Bara Barakat^1,*, Bilal Al-Absi¹, Boris Hadaschik², Christian Rehme², Samer Schakaki³, Joerg Bauer¹

Canadian Journal of Urology, Vol.33, No.1, pp. 105-116, 2026, DOI:10.32604/cju.2026.071284 - 28 February 2026

Abstract Background: Artificial intelligence (AI)-assisted three-dimensional (3D) surgical platforms, integrated with augmented reality, have the potential to improve intraoperative anatomical recognition and provide surgeons with an immersive, dynamic operating environment during uro-oncological procedures. This review aims to examine the current applications of AI in robotic uro-oncology, with a particular focus on its role in facilitating intraoperative navigation during complex surgeries. Methods: A systematic literature search was performed across PubMed, the National Library of Medicine, MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, and Google Scholar to identify relevant studies published up to July 2025.… More >

Samar Taha Yousif^1,2, Firas Basim Ismail^1,3,*, Ammar Al-Bazi⁴, Alaa Abdulhady Jaber⁵, Sivadass Thiruchelvam¹

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.073955 - 27 February 2026

Abstract Thermal power plants are the main contributors to greenhouse gas emissions. The prediction of the emission supports the decision makers and environmental sustainability. The objective of this study is to enhance the accuracy of emission prediction models, supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts. This paper presents a data-driven approach for the accurate prediction of gas emissions, specifically nitrogen oxides (NOx) and carbon monoxide (CO), in natural gas power plants using an optimized hybrid machine learning framework. The proposed model integrates a Feedforward Neural Network (FFNN)… More > Graphic Abstract

Ichiro Kutsuna^1,2,*, Masanao Ikeya^2,3, Akane Fujii², Aiko Hoshino⁴, Kazuya Sakai¹

International Journal of Mental Health Promotion, Vol.28, No.2, 2026, DOI:10.32604/ijmhp.2025.074249 - 27 February 2026

Abstract Background: In mental health, recovery is emphasized, and qualitative analyses of service users’ narratives have accumulated; however, while qualitative approaches excel at capturing rich context and generating new concepts, they are limited in generalizability and feasible data volume. This study aimed to quantify the subjective life history narratives of users of psychiatric home-visit nursing using natural language processing (NLP) and to clarify the relationships between linguistic features and recovery-related indicators. Methods: We conducted audio-recorded and transcribed semi-structured interviews on daily life verbatim and collected self-report questionnaires (Recovery Assessment Scale [RAS]) and clinician ratings (Global Assessment of… More >

Zhiyong Sun^*

Journal of Psychology in Africa, Vol.36, No.1, pp. 33-43, 2026, DOI:10.32604/jpa.2026.074741 - 26 February 2026

Abstract This mixed-methods study investigated how AI-enhanced English as a Foreign Language (EFL) learning environments influence students’ psychological well-being through the mediating roles of motivation and language learning anxiety and the moderating role of trust. Participants were Chinese university students (N = 310, 62% female, mean age = 18.9, SD = 0.8), of whom 15 completed interviews to both add to and to clarify the evidence from the surveys. Structural equation modeling results revealed that AI use had significant indirect effects on well-being through increased motivation and reduced language learning anxiety. Trust in AI significantly moderated… More >

Mubarak Alanazi^1,*, Junaid Rashid²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077956 - 26 February 2026

Abstract Wind turbine blade defect detection faces persistent challenges in separating small, low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints. Conventional image-processing pipelines struggle with scalability and robustness, and recent deep learning methods remain sensitive to class imbalance and acquisition variability. This paper introduces TurbineBladeDetNet, a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types. Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability. The model achieves 97.14% accuracy, 98.65% More >

Ka-Kyung Kim, Joon-Seok Kim, Dong-Hyuk Shin, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077315 - 26 February 2026

Abstract As attack techniques evolve and data volumes increase, the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential. Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems. However, its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm. To address these concerns, this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments, examining both their benefits and associated… More > Graphic Abstract

Itthidet Thawon^1,2, Duy Vo^3,4, Tinh Quoc Bui^3,4, Kanya Rattanamongkhonkun¹, Chakkapong Chamroon¹, Nakorn Tippayawong¹, Yuttana Mona¹, Ramnarong Wanison¹, Pana Suttakul^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077044 - 26 February 2026

Abstract Physics-informed neural networks (PINNs) have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training. Their ability to enforce differential equations, constitutive relations, and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models, particularly for solid and structural mechanics, where data are often limited or noisy. This review offers a comprehensive assessment of recent developments in PINNs, combining bibliometric analysis, theoretical foundations, application-oriented insights, and methodological innovations. A bibliometric survey indicates a rapid increase in publications on PINNs since 2018,… More >

Fazal Ur Rehman¹, Yazeed Alkhrijah², Syed Muhammad Usman³, Muhammad Irfan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.076332 - 26 February 2026

Abstract Automated detection of Motor Imagery (MI) tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation. Prediction of MI tasks can be performed with the help of Electroencephalogram (EEG) signals recorded by placing electrodes on the scalp of subjects; however, accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process, the extraction of a feature vector with high interclass variance, and accurate classification. The proposed method consists of preprocessing, feature extraction, and classification. First, EEG signals are denoised using a… More >

Silvia Alayón^1,*, Carlos Martín¹, Jesús Torres¹, Manuel Bacallado¹, Rosa Aguilar¹, Guzmán Savirón²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.073488 - 26 February 2026

Abstract Business Process Modelling (BPM) is essential for analyzing, improving, and automating the flow of information within organizations, but traditional approaches based on manual interpretation are slow, error-prone, and require a high level of expertise. This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation (BPMN) diagrams solely from verbal descriptions of the processes to be modeled, utilizing Large Language Models (LLMs) and multimodal Artificial Intelligence (AI). Experimental results, based on video recordings of process explanations provided by an expert from an organization (in this case,… More >

Waseem Akhtar^1,2, Mahwish Ilyas³, Romana Aziz^4,*, Ghadah Aldehim⁴, Tassawar Iqbal⁵, Muhammad Ramzan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.071669 - 26 February 2026

Abstract Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of… More >