Chanyoung Kim, Jion Kim, Byeong-Seok Shin^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080992 - 15 June 2026

Abstract Diffusion-based methods have substantially improved the performance of full-body Text-to-Motion (T2M) generation from natural language descriptions. Despite this progress, accurately capturing the fine-grained semantics of composite prompts remains challenging. Approaches that rely solely on a single global text condition often fail to retain part-specific semantic cues, leading to deviations in the motions of certain body parts from the intended descriptions. Recent methods have attempted to address this by incorporating both global and local conditions, yet these are typically combined using fixed ratios or applied in separate stages, which restricts their adaptability to evolving semantic requirements… More > Graphic Abstract

Xiangqin Chen^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079663 - 15 June 2026

Abstract Simultaneous localization and mapping (SLAM) must remain reliable when sensing suites and operating conditions vary across platforms and deployments. Beyond correspondence degradation, a dominant deployment failure mode is misweighted constraints: under distribution shift, uncertainty estimates can become miscalibrated, allowing a small set of overconfident factors to dominate iterative optimization and destabilize inference. This article presents conformal-calibrated foundation-factor graph SLAM (CF2-SLAM), a sensor-agnostic framework that combines frozen foundation representations with lightweight probabilistic factor heads that emit explicit residuals and covariances, and a classical factor-graph back-end for principled multi-modal fusion. To mitigate systematic misweighting under shift, More >

Aihua Wu, Chenlu Huang^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078692 - 15 June 2026

Abstract Three-dimensional (3D) point cloud semantic segmentation is a core task in indoor scene understanding, providing detailed semantic information about spatial structures and object categories in indoor environments. Although methods based on deep learning have made steady progress in recent years, accurately segmenting complex indoor scenes remains challenging due to the unordered nature of point clouds and variations across large scales. Most existing networks have limited capability for multi-scale feature aggregation and struggle to balance local geometric details with global semantic context. These issues are further exacerbated by hierarchical downsampling, which often leads to the loss… More >

Marco Roccetti^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.082258 - 27 May 2026

Abstract Geospatial health risk signals, characterized by associations with high magnitude statistical significance, may frequently originate from circumscribed observational data streams. When these signals are fueled by massive N-size datasets, the large dimensional scale of the sample can induce a misleading interpretation of local evidence as a statistically significant risk inflation. The objective of this study is to verify whether such health risk configurations constitute geospatial structural artifacts: namely, stochastic distortions generated by the spatial information of local health repositories that, despite their massive scale, may remain fundamentally distant from broader contextual realities. To this aim,… More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

T. I. Eldho^1,*, Sanjukta Das¹, Aatish Anshuman², Tinesh Pathania³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078705 - 27 May 2026

Abstract In recent years, meshless methods have been increasingly applied to the simulation of various engineering problems due to their inherent advantages over traditional mesh-based approaches, including greater flexibility, independence from predefined meshing, simpler adaptive analysis, improved automation, and suitability for complex problems. Several meshless methods have been used for porous media simulation, and are broadly categorized into collocation, global weak form and local weak form methods. In this study, a comprehensive comparison of the applicability of these three categories of meshless methods for simulating coupled flow and transport problems in porous media is presented. The… More > Graphic Abstract

Thanh Tuan Nguyen¹, Cuong Nguyen Dinh Hoa^2,3,*

Intelligent Automation & Soft Computing, Vol.41, pp. 1-24, 2026, DOI:10.32604/iasc.2026.078344 - 12 May 2026

Abstract Accurate crude oil price forecasting is critical for global economic stability but remains an exceptionally challenging task due to the data’s complex, non-linear, and non-stationary nature. Deep learning models like LSTMs are widely favored. However, the dominant research trend currently focuses on increasingly complex hybrid and ensemble architectures. These models often suffer from high computational overhead, intricate tuning processes, and potential overfitting, raising critical questions about their necessity. In this paper, we challenged the assumption that complexity is required for high performance by proposing and evaluating a streamlined 1D-CNN model. We conducted a comprehensive evaluation… More >

Sajid Ullah Khan^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079440 - 08 May 2026

Abstract Healthcare, education, technological advancement, and farming are the key challenges facing developing countries, with agriculture unquestionably playing an important role in economic growth. Ensuring adequate food production is essential for citizens’ survival, as it is anticipated that efforts in this area would result in increased food productivity. A key approach to enhancing field productivity involves meticulous care of its components, starting with the production of crops. Wheat leaf rust poses a severe threat, particularly to young seedlings, constituting a significant fungal disease that can cause a 25% reduction in wheat productivity. To overcome these issues,… More >

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Raja Azlina Raja Mahmood¹, Faten A. Saif², Huda Althumali³, Chedia Jarray⁴, Umar Ali Bukar⁵, Mohammed Sani Adam⁶

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.074678 - 08 May 2026

Abstract Non-terrestrial networks (NTNs), encompassing unmanned aerial vehicles (UAVs), low-/high-altitude platforms (LAPs/HAPs), and satellite systems, are increasingly enabling Internet of Things (IoT) applications beyond the limits of terrestrial infrastructure. By combining UAV mobility with satellite and HAP coverage, NTN-assisted IoT supports diverse use cases, including remote sensing, smart cities, intelligent transportation, and emergency response. This paper presents a systematic mapping of machine learning (ML) research in NTN-assisted IoT with a focus on security-related aspects. A keyword co-occurrence analysis of over 2000 publications identifies twelve thematic clusters, including three clusters directly related to security, privacy, and trust.… More >

Linkang Wang¹, Bowei Xie^2,3,4,*, Zhiqiang Liu¹, Lijing Yi^2,3,4, Mu Du^2,3,4,*

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2026.076486 - 30 April 2026

Abstract The Local Surface Plasma Resonance (LSPR) of spherical metal particles is typically only observed within the visible spectrum. This inherent property renders modulation through alterations in radius or material challenging, significantly constraining its practical applications. In this work, we propose a super-elliptic gold nanoparticle model that allows for the continuous modulation of particle geometry from spherical to star-like shapes using a single roundness parameter (e). Unlike conventional nanorods or discrete nanostars, this geometry provides a unified framework to investigate the evolution of multipole resonances. The radiation characteristics of super elliptic gold nanoparticles in the range of… More >