Search Results (3,164)

Background/Objectives: Health education plays a vital role in training health professionals and caregivers, supporting both prevention and the promotion of self-care. In this context, technology serves as a valuable ally by enabling continuous and flexible learning. Among the various domains of health education, nutrition stands out as a key element in the management of Amyotrophic Lateral Sclerosis (ALS), helping to prevent malnutrition and enhance patient well-being. Accordingly, this study aimed to evaluate the effectiveness of the teaching and learning processes within a learning pathway focused on food and nutrition in the context of ALS. Methods: This study adopted a longitudinal, quantitative design. The learning pathway, titled “Food and Nutrition in ALS,” consisted of four self-paced and self-instructional Massive Open Online Courses (MOOCs), offered through the Virtual Learning Environment of the Brazilian Health System (AVASUS). Participants included health professionals, caregivers, and patients from all five regions of Brazil. Participants had the autonomy to complete the courses in any order, with no prerequisites for enrollment. Results: Out of 14,263 participants enrolled nationwide, 182 were included in this study after signing the Informed Consent Form. Of these, 142 (78%) completed at least one course and participated in the educational intervention. A significant increase in knowledge was observed, with mean pre-test scores rising from 7.3 (SD = 1.8) to 9.6 (SD = 0.9) on the post-test across all courses (p < 0.001). Conclusions: The self-instructional, technology-mediated continuing education model proved effective in improving participants’ knowledge about nutrition in ALS. Future studies should explore knowledge retention, behavior change, and the impact of such interventions on clinical outcomes, especially in multidisciplinary care settings. Full article

Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute illnesses. Additionally, the study explored the practical challenges associated with validating AI tools used for homeopathy and sought to generate insights on the potential value and limitations of these tools in the management of acute health complaints. Method: Randomly selected cases at a homeopathy teaching clinic (n = 100) were entered into a commercially available homeopathic remedy finder to investigate the consistency between automated and live recommendations. Client symptoms, medical disclaimers, remedies, and posology were compared. The findings of this study show that the purpose-built homeopathic remedy finder is not a one-to-one replacement for a live practitioner. Result: In the 100 cases compared, the automated online remedy finder provided between 1 and 20 prioritized remedy recommendations for each complaint, leaving the user to make the final remedy decision based on how well their characteristic symptoms were covered by each potential remedy. The live practitioner-recommended remedy was included somewhere among the auto-mated results in 59% of the cases, appeared in the top three results in 37% of the cases, and was a top remedy match in 17% of the cases. There was no guidance for managing remedy responses found in live clinical settings. Conclusion: This study also highlights the challenge and importance of validating AI remedy recommendations against real cases. The automated remedy finder used covered 74 acute complaints. The live cases from the teaching clinic included 22 of the 74 complaints. Full article

Background: Point-of-care ultrasound (POCUS) is gaining recognition as a valuable diagnostic tool in various fields of medicine, including pediatrics. Its application at the point of care enables real-time clinical decision-making, which is particularly advantageous in pediatric settings. Although global interest in POCUS is growing, many European countries—including Poland—still lack formal training programs for POCUS at both the undergraduate and postgraduate levels. Nevertheless, the number of pediatricians incorporating POCUS into their daily clinical practice in Poland is increasing. However, the extent of its use and perceived value among pediatricians remains largely unknown. This study aimed to evaluate the current level of POCUS utilization in pediatric care in Poland, focusing on pediatricians’ self-assessed competencies, perceptions of its clinical utility, and key barriers to its implementation in daily practice. Methods: This cross-sectional study was conducted between July and August 2024 using an anonymous online survey distributed to pediatricians throughout Poland via national professional networks, with a response rate of 7.3%. Categorical variables were analyzed using the chi-square test of independence to assess the associations between key variables. Quantitative data were analyzed using descriptive statistics, and qualitative data from open-ended responses were subjected to a thematic analysis. Results: A total of 210 pediatricians responded. Among them, 149 (71%) reported access to ultrasound equipment at their workplace, and 89 (42.4%) reported having participated in some form of POCUS training. Only 46 respondents (21.9%) reported frequently using POCUS in their clinical routine. The self-assessed POCUS competence was rated as low or very low by 136 respondents (64.8%). While POCUS was generally perceived as a helpful tool in facilitating and accelerating clinical decisions, the main barriers to implementation were a lack of formal training and limited institutional support. Conclusions: Although POCUS is perceived as clinically valuable by the surveyed pediatricians in Poland, its routine use remains limited due to training and systemic barriers. Future efforts should prioritize the development of a validated, competency-based training framework and the implementation of a larger, representative national study to guide the structured integration of POCUS into pediatric care. Full article

In the finite control set model predictive control (FCSMPC) algorithm for a converter based on a neural network, the optimal control variables computed by neural network controllers achieve decoupling between the optimal FCSMPC algorithm design and online computational burden. However, the limited generalization capability of neural network controllers leads to degraded control performance when converter load types vary, so it is essential to design switching rules for neural network controller model parameters tailored to different load types and rapidly identify the converter load type. To address this issue, this article designs a switching strategy for neural network controller model parameters of converters and employs a convolutional neural network (CNN) to identify the converter load type. The CNN-based identification achieves adaptive switching of controller model parameters based on detected load types, ensuring consistent control performance across different converter load types. Simulation results demonstrate that load-type identification based on the CNN achieves alignment between neural network controller model parameters and load types, and the adaptability of converter neural network controllers is enhanced significantly. The effectiveness and feasibility of the proposed method are validated. Full article

CareCoach seeks to enhance self-efficacy in family caregivers of people living with dementia and has been feasibility tested in a multicentre randomised controlled trial. The intervention offers two face-to-face sessions with a trained coach and access to an online platform with nine modules. This paper reports findings from an embedded qualitative process evaluation assessing implementation from the implementer’s (‘coach’s’) (n = 8) perspective using individual interviews and implementer group discussions. Qualitative data were transcribed verbatim, inductively coded and analysed using Normalisation Process Theory. Implementers demonstrated (1) ‘Coherence’ by seeking to understand how CareCoach compared to current practice, highlighting the importance of supporting coaches to differentiate and identify boundaries between their new ‘coach role’ and usual practice; (2) ‘Cognitive Participation’ by reviewing training and resources to understand their role own responsibilities and facilitate delivery of coaching sessions; group supervision and peer support were also emphasised; (3) ‘Collective Action’ through interactions with carers to deliver key behavioural aspects such as goal setting, problem solving, and providing feedback; and (4) ‘Reflexive Monitoring’ by appraising the intervention to gain useful insights that could facilitate refinement of CareCoach training and delivery. This study provides a theoretically informed understanding of the implementation of CareCoach for caregivers of people living with dementia and provides recommendations to enhance training for coaches, intervention delivery and carer engagement. Full article

Effective energy management in Low Earth Orbit satellites is critical, as inefficient energy management can significantly affect mission objectives. The dynamic and harsh space environment further complicates the development of effective energy management strategies. To address these challenges, we propose a Deep Reinforcement Learning approach using Deep-Q Network to develop an adaptive energy management framework for Low Earth Orbit satellites. Compared to traditional techniques, the proposed solution autonomously learns from environmental interaction, offering robustness to uncertainty and online adaptability. It adjusts to changing conditions without manual retraining, making it well-suited for handling modeling uncertainties and non-stationary dynamics typical of space operations. Training is conducted using a realistic satellite electric power system model with accurate component parameters and single-orbit power profiles derived from real space missions. Numerical simulations validate the controller performance across diverse scenarios, including multi-orbit settings, demonstrating superior adaptability and efficiency compared to conventional Maximum Power Point Tracking methods. Full article

This study examines how teacher–student relationships and school engagement change across online and in-school learning, based on the experiences of 105 cross-regional secondary vocational students in China. Using questionnaire surveys, the study explores students’ perceptions and learning needs in both settings. The findings confirm that teachers play a consistently positive role in promoting student engagement across both online and in-school learning modalities. While affective engagement was higher during online learning, driven by stronger teacher responsiveness and improved student–teacher relationships, students reported increased pride in their schools after returning home, reflecting a renewed appreciation. In-school learning was associated with higher behavioral engagement and greater motivation, despite tensions over intensified academic tasks. Online learning facilitated cognitive engagement through easier vocabulary searches; nevertheless, poor home environments reduced motivation. Enhancing engagement may require offering students autonomy, valuing their input, and clarifying the relevance of the learning content. Full article

Background: The purpose of this study was to evaluate Achilles tendon ruptures (ATR) in NHL players and the effects on return to play and player performance metrics. The incidence, mechanism of injury, management strategy, return to play (RTP), and post-injury were assessed from official online sports databases. Methods: A retrospective review of NHL players who sustained a partial or complete tear of the Achilles tendon from 2008 to 2024 was performed. Data were collected from NHL injury databases and media reports, and included player demographics, injury mechanism, treatment, and post-injury performance metrics. A Wilcoxon signed rank test was used to compare pre-injury and post-injury performance metrics, with significance set at p < 0.05. Results: Here, 15 NHL players with a mean age of 27.8 years were identified, with a prevalence rate of 0.125 injuries per 10,000 athletic exposures. Overall, 73.3% of ATRs were non-contact in nature, with 60.0% of ATRs occurring during off-season training. Fourteen players were managed with non-operative treatment, with no re-ruptures reported. The RTP rate was 93.3%, with players missing a mean number of 45.7 games. However, there was a deterioration in post-injury performance metrics, including games played per season, plus/minus rating, and time on ice per game post-injury. Conclusions: This study found that Achilles tendon ruptures are an uncommon injury in NHL players, with a prevalence rate of 0.125 injuries per 10,000 athletic exposures. A high RTP rate of 93.3% was observed in this cohort. However, there was a deterioration in post-injury performance metrics, including games played per season, plus/minus rating, and time on ice per game post-injury, highlighting the potential devastating sequelae of ATRs in elite NHL athletes. Full article

Background: This study examines current trends in Canada using data from the Canadian Joint Replacement Registry (CJRR) and includes a national survey to understand the varied uptake of cement for femoral stem fixation. Methods: The survey was available online and the website link was distributed to all orthopaedic surgeons through the Canadian Orthopaedic Association between September and December 2022. The CJRR obtained data from the Canadian Institute for Health Information (CIHI), and information pertaining to patients 55 years of age and older who underwent hemiarthroplasty for hip fracture in Canada between April 2017 and March 2022 was used. Results: Most respondents practiced in an academic community setting (52%). Only 53% of respondents reported using cement, and 71% indicated that cemented fixation was the best practice. The main reasons for using uncemented stems were less operative time (23%), cement disease concerns (11%), and surgeons’ comfort (10%). Similarly, CJRR data showed only 51% cemented fixation among 42,386 hemiarthroplasties performed between 2017 and 2022. The proportion of cemented implants varied by province, but overall, the increase in the use of cement from 2017 to 2022 was from 42.9% to 57.7%. Conclusions: This study demonstrates variability in the use of cement for femoral fixation despite solid evidence showing improved outcomes using cement. Some of the main reasons in favour of uncemented stems include operative time, surgical training, and concerns about cement disease. Establishing clear position statements and guidelines supporting cemented fixation may be prudent to build universal consensus on this practice. Full article

Piano fingering estimation remains a complex problem due to the combinatorial nature of hand movements and no best solution for any situation. A recent model-free reinforcement learning framework for piano fingering modeled each monophonic piece as an environment and demonstrated that value-based methods outperform probability-based approaches. Building on their finding, this paper addresses the more complex polyphonic fingering problem by formulating it as an online model-free reinforcement learning task with a novel training strategy. Thus, we introduce a novel Elite Episode Replay (EER) method to improve learning efficiency by prioritizing high-quality episodes during training. This strategy accelerates early reward acquisition and improves convergence without sacrificing fingering quality. The proposed architecture produces multiple-action outputs for polyphonic settings and is trained using both elite-guided and uniform sampling. Experimental results show that the EER strategy reduces training time per step by 21% and speeds up convergence by 18% while preserving the difficulty level and result of the generated fingerings. An empirical study of elite memory size further highlights its impact on training performance in solving piano fingering estimation. Full article

Internal crack defects in high-strength low-alloy (HSLA) steels during continuous casting pose significant challenges to downstream processing and product reliability. However, due to the inherent class imbalance in industrial defect datasets, conventional machine learning models often suffer from poor sensitivity to minority class instances. This study proposes a predictive framework that integrates conditional tabular generative adversarial network (CTGAN) for synthetic minority sample generation and CatBoost for classification. A dataset of 733 process records was collected from a continuous caster, and 25 informative features were selected using mutual information. CTGAN was employed to augment the minority class (crack) samples, achieving a balanced training set. Feature distribution analysis and principal component visualization indicated that the synthetic data effectively preserved the statistical structure of the original minority class. Compared with the other machine learning methods, including KNN, SVM, and MLP, CatBoost achieved the highest metrics, with an accuracy of 0.9239, precision of 0.9041, recall of 0.9018, and F1-score of 0.9022. Results show that CTGAN-based augmentation improves classification performance across all models. These findings highlight the effectiveness of GAN-based augmentation for imbalanced industrial data and validate the CTGAN–CatBoost model as a robust solution for online defect prediction in steel manufacturing. Full article

Freezing of gait (FoG) impairs mobility and daily functioning and increases the risk of falls, leading to a reduced quality of life (QoL) in people with Parkinson’s disease (PD). The Cue2walk, a wearable smart cueing device, can detect FoG and hereupon provides rhythmic cues to help people with PD manage FoG in daily life. This study investigated the user experiences and device usage of the Cue2walk, and its impact on health-related QoL, FoG and daily activities. Twenty-five users of the Cue2walk were invited to fill out an online survey, which included a modified version of the EQ-5D-5L, tailored to the use of the Cue2walk, and its scale for health-related QoL, three FoG-related questions, and a question about customer satisfaction. Sixteen users of the Cue2walk completed the survey. Average device usage per day was 9 h (SD 4). Health-related QoL significantly increased from 5.2/10 (SD 1.3) to 6.2/10 (SD 1.3) (p = 0.005), with a large effect size (Cohen’s d = 0.83). A total of 13/16 respondents reported a positive effect on FoG duration, 12/16 on falls, and 10/16 on daily activities and self-confidence. Customer satisfaction was 7.8/10 (SD 1.7). This pilot study showed that Cue2walk usage per day is high and that 15/16 respondents experienced a variety of positive effects since using the device. To validate these findings, future studies should include a larger sample size and a more extensive set of questionnaires and physical measurements monitored over time. Full article

This paper presents the integrated study of the funerary ara of Ofilius Ianuarius, discovered within the burial complex of Via Appia Antica 39, and explores its digital stratigraphic recontextualisation through two 3D semantic workflows. The research aims to evaluate the potential of stratigraphic 3D modelling as a tool for post-excavation analysis and transparent archaeological interpretation. Starting from a set of georeferenced photogrammetric models acquired between 2023 and 2025, the study tests two workflows: (1) an EMF-based approach using the Extended Matrix, Blender, and EMviq for stratigraphic relationship modelling and online visualisation; (2) a semantic integration method using the .gltf format and the CRMArcheo Annotation Tool developed in Blender, exported to the ATON platform. While both workflows enable accurate 3D documentation, they differ in their capacity for structured semantic enrichment and interoperability. The results highlight the value of combining reality-based models with semantically linked stratigraphic proxies and suggest future directions for linking archaeological datasets, ontologies, and interactive digital platforms. This work contributes to the ongoing effort to foster transparency, reproducibility, and accessibility in virtual archaeological reconstruction. Full article

This study proposed an online early lameness detection method for dairy cow health management to overcome the inability of wearable sensor-based methods for online detection and low sensitivity to early lameness. Wearable IMU sensors collected acceleration data in stationary and moving states; a threshold discrimination module using variance of motion-direction acceleration was designed to distinguish states within 2 s, enabling rapid data screening. For moving-state windowed data, the InceptionTime network was modified with YOLOConv1D and SeparableConv1D modules plus Dropout, which significantly reduced model parameters and helped mitigate overfitting risk, enhancing generalization on the test set. Typical gait features were fused with deep features automatically learned by the network, enabling accurate discrimination among healthy, mild (early) lameness, and severe lameness. Results showed that the online detection model achieved 80.6% dairy cow health status detection accuracy with 0.8 ms single-decision latency. The recall and F1 score for lameness, including early and severe cases, reached 89.11% and 88.93%, demonstrating potential for early and progressive lameness detection. This study improves lameness detection efficiency and validates the feasibility and practical value of wearable sensor-based gait analysis for dairy cow health management, providing new approaches and technical support for monitoring and early intervention on large-scale farms. Full article

Background: Musculoskeletal disorders significantly affect healthcare professionals, particularly physiotherapists, due to the physical demands of their work. The link between physical ailments and psychological distress is especially prominent in clinical settings. Objectives: To assess the prevalence of work-related low back pain (LBP), stress, anxiety, and depression among physiotherapists in Saudi Arabia, and to identify associated local risk factors. Methods: A cross-sectional study using convenience sampling included 710 licensed physiotherapists across Saudi Arabia. Participants completed an online survey containing demographic data and the validated measures, including the Visual Analog Scale (VAS) for pain, the Oswestry Disability Index (ODI), and the Depression, Anxiety, and Stress Scale-21 (DASS-21) for psychological distress. Data were analysed using descriptive statistics, chi-square tests, correlation, and regression analyses. Results: Of 710 responses, 697 were valid; 378 physiotherapists reported work-related LBP. The mean pain intensity was 4.6 (SD = 1.6), with 54.2% experiencing moderate to severe disability. Mental health results showed 49.7% had depressive symptoms and 33.9% experienced some level of anxiety. Significant correlations were observed between disability and psychological distress (anxiety: r = 0.382; depression: r = 0.375; stress: r = 0.406; all p < 0.001). Regression analyses indicated psychological distress significantly predicted disability, with R² values ranging from 0.125 to 0.248, being higher among inpatient physiotherapists. Conclusions: This study reveals a high prevalence of LBP and psychological distress among Saudi physiotherapists, with stress being the strongest predictor of LBP severity. Integrated ergonomic and mental health interventions, including workplace wellness programs and psychological support, are recommended to reduce risks and promote a healthier, more sustainable physiotherapy workforce. Full article