CampusX: An IoT-Powered Real-Time Monitoring System for University Campuses

There is an ever-urgent need for accessing real-time crowdedness and airflow information for indoor study spaces in universities, for example, to control COVID-19 transmission risk. Even before the pandemic, many students spent valuable time finding suitable study areas with proper lighting, low noise, and ample seating. This paper presents a pilot system, CampusX, which aims to provide students with useful real-time information about study spaces on campus. Our system collects and analyzes environmental data before presenting them to students as useful information. This helps them to select the most suitable study spaces. The main components of this system include a sensor platform, data collection and processing pipelines, networking, and an interactive web-application. © 2023 IEEE.

Optimization of a microgrid for the new post-covid-19 pandemic energy demand using PV-wind-biogas in Brazil

Optimizing the configuration of a microgrid allows to reduce energy losses and costs. Due to the new demand profile that emerged with the covid-19 pandemic with remote work, it is important to study its impact on the dimensioning of a microgrid, as well as the way in which renewable resources are distributed and dimensioned, in order to promote its use. Thus, this paper aims to propose the optimal configuration of distributed energy resources that meet two scenarios, pre-pandemic and post-pandemic, of electricity demand in a household in Salvador, Brazil. Therefore, an optimization problem was formulated in GAMS using environmental data and electricity demand, as well as the costs involved in the implementation and operation of the system, considering the resources of solar, wind and biogas energy. There was a change in the post-pandemic scenario, with the tendency to increase the use of solar energy, due to the demand being distributed throughout the day. It was observed for both scenarios that biogas energy had the greatest participation in domestic energy generation, followed by solar and wind energy. Therefore, the use of biogas in combination with other renewable resources can minimize costs and, at the same time, meeting the energy demand of a residence. In addition, this contributes to the environmental and economic sustainability of the region, as consumers begin to produce their own energy using renewable resources. © 2021 IEEE

Early prediction of SARS-CoV-2 reproductive number from environmental, atmospheric and mobility data: A supervised machine learning approach.

INTRODUCTION: SARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t. Current strategies to calculate Rt reflect the number of secondary cases after several days, due to a delay from symptoms onset to reporting. We propose a complementary Rt estimation using supervised machine learning techniques to predict short term variations with more timely results. MATERIAL AND METHODS: Our primary goal was to predict Rt of the current day in the twelve provinces of Lombardy with the highest possible accuracy, and with no influence of the local testing strategies. We gathered data about mobility, weather, and pollution from different public sources as a proxy of human behavior and public health measures. We built four supervised machine learning algorithms with different strategies: the outcome variable was the daily median Rt values per province obtained from officially adopted algorithms. RESULTS: Data from 243 days for every province were presented to our four models (from February 15th, 2020, to October 14th, 2020). Two models using differential calculation of Rt instead of the raw values showed the highest mean coefficient of determination (0.93 for both) and residuals reported the lowest mean error (-0.03 and 0.01) and standard deviation (0.13 for both) as well. The one with access to the value of Rt of the day before heavily relied on that feature for prediction, while the other one had more distributed weights. DISCUSSION: The model that had not access to the Rt value of the previous day and used Rt differential value as outcome (FDRt) was considered the most robust according to the metrics. Its forecasts were able to predict the trend that Rt values would have developed over different weeks, but it was not particularly accurate in predicting the precise value of Rt. A correlation among mobility, atmospheric, features, pollution and Rt values is plausible, but further testing should be performed.