ABSTRACT
The COVID-19 pandemic has intensely impacted humanities globally. This scenario has laid down many protocols and procedures to mitigate the risk and thus ensure the safety of individuals. The pandemic has augmented the demand of contactless mechanism especially in the public places.This paper presents novel machine learning and internet of things based solution for contactless entry to premises following mandatory checks at the entry as per COVID-19 protocols. The proposed model senses the body temperature and detects the face mask of an individual prior to entry. The entrance is allowed through contactless opening of the gate only if the body temperature is within prescribed limits and the face mask is properly put on.The current work uses a machine learning model for detection of the face mask which uses the real time image during screening;the algorithm is trained using the data sets with and without mask. The temperature screening is carried out with temperature sensor connected to the Arduino processor.A prototype model using Arduino is prepared based on the inputs received from the temperature sensor and the Machine learning Algorithm. The gate shall open for entry if a person has normal body temperature and wearing a proper face mask, else, will be restricted through a custom alert. Also a track of the number of persons entering is monitored and sent to a web portal on real time basis to account for overcrowding.The model ensures a contactless check at the entry, thus, controlling the outbreak of the Covid situation. © 2022 IEEE.
ABSTRACT
At the 2019 and 2021 International Conference on Environmental Psychology, discussions were held on the future of conferences in light of the enormous greenhouse gas emissions and inequities associated with conference travel. In this manuscript, we provide an early career researcher (ECR) perspective on this discussion. We argue that travel-intensive conference practices damage both the environment and our credibility as a discipline, conflict with the intrinsic values and motivations of our discipline, and are inequitable. As such, they must change. This change can be achieved by moving toward virtual and hybrid conferences, which can reduce researchers' carbon footprints and promote equity, if employed carefully and with informal exchange as a priority. By acting collectively and with the support of institutional change, we can adapt conference travel norms in our field. To investigate whether our arguments correspond to views in the wider community of ECRs within environmental psychology, we conducted a community case study. By leveraging our professional networks and directly contacting researchers in countries underrepresented in those networks, we recruited 117 ECRs in 32 countries for an online survey in February 2022. The surveyed ECRs supported a change in conference travel practices, including flying less, and perceived the number of researchers wanting to reduce their travel emissions to be growing. Thirteen percent of respondents had even considered leaving academia due to travel requirements. Concerning alternative conference formats, a mixed picture emerged. Overall, participants had slightly negative evaluations of virtual conferences, but expected them to improve within the next 5 years. However, ECRs with health issues, facing visa challenges, on low funding, living in remote areas, with caretaking obligations or facing travel restrictions due to COVID-19 expected a switch toward virtual or hybrid conferences to positively affect their groups. Participants were divided about their ability to build professional relationships in virtual settings, but believed that maintaining relationships virtually is possible. We conclude by arguing that the concerns of ECRs in environmental psychology about current and alternative conference practices must be taken seriously. We call on our community to work on collective solutions and less travel-intensive conference designs using participatory methods.
ABSTRACT
Introduction Epidemiological data collection is often challenged by low response and, in the case of cohorts, poor long-term compliance, i.e. a high drop-out. For the correct recording of incident or recurring health events, that are subject to recall difficulties, gathering of data during the event and immediate response of the participants is crucial. This is especially true when biosampling that catches a transient biological situation like COVID-19 is involved. In addition, emerging research topics (e.g. pandemics like the current SARS-CoV-2) demand a flexible approach regarding content while allowing for complex and varying study designs. To meet these needs, we developed an eResearch system for prospective monitoring and management of incident health events (PIA). Methods Programming PIA focusses on IT security and data protection as well as aiming for a user-friendly and motivating design e.g. through feedback for study participants. The main building blocks of the infrastructure are identical functionalities in web-based, iOS and Android compatible application to strengthen the user acceptance of the participants. The backend consists of services and databases, which are all containerised using Docker containers. All programming is based on the JavaScript ecosystem as this is widely used and well supported. Results PIA offers complete management of observational epidemiological studies with six different roles: PIA administrator, researcher, participant manager, study nurse, consent manager and participant. Each role has a specific interface, so that different functions e.g. implementation of new questionnaires, administration of biosamples or management of participant contacts can be performed by different personae. PIA can be integrated in the IT system of ongoing studies like the German National Cohort but also used as stand-alone system. The software is open source (AGPL3.0): https://github.com/hzi-braunschweig/pia-system. Discussion Despite the abundance of existing Electronic Data Capture Systems (EDC systems), we developed our own generic tool that combines monitoring and management in order to use it for specific applications e.g. in certain pre-existing epidemiological studies or for syndromic surveillance in the current pandemic. Hence, PIA is continuously adapted to emerging requirements. Currently, systematic feedback from users is collected. We aim to improve the user experience of PIA as well as provide further feedback and additional elements like gamification in the future.
ABSTRACT
Eating disorders are multifaceted problems with various risk factors, including the sociocultural context, social media, society's beauty standards, personality, and genetics. The coronavirus disease 2019 (COVID-19) pandemic has been a cause of stress among university students, as well as inducing changes in their physical activity and eating habits. Objective: The objectives of this study were to evaluate the changes in body mass index and risk of developing eating disorders among university students during the COVID 19 pandemic. Methods: This was a cross-sectional study of 1004 female students recruited from a university in Riyadh, Saudi Arabia. Data were collected from December 2020 to March 2021 through a self-administered questionnaire comprising three parts: sociodemographic items, the Eating Attitudes Test, and an evaluation of behavioral changes during the COVID-19 pandemic. Results: Most participants were aged 18-24 years, single, lived with their parents, and had a moderate to high family income. There was a significant relationship between the risk of developing eating disorders and marital status (p < 0.001). College type (p < 0.003), fast food consumption (p = 0.010), and engaging in exercise (p < 0.001) were also significant factors. Based on categorizations of risk levels derived from the literature, about 31.5% of the participants had a high risk of developing eating disorders. Conclusion: According to our results, eating disorders are relatively common among Saudi female undergraduate students. Thus, educational programs that aim to increase this population's awareness concerning appropriate nutrition and body weight are needed.
ABSTRACT
Background: Incidence of Acute Kidney Injury (AKI) among COVID-19 patients is 35%. Requirement for Renal replacement therapy (RRT) is estimated to be 15%-20%. We aimed to identify risk factors associated with mortality and need for RRT in COVID-19 patients with AKI. We also estimated burden of the pandemic on inpatient hemodialysis (HD) unit. Methods: Inpatients above 18 years, diagnosed with COVID-19 on RT-PCR between March-June 2020 were included in the study. AKI was defined using KDIGO guidelines. Data collected included demographics, serum creatinine, time to AKI, comorbidities, albuminuria, need for RRT and intubation. All inpatient HD sessions from January 2016 to June 2020 were included to estimate burden of COVID-19. CVVHD, PIRRT and PD were excluded. Statistical analysis included logistic regression, ANOVA, z-test for proportions and Chi-square test. Interrupted time series analysis using Auto Regression Interference and Moving Average (ARIMA) was used to predict proportion of bedside HD sessions from January 2020. Results: 1991 patients positive for COVID-19 on RT-PCR were included. 683 (34.2%) were found to have AKI. 185 patients (27.1%) required RRT. Mortality among AKI patients was 64.7%. Age (OR=1.04;CI 1.03 to 1.06), AKI after 1 week (OR=2.15;CI 1.06 to 4.35), albuminuria (OR=2.57;CI 1.11 to 5.93), need for RRT (OR=2;CI 1.26 to 3.19) and intubation (OR=4.6;CI 2.71 to 7.75) were the mortality risk factors. Albuminuria (OR=2.97;CI 1.04 to 8.46), CKD (OR=3.5;CI 1.67 to 7.34) and intubation (OR=7.8;CI 5.14 to 11.91) were the risk factors for RRT. Diabetes and hypertension did not increase mortality or the need for RRT. To estimate the burden of pandemic, 24086 HD sessions between Jan. 2016 to June 2020 were analyzed. Proportion of bedside HD was significantly higher in 2020 when compared to previous years (p<0.01) due to isolation protocols. ARIMA model showed a significant difference in the mean proportion of bedside HD sessions for 2020 between observed and expected values (p<0.01). Personnel requirement showed an extra burden of 870 nurse-hours with March-April accounting for 76%. This was due to increased number of bedside sessions requiring a 1:1 nurse-patient ratio as opposed to in unit sessions where nurse-patient ratio is 1:2. Conclusions: Time to AKI, albuminuria and RRT are important risk factors for mortality in COVID-19 patients with AKI.