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Apr 27, 2026Open Access
Artificial intelligence (AI) has moved from a peripheral tool to a structural component of contemporary marketing strategy, promising unparalleled efficiency in demand forecasting, audience segmentation, and the delivery of individualised customer experiences. Yet the gap between what predictive analytics enables technically and what organisations achieve relationally remains poorly understood. This study adopts a qualitative, interpretive design to investigate three interrelated questions: 1) h...
Apr 23, 2026Open Access
Objective: This systematic review aimed to evaluate the validity of artificial intelligence (AI)-based models applied to orthodontic diagnosis. Materials and Methods: A comprehensive electronic search was conducted in five databases—PubMed, ScienceDirect, Google Scholar, Web of Science, and the Cochrane Library—using the MeSH terms artificial intelligence, orthodontics, orthodontic diagnosis, neural networks, and machine learning. After applying predefined inclusion and exclusion criteria, nine ...
Apr 20, 2026Open Access
Students with moderate intellectual disabilities face considerable challenges when engaging with traditional educational and training approaches, making the learning process demanding for both learners and their instructors. Against the backdrop of rapid advancements in artificial intelligence and natural language processing (NLP), cognitive computing has proven highly effective in creating interactive learning environments that respond dynamically to the individual needs of students with disabi...
Apr 10, 2026Open Access
The ability to communicate effectively in English is fundamental to academic success in Nigerian universities, yet persistent low achievement in the compulsory “Use of English” course remains a concern, particularly in specialized institutions like maritime universities where proficiency underpins both academic performance and professional competence. This study addresses the limitation of reactive academic support systems by employing unsupervised machine learning to analyze English language le...
Mar 20, 2026Open Access
Drawing on journal articles indexed in CNKI and the Web of Science Core Collection, this study conducts a systematic review and comparative analysis of Chinese and international research on AI-empowered foreign language and translation teaching. Specifically, 104 Chinese literature pieces published between 2020 and 2025, and 200 English literature pieces published between 2006 and 2025 were included in the survey. By adopting bibliometric methods and knowledge mapping with CiteSpace, the study e...
Mar 20, 2026Open Access
Lithium remains the most effective long-term treatment for bipolar disorder, yet its therapeutic benefits are offset by a well-established risk of chronic kidney disease (CKD). Anticipating lithium-associated renal impairment is clinically challenging because the underlying mechanisms are subtle, multi-variate, and evolve dynamically with cumulative exposure. In this study, we develop a transparent, end-to-end machine-learning framework for early detection of lithium-induced kidney damage using ...
Mar 20, 2026Open Access
Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant risk of progressive renal impairment in a subset of patients. Early recognition of individuals vulnerable to lithium-associated nephrotoxicity is clinically critical but challenging, given the heterogeneous evolution of renal biomarkers, nonlinear exposure effects, and complex interactions with comorbidities and concomitant medications. In this work, we introduce a multimod...
Mar 18, 2026Open Access
Emotion regulation emerges from coordinated dynamics across autonomic, cardiovascular, electrodermal, thermoregulatory, and motor systems. Although wearable devices can continuously capture these signals, most predictive models either analyse each modality in isolation or fuse them via simple feature concatenation, which obscures the structured cross-system interactions that likely underpin successful regulation. We introduce a graph neural network (GNN) framework that encodes multimodal wearabl...
Mar 18, 2026Open Access
Anticipating short-term affective instability in bipolar disorder represents a longstanding challenge in computational psychiatry. Early signalling of transitions from stable to depressive, manic, or mixed states could support just-in-time interventions, yet real-world digital phenotyping datasets rarely provide dense temporal sampling, precise transition labels, or sufficient event frequency for training predictive models. To address these methodological barriers, we construct a clinically prin...
Mar 17, 2026Open Access
Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational psychiatry. Real-world datasets from smartphones and wearables often suffer from sparse labels, heterogeneous sampling, and highly imbalanced case control ratios, limiting the development of robust models. To explore these challenges under controlled conditions, we construct a clinically inspired synthetic dataset of daily behavioural trajectories for 200 virtual subjects monitored...
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