Shohreh Moradpour^1,#, Shuai Zhao^2,#, Mojgan Entezari¹, Shamsollah Ayoubi^3,*, Seyed Roohollah Mousavi⁴

Revue Internationale de Géomatique, Vol.34, pp. 809-829, 2025, DOI:10.32604/rig.2025.069538 - 06 November 2025

Abstract Accurate spatial prediction of soil organic carbon (SOC) and soil inorganic carbon (SIC) is vital for land management decisions. This study targets SOC/SIC mapping challenges at the watershed scale in central Iran by addressing environmental heterogeneity through a random forest (RF) model combined with bootstrapping to assess prediction uncertainty. Thirty-eight environmental variables—categorized into climatic, soil physicochemical, topographic, geomorphic, and remote sensing (RS)-based factors—were considered. Variable importance analysis (via) and partial dependence plots (PDP) identified land use, RS indices, and topography as key predictors of SOC. For SIC, soil reflectance (Bands 5 and 7, ETM+), topography, More > Graphic Abstract

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 >

Zhe Liu^1,2,*, Jiahao Shi³, Dania Santina⁴, Yulong Huang¹, Nabil Mlaiki⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3531-3555, 2025, DOI:10.32604/cmes.2025.071145 - 30 September 2025

Abstract The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations. However, traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data, as they rely on a single-dimensional membership value. To overcome these limitations, we propose an auto-weighted multi-view neutrosophic fuzzy clustering (AW-MVNFC) algorithm. Our method leverages the neutrosophic framework, an extension of fuzzy sets, to explicitly model imprecision and ambiguity through three membership degrees. The core novelty of AW-MVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions More >

Shige Wang¹, Yalong Liang², Lian Huang³, Pei Li^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2729-2748, 2025, DOI:10.32604/cmc.2025.068351 - 23 September 2025

Abstract This study introduces a hybrid Cuckoo Search-Deep Neural Network (CS-DNN) model for uncertainty quantification and composition optimization of Na_1/2Bi_1/2TiO₃ (NBT)-based dielectric energy storage ceramics. Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate (1 − x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD, SEM, and electrical characterization. The key innovation lies in integrating the CS metaheuristic algorithm with a DNN, overcoming local minima in training and establishing a robust composition-property prediction framework. Our model accurately… More >

Manuel J. C. S. Reis^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3023-3039, 2025, DOI:10.32604/cmc.2025.066390 - 23 September 2025

Abstract The Vehicle Routing Problem with Time Windows (VRPTW) presents a significant challenge in combinatorial optimization, especially under real-world uncertainties such as variable travel times, service durations, and dynamic customer demands. These uncertainties make traditional deterministic models inadequate, often leading to suboptimal or infeasible solutions. To address these challenges, this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms (GA) with Local Search (LS), while incorporating stochastic uncertainty modeling through probabilistic travel times. The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance. This adaptivity enhances the algorithm’s… More >

Shanshan Liu¹, Liang Chang^1,*, Guanyu Hu^1,2, Shiyu Li¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1113-1134, 2025, DOI:10.32604/cmc.2025.067240 - 29 August 2025

Abstract The evidential reasoning (ER) rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty. However, traditional ER implementations rely on two critical limitations: 1) unrealistic assumptions of complete evidence independence, and 2) a lack of mechanisms to differentiate causal relationships from spurious correlations. Existing similarity-based approaches often misinterpret interdependent evidence, leading to unreliable decision outcomes. To address these gaps, this study proposes a causality-enhanced ER rule (CER-e) framework with three key methodological innovations: 1) a multidimensional causal representation of evidence to capture dependency structures; 2) probabilistic quantification of causal… More >

Yizhe Han^1,2, Guangjing Huang¹, Fei Xiao¹, Zhiyin Huang^3,*, Yuting Dai¹

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1657-1671, 2025, DOI:10.32604/fdmp.2025.067528 - 31 July 2025

Abstract During high-speed forward flight, helicopter rotor blades operate across a wide range of Reynolds and Mach numbers. Under such conditions, their aerodynamic performance is significantly influenced by dynamic stall—a complex, unsteady flow phenomenon highly sensitive to inlet conditions such as Mach and Reynolds numbers. The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil. In this study, we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils, accounting for uncertainties in inlet parameters. A computational fluid dynamics (CFD) model… More >

Bingcai Wei¹, Hui Liu^1,*, Chuang Qian², Haoliang Shen³, Yibiao Chen³, Yixin Wang³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5095-5109, 2025, DOI:10.32604/cmc.2025.065812 - 30 July 2025

Abstract Within the domain of low-level vision, enhancing low-light images and removing sand-dust from single images are both critical tasks. These challenges are particularly pronounced in real-world applications such as autonomous driving, surveillance systems, and remote sensing, where adverse lighting and environmental conditions often degrade image quality. Various neural network models, including MLPs, CNNs, GANs, and Transformers, have been proposed to tackle these challenges, with the Vision KAN models showing particular promise. However, existing models, including the Vision KAN models use deterministic neural networks that do not address the uncertainties inherent in these processes. To overcome… More >

Adam Khalid¹, Anazida Zainal¹, Fuad A. Ghaleb², Bander Ali Saleh Al-rimy³, Yussuf Ahmed^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5665-5691, 2025, DOI:10.32604/cmc.2025.063451 - 30 July 2025

Abstract Advanced Persistent Threats (APTs) represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour, long-term persistence, and ability to bypass traditional detection systems. The complexity of real-world network data poses significant challenges in detection. Machine learning models have shown promise in detecting APTs; however, their performance often suffers when trained on large datasets with redundant or irrelevant features. This study presents a novel, hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data. It combines Mutual Information (MI), Symmetric… More >

Xiaolong Xiao¹, Xiaoxing Lu^1,*, Ziran Guo¹, Jian Liu¹, Shenglong Wu², Ye Cai²

Energy Engineering, Vol.122, No.8, pp. 3117-3132, 2025, DOI:10.32604/ee.2025.065631 - 24 July 2025

Abstract With the popularization of microgrid construction and the connection of renewable energy sources to the power system, the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent, and the accuracy of typical scenario predictions is low. In order to improve the accuracy of scenario prediction under source and load uncertainty, this paper proposes a typical scenario identification model based on random forests and order parameters. Firstly, a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids, taking into account source-load… More >