is firstly employed to determine the adjustable range of aggregated power within the VPP, facilitating an assessment of its potential contribution to power supply support. Then, an optimal dispatch model based on More > Graphic Abstract

Mintao Xu^1,#, Zichao Jin^1,#, Yangyang Tian¹, Jingcheng Zhang^1,*, Huiqin Ma¹, Yujin Jing¹, Jiangxing Wu², Jing Zhai²

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4059-4078, 2025, DOI:10.32604/phyton.2025.074422 - 29 December 2025

Abstract The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security. Data-driven forecasting models have emerged as an effective approach to support early warning and management, yet the lack of user-friendly tools for model development remains a major bottleneck. This study presents the Multi-Scenario Crop Disease Forecasting Modeling System (MSDFS), an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training, evaluation, and deployment-across four representative scenarios: static point-based, static grid-based, dynamic point-based, and dynamic grid-based. Unlike conventional frameworks, MSDFS emphasizes modeling flexibility, allowing… More >

Qingliang Wang¹, Chengkai Liu¹, Zhaohui Zhou¹, Ye Han¹, Luebin Fang², Moxuan Zhao³, Xiao Cao^3,*

Energy Engineering, Vol.122, No.11, pp. 4715-4732, 2025, DOI:10.32604/ee.2025.068105 - 27 October 2025

Abstract Grid-supplied load is the traditional load minus new energy generation, so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid. In addition, with the expansion of the power system and the increase in the frequency of extreme weather events, the difficulty of grid-supplied load forecasting is further exacerbated. Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load, especially under extreme weather conditions. This paper proposes a novel grid-supplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism (CNN-BiLSTM-Attention). The model utilizes transfer… More >

Mohammed Albekairi¹, Radhia Khdhir^2,*, Amina Magdich³, Somia Asklany^4,*, Ghulam Abbas⁵, Amr Yousef ^6,7

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3607-3644, 2025, DOI:10.32604/cmes.2025.069681 - 30 September 2025

Abstract License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation, which lead to pixel displacements. This article introduces a Displacement Region Recognition Method (DR2M) to address such a problem. This method operates on displaced features compared to the training input observed throughout definite time frames. The technique focuses on detecting features that remain relatively stable under haze, using a frame-based analysis to isolate edges minimally affected by visual noise. The edge detection failures are identified using a bilateral neural network through displaced feature training. The training converges bilaterally… More >

Ruiheng Pan^*, Shuyan Wang, Yihan Huang, Gang Ma

Energy Engineering, Vol.122, No.8, pp. 3079-3098, 2025, DOI:10.32604/ee.2025.065124 - 24 July 2025

Abstract Contemporary power network planning faces critical challenges from intensifying climate variability, including greenhouse effect amplification, extreme precipitation anomalies, and persistent thermal extremes. These meteorological disruptions compromise the reliability of renewable energy generation forecasts, particularly in photovoltaic (PV) systems. However, current predictive methodologies exhibit notable deficiencies in extreme weather monitoring, systematic transient phenomena analysis, and preemptive operational strategies, especially for cold-wave weather. In order to address these limitations, we propose a dual-phase data enhancement protocol that takes advantage of Time-series Generative Adversarial Networks (TimeGAN) for temporal pattern expansion and the K-medoids clustering algorithm for synthetic data… More >

Xiaoni Sun¹, Jiming Li², Zhiqiang Zhao², Guodong Jing², Baojun Chen², Jinrong Hu³, Fei Wang², Yong Zhang^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2121-2149, 2025, DOI:10.32604/cmc.2025.063373 - 03 July 2025

Abstract Weather forecasting is crucial for agriculture, transportation, and industry. Deep Learning (DL) has greatly improved the prediction accuracy. Among them, Graph Neural Networks (GNNs) excel at processing weather data by establishing connections between regions. This allows them to understand complex patterns that traditional methods might miss. As a result, achieving more accurate predictions becomes possible. The paper reviews the role of GNNs in short-to medium-range weather forecasting. The methods are classified into three categories based on dataset differences. The paper also further identifies five promising research frontiers. These areas aim to boost forecasting precision and More >

Mohammed J. Alshukri^1,2, Amjed Al-Khateeb³, Ahmed Mohsin Alsayah², Adel A. Eidan⁴, Hameed B. Mahood^5,6,*

Energy Engineering, Vol.122, No.4, pp. 1359-1372, 2025, DOI:10.32604/ee.2025.060613 - 31 March 2025

Abstract This research presents a new method to boost the efficiency of evaporative coolers by integrating magnetized water and a heat exchanger. Magnetized water, known for its high evaporation rate and reduced surface tension, offers a promising way to enhance air cooler performance. Additionally, the advanced heat exchanger both improves air cooling capacity and controls humidity levels. Aloni 100 L, a locally manufactured evaporative cooling system, and tap water were used in experiments. Tap water was magnetized using recycled magnets extracted from computer hard drives. Twenty-six magnets meticulously arranged within rectangular grooves, each with a minimum… More >

Ruoxin Li^1,*, Shaoxiong Wu¹, Fengping Deng¹, Zhongli Tian¹, Hua Cai¹, Xiang Li¹, Xu Xu¹, Qi Liu^2,3

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2969-2984, 2025, DOI:10.32604/cmc.2025.060230 - 17 February 2025

Abstract Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and… More >

Mingguan Zhao^1,2,*, Xinsheng Dong^1,2, Yang Yang^1,2, Meng Li^1,2, Hongxia Wang^1,2, Shuyang Ma^1,2, Rui Zhu³, Xiaojing Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2091-2109, 2025, DOI:10.32604/cmes.2025.059169 - 27 January 2025

Abstract Ice cover on transmission lines is a significant issue that affects the safe operation of the power system. Accurate calculation of the thickness of wire icing can effectively prevent economic losses caused by ice disasters and reduce the impact of power outages on residents. However, under extreme weather conditions, strong instantaneous wind can cause tension sensors to fail, resulting in significant errors in the calculation of icing thickness in traditional mechanics-based models. In this paper, we propose a dynamic prediction model of wire icing thickness that can adapt to extreme weather environments. The model expands… More >