Yunlong Teng¹, Ying Liu², Jianhong Liang¹, Jinshang Luo^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082594 - 30 June 2026

Abstract Over the past decades, surrogate model-aided reliability analysis approaches grounded in active learning have undergone extensive development. However, Gaussian process models like Kriging suffer from severe computational burdens when handling high-dimensional problems or large samples. Conversely, machine learning algorithms such as extreme learning machines exhibit high computational efficiency but lack variance output and stability, making them difficult to employ for adaptive active learning strategies. To address these limitations, this study proposes a population Monte Carlo method based on an adaptive closed neuron extreme learning machine. First, a closed neuron strategy uses a consistency metric to… More >

Riad Mollik Babu¹, Md Shafiul Alam^2,*, Md. Hasibur Rahman³, Mohammad Ali², Md. Alamgir Hossain⁴, Md. Arifuzzaman⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082366 - 30 June 2026

Abstract The increasing penetration of solar photovoltaic (PV) systems into power grids poses challenges due to their inherent intermittency and variability, which can compromise grid stability and reliability. Hybridizing solar PV with wind energy and battery energy storage system (BESS) offers a promising solution by leveraging resource complementarity and providing fast frequency response. This study presents a techno-economic and environmental assessment of a hybrid renewable energy system. Wind turbines and a BESS are integrated with the existing 7.5 MW Sirajganj Solar PV Power Plant in Bangladesh. The proposed hybrid configuration is evaluated using real-world operational data… More >

Thao Nguyen-Trang^1,2,*, Hiep Ha-Hoang³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080808 - 27 May 2026

Abstract Solving differential equations (DEs), including ordinary differential equations (ODEs) and partial differential equations (PDEs), is fundamental to scientific computing and engineering. The development of deep learning has led to Physics-Informed Neural Networks (PINNs), in which physical laws are embedded directly into the loss function. However, PINNs inherit the intrinsic instability of deep neural networks (DNNs) and lack an effective mechanism for Uncertainty Quantification (UQ). This paper proposes a stochastic ensemble framework to address these limitations. The proposed method is a double-stochastic ensemble framework that combines bagging (via bootstrap resampling and randomized collocation points) with Monte… More >

Jian Wang¹, Gongqiang Yang^1,*, Yufeng Sun², Gangui Yan¹, Jie Long³

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.072828 - 27 May 2026

Abstract Given that the power grid partitioning method relying mainly on line reactive power flow information sees frequent changes in partitioning results with reactive power flow fluctuations under high-proportion fixed-power-factor PV-connected distribution networks, and traditional distributed PV collaborative optimization fails to adapt due to such changes, a stable partitioning and distributed PV collaborative optimization method for this scenario is proposed. Firstly, the Gaussian mixture model (GMM) is used to characterize the characteristics of PV reactive power output, obtaining the typical curve of PV reactive power output. Secondly, the Monte Carlo Simulation (MCS) probabilistic power flow calculation… More >

Long Li, Hengyang Wu^*, Na Wang

Journal on Artificial Intelligence, Vol.8, pp. 169-182, 2026, DOI:10.32604/jai.2026.077462 - 23 March 2026

Abstract Zero-shot learning as an emerging approach in image captioning techniques, has garnered significant attention from researchers in recent years due to its ability to accomplish tasks without requiring specific category training data. Existing zero-shot image captioning schemes largely rely on traditional language models, which exhibit low efficiency and suboptimal generation quality. To address this issue, this study proposes Hamiltonian Monte Carlo for Image Captioning (HMCIC). This method first models the image captioning task as a probabilistic sampling problem in parameter space, integrating semantic matching and syntactic coherence into an energy function to guide the generation… More >

Chao Li^1,3,#, Jianing Wang^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071242 - 12 January 2026

Abstract Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a More >

Ricardo Dominico Da Silva^1,2, Jangkung Raharjo^1,3,*, Sudarmono Sasmono^1,3

Energy Engineering, Vol.122, No.12, pp. 5073-5090, 2025, DOI:10.32604/ee.2025.071545 - 27 November 2025

Abstract The rapid development of technology has led to an ever-increasing demand for electrical energy. In the context of Timor-Leste, which still relies on fossil energy sources with high operational costs and significant environmental impacts, electricity load forecasting is a strategic measure to support the energy transition towards the Net Zero Emission (NZE) target by 2050. This study aims to utilize historical electricity load data for the period 2013–2024, as well as data on external factors affecting electricity consumption, to forecast electricity load in Timor-Leste in the next 10 years (2025–2035). The forecasting results are expected… More >

Yifei Wang, Zhao Zhang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011121

Abstract Additive manufacturing (AM) reveals high anisotropy in mechanical properties due to the thermal accumulation induced microstructures. How to reveal the internal connection between the microstructures and the mechanical properties in additive manufacturing is a challenge. There are many methods to predict the mechanical properties based on the microstructural evolutions in additive manufacturing [1–3]. Here we summarized the main methods for the prediction of the mechanical properties in additive manufacturing, including crystal plasticity finite element method (CPFEM), dislocation dynamics (DD), and molecular dynamics (MD). We systematically examine these primary approaches for mechanical property predictions in AM,… More >

Yingying Guo¹, Ziyu Cui², Jibing Shen¹, Pei Li^3,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4519-4550, 2025, DOI:10.32604/cmc.2025.071504 - 23 October 2025

Abstract Accurate simulation of acoustic wave propagation in complex structures is of great importance in engineering design, noise control, and related research areas. Although traditional numerical simulation methods can provide precise results, they often face high computational costs when applied to complex models or problems involving parameter uncertainties, particularly in the presence of multiple coupled parameters or intricate geometries. To address these challenges, this study proposes an efficient algorithm for simulating the acoustic field of structures with adhered sound-absorbing materials while accounting for ground reflection effects. The proposed method integrates Catmull-Clark subdivision surfaces with the boundary… More >

Miguel A. Millán^1,*, Rubén A. Galindo², Fausto Molina-Gómez¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2923-2941, 2025, DOI:10.32604/cmes.2025.069356 - 30 September 2025

Abstract Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes, resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns. This complexity poses significant challenges for slope stability analysis, requiring the development of specialized techniques to address these issues. This research presents a numerical methodology that incorporates spatial variability, nonlinear material characterization, and probabilistic analysis using a Monte Carlo framework to address this issue. The heterogeneous structure is represented by randomly assigning different lithotypes across the slope, while maintaining predefined global proportions. This contrasts with the more common approach… More >