Sathishkumar Veerappampalayam Easwaramoorthy^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.087043 - 27 July 2026

Abstract This article has no abstract. More >

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

Adrián Bañuls-Arias, Cipriano Galindo, Ana Cruz-Martín, Manuel Castellano-Quero, Juan M. Gandarias, Juan-Antonio Fernández-Madrigal, Vicente Arévalo-Espejo^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085976 - 27 July 2026

Abstract The basic operation of a mobile robot is navigating to some target, avoiding collisions and possibly minimizing other criteria. A diversity of methods have been developed since the past century, and the research is still active, but there is one aspect that is often neglected: the duration of the steps in which computational devices divide the navigation process. Usually, it is set heuristically to a small, constant value for sampling observations frequently enough to ensure safety; however, each robot and environment has particularities that can make such a fixed timestep sub-optimal under some criteria. This… More >

Guangyu Xu^1,2, Yuxi Ban¹, Legend Zhang³, Junmin Lyu³, Feng Bao⁴, Wenfeng Zheng^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085484 - 27 July 2026

Abstract High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence (GeoAI). Existing CNN-based methods are effective for local and multiscale feature extraction but often lack progressive cross-scale semantic propagation, while attention- and Transformer-based methods improve global spatial modeling but generally ignore frequency-domain regularities. To address these limitations, this study proposes a Multiscale Long-Distance Feature Aggregation Network (MLFANet), a unified spatial-frequency segmentation framework for high-resolution remote sensing imagery. MLFANet introduces three key components: a Multiscale Global Dependency Extraction module for cascaded cross-scale contextual refinement, an FFT-based frequency-domain branch with learnable global filtering for… More >

Jorge Gómez-Navalón, Ronghao Pan, Tomas Bernal-Beltrán, José Antonio García-Díaz^*, Rafael Valencia-García

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085437 - 27 July 2026

Abstract Traditionally, speech emotion recognition has relied on supervised models that require task-specific training and annotated data. However, the recent emergence of audio-language models introduces a more flexible paradigm that enables multimodal reasoning through speech and natural language interaction. Nevertheless, their effectiveness for emotion recognition remains unclear. In this study, we evaluate audio-language models for speech emotion classification using the Spanish MEACorpus dataset and compare three approaches: prompt-based inference, embedding-based classification with lightweight classifiers, and instruction-tuned models with parameter-efficient fine-tuning plus a hybrid architecture based on class-specific confidence-driven routing. Our results show that the hybrid approach… More >

Ali Raza^1,*, Umar Shafique¹, Marek Lampart¹, Dumitru Baleanu², Emad Fadhal³, Hadil Alhazmi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084939 - 27 July 2026

Abstract Kawasaki disease (KD) is an acute, self-limited pediatric vasculitis of unknown etiology and is one of the leading causes of acquired coronary artery complications in children. Endothelial dysfunction, vascular endothelial growth factor (VEGF) activity, adhesion molecule/chemokine activation, and inflammatory cytokine responses play important roles in its pathogenesis. This paper presents a delay differential equation model with stochastic perturbations to study lesion-level inflammatory mechanisms involved in Kawasaki disease pathogenesis. The model describes interactions among healthy endothelial cells, vascular endothelial growth factor (VEGF), adhesion molecules/chemokines, and inflammatory cytokine activity. Mathematically, endothelial-cell injury promotes VEGF production, VEGF contributes… More >

Sachin Tom, Eva Gutheil^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084886 - 27 July 2026

Abstract A single droplet heating, vaporization, and detailed combustion model is developed for pure p-xylene (p-C₈H₁₀) in hot air. p-C₈H₁₀ is a combustible solvent in precursor solutions, for instance, with titanium tetraisopropoxide (TTIP) for the production of TiO2 nanoparticles. In the present one-dimensional mathematical model, a spherically symmetric p-xylene droplet in hot air is considered, resolving both the droplet (liquid phase) and the ambience (gas phase). The calculation of the vaporization rate includes the Stefan velocity at the droplet surface. In the gas phase, a detailed chemical reaction scheme is used. Elementary reactions are… More >

Tanzila Saba¹, Muhammad Mujahid¹, Faten S. Alamri^2,*, Roaa Khalil Mohamed Ali Abed³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084816 - 27 July 2026

Abstract In the era of artificial intelligence, pattern recognition techniques have become fundamental in advancing medical image processing, diagnosis, and automated disease classification systems. Among various clinical challenges, breast cancer is the second most dangerous leading cause of death in women worldwide. Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates. This study proposes a dynamic graph multi-scale network for breast cancer diagnosis, integrated with multi-scale convolutional feature extraction, a squeeze-and-excitation block, and a graph convolutional network to jointly model local spatial features… More > Graphic Abstract

Yunus Celik^*, Burhan Necati Kiziloglu

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084744 - 27 July 2026

Abstract This study investigates the passive amplification of aerodynamic excitation forces through coordinated bluff-body geometric modifications to quantify the vortex-induced vibration (VIV) energy harvesting potential of stationary cylinders in the laminar regime. Two-dimensional laminar simulations on fixed bodies isolate geometric effects from structural feedback. Circumferential biomimetic grooves are first optimised on a circular baseline at Re=200 using a Taguchi orthogonal array (L9), identifying groove amplitude as the dominant control parameter and selecting N=24, Amp=5% as the optimal configuration, which yields a 21% increase in the root-mean-square lift coefficient (Cℓ,rms) and… More >

Muhammad Zaheer Sajid¹, Muhammad Fareed Hamid², Nauman Ali Khan^2,3,*, Imran Qureshi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084724 - 27 July 2026

Abstract Digital pathology is rapidly transforming histopathological diagnosis, yet many existing deep learning models treat all spatial regions uniformly and do not exploit the multi-frequency structure of tissue, which limits both diagnostic accuracy and computational efficiency. This paper proposes WaSA-Net, an end-to-end architecture that integrates three complementary modules for histopathological image analysis. First, the Wavelet-Guided Tokenization (WGT) module decomposes input images into frequency-aware representations using learnable wavelet-like filters, so that both global tissue structures and fine-grained cellular patterns are exposed to attention from the first layer. Second, the Dynamic Sparse Attention with Pathology Priors (DSA-PP) module… More >