Search Results (1,695)

Aim: Self-stigma represents a major barrier to recovery among individuals with severe mental illness (SMI). This review aimed to identify and synthesize the available evidence on the effectiveness of non-pharmacological interventions for reducing self-stigma in adults with SMI, while also exploring physical appearance care as a potentially relevant but under-researched area. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines and the Cochrane Handbook recommendations. The review protocol was registered in PROSPERO (CRD420251013333). Data Sources: A comprehensive search was conducted across multiple databases, including the Virtual Health Library, PubMed, Web of Science, the Cochrane Library, and EBSCOhost databases. A snowball search of reference lists was also performed. Studies published in English or Spanish within the past ten years were included. Review Methods: Two independent reviewers screened titles, abstracts, and full texts according to predefined criteria. Methodological quality was assessed using the Critical Appraisal Skills Programme España (CASPe). A qualitative synthesis was conducted for all included studies, and a random-effects meta-analysis was performed for studies providing sufficient quantitative data. Standardized mean differences and heterogeneity statistics were calculated. Results: Twenty-eight studies were included in the qualitative synthesis, and twelve were eligible for meta-analysis. Multicomponent interventions integrating psychoeducation, cognitive restructuring, narrative approaches, and social support showed the most consistent effects across the evaluated outcome domains. Meta-analytic findings indicated small-to-moderate reductions in self-stigma and improvements in hope, with low levels of statistical heterogeneity across outcomes. Effects on self-esteem, quality of life, self-efficacy, and psychiatric symptomatology were limited or inconsistent across studies. No studies specifically evaluated interventions focused on physical appearance care. Conclusions: Non-pharmacological interventions appear to produce modest but potentially meaningful reductions in self-stigma among individuals with SMI, particularly when delivered through multicomponent psychosocial approaches that integrate psychoeducation, cognitive restructuring, narrative techniques, and social support. Full article

This study maps the intellectual structure and thematic evolution of buy now, pay later (BNPL) research published between 2010 and 2025, with particular attention to how impulsive buying and post-purchase regret are positioned within the broader BNPL knowledge domain. Drawing on an integrated bibliometric science mapping and focused behavioral synthesis approach, the study first mapped a broad Scopus dataset of BNPL-related digital consumer credit and deferred payment research published between 2010 and 2025. This dataset was used for performance analysis and VOSviewer-based science mapping. A second, narrower PRISMA-guided screening process was then applied to identify empirical studies that directly examined BNPL-related behavioral and psychological outcomes, resulting in 13 studies retained for focused qualitative synthesis. The bibliometric findings show that BNPL scholarship expanded sharply after 2020, with research concentrated in marketing, consumer behavior, fintech, and digital commerce outlets. The science mapping results reveal a fragmented field structured around digital finance adoption, impulsive consumption, consumer vulnerability, and emerging ethical and regulatory concerns. The systematic synthesis further indicates that BNPL-related mechanisms, including installment framing, urgency cues, perceived affordability, and reduced payment salience, are consistently associated with impulsive buying tendencies. However, post-purchase regret remains underexamined and is rarely modeled as a distinct emotional outcome. By integrating bibliometric evidence with behavioral synthesis, this study clarifies how BNPL research has developed, where conceptual fragmentation remains, and why future studies should connect digital payment design, cognitive distortions, impulsive purchasing, and post-purchase emotional consequences within more comprehensive theoretical models. The study contributes by offering a structured research agenda for advancing responsible BNPL scholarship, consumer protection, and future digital finance research. Full article

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021–2026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (κ = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848–0.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL’s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field’s priority research needs. Full article

Objective: Language delay (LD) is a common developmental condition in which children fail to achieve age-appropriate language milestones, affecting communication, cognition, and social integration. It affects approximately 1 in 14 preschool children and may have long-term consequences into adulthood. The period from 12 to 36 months is a critical window for language development, during which children begin to comprehend and produce their first words. Early identification of risk factors during this stage is essential for timely intervention. However, in Vietnam, data on factors associated with language delay in this age group remain limited. Therefore, this study aimed to identify factors associated with language delay in children aged 12–36 months. Methods: A case–control study was conducted, including 55 children with language delay and 55 typically developing children aged 12–36 months. Personal, familial, medical, and environmental data were collected using structured questionnaires. Univariate and multivariable logistic regression analyses were performed to identify factors associated with language delay. Results: A total of 110 children (43 boys and 67 girls) were included. The strongest risk factor was the use of screens to calm or occupy children (OR = 36.6; p < 0.001). Early bilingual exposure was a significant protective factor (OR = 0.12; p = 0.014), while shared reading or picture viewing showed a strong but borderline protective effect (OR = 0.23; p = 0.051). Conclusions: The use of screens to calm or occupy children was the main risk factor for language delay, whereas early bilingual exposure and shared reading or picture viewing were protective factors. These findings highlight the importance of limiting non-interactive screen use and promoting interactive language activities to support early language development. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

As augmented reality head-up display (AR-HUD) becomes increasingly integrated into intelligent vehicles, inappropriate interface designs may increase drivers’ cognitive workload and delay hazard responses. This study investigates how cognitive style, driving task type, and AR-HUD navigation design jointly influence drivers’ behavioral performance and visual attention. A total of 55 participants were recruited and screened using the Group Embedded Figures Test, with 38 drivers finally selected for a 2 × 4 × 2 driving-simulation experiment comparing world-fixed (WF) and screen-fixed (SF) interfaces across goal-directed and stimulus-driven tasks. Reaction times and eye-tracking indicators were analyzed using generalized linear models. Results show that stimulus-driven tasks significantly increased reaction times, with rear-vehicle scenarios producing the longest responses (mean = 1.420). During lane-change tasks, WF displays significantly reduced fixation duration (p < 0.001) and fixation counts (p < 0.001), whereas SF displays improved attentional efficiency during pedestrian-warning tasks. In addition, field-dependent drivers exhibited significantly larger pupil diameters, indicating higher cognitive workload. These findings provide sensor-based evidence for AR-HUD systems that dynamically optimize interface presentation according to task context and workload conditions. Full article

Background: Live music, understood as real-time musical performance delivered in the physical presence of patients or other participants, is increasingly incorporated into healthcare settings as an arts-based, non-pharmacological practice intended to support well-being and humanize care. While previous reviews have examined a broad range of music-based interventions in healthcare, limited attention has been given specifically to live music, its contextual characteristics, and the values attributed to its use within hospital environments. Objectives: This scoping review aims to map and synthesize the literature on live music in healthcare settings, focusing on clinical contexts, populations involved, and the therapeutic, psychosocial, and environmental values reported. Methods: A scoping review was conducted following the framework of Arksey and O’Malley. Searches were performed in Web of Science, Scopus and Pubmed using terms related to live music and healthcare settings. Studies published in English or Spanish over the past 20 years were considered. After screening titles, abstracts, and full texts, 81 studies met the inclusion criteria. Results: The studies covered diverse hospital units and patient groups, particularly oncology, neonatal and intensive care, palliative care, and haemodialysis. Reported outcomes were mainly psychological and emotional, including reductions in anxiety, stress, and distress, alongside improvements in mood, well-being, and quality of life. Cognitive, physiological, and environmental benefits were also identified, emphasizing the role of live music in creating supportive and humanized care environments. Most studies were conducted in Europe and North America. Conclusions: Live music is widely implemented in healthcare settings and is associated with benefits extending beyond symptom reduction to experiential and humanizing dimensions of care. This scoping review provides an overview of the existing evidence base and identifies directions for future research in arts and health. Full article

Background: Mild Cognitive Impairment (MCI) is a clinical state between normal aging and dementia. It may involve impairment in one or several cognitive domains. MCI offers a key window for maintaining cognitive function and studying how deficits develop in the elderly, making it of great research value. Measurement tools for screening MCI are not yet standardized in China. The accuracy of diagnostic criteria and threshold values needs improvement. Previous studies on the neural mechanisms of MCI have examined various aspects, but the changes in the white matter microstructure in older adults with MCI remain unclear. Most past studies used Fractional Anisotropy (FA) analysis to examine changes in white matter fiber orientation, often ignoring fiber density. As a result, findings are often contradictory or difficult to interpret. Therefore, it is necessary to assess cognitive function in MCI populations using more comprehensive and standardized measurement tools. It is also important to explore the association between changes in white matter microstructure and cognitive function in MCI by analyzing FA and Mean Diffusivity (MD). Methods: First, we assessed cognitive function using the Cognitive Function Measurement Scale for the Elderly, developed by Beijing Normal University, with diagnoses based on the NIA-AA (National Institute on Aging—Alzheimer’s Association) criteria. Second, we employed Diffusion Tensor Imaging (DTI) combined with Tract-Based Spatial Statistics (TBSS) to investigate alterations in the white matter fiber tract integrity in individuals with MCI. Based on the metrics used, this study was divided into two analytical approaches: Analysis Mode 1 utilized FA to explore changes in white matter fiber orientation in the MCI group. Analysis Mode 2 utilized MD to examine changes in white matter fiber density in the MCI group. Third, we further explored the association between alterations in the white matter fiber tract integrity and cognitive function in individuals with MCI. Specifically, FA and MD values from brain regions showing significant differences between the MCI and normal control groups were extracted and correlated with cognitive test scores. Results: According to the results of the community measurement survey, the prevalence of MCI among the elderly in Shenzhen is approximately 21.54%. Individuals with MCI exhibited functional decline in memory, attention, language, executive function, and spatial processing. DTI results indicated that (1) FA values across the brain’s white matter fiber tracts showed a decreasing trend in the elderly with MCI, with no areas exhibiting significantly higher FA values. Specifically, FA values were significantly lower in the corpus callosum, internal capsule, corona radiata, thalamic radiation, external capsule, superior fronto-occipital fasciculus, and cingulum (cingulate gyrus). (2) White matter fiber tracts with significantly reduced FA values also demonstrated significantly increased MD values. Additionally, MD values in the cingulum (hippocampus), inferior cerebellar peduncle, and corticospinal tract were significantly reduced in the MCI group. (3) Correlation analysis revealed that the significant differences in FA and MD values within the white matter fiber tracts of older adults with MCI were correlated with scores on several cognitive tests. Conclusions: In the present study, older adults with MCI tended to exhibit functional decline across multiple cognitive domains and relatively extensive microstructural white matter damage. Observations suggested that white matter fiber density may be informative regarding these microstructural alterations, indicating that diffusion biomarkers in key regions such as the cingulum (hippocampus) warrant further investigation. Full article

Background: Type 2 Diabetes Mellitus (T2DM) is associated with a vascular-executive cognitive decline profile that early impacts complex daily tasks. Despite the increased risk of Mild Cognitive Impairment (MCI) in this population, there is a critical shortage of instruments specifically validated for this group. This scoping review aims to identify the instruments used to assess functionality in individuals with T2DM and MCI and to map their psychometric properties. Methods: We conducted a scoping review based on the JBI methodology and PRISMA-ScR guidelines. The search was performed across several electronic databases (PubMed, Cochrane Library, Web of Science, Scopus and SciELO), up to March 2026, focusing on the intersection of T2DM, mild cognitive impairment, and the psychometric properties of functional scales. Results: Our search identified only three studies meeting the eligibility criteria. The functional instruments evaluated across these publications were the ADCS-ADL scale, the A-FAQ, and a predictive nomogram including the Lawton-Brody scale. Methodological approaches, sample configurations and reported outcomes varied substantially within the included literature, with no comparative validation studies conducted among homogeneous T2DM cohorts. Conclusions: The notable scarcity and marked heterogeneity of the available literature prevent any definitive conclusions regarding the comparative diagnostic superiority of current functional scales. While gradated instruments show conceptual compatibility with the executive-vascular cognitive decline profile of T2DM, their psychometric properties remain unvalidated in this specific population. Future research should prioritize longitudinal validation designs in homogeneous diabetic cohorts to standardize screening protocols calibrated to metabolic and vascular variations. Full article

Accurate early screening of Alzheimer’s disease (AD) is crucial, yet traditional diagnostic methods are often limited by invasiveness or high costs. Therefore, there is a critical need for non-invasive biomarkers that enable precise and accessible screening. In this study, we propose a multi-modal digital biomarker framework designed to accurately detect AD by evaluating impairments across multiple cognitive domains, such as language, working memory, and visuospatial attention. By leveraging voice and video data, our approach significantly enhances user accessibility and real-world applicability. We validated the proposed framework using a dataset of 128 participants, comprising 77 healthy controls (HCs) and 51 patients with AD. While individual cognitive tasks yielded F1-scores ranging from 69.23% to 77.78% and sensitivities from 69.23% to 80.77%, our ensemble strategy significantly enhanced detection performance, achieving an F1-score of 83.64% and a sensitivity of 88.46%. These findings confirm that the proposed multi-modal digital biomarker framework, enhanced via ensembling, provides a highly accurate, scalable, and practical solution for the non-invasive screening and detection of AD. Full article

Background/Objectives: The microbiota–gut–brain axis (MGBA) plays a pivotal role in cognitive function, making psychobiotics a promising strategy for managing neurodegenerative diseases. Lactic acid bacteria (LAB) from traditional fermented foods represent a valuable source of candidate strains, and multi-strain consortia may offer enhanced therapeutic efficacy through synergistic effects. This study evaluated the functional and psychobiotic potential of three lactic acid bacteria (LAB) strains isolated from fermented foods, assessed as monocultures and a multi-strain consortium (MIX). Methods: The research encompassed an initial screening of the individual strains and the MIX, assessing their adhesion to mucin, stability in a static in vitro digestion model, and amino acid profiling. Subsequently, the LAB MIX underwent long-term evaluation in a dynamic gastrointestinal model (SHIME^®) inoculated with microbiota from a patient with Alzheimer’s disease, during which alterations in gut microbiota composition and amino acid metabolism were analyzed. Results: The LAB MIX demonstrated high stability under digestive stress and effective mucoadhesive properties. Furthermore, the consortium demonstrated a distinct metabolic signature, driving enhanced functional effects that complemented or exceeded those observed in individual monocultures. In the SHIME^® model, the MIX induced significant, site-specific shifts in microbial composition, notably increasing lactobacilli abundance. These taxonomic changes correlated with an enriched metabolic profile, including elevated levels of GABA precursors and amino acids with antioxidant potential, which are crucial for MGBA modulation. Conclusions: These results identify the LAB consortium as a compelling psychobiotic candidate. Further in-depth in vivo and clinical studies are required to validate its therapeutic potential for MGBA modulation. Full article

Affected by inter-annotator cognitive differences, fatigue effects, and data poisoning, training data inevitably contains a certain proportion of noise, which severely impairs model performance. Traditional manual verification is costly and inefficient, while existing automatic detection methods generally suffer from limited precision, poor interpretability, and insufficient robustness. This paper proposes a noise label detection and correction method based on Bayesian weighted consensus inference. First, an ensemble of multiple lightweight heterogeneous models is constructed, and model prior knowledge and dataset noise are obtained on a clean validation set. Second, the model ensemble predicts noisy samples to extract two-dimensional consensus evidence. Then, prior knowledge and consensus evidence are fused, and the posterior probability of label noise is calculated via Bayesian inference to generate correction suggestions. Finally, high-confidence noisy labels are precisely screened based on the posterior probability threshold. Experimental results on three datasets show that the proposed method achieves a precision of 96.50%, a recall of 98.61%, an F₁-score of 97.54%, and a correction accuracy of 95.53%, with improvements of 5–20% over mainstream methods. With a computational cost comparable to that of basic ensemble methods, the proposed approach achieves a favorable balance among precision, robustness, and interpretability. It thus offers a promising and cost-effective solution for automated quality control of large-scale annotated datasets, especially in text classification tasks. Full article

Background/Objectives: Cognitive decline and dementia represent a growing global crisis, affecting over 57 million individuals worldwide, projected to exceed 150 million by 2050. The 2024 Lancet Commission identified hearing loss as the single largest modifiable dementia risk factor (~7% population-attributable fraction). Obstructive sleep apnea (OSA), affecting ~936 million adults, is an increasingly recognized contributor yet remains underdiagnosed, especially in low- and middle-income countries (LMICs). This review synthesizes evidence on the global burden of cognitive decline associated with both conditions, evaluates causality debates, and identifies research gaps. Methods: Following SANRA guidelines, a search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library through February 2026. Original studies, systematic reviews, meta-analyses, and WHO/GBD reports were included; editorials and non-English publications were excluded. After deduplication, 3847 records were screened, and 96 studies met the inclusion criteria. Results: OSA has been linked to cognitive decline through several plausible mechanisms, including intermittent hypoxia, sleep fragmentation, impaired glymphatic clearance, and amyloid-beta accumulation, though the directionality of these associations requires confirmation from longitudinal studies. Hearing loss contributes to cognitive load, social isolation, and cortical reorganization. Both conditions disproportionately affect LMICs, where access to diagnosis and treatment remains limited. CPAP and hearing rehabilitation show cognitive benefits when initiated early, though evidence for reversing established impairment remains limited. A synergistic interaction between the two conditions is biologically plausible but empirically underexplored. Conclusions: OSA and hearing loss are highly prevalent conditions associated with increased dementia risk, though the certainty of causal relationships and the magnitude of intervention effects differ between the two conditions and across the available evidence. Integrated screening and early intervention could yield substantial neuroprotective benefits in high-risk populations and LMICs. Future longitudinal studies should examine combined cognitive trajectories and optimal intervention timing. Full article

Probabilistic risk assessment of complex offshore oil and gas systems is often challenged by scarce statistical data and multiple uncertainties. Traditional point-value probability and standard Bayesian networks cannot fully represent and propagate these uncertainties, which may mislead high-risk security decision-making. To address this issue, this paper proposes a new hybrid risk assessment framework that combines interval-valued T-spherical fuzzy sets (IVTSFS) with credal networks (CN). First, IVTSFS is used to quantify the subjective risk perception of multiple experts, effectively capturing hesitancy, fuzziness, and group disagreement. An improved probability mapping mechanism is introduced to align linguistic evaluations with objective failure frequency spaces, thereby avoiding systemic transformation biases. Subsequently, the interval conditional probability table is constructed using the imprecise leakage noise-OR model, which alleviates the problem of parameter dimension explosion in complex causal structure and explicitly retains the parameter uncertainty. The 2U algorithm is then applied to perform accurate interval inference in CN. The feasibility and comparative advantages of the method are illustrated in the actual case of the single-point mooring system. The results clearly output the upper and lower bounds of the system failure risk, and identify the key vulnerable nodes through diagnostic reasoning and sensitivity analysis. This study has theoretical contributions in fuzzy decision-making and uncertainty modeling. By unifying advanced fuzzy cognitive quantification and imprecise probability propagation, it provides a structured uncertainty representation tool for expert-informed risk screening under data scarcity. Full article

Background and Objectives: Artificial intelligence (AI) is increasingly integrated into healthcare management and pharmaceutical marketing workflows, yet determinants of AI adoption intention among non-clinical professionals remain under-studied in Central and Eastern Europe. This cross-sectional study quantified AI adoption intention (AAI) across three professional groups and examined its organizational, cognitive, attitudinal, and regulatory correlates. Methods: We surveyed 127 Romanian professionals (43 hospital administrators, 42 pharmaceutical marketing professionals, 42 community pharmacy managers) using a 46-item structured instrument. The instrument combined items adapted from UTAUT/TAM and organizational-readiness measures with study-specific AI-marketing, AI-literacy, and regulatory-literacy items; Analyses included ANOVA with Tukey HSD, Spearman correlations, age-adjusted OLS regression with HC3 robust standard errors, bootstrap indirect-effect analysis, moderation, exploratory k-means clustering, and exploratory logistic/ROC analysis. Results: AAI differed across groups: pharmaceutical marketing 4.33 ± 0.50, hospital administrators 3.39 ± 0.47, and pharmacy managers 2.88 ± 0.54; all pairwise Tukey contrasts p < 0.001. In the multivariable model (R² = 0.833)—interpreted cautiously because conceptually related adoption constructs may overlap despite acceptable collinearity diagnostics—perceived usefulness, organizational readiness, and perceived ease of use were the strongest associated factors, while data governance concern was the main negative correlate. Perceived usefulness statistically accounted for 61.7% of the AI literacy–AAI indirect association, and regulatory literacy moderated the AI literacy–AAI association. An exploratory age-adjusted logistic model showed high within-sample discrimination for top-tertile AAI but should be interpreted as convergent validity among survey constructs rather than as a validated screening tool. Conclusions: AI adoption intention in Romanian medical management and pharmaceutical marketing is associated mainly with perceived usefulness and organizational readiness, tempered by data governance concern and regulatory knowledge. Longitudinal, multi-site, real-world implementation studies with external validation are needed. Full article