Chia-Hui Liu^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.072887 - 09 December 2025

Abstract The Industrial Internet of Things (IIoT) has emerged as a cornerstone of Industry 4.0, enabling large-scale automation and data-driven decision-making across factories, supply chains, and critical infrastructures. However, the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping, data tampering, and device impersonation. While digital signatures are indispensable for ensuring authenticity and non-repudiation, conventional schemes such as RSA and ECC are vulnerable to quantum algorithms, jeopardizing long-term trust in IIoT deployments. This study proposes a lightweight, stateless, hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT. The… More >

Tanvir Fatima Naik Bukht^1,2, Yanfeng Wu¹, Nouf Abdullah Almujally³, Shuoa S. AItarbi⁴, Hameedur Rahman², Ahmad Jalal^2,5,*, Hui Liu^1,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4013-4036, 2025, DOI:10.32604/cmes.2025.071850 - 23 December 2025

Abstract Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining… More >

R. Yoga Indra Eniya¹, K. Vijayakumar², B. Vigneashwari^3,*

Chalcogenide Letters, Vol.22, No.11, pp. 971-985, 2025, DOI:10.15251/CL.2025.2211.971

Abstract Nanocrystals (~16 nm) of semiconducting lead sulphide (PbS) were synthesized using the coprecipitation method, which was characterized for phase and compositional purity. These ultrafine particles of PbS exhibited quantum confinement characteristics, which were revealed by blue-shifting in optical absorption using UV-DRS analysis. These QDs of PbS were driven under the influence of the applied electric field using monodispersed colloidal suspension on the Indium-Tin-Oxide (ITO) substrate using the electrophoretic deposition technique (EPD). The formation of self-organized arrays of PbS quantum dots (QDs) and their stacked assemblies was achieved through EPD. Interestingly, neither complexing agents nor templates More >

Rodolphe Griset^1,#,*, Ioannis Lavdas^2,§, Jiří Guth Jarkovský³

Journal of Quantum Computing, Vol.7, pp. 81-105, 2025, DOI:10.32604/jqc.2025.066064 - 08 December 2025

Abstract Quantum search has emerged as one of the most promising fields in quantum computing. State-of-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution. These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces. Unfortunately, the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure, rendering this kind of… More >

Fatemeh Mollaamin^1,*, Majid Monajjemi²

Energy Engineering, Vol.122, No.12, pp. 4971-4986, 2025, DOI:10.32604/ee.2025.069945 - 27 November 2025

Abstract Delving alternative high-performance anodes for lithium-ion batteries have always attracted scientist attention. A wide-bandgap semiconductor with excellent mechanical properties, “silicon carbide (SiC)”, has been introduced as the anode electrode. Two-dimensional SiC has special hybridization which can build it as an appropriate substitution for graphene. Energy storage technologies are keys in the extension and function of electric devices. To keep up with steady innovations in saving energy technologies, it is essential to progress corresponding practical strategies. In this research article, SiC has been designed and characterized as an anode electrode for lithium (Li), sodium (Na), beryllium… More > Graphic Abstract

Yazeed Alkhrijah¹, Marwa Fahim², Syed Muhammad Usman³, Qasim Mehmood³, Shehzad Khalid^4,5,*, Mohamad A. Alawad¹, Haya Aldossary⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2339-2355, 2025, DOI:10.32604/cmes.2025.071512 - 26 November 2025

Abstract Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a… More >

Abrar M. Alajlan^1,*, Abdul Razaque²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 919-946, 2025, DOI:10.32604/cmes.2025.071996 - 30 October 2025

Abstract Biometric authentication provides a reliable, user-specific approach for identity verification, significantly enhancing access control and security against unauthorized intrusions in cybersecurity. Unimodal biometric systems that rely on either face or voice recognition encounter several challenges, including inconsistent data quality, environmental noise, and susceptibility to spoofing attacks. To address these limitations, this research introduces a robust multi-modal biometric recognition framework, namely Quantum-Enhanced Biometric Fusion Network. The proposed model strengthens security and boosts recognition accuracy through the fusion of facial and voice features. Furthermore, the model employs advanced pre-processing techniques to generate high-quality facial images and voice… More >

Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

Zhen Gong^#, Siyu Chen^#, Zhenyu Feng, Dawang Li, Le Zhang, Meiting Xu, Yanping Lin, Huixin Huang, Dan Jiang, Caiyi Wu, Yichun Ke, Zhonghui Du^*, Ning Zhao, Hongbo Liu^*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.9, pp. 2101-2129, 2025, DOI:10.32604/fdmp.2025.068946 - 30 September 2025

Abstract Quantum dot inks (QDIs) represent an emerging functional material that integrates nanotechnology and fluid engineering, demonstrating significant application potential in flexible optoelectronics and high-color gamut displays. Their wide applicability is due to a unique quantum confinement effect that enables precise spectral tunability and solution-processable properties. However, the complex fluid dynamics associated with QDIs at micro-/nano-scales severely limit the accuracy of inkjet printing and pattern deposition. This review systematically addresses recent advances in the hydrodynamics of QDIs, establishing scientific mechanisms and key technical breakthroughs from an interdisciplinary perspective. Current research has focused on three optimization directions:… More >

Sunawar Khan¹, Tehseen Mazhar^1,2,*, Naila Sammar Naz¹, Fahed Ahmed¹, Tariq Shahzad³, Atif Ali⁴, Muhammad Adnan Khan^5,*, Habib Hamam^6,7,8,9

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2347-2401, 2025, DOI:10.32604/cmc.2025.066932 - 23 September 2025

Abstract Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron… More >