Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Hongsheng Su, Zhensheng Teng^*, Zihan Zhou

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069495 - 27 January 2026

Abstract Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy, replacement-based maintenance practices that deviate from actual operational conditions, and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization, this study proposes a Time-Based Incomplete Maintenance (TBIM) strategy incorporating reliability constraints through stochastic differential equations (SDE). By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions, a high-precision SDE degradation model is constructed, achieving 16% residual reduction compared to… More >

Jiale Hu, Fan Chen^*, Yue Yang, Man Wang

Journal on Artificial Intelligence, Vol.8, pp. 51-64, 2026, DOI:10.32604/jai.2026.075257 - 22 January 2026

Abstract To enhance the operational performance of energy storage systems (ESS), this paper proposes an optimal dispatch strategy that jointly considers reliability and economic efficiency. First, we formulate a cost-minimization model that includes ESS dispatch costs, wind and photovoltaic (PV) curtailment costs, and load loss costs, while explicitly enforcing power supply reliability constraints. Next, we develop a comprehensive evaluation indicator system that integrates reliability, economic performance, renewable-energy utilization, and ESS technical indicators, thereby addressing the limitations of single-indicator assessments. Finally, a case study using real data from a region in China shows that the proposed strategy More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

Zhenhao Zhang^1,*, Yong Tian^1,2, Yuanzhi Cao¹, Tao Chen¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010846

Abstract Accurate calculation of the failure probability of structural components was crucial for full-probability level structural design. However, current design codes typically use uniform design factors, which fail to accurately reflect the true failure probability of structures. In this paper, based on the direct probability integral method (DPIM) and combining different design parameter iterative calculation strategies, the full-probabilistic design methods for single-parameter and multi-parameter were proposed, and their accuracy advantages in structural reliability design were verified by engineering examples. Furthermore, this study compares the partial factor method, the design value method, the direct probability design method,… More >

Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*

Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025

Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

Qiuyu Lu¹, Yunqi Yan², Yang Liu¹, Ying Chen^2,*, Yinguo Yang¹, Tannan Xiao³, Guobing Wu¹

Energy Engineering, Vol.122, No.12, pp. 4799-4814, 2025, DOI:10.32604/ee.2025.069131 - 27 November 2025

Abstract The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system (ECS) designs that prioritize lifetime operational reliability. Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints. This paper introduces a novel, two-stage optimization framework for offshore wind farm (OWF) ECS planning that systematically incorporates reliability. The first stage employs Mixed-Integer Linear Programming (MILP) to determine an optimal radial network topology, considering linearized reliability approximations and geographical constraints. The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained More >

Khadija Slimani^1,2,*, Samira Khoulji², Hamed Taherdoost^3,4, Mohamed Larbi Kerkeb⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4667-4686, 2025, DOI:10.32604/cmc.2025.070173 - 23 October 2025

Abstract The integration of the dynamic adaptive routing (DAR) algorithm in unmanned aerial vehicle (UAV) networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6G. DAR’s innovative framework incorporates real-time path adjustments, energy-aware routing, and predictive models, optimizing reliability, latency, and energy efficiency in UAV operations. This study demonstrated DAR’s superior performance in dynamic, large-scale environments, proving its adaptability and scalability for real-time applications. As 6G networks evolve, challenges such as bandwidth demands, global spectrum management, security vulnerabilities, and financial feasibility become prominent. DAR aligns with these demands by offering More >

Shuoshuo Shen^1,2, Jin Cheng^1,2,*, Zhenyu Liu², Jianrong Tan^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.011752

Abstract Motion stability assessment of industrial robots subject to complex dynamic properties and multi-source uncertainties in open environments registers an important yet challenging task [1–5]. To tackle this task, this study proposes a new reliability-based motion stability analysis method for industrial robots, which incorporates the moment-based method and Bayesian inference-guided probabilistic model updating strategy. To start with, the comprehensive motion system model of industrial robots is established by integrating the control, drive, and multi-body motion models. The reliability-based stability model of industrial robots is presented considering the uncertainty of parameters. Subsequently, the fractional exponential moments are… More >

Xi Chen^1,3, Wanru Lin¹, Yuefu Liu^2,*

Journal of Psychology in Africa, Vol.35, No.5, pp. 661-669, 2025, DOI:10.32604/jpa.2025.068787 - 24 October 2025

Abstract Accurate assessment of climate anxiety is crucial, yet the cross-cultural transportability of existing instruments remains an open question. This study translated and validated the Hogg Climate Anxiety Scale for the Chinese context. A total of 959 students (females = 69.7%; M age = 19.60 years, SD = 1.40 years) completed the Hogg Climate Anxiety Scale, with the Climate Change Anxiety Scale and the Anxiety Presence Subscale served as criterion measures for concurrent validity. Test–retest reliability was evaluated with a subset after one month. Confirmatory factor analysis supported the original four-factor structure and measurement invariance across genders.… More >