Abeer S. Hadad¹, Fahman Saeed², Adeeb A. Ahmed^3,*, Jiangbin Zheng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083873 - 27 July 2026

Abstract Loop invariant inference is fundamental to program verification, yet it remains particularly challenging for multipath loops, where different execution paths may exhibit incompatible behaviors across feasible executions. In such settings, invariants that are both sound and sufficiently precise often require disjunctive forms, whose automatic inference remains difficult. This paper presents an efficient, path-sensitive, counterexample-guided framework for automated loop invariant inference. Our approach leverages a Path Dependency Automaton (PDA) to systematically decompose the semantics of multipath loops by modeling feasible execution paths independently. Building on this decomposition, we introduce a localized, path-guided Counterexample-Guided Invariant Refinement (CEGIR) More >

Alireza Norouzpour Shahrbejari¹, Nafiseh Pishbin², Mohammad Reza Maghami^3,*, Mazlan Mohamed^4,*, Mohammad Golmohammad¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083763 - 27 July 2026

Abstract This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting, sizing, and scenario-based operation of energy storage systems (ESSs) in renewable-integrated distribution networks. The proposed model concurrently addresses technical, economic, and reliability objectives—minimizing active power losses (PL), voltage deviation (VD), expected energy not supplied (EENS), and short-circuit level (SCL), while maximizing voltage sensitivity index (VSI) and power-loss sensitivity factor (PLSF). A Particle Swarm Optimization (PSO) algorithm with weighted-sum scalarization is employed to solve this complex, nonlinear optimization problem and effectively balance the conflicting operational goals. The framework is validated using IEEE… More >

Sahil Tripathi¹, Manaswi Kulahara², Abdul Khader Jilani Saudagar³, Hatoon S. AlSagri^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083755 - 27 July 2026

Abstract Spatial reasoning, defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding. However, existing works such as Bidirectional Encoder Representations from Transformers (BERT)-based spatial Question Answering (QA) models and neuro-symbolic models rely on dataset-specific patterns, leading to shortcut learning, where reliance on superficial lexical cues rather than true relational understanding. Recent Large Language Models (LLMs)-based works, including fine-tuning and Chain-of-Thought (CoT) prompting, partially alleviate shortcut learning but remain limited by non-causal reasoning, where predictions depend on spurious correlations rather than stable relational structure. To address these… More >

Kiran Asma, Muhammad Asif Zahoor Raja^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083532 - 27 July 2026

Abstract The accelerated digital transformation of critical infrastructure has yielded unprecedented system interconnectivity, enhancing operational efficiency, simultaneously expanding the epidemiological propagation surface in heterogeneous networks. A novel machine learning-driven neuroarchitecture is designed in the present study, leveraging multilayer autoregressive exogenous neural networks (ARXNNs) iteratively trained with the Levenberg Marquardt (LM) algorithm, i.e., ARXNNs-LM, to address the intricate temporal dynamics of nonlinear fractional epidemiological computer virus propagation in the networks. The proposed ARXNNs-LM methodology effectively models the dynamic state transitions between susceptible, infected, and recovered systems. The dataset is synthesized through the application of the Grünwald–Letnikov (GL)… 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