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Paaske Dotson posted an update 6 months ago
Therefore, it is critical that multidisciplinary service providers, including health professionals, employers, social services providers, educational institutions and community organizations, move toward online service delivery, so that homebound HIV-positive LGBT+ individuals are secured with a wide range of care options. Non-judgemental, tele-counseling may bridge the gap to mental health services. Community clinics catering to HIV-positive and/or LGBT+ clients may consider precociously supplying essential amenities, such as Preexposure (PrEP)/postexposure prophylaxis (PEP), condoms, emergency contraception, and sterile needles. Lastly, efforts directed at the sustenance of at-risk/HIV-positive LGBT+ health should persevere, even after the pandemic.This study presents the relationships between ambient air pollutants and morbidity and emergency department visits among children and adults performed in Great Casablanca, the most populated and economic region in Morocco. This research was analyzed using conditional Poisson model for the period 2011-2013. In the period of study, the daily average concentrations of SO2, NO2, O3 and PM10 in Casablanca were 209.4 µg/m3, 61 µg/m3, 113.2 µg/m3 and 75.1 µg/m3, respectively. In children less than 5 years old, risk of asthma could be increased until 12% per 10 µg/m3 increase in NO2, PM10, SO2 and O3. In children over 5 years and adults, an increase of 10 µg/m3 air pollutant can cause an increase until 3% and 4% in respiratory consultations and acute respiratory infection, respectively. Similarly, impact on emergency department visits due to respiratory and cardiac illness was established. Our results suggest a not negligible impact on morbidity of outdoor air pollution by NO2, SO2, O3, and PM10.Food safety is a public health concern because foodborne diseases have been increasing in recent years due to several factors such as urbanization, globalization and changes in consumer habits. Many countries in the world, including Turkey have upgraded their laws about food and personnel hygiene in the catering industry and undertaken changes to the organizational structure of their regulatory institutions to protect consumers’ health. In this study, it was aimed to evaluate the perceptions of food processors on food safety and to determine whether there has been a change in this matter over the last 12 years. The data has been collected by conducting face to face interviews and having 500 employees from the sector fill in a questionnaire constructed for this purpose. The responses to the questionnaire have been measured by assigning ‘hygiene perception points’ to each respondent according to their replies. These hygiene perception points have been analysed in terms of gender, age, educational level and work experience of the employees involved. SB273005 The results have revealed that employees between the ages of 26-34, women, university graduates have a higher level of perception of hygiene than other age groups, men, those with lower education levels, respectively. Hygiene perception points were found to be higher compared to the results obtained 12 years ago. The positive changes observed in the hygiene perception points are thought to result from the differences in the legislation of the years in which both studies were conducted. It is thought that the obligatory of providing hygiene and food safety training to individuals working in the catering sector with law changes leads to positive changes in the employees. Legally compulsory training activities can overcome many sanitation and safety problems that result from misinformed or uninformed employees.Modelling and simulation methods can play an important role in guiding public health responses to infectious diseases and emerging health threats by projecting the plausible outcomes of decisions and interventions. The 2003 SARS epidemic marked a new chapter in disease modelling in Canada as it triggered a national discussion on the utility and uptake of modelling research in local and pandemic outbreaks. However, integration and application of model-based outcomes in public health requires knowledge translation and contextualization. We reviewed the history and performance of Pan-InfORM (Pandemic Influenza Outbreak Research Modelling), which created a national infrastructure in Canada with a mandate to develop innovative knowledge translation methodologies to inform policy makers through modelling frameworks that bridge the gaps between theory, policy, and practice. This review demonstrates the importance of a collaborative infrastructure as a “Community of Practice” to guide public health responses, especially in the context of emerging diseases with substantial uncertainty, such as the COVID-19 pandemic. Dedicated resources to modelling and knowledge translation activities can help create synergistic strategies at the global scale and optimize public health responses to protect at-risk populations and quell socioeconomic and health burden.The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher’s group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models.
As a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds.
A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.
A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.
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