Shasha Li, Bin Ji^*, Xiaodong Liu, Jun Ma, Jie Yu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076083 - 12 March 2026

Abstract Aligning natural language with operating system (OS) commands allows users to perform complex computer tasks through simple natural language descriptions. However, due to the complex nature of natural language, it still remains challenging to achieve precise alignment. In this paper, we present ComAlign, a Chinese benchmark dataset that pairs Chinese natural language descriptions with corresponding OS commands. ComAlign covers a broad range of 82 distinct OS command types with a total of 1811 natural language descriptions. We elaborate on the construction of ComAlign and construct three baselines to evaluate the alignment accuracy on ComAlign. Experimental More >

Shih-Ming Cho¹, Sung-Wen Wang¹, Min-Chie Chiu^2,*, Shao-Chun Chen¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074911 - 12 March 2026

Abstract To address crop depredation by intelligent species (e.t, macaques) and the habituation from traditional methods, this study proposes an intelligent, closed-loop, adaptive laser deterrence system. A core contribution is an efficient multi-stage Semi-Supervised Learning (SSL) and incremental fine-tuning (IFT) framework, which reduced manual annotation by ~60% and training time by ~68%. This framework was benchmarked against YOLOv8n, v10n, and v11n. Our analysis revealed that YOLOv12n’s high Signal-to-Noise Ratio (SNR) (47.1% retention) pseudo-labels made it the only model to gain performance (+0.010 mAP) from SSL, allowing it to overtake competitors. Subsequently, in the IFT stress test,… More >

HeeSeok Choi¹, Joon-Min Gil^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074511 - 12 March 2026

Abstract Given the growing number of vehicle accidents caused by unintended acceleration and braking failure, verifying Sudden Unintended Acceleration (SUA) incidents has become a persistent challenge. A central issue of debate is whether such events stem from mechanical malfunctions or driver pedal misapplications. However, existing verification procedures implemented by vehicle manufacturers often involve closed tests after vehicle recalls; thus raising ongoing concerns about reliability and transparency. Consequently, there is a growing need for a user-driven framework that enables independent data acquisition and verification. Although previous studies have addressed SUA detection using deep learning, few have explored… More >

Changxu Zhao¹, Jianping Liu^1,*, Xiaofeng Wang¹, Wei Sun², Libo Liu³, Haiyu Ren¹, Pan Liu¹, Qiantong Wang¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074409 - 12 March 2026

Abstract Foundation models are reshaping artificial intelligence, yet their deployment in specialised domains such as agricultural question answering (AQA) still faces challenges including data scarcity and barriers to domain-specific knowledge. To systematically review recent progress in this area, this paper adopts a task–paradigm perspective and examines applications across three major AQA task families. For text-based QA, we analyse the strengths and limitations of retrieval-based, generative, and hybrid approaches built on large language models, revealing a clear trend toward hybrid paradigms that balance precision and flexibility. For visual diagnosis, we discuss techniques such as cross-modal alignment and More >

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073832 - 12 March 2026

Abstract The Internet of Things (IoT) and cloud computing have significantly contributed to the development of smart cities, enabling real-time monitoring, intelligent decision-making, and efficient resource management. These systems, particularly in IoT networks, rely on numerous interconnected devices that handle time-sensitive data for critical applications. In related approaches, trusted communication and reliable device interaction have been overlooked, thereby lowering security when sharing sensitive IoT data. Moreover, it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment. In this research, an Artificial Intelligence (AI) recommended fault-tolerant framework is proposed that leverages blockchain More >

Mingwei Zhan^1,#, Zhaokai Zhou^2,#, Jianpeng Zhang^3,#, Xin Wang⁴, Canxuan Li⁵, Bochen Pan⁶, Zhanyang Luo⁷, Wenjie Shi⁸, Yongjie Wang⁹, Minglun Li¹⁰, Weizhuo Wang^11,*, Run Shi^12,*, Jingyu Zhu^1,13,*

Canadian Journal of Urology, Vol.33, No.1, pp. 35-49, 2026, DOI:10.32604/cju.2026.076084 - 28 February 2026

Abstract Artificial intelligence (AI) is transforming the diagnostic landscape of malignant tumors in the urinary system, including prostate cancer, bladder cancer, and renal cell carcinoma (RCC). By integrating imaging, pathology, and molecular data, AI enhances the precision and reproducibility of tumor detection, grading, and risk stratification. In prostate cancer, AI-assisted multiparametric Magnetic resonance imaging (MRI) and digital pathology systems improve lesion localization and Gleason scoring. For bladder cancer, deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging (CT/MRI) enable real-time lesion segmentation and non-invasive biomarker prediction, such as Programmed Cell Death-Ligand 1 (PD-L1) expression. More >

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 >