Zhuoxi Feng^#,1, Jinyang Liu^#,2, Zihao Wu¹, Ziran Geng¹, Zhimin Liu^1,*

Congenital Heart Disease, Vol.20, No.5, pp. 625-636, 2025, DOI:10.32604/chd.2025.069214 - 30 November 2025

Abstract Congenitally corrected transposition of the great arteries (CCTGA) is a rare congenital heart disease characterized by atrioventricular, ventriculoarterial, and conduction system discordance, commonly accompanied by atrioventricular block (AVB). Pacing in patients with CCTGA and AVB (both pediatric and adult) poses challenges in strategy selection, procedural complexity, and clinical decision-making due to limited evidence. Conventional morphological left ventricular pacing is widely adopted but may induce ventricular dyssynchrony, heart failure, and tricuspid valve dysfunction. While cardiac resynchronization therapy serves as an upgrade for pacing-induced cardiomyopathy and heart failure, its application may be limited by coronary sinus anatomical… More >

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1303-1347, 2025, DOI:10.32604/cmes.2025.071302 - 26 November 2025

Abstract Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the More >

Hamed Alqahtani¹, Gulshan Kumar^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1439-1479, 2025, DOI:10.32604/cmes.2025.067738 - 31 August 2025

Abstract Domain Generation Algorithms (DGAs) continue to pose a significant threat in modern malware infrastructures by enabling resilient and evasive communication with Command and Control (C&C) servers. Traditional detection methods—rooted in statistical heuristics, feature engineering, and shallow machine learning—struggle to adapt to the increasing sophistication, linguistic mimicry, and adversarial variability of DGA variants. The emergence of Large Language Models (LLMs) marks a transformative shift in this landscape. Leveraging deep contextual understanding, semantic generalization, and few-shot learning capabilities, LLMs such as BERT, GPT, and T5 have shown promising results in detecting both character-based and dictionary-based DGAs, including… More >

Chunhong Guo¹, Huifang Liu², Jingfu Guo^3,*

Journal of Psychology in Africa, Vol.35, No.3, pp. 411-418, 2025, DOI:10.32604/jpa.2025.065763 - 31 July 2025

Abstract We explored the effects of algorithmic opacity on employees’ playing dumb and evasive hiding rather than rationalized hiding. We examined the mediating role of job insecurity and the moderating role of employee-AI collaboration. Participants were 421 full-time employees (female = 46.32%, junior employees = 31.83%) from a variety of organizations and industries that interact with AI. Employees filled out data on algorithm opacity, job insecurity, knowledge hiding, employee-AI collaboration, and control variables. The results of the structural equation modeling indicated that algorithm opacity exacerbated employees’ job insecurity, and job insecurity mediated between algorithm opacity and More >

Hossana Maghiri Sufficient^*, Abdulazeez Murtala Mohammed, Bashir Danjuma

Journal of Cyber Security, Vol.7, pp. 239-253, 2025, DOI:10.32604/jcs.2025.066312 - 14 July 2025

Abstract The rapid integration of artificial intelligence (AI) into ethical hacking practices has transformed vulnerability discovery and threat mitigation; however, it raises pressing ethical questions regarding responsibility, justice, and privacy. This paper presents a PRISMA-guided systematic review of twelve peer-reviewed studies published between 2015 and March 2024, supplemented by Braun and Clarke’s thematic analysis, to map four core challenges: (1) autonomy and human oversight, (2) algorithmic bias and mitigation strategies, (3) data privacy preservation mechanisms, and (4) limitations of General Data Protection Regulation (GDPR) and the European Union’s AI Act in addressing AI-specific risks, alongside the… More >

K. Sinivasan¹, N. Vishnu Ganesh^1,*, G. Hirankumar², M. Al-Mdallal Qasem^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1029-1049, 2025, DOI:10.32604/fdmp.2025.064503 - 30 May 2025

Abstract In this study, an analytical investigation is carried out to assess the impact of magnetic field-dependent (MFD) viscosity on the momentum and heat transfers inside the boundary layer of a Jeffrey fluid flowing over a horizontally elongating sheet, while taking into account the effects of ohmic dissipation. By applying similarity transformations, the original nonlinear governing equations with partial derivatives are transformed into ordinary differential equations. Analytical expressions for the momentum and energy equations are derived, incorporating the influence of MFD viscosity on the Jeffrey fluid. Then the impact of different parameters is assessed, including magnetic More >

Nguyen Khac Toan¹, Ho Nguyen Anh Tuan², Nguyen Truong Thinh^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1273-1295, 2025, DOI:10.32604/cmes.2025.064218 - 30 May 2025

Abstract This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods. The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery. The approach leverages advanced image processing techniques, 3D Morphable Models (3DMM), and deformation techniques to overcome the limitations of deep learning models, particularly addressing the interpretability issues commonly encountered in medical applications. The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm. Sub-landmarks… More > Graphic Abstract

Xinchao Han^1,2, Aojun Zhang^1,2, Runchuan Li^1,2,*, Shengya Shen³, Di Zhang^1,2, Bo Jin^1,2, Longfei Mao^1,2, Linqi Yang^1,2, Shuqin Zhang^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3443-3465, 2025, DOI:10.32604/cmc.2024.059403 - 17 February 2025

Abstract Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, More >

Manouk H. C. Linderhof^1,#, Hoang H. Nguyen^1,2,#, Annemien E. van den Bosch¹, Mathijs S. van Schie¹, Vehpi Yildirim¹, Yannick J. H. J. Taverne³, Natasja M. S. de Groot^1,*

Congenital Heart Disease, Vol.19, No.6, pp. 577-592, 2024, DOI:10.32604/chd.2025.062129 - 27 January 2025

Abstract Surgical ablation (SA) has become an essential rhythm-control strategy for managing tachyarrhythmias in patients with congenital heart disease. Atrial tachyarrhythmias, such as atrial flutter and atrial fibrillation, are prevalent in congenital heart disease, affecting up to 50% of patients, and pose significant risks, including increased morbidity and mortality. Ventricular tachyarrhythmias, though less common, can lead to sudden cardiac death, particularly in conditions like Tetralogy of Fallot. Prior studies suggested that SA for tachyarrhythmias in patients with congenital heart disease offers significant benefits, including superior long-term rhythm control compared to catheter ablation (CA). Atrial tachyarrhythmia burden… More > Graphic Abstract

Juhwan Kim, Baehoon Son, Jihyeon Yu, Joobeom Yun^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3371-3393, 2024, DOI:10.32604/cmc.2024.057234 - 18 November 2024

Abstract Digital forensics aims to uncover evidence of cybercrimes within compromised systems. These cybercrimes are often perpetrated through the deployment of malware, which inevitably leaves discernible traces within the compromised systems. Forensic analysts are tasked with extracting and subsequently analyzing data, termed as artifacts, from these systems to gather evidence. Therefore, forensic analysts must sift through extensive datasets to isolate pertinent evidence. However, manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive. Previous studies addressed such inefficiencies by integrating artificial intelligence (AI) technologies into digital forensics. Despite the efforts in previous studies, artifacts were… More >