Search Results (1,723)

Extended reality (XR) has increasingly been applied to nursing practicum education; however, most systems rely on controller-based interfaces that limit precise capture of continuous fine motor performance and objective assessment. This study developed and validated a sensor-integrated, controller-free XR nursing practicum system (Smart Nursing v1.0) grounded in continuous precision sensing. Based on internationally standardized intravenous injection protocols, the system integrated optical hand tracking and speech recognition to quantify hand kinematics, spatial accuracy, procedural sequencing, and verbal compliance. A three-phase validation framework was implemented. Internal technical verification confirmed stable real-time performance (≥60 FPS) and consistent action recognition. In a user-based study involving 63 undergraduate nursing students, XR-based automated scores demonstrated high agreement with expert instructor ratings (ICC = 0.932, 95% CI = 0.91–0.96, p < 0.001). XR baseline scores significantly predicted post-training performance (β = 0.632, p < 0.001) and showed significant incremental validity beyond instructor pre-training scores (ΔR² = 0.186, p < 0.001). Independent verification confirmed high recognition accuracy (100%) and system stability. These findings indicate that precision sensing enables XR environments to function as reliable performance measurement systems, supporting standardized non-face-to-face nursing practicum education. Full article

Full-penetration laser welding (FPLW) is increasingly adopted in manufacturing pipelines, yet its industrial scalability is constrained by in-process defect formation, particularly incomplete penetration. To address this, we propose a sensor-driven framework for non-destructive monitoring and automated defect detection that uses infrared (IR) thermography as the primary in situ sensing modality and applies deep learning to the acquired thermal signals. High-speed IR camera recordings were processed to track spatter and the weld zone, yielding a time series of physically interpretable spatiotemporal features (mean spatter area, mean spatter temperature, number of spatters, and mean welding zone temperature). Defect recognition is formulated as a multi-label classification problem targeting incomplete penetration, sagging, shrinkage groove, and linear misalignment, and multiple temporal models were evaluated on the same sensor-derived feature sequences. Experimental validation on 09G2S pipeline steel demonstrates that the proposed time series pipeline based on a hybrid CNN–transformer achieves a mean Average Precision (mAP) of 0.85 while preserving near-real-time inference on a CPU. The results indicate that IR thermography-based spatter dynamics provide actionable sensing signatures for automated defect prediction and can serve as a foundation for closed-loop quality control in industrial laser pipeline welding. Full article

Burnout among long-term care workers is a public health concern, yet mixed-methods evidence from China is scarce. To examine multilevel correlates of burnout, a convergent mixed-methods study using a Social–Ecological Framework was conducted. In the quantitative strand, 494 workers were surveyed using two-stage cluster sampling, and probability-weighted multivariable linear regression examined factors associated with emotional exhaustion, depersonalization, and reduced personal accomplishment. In the qualitative strand, 15 participants completed semi-structured interviews; transcripts were managed in MAXQDA 2025 and analyzed thematically. Burnout was common (30.77% mild, 33.00% moderate, 17.00% severe). Quantitative findings showed that burnout dimensions were associated with gender, age, marital status, employment arrangement, institution type, training intensity, caregiver burden, and recognition of the long-term care insurance policy (p < 0.05). Qualitative findings highlighted cognitive adaptation, emotional reciprocity with older adults, organizational training and support, and policy recognition as potential buffering resources. These findings suggest that burnout is shaped by influences across multiple levels. Coordinated efforts may help alleviate burnout by strengthening training systems, reducing caregiving burden, enhancing recognition of long-term care policies, and elevating the societal value of care work. Future research should validate these potential courses of action through longitudinal or intervention studies. Full article

Studies of vocal communication often focus on the messages that calls and songs convey related to reproductive activities, foraging, predator avoidance, social bonding, individual recognition, and conflict resolution. We consider ways in which vocalizations may dynamically mediate social interactions at a more basic level, through collective coordination of movements and the enhancement of spatial perception. From this perspective, animals may vocalize to probe the locations, movements, and intentions of others, to manipulate position changes by listeners, or to increase their own capacity to localize sounds. An animal’s capacity to flexibly adjust vocalizations, both in real-time and over longer periods, can increase their ability to monitor and influence conspecifics independently of any information that may be encoded within those vocalizations. Beyond simply conveying messages, reproductive fitness, or emotional states, an animal’s ability to modulate vocalizations may dynamically affect its future action plans and social roles within a group. Identifying situational, life-history, and sociospatial factors that determine how animals vocally interact in real-time is key to understanding how an animal’s vocalizations relate to its own actions and the actions of others. Full article

Duchenne muscular dystrophy (DMD) is a severe X-linked neuromuscular disorder caused by loss-of-function mutations in the dystrophin gene, leading to progressive muscle degeneration, motor decline, respiratory compromise, and cardiomyopathy. Diagnosis typically occurs in early childhood following recognition of motor delays, markedly elevated creatine kinase, and confirmatory genetic testing. Over the past decade, the therapeutic landscape for DMD has expanded substantially, evolving from exclusively supportive care to patient-centric multifaceted treatment paradigms, including corticosteroids, mutation-specific therapies, small molecule disease-modifying approaches, and gene replacement strategies. Despite these advances, no currently available therapy restores full-length dystrophin or completely halts disease progression. This review provides a clinically oriented comprehensive overview of currently Food and Drug Administration (FDA)-approved medications for DMD, with particular emphasis on corticosteroids, exon-skipping therapies, nonsense mutation readthrough agents, recently approved gene therapy, and select ongoing gene therapy trials. We summarize mechanisms of action, clinical efficacy, safety considerations, regulatory status, and highlight the challenges of integrating these therapies into longitudinal care. Through illustrative clinical vignettes, we highlight the real-world complexity of treatment selection, shared decision-making, and longitudinal care planning in contemporary DMD management. Full article

Diet is a major, modifiable determinant of cardiometabolic, cancer, and inflammatory disease risk, yet individuals frequently exhibit substantial heterogeneity in metabolic and clinical responses to similar dietary exposures. Genetic susceptibility and its interplay with diet plausibly contribute to this variability, motivating gene–diet (G×D) interaction research and the broader ambition of precision nutrition. Translation has lagged, however, because interaction effects are typically modest, context-dependent, and difficult to reproduce, particularly in the presence of pervasive dietary measurement error, heterogeneous exposure definitions, and stringent multiplicity correction. A methodologically oriented synthesis is presented across eight domains of contemporary G×D epidemiology: classical regression interaction models; efficient study designs; dietary assessment and measurement error; dietary patterns, mixtures, and non-linear modeling; genome-wide and polygenic approaches; causal inference frameworks; multi-omics integration; and machine learning. Central concepts include the recognition that “interaction” is a scale-dependent estimand and that transparent reporting of coding choices and effect-modification metrics—including additive interaction when relevant for public health interpretation—is essential. Credible inference further depends on high-quality, harmonized dietary phenotyping with explicit energy adjustment and, where feasible, biomarker calibration, alongside robust control of population structure and gene–diet correlation using ancestry adjustment, mixed models, and family-based designs. Genome-wide and polygenic risk-based approaches expand discovery potential but require disciplined multiplicity strategies, discovery-replication workflows, and explicit evaluation of portability and equity across ancestries. Causal inference methods can strengthen etiologic interpretation when assumptions are defensible and sensitivity analyses are routinely implemented. Multi-omics and machine learning may enhance mechanistic and predictive insight, but only under rigorous quality control, validation, and reproducible pipelines. Overall, harmonized measurement, clear estimands, multi-ancestry replication, and integrated evidence pipelines are pivotal for producing robust and actionable G×D evidence. Full article

Overtourism has intensified socio-environmental pressures in popular destinations, raising concerns about ethical responsibility and sustainable behavior among tourism actors and visitors. In this study, we explored how environmental awareness and ethical values shape behavioral intentions under overtourism pressure by combining a systematic literature review with qualitative field data from Bali. Through a PRISMA-based review of 100 peer-reviewed journal articles published between 2015 and 2024, we synthesized evidence on environmental ethics, responsible tourism, and pro-environmental behavioral mechanisms. The review reveals that increasing scholarly attention is being paid to ethical norms, emotional engagement, and contextual constraints but shows that there is limited empirical understanding of how these factors are experienced in practice by local actors and domestic tourists. To address this gap, qualitative interviews were conducted with three key stakeholders, including accommodation and tourism service providers, and 10 domestic tourists. Thematic analysis identifies three interrelated mechanisms influencing behavioral intention: (a) recognition of environmental risk and destination vulnerability, (b) ethical reasoning and sense of collective responsibility, and (c) structural barriers shaped by convenience, economic pressures, and weak governance. While participants express strong environmental awareness and moral concern, behavioral intentions are often constrained by limited information, the perceived ineffectiveness of individual actions, and a lack of regulatory enforcement. This study contributes to the sociological literature on sustainable tourism by elucidating how ethics and awareness translate into intention under overtourism pressure. We report the practical implications for ethical communication, stakeholder collaboration, and participatory governance. Full article

Social media has become a key venue where PhD graduates seek career information, compare experiences, and negotiate uncertainty. Drawing on information behavior and sense-making perspectives, this study examines how returnee PhDs from non-core study destinations discuss employment challenges in China’s academic labor market when credential signals are contested. Using Korean-trained PhDs as a theoretically motivated exemplary case, we collected 1149 publicly available posts from Xiaohongshu, a Chinese social media platform, and applied BERTopic to identify latent themes, followed by qualitative close reading of representative posts to interpret discourse functions. The model yielded ten topics, and semantic association analysis indicates substantial overlap among high-frequency topics, suggesting intertwined concerns rather than neatly separated issue domains. The four most prevalent topics account for 72.06% of the corpus, centering on credential recognition, job-search pathways, informal screening rules, and intersecting age- and gender-related pressures. Qualitative readings further reveal recurring discursive moves, including exposing tacit hiring heuristics, contesting stigmatizing labels (e.g., “water PhD,” a derogatory term implying low-quality credentials), and exchanging actionable strategies across regions and career tracks. Overall, the findings point to discursive convergence under evaluation uncertainty: when formal criteria are ambiguous and institutional signals are unreliable, participants turn to social media to stabilize expectations by triangulating cases and iteratively refining shared interpretations of the job market. This study contributes empirical evidence on uncertainty-driven information practices in highly educated labor markets and demonstrates the value of combining topic modeling with qualitative interpretation to capture online collective sense-making. Full article

This study addresses the growing vulnerability of societies to extreme weather events intensified by climate change and explores how Secondary Education can foster sustainability competences aligned with the European GreenComp framework. A mixed-methods design was used, combining a content analysis of 279 curricular units from educational legislation and STEM subjects in Compulsory Secondary Education and Baccalaureate, a questionnaire administered to 190 students, and the design and classroom implementation of a GreenComp-based teaching intervention. The curricular analysis revealed uneven integration of sustainability competences across STEM disciplines, with stronger presence in Biology, Geology and Technology, and limited representation in Mathematics and Physics and Chemistry. Student perceptions showed fragmented understandings of extreme weather events, their causes and consequences, and limited awareness of global frameworks such as the SDGs and COP meetings. The implemented teaching sequence improved students’ knowledge of extreme events, strengthened their recognition of links with climate change, and increased awareness of mitigation, adaptation, and the role of education and political action. Overall, the findings highlight both opportunities and gaps in current curricula and demonstrate the potential of contextualized, inquiry-based STEM approaches to develop sustainability competences and better prepare students to face extreme weather events. Full article

Recent advances in chiral analytical chemistry have revealed that fermented and natural foods contain substantial amounts of D-amino acids (D-AAs), the mirror-image counterparts of L-amino acids, leading to their recognition as nutraceutical components with potential health relevance. Although clinical evidence provides only limited support for their therapeutic efficacy, commercial expectations have outpaced scientific validation, and recent safety concerns emphasize the need for critical evaluation. In this review, we integrate findings from food chemistry, microbiology, biochemistry, physiology, and clinical research to provide a critical overview of dietary D-AAs. We examine how dietary exposure, microbial metabolism, host clearance capacity, and redox status collectively shape their context-dependent biological effects. We highlight the mechanistic linkage between D-amino acid oxidase (DAAO)-mediated hydrogen peroxide (H₂O₂) generation and organ-specific vulnerability, thereby clarifying the molecular basis of their “double-edged sword” actions. Within this interdisciplinary framework, we propose that D-AAs function as context-dependent “contronymic” molecules in cellular communication. By distinguishing physiological regulation, experimental modulation, and clinical application, this review aims to support evidence-based nutraceutical strategies and safety assessments that harness the potential benefits of D-AAs while minimizing associated risks. Full article

Storm surges are catastrophic marine disasters that pose severe threats to coastal populations, making the rapid extraction of key information from multi-source texts critical for effective emergency response. However, existing extraction methods often struggle with complex linguistic challenges, such as identifying nested entities (e.g., overlapping geographic names), capturing relationships across long texts, and handling the disparity between formal official reports and unstructured social media data. To address these limitations, this study proposes a Storm Surge Knowledge Extraction Model (SSKEM) based on Global Pointer Networks. By constructing a domain-specific dataset of 4000 records from government bulletins, news reports, and social media, the proposed model utilizes a unified matrix decoding mechanism to treat entity and relation extraction as a holistic task. Experimental results demonstrate that the model achieves an F1-score of 88.4%, outperforming robust baseline models by 5.5%. Notably, it improves the recognition accuracy of complex nested entities by 13.7% and enhances the recall rate for cross-sentence relations by 18.2%. Furthermore, the model exhibits high computational efficiency, processing speed suitable for real-time applications, and effectively bridges the performance gap between standardized and fragmented data sources. This research provides a robust technical solution for transforming heterogeneous disaster big data into actionable knowledge for decision-support systems. Full article

Addressing the challenges of large-scale gesture recognition models, high computational complexity, and inefficient deployment on embedded devices, this study designs and implements a visual dynamic gesture recognition system based on a lightweight CNN-LSTM-DSA model. The system captures user hand images via a camera, extracts 21 keypoint 3D coordinates using MediaPipe, and employs a lightweight hybrid model to perform spatial and temporal feature modeling on keypoint sequences, achieving high-precision recognition of complex dynamic gestures. In static gesture recognition, the system determines the gesture state through joint angle calculation and a sliding window smoothing algorithm, ensuring smooth mapping of the servo motor angles and stability of the robotic hand’s movements. In dynamic gesture recognition, the system models the key point time series based on the CNN-LSTM-DSA hybrid model, enabling accurate classification and reproduction of gesture actions. Experimental results show that the proposed system demonstrates good robustness under various lighting and background conditions, with a static gesture recognition accuracy of up to 96%, dynamic gesture recognition accuracy of 90.19%, and an overall response delay of less than 300 ms. Full article

West Nile virus (WNV) circulates endemically in Romania, yet prevention of WNV infection in horses largely depends on owner-driven decisions that require accurate risk perception and veterinary guidance. A cross-sectional online survey was carried out between May and November 2025 to evaluate the knowledge, attitudes, and preventive practices (KAP) regarding WNV among 227 horse owners from various Romanian regions. In total, 67.4% of respondents had previously heard of WNV. The main transmission route was correctly identified as mosquito bites by 49.8% of participants, while 32.2% answered “don’t know” or presented misconceptions: horse-to-horse contact (9.3%), tick bites (10.6%) and blood transfusion (0.4%). Recognition of clinical signs was limited, with fever (31.3% of respondents) and gait abnormalities or ataxia (24.7% of respondents) being most frequently mentioned, followed by inappetence (19.4% of respondents), seizures (18.1% of respondents), coughing (8.8% of respondents), and abortions (10.6% of respondents); 47.6% of respondents were unable to identify any specific signs. Awareness of the existence of an equine WNV vaccine was reported by 23.8% of respondents, while only 4.0% indicated that their horses had been vaccinated. The most common preventive measures included the use of insecticides in stables (61.2%) and topical repellents on horses (55.5%), whereas environmental control actions such as removing standing water (14.1%) or avoiding swampy areas (11.9%) were less frequent; 19.4% reported taking no preventive measures. Veterinary communication was limited, with only 17.2% of respondents having received information about WNV from a veterinarian, and 21.6% perceiving a real risk of infection in Romania. Overall, the data show a marked disconnect between awareness and actionable prevention (particularly vaccination and environmental mosquito control), indicating that targeted owner education must be paired with structured veterinary communication to translate knowledge into preventive uptake in endemic settings. Full article

An intelligent multi-step action recognition system was designed for long-term caregiver training and assessment. Leveraging MediaPipe for precise and real-time human pose estimation, the system extracts detailed spatiotemporal body and hand keypoints. Temporal convolutional networks are employed to effectively capture temporal dependencies and complex features from sequential motion data. Dynamic time warping provides robust sequence alignment, allowing flexible comparison between performed actions and standard templates despite temporal variations in execution speed or style. A finite state machine imposes logical constraints by modeling expected action step sequences, enabling accurate detection of sequence anomalies or deviations. This hybrid architecture supports comprehensive evaluation and real-time feedback, facilitating improved caregiver skill acquisition, process adherence, and quality control within long-term care settings. The system aims to advance digital transformation in healthcare education by providing a scalable, precise, and adaptive training solution. Full article

Background/Objectives: Transphyseal fracture of the distal humerus (TFDH) is a rare but clinically important pediatric elbow injury that predominantly affects children under 3 years of age. Due to the radiolucent nature of the cartilaginous distal humeral epiphysis in this age group, TFDH is often misdiagnosed as elbow dislocation, supracondylar fracture, or lateral/medial condyle fracture. Time pressures, limited pediatric musculoskeletal expertise, and incomplete clinical histories in emergency settings further compound this diagnostic challenge. Despite the importance of early and accurate diagnosis to prevent complications such as cubitus varus, systematic studies on diagnostic pitfalls and strategies for improving recognition remain scarce. We therefore aim to characterize misclassification patterns, standardize radiographic cues, and evaluate management outcomes. Methods: We conducted a single-center retrospective review of 25 pediatric patients with TFDH who were misdiagnosed at initial presentation between 2012 and 2022. Clinical records, radiographic features, treatment modalities, and complications were analyzed over a minimum follow-up period of 24 months. Results: All 25 cases were initially misdiagnosed. The most common misdiagnoses were supracondylar and lateral condyle fractures (each 6/25, 24%), followed by elbow dislocation (4/25, 16%). Misclassification was primarily attributed to failure to assess global forearm–humerus alignment and misinterpretation of the radiocapitellar line. All patients underwent emergency management, with 18/25 (72%) receiving closed reduction and percutaneous K-wire fixation, and 7/25 (28%) undergoing closed reduction and cast immobilization. Cubitus varus developed in 5/25 (20%) overall and was more frequent after closed reduction with cast immobilization (3/7, 43%) than after K-wire fixation (2/18, 11%). Overall, 92% achieved excellent functional outcomes according to the Mayo Elbow Performance Index (MEPI). The implementation of a targeted curriculum improved diagnostic accuracy among trainees from 70% to 100%. Conclusions: TFDH poses substantial cognitive and radiographic diagnostic challenges. A structured radiographic assessment, early senior review, and targeted education may improve recognition and outcomes. These findings offer actionable insights to enhance diagnostic accuracy and optimize care for this vulnerable patient population. Full article