Yuxin Tian¹, Wangxin Yu¹, Wanqing Yuan¹, Qingquan Liu^1,*, Xiaoliang Wang^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078776 - 30 March 2026

Abstract Granular flow, such as hopper discharge and debris flows, involves complex multi-scale, multi-phase, and multi-physics coupling, posing significant challenges for numerical simulation. Over the past two decades, methods like the Discrete Element Method (DEM), Smoothed Particle Hydrodynamics (SPH), and Depth-Averaging Method (DAM), have been developed to address these problems. However, their applicability across different scales remains unclear due to differences in physical assumptions and numerical algorithms. Therefore, a comprehensive evaluation is critically needed. This study selects three typical methods (DEM, SPH, and DAM) to examine their convergence behavior, boundary condition implementation, and limitations in physical More >

Laura Lacomba-Trejo^1,*, Francisco González-Sala¹, Sandra Simó², Florencia Talmón-Knuser³

International Journal of Mental Health Promotion, Vol.28, No.3, 2026, DOI:10.32604/ijmhp.2026.077053 - 31 March 2026

Abstract Backgrounds: Somatization and eating-related problems in adolescents living in residential care may be shaped by the interplay of risk and protective factors, including gender, relational trauma, attachment patterns, emotional intelligence, and perceived social support. This study examined how gender, relational trauma, attachment dimensions, resilience, and emotional intelligence contribute to the presence of somatic and eating difficulties in this population. Methods: The sample included 46 adolescents (63% female; ages 12–17, Mean = 14.85, Standard Deviation (SD) = 1.49) residing in child protection institutions in Uruguay. Participants completed self-report measures assessing childhood relational trauma (CaMir), attachment dimensions (anxiety… More > Graphic Abstract

Xiaoting Hou¹, Jingjing Zhao¹, Yuxin Shi¹, Yuhua Li^2,*, Shufen Xing^1,*

International Journal of Mental Health Promotion, Vol.28, No.3, 2026, DOI:10.32604/ijmhp.2026.074010 - 31 March 2026

Abstract Background: The growing parenting stress among Chinese mothers in recent years raises concerns about its impact on adolescent internalizing problems. The purpose of this study was to examine the curvilinear relationship between maternal parenting stress and internalizing problems in adolescents, and further explore the moderating effects of family socioeconomic status (SES) and adolescent gender. Methods: Data were collected from 405 mothers and adolescents (203 boys, Mean_age = 12.23) across five cities (Beijing, Hebei, Shanxi, Shenzhen, and Shandong) in China, who completed self-report measures of maternal parenting stress and internalizing problems. Descriptive statistics and multiple regression analyses were… More >

Saeid Sheikhi^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077788 - 12 March 2026

Abstract Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems, yet existing methods often struggle with balancing exploration and exploitation across diverse problem landscapes. This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization (PWO) algorithm. The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves, also known as African wild dogs in the wild, particularly their unique consensus-based voting rally mechanism, a behavior fundamentally distinct from the social dynamics of grey wolves. In this innovative process, pack members explore different areas… More >

Jia Wu^1,#, Wei Luo^2,#, Qianguo Xiao¹, Qinhong Xie¹, Xiaodong Li¹, Taiyong Bi^1,*, Hui Kou^1,*

Journal of Psychology in Africa, Vol.36, No.1, pp. 97-106, 2026, DOI:10.32604/jpa.2026.072761 - 26 February 2026

Abstract This study examines the mediating role of positive psychological capital and the moderating role of ethnicity in the relationship between mindfulness and internalizing/externalizing problems among adolescents. The study sample comprized Chinese adolescents (N = 637 ethnic minority; females = 40.97%, meam age = 12.68, SD = 0.49 years; N = 636 Han; females = 49.06%, mean age = 12.71, SD = 0.47 years). The participants completed the Child and Adolescent Mindfulness Measure, the Positive Psycap Questionnaire, and the Youth Self-Report. Results from the moderated mediation analysis showed mindfulness was negatively associated with both internalizing and externalizing More >

Le Thi Hong Van^1,*, Le Duc Thuan¹, Pham Van Huong¹, Nguyen Hieu Minh²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075027 - 10 February 2026

Abstract Optimizing convolutional neural networks (CNNs) for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy. This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection. Unlike conventional single-objective approaches, the proposed method formulates a global multi-objective fitness function that integrates accuracy, precision, recall, and model size (speed/model complexity penalty) with adjustable weights. This design enables both single-objective and weighted-sum multi-objective optimization, allowing adaptive selection of optimal CNN configurations for diverse deployment… More >

Shao-Qiang Ye^*, Azlan Mohd Zain, Yusliza Yusoff

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073411 - 10 February 2026

Abstract Engineering optimization problems are often characterized by high dimensionality, constraints, and complex, multimodal landscapes. Traditional deterministic methods frequently struggle under such conditions, prompting increased interest in swarm intelligence algorithms. Among these, the Cuckoo Search (CS) algorithm stands out for its promising global search capabilities. However, it often suffers from premature convergence when tackling complex problems. To address this limitation, this paper proposes a Grouped Dynamic Adaptive CS (GDACS) algorithm. The enhancements incorporated into GDACS can be summarized into two key aspects. Firstly, a chaotic map is employed to generate initial solutions, leveraging the inherent randomness… More >

Chih-Chieh Chang^*, Khairul Izyan Bin Anuar, Yu-Hwa Liu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073177 - 10 February 2026

Abstract Small datasets are often challenging due to their limited sample size. This research introduces a novel solution to these problems: average linkage virtual sample generation (ALVSG). ALVSG leverages the underlying data structure to create virtual samples, which can be used to augment the original dataset. The ALVSG process consists of two steps. First, an average-linkage clustering technique is applied to the dataset to create a dendrogram. The dendrogram represents the hierarchical structure of the dataset, with each merging operation regarded as a linkage. Next, the linkages are combined into an average-based dataset, which serves as… More >

Farhad Soleimanian Gharehchopogh^*, Keyvan Fattahi Rishakan

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073808 - 29 January 2026

Abstract Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences. Metaheuristic algorithms, in particular, have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships. The Mountain Gazelle Optimizer (MGO) is notably effective but struggles to balance local search refinement and global space exploration, often leading to premature convergence and entrapment in local optima. This paper presents the Improved MGO (IMGO), which integrates three synergistic enhancements: dynamic chaos mapping using piecewise chaotic sequences to boost exploration diversity; Opposition-Based Learning (OBL) with adaptive, diversity-driven activation to speed up… More >

Yan Kong¹, Xinpeng Guo², Chih-Hsien Hsia^3,4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071269 - 12 January 2026

Abstract With the development of technology, diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization (CO) problems, particularly in tackling Non-deterministic Polynomial-time hard (NP-hard) problems such as the Traveling Salesman Problem (TSP). However, existing diffusion model-based solvers typically employ a fixed, uniform noise schedule (e.g., linear or cosine annealing) across all training instances, failing to fully account for the unique characteristics of each problem instance. To address this challenge, we present Graph-Guided Diffusion Solvers (GGDS), an enhanced method for improving graph-based diffusion models. GGDS leverages Graph Neural Networks (GNNs) to capture graph structural information… More >