Search Results (4,040)

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%. Full article

Eye-movement assessment is a key component of neurological evaluation, offering valuable insights into neural deficits and underlying mechanisms. This narrative review explores the emerging subject of digital eye-movement outcomes (DEMOs) and their potential as sensitive biomarkers for neurological impairment. Eye tracking has become a useful method for investigating visual system functioning, attentional processes, and cognitive mechanisms. Abnormalities in eye movements, such as altered saccadic patterns or impaired smooth pursuit, can act as important diagnostic indicators for various neurological conditions. The non-invasive nature, cost-effectiveness, and ease of implementation of modern eye-tracking systems makes it particularly attractive in both clinical and research settings. Advanced digital eye-tracking technologies and analytical methods enable precise quantification of eye-movement parameters, complementing subjective clinical evaluations with objective data. This review examines how DEMOs could contribute to the localisation and diagnosis of neural impairments, potentially serving as useful biomarkers. By comprehensively exploring the role of eye-movement assessment, this review aims to highlight the common eye-movement deficits seen in neurological injury and disease by using the examples of mild traumatic brain injury and Parkinson’s Disease. This review also aims to enhance the understanding of the potential use of DEMOs in diagnosis, monitoring, and management of neurological disorders, ultimately improving patient care and deepening our understanding of complex neurological processes. Furthermore, we consider the broader implications of this technology in unravelling the complexities of visual processing, attention mechanisms, and cognitive functions. This review summarises how DEMOs could reshape our understanding of brain health and allow for more targeted and effective neurological interventions. Full article

Bone fractures are associated with hypoxia, but no longitudinal studies of perfusion measurements in human patients have been reported despite the clinical and research potential. In this longitudinal observational cohort study, the near-infrared spectroscopy (NIRS) device PortaMon was used to assess oxy-(O₂Hb), deoxy-(HHb) and total (tHb) haemoglobin, as well as the differences between O₂Hb and HHb (Hb_Diff) and the tissue saturation index (TSI) at three different depths in the fracture gap. Linear mixed effect models were fitted to analyse time effects. One-way ANOVAs were conducted to compare groups. The time points corresponding to minima were calculated via linear regression. In this study, 11 patients with tibial shaft fractures underwent longitudinal measurements. Additionally, 9 patients with diagnosed tibial shaft nonunion and 23 age-matched controls were measured once. In the longitudinal group, all fractures healed, and decreases in O₂Hb and Hb_Diff (all p < 0.05) were observed, with minima occurring 19–21 days after fracture. O₂Hb values in nonunion patients did not differ from the minima in longitudinally measured union patients, whereas differences in HHb and tHb were significant (all p < 0.05). Previously, the onset of hypoxia has been assumed to be much faster. The characteristic trajectories of the NIRS parameters O₂Hb and Hb_Diff can be used to fulfil the need for a non-invasive method to monitor fracture healing. These results suggest that NIRS could supplement radiographs and clinical impressions in daily clinical practice and may enable earlier diagnosis of nonunion. Full article

Artificial intelligence (AI)-based digital patient twins have the potential to make breakthroughs in research and clinical practices in rehabilitation. They make it possible to personalise treatment plans by simulating different rehabilitation scenarios and predicting patient-specific outcomes. DTs can continuously monitor a patient’s progress, adjusting therapy in real time to optimise recovery. They also facilitate remote rehabilitation by providing virtual models that therapists can use to guide patients without having to be physically present. Digital twins (DTs) can help identify potential complications or failures at an early stage, enabling proactive interventions. They also support the training of rehabilitation professionals by offering realistic simulations of different patient conditions. They can also increase patient engagement by visualising progress and potential future outcomes, motivating adherence to therapy. They enable the integration of multidisciplinary care, providing a common platform for different professionals to collaborate and improve rehabilitation strategies. The article aims to trace the current state of knowledge, research priorities, and research gaps in order to properly guide further research and shape decision support in rehabilitation. Full article

Bridge maintenance faces challenges regarding data management and decision-making efficiency, primarily owing to the manual processing of extensive inspection data and the absence of integrated digital solutions. This study addresses these challenges by proposing a health index (HI)-based bridge evaluation framework for prestressed concrete bridges, based on building information modeling (BIM) for inspection data integration and long short-term memory (LSTM) models for accurate deterioration prediction. The proposed framework categorizes and analyzes bridge deterioration data from inspection reports and develops a predictive LSTM model that allows quantitative bridge evaluation based on accumulated historical data. The results demonstrate that this approach enhances the efficiency and accuracy of bridge condition evaluation while enabling long-term prediction of deterioration trends. In a case study of a bridge, the bridge-level HI value decreased by 17% over 16 years, while the condition grade remained unchanged. The findings suggest that integrating BIM with HI-based bridge evaluation can support sustainable bridge maintenance strategies. Full article

Nurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare. Full article

As global sustainable development goals progress, improving the welfare of vulnerable rural populations has become a critical issue for many countries. Using data from the China Family Panel Studies (CFPS) from 2014 to 2022, this paper examines the impact of Digital Rural Construction (DRC) on the welfare of disadvantaged rural children. Specifically, it explores the mechanisms through which DRC enhances child welfare, focusing on information access, educational resource availability, and medical service accessibility. Additionally, the roles of government and non-governmental organizations in moderating this process are analyzed. The findings show that DRC significantly improves children’s health, education, and psychological well-being. Furthermore, interventions by governments and non-governmental organizations amplify these positive effects, with non-governmental interventions proving particularly effective. This study not only addresses a gap in the literature on the mechanisms linking DRC and child welfare but also offers valuable insights for policymakers from a sustainability perspective. Full article

Background/Objectives: Magnesium (Mg)-based food supplements contribute to the maintenance of adequate levels of Mg that are essential for overall health and well-being. The aim of this double-blind, randomized, cross-over clinical study was to assess the plasma Mg levels in volunteers following the oral administration of a magnesium-based nutraceutical ingredient, MAGSHAPE^TM microcapsules (Mg-MS), in comparison to other commonly used magnesium sources, including the following: Mg Oxide (MgO), Mg Citrate (Mg-C), and Mg bisglycinate (Mg-BG). Methods: A total of 40 healthy women and men were put on a low-Mg diet for 7 days, and after 8 h of fasting, a blood sample was taken from a digital puncture before (0 h) and 1 h, 4 h, and 6 h after the oral intake of each product. Results: Our results showed that the blood plasma levels of Mg increased significantly at all tested time-points after the oral intake of Mg-MS, while the blood plasma levels of Mg increased significantly only after 1 and 4 h of the oral intake of MgO and Mg-C, respectively. However, no significant increase in Mg levels was observed upon the intake of Mg-BG. Interestingly, the Mg-MS microencapsulation technology was observed to enable a sustained increase in plasma Mg levels over the duration of this study, i.e., 1, 4, and 6 h after oral intake. A direct comparison of the increase in plasma Mg levels over the 6 h period revealed that the Mg-MS microencapsulation technology significantly increased Mg bioavailability compared to the non-microencapsulated MgO. Our study also showed that, compared to the other Mg sources tested, the Mg-MS microencapsulation technology reduced adverse side effects commonly associated with Mg supplementation, specifically with regard to increased intestinal motility and sensations of gastric heaviness following oral administration. Conclusions: Altogether, this clinical study introduced MAGSHAPE^TM microcapsules as a bioavailable and well-tolerated alternative to existing Mg-based ingredients used in food supplements. Full article

This study investigates the biomechanical differences between typically developed (TD) individuals and those with contralateral hemiplegia (CH) using musculoskeletal modeling in OpenSim. Ten TD participants and ten CH patients were analyzed for joint angles and external joint moments around the three anatomical axes: frontal, sagittal, and transverse. The analysis focused on hip, pelvis, lumbar, knee, ankle, and subtalar joint movements, leveraging MRI-derived bone length data and gait analysis. Significant differences (p < 0.05) were observed in hip flexion, pelvis tilt, lumbar extension, and ankle joint angles, highlighting the impact of hemiplegia on these specific joints. However, parameters like hip adduction and rotation, knee moment, and subtalar joint dynamics did not show significant differences, with p > 0.05. The comparison of joint angle and joint moment correlations between TD and CH participants highlights diverse coordination patterns in CH. Joint angles show significant shifts, such as HF and LR (−0.35 to −0.97) and PR and LR (0.22 to −0.78), reflecting disrupted interactions, while others like HR and LR (0.42 to 0.75) exhibit stronger coupling in CH individuals. Joint moments remain mostly stable, with HF and HA (0.54 to 0.53) and PR and LR (−0.51 to −0.50) showing negligible changes. However, some moments, like KA and HF (0.11 to −0.13) and PT and KA (0.75 to 0.67), reveal weakened or altered relationships. These findings underscore biomechanical adaptations and compensatory strategies in CH patients, affecting joint coordination. Overall, CH individuals exhibit stronger negative correlations, reflecting impaired coordination. These findings provide insight into the musculoskeletal alterations in hemiplegic patients, potentially guiding the development of targeted rehabilitation strategies. Full article

Background: Young women spend 50 min daily on social media (SM); thus, SM platforms are promising for health interventions. This study tested the feasibility and preliminary efficacy of the co-designed SM intervention the Daily Health Coach (DHC). The DHC is a 3-month healthy lifestyles intervention programme, targeting eating, physical activity, and social wellbeing behaviours in women aged 18–24, via the dissemination of health and nutrition content on social media platform Instagram. Methods: The programme was tested using an assessor-blinded, two-arm pilot randomised controlled trial with 46 participants over 12 weeks. Engagement was assessed via SM metrics; acceptability via post-programme questionnaires; and feasibility included retention, randomisation, recruitment, and data collection. Secondary outcomes—dietary quality, physical activity, social influence, disordered eating behaviours, body image, and digital health literacy—were assessed using validated surveys. Analyses included t-tests, chi-squared tests, and linear mixed models. The treatment effects were estimated by testing mean score differences from baseline to 3 months for intention-to-treat populations. Results: The DHC scored 83.6% for programme satisfaction. Over time, a significant decrease in body image disturbance was observed (p = 0.013). A significant group-by-time interaction for digital health literacy (p = 0.002) indicated increased ability to discern evidence-based nutrition information (p = 0.006). The waitlist control group showed increased social influence compared to the intervention group (p = 0.034). No other significant changes were observed. Conclusion: The DHC is a feasible and acceptable method for disseminating nutrition information. Larger studies are needed to determine efficacy. Full article

Automatic speech recognition (ASR) and natural language processing (NLP) play key roles in advancing human–technology interactions, particularly in healthcare communications. This study aims to enhance French-language online mental health platforms through the adaptation of the QuartzNet 15 × 5 ASR model, selected for its robust performance across a variety of French accents as demonstrated on the Mozilla Common Voice dataset. The adaptation process involved tailoring the ASR model to accommodate various French dialects and idiomatic expressions, and integrating it with an NLP system to refine user interactions. The adapted QuartzNet 15 × 5 model achieved a baseline word error rate (WER) of 14%, and the accompanying NLP system displayed weighted averages of 64.24% in precision, 63.64% in recall, and an F1-score of 62.75%. Notably, critical functionalities such as ‘Prendre Rdv’ (schedule appointment) achieved precision, recall, and F1-scores above 90%. These improvements substantially enhance the functionality and management of user interactions on French-language digital therapy platforms, indicating that continuous adaptation and enhancement of these technologies are beneficial for improving digital mental health interventions, with a focus on linguistic accuracy and user satisfaction. Full article

Background: Visual impairment can significantly impact an individual’s daily activities. Patients require regular monitoring, typically occurring within hospital eye services. Capacity constraints have necessitated innovative solutions to improve patient care. Existing digital solutions rely on task-based digital home monitoring such as visual acuity testing. These require active involvement from patients and do not typically offer an indication of quality of life. Digital phenotyping refers to the use of personal digital devices to quantify passive behaviour for detecting clinically significant changes in vision and act as biomarkers for disease. Its uniqueness lies in the ability to detect changes passively. The objective was to co-design an accessible smartphone app (OverSight) for the purposes of digital phenotyping in people with sight impairment. Methods: Development of OverSight included stakeholder consultations following principles of user-centred design. Apple iOS software frameworks (HealthKit, ResearchKit, and SensorKit) and a SwiftUI developer toolkit were used to enable the collection of active and passive data streams. Accessibility and usability were assessed using the System Usability Scale (SUS) and feedback following a 3-month pilot study. Consultations with patients informed the design of OverSight, including preferred survey scheduling and the relevancy of patient support resources. Results: Twenty visually impaired participants (mean age 42 ± 19 years) were recruited to the pilot study. The average score on the SUS was 76.8 (±8.9), indicating good usability. There was a statistically significant moderate negative correlation between SUS scores and visual acuity in both the better (r = −0.494; p ≤ 0.001) and worse eye (r = −0.421; p ≤ 0.001). Conclusions: OverSight offers promising potential for collecting patient-generated health data for the purposes of digital phenotyping in patients with eye disease. Through further testing and validation, this novel approach to patient care may ultimately provide opportunities for remote monitoring in ophthalmology. Full article

Background/Objectives: Baobab (Adansonia digitate L.) is an underutilized species and edible parts (fruits, leaves and seeds) contribute to food security and human health in tropical areas. Although the fruits have attracted greater research interest and have recently been approved for consumption in EU countries, the leaves are traditionally consumed but they have yet to be studied from an interventional perspective. The aim of this study was to propose a protocol for a dietary intervention using baobab leaves (BLs) to achieve the recommended reference values for proteins and minerals (K, Ca, Mg, Na, Fe, Mn) for different target groups of the Sudanese population. Methods: Dry matter, crude fat, protein and ash content, mineral content (Na, Mg, K, Ca, Fe, Mn), total phenolic, and flavonoid compounds were determined in BLs from six different areas. To assess the health and nutrition status in Sudan, time-series data (2013–2023) from the DataBank Health Nutrition and Population Statistics database were used. The reference values for nutrients recommended by the European Food Safety Authority were used to estimate the amount of baobab leaf intake (BLI, g/day). Results: For each nutrient, the study area with the lowest amount of BLs to be consumed is recommended. Leaves from the area of El Gari (BN3) 18.312 g/day and 30.712 g/day are recommended for K and Ca, which are particularly beneficial for children aged 1–3 years and lactating women. Leaves from Kor Tagat (KR1) are suitable for sodium intake, requiring approximately 13–23 g/day across all age groups. Leaves from Kazgil (KR2) (46–81 g/day), (35–66 g/day), (0.48–0.68 g/day), and (4–6 g/day) are optimal for fulfilling the daily requirements of magnesium, iron, manganese, and protein in this order. Conclusions: The systematic inclusion of BLs in the diet can positively support the nutritional status of various demographics. Moreover, the findings of this study demonstrated the foundation for public health and nutritional policy-makers on how they will tackle malnutrition and food insecurity worldwide by incorporating naturally available diets and nutritious alternatives. Recommendation: Further research should focus on assessing the nutritional composition factors that could affect the absorption of nutrients such as phytates and oxalates and investigating the in vitro bioavailability of the elements. Full article

Many children on the autism spectrum engage in challenging behaviors, like aggression, due to difficulties communicating and regulating their stress. Identifying effective intervention strategies is often subjective and time-consuming. Utilizing unobservable internal physiological data to predict strategy effectiveness may help simplify this process for teachers and parents. This study examined whether heart rate data can predict strategy effectiveness. Teachers and coders from the research team recorded behavioral and heart rate data over three months for each participating student on the autism spectrum using the KeepCalm app, a platform that provides in-the-moment strategy suggestions based on heart rate and past behavioral data, across 226 instances of strategy interventions. A binary logistic regression was performed to assess whether heart rate reduction, time to return to heart rate baseline, and documented skills and challenging behaviors predicted strategy effectiveness. Results suggested that heart rate reduction may be a significant predictor, and supported the existing practice of using behavioral patterns as proxies for strategy effectiveness. Additional analyses indicate proactive strategies are more effective and are associated with greater reduction in heart rate, relative to reactive strategies. Further exploration of how internal physiological data can complement observable behaviors in assessing intervention strategy effectiveness is warranted given the novelty of our findings. Full article

Background/Objectives: Infection prevention and control (IPC) is essential to ensure the safety of dental personnel and patients. This study aimed to assess the knowledge and compliance of dental undergraduate students, interns, and postgraduate students with IPC measures in prosthodontic procedures. Methods: A cross-sectional observational study was conducted at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, involving 216 participants selected using stratified random sampling. A validated questionnaire was used to assess knowledge and compliance. Statistical analyses, including the Mann–Whitney U test and Kruskal–Wallis test, were conducted to explore factors influencing knowledge and compliance levels. Results: Participants demonstrated a high level of IPC knowledge, with 93.55% correctly identifying the goal of infection control. However, gaps were noted, such as only 41.23% recognizing the recommended handwashing duration. Sex differences in knowledge were marginally statistically significant (p < 0.05), while academic level showed no significant association. Compliance was high in some areas, such as handwashing after treating patients (81.11%), but lower in others, such as disinfecting digital equipment between patients (36.87%). Higher self-confidence was significantly associated with greater knowledge scores (p < 0.05), while self-satisfaction with knowledge did not correlate with knowledge levels. Conclusions: This study highlights strong IPC measures knowledge and compliance during prosthodontic procedures among dental personnel, with some gaps in understanding and practice. Addressing these gaps through targeted training and standardized guidelines can further enhance safety and infection control in clinical settings, benefiting both patients and healthcare providers. Full article