Search Results (1,627)

In recent years, the advancement of various applications, data mining, technologies, and socio-technical systems has led to the development of interactive platforms that enhance user experiences through personalization. In the sports domain, users can access training plans, routes and healthy habits, all in a personalized way thanks to sports recommender systems. These recommendation engines are fueled by rich datasets that are collected through continuous monitoring of users’ activities. However, their potential to address user profiling is limited to single users and not to the dynamics of groups of sportsmen. This paper introduces GoSS-Rec, a Group-oriented Segment Sequence Recommender System, which is designed for groups of cyclists who participate in fitness activities. The system analyzes collective preferences and activity records to provide personalized route recommendations that encourage exploration of diverse cycling paths and also enhance group activities. Our experiments show that GoSS-Rec, which is based on Prod2vec, consistently outperforms other models on diversity and novelty, regardless of the group size. This indicates the potential of our model to provide unique and customized suggestions, making GoSS-Rec a remarkable innovation in the field of sports recommender systems. It also expands the possibilities of personalized experiences beyond traditional areas. Full article

Blood-based biomarkers allow monitoring of an individual’s health status and provide insights into metabolic and inflammatory processes in conditions like obesity, cardiovascular, and liver diseases. However, selecting suitable biomarkers and optimizing analytical assays presents challenges, is time-consuming and laborious. Moreover, knowledge of potential sex differences remains incomplete as research is often carried out in men. This study aims at enabling researchers to make informed choices on the type of biomarkers, analytical assays, and dilutions being used. More specifically, we analyzed plasma concentrations of >90 biomarkers using commonly available ELISA or electrochemiluminescence-based multiplex methods, comparing normal weight (BMI < 25; n = 40) with obese (BMI > 30; n = 40) adult blood donors of comparable age. To help choose optimal biomarker sets, we grouped frequently employed biomarkers into biological categories (e.g., adipokines, acute-phase proteins, complement factors, cytokines, myokines, iron metabolism, vascular inflammation), first comparing normal-weight with obese persons, and thereafter exploratively comparing women and men within each BMI group. Many biomarkers linked to chronic inflammation and dysmetabolism were elevated in persons with obesity, including several adipokines, interleukins, chemokines, acute-phase proteins, complement factors, and oxidized LDL. Further exploration suggests sex disparities in biomarker levels within both normal-weight and obese groups. This comprehensive dataset of biomarkers across diverse biological domains constitutes a reference resource that may provide valuable guidance for researchers in selecting appropriate biomarkers and analytical assays for own studies. Moreover, the dataset highlights the importance of taking possible sex differences into account. Full article

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

Background: Family caregivers are a vital yet often under-recognized part of the healthcare system. They provide essential emotional, physical, and logistical support to individuals with illness, disability, or frailty, and their contributions improve continuity of care and reduce system strain. However, many healthcare and social service providers are not equipped to meaningfully engage caregivers as partners. In Alberta, stakeholders validated the Caregiver-Centered Care Competency Framework and identified the need for a three-tiered education model—Foundational, Advanced, and Champion—to help providers recognize, include, and support family caregivers across care settings. This paper focuses on the development and early evaluation of the Advanced Caregiver-Centered Care Education modules, designed to enhance the knowledge and skills of providers with more experience working with family caregivers. The modules emphasize how partnering with caregivers benefits not only the person receiving care but also improves provider effectiveness and supports better system outcomes. Methods: The modules were co-designed with a 154-member interdisciplinary team and grounded in the competency framework. Evaluation used the first three levels of the Kirkpatrick–Barr health workforce education model. We analyzed pre- and post-surveys from the first 50 learners in each module using paired t-tests and examined qualitative feedback and SMART goals through inductive content analysis. Results: Learners reported a high level of satisfaction with the education delivery and the knowledge and skill acquisition. Statistically significant improvements were observed in 53 of 54 pre-post items. SMART goals reflected intended practice changes across all six competency domains, indicating learners saw value in engaging caregivers as partners. Conclusions: The Advanced Caregiver-Centered Care education improved providers’ confidence, knowledge, and skills to work in partnership with family caregivers. Future research will explore whether these improvements translate into real-world practice changes and better caregiver experiences in care planning, communication, and navigation. Full article

Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral–temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques. Full article

Background/Objectives: This study investigated the association between cochlear implant (CI) users’ assessed perception of musical sound quality and their subjective music perception and music-related quality of life (QoL). The aim was to provide a comprehensive evaluation by integrating a relatively objective Turkish Multiple Stimulus with Hidden Reference and Anchor (TR-MUSHRA) test and a subjective music questionnaire. Methods: Thirty CI users and thirty normal-hearing (NH) adults were assessed. Perception of sound quality was measured using the TR-MUSHRA test. Subjective assessments were conducted with the Music-Related Quality of Life Questionnaire (MuRQoL). Results: TR-MUSHRA results showed that while NH participants rated all filtered stimuli as perceptually different from the original, CI users provided similar ratings for stimuli with adjacent high-pass filter settings, indicating less differentiation in perceived sound quality. On the MuRQoL, groups differed on the Frequency subscale but not the Importance subscale. Critically, no significant correlation was found between the TR-MUSHRA scores and the MuRQoL subscale scores in either group. Conclusions: The findings demonstrate that TR-MUSHRA is an effective tool for assessing perceived sound quality relatively objectively, but there is no relationship between perceiving sound quality differences and measures of self-reported musical engagement and its importance. Subjective music experience may represent different domains beyond the perception of sound quality. Therefore, successful auditory rehabilitation requires personalized strategies that consider the multifaceted nature of music perception beyond simple perceptual judgments. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

DNA connects the domains of genetic regulation and environmental interactions and plays a crucial role in neural development and cognitive function. The complex roles of genetic and epigenetic processes in brain development, synaptic plasticity, and higher-order cognitive abilities were reviewed in this study. Neural progenitors are formed and differentiated according to genetic instructions, whereas epigenetic changes, such as DNA methylation, dynamically control gene expression in response to external stimuli. These processes shape behavior and cognitive resilience by influencing neural identity, synaptic efficiency, and adaptation. This review also examines how DNA damage and repair mechanisms affect the integrity of neurons, which are essential for memory and learning. It also emphasizes how genetic predispositions and environmental factors interact to determine a person’s susceptibility to neurodegenerative disorders, such as Parkinson’s and Alzheimer’s diseases. Developments in gene-editing technologies, such as CRISPR, and non-viral delivery techniques provide encouraging treatment avenues for neurodegenerative disorders. This review highlights the fundamental role of DNA in coordinating the intricate interactions between molecular and environmental factors that underlie brain function and diseases. Full article

Background: Fibromyalgia (FM) is a chronic condition characterized by widespread pain, fatigue, cognitive difficulties, and psychological symptoms. Patients often present distinct personality traits and psychopathological patterns associated with symptom severity. Objective: To examine psychopathological profiles in FM patients based on functional, physical–somatic, and emotional impairment domains, as well as on cumulative disease severity. Materials and Methods: A cross-sectional study was conducted with 70 women clinically diagnosed with FM at a specialized Fibromyalgia Unit. Psychological functioning was assessed using the Personality Assessment Inventory, and disease impact was measured with the Fibromyalgia Impact Questionnaire. Hierarchical cluster analyses were used to classify participants into mild and severe clusters across FIQ domains, and psychological profiles were compared. Results: Patients with severe functional impairment had more affective dysregulation (76.43 vs. 70.20, p < 0.01) and somatic complaints (85.57 vs. 79.76, p < 0.05) than those with mild impairment. The severe–physical cluster showed greater mood instability, somatization, and suicidal ideation (60.94 vs. 53.61, p < 0.05). The severe–emotional cluster had higher rates of major depression (85.71% vs. 64.28%) and persistent depressive disorder (76.19% vs. 70.61%, p < 0.05). Severe showed more emotional instability and somatization, distinguishing it from mild. Greater cumulative severity intensified depressive and somatic disorders. Discussion: Findings support FM’s biopsychosocial profile, where emotional distress may relate to psychological and physical symptoms, reinforcing the need for personalized, multidisciplinary care and comprehensive assessment. Full article

This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity using Khanmigo, a domain-specific AI platform for K-12 education, PTs explored AI-supported instructional tasks. Post-activity data were analyzed using PLS-SEM. The results showed that perceived usefulness (PU), perceived ease-of-use (PEU), and self-efficacy (SE) significantly predicted behavioral intention (BI) to adopt GenAI, with SE also influencing both PU and PEU. Conversely, personal innovativeness in IT and perceived cyber risk showed insignificant effects on BI or PU. The findings underscored the evolving dynamics of TAM constructs in GenAI contexts and highlighted the need to reconceptualize ease-of-use and risk within AI-mediated environments. Practically, the study emphasized the importance of preparing PTs not only to operate AI tools but also to critically interpret and co-design them. These insights inform both theoretical models and teacher education strategies, supporting the ethical and pedagogically meaningful integration of GenAI in K-12 education. Theoretical and practical implications are discussed. Full article

Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article

Background: Delirium is a common, underdiagnosed neuropsychiatric syndrome in older adults, associated with high mortality and functional decline. Given its multifactorial nature and overlap with frailty, radiological markers may improve risk stratification in the emergency department (ED). Methods: We conducted a retrospective study on a small sample of 30 patients diagnosed with delirium in the emergency department who had recently undergone brain, thoracic, or abdominal CT scans for unrelated clinical indications. Using post-processing software, we analyzed radiological markers, including coronary artery calcifications (to estimate vascular age), cerebral atrophy (via the Global Cortical Atrophy scale), and cachexia (based on abdominal fat and psoas muscle volumetry). Results: Five domains were identified as significant predictors of 12-month mortality in univariate Cox regression: vascular age, delirium etiology, cerebral atrophy, delirium subtype (hyperactive vs. hypoactive), and cachexia. Based on these domains, we developed an exploratory 10-point delirium score. This score demonstrated acceptable diagnostic accuracy for mortality prediction (sensitivity 0.93, specificity 0.73, positive predictive value 0.77, negative predictive value 0.91) in this limited cohort. Conclusions: While preliminary and based on a small, retrospective sample of 30 patients, this multidimensional approach integrating clinical and radiological data may help improve risk stratification in elderly patients with delirium. Radiological phenotyping, particularly in terms of vascular aging and sarcopenia/cachexia, offers objective insights into patient frailty and could inform more personalized treatment pathways from the ED to safe discharge home, pending further validation. Full article