Dae-Kyoo Kim^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2549-2572, 2025, DOI:10.32604/cmc.2025.066190 - 03 July 2025

Abstract Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems. However, it must also adhere to critical quantum constraints, notably the no-cloning theorem, which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography, secure communication, and error correction. While existing quantum circuit representations implicitly honor such constraints, they lack formal mechanisms for early-stage verification in software design. Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software. This paper presents a formal… More >

Jianping Li, Wan Xiong, Tenghang Zhang, Hao Cheng, Kun Shen, Miaojin He, Yu Zhang, Junxin Song, Ying Deng^*, Qiaowang Chen^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2189-2216, 2025, DOI:10.32604/cmc.2025.066128 - 03 July 2025

Abstract Tungsten carbide-based (WC-based) cemented carbides are widely recognized as high-performance tool materials. Traditionally, single metals such as cobalt (Co) or nickel (Ni) serve as the binder phase, providing toughness and structural integrity. Replacing this phase with high-entropy alloys (HEAs) offers a promising approach to enhancing mechanical properties and addressing sustainability challenges. However, the complex multi-element composition of HEAs complicates conventional experimental design, making it difficult to explore the vast compositional space efficiently. Traditional trial-and-error methods are time-consuming, resource-intensive, and often ineffective in identifying optimal compositions. In contrast, artificial intelligence (AI)-driven approaches enable rapid screening and… More >

Tien-Tuan Dao¹, Lan-Nhi Tran-Ngoc^2,3, Trong-Pham Nguyen-Huu^2,3, Khanh-Linh Dinh-Bui^2,3, Nhat-Minh Nguyen^2,3, Ngoc-Bich Le^2,3, Tan-Nhu Nguyen^2,3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3345-3369, 2025, DOI:10.32604/cmc.2025.065279 - 03 July 2025

Abstract Skull structures are important for biomechanical head simulations, but they are mostly reconstructed from medical images. These reconstruction methods harm the human body and have a long processing time. Currently, skull structures can be straightforwardly predicted from the head, but a full head shape must be available. Most scanning devices can only capture the face shape. Consequently, a method that can quickly predict the full skull structures from the face is necessary. In this study, a novel face-to-skull prediction procedure is introduced. Given a three-dimensional (3-D) face shape, a skull mesh could be predicted so… More >

Dong-Hyuk Shin¹, Jae-Jun Ha¹, Ieck-Chae Euom^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3795-3838, 2025, DOI:10.32604/cmes.2025.066727 - 30 June 2025

Abstract The accelerated global adoption of electric vehicles (EVs) is driving significant expansion and increasing complexity within the EV charging infrastructure, consequently presenting novel and pressing cybersecurity challenges. While considerable effort has focused on preventative cybersecurity measures, a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents, a gap exacerbated by system heterogeneity, distributed digital evidence, and inconsistent logging practices which hinder effective incident reconstruction and attribution. This paper addresses this critical need by proposing a novel, data-driven forensic framework tailored to the EV charging infrastructure, focusing on the systematic identification, classification,… More >

Amani Baazeem¹, Muhammad Shoaib Arif^2,*, Yasir Nawaz³, Kamaleldin Abodayeh²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3463-3489, 2025, DOI:10.32604/cmes.2025.066299 - 30 June 2025

Abstract The numerical approximation of stochastic partial differential equations (SPDEs), particularly those including q-diffusion, poses considerable challenges due to the requirements for high-order precision, stability amongst random perturbations, and processing efficiency. Because of their simplicity, conventional numerical techniques like the Euler-Maruyama method are frequently employed to solve stochastic differential equations; nonetheless, they may have low-order accuracy and lower stability in stiff or high-resolution situations. This study proposes a novel computational scheme for solving SPDEs arising from a stochastic SEIR model with q-diffusion and a general incidence rate function. A proposed computational scheme can be used to… More >

Kirubavathi G.^1,*, Arjun Pulliyasseri¹, Aswathi Rajesh¹, Amal Ajayan¹, Sultan Alfarhood^2,*, Mejdl Safran², Meshal Alfarhood², Jungpil Shin³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3005-3031, 2025, DOI:10.32604/cmes.2025.065698 - 30 June 2025

Abstract The exponential expansion of the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Transportation Management of Things (TMoT) produces vast amounts of real-time streaming data. Ensuring system dependability, operational efficiency, and security depends on the identification of anomalies in these dynamic and resource-constrained systems. Due to their high computational requirements and inability to efficiently process continuous data streams, traditional anomaly detection techniques often fail in IoT systems. This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems. Extensive experiments were carried out on multiple real-world datasets, achieving… More >

Tawfeeq Shawly^1,2, Ahmed A. Alsheikhy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3033-3064, 2025, DOI:10.32604/cmes.2025.065264 - 30 June 2025

Abstract Epilepsy is a long-term neurological condition marked by recurrent seizures, which result from abnormal electrical activity in the brain that disrupts its normal functioning. Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models, which inadequately represent the continuous dynamics of electroencephalogram (EEG) signals. To overcome this limitation, we introduce an innovative approach that employs Neural Ordinary Differential Equations (NODEs) to model EEG signals as continuous-time systems. This allows for effective management of irregular sampling and intricate temporal patterns. In contrast to conventional techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural… More >

Yi Yang, Che-Yu Lin^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2767-2783, 2025, DOI:10.32604/cmes.2025.064459 - 30 June 2025

Abstract In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries, it is important to develop science-based strategies for optimally designing training programs. The purpose of the present study is to develop a novel method by the combined use of optimal control theory and a training-performance model for designing optimal training programs, with the hope of helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining. The training-performance model used in the proposed optimal… More >

Sahbi Boubaker^1,*, Sameer Al-Dahidi², Faisal S. Alsubaei³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3583-3614, 2025, DOI:10.32604/cmes.2025.063957 - 30 June 2025

Abstract The transportation and logistics sectors are major contributors to Greenhouse Gase (GHG) emissions. Carbon dioxide (CO₂) from Light-Duty Vehicles (LDVs) is posing serious risks to air quality and public health. Understanding the extent of LDVs’ impact on climate change and human well-being is crucial for informed decision-making and effective mitigation strategies. This study investigates the predictability of CO₂ emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers, their CO₂ emission levels, and key influencing factors. Specifically, six Machine Learning (ML) algorithms, ranging from simple linear models to complex non-linear models, were applied under… More >

Mohammed Althubyani^1,*, Nidal E. Taha², Khdija O. Taha², Rasmiyah A. Alharb², Sayed Saber^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3491-3521, 2025, DOI:10.32604/cmes.2025.061640 - 30 June 2025

Abstract This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo (ABC) sense. Unlike traditional epidemiological models that rely on classical or Caputo fractional derivatives, the proposed model incorporates nonlocal memory effects, hereditary properties, and complex transmission dynamics through fractal-fractional calculus. The Atangana-Baleanu operator, with its non-singular Mittag-Leffler kernel, ensures a more realistic representation of disease progression compared to classical integer-order models and singular kernel-based fractional models. The study establishes the existence and uniqueness of the proposed system and conducts a comprehensive stability analysis, including local More >