Bao Nguyen^*, Ngan Nguyen Xuan Phuong^*

Intelligent Automation & Soft Computing, Vol.41, pp. 105-137, 2026, DOI:10.32604/iasc.2026.088941 - 21 September 2026

Abstract Autonomous unmanned aerial vehicles (UAVs) operating in dense three-dimensional environments require predictive models that can represent multiple plausible futures while estimating the safety consequences of these futures. This paper presents a hybrid diffusion world model for short-horizon UAV trajectory forecasting and probabilistic collision-risk estimation. The model conditions on historical UAV states and executed actions, depth observations, and safety-context variables to generate multiple future relative-motion trajectories over a 1.0-s prediction horizon, while jointly estimating collision probability, near-miss probability, and obstacle-clearance information. A task-specific synthetic UAV dataset based on locations in Vietnam containing 1000 in-distribution episodes and… More >

Zongzheng Jiao¹, Shuaikang Yang¹, Junlong Yin¹, Shaohui Wang², Minpo Jung^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.8, 2026, DOI:10.32604/fdmp.2026.087218 - 04 September 2026

Abstract Hydro-mechanical (HM) simulations of porous-media systems—such as those used in geotechnical engineering, groundwater flow, formation consolidation, and underground-structure safety assessment—become computationally expensive when large material- and load-parameter spaces must be explored for design optimization, uncertainty quantification, or real-time decision support. Although data-driven surrogate models can accelerate such analyses, it remains unclear whether explicitly conditioning a history-based predictor on physical parameters offers a meaningful advantage over learning directly from the temporal evolution of the physical fields. This study systematically benchmarks five physical-parameter conditioning strategies—token concatenation, feature-wise linear modulation (FiLM), weak FiLM, adaptive instance normalization (AdaIN), and… More >

Bai Xiao^1,*, Jingjun Bu¹, Binbin Du², Yulin Ge², Jian Gao²

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.081408 - 30 August 2026

Abstract In order to solve the problem of the mismatch between the supply of electric vehicle charging stations and charging demand of electric vehicle charging stations caused by the rapid growth of electric vehicles, and the difficulty in determining the optimal site and capacity of electric vehicle charging stations, an optimisation method for the siting and sizing of electric vehicle charging stations considering the accessibility of user charging was proposed. Firstly, the road network topology structure is established in the GIS environment, and the shortest time path of the user is planned by establishing the road… More >

Longqiao Hu¹, Yunjin Wang^1,*, Jia Liu¹, Jiacheng Yin¹, Siyu Zhang², Mengyu Li¹, Qi Wu¹, Jiawei Li¹, Fujian Zhou³

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.079317 - 30 August 2026

Abstract The flow mechanisms of shale oil are inherently complex, characterized by diverse occurrence states. Establishing a robust production forecasting model is essential for thoroughly evaluating well deliverability and the efficacy of reservoir stimulation. While conventional analytical techniques, numerical reservoir simulation, and production decline analysis constitute standard practice, they often struggle to balance computational efficiency with predictive fidelity. Intelligent algorithms offer superior precision in short-term forecasting following extensive data training; however, they frequently exhibit poor generalization and underfitting during long-term performance projections. Consequently, integrating domain-specific production decline theory as physical constraints represents a strategic pathway to… More >

Yunlong Du¹, Shuyi Zhuang^2,*, Zhigang Ye², Qiangsheng Bu², Yun Chai¹, Yuanbing Wang³

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2025.074702 - 30 August 2026

Abstract Under the dual challenges of global warming and energy transition, improving the short-term forecasting accuracy of surface solar radiation is of great practical importance for photovoltaic (PV) power integration. In this study, a short-term solar radiation forecasting model based on the XGBoost machine learning algorithm was developed for Jiangsu Province by integrating multispectral data from the Fengyun-4A (FY-4A) geostationary satellite with ground-based meteorological observations. The model incorporated 18 input features—including satellite reflectance, solar zenith angle, normalized difference vegetation index (NDVI), elevation, and land-cover data—to dynamically predict ground horizontal irradiance (GHI) with 0–4 h lead times.… More >

Honglian Gao¹, Qingsong Zhang¹, Zeming Chen¹, Lianchen Li¹, Quanhui Liu¹, Yuxiang Tian¹, Xianfeng Xu^2,*

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.074052 - 30 August 2026

Abstract With the increasing penetration rate of distributed generators (DGs) in the distribution network, DGs with independent power supply capability provide strong support for distribution network fault recovery. Traditional fault reconfiguration methods often rely on static load priorities and fail to fully consider the time-varying dynamic characteristics of outage costs, resulting in shortcomings in the economy and adaptability of restoration strategies. To address this, this paper proposes a fault reconfiguration method that integrates day-ahead prediction and a dynamic load restoration set. Firstly, a Long Short-Term Memory network optimized by Variational Mode Decomposition and the Marine Predators… More >

Yan-Wei Li, David Chunhu Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086216 - 28 August 2026

Abstract As urban transportation systems grow increasingly complex, accurate and interpretable traffic congestion forecasting is critical. While existing deep learning models utilize graph neural networks (GNNs) and attention mechanisms, they often struggle with physical consistency under extreme scenarios. To address this, we propose the Physics-Informed Explainable Spatial-Temporal Graph Attention Network (PI-X-STGAT). Our framework models road segments as graph nodes, integrating traffic, weather, and cyclical temporal features. The architecture comprises a Context-Aware Graph Attention Network (GAT) enhanced with Node Adaptive Parameter Learning (NAPL) for capturing dynamic spatial dependencies, a Gated Recurrent Unit (GRU) layer for temporal evolution,… More >

Bindu Vadlamudi¹, Subhojit Dawn^1,*, Ishwarya Devarakonda¹, Sri Hari Priya Lanka¹, Sujan Turaka¹, Taha Selim Ustun^2,*

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.083555 - 06 August 2026

Abstract Agricultural irrigation consumes a large share of electricity in rural areas, creating predictable peak conditions on distribution systems that can lead to grid instability and unreliability. Classic load-forecasting and scheduling methods are time-consuming and unable to respond rapidly to fluctuating irrigation demand. Additionally, most traditional methods require a stable internet connection to function and therefore cannot readily adapt to seasonal changes or crop-specific irrigation requirements. This creates inefficiencies in energy consumption and inconsistencies in water delivery to consumers. To reduce these drawbacks, this research proposes a framework for irrigation forecasting and dynamic scheduling for agricultural… More >

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

Rana Muhammad Adnan Ikram¹, Jing-Cheng Han^1,*, Ahmed A. Ewees², Mo Wang³, Ozgur Kisi^4,5,6,*, Salim Heddam⁷, Mohammad Zounemat-Kermani⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083313 - 27 July 2026

Abstract Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four… More >