Search Results (646)

Background and Objectives: Chronic non-specific neck pain (CNNP) is common among desk-based workers exposed to increased ergonomic risk and is accompanied by functional impairments. This study examined associations between exercise participation and pain intensity, upper-extremity disability, body awareness, and perceived sagittal trunk appearance in this population. Materials and Methods: This cross-sectional study included 250 of 512 screened desk-based workers with CNNP, pain severity ≥4/10, and a Rapid Upper Limb Assessment (RULA) score ≥ 3/7. Physical activity, upper-extremity disability, body awareness, perceived sagittal trunk appearance, and ergonomic risk were assessed using the IPAQ-SF, QuickDASH, Body Awareness Rating Questionnaire (BARQ), KyphoTAPS, and a video-call-based RULA. Results: Participants reporting exercise participation had lower QuickDASH scores (p = 0.003, d = −0.42) and higher BARQ total scores (p = 0.023, d = 0.33) than non-exercisers. BARQ function and awareness scores were higher in this group (p = 0.004 and p = 0.033), while pain severity, perceived sagittal trunk appearance, and ergonomic risk were similar between groups. QuickDASH scores were positively correlated with pain severity (rho = 0.636–0.727) and negatively correlated with BARQ total scores (rho = −0.477). RULA scores showed a weak positive correlation with general neck pain (rho = 0.288) (FDR-adjusted p < 0.002). Exploratory adjusted analyses showed that participants reporting regular exercise participation had lower QuickDASH and higher BARQ total scores than non-exercisers in all models. Participants reporting low-frequency exercise participation had lower QuickDASH scores than non-exercisers only in Model 4. Conclusions: Self-reported exercise participation may be associated with lower QuickDASH scores and higher BARQ total scores in desk-based workers with CNNP and increased ergonomic risk. Because maintaining consistent exercise participation can be challenging in occupational settings, future studies should investigate feasible exercise strategies to address pain-related, functional, and perceptual outcomes among occupational populations exposed to prolonged ergonomic loading. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is caused by excessive fat accumulation in the liver (steatosis) and affects approximately 38% of adults globally. MASLD may progress from simple steatosis to fibrosis and cirrhosis and is closely related to other cardio-metabolic conditions (e.g., obesity and type 2 diabetes), posing a risk factor for cardiovascular disease. Currently, lifestyle modification and weight reduction remain the main initial treatment options. Existing data suggest that there is low awareness among patients regarding MASLD diagnosis and its subsequent management. Therefore, this study aimed to develop a rich understanding of the lived experiences, concerns, and support needs of adults with MASLD. Methods: A qualitative design was applied, utilizing semi-structured interviews. Adults living with MASLD were invited to talk about their diagnosis and discuss their lived experiences. Participants were interviewed by telephone or video call, and each interview was transcribed and analyzed with a reflexive thematic analysis approach. Results: Twenty-five adults with MASLD (age range: 24–79 years; 40% men) were interviewed. The emergent key themes related to communication and emotions at diagnosis; independently seeking further information; lived experiences post-diagnosis; support needs; and future concerns. Many participants reported receiving the diagnosis incidentally, and a number of issues were raised regarding lack of clarity at the point of diagnosis. Additional concerns included information obtainable via the internet, symptoms, social relationships, stigma, and lifestyle modification. Future anxieties related mainly to fears of disease progression, while support needs were predominantly focused on information and follow-up. Conclusions: The concerns and support needs identified by this study highlight key issues/themes that should inform education and support initiatives by relevant healthcare services aiming to improve the lived experiences and holistic management of adults with MASLD. Full article

Aging residential buildings constructed under outdated design standards create an urgent need for scalable, evidence-based indoor safety assessment methods. Conventional manual inspections rely on subjective checklists, lack audit trails, and are impractical for widespread deployment. This study presents a vision-based digital twin and AI agent framework that converts a single continuous smartphone video into an explainable, evidence-constrained safety assessment. The pipeline employs MASt3R-SLAM to reconstruct a metric-scale 3D point cloud from monocular video, calibrated with AprilTag fiducials for absolute scale. SpatialLM parses the geometry to extract semantic entities and spatial relationships. Risk guidelines are formalized into a computable Risk Prototype structure, unified within a hierarchical SceneState data structure that binds geometric measurements, semantic labels, image observations, and regulatory knowledge. A LangGraph-based AI agent conducts a dual-pathway assessment: an initial whole-dwelling scan followed by iterative follow-up queries invoking tool calls for measurement, knowledge retrieval, or visual cross-checking. In a pilot validation across five heterogeneous residences, with detailed manual comparison in two representative cases, the framework achieved risk recall rates of 77.8–100% and precision rates of 45.0–70.0% against the single-assessor manual reference. The average judgment closure rate was 71.7%, with spatial granularity enhancement of up to 2.2× in complex environments. These results suggest that the framework can achieve risk coverage comparable to manual checklist inspection while offering enhanced granularity in complex environments and quantitative precision in well-defined spaces. Full article

Recognizing facial emotions automatically from images/videos (FER) still represents a difficult problem for emotion computing, mainly due to variations in the face pose, lighting, occlusion, facial features, and expression intensity in the wild. Recent CNN–Transformer-based hybrid models like POSTER have leveraged local feature learning, landmark guidance, and global dependency modeling to achieve strong performance. Yet these methods give the main focus to spatial and contextual representations while not really going deep into adaptive channel-wise feature importance over multi-scale representations. As different feature channels represent emotions in varying degrees, it is likely that by treating all feature channels equally, one would limit the ability of the learned features to discriminate effectively. To overcome this weakness, this article presents a ResNet-18–Transformer landmark-guided module called SE-POSTER that fuses lightweight Squeeze-and-Excitation (SE) attention modules into the multi-scale feature pyramid of the baseline POSTER architecture. The proposed method carries out feature channel recalibration adaptively at the level of features before Transformer-based global attention modeling, thus allowing the network to focus on emotionally informative feature channels and suppress less relevant responses. The inclusion of SE attention in the network enhances fine, mid, and global levels of feature representations at a very low cost in terms of computation. On the basis of the RAF-DB, FERPlus, and AffectNet datasets, enormous experiments prove that the SE-POSTER framework proposed is capable of steadily boosting recognition accuracy relative to the baseline POSTER and several state-of-the-art FER methods. Especially, the proposed model delivers 92.78% accuracy on RAF-DB while it also shows better robustness and generalization capability under difficult real-world conditions. Moreover, additional ablation studies reveal that multi-level channel recalibration is effective in improving discriminative emotional feature learning. Full article

The performance of Network-on-Chip (NoC) architectures is severely limited by the high dynamic power consumption and interconnect crosstalk with the scaling of deep sub-micron (DSM) technology. The coupling capacitance between wires dominates the self-capacitance in dense interconnects, and the coupling transitions contribute the most to the link power dissipation. In this paper, we propose a new low-power error correction coding scheme called Parity-based Multi-bit Transient Error Correction (PMTEC), which can simultaneously deal with self-transitions and coupling transitions in NoC links. The proposed method reduces the effective switching activity by employing a balanced encoding strategy, unlike existing coding techniques, which typically degrade the signal integrity or impose significant power overheads for particular data patterns. Experimental analysis demonstrates that PMTEC achieves strong link power reductions on various data profiles, including text, image, and video traffic, without the negative overheads of cutting-edge techniques like Duplicated Two-Dimensional Parities (DTDPs) and Crosstalk-Aware Transient Error Correction (CATEC). Furthermore, the codec’s parametric synthesis reveals a very small footprint, taking up only 2472.018 $\mathsf{\mu }{\mathrm{m}}^2$ of silicon area and using a negligible 0.219 $\mathsf{\mu }$W of power, which are 87.57% and 99.20% reductions over CATEC, respectively. The suggested work offers a scalable and energy-efficient solution for dependable on-chip communication in power-constrained systems, despite requiring a 15.371 ns latency trade-off to correct 8-bit bursts. Full article

Guided by the ideational theory of populism and Jowett and O’Donnell’s framework for propaganda analysis, this study examines Donald Trump’s usage of propagandistic language and emotional appeals. The study classifies his posts on Truth Social during the 2024 American presidential election into the seven categories of propaganda tactics outlined by the Institute of Propaganda Analysis. These techniques include name calling, glittering generalities, transfer methods, testimonials, plain folk appeals, card stacking and bandwagoning. A deductive content analysis was performed on a corpus of the text of 1319 Truth Social posts (“truths”) posted by Donald Trump during the election, excluding image- and video-based posts. This study found a vast majority contain at least one classic propaganda technique. A contribution of this study is that it determines not only which tactics he uses as a case study of a populist communicator but who he targets and with which tactics. The study found five main targets: political opponents, political allies, immigrants, institutions, and foreign leaders. Findings demonstrate Donald Trump does not use propaganda techniques monolithically but rather as dependent on his target and with distinct patterns, to produce and reinforce narratives about in- and out-group messaging. Full article

Background: The COVID-19 pandemic was accompanied by an infodemic that promoted the use of so-called “miracle products” lacking scientific evidence, posing significant public health risks. Despite increasing concern, evidence on effective educational strategies to counteract this misinformation remains limited, particularly in Latin America. Methods: A quasi-experimental pre–post study without a control group was conducted among university students in Mexico City between February and June 2021. Participants were recruited via Facebook using a snowball sampling approach. A validated nine-item questionnaire assessed knowledge about miracle products before and after exposure to an educational video intervention. Paired statistical analyses were performed to evaluate changes in knowledge. Results: A total of 157 participants completed the pre-test, and 103 completed the post-test. The intervention resulted in a significant increase in knowledge scores, from a mean of 5.98 (SD = 1.73) to 9.05 (SD = 1.54) (p < 0.001). Significant improvements were observed in eight of nine items, with the largest increases in knowledge related to high-risk substances and reporting mechanisms. No significant baseline differences were found between participants who completed and those who did not complete the post-test. Conclusions: The video-based educational intervention was effective in improving knowledge about miracle products during COVID-19. These findings support the use of digital health education strategies as scalable tools to combat misinformation, particularly in resource-constrained settings. However, further research using controlled designs is needed to assess long-term effects and behavioral outcomes. Full article

Product recognition in e-commerce live streaming is hindered by rapid viewpoint changes, occlusions, motion blur, and inconsistencies between visual and spoken information. Existing approaches typically focus on individual components such as detection, OCR, or speech recognition, which limits their effectiveness in end-to-end structured product understanding. To address this problem, we propose an integrated framework that combines task-oriented keyframe selection with multimodal semantic fusion. The framework first uses D-FINE to localize product regions and then selects informative frames through two complementary strategies. Strategy A considers both detection confidence and Laplacian-based sharpness, while Strategy B combines detection confidence with a learned quality component estimated by an EfficientNetV2-M regression model. OCR, visual-semantic recognition, and ASR are then applied to extract complementary evidence, and a Qwen3.5-27B large language model is used to structure and fuse multimodal evidence into standardized product outputs, including brand, product name, and category. Experiments on an in-house e-commerce livestreaming dataset demonstrate substantial gains over a last-frame baseline. Strategy B achieves the best overall result, improving the Perfect Match Rate from 0.609 to 0.775 and the Semantic Similarity from 0.697 to 0.802. Ablation studies further show that the full multimodal framework consistently outperforms unimodal and dual-modality variants under both frame selection strategies. In addition, Top-K analysis indicates that single-frame inference provides a practical balance between OCR evidence completeness and efficiency. Efficiency analysis shows that the per-video API monetary cost remains low under the pricing configuration used in this study, while API latency is mainly limited by Qwen3.5-27B LLM calls for evidence structuring and final fusion. Overall, the proposed framework offers an effective and extensible solution for structured product recognition in complex live-streaming scenarios. Full article

Background/Objectives: Health systems across Europe are increasingly challenged by population ageing, multimorbidity, and persistent inequities in access to care. Community Nursing has emerged as a promising approach to strengthening preventive, community-based services, yet evidence on stakeholder-perceived needs during early implementation remains limited. This study aimed to explore how different types of needs are perceived and articulated during the early implementation of Community Nursing in Austria. Methods: An interpretive descriptive qualitative study was conducted as part of an Austrian Community Nursing pilot project. Semi-structured interviews were carried out with eleven stakeholders, including informal caregivers, network partners (e.g., local healthcare providers), and local political decision-makers. Data were analyzed using qualitative content analysis, guided deductively by Bradshaw’s Taxonomy of Needs and complemented by inductive sub-category development. Interviews were conducted via telephone or video call (Zoom) and ranged from approximately 30 min to an hour. Results: Normative needs reflected expectations for preventive services, continuity of care, advocacy, and sustainable organizational structures. Felt needs centered on trust, emotional security, and relational continuity. Expressed needs became visible through active use of Community Nursing services, including preventive programs, transitional care support, and administrative navigation. Comparative needs highlighted geographic inequities and differences between municipalities with and without access to Community Nursing. Across stakeholder groups, concerns regarding long-term financing and sustainability were prominent. Conclusions: The findings suggest that Community Nursing addresses multiple stakeholder-perceived needs simultaneously, particularly by providing relational, accessible, and preventive support. However, sustained impact depends on stable funding and systemic integration beyond pilot phases. These results offer transferable insights for the development and scaling of community-based nursing models in ageing societies. Full article

Background/Objectives: Wheelchair users with multiple sclerosis (MS) often face barriers that restrict participation in physical activity and exercise training. This manuscript reports on participant feedback to guide evaluating and refining a novel exercise training program, Seated Physical activity INtervention (SPIN). SPIN was adapted from the Guidelines for Exercise in MS (GEMS) approach using a three-step community-engaged research framework based on meeting the needs of wheelchair users with MS. Methods: Semi-structured interviews were conducted with 9 participants who completed the 16-week SPIN intervention. The key SPIN intervention components were the exercise prescription, exercise equipment, and behavioral coaching grounded in Social Cognitive Theory. Formative interview domains included overall experience, enjoyable and missing components, delivery modifications, barriers, lessons learned, and additional research topics of interest. Data were analyzed and reported using a rapid qualitative analysis approach. Results: Interviews averaged 16 ± 10 min. Participants reported enjoying SPIN, noting program strengths as being flexible and appropriate for individuals with MS, receiving coaching calls by knowledgeable staff that offered support and accountability, and receiving exercise equipment and video demonstrations. Participants also identified strategies for enhancing the program such as including peer support, offering real-time feedback during exercise, and adding other wellness behavior topics (e.g., diet). Conclusions: The results offer helpful ideas to consider when developing exercise training programs for wheelchair users with MS and other disabilities that may improve health and well-being. Full article

There is a pressing need for education regarding sustainability and previous research has focused more on students and less on teachers. This article explores teachers’ understandings of using the game Climate Call, which covers carbon dioxide content, in the General Science classroom to teach sustainability. This case study involved four teachers and six upper secondary classes in Sweden, from whom data was collected through fieldnotes, video recordings and interviews. The data has been analysed through the framework of the didactical tetrahedron, modelling the interactions between teacher, student, sustainability and the game in teaching and learning. The results indicate that teachers recognise new opportunities for teaching sustainability and for using the game’s content to highlight other aspects of the subject. The game also creates new interaction opportunities between students and teachers, though not all interactions were without obstacles. Full article

This article examines the utilization of WhatsApp as an alternative communication tool for disseminating visual content among social activists during protests. While WhatsApp is typically conceptualized as an interpersonal or group messaging platform, research on its role as an infrastructure for alternative media and citizen journalism remains limited. The study focuses on the “Tikva” group, established at the onset of the public struggle against the 2023 judicial reform in Israel, which evolved into a nine-month mass protest movement described as one of the largest in the country’s history. Through qualitative thematic content analysis of videos distributed within the group, the article explores how WhatsApp functions simultaneously as a channel for digital activism and as a site of bottom-up, democratic, non-institutional news production. The findings indicate two primary trends: functionally, WhatsApp operates as a mechanism for resource mobilization, calls to action in physical and digital spaces, and the cultivation of belonging and solidarity among activists facing institutional power; in terms of content and production, the videos articulate an anti-hegemonic discourse and challenge mainstream media conventions. The analysis shows how these videos dismantle delegitimizing frames and construct a counter-narrative depicting protesters as citizens defending democracy, thereby sustaining the protest movement’s momentum. Full article

Climate hazards increasingly unfold as information crises alongside physical impacts, producing rapid shifts in what people search for and discuss online. This case study demonstrates how AI-supported analysis of online data can complement conventional disaster intelligence by providing a scalable social sensing layer for climate hazard resilience in Galicia. It integrates Google Trends as a proxy for changing public attention and information demand, and YouTube videos and comment threads to capture public sensemaking and resilience-relevant signals. Monthly Google Trends series were used for eight hazards, with floods showing the highest mean interest, followed by wildfires and heatwaves. For the three highest-salience hazards, the study analyzed YouTube comments using gpt-5-mini to extract sentiment, emotions, topics, institutional trust cues, collective efficacy cues, calls to action, impacts, vulnerable groups, and coping actions. The corpus included 184 heatwave comments, 20,427 wildfire comments, and 4882 flood comments. Across hazards, discourse is predominantly negative but differs in structure. Heatwave threads skew toward mockery and normalization, wildfire threads center on anger, governance and low institutional trust, and flood threads combine solidarity with demands for localized warnings and guidance. The study translates comment-level signals into traceable policy recommendations emphasizing actionable risk communication, early warning and response capacity, and trust-building practices. The study concludes with an operational pipeline concept for continuous monitoring and dashboard-based decision support, while emphasizing limitations related to Google Trends sampling and normalization, platform and API biases, and model-mediated uncertainty. Full article

Individuals with type 2 diabetes mellitus (T2D) are at increased cardiovascular risk. Although exercise is an important strategy for reducing cardiometabolic risk, accessible and scalable intervention delivery strategies, such as synchronous telehealth programs, remain underexplored. This randomized clinical trial (RED Study; NCT05362071) investigated the effects of a 12-week synchronous telehealth exercise program on clinical, functional, and psychosocial outcomes in adults with T2D. Thirty-three participants (55.8 ± 10.1 years) were randomized to an intervention group (INT; n = 17), which performed supervised combined aerobic and resistance exercise via video calls (2–3 sessions/week), or a control group (CON; n = 16). Glycated hemoglobin (HbA1c) was the primary outcome. Secondary outcomes included capillary blood glucose, blood pressure, functional performance, and psychosocial parameters. Assessments were conducted at baseline and post-intervention by blinded evaluators, and analyses were conducted using linear mixed-effects models in an intention-to-treat analysis. No significant interaction effect was observed for HbA1c (p > 0.05). However, significant group × time interactions favored the INT for functional performance outcomes, including the 30 s Chair Stand (p = 0.02), Arm Curl (p < 0.001), Timed Up and Go (p = 0.01), and 2-Minute Step Test (p = 0.01), as well as sleep quality (p < 0.001). Depressive symptoms decreased over time (p = 0.03) in both groups. Additionally, the INT showed reductions in post-session capillary blood glucose across mesocycles 1, 2, and 4 (p = 0.03). The synchronous telehealth exercise program was not superior to the control condition in reducing HbA1c; however, it improved functional performance, enhanced sleep quality, and promoted acute reductions in glycemic levels in individuals with T2D. Full article

Background/Objectives: Cardiopulmonary resuscitation (CPR) skill assessments are susceptible to evaluator subjectivity, cognitive fatigue, and observational limitations. Although recent advances in multimodal artificial intelligence have increased the possibility of automated video-based assessment, its validity for clinical skill evaluation remains insufficiently examined. Methods: In this cross-sectional study, we enrolled 130 laypersons who underwent Basic Life Support training and skill testing. Twenty recordings were used for prompt development and 110 recordings were analyzed. Expert evaluators and GPT-4o independently assessed participants’ skills using a 12-item checklist. The manikin sensor data were the reference standard for the four chest compression metrics. Agreement was evaluated using Gwet’s agreement coefficient 1 (AC1) and intraclass correlation coefficient (2,1). Diagnostic accuracy, sensitivity, and specificity were compared using McNemar’s test. Results: Procedural items such as confirming cardiac arrest, calling 119, and requesting an automated external defibrillator showed a near-perfect agreement between experts and GPT-4o (AC1 > 0.8). However, the agreement was poor for the compression depth (AC1 = 0.374) and full chest recoil (AC1 = 0.355). Experts demonstrated high sensitivity (77.8–84.3%) but low specificity (24.6–47.8%), whereas GPT-4o showed low sensitivity (35.6–40.6%) but high specificity (69.2–76.1%). Conclusions: GPT-4o cannot serve as a standalone evaluator because of its inherent limitations in inferring three-dimensional spatial information from two-dimensional videos. However, its high agreement on procedural items and complementary error patterns with that of human evaluators on compression metrics suggests its potential as a decision support tool to mitigate expert leniency bias in CPR education. Full article