Search Results (10,216)

English proficiency is vital for non-native speakers’ career development, yet classroom instruction alone cannot meet practical demands, making informal digital learning of English (IDLE) increasingly important. Artificial intelligence (AI), with conversational and multimodal functions, offers new opportunities for IDLE. However, existing research on AI-mediated IDLE has predominantly focused on language majors and often relied on a single methodological lens, neglecting STEM undergraduates and the complex network dynamics among motivational factors. However, research has largely focused on language majors, leaving STEM majors underexplored. Guided by the Hedonic-Motivation System Adoption Model (HMSAM), this study analyzed data from 413 Chinese STEM majors using partial least squares structural equation modeling (PLS-SEM, SmartPLS 4.0) and psychological network analysis (PNA, R 4.5.3). PLS-SEM results showed that enjoyment was the strongest direct predictor of AI-IDLE, followed by focused immersion, perceived usefulness, and curiosity. Control contributed indirectly via focused immersion, while boredom was non-significant. Perceived ease of use influenced AI-IDLE only through cognitive and emotional pathways. The model explained 58.1% of the variance. PNA further identified enjoyment, focused immersion, and control as central nodes, while the link between perceived usefulness and AI-IDLE was non-significant. These findings suggest that Chinese STEM undergraduates’ AI-IDLE is primarily driven by intrinsic hedonic motivations rather than utilitarian evaluations. The study provides empirical support for designing AI tools that enhance enjoyment and control to foster STEM students’ extracurricular English engagement. Full article

Background/Objectives: The health status of institutionalized older adults is determined by the interaction of functional, cognitive, nutritional, anthropometric, and biochemical factors, which may not be adequately captured through single-domain assessments. Within the framework of the VIVA Project (Vulnerability Index: Valencia institutionalized Adults), this study aimed to characterize institutionalized older adults during the COVID-19 pandemic using an integrated multidimensional approach and to explore clinically interpretable vulnerability profiles. Methods: This cross-sectional study included 124 residents from 10 nursing homes of Valencia, Spain. Data were obtained from institutional records and included age, sex, body mass index (BMI), Barthel Index, Mini-Examination of Cognition (MEC), Tinetti scale, Mini Nutritional Assessment-Short Form (MNA-SF), and biochemical markers related to protein status, lipid metabolism, micronutrient availability, and renal function. An exploratory VIVA multidimensional index was constructed from nine standardized variables, and k-means clustering was applied to these variables rather than to a single summed score to identify residents’ phenotypes. An exploratory logistic regression model was used to assess the internal discrimination of the high-vulnerability phenotype. Results: The cohort showed marked heterogeneity across functional, cognitive, nutritional, anthropometric, and biochemical domains. Cluster analysis identified three clinically interpretable phenotypes ranging from lower to higher vulnerability. Functional impairment, particularly the Barthel Index and Tinetti score, was the main driver of separation between phenotypes, while biochemical markers contributed to refining profile discrimination. The exploratory logistic regression model showed high internal discrimination for the high-vulnerability phenotype, supporting the internal coherence of the integrated framework. Conclusions: An integrated multidimensional framework may be useful for characterizing vulnerability among institutionalized older adults and supporting risk stratification in long-term care settings. The logistic regression findings, including the high AUC, should be interpreted only as evidence of internal discrimination and internal coherence of the exploratory construct, not as evidence of external validity, reproducibility, diagnostic accuracy, or future predictive utility. Full article

Background: Post-traumatic growth (PTG) refers to positive psychological changes resulting from the struggle with highly challenging or traumatic life events. Psychosocial interventions have demonstrated efficacy in promoting psychological well-being in the aftermath of traumatic experiences. Cognitive Behavioral Therapy (CBT) is among the most extensively studied such interventions, aligning with the PTG model’s prerequisites for growth. Objective: The aim of this systematic review was to assess the efficacy of CBT and CBT-based interventions in promoting PTG. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching PubMed, Scopus, and Google Scholar databases from inception to December 2024. Eligibility criteria included: (a) the inclusion of a CBT or CBT-based intervention, (b) measurement of PTG using the Post-Traumatic Growth Inventory (PTGI), (c) study participants having experienced traumatic life events, and (d) articles written in English. Risk of bias was assessed independently by two reviewers. Due to the heterogeneity of included studies, a qualitative narrative synthesis approach was adopted. Risk of bias was assessed using the RoB-2 tool for RCTs, ROBINS-1 for quasi-experimental studies and Newcastle–Ottawa scale for cohort studies. Certainty of evidence, assessed using the GRADE framework, is considered low. Results: A total of 19 studies were included (13 randomized controlled trials, 3 quasi-experimental, and 3 longitudinal studies). While traditional CBT produced mixed results in fostering PTG, CBT-based therapeutic protocols—particularly those explicitly designed to target PTG or incorporating structured cognitive–emotional techniques—demonstrated more consistent benefits. Limitations of the included studies include measurement of PTG as a secondary outcome, small sample sizes, and the presence of confounding variables. Conclusions: Further high-quality, multicenter randomized controlled trials with standardized protocols are needed to clarify the role of CBT in promoting growth after trauma. Full article

This paper examines how the current state of affective computing is limited by its reliance on theories that treat emotions as static, isolated states, and argues that the holistic and process-oriented theory of mind from Yogācāra Buddhism offers a more sophisticated alternative, viewing emotion as an experience deeply integrated with cognition, volition, and somatic awareness. As a case study, this paper proposes a framework for sentiment analysis inspired by Yogācāra principles, based upon the Chinese Buddhist text Mahāyāna Treatise on the Hundred Dharmas Illuminating the Gate. This multi-aspect annotation system analyzes emotional expressions across five key dimensions corresponding to Yogācāra’s “ever-present” Mental Factors. By mapping emotions in this compositional manner, the framework provides a more granular and context-rich understanding of human sentiment than current methods allow. This paper thus serves as a call to diversify AI’s theoretical foundations, demonstrating through this Yogācāra case study how engagement with insights from different traditions can resist the top-down “theoretical monopoly” of Western psychological models, which flattens the rich diversity of human affective experience into a single, dominant paradigm. Full article

Background/Objectives: Assessment of cognitive function in patients with dementia is often hindered by functional and environmental barriers. Although caregiver reports are an alternative source, their clinical utility for estimating patients’ cognitive function remains uncertain. This study aimed to estimate cognitive function using caregiver-reported data combined with patient demographics and to evaluate its clinical utility. Methods: This retrospective cross-sectional study enrolled participants who visited a memory clinic and completed the Montreal Cognitive Assessment (MoCA) for cognitive assessment, together with caregiver-reported questionnaires for activities of daily living (ADL) and neuropsychiatric symptoms (NPS). Multivariable linear regression models were constructed to predict the MoCA score, with Model 1 including demographics, ADL, and NPS as covariates and Model 2 further incorporating clinical diagnosis. The intraclass correlation coefficient, Bland–Altman analysis, and regression error characteristic curves were assessed. Results: Among 2650 participants (56.5% women; mean age, 70.4 years), the NPS variable was excluded from both models. Model 1, which included demographics and ADL, explained 65.4% of the variance, whereas Model 2, which incorporated clinical diagnosis, explained 75.9%. Model 2 yielded an intraclass correlation coefficient of 0.853, compared to 0.778 for Model 1. At a 4-point error tolerance, Model 2 yielded an accuracy of 75.5%. Bland–Altman biases were near zero, with 95% limits of agreement of approximately ±7 points for Model 2. Conclusions: MoCA scores can be estimated using caregiver-reported ADL scores, demographics, and clinical diagnosis. NPS scores provided no additional predictive value when these factors were included. These models provide valid quantitative tools for indirect cognitive assessment when in-person testing is impossible. Full article

Despite extensive research on social engineering victimisation on social networking sites (SNSs) across the Internet, user susceptibility continues to increase, indicating that existing explanatory models remain incomplete. Previous studies have predominantly examined susceptibility through isolated factors, including individual traits, message characteristics, or source attributes, while often overlooking how evolving Internet-based SNS environments interact with human and social factors. To address this gap, this study presents an interdisciplinary qualitative investigation into emerging determinants of user susceptibility to social engineering cyberattacks (SECAs) on Internet-enabled SNS platforms. Drawing on in-depth interviews with 18 experts from cybersecurity, psychology, sociology, criminology, and linguistics, the study captures perspectives that are rarely integrated within a single analytical framework. Using NVivo 14 and inductive thematic analysis, six core themes and seven sub-themes were identified, revealing previously underexplored cognitive-emotional, social-relational, and platform-mediated mechanisms of victimisation. The key contribution of this research is not the identification of entirely new susceptibility factors, but the development of an interdisciplinary framework that integrates these previously disconnected dimensions. By foregrounding the role of SNS design affordances within the broader Internet ecosystem and their interaction with human cognition and social dynamics, this study advances current understanding beyond fragmented models of user vulnerability. The findings provide a novel conceptual foundation for future empirical research and inform the design of more effective, context-aware mitigation and awareness strategies for SECAs on Internet-based SNSs. Full article

Chronic pain is increasingly understood as a multidimensional condition in which, in a substantial subgroup of patients, a protective symptom can evolve into a persistent maladaptive disorder of the nervous system, while in others it may remain closely tied to ongoing mechanical or structural factors. Central sensitization (CS) represents a key mechanism underlying this transition, characterized by enhanced neural responsiveness and impaired endogenous pain inhibition, leading to a dissociation between pain and tissue pathology. The aim of this narrative review is to critically discuss current evidence on CS as a mechanism-based explanation for persistent pain, using lumbar disk herniation (LDH) as a clinical model of the radiological-clinical mismatch, and to discuss its direct implications for identifying sensitized phenotypes, multimodal assessment, and rehabilitation strategies. A total of 77 sources published between 2006 and 2026 were synthesized. These reviewed sources demonstrate that identification of the sensitized phenotype requires a multimodal assessment approach combining self-report measures, such as the Central Sensitization Inventory (CSI), with psychophysical methods including quantitative sensory testing (QST) and conditioned pain modulation (CPM). Cognitive-emotional factors are also critical, as postoperative kinesiophobia affects approximately 38.3% of LDH patients and is associated with increased pain intensity and reduced self-efficacy. Management strategies reported in these publications focus on mechanism-based interventions, particularly pain neuroscience education (PNE) and graded, time-contingent exercise, which aim to modify pain-related cognitions and restore endogenous inhibitory processes. These approaches may be supported by adjunctive therapies, including dry needling (DN), electro-dry needling (EDN), centrally acting pharmacological agents (e.g., serotonin–norepinephrine reuptake inhibitors [SNRIs] and gabapentinoids), and psychologically informed treatments such as cognitive behavioral therapy (CBT). While surgical decompression may reduce CS-related symptoms, preoperative sensitization does not necessarily predict poorer outcomes, highlighting the interaction between peripheral and central mechanisms. Adopting a sensitization-informed perspective may encourage a broader integration of contemporary pain models alongside traditional structural views in lumbar disc herniation clinical care. Full article

Background/Objectives. Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder characterized by dopaminergic neuron loss, α-synuclein pathology, neuroinflammation, and cognitive decline. Synaptamide (N-Docosahexaenoylethanolamine (DHEA)) is an endogenous lipid mediator with documented anti-inflammatory and neurogenic properties, but its effects in PD models remain unexplored. This study aimed to evaluate the neuroprotective potential of synaptamide in a subchronic MPTP-induced mouse model of PD. Methods. Male C57BL/6 mice received MPTP (30 mg/kg/day, i.p., 5 days) with or without synaptamide (10 mg/kg/day, s.c., 13 days). Behavioral tests (open field, Y-maze, elevated plus maze, novel object recognition (NOR)) were performed, followed by immunohistochemical analysis of tyrosine hydroxylase (TH)-positive neurons in the substantia nigra, and Western blotting for α-synuclein, p-α-synuclein, TH, and IL1β in brain homogenates and serum. In vitro Neuro-2a cells were co-treated with MPP⁺ (100 µM) and synaptamide (0.1–10 µM) for cytotoxicity assessment (MTS assay). Results. Synaptamide (10 µM) significantly attenuated MPP⁺-induced cytotoxicity in Neuro-2a cells. In vivo, MPTP caused a marked loss of TH⁺-neurons in the substantia nigra, which was prevented by synaptamide treatment. Importantly, this subchronic MPTP model recapitulates early biochemical alterations (e.g., α-synuclein phosphorylation at Ser129) rather than mature Lewy body pathology, a limitation that should be considered when interpreting these findings. Although no motor deficits or anxiety-like behavior were observed, the NOR test revealed MPTP-induced long-term memory impairment, which was fully restored by synaptamide. Conclusions. These findings suggest that synaptamide may exert effects on pathological processes associated with PD, warranting further investigation into its potential role in combination or supportive therapy for this disease. Full article

Prenatal alcohol exposure (PAE) is recognized as a major public health concern due to its profound and lasting effects on the central nervous system (CNS) and its ability to induce fetal alcohol spectrum disorders (FASD), which encompass a wide range of cognitive, behavioural, and neuropsychiatric disorders that persist throughout life. Experimental and clinical studies have identified several mechanisms underlying ethanol impairing brain development, including apoptosis, oxidative stress, disruption of morphogen and growth factor signalling pathways, impaired neuronal proliferation and migration, neurotransmitter systems’ dysfunction, glial cells damage associated with deficient myelination, vascular and blood–brain barrier (BBB) alterations, and lasting epigenetic reprogramming. However, to date no widely accepted integrative framework explaining how these impairments underline the heterogeneous phenotype observed in FASD is available. The present brings together developmental neurobiology and computational neuroscience to conceptualize PAE as a disorder of emerging neural and functional architecture. Here, we summarize the pharmacokinetics of ethanol in pregnancy, critical windows of vulnerability, and the classical pathways of alcohol teratogenesis affecting neuronal survival, migration, synaptogenesis, myelination, and gene regulation. We have also reviewed MRI, diffusion imaging, and EEG/MEG evidence showing altered brain volumes, white matter microstructure, functional connectivity, and network organization in individuals with PAE. Finally, we propose a systems-level model that conceptualizes PAE as a disorder of emerging neuro-computational architecture, in which ethanol-induced cellular and molecular perturbations collectively alter the building blocks and self-organization rules of brain network assembly. Full article

The present study investigates the semantic structure, dominant themes, and temporal evolution of research on giftedness in early childhood through a comparative topic modeling approach. A final analytic sample (n = 518) of peer-reviewed journal articles indexed in the Scopus and Web of Science databases was analyzed. Three topic modeling methods, Latent Dirichlet Allocation (LDA), Structural Topic Modeling (STM), and BERTopic, were systematically compared using multiple evaluation metrics. BERTopic demonstrated the strongest overall performance, producing approximately 11% higher coherence than STM and approximately 34% higher coherence than LDA. In terms of diversity, it achieved 14% to 17% greater thematic variety and, according to the Gini coefficient, revealed a 58% to 60% more balanced thematic distribution. BERTopic-based analyses identified five major thematic axes: Socio-Linguistic Development and Family Context, Psychometric Intelligence, Identification, and Cognitive Differences, Program Access, Identification, and Educational Equity, Early Academic Skills and Cognitive Development, and Creativity, Higher-Order Thinking, and Enrichment Programs. Thematic mapping and topic similarity analysis were used to examine the semantic structure of the field, while linear regression-based trend analysis over the 1918–2026 publication period showed that family context, socio-linguistic development, and equity-related themes have gained increasing importance over time, whereas psychometric identification largely maintained its central position within the field. These findings indicate that the field is moving toward a more inclusive, semantically grounded, and equity-oriented perspective. However, they should be interpreted in light of the study’s reliance on article abstracts, the sensitivity of BERTopic clustering parameters, and the use of linear trend modeling. Full article

Background/Objectives: Adult epilepsy is clinically heterogeneous, and individual clinical predictors may not fully capture the multidimensional burden associated with drug-resistant epilepsy (DRE). This study aimed to identify latent clinical phenotypes in adults with epilepsy and examine their cross-sectional associations with DRE and broader disease burden. Methods: This regional observational cohort study used a source database of 1100 patients with epilepsy. After excluding two patients aged <18 years, the adult analytic cohort included 1098 patients. Complete-case latent class analysis (LCA) was performed in 1054 patients using age at onset, disease duration, seizure type, seizure frequency, serial seizures/status, postictal confusion, neurological status, neuroimaging category, and number of antiseizure medications. Model selection was based on statistical fit, class size, and clinical interpretability. Internal clinical validation outcomes included DRE, quality of life, cognitive screening, and stigma scores. Post hoc characterization described the classes by epilepsy etiology, derived epilepsy type, and seizure categories aligned with current terminology. Results: A three-class solution was selected, with class sizes of 314, 465, and 275. DRE prevalence increased stepwise across classes: 5.7%, 14.2%, and 33.1%, respectively (p < 0.001). In adjusted analysis, Class 2 had higher odds of DRE than Class 1 (odds ratio 2.70, 95% confidence interval 1.56–4.67), while Class 3 showed the strongest association (odds ratio 8.19, 95% confidence interval 4.15–16.16; both p < 0.001). Higher-burden classes showed lower quality-of-life and cognitive scores and higher stigma scores. Conclusions: LCA identified three clinically interpretable, burden-enriched phenotypic profiles associated with a stepwise gradient in DRE and broader multidimensional disease burden. These cross-sectional profiles may provide a useful framework for describing clinical heterogeneity in adult epilepsy and generating hypotheses for future validation studies. Full article

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by amyloid-β (Aβ) deposition, tau hyperphosphorylation, neuroinflammation, mitochondrial dysfunction, and oxidative stress. Despite recent advances, current therapies offer little benefit, and AD remains a significant challenge. Polyunsaturated fatty acids (PUFAs), particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have attracted attention for their neuroprotective effects primarily through anti-inflammatory and antioxidant properties, but also for their ability to influence membrane fluidity and neuronal function. DHA is the predominant omega-3 PUFA in nerve cell membranes and is critical for synaptic plasticity and cognitive function. Some evidence has demonstrated that marine omega-3 supplementation reduces Aβ deposition, modulates microglial activation, and prevents cognitive decline in animal models. Even with heterogeneous results, preclinical and clinical studies suggest that long-term DHA/EPA supplementation can improve cognitive function in subjects with mild cognitive impairment (MCI) and reduce neuroinflammation markers. However, individual variability and brain bioavailability pose significant challenges. This review summarizes and discusses the current knowledge on the importance of PUFAs for human health, exploring novel mechanistic hypotheses, such as the effect of omega-3 fatty acids on brain iron homeostasis, the microbiota–gut–brain axis, the glymphatic system, and miRNAs. Furthermore, it focuses on the therapeutic potential of PUFAs in the treatment of AD and proposes future directions for translational research. Full article

Short videos have become a dominant format in digital commerce, enabling brands to engage consumers and drive purchases through dynamic and visually rich content. This highlights the need for a more nuanced understanding of visual framing strategies, that is, what elements are shown and how they are presented. Drawing on Cognitive Load Theory, this study explores the impact of visual compositional framing strategies and their dynamics on consumer engagement and sales. Applying a CNN-based deep learning model, 249,043 images (video frames) extracted from 3426 book-related short sales videos on Douyin are classified into one of three categories: functional, contextual, or social, according to the visual composition of the frame. Further econometric modeling reveals distinct effects of such framing categories: functional framing is positively associated with both engagement and sales, contextual framing relates to higher sales only, while social framing relates positively to engagement but negatively to sales. From a dynamic perspective, frequent transitions between framing types within a short video increase visual complexity, which reduces both engagement and sales and moderates the effects of specific framing strategies. These findings advance theoretical understanding of visual framing in dynamic media environments and offer practical insights for designing more effective short video content. Full article

The rapid evolution of data-driven enterprises demands scalable and intelligent systems capable of managing substantial volumes of heterogeneous data in real time. However, traditional systems lack a holistic approach to managing distributed data engineering, real-time analytics, and intelligent decision-making. To address these limitations, this paper proposes a Cognitive Lakehouse Framework that integrates distributed data processing, transformer-based deep learning, real-time analytics, and autonomous decision intelligence. Data are gathered from high-velocity, heterogeneous streams using Apache Kafka. Subsequently, data are processed using the hybrid batch/streaming paradigm, implemented via Apache Spark and Apache Flink, providing low latency and scalability. For data storage, a unified lakehouse layer is created using Delta Lake and Apache Iceberg, both of which support ACID transactions and schema evolution. In addition, transformer-based Deep Learning (DL) algorithms are utilized to capture temporal dependencies for predictive analytics, anomaly detection, and adaptive learning. Model lifecycle management is handled by MLflow, while ClickHouse and Apache Druid are used for real-time analytics. The architecture uses microservices and an event-driven approach on Kubernetes, and the workflow is automated with Apache Airflow. The performance assessment is conducted using TPC-H, TPC-DS, and real-time stream data to measure latency, throughput, and accuracy. Data quality, security, and compliance are provided by governance layers consisting of Apache Ranger and Apache Atlas. Experimental results show that significant gains can be made in terms of performance, with an accuracy of 98.5%, a query response time of 120 ms, a peak throughput of 85,000 records/s, and an end-to-end latency of 95 ms. Full article

While individual determinants of students’ well-being are well established, less is known about the association with the institutional context. This study evaluates institutional-level factors associated with students’ mental health while controlling for individual characteristics. The cross-sectional analysis used data from 12 German institutions (n = 13,715) collected in the StudiBiFra survey on study conditions and student mental health. Individual-level variables included gender, age, study subject group, and four mental health variables (general well-being, depressiveness, cognitive stress, and exhaustion). Institution-level variables comprised institution type, excellence status, multi-campus structure, size, and satisfaction with the quality of health promotion services. Multilevel binary logistic regression models were applied to examine associations between institutional characteristics and mental health outcomes, adjusting for individual factors. Students enrolled at universities of applied sciences showed a lower likelihood of reporting depressiveness and exhaustion. Higher levels of depressiveness and cognitive stress were observed among students at medium-sized institutions compared to small ones. Students not enrolled at institutions with excellence status had lower risks of depressiveness, stress, and exhaustion. Additionally, higher satisfaction with institutional health promotion services was associated with reduced odds of depressiveness. Institutional factors are related to students’ mental health beyond individual characteristics, highlighting the need for a holistic, setting-based approach. Full article