Density variability of COVID-19 face mask litter: A cautionary tale for pandemic PPE waste monitoring.

Despite the requirement for data to be normally distributed with variance being independent of the mean, some studies of plastic litter, including COVID-19 face masks, have not tested for these assumptions before embarking on analyses using parametric statistics. Investigation of new data and secondary analyses of published literature data indicate that face masks are not normally distributed and that variances are not independent of mean densities. In consequence, it is necessary to either use nonparametric analyses or to transform data prior to undertaking parametric approaches. For the new data set, spatial and temporal variance functions indicate that according to Taylor's Power Law, the fourth-root transformation will offer most promise for stabilizing variance about the mean.

Standardized Risk Mitigation Measurement in Extended Reality Environments Utilizing the IEEE Experience API (xAPI) Standard

Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored. © 2022 IEEE.

Data transformation in the Infectious Disease Surveillance System for the Public Health Information Management

This paper aims to solve the problem of large number of COVID-19 patients with paper form for COVID-19 investigation and to communicate between medical staffs and public health office center. We applied the agile model to design and develop data transformation in the infectious disease surveillance system to help a project to adapt to change requests quickly. This data transformations provide the public health emergency surveillance such as routine surveillance, COVID-19 surveillance, event-based surveillance and outbreak investigation. This approach can be useful to the Infectious disease situation. In additional, it can be provided greater contributions to manage public health data in term communicable diseases in the future. © 2022 IEEE.