Abdulrahman K. Sinno¹, Aisha Mustapha¹, Navya Nair¹, Simona Zaami², Lina De Paola², Valentina Billone³, Eleonora Conti³, Giuseppe Gullo^3,*, Pasquale Patrizio⁴

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.072176 - 30 December 2025

Abstract Cervical cancer related to human papillomavirus (HPV) is a leading cause of cancer-related mortality among women worldwide. Cancer cells release fragments of their DNA, known as circulating tumor DNA (ctDNA), which can be detected in bodily fluids. A PubMed search using the terms “ctHPV” or “circulating tumor DNA” and “cervical cancer”, limited to the past ten years, identified 104 articles, complemented by hand-searching for literature addressing medico-legal implications. Studies were evaluated for relevance and methodological quality. Detection and characterization of circulating tumor HPV DNA (ctHPV DNA) have emerged as promising tools for assessing prognosis and More >

M. N. Abd-Al Ameer, Iman S. Kareem, Ali A. Ismaeel^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.073437 - 27 December 2025

Abstract Electrical and electronic devices face significant challenges in heat management due to their compact size and high heat flux, which negatively impact performance and reliability. Conventional cooling methods, such as forced air cooling, often struggle to transfer heat efficiently. In contrast, thermoelectric coolers (TECs) provide an innovative active cooling solution to meet growing thermal management demands. In this research, a refrigerant based on mono ethylene glycol and distilled water was used instead of using gases, in addition to using thermoelectric cooling units instead of using a compressor in traditional refrigeration systems. This study evaluates the… More > Graphic Abstract

K. Naga Venkata Siva¹, Damodhar Reddy², P. Krishna Murthy³, Kiran Kumar Pulamolu⁴, M. Dharani⁵, T. Venkatakrishnamoorthy^6,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.072982 - 27 December 2025

Abstract Conventional multilevel inverters often suffer from high harmonic distortion and increased design complexity due to the need for numerous power semiconductor components, particularly at elevated voltage levels. Addressing these shortcomings, this work presents a robust 15-level Packed U Cell (PUC) inverter topology designed for renewable energy and grid-connected applications. The proposed system integrates a sensor less proportional-resonant (PR) controller with an advanced carrier-based pulse width modulation scheme. This approach efficiently balances capacitor voltage, minimizes steady-state error, and strongly suppresses both zero and third-order harmonics resulting in reduced total harmonic distortion and enhanced voltage regulation. Additionally, More >

Guangyu Huo, Chang Su, Xiaoyu Zhang^*, Xiaohui Cui, Lizhong Zhang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.072147 - 09 December 2025

Abstract Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space More >

Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >

Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-49, 2026, DOI:10.32604/cmc.2025.070918 - 09 December 2025

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Maryan Rizinski^1,2,*, Dimitar Trajanov^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-34, 2026, DOI:10.32604/cmc.2025.069678 - 10 November 2025

Abstract Artificial intelligence (AI) is reshaping financial systems and services, as intelligent AI agents increasingly form the foundation of autonomous, goal-driven systems capable of reasoning, learning, and action. This review synthesizes recent research and developments in the application of AI agents across core financial domains. Specifically, it covers the deployment of agent-based AI in algorithmic trading, fraud detection, credit risk assessment, robo-advisory, and regulatory compliance (RegTech). The review focuses on advanced agent-based methodologies, including reinforcement learning, multi-agent systems, and autonomous decision-making frameworks, particularly those leveraging large language models (LLMs), contrasting these with traditional AI or purely… More >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Zheng Yao^*, Puqing Chang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068248 - 10 November 2025

Abstract As Internet of Things (IoT) applications expand, Mobile Edge Computing (MEC) has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices. Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies, conflicting objectives, and limited resources. This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC. We jointly consider task heterogeneity, high-dimensional objectives, and flexible resource scheduling, modeling the problem as a Many-objective optimization. To solve it, we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on More >

Kyung-Hyun Suh^1,*, Sung-Jin Chung¹, Goo-Churl Jeong¹, Kunho Lee¹, Ji-Hyun Ryu²

International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1829-1845, 2025, DOI:10.32604/ijmhp.2025.071879 - 31 December 2025

Abstract Background: Adolescents increasingly engage with social media for connection, self-expression, and identity exploration. This growing digital engagement has raised concerns about its potential risks and mental health implications. Methods: This narrative review examines literature on adolescent social media use by exploring underlying motivations, risk and protective factors across personal, environmental, and digital domains, with a focus on mental health outcomes. Results: Individual vulnerabilities—such as low self-esteem, impulsivity, and poor sleep—interact with contextual factors like peer pressure and family conflict to elevate risks. Digital environments shaped by algorithmic feeds, feedback mechanisms, and curated content promote social comparison and More >