Matthew Starr¹, Lori Unruh-Snyder^1,*, Luke Gatiboni¹, Koralalage Jayaratne²

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.072898 - 28 April 2026

Abstract Biostimulants, categorized as microbial or non-microbial, including humic substances, seaweed extracts, chitosan, or protein hydrolysates (PHs), have gained significant attention in modern agriculture for their ability to enhance crop productivity, improve nutrient use efficiency, and increase resilience to abiotic and biotic stresses, while reducing dependence on conventional agrochemicals. This review synthesizes the historical development, classification, mechanisms of action, and agronomic benefits of biostimulants, with a particular emphasis on PHs, which are mixtures of amino acids, peptides, and polypeptides derived from plant or animal proteins through enzymatic, chemical, or thermal hydrolysis. The concept of biostimulants has… More >

Mataanana Mulavu^1,*, Dana Seale², J. Paul Seale³, Sion K. Harris⁴, Tulani Francis L. Matenga¹, Mwitwa Mugode¹, Shimeo Sakanya¹, Jonathan M. Tirrell⁵, Phillip Chimponda⁶, Wilbroad Mutale⁷, Mutale Sampa⁸, Oliver Mweemba¹

Journal of Psychology in Africa, Vol.36, No.2, pp. 219-230, 2026, DOI:10.32604/jpa.2026.071580 - 29 April 2026

Abstract Unhealthy substance use is high among adolescents and young people in Zambia. Conceivably, a character strength approach could help reduce alcohol and other psychoactive substance use among young adults. We tested the efficacy of a positive psychology based group-based character strengths prevention and recovery program for alcohol risk reduction among Zambian young adults. The primary study participants included schoolchildren and community members. We conducted 8 focus group discussions (FGDs) with school students (aged 13 to 17) and community youth (ages 18–24), 12 FGDs with parents, teachers, family, friends, and group leaders. Moreover, we conducted 8… More >

Rafael de Oliveira Silva^1,2,*, Roberto Outa³, Fábio Roberto Chavarette⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080607 - 27 April 2026

Abstract The reliable detection of cracks in engineering materials remains a fundamental challenge in nondestructive testing, especially in applications that require automated inspection, reduced instrumentation costs, and robustness under noisy operational conditions. Traditional nondestructive evaluation techniques often rely on complex sensing setups or expert-dependent interpretation, which can limit scalability and real-time applicability. In this context, this study addresses the scientific problem of achieving reliable and automated crack detection using simplified sensing architectures combined with intelligent data-driven analysis. This work proposes an intelligent signal classification framework for crack detection in polymeric materials based on machine learning and… More >

Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080595 - 27 April 2026

Abstract Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from LiDAR. However, cross-modal knowledge transfer remains challenging due to the inherent modality heterogeneity between LiDAR and camera data, which often leads to instability during training. In this work, we find that these instabilities are closely related to distribution mismatch in the cross-modal feature space and noisy teacher signals. To address this issue, we propose a novel distribution-aware cross-modal distillation framework, named DA-T3D. Specifically, we first explicitly model the LiDAR teacher’s Bird’s-Eye-View (BEV) feature distribution and use… More >

Mamoona Jabbar, Uzma Jamil^*, Muhammad Younas, Bushra Zafar

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080241 - 27 April 2026

Abstract Magnetic Resonance Imaging is one of the most commonly used neuro-oncology imaging modalities, which is a non-invasive mode of imaging and helps in detecting brain abnormalities in an effective way. Earlier researchers have demonstrated that brain tumor segmentation and classification can be effectively performed using deep learning techniques. Existing studies are primarily aimed at increasing prediction accuracy and provide insignificant consideration to model interpretability, limiting their practical application in clinical practice. To address this limitation, this research presents a two-stage explainable deep learning model, which combines transformer-based segmentation with an ensemble classification model that is… More >

Zelong Liang^1,2, Tinh Quoc Bui^3,4, Zhichao Dong⁵, Weihua Li^1,*, Yingjun Wang^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079830 - 27 April 2026

Abstract Topology optimization (TO) has become a core computational paradigm for structural design by defining optimality through physics-based objectives and constraints. However, practical engineering design often involves incomplete and imperfect physical modeling due to multi-physics coupling, manufacturing uncertainty, and computational constraints, leaving critical design factors insufficiently captured in purely physics-driven formulations. In parallel, data-driven and generative methods have enabled rapid topology generation and intent-aware design exploration, yet often weaken explicit optimality guarantees. This review argues that these seemingly divergent developments can be organized under a unified information–physics perspective. We term this emerging field Topology Optimization Informatics… More > Graphic Abstract

Ali S. Alghamdi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079763 - 27 April 2026

Abstract The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks, which complicates the planning and decision-making process. This paper presents a dual-objective stochastic optimization framework for the optimal allocation of wind DG, considering dynamic network reconfiguration across multiple loading conditions. Probabilistic modeling of wind speed is integrated using the Weibull distribution and the associated wind power uncertainty is discretized through a scenario-based point estimation method. Variability in load is accounted for by considering multiple loading levels, and the integrated uncertainty space is constructed as the Cartesian product of wind… More >

Tian Niu, Kaizhao Zhang, Zhongxue Gan, Wenchao Ding^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079324 - 27 April 2026

Abstract Ensuring operational safety for autonomous vehicles is a critical challenge in modern engineering, particularly due to the intricate interactions among diverse traffic participants. Traditional approaches often treat planning and prediction as unidirectional processes, failing to capture the dynamic, game-theoretic nature of real-world traffic. In the context of Digital Twins, there is an urgent need for high-fidelity virtual representations that can model the continuous, bidirectional evolution of the ego vehicle and surrounding agents to support robust decision-making under uncertainty. To address these limitations, a novel framework named Planning by Simulation with mutual influence prediction is proposed,… More >

Prabhat Das^1,2, Dibya Jyoti Bora¹, Sajal Saha², Cheng-Chi Lee^3,4,*, Hirak Mazumdar²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079203 - 27 April 2026

Abstract Real-time data processing is essential in the evolving landscape of IoT applications, ensuring efficiency, reliability, and adaptability. However, conventional clustering algorithms often face difficulties in managing high-frequency, continuous IoT data streams due to limited adaptability and high computational overhead. To address these challenges, this study proposes a resilient adaptation of the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm, tailored specifically for streaming IoT data. The enhanced approach dynamically recalculates clusters and determines the optimal number of clusters using the KneeLocator method. Unlike the original batch-oriented BIRCH, the modified version processes data incrementally, enabling More >

Md Jalal Uddin Rumi, Xiaowei Zeng^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079021 - 27 April 2026

Abstract Cohesive zone models (CZMs) are widely used to simulate interfacial fracture, where the post-peak softening branch of the traction–separation law (TSL) can strongly influence both the predicted response and the numerical behavior, particularly when the fracture process zone is not small relative to the structure. In Abaqus, however, cohesive elements are natively restricted to bilinear and linear–exponential TSLs, and implementing other softening behaviors typically requires user subroutines, which requires advanced knowledge and limits rapid model development and testing. This work exploits Abaqus’s tabular damage-evolution capability in a different way by constructing the damage variable analytically… More >