Percepção de aprendizagem on-line na disciplina de Biomateriais / Perception of online learning in the biomaterials course

Com as universidades fechadas e a implementação do Ensino Remoto Emergencial, as atividades curriculares ocorreram através de plataformas digitais. O objetivo do presente trabalho foi avaliar a percepção de aprendizagem on-line na disciplina de Biomateriais da Faculdade de Odontologia da Universidade Federal Fluminense no período da pandemia. O questionário COLLES (Constructivist OnLine Learning Environment Survey) foi enviado individualmente por e-mail aos cinquenta alunos, apresentando 24 declarações divididas em seis quesitos: relevância, reflexão crítica, interatividade, apoio dos tutores, apoio entre os colegas e compreensão; e para cada declaração cinco opções de resposta: quase sempre, frequentemente, algumas vezes, raramente e quase nunca. Quarenta e um alunos responderam. A soma das médias obtidas em quase sempre e frequentemente foi de 87,2% para relevância, 70% para reflexão crítica, 33,9% para interatividade, 47,6% para apoio dos tutores, 44,2% para apoio dos colegas e 89,5% para compreensão. Concluiu-se que a relevância, a reflexão crítica e a compreensão apresentaram melhores resultados, enquanto a interatividade, o apoio entre os colegas e o apoio dos tutores demonstraram necessidade de aprimoramento. E apesar das limitações do ERE, a avaliação positiva dos alunos evidenciou esta modalidade de educação on-line como uma solução plausível.

With universities closed and the implementation of Emergency Remote Teaching, curricular activities took place through digital platforms. The objective of this study was to assess the perception of online learning in the Biomaterials course at the Dental School of the Federal Fluminense University during the pandemic. The COLLES questionnaire (Constructivist OnLine Learning Environment Survey) was individually sent via email to fifty students, presenting 24 statements divided into six aspects: relevance, critical reflection, interactivity, tutor support, peer support, and comprehension. For each statement, there were five response options: almost always, often, sometimes, rarely, and almost never. Forty-one students responded. The sum of the averages obtained for almost always and often was 87.2% for relevance, 70% for critical reflection, 33.9% for interactivity, 47.6% for tutor support, 44.2% for peer support, and 89.5% for comprehension. It was concluded that relevance, critical reflection, and comprehension showed better results, while interactivity, peer support, and tutor support demonstrated a need for improvement. Despite the limitations of Emergency Remote Teaching, the positive evaluation from the students highlighted this mode of online education as a plausible solution.

Real-road NOx and CO2 emissions of city and highway China-6 heavy-duty diesel vehicles.

The emission of heavy-duty vehicles has raised great concerns worldwide. The complex working and loading conditions, which may differ a lot from PEMS tests, raised new challenges to the supervision and control of emissions, especially during real-world applications. On-board diagnostics (OBD) technology with data exchange enabled and strengthened the monitoring of emissions from a large number of heavy-duty diesel vehicles. This paper presents an analysis of the OBD data collected from more than 800 city and highway heavy-duty vehicles in China using remote OBD data terminals. Real-world NOx and CO2 emissions of China-6 heavy-duty vehicles have been examined. The results showed that city heavy-duty vehicles had higher NOx emission levels, which was mostly due to longer time of low SCR temperatures below 180°C. The application of novel methods based on 3B-MAW also found that heavy-duty diesel vehicles tended to have high NOx emissions at idle. Also, little difference had been found in work-based CO2 emissions, and this may be due to no major difference were found in occupancies of hot running.

Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China.

Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.

Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning.

Introduction: The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province. Methods: Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989-2019). Results: The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0.95 and 0.83, and RMSE of 2.98 Mg C ha-1 and 4.93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest AGC in Lishui City over the past three decades, escalating from 1.36×107 Mg C in 1989 to 6.16×107 Mg C in 2019. Discussion: This study provided a set of effective hyperparameters and model of machine learning suitable for subtropical forests and a reference data for improving carbon sequestration capacity of subtropical forests in Lishui City.

Accuracy and role of consumer facing wearable technology for continuous monitoring during endoscopic procedures.

Background: Consumer facing wearable devices capture significant amounts of biometric data. The primary aim of this study is to determine the accuracy of consumer-facing wearable technology for continuous monitoring compared to standard anesthesia monitoring during endoscopic procedures. Secondary aims were to assess patient and provider perceptions of these devices in clinical settings. Methods: Patients undergoing endoscopy with anesthesia support from June 2021 to June 2022 were provided a smartwatch (Apple Watch Series 7, Apple Inc., Cupertino, CA) and accessories including continuous ECG monitor and pulse oximeter (Qardio Inc., San Francisco, CA) for the duration of their procedure. Vital sign data from the wearable devices was compared to in-room anesthesia monitors. Concordance with anesthesia monitoring was assessed with interclass correlation coefficients (ICC). Surveys were then distributed to patients and clinicians to assess patient and provider preferences regarding the use of the wearable devices during procedures. Results: 292 unique procedures were enrolled with a median anesthesia duration of 34 min (IQR 25-47). High fidelity readings were successfully recorded with wearable devices for heart rate in 279 (95.5%) cases, oxygen in 203 (69.5%), and respiratory rate in 154 (52.7%). ICCs for watch and accessories were 0.54 (95% CI 0.46-0.62) for tachycardia, 0.03 (95% CI 0-0.14) for bradycardia, and 0.33 (0.22-0.43) for oxygen desaturation. Patients generally felt the devices were more accurate (56.3% vs. 20.0% agree, p < 0.001) and more permissible (53.9% vs. 33.3% agree, p < 0.001) to wear during a procedure than providers. Conclusion: Smartwatches perform poorly for continuous data collection compared to gold standard anesthesia monitoring. Refinement in software development is required if these devices are to be used for continuous, intensive vital sign monitoring.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach.

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

Clinical Interventions Following Escalations from a Continuous Respiratory Monitoring Service in Patients With Chronic Obstructive Pulmonary Disease.

Background: Continuous respiratory monitoring can support integrated care for chronic obstructive pulmonary disease (COPD) patients, by coupling them with remote clinical personnel who triage patients in coordination with their health care providers. When deploying such services, there remains uncertainty surrounding outcomes when at-risk patients are proactively identified and escalated for provider evaluation. This study presents findings from a service deployed in a real-world COPD cohort by analyzing the clinical interventions made during in-person and telehealth pulmonary outpatient visits following remote escalations. Methods: A single-center, retrospective, observational study of real-world COPD patients at a multi-site pulmonary practice was conducted. Patients who were enrolled in a continuous respiratory monitoring service for at least one year and were seen by a provider within seven days of an escalation by the service (N=168) were included. To evaluate the potential impact of these escalations on provider and patient burden, medical charts from outpatient visits were manually reviewed and grouped into six categories based on the clinical action(s) taken by the provider. Results: A total of 245 outpatient visits occurred from 168 patients within seven days of escalation. Of the 245 visits, 206 (84.1%) resulted in clinical intervention and 163 (66.5%) resulted in treatment consistent with acute exacerbations of COPD (AECOPDs). 1.6% of the outpatient visits resulted in referral to the emergency room. Conclusion: Provider encounters occurring following the escalation of a patient from a continuous respiratory monitoring service consistently resulted in that provider administering a treatment to the escalated patient.

Sociodemographic predictors of successful screening and subsequent randomization in a digital health hypertension intervention.

Introduction: Clinical trials often enroll nonrepresentative participant samples, limiting generalizability of trial findings. The current analysis explores the influences of remote recruitment and screening protocols on participation in a digital health intervention (DHI) to promote the evidence-based Dietary Approaches to Stop Hypertension (DASH) eating pattern. Methods: Nourish was a 12-month randomized controlled trial comparing the effectiveness of a DHI to an attention control arm among US adults with hypertension. Participants were recruited using digital approaches; eligible individuals completed several screening steps. We examined associations between sociodemographics and mobile technology use and completion of each screening step and compared those characteristics between randomized and nonrandomized participants (those consented but were screened out before randomization). Results: A total of 678 adults consented to participate in Nourish; 44% of those consented were randomized (n = 301). Those randomized possessed a higher education level (p < 0.0001); were more likely to use health-related apps (p < 0.0001) and wearables (p < 0.0001); and were older (p = 0.01) than nonrandomized individuals. Randomized adults were more likely to use a desktop/laptop/tablet for Internet access (vs mobile phones) (p = 0.01). No significant association was observed existed between sex, race, ethnicity, income, or geographic density of residence and subsequent randomization. Conclusions: Participants with lower education levels or limited experience in using mobile technologies may require additional support to participate in DHIs. Future research is needed to evaluate remote clinical trial procedures and impacts on generalizability to achieve equitable clinical trial participation.

YOLO-Faster: An efficient remote sensing object detection method based on AMFFN.

As a pivotal task within computer vision, object detection finds application across a diverse spectrum of industrial scenarios. The advent of deep learning technologies has significantly elevated the accuracy of object detectors designed for general-purpose applications. Nevertheless, in contrast to conventional terrestrial environments, remote sensing object detection scenarios pose formidable challenges, including intricate and diverse backgrounds, fluctuating object scales, and pronounced interference from background noise, rendering remote sensing object detection an enduringly demanding task. In addition, despite the superior detection performance of deep learning-based object detection networks compared to traditional counterparts, their substantial parameter and computational demands curtail their feasibility for deployment on mobile devices equipped with low-power processors. In response to the aforementioned challenges, this paper introduces an enhanced lightweight remote sensing object detection network, denoted as YOLO-Faster, built upon the foundation of YOLOv5. Firstly, the lightweight design and inference speed of the object detection network is augmented by incorporating the lightweight network as the foundational network within YOLOv5, satisfying the demand for real-time detection on mobile devices. Moreover, to tackle the issue of detecting objects of different scales in large and complex backgrounds, an adaptive multiscale feature fusion network is introduced, which dynamically adjusts the large receptive field to capture dependencies among objects of different scales, enabling better modeling of object detection scenarios in remote sensing scenes. At last, the robustness of the object detection network under background noise is enhanced through incorporating a decoupled detection head that separates the classification and regression processes of the detection network. The results obtained from the public remote sensing object detection dataset DOTA show that the proposed method has a mean average precision of 71.4% and a detection speed of 38 frames per second.

The AlzMatch Pilot Study - Feasibility of Remote Blood Collection of Plasma Biomarkers for Preclinical Alzheimer's Disease Trials.

BACKGROUND: Advances in plasma biomarkers to detect Alzheimer's disease (AD) biology allows researchers to improve the efficiency of participant recruitment into preclinical trials. Recently, protein levels of plasma amyloid-beta and tau proteins have been shown to be predictive of elevated amyloid in brain. Online registries, such as the Alzheimer's Prevention Trials (APT) Webstudy, include and follow participants using remote assessments to facilitate efficient screening and enrollment of large numbers of individuals who may be at higher risk for AD. OBJECTIVES: The AlzMatch Pilot Study investigated the feasibility of recruiting individuals from an online registry for blood sample collection at community-based phlebotomy centers and plasma biomarker quantification to assess an individual's eligibility for AD preclinical trials. DESIGN: Pilot feasibility study with co-primary outcomes. SETTING: This pilot feasibility study included participants from the APT Webstudy, the remote assessment arm of the Trial-ready cohort for Preclinical and Prodromal AD (TRC-PAD) Platform. Novel design included collection of electronic consent, use of community laboratories for plasma collection, mass spectrometry-based biomarker assay, and telephone communication of plasma biomarker screening eligibility. PARTICIPANTS: Participants invited to the AlzMatch pilot feasibility study were active in the APT Webstudy, 50 years of age or older, resided within 50 miles of both a Quest Diagnostics Patient Services Center (a national diagnostic laboratory with convenient locations for sample collection and processing) and one of six TRC-PAD vanguard clinical trial sites, had no self-reported dementia diagnosis, were able to communicate in English and engaged with the APT Webstudy within the prior 6 months. MEASUREMENTS: Primary feasibility outcomes were completion of electronic consent (e-consent) for invited participants and collection of usable blood samples. Additional feasibility outcomes included invitation response rate, plasma biomarker eligibility status (based on amyloid beta-42/40 [Aß42/40] concentration ratio), ApoE proteotype, and trial inclusion criterion), and completion of telephone contact to learn eligibility to screen for a study. RESULTS: 300 APT Webstudy participants were invited to consent to the AlzMatch study. The AlzMatch e-consent rate was 39% (n=117) (95% CI of 33.5%-44.5%) overall, which was higher than the expected rate of 25%. Similar consent rates were observed across participants based on self-defined sex (41% Female (n=75), 37% Male (n=42)) and race and ethnicity (37% from underrepresented groups (URG) (n=36), 40% not from URG (n=79)). Among those that consented (n=117), plasma was successfully collected from 74% (n=87) (95% CI of 66%-82%), with similar rates across sex (76% Female (n=57), 71% Male (n=30)) and race and ethnicity (75% URG (n=27) and 75% not from URG (n=59)). 60% (n=51) of participants with plasma biomarker results were eligible to screen for future preclinical AD trials. CONCLUSION: Electronic consent of participants through an online registry, blood sample collection at community-based centers, plasma biomarker quantification and reporting, and biomarker assessments for study eligibility were all feasible with similar engagement rates across demographic groups. Although this pilot was a small and selective sample, participants engaged and consented at higher than expected rates. We conclude that collecting blood at community laboratories for biomarker analyses may improve accessibility beyond research, and may facilitate broader access for clinical use of AD plasma biomarkers. Based on our results, an expanded version of the AlzMatch study is underway, which involves expanding invitations to additional APT Webstudy participants and clinical trial sites.

Assessing Telehealth in Palliative Care: A Systematic Review of the Effectiveness and Challenges in Rural and Underserved Areas.

The integration of telehealth into palliative care has garnered significant attention due to its potential to enhance both access and quality of care, particularly for patients in rural and underserved areas. This interest stems from the need to address geographical and logistical barriers that traditionally hinder palliative care delivery. Despite its potential benefits, the effectiveness of telehealth and the challenges associated with its implementation remain underexplored, necessitating further investigation. This study aims to critically evaluate the effectiveness of telehealth in palliative care by focusing on several key areas: its impact on access to care, symptom management, patient satisfaction, and cost-effectiveness. To achieve this, a systematic review was conducted, synthesizing data from various studies that investigated telehealth interventions within palliative care settings. The review employed a comprehensive search strategy across electronic databases, concentrating on randomized controlled trials (RTCs) published between 2014 and 2024. To ensure the reliability of the findings, low-quality and unrelated studies were excluded, and the remaining studies were meticulously analyzed for bias and methodological quality. The review's findings indicate that telehealth significantly enhances access to palliative care, allowing patients to receive timely and appropriate care without the need for extensive travel. It also improves symptom management and patient satisfaction, aligning to provide patient-centered care. Additionally, telehealth is cost-effective by reducing expenses associated with travel and in-person visits. These benefits highlight telehealth's potential to address some of the critical challenges in palliative care delivery. Despite its advantages, implementing telehealth in palliative care is not without challenges. Technological barriers, such as inadequate infrastructure and device limitations, pose significant hurdles. Integration issues, including the need for seamless incorporation into existing care systems, and varying levels of digital literacy among patients and caregivers, also impact the effectiveness of telehealth. Addressing these challenges is crucial for optimizing telehealth's implementation. Ensuring that telehealth solutions are accessible, user-friendly, and well-integrated into care practices is essential for fully leveraging its potential benefits.

Non-invasive remote ischemic preconditioning for patients with heart failure undergoing cardiac catheterization: a network meta-analysis of randomized controlled trials.

OBJECTIVE: This study aimed to evaluate the efficacy of six non-invasive remote ischemic preconditioning (RIPC) interventions during the nursing care of patients with heart failure (HF) prior to cardiac catheterization. METHODS: A comprehensive search of nine Chinese and English online databases was conducted from the date of their inception to June 2023 to identify randomized controlled trials (RCTs) investigating RIPC in patients with HF prior to cardiac catheterization. Two independent investigators screened the articles, extracted data, and assessed their quality. The risk of bias was evaluated using the Cochrane risk-of-bias tool, and a network meta-analysis was conducted using R software. RESULTS: Four trials involving 511 patients with a low risk of bias were included in the analysis. Six non-invasive RIPC interventions were identified, all demonstrating effectiveness in reducing the incidence of contrast-induced acute kidney injury (CI-AKI). Among these, Intervention F (applying up to 50 mmHg above the resting systolic pressure for 5 min to the dominant leg or upper limb, repeated three times with an 18-minute interval) was deemed optimal, although the timing of the procedure was not specified. Intervention D (applying up to 200 mmHg pressure to the upper limb for 5 min, repeated four times with 5-minute intervals, within 45 min prior to cardiac catheterization, ) was considered suboptimal. CONCLUSION: Although Intervention D was recommended as the preferred option, none of the four trials examined its impact on the cardiac function of patients with HF. Large-scale, multi-center RCTs are required, with outcome indicators including cardiac function and the occurrence of CI-AKI, to better understand the therapeutic effects of RIPC on HF and reduce the incidence of CI-AKI. This will provide a more robust foundation for clinical practice.

Reactivation of stable neovascular age-related macular degeneration following treat-and-extend regimen discontinuation.

BACKGROUND: To describe the incidence and pattern of reactivation of neovascular age-related macular degeneration (nAMD) following successful treatment with treat-and-extend intravitreal anti-vascular endothelial growth factor therapy. METHODS: Consecutive patients with treated nAMD who did not require further treatment over a 6-month period and who attended their 3-monthly optical coherence tomography monitoring clinic in Moorfields Eye Hospital from 1 November 2019 to 31 January 2020 were included. Patients with diagnoses of macular neovascularization other than AMD, and patients with incomplete data were excluded. Baseline demographics recorded were age, sex, race, laterality, cause of macular neovascularization, drug, number of injections, and duration of treatment. Date, setting, symptoms, and time to retreatment were collected among patients with disease reactivation. RESULTS: The medical records of 286 patients were included. Most patients were female (64.3%), white (68.18%), and were receiving aflibercept monotherapy (55.2%). Mean number of injections at baseline was 17.79 ± 11.74 (range 3-62) with a mean treatment duration of 39.47 ± 30.68 months (range 2-139). Reactivation of AMD was identified in 32.2% of cases with 87% of recurrences identified via scheduled visit. The most common symptom was blurring of vision in 44.6%, while 39.1% were asymptomatic. Mean time from baseline to retreatment was 29.37 ± 22.40 months (range 5-104), with 20.7%, 73.9% and 88.04% of these patients requiring retreatment within 1, 3, and 5 years, respectively. CONCLUSIONS: Despite prior treatment with no reactivation in 6 months, 32.2% reactivate, 73.9% of which within 3 years. A significant proportion, 39.1%, reactivated without symptoms necessitating regular monitoring in the first 5 years.

Spatial ecology, biodiversity, and abiotic determinants of Congo's bai ecosystem.

Canopy gaps are foundational features of rainforest biodiversity and successional processes. The bais of Central Africa are among the world's largest natural forest clearings and thought to be critically important islands of open-canopy habitat in an ocean of closed-canopy rainforest. However, while frequently denoted as a conservation priority, there are no published studies on the abundance or distribution of bais across the landscape, nor on their biodiversity patterns, limiting our understanding of their ecological contribution to Congolese rainforests. We combined remote sensing and field surveys to quantify the abundance, spatial distribution, shape, size, biodiversity, and soil properties of bais in Odzala-Kokoua National Park (OKNP), Republic of the Congo (hereafter, Congo). We related bai spatial distribution to variation in hydrology and topography, compared plant community composition and 3D structure between bais and other open ecosystems, quantified animal diversity from camera traps, and measured soil moisture content in different bai types. We found bais to be more numerous than previously thought (we mapped 2176 bais in OKNP), but their predominantly small size (80.7% of bais were <1 ha), highly clustered distribution, and restriction to areas of low topographic position make them a rare riparian habitat type. We documented low plant community and structural similarity between bai types and with other open ecosystems, and identified significant differences in soil moisture between bai and open ecosystem types. Our results demonstrate that two distinct bai types can be differentiated based on their plant and animal communities, soil properties, and vegetation structure. Taken together, our findings provide insights into how bais relate to other types of forest clearings and on their overall importance to Congolese rainforest ecosystems.

An ICT-enabled community oriented primary care intervention in mining communities during COVID-19 (2019-2022): perceived changes in the role and place of community health workers.

BACKGROUND: This is a study of service provider perceptions of the place, role and practices of CHWs in a four-year, large-scale private sector funded, public service ICT-enabled COPC intervention with rural and remote mining communities. Like all South African communities, apart from large mining house employees and some contractors, most people use available public healthcare services and private traditional as well as limited allopathic private sector providers. In addition to the limitations of facility centred primary healthcare and a fragmented health care system, the many negative health effects of mining on the communities, go unattended. METHODS: This is a rapid, qualitative pragmatic study. Using site and participation convenience sampling, 37 semi-structured individual or group interviews were conducted with 57 stakeholders from 38 of the 135 intervention PHC facilities. Using a data driven, inductive approach, the results were analysed thematically in terms of perceived changes in the role and place of CHWs. RESULTS: CHWs registered 42 490 households and captured the demographic and social profiles as well as the health status of over 154 910 individuals using AitaHealth™. These data provided healthcare professionals and managers with knowledge about community demographics, at-risk groups and vulnerable individuals. The intervention changed the locational focus of CHW practice and expanded their scope of work and competencies in household comprehensive health education, advice and care. It led to a growth in community and professional confidence in CHWs as trusted members of mining community PHC teams and to more focused and efficient clinic work. CONCLUSION: This ICT-enabled COPC intervention adopted a comprehensive approach to healthcare delivery that started by including CHWs in PHC teams and locating them in communities. Inclusive and systematic continuous learning, clinically-led CHW service support and ICT-enabled information technology engendered trust in CHWs as competent PHC members, and grew community confidence in them and the PHC system as a whole. Although health, care and other professionals and workers valued the changes the intervention brought to their work as well as people's lives in underserved and vulnerable mining communities, its sustainability is contingent on the vagaries of political will and financial commitment.

Research on remote sensing ecological livability index based on Google Earth Engine: a case study from Urumqi-Changji-Shihezi urban cluster.

The U-Chang-Shi (Urumqi-Changji-Shihezi) urban cluster, located at the heart of Xinjiang, boasts abundant natural resources. Over the past two decades, rapid urbanization, industrialization, and climate change have significantly threatened the region's ecological livability. To comprehensively, scientifically, and objectively assess the ecological livability of this area, this study leverages the Google Earth Engine (GEE) platform and multi-source remote sensing data to develop a comprehensive evaluation metric: the Remote Sensing Ecological Livability Index (RSELI). This aims to examine the changes in the ecological livability of the U-Chang-Shi urban cluster from 2000 to 2020. The findings show that despite some annual improvements, the overall trend in ecological livability is declining, indicating that the swift pace of urbanization and industrialization has placed considerable pressure on the region's ecological environment. Land use changes, driven by urban expansion and the growth in agricultural and industrial lands, have progressively encroached upon existing green spaces and water bodies, further deteriorating the ecological environment. Additionally, the region's topographical features have influenced its ecological livability; large terrain fluctuations have made soil erosion and geological disasters common. Despite the central plains' vast rivers providing ample water resources, over exploitation and ill-conceived hydrological constructions have led to escalating water scarcity. The area near the Gurbantunggut Desert in the north, with its extremely fragile ecological environment, has long been unsuitable for habitation. This study provides a crucial scientific basis for the future development of the U-Chang-Shi urban cluster and hopes to offer theoretical support and practical guidance for the sustainable development and ecological improvement of the region.

Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AI.

The rapid reduction of forests due to environmental impacts such as deforestation, global warming, natural disasters such as forest fires as well as various human activities is an escalating concern. The increasing frequency and severity of forest fires are causing significant harm to the ecosystem, economy, wildlife, and human safety. During dry and hot seasons, the likelihood of forest fires also increases. It is crucial to accurately monitor and analyze the large-scale changes in the forest cover to ensure sustainable forest management. Remote sensing technology helps to precisely study such changes in forest cover over a wide area over time. This research analyzes the impact of forest fires over time, identifies hotspots, and explores the environmental factors that affect forest cover change. Sentinel-2 imagery was utilized to study changes in Brunei Darussalam's forest cover area over five years from 2017 to 2022. An object-based approach, Simple Non-Iterative Clustering (SNIC), is employed to cluster the region using NDVI values and analyze the changes per cluster. The results indicate that the area of the clusters reduced where fire incidence occurred as well as the precipitation dropped. Between 2017 and 2022, the increased forest fires and decreased precipitation levels resulted in the change in cluster areas as follows: 66.11%, 69.46%, 68.32%, 73.88%, 77.27%, and 78.70%, respectively. Additionally, hotspots in response to forest fires each year were identified in the Belait district. This study will help forest managers assess the causes of forest cover loss and develop conservation and afforestation strategies.

Prerequisites and barriers to telerehabilitation in patients with neurological conditions: A narrative review.

BACKGROUND: A field of study that uses telerehabilitation (TR) is neurorehabilitation; however, standards for medical and technological applications, medicolegal and ethical regulations, and other aspects of neuro-TR are still being developed. OBJECTIVE: To address the prerequisites and barriers for implementing TR in neurorehabilitation in the light of present findings. METHODS: A narrative review was conducted based on specific questions about the prerequisites for neuro-TR and barriers to its implication. According to a foreground search strategy in the context of neurorehabilitation using TR in neurological patient population, PubMed, EMBASE and Cochrane databases were searched and reviewed. RESULTS: Barriers and prerequisites for neuro-TR were mostly grouped under the categories of administrative/organizational, human (beneficiaries/providers), technical, and ethical. Apart from the technical framework, knowledge and the presence of an administrative leader responsible for overseeing TR are crucial prerequisites. The internet and technological constraints rank highest among the barriers. CONCLUSION: Since neuro-TR is relatively new with minimal guidelines and regulations, highly technologic, and lack of established practices, it is imperative to determine and fully comprehend the criteria for its uses. After the prerequisites are established, it is imperative to recognize and address implementation constraints, which may differ depending on the community's infrastructure and neurologic condition.

Escalation Pathways of Remote Patient Monitoring Programs for COVID-19 Patients in Canada and the United States: A Rapid Review.

Introduction: During the COVID-19 pandemic, hospitals in North America were overwhelmed with COVID-19 patients and had limited capacity to admit patients. Remote patient monitoring (RPM) programs were developed to monitor COVID-19 patients at home and reduce disease transmission and the demand on hospitals. A critical component of RPM programs is effective escalation pathways. The purpose of this review is to synthesize the implementation of escalation pathways of RPM programs for COVID-19 patients in Canada and the United States. Methods: The search identified 563 articles from Embase, PubMed, and Scopus. Following title and abstract screening, 131 were selected for full-text review, and 26 articles were included. Data were extracted on study location, patient eligibility and program size, data collection, monitoring team, escalation criteria, and escalation response. Results: The included studies were published between 2020 and 2022; 3 in Canada and 23 in the United States. The RPM programs collected physiological vital signs and symptom data, which were inputted manually by patients and health care workers or synced automatically. Escalations were triggered automatically or following manual review by nurses and physicians when signs and symptoms were concerning or reached a specific threshold. Escalations included emergency department referrals, physician appointments, and increased monitoring. Conclusion: Many decisions are required when designing RPM escalation pathways for patients with COVID-19, which is crucial to promptly address patients' changing health statuses and clinical needs. Future research is needed to evaluate the effectiveness of escalation pathways for COVID-19 patients through performance metrics and patient and health care worker experience.