The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.

In Brazil, training capable bioinformaticians is done, mostly, in graduate programs, sometimes with experiences during the undergraduate period. However, this formation tends to be inefficient in attracting students to the area and mainly in attracting professionals to support research projects in research groups. To solve these issues, participation in short courses is important for training students and professionals in the usage of tools for specific areas that use bioinformatics, as well as in ways to develop solutions tailored to the local needs of academic institutions or research groups. In this aim, the project “Bioinformática na Estrada” (Bioinformatics on the Road) proposed improving bioinformaticians’ skills in undergraduate and graduate courses, primarily in the countryside of the State of Pará, in the Amazon region of Brazil. The project scope is practical courses focused on the areas of interest of the place where the courses are occurring to train and encourage students and researchers to work in this field, reducing the existing gap due to the lack of qualified bioinformatics professionals. Theoretical and practical workshops took place, such as Introduction to Bioinformatics, Computer Science Basics, Applications of Computational Intelligence applied to Bioinformatics and Biotechnology, Computational Tools for Bioinformatics, Soil Genomics and Research Perspectives and Horizons in the Amazon Region. In the end, 444 undergraduate and graduate students from higher education institutions in the state of Pará and other Brazilian states attended the events of the Bioinformatics on the Road project.

Weed infestation is an essential factor in sugarcane productivity loss. The use of remote sensing data in conjunction with Artificial Intelligence (AI) techniques, can lead the cultivation of sugarcane to a new level in terms of weed control. For this purpose, an algorithm based on Convolutional Neural Networks (CNN) was developed to detect, quantify, and map weeds in sugarcane areas located in the state of Alagoas, Brazil. Images of the PlanetScope satellite were subdivided, separated, trained in different scenarios, classified and georeferenced, producing a map with weed information included. Scenario one of the CNN training and test presented overall accuracy (0,983), and it was used to produce the final mapping of forest areas, sugarcane, and weed infestation. The quantitative analysis of the area (ha) infested by weed indicated a high probability of a negative impact on sugarcane productivity. It is recommended that the adequacy of CNN’s algorithm for Remotely Piloted Aircraft (RPA) images be carried out, aiming at the differentiation between weed species, as well as its application in the detection in areas with different culture crops

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O pavimento pode ser definido com uma estrutura de múltiplas camadas semi-infinitas cuja função é transmitir os esforços causados pelo tráfego no terreno de fundação (subleito), além de proporcionar aos usuários boas condições de conforto e segurança. Sabemos que a morte no trânsito é responsável por gerar grandes despesas ao governo, desta forma, a segurança das estradas é crucial para o desenvolvimento do país. A textura superficial dos pavimentos condiciona diversos aspectos relacionados ao conforto e a segurança dos usuários sobretudo o efeito de aderência pneu/pavimento e hidroplanagem. O presente estudo avalia as principais características da macroestrutura do pavimento ao longo do traçado da Avenida NS-15 entre as avenidas Juscelino Kubitscheck e LO-08 e verifica se estes valores estão em concordância com as normas regulamentadoras ASTM E 965-96 e ASTM E 2380. Para tal, foram realizados os ensaios de Mancha de Areia e de Drenabilidade, de acordo os valores de referência no Brasil.