Health Management Approach Research Articles

Nanoemulsion and nanoemulgel-based carriers as advanced delivery tools for the treatment of oral diseases.

Oral diseases rank among the most widespread ailments worldwide posing significant global health and economic challenges affecting around 3.5billion people, impacting the quality of life for affected individuals. Dental caries, periodontal disease, bacterial and fungal infections, tooth loss and oral malignancies are among the most prevalent global clinical disorders contributing to oral health burden. Traditional treatments for oral diseases often face challenges such as poor drug bioavailability, breakdown of medication in saliva, inconsistent antibiotic levels at the site of periodontal infection as well as higher side effects. However, the emergence of nanoemulgel (NEG) as an innovative drug delivery system offers promising solutions where NEG combines the advantages of both nanoemulsions (NEs) and hydrogels providing improved drug solubility, stability, and targeted delivery. Due to their minuscule size and ability to control drug release, NEGs hold promise for improving treatment of oral diseases, where versatility of these delivery systems makes them suitable for various applications, including topical delivery in dentistry. This review concisely outlines the anatomy of the oral environment and investigates the therapeutic potential of NE-based gels in oral disorder treatment. It thoroughly examines the challenges of drug delivery in the oral cavity and proposes strategies to improve therapeutic efficacy, drawing attention to previous research reports for comparison. Through comprehensive analysis, the review highlights the promising role of NEGs as a novel therapeutic approach for oral health management via research advancements and their clinical translation. Additionally, it provides valuable insights into future research directions and development opportunities in this area.

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.

An Innovative Public Health Solution with European Features for an American Problem

Abstract Problem The Eastern Shore of Maryland is a beautiful region that boasts a multicultural community of 455,000 year-round residents. Despite its rural location, the coastal lifestyle attracts retirees, making it the state’s oldest population. Unfortunately, the region also has the highest socioeconomic disparities in the state, which creates difficulties in accessing healthcare. This is a significant public health issue that results in costly barriers to preventive care and timely, accounting for $320 billion in unnecessary healthcare spending. Description of the problem Maryland’s healthcare funding formulas favor larger health systems in urban areas, leading to health inequities. To address this, we propose an innovative scenario analysis model partially based on European public health approaches. This model will adjust funding formulas based on each region’s specific needs and challenges, promoting fairness and equity in healthcare funding. Results Adopting this population health management approach would have built-in efficiencies: In the United States, healthcare spending tends to be higher among underserved populations due to delayed care, access challenges, missed diagnoses, and limited access to the latest scientific advances as well as proper preventive services. Lessons The proposed scenario-based public health funding approach targeting the needs of the underserved population on the Eastern Shore of Maryland could yield significant positive health outcomes and health savings generating a ripple effect across the state, and perhaps even the country. The lessons learned in how other countries handle public health funding could lead to global collaboration. Key messages • Our approach aims to provide a practical solution to unnecessary healthcare spending. • We must adapt to the changing tides to stay on course.

Study on the intervention effect of HCH integrated SMG health management model on community high-risk group of cardiovascular disease.

This study aimed to evaluate the integration of the Hospital-Community-Home (HCH) model with the Self-Mutual-Group (SMG) health management model for high-risk populations with cardiovascular disease in the Yuhua community of Shijiazhuang city. The study focused on implementing care interventions (HCH, SMG) with a specific emphasis on SMG to promote beneficial views/behaviors, enhance self-efficacy/agency, and address detrimental determinants of health, ultimately leading to durable changes and healthier lifestyles. Comparing the HCH model with the combined SMG model helps to comprehensively assess the strengths and weaknesses of different health management approaches. This comparison contributes to theoretical innovation and practical development in the field of health management, as well as improving patients' health outcomes and quality of life. This study employed a quasi-experimental design. Using stratified sampling, individuals who underwent health examinations in Community A and Community B from Shijiazhuang city between May 2023 and August 2023 were randomly selected. After informing the participants about the study and obtaining informed consent via telephone, high-risk patients with cardiovascular disease were screened based on their medical examination reports. Data on lifestyle behaviors, self-efficacy, medical responses, quality of life, and readmission rates were collected and compared before and after the intervention. A total of 526 eligible participants were included, with 241 in the control group and 285 in the study group. After the intervention, there was no significant change in the proportions of smokers, alcohol consumers, and individuals engaging in leisure exercises in the control group. However, in the study group, the proportions of smokers and alcohol consumers significantly decreased, while the proportion of individuals engaging in leisure exercises significantly increased. After the intervention, both the study group and the control group showed significant increases in scores on the General Self-Efficacy Scale-Schwarzer (GSES) and the Seattle Angina Questionnaire (SAQ), with the study group scoring significantly higher than the control group. Avoidance and surrender scores significantly increased after the intervention, with the study group scoring significantly lower than the control group. Confrontation scores significantly increased after the intervention, with the study group scoring significantly higher than the control group. During the follow-up period, the study group had a significantly lower readmission rate than the control group. The integration of HCH with SMG health management model can significantly improve lifestyle behaviors, optimize medical responses, enhance self-efficacy and quality of life, and significantly reduce readmission rates among high-risk populations with cardiovascular disease.

GAN-Based Multi-Task Learning Approach for Prognostics and Health Management of IIoT

Health assessment (HA) & remaining useful life (RUL) estimation, the two essential pillars of the prognostics and health management (PHM) paradigm, help improve industrial equipment reliability while reducing maintenance costs. However, reported works treat HA and RUL estimation as disjoint problems though there exist unexploited similarities among these related issues. Additionally, in practical industrial working scenarios, equipment(s) stay in a normal condition for most of its lifespan, leading to a disproportionate training dataset, hampering the prediction accuracy. To overcome the above problems, we propose a data-driven multi-task learning framework aided by a novel least squares recurrent auxiliary classifier generative adversarial network (LS-R-ACGAN). LS-R-ACGAN employs recurrent neural networks (RNNs) in its generator & discriminator networks for multi-variate fault data generation while overcoming the vanishing gradient problem of ACGANs. Post-data-augmentation, a balanced training dataset, trains a multi-task learning model based on a deep gated RNN (DGRU) for joint HA and RUL estimation. Our simulations use the C-MAPSS dataset for testing the proposed approach’s accuracy. The final results showcase improvements by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 3.54% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 2.38% on the RMSE and Score metric over existing state-of-the-art works suggesting its competitiveness and competence for real-world implementations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study attacks multiple presumptions concerning the maintenance of complex systems including, ample availability of fault samples alongside normal samples and solving only one of the problems, i.e., health assessment (HA) or remaining useful life (RUL) estimations rather than both. Popular artificial intelligence (AI) based solutions rely heavily upon the quantity of training data for desired performance outcomes. However, in real-life industrial scenarios, fault instances are rare, inherently resulting in an imbalanced dataset. Additionally, it is both labor-intensive and costly to repeat multiple experiments for gathering required data as the equipment(s) pass through short-lived fault states. Thus conventional AI-based solutions may end up producing biased predictions. This work proposes to solve the imbalanced training dataset problem along with joint HA and RUL estimations, in one solution pipeline, currently unexplored in majority of the existing works in the literature. LS-R-ACGAN generates ample diverse fault samples for reducing the degree of imbalance between normal and fault classes. Subsequently, after data augmentation, the balanced training dataset helps train a multi-task learning model for joint HA and RUL estimations. The proposed framework has been implemented and tested on a benchmark dataset proving its superiority over multiple existing methods in the literature.

Feasibility and preliminary effectiveness of a population health management approach to improve cardiovascular risk management in primary care: a mixed-method implementation study

Feasibility and preliminary effectiveness of a population health management approach to improve cardiovascular risk management in primary care: a mixed-method implementation study

Management to Reduce Impact of Cerebral Coenurosis in Bonga Sheep in Kafa Zone, South Western Ethiopia

Coenurosis, a parasitic and fatal disease, is widespread in the highlands of Ethiopia where 75% of the country’s sheep population are found. There is significant prevalence of the disease in the current study area. Therefore, the current study was conducted with the objectives to reduce impact of cerebral coenurosis in sheep production in the study area through integrating different prevention and control options and to raise the community awareness about disease. Questionnaire surveys, coproscopic examination of dog faeces and household level surveillance was used to collect data on reported coenurosis cases and taeniid infections. Awareness creation trainings and regular deworming of dogs were used as an intervention to reduce the prevalence of the disease. A total of 107, 134, 153, and 124 dogs were dewormed during the first, second, third, and fourth rounds during the study period respectively. Eggs were detected in 58.53% (95% CI: 47.4- 68.86) of pre-deworming fecal samples of dogs. Eggs were detected in 24.18% (95% CI: 40.0–49.7) of post-intervention fecal samples, 34.35% less than pre-intervention. At the beginning of the intervention the level of awareness of the farmers in the intervention sites about the cause of the disease was 12.2% which was increased to 51.03% at the end of the intervention. The community-based integrated animal health management approach for the control of coenurosis has the potential to be scaled-up across the country, thereby reducing economic losses of communities where the disease is endemic.

Impact of integrative care on cardiovascular disease risk in newly diagnosed type 2 diabetes mellitus patients: A BI-VitalLife Cohort study.

Type 2 diabetes mellitus (T2DM), a chronic metabolic disorder, significantly increases cardiovascular disease (CVD) risk. Integrative care (IC) offers a personalized health management approach, utilizing various interventions to mitigate this risk. However, the impact of IC on CVD risk in newly diagnosed T2Dm patients remains unclear. This study aims to assess the differences in CVD risk development within 120 months following a new diagnosis of T2DM, using real-world data from Bumrungrad International Hospital and Vitallife Scientific Wellness Center. This study utilized the BI-VitalLife Cohort dataset that contains de-identified demographics, vitals, diagnoses and clinical information, laboratory and radiological data, medications, and treatments of more than 2.8 million patients who visited Bumrungrad International Hospital and/or VitalLife Scientific Wellness Center from June 1, 1999, to May 31, 2022. This study focused on newly diagnosed T2DM patients, defined according to American Diabetes Association criteria. We compared CVD risk between the IC and conventional care (CC) groups using the Kaplan-Meier curve and Cox proportional hazard model, adjusted for age, sex, and laboratory values. Propensity score matching was employed to enhance comparability. Of the 5,687 patients included, 236 were in the IC group and 5,451 in the CC group. The IC group, characterized by a lower age at T2DM diagnosis, showed favorable hematological and metabolic profiles. The Cox proportional hazard ratios revealed a significantly lower CVD risk in the IC group within 120 months post-T2DM diagnosis compared to the CC group, consistent even after adjusting for confounding factors. Propensity score-matched analysis supported these findings. Personalized integrative care may offer a significant advantage in reducing CVD risk among newly diagnosed T2DM patients compared to conventional care, even when considering various confounding factors. This study sheds light on the potential of integrative care in informing treatment strategies for T2DM patients at risk of developing CVD.

A laser-Engraved Wearable Electrochemical Sensing Patch for Heat Stress Precise Individual Management of Horse.

In point-of-care diagnostics, the continuous monitoring of sweat constituents provides a window into individual's physiological state. For species like horses, with abundant sweat glands, sweat composition can serve as an early health indicator. Considering the salience of such metrics in the domain of high-value animal breeding, a sophisticated wearable sensor patch tailored is introduced for the dynamic assessment of equine sweat, offering insights into pH, potassium ion (K+), and temperature profiles during episodes of heat stress and under normal physiological conditions. The device integrates a laser-engraved graphene (LEG)sensing electrode array, a non-invasive iontophoretic module for stimulated sweat secretion, an adaptable signal processing unit, and an embedded wireless communication framework. Profiting from an admirable Truth Table capable of logical evaluation, the integrated system enabled the early and timely assessment for heat stress, with high accuracy, stability, and reproducibility. The sensor patch has been calibrated to align with the unique dermal and physiological contours of equine anatomy, thereby augmenting its applicability in practical settings. This real-time analysis tool for equine perspiration stands to revolutionize personalized health management approaches for high-value animals, marking a significant stride in the integration of smart technologies within the agricultural sector.

A prognostic and health management approach using colour fade to determine the condition of silk in a museum display environment

Silk is highly susceptible to fade, often resulting in colour loss to the fabric even when displayed in controlled museum environments. This study applies the engineering-inspired Prognostics and Health Management (PHM) approach to assess the remaining useful life (RUL) of silk fabric in a museum environment, focussing on predicting colour fade as an indicator of degradation. A novel mathematical model is developed to forecast cumulative colour fade of silk upholstery exhibited at the Great Gallery in The Wallace Collection, London. Unlike traditional accelerated aging methods, this model utilises naturally aged samples to estimate the rate of colour change over time. The main contribution of this work lies in the methodology and computational framework for model development using environmental data and conditions of silk in the museum environment. The proposed PHM methodology allows for continuous modifications of the colour fade model to improve accuracy by deploying new data from different silk collections and environments. The article demonstrates a model-based approach for informed decision making in museums regarding display and storage of silk upholstery.

An emerging tool in healthcare: wearable surface-enhanced Raman Spectroscopy

This perspective explores the progressive domain of wearable surface-enhanced Raman spectroscopy (SERS), underscoring its potential to revolutionize healthcare. As an advanced variation of traditional Raman spectroscopy, SERS offers heightened sensitivity in detecting molecular vibrations. Applied in wearable technology, it provides a mechanism for continuous, non-invasive, real-time monitoring of chemical and biomolecular processes in the human body through biofluids such as sweat and tears. This underscores its immense potential in enabling early disease detection and facilitating personalized medicine. However, the adoption of wearable SERS is not without challenges, which include device miniaturization, reliable biofluid sampling, user comfort, biocompatibility, and data interpretation. Nevertheless, this perspective emphasizes that the fast-paced advancements in nanotechnology and data sciences render these challenges surmountable. In summary, the perspective presents wearable SERS as a promising innovation in healthcare’s future landscape. It has the potential to enhance individual health outcomes significantly and lower healthcare costs by promoting a preventive health management approach.

A comparative study of population management approaches in infectious disease control, population management of fisheries and wildlife, and integrated pest management in agriculture

AbstractThis paper provides a comprehensive study on the similarities and differences between infectious disease control in public health and various population management approaches, including fisheries resource management, wildlife management, and integrated pest management in agriculture. We aim to identify key strategies, and potential synergies that can contribute to the development of population management strategies within these domains. For example, scientists provide options but leave policy making itself to stakeholders, choose monitoring indicators with as little time lag or change thresholds between increasing and decreasing phases to account for time lag, consider economic feasibility, and clarify accountability, which are common to all fields. There are many elements to incorporate methods from other fields while making use of the accumulation gained in each field.

Unsupervised Bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems

In the prognostics and health management of mechanical systems, achieving high reliability requires high-quality data for analysis and model training to avoid intensive misalarms and false detections. This research introduces a novel unsupervised Bayesian change-point detection approach specifically tailored to improve the accuracy and dependability of data used in the performance evaluation, fault diagnostics, and predictive maintenance of such systems. By leveraging this method, the precision of change-point detection in operational data is significantly heightened, effectively discerning between normal conditions and potential outliers within the recorded data sequences. The proposed approach replaces the reliance on traditional outlier detection methods with an innovative utilization of prediction error metrics, enriched with statistical analysis to assess and bolster the robustness of the detection algorithm. Conducted case studies on data from the supervisory control and data acquisition (SCADA) system of 92 wind turbines highlight the advanced data cleaning capabilities of our method, which markedly outperforms previous techniques including quartile, Gaussian mixture models, isolation forests, and local outlier factor algorithms. The findings illustrate the methodology’s efficacy in obtaining rcleaned data, which are crucial for the reliable prognostics and health management frameworks of mechanic system, and enhance the confidence in feature extraction and performance prediction.

Assembling a population health management maturity index using a Delphi method

BackgroundAlthough local initiatives commonly express a wish to improve population health and wellbeing using a population health management (PHM) approach, implementation is challenging and existing tools have either a narrow focus or lack transparency. This has created demand for practice-oriented guidance concerning the introduction and requirements of PHM.MethodsExisting knowledge from scientific literature was combined with expert opinion obtained using an adjusted RAND UCLA appropriateness method, which consisted of six Dutch panels in three Delphi rounds, followed by two rounds of validation by an international panel.ResultsThe Dutch panels identified 36 items relevant to PHM, in addition to the 97 items across six elements of PHM derived from scientific literature. Of these 133 items, 101 were considered important and 32 ambiguous. The international panel awarded similar scores for 128 of 133 items, with only 5 items remaining unvalidated. Combining literature and expert opinion gave extra weight and validity to the items.DiscussionIn developing a maturity index to help assess the use and progress of PHM in health regions, input from experts counterbalanced a previous skewedness of item distribution across the PHM elements and the Rainbow Model of Integrated Care (RMIC). Participant expertise also improved our understanding of successful PHM implementation, as well as how the six PHM elements are best constituted in a first iteration of a maturity index. Limitations included the number of participants in some panels and ambiguity of language. Further development should focus on item clarity, adoption in practice and item interconnectedness.ConclusionBy employing scientific literature enriched with expert opinion, this study provides new insight for both science and practice concerning the composition of PHM elements that influence PHM implementation. This will help guide practices in their quest to implement PHM.

Development and first steps towards implementing population health management in Belgium.

Background: Although the recent pilot projects on integrated care for the chronically ill fit within a population needs-based approach in Belgium, awareness and implementation of population health management is still limited and in its early stages. Therefore, this study aims to identify the views of Belgian stakeholders on hurdles and avenues to introduce population health management, to design a population health management approach to secondary prevention of atherosclerotic cardiovascular disease, and to propose actionable recommendations to roll out population health management in Belgium.&#x0D; Methods: Eleven Belgian high-level decision makers in medicine, policy and science participated in two focus group sessions held between October and December 2021. Discussions were informed by a literature review and a semi-structured guide. Focus group sessions were recorded and discussions were transcribed ad verbatim. An inductive thematic analysis of discussions was carried out.&#x0D; Results: Focus group participants highlighted multiple inter-related barriers and recommendations towards the introduction of population health management in Belgium. In particular, they referred to the responsibilities of the three policy levels within the Belgian political system, the shared accountability for the health of the population, the importance of a learning health system supporting population health management, the development of appropriate payment models, data and knowledge infrastructure, collaborative relationships and community involvement. These barriers and recommendations were illustrated by a case study setting up a population health management approach to secondary prevention of atherosclerotic cardiovascular disease in Belgium.&#x0D; Conclusions: In order to speed up the introduction of population health management in Belgium, this study advocates: to instill a sense of urgency starting from a joint population-oriented vision; to move to a learning health system which makes use of a data and knowledge infrastructure; to set up new payment model pilots; to impose collaborative relationships and responsibilities; and to engage in continuous dialogue with communities of practice.

Data driven population health management: first steps to an integrated approach in Leuven Cares (Zorgzaam Leuven)

In 2018 we started the Leuven Cares project in the city of Leuven. The aim of this project is to develop, implement and evaluate the necessary changes to enable integrated care.&#x0D; We installed a proactive approach to manage the health and wellbeing of the population of Leuven, to move away from the provision of reactive, demand-led care.&#x0D; All inhabitants of Leuven are targeted, 102000 people. All relevant healthcare organizations participate in the project, which is financially supported by the federal government and by 60 local organizations.&#x0D; We use a data driven population health management approach based on 4 building blocks:&#x0D; 1.Know your population health’s needs: obtaining data from the population for constant re-evaluation of the interventions at as many levels as possible. Define clearly for every action the type and subgroup of patients.&#x0D; 2.Engage with your population: collaboration in integrated neighborhood teams at a local level of 5000 to 10000 inhabitants. Map clearly local level interventions (5000 to 10000 inhabitants) versus meso level (100000 inhabitants) using natural existing networks, functional collaborations and structures.&#x0D; 3.Manage your population: different interventions or different types of patients starting with the correct stratification. Every intervention and intermediate evaluation is based as much as possible on data, existing or collected data.&#x0D; 4.Provide support from an integration management team: a so called integrator structure as a backoffice to permanently catalyze the transition towards population based integrated care. &#x0D; Our results (September 2022):&#x0D; -50%-60 % of primary caregivers participated in at least one Leuven Cares action.&#x0D; -for the various Leuven Cares actions, a total of 6500 patients were included on an individual basis and stratified within the ‘Leuven cares stratification map’.&#x0D; -the stakeholder consortium consists of more than 73 partners and is still augmenting. This consortium consists of partners representing health care organizations, social welfare organizations, academic partners, local health insurance partners and other broadly oriented local partners.&#x0D; -the “integrator” function was constructed as a complex conceptual entity inside the global governance arrangement. It consists in total of more than 40 individuals and a small coordination team of 3 FTE functions.&#x0D; Lessons learned and next steps: At a micro level, a key facilitator for a population health management approach is to install integrated neighborhood teams of primary care professionals, to manage the population. At the meso level, create a strong integrator function with a diversity of liaison arms in the different partner organizations. The integrator needs support of a central backoffice team with visionary leadership and management skills to facilitate a population-oriented approach.&#x0D; The effects on the entire population of this project and the various subactions of this project will be studied in the coming years. The ongoing process shows good participation of professional caregivers and their organizations, a need for leadership, a collaborative attitude, a data- and evidence-based approach and a strong local integrator supporting the transition towards integrated care. In a next step, we want to measure the long-term impact of this project on relevant quintuple aim parameters.&#x0D;

Strengthening a Dutch initiative with action research; community involvement one of the key stumbling blocks

Introduction: There is a strong call from integrated care initiatives for practical guidance, including translating existing scientific knowledge into the local context. This covers several themes such as regional governance, adjusting payment models, and community involvement. In the presented project, we used an action research approach to support the team of HealthKIC implementing the ‘Kavelmodel’ in the Netherlands. HealthKIC supports regions that are willing to change using a population health management approach by facilitating the implementation together with stakeholders on all levels. The Kavelmodel focusses on improving the health and wellbeing of the population of the involved regions, ‘Vallei Vitaal &amp; Gezond’ and ‘8terhoek Gezond’ for now. &#x0D; Methods: Together with the HealthKIC-team we co-designed an action research process looking to evaluate the tools and approaches that were used by the HealthKIC-team to enable key themes and issues in the regions and with system-level stakeholders. Working with an emergent paradigm perspective in this complex and dynamic environment, we attempted to balance the flexibility in the process to adapt to emergent circumstances, while still have the ability to validate the findings. The process focussed on exchanging knowledge and lessons learned between science and practice using learning cycles. &#x0D; Results: While all above-mentioned themes were discussed, one key stumbling block was community engagement. Using several learning cycles in different settings, we were able to continuously stress the importance of it, share knowledge and practical examples, and discuss next steps. However, both system-level stakeholders and regional stakeholders remained reluctant to adopt a form of community involvement and haven’t implemented it yet. The actual doing, listening to community members and hand over some control, was perceived the most difficult and crucial aspect for stakeholders on all levels. Reflection sessions about this theme revealed the main reason for not taking action was feeling insecure about how to progress. As was seen with other themes, this hesitation often led to people falling back into old habits and working on other day-to-day tasks. &#x0D; Discussion: As is evidenced by the literature and practice, community involvement often remains theoretical. We tried out a new way of working using learning cycles to provide practical guidance, but it didn’t result in effective implementation of community involvement yet. Having the knowledge of the importance of and how to involve community members proved to be not enough. Therefore, we still need to search for the most effective strategy to include community members successfully in these kind of initiatives. One of the lessons learned is that it is important is to consider the attitude and commitment of the stakeholders involved. The project leaders need to feel supported by them to take action. Next to that, the community involvement needs to fit within the bigger picture of the initiative, supported by a joint vision, sustainable resources, and a learning environment. These are crucial insights into why community involvement is still lacking. Looking forward, having PHM-coaches in the region could bring the necessary assurance for people to work differently and in new and unexpected circumstances.

Principles for effective management of scabies outbreaks

Background: The contagious skin infestation scabies, is caused by the mite &lt;i&gt;Sarcoptes scabiei&lt;/i&gt; var. &lt;i&gt;hominis&lt;/i&gt;. These microscopic organisms navigate under the skin surface to lay eggs, creating burrows that cause notable discomfort and itching and enable the maturation and subsequent proliferation of the mites. Spread to other hosts can occur through direct, often prolonged, skin-to-skin contact or indirectly through shared items such as clothing, bedding, or towels.Current Concepts: Management of a scabies outbreak encompasses several pivotal steps: accurate diagnosis, comprehensive treatment with topical scabicides to kill the mites, rigorous environmental control (including washing all possibly infested materials in hot water), meticulous contact tracing of all close contacts, administration of symptomatic relief through antihistamines and corticosteroids, and meticulous follow-up to verify successful treatment outcomes.Discussion and Conclusion: The prevention of further infestation and recurrent outbreaks is crucial, necessitating a holistic, integrative public health management approach. This approach involves consistent communication and public education about preventive measures and prompt treatment strategies to curtail widespread infestation, thereby safeguarding community health.

A Comparison of Indonesia and Malaysia Legal Responses in Handling Covid-19

Objective: This paper aims to know about COVID-19 pandemic has become a global threat and spread of the virus reaches Indonesia and Malaysia. The response to Covid-19 has been governed by Indonesia and Malaysia laws and legislation. This paper aimed at assessing why was the legal response of Malaysia more successful in handling COVID-19 than that of Indonesia?&#x0D; &#x0D; Method: This research used a normative legal method.&#x0D; &#x0D; Result: The results revealed that (1) Indonesia and Malaysia issued new laws and regulations, but Indonesia uses a health and disaster management approach, whereas Malaysia uses a health and law enforcement approach; (2) The structure for handling COVID-19 in Indonesia is performed by the COVID-19 Task Force, while Malaysia is carried out by The National Security Council; (3) Indonesia imposes large-scale social restrictions, whereas Malaysia enforces the Movement Control Order.&#x0D; &#x0D; Conclusion: This paper argues that Malaysia’s legal response through a health and law enforcement approach performed by The National Security Council has been more successful in reducing the number of victims and the spread of COVID-19. Indonesia can emulate Malaysia by using a law enforcement approach to dealing with COVID-19 in addition to a health and disaster management approach. Malaysia has been more successful in handling COVID-19 than Indonesia.

The ROAD to discovery: Machine-learning-driven anomaly detection in radio astronomy spectrograms

Context.As radio telescopes increase in sensitivity and flexibility, so do their complexity and data rates. For this reason, automated system health management approaches are becoming increasingly critical to ensure nominal telescope operations.Aims.We propose a new machine-learning anomaly detection framework for classifying both commonly occurring anomalies in radio telescopes as well as detecting unknown rare anomalies that the system has potentially not yet seen. To evaluate our method, we present a dataset consisting of 6708 autocorrelation-based spectrograms from the Low Frequency Array (LOFAR) telescope and assign ten different labels relating to the system-wide anomalies from the perspective of telescope operators. This includes electronic failures, miscalibration, solar storms, network and compute hardware errors, among many more.Methods.We demonstrate how a novel self-supervised learning (SSL) paradigm, that utilises both context prediction and reconstruction losses, is effective in learning normal behaviour of the LOFAR telescope. We present the Radio Observatory Anomaly Detector (ROAD), a framework that combines both SSL-based anomaly detection and a supervised classification, thereby enabling both classification of both commonly occurring anomalies and detection of unseen anomalies.Results.We demonstrate that our system works in real time in the context of the LOFAR data processing pipeline, requiring &lt;1ms to process a single spectrogram. Furthermore, ROAD obtains an anomaly detection F-2 score of 0.92 while maintaining a false positive rate of 2%, as well as a mean per-class classification F-2 score of 0.89, outperforming other related works.