Yongfeng Zhang^1,*, Jie Chen^2,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088561 - 15 September 2026

Abstract Speaker verification on phones, wearables, and voice-enabled Internet-of-Things gateways must balance local privacy with reliable decisions under adverse audio. Existing privacy-preserving verification schemes generally protect a fixed representation, whereas edge-offloading policies usually adapt computation without attaching an explicit feature-disclosure budget; neither line alone coordinates trial uncertainty, communication state, and privacy expenditure. This paper presents privacy-preserving uncertainty-aware adaptive feature offloading (P-UAFO), an edge-intelligence framework that keeps raw audio local and transmits only clipped, projected, quantized, and Gaussian-perturbed intermediate features when their expected benefit justifies resource cost. Its online pipeline first estimates decision uncertainty and resource state,… More >

Rihem Farkh¹, Ghislain Oudinet², Alaeddine Moussa³, Yasser Fouad^4,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086607 - 15 September 2026

Abstract Autonomous driving systems must reason not only about the current scene but also about how the environment may evolve under alternative actions. Although predictive world models can generate future latent rollouts, these rollouts are often consumed directly by planners or explanation modules without an explicit and auditable interpretation stage. This paper presents a predictive and explainable driving framework centered on a Future Interpretation Module (FIM), which transforms action-conditioned future rollouts into structured descriptors, including risk trend, peak risk, time-to-critical, minimum clearance, predicted collision, dominant predicted event, and confidence. An aligned latent interface, trained with feature-alignment… More >

Chuanlin Jian¹, Yuanchen Sun², Xiangcheng Liu¹, Xuming Huang³, Samaneh Beheshti Kashi⁴, Xing Hu^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086441 - 15 September 2026

Abstract Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a… More >

Hadeel Alsolai¹, Muhammad Waqas Ahmed², Bayan Alabdullah¹, Fatimah Alhayan¹, Mohammed Alonazi³, Ahmad Jalal^4,5, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085838 - 15 September 2026

Abstract Robust scene-level classification and semantic understanding from aerial and disaster-related imagery are essential for intelligent vision systems deployed in emergency response, UAV-based monitoring, and safety-critical environments. However, existing deep learning approaches, including convolutional neural networks and Vision Transformers (ViTs), often struggle to simultaneously capture fine-grained local object characteristics and global semantic scene context, while also lacking reliable uncertainty estimation mechanisms for trustworthy decision-making. To address these limitations, this paper proposes MS-SLCA-ViT, a novel multi-scale scene–local cross-attention Vision Transformer framework for robust and uncertainty-aware image scene understanding. The proposed architecture introduces three major contributions. First, a… More >

Chin-Shiuh Shieh¹, Thanh-Lam Nguyen¹, Thanh-Tuan Nguyen^2,*, Xuan-Huy Nguyen², Chau-Tan-Phat Le², Mong-Fong Horng^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084993 - 15 September 2026

Abstract Internet of Things (IoT) intrusion detection systems face increasing pressure from adversarial attacks that can manipulate not only feature vectors but also the relational structure on which graph based models rely. This paper proposes Trust Filtered GraphSAGE (TF-SAGE), a graph based intrusion detection system (IDS) pipeline in which edges are assigned trust scores, filtered before message passing, and coupled with uncertainty aware inference to reduce overconfident decisions under unstable neighborhoods. The model is evaluated on NF-ToN-IoT-v2 as the main benchmark and CICIIoT2025 as an independent confirmation benchmark under the same FSAA and GSAA evaluation protocol.… More >

Dan Wu^1,2, Hongchen Luo¹, Daniel Hall³, Francis Cheung⁴, Yingrui Yin¹, Shanyue Li¹, Shuhan Jiang⁵, Duo Jiang^1,2,*

International Journal of Mental Health Promotion, Vol.28, No.8, 2026, DOI:10.32604/ijmhp.2026.083732 - 31 August 2026

Abstract Background: As social development accelerates and workforce competition intensifies, uncertainty is increasingly recognized as a core component of contemporary stress, particularly in the rapidly expanding platform (gig) economy. Platform delivery riders operate under algorithmic control, unstable income, and opaque evaluation systems, yet the magnitude and correlates of their uncertainty stress remain under-investigated. This study aimed to quantify the level of uncertainty stress among platform delivery riders and to identify work- and lifestyle-related factors associated with it. Methods: A cross-sectional correlational design was used. Between August and December 2022, platform delivery riders in Shenzhen and Guangzhou (two… More >

Yi Sui^*, Zhibin Song, Zhuopeng Shi, Yiliang Hao, Yawei Zhao

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.079162 - 30 August 2026

Abstract As the core hub of energy production and consumption, the scientificity of power grid investment allocation directly affects renewable energy integration and power supply reliability. However, regional development stages exhibit significant uncertainty due to factors such as policy orientation and economic-technological progress. Existing power grid investment decision-making methods mostly ignore regional development differences, making it difficult to balance regional development equity and investment efficiency. To address this, this paper proposes a two-stage robust optimization model for power grid investment considering the uncertainty of regional development stages. Firstly, a comprehensive evaluation index system covering operational, technical,… More >

Jia Chen¹, Zhicheng Liao¹, Mengdi Hou², Jianbo Huang^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085729 - 28 August 2026

Abstract Extrusion-based 3D-printed concrete (3DPC) imposes a dual constraint on mix design: fresh-state printability and hardened compressive strength must both be maintained within a narrow water-to-binder window, making data-driven prediction tools essential for reducing experimental iteration. This study evaluates 20 regression algorithms on 254 experimental records spanning plain printable mortars to high-fibre reinforced composites (CS: 11.1–189.0 MPa). Four physically motivated composite variables encoding cement blend potency, cumulative supplementary cementitious material (SCM) substitution, fibre volumetric stiffness, and water-to-sand ratio are constructed; Boruta-based selection retains 11 of 17 candidate features. CatBoost achieves the highest 30-run mean performance ($R^2=0.8968\pm 0.0505$;… More >

Shamsh Parveen¹, Mehtab Alam^2,*, Ashraf Ali³, Abdullah Alourani⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083687 - 28 August 2026

Abstract Accurate prediction of complex failure modes in anisotropic composite structures—specifically matrix cracking, fiber rupture, and delamination (stratification)—remains a central challenge in computational fracture mechanics. The primary goal of this work is to bridge the gap between high-fidelity physical modeling and computational efficiency. While the extended finite element method (XFEM) enables mesh-independent crack modeling, its computational cost limits scalability. This work proposes a deep learning–accelerated extended finite element framework (DL-XFEM) that couples physically admissible XFEM fields with a neural network surrogate to predict incremental crack growth. XFEM is employed to generate stress-intensity factors and fracture-consistent state… More >

Kai Luo^1,2,*, Yuzhi Chen³

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.079382 - 24 August 2026

Abstract Material property variability in carbon fiber-reinforced polymer composites is a major source of uncertainty in quantitative Lamb wave-based delamination imaging. Even minor deviations in elastic properties can alter dispersion characteristics and wave propagation behavior, thereby reducing the reliability of imaging-based assessments. To systematically investigate this effect, the present study examines the influence of subtle material variations on Lamb wave responses through combined numerical modeling and finite element simulations. Time-of-flight features at the excitation frequency are extracted using a continuous wavelet transform with Morlet wavelets, enabling robust identification of mode-dependent arrival information. Within a finite element… More >