Desenvolvimento de uma placa eletrônica do sistema mínimo da plataforma LAICAnSat

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Curso de Graduação em Engenharia de Controle e Automação, 2015.O presente trabalho apresenta o desenvolvimento de uma placa eletrônica do sistema mínimo da plataforma LAICAnSat. O objetivo do trabalho é padronizar, implementar e desenvolver uma placa eletrônica que consiste no computador de bordo do projeto. Um dos requisitos dessa padronização foi a utilização do padrão de placa PC104 que se encaixa perfeitamente no padrão CubeSat. O sistema embarcado possui um receptor GPS, Xbee, sensor de temperatura, pressão, umidade e sensores de posicionamento. É apresentado um histórico das missões passadas e, em seguida, é apresentado as decisões tomadas em relação aos novos requisitos do projeto gerados pelos experimentos passados. É apresentado em detalhes o novo hardware do projeto junto com os testes feitos na placa. O resultado do estudo apresentou uma placa compatível com o objetivo do projeto.This work presents the development of an electronic board of the minimum LAICAnSat platform system. The objective is to standardize, deploy and develop an electronic board consisting of the project on-boad computer. One of the requirements of this standardization was using the PC104 board pattern that fits perfectly into the CubeSat standard. The embedded system has a GPS receiver, Xbee, temperature sensor, pressure, humidity and positioning sensors. A history of the past missions is displayed and then is presented the decisions taken in relation to the new project requirements generated by past experiments. It is presented in detail the new hardware design along with the tests done on the board. The study results showed a board compatible with the project objective

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages