Emrah Altun^1,*, Christophe Chesneau², Atacan Erdis¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083459 - 27 July 2026

Abstract Modeling bounded response variables is an important problem in computational statistics, especially in applications involving skewed, heavy-tailed data. In such cases, the modal regression is a robust alternative to traditional mean-based modeling approaches. In this study, a new bounded distribution, called the extended Bradford distribution, is proposed as a flexible extension of the classical Bradford distribution. By incorporating an additional shape parameter, the corresponding model can capture various shape structures, such as left and right skewness, increasing, and bathtub hazard shapes. The new distribution provides an explicit expression for the mode, making it suitable for More >

Ki-Young Shin¹, Soonmo Kwon², Kyudong Park^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083410 - 27 July 2026

Abstract Multimodal AI systems often suffer from “over-informing”, where excessive raw visual input introduces noise that distracts from task-relevant decisions. Motivated by selective human attention strategies, we propose ARS-MMT (Attention and Reasoning through Source Sentences for Multimodal Machine Translation), an architecture that operationalizes a “look-and-think” pipeline: a source-language encoder first builds contextualized linguistic representations, a relation reasoning network then produces a query-conditioned visual channel, and a multimodal decoder generates the translation conditioned in parallel on the encoded text and on this visual channel. We quantify the contribution of the visual modality through a controlled ablation: zeroing… More >

Kazutaka Toda¹, Yuntao Wang^1,*, Hyungrok Jo², Yang Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083407 - 27 July 2026

Abstract The rapid evolution of quantum computing poses a fundamental challenge to classical public-key cryptosystems, accelerating the adoption of lattice-based post-quantum cryptography in large-scale digital infrastructures, including Future Mobile Internet Technologies (FMIT) and their convergence applications (FMIT-CA). As lattice-based cryptography is expected to play an important role in such environments, accurate hardness estimation and parameter assessment of underlying lattice problems have become increasingly important. Since the security of these cryptographic schemes is closely related to the computational hardness of the Shortest Vector Problem (SVP), improving practical SVP-solving techniques contributes indirectly to the security evaluation of such… More >

Rana Muhammad Adnan Ikram¹, Jing-Cheng Han^1,*, Ahmed A. Ewees², Mo Wang³, Ozgur Kisi^4,5,6,*, Salim Heddam⁷, Mohammad Zounemat-Kermani⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083313 - 27 July 2026

Abstract Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four… More >

Mohammed Ansar Mohammed Manaz^1,*, Shang Ping Hong², Tzung-Lin Lee¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083270 - 27 July 2026

Abstract Silicon Carbide Metal Oxide Semiconductor Field Effect Transistors (SiC MOSFETs) have superior characteristics compared to traditional Silicon-based switching devices. SiC devices can support fast switching speeds and high blocking voltages. Due to limited historical data and rapid technological improvements, there is not enough field data to correctly evaluate the reliability of the state-of-the-art SiC MOSFETs. An accurate model of their reliability and aging characteristics is needed to expedite their rapid commercial adoption in mission-critical applications, such as offshore wind farms and electric vehicles. Classical handbook-based methods produce large errors due to their inability to correctly… More >

Junho Lee, Sanghyun Park, Younghun Lee, Namsu Kim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083185 - 27 July 2026

Abstract Monitoring the conditions of electric motors in industrial applications is an essential step for ensuring safety and reducing maintenance costs. This paper deals with one of the most frequent winding failures—the inter-turn short fault of an interior permanent magnet synchronous motor. A novel high-fidelity co-simulation framework toward a digital twin-based approach combining Maxwell simulation in finite element method (FEM) for the motor and control system in system software for the inverter is presented. An analysis of the motor based on a 2D FEM model is performed considering the motor topology and non-linear properties, and inductance… More >

Yiyan Zhang^1,*, Yi Xin², Qin Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082841 - 27 July 2026

Abstract Medical data has specificity compared to other fields of data, and the description of medical data characteristics is still in a qualitative stage. This study included 293 sub-datasets of 138 independent datasets. First, data preprocessing was performed using methods such as incomplete data removal, inconsistent data normalization, and data integration. Then, the characteristics of 293 research datasets were quantified using 26 indicators in three categories: simple indicators, statistical indicators, and informational indicators. Furthermore, statistical analysis was performed on the above-mentioned quantitative characteristics, and stepwise regression and decision tree methods were used for modeling learning. The… More >

Daniel Martín-Pérez¹, Francesc Rodríguez-Díaz¹, David Gutiérrez-Avilés², Alicia Troncoso¹, Francisco Martínez-Álvarez^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082712 - 27 July 2026

Abstract Hybrid classical-quantum architectures have emerged as a practical response to the data and compute demands of modern deep learning, since a pretrained classical backbone can carry the feature extraction while a compact quantum head provides the trainable component. Quantum transfer learning is the most active instance of this idea. However, existing quantum transfer learning pipelines have been evaluated in isolation, typically on a single software framework and without a structured treatment of noise or statistical significance, which makes it difficult to assess how this paradigm contributes over fair classical baselines. A methodological benchmark for quantum… More >

Jiawang Zou, Annan Jiang^*, Xinzhi Wang, Hao Huang

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082647 - 27 July 2026

Abstract Accurate forecasting of settlement in buildings adjacent to shield tunnels remains a critical challenge in underground engineering due to complex spatiotemporal interactions and nonlinear relationships among multi-source monitoring data and construction parameters. To address this issue, a Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Network–Long Short-Term Memory–Transformer (CNN-LSTM-Transformer) hybrid framework is proposed, explicitly incorporating the relative spatial relationship between the shield excavation face and adjacent structures. In this framework, the Convolutional Neural Network (CNN) module extracts spatial features from monitoring data and tunneling parameters, capturing interdependencies among different construction indicators and reflecting local spatial heterogeneity of… More > Graphic Abstract

José González-Cabrero^1,2, Carmelo Gómez^1,2, Manuel Paredes³, Francisco Cavas^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082643 - 27 July 2026

Abstract The development of accurate digital twin models of the human cornea is a key factor for planning and monitoring eye treatments and clinical supervision. Corneal tissue can be simulated with the implementation of hyperelastic models based on strain energy density functions. However, the number of hyperelastic models and the parameters’ variation that define these models hinder comparison across different studies. Furthermore, parameter calculations based on a single test are an ill-posed problem. In this research, a novel sequential methodology based on collagen fibril crimping strain threshold has been implemented to calculate the corneal material’s parameters. More > Graphic Abstract