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Background Controlling the COVID-19 outbreak in Brazil is a challenge due to the population’s size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. Objective The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. Methods Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models’ performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. Results Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. Conclusions The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

Resumo-O aproveitamento da ortogonalidade entre linhas de uma matriz wavelet tem sido explorada para obter sistemas de comunicac ¸ão sem fio que apresentam desempenho comparável, e em alguns casos superior, aos que utilizam códigos espáciotemporais. Entretanto, uma limitac ¸ão dos sistemas wavelet propostos até o momento é a eficiência espectral (EE) que fica limitada a 1 bit/s/Hz. A combinac ¸ão de símbolos wavelet em um símbolo do canal pode aumentar a eficiência espectral desses sistemas, e neste trabalho propomos o uso da func ¸ão geradora de momentos para, a partir de uma matriz de codificac ¸ão, obter as sequências de símbolos wavelet e suas probabilidades conjuntas, e através de um exemplo, mostra-se que a combinac ¸ão de pares de símbolos wavelet permite aumentar a EE para 2 bits/s/Hz. O método proposto é geral e permite determinar, para uma dada matriz de codificac ¸ão wavelet quais sequências de tamanho N podem ser obtidas, o que abre a possibilidade de realizar combinac ¸ões de sequência de símbolos wavelet para obter sistemas com eficiência espectral maiores.Palavras-Chave-Wavelet, codificac ¸ão, eficiência espectral, func ¸ão geradora, momentos.

Context. The measurement of diffuse 21-cm radiation from the hyperfine transition of neutral hydrogen (Hi signal) in different redshifts is an important tool for modern cosmology. However, detecting this faint signal with non-cryogenic receivers in single-dish telescopes is a challenging task. The BINGO (Baryon Acoustic Oscillations from Integrated Neutral Gas Observations) radio telescope is an instrument designed to detect baryonic acoustic oscillations (BAOs) in the cosmological Hi signal, in the redshift interval 0.127 ≤ z ≤ 0.449. Aims. This paper describes the BINGO radio telescope, including the current status of the optics, receiver, observational strategy, calibration, and the site. Methods. BINGO has been carefully designed to minimize systematics, being a transit instrument with no moving dishes and 28 horns operating in the frequency range 980 ≤ ν ≤ 1260 MHz. Comprehensive laboratory tests were conducted for many of the BINGO subsystems and the prototypes of the receiver chain, horn, polarizer, magic tees, and transitions have been successfully tested between 2018-2020. The survey was designed to cover ∼ 13% of the sky, with the primary mirror pointing at declination δ = −15 • . The telescope will see an instantaneous declination strip of 14.75 • . Results. The results of the prototype tests closely meet those obtained during the modeling process, suggesting BINGO will perform according to our expectations. After one year of observations with a 60% duty cycle and 28 horns, BINGO should achieve an expected sensitivity of 102 µK per 9.33 MHz frequency channel, one polarization, and be able to measure the Hi power spectrum in a competitive time frame.

Placed at the core of Smart Grids metering infrastructure are the Smart Meters, devices that not only measure electric energy consumption of a customer but also report data back to the utility. This process effectivelly sets a stage for a two-way communications link between meters and the utility, which may be used to improve not only the planning but also the operation of the grid. Data submitted goes through the Advanced Metering Infrastructure and is finally delivered to the utility at the edge of the topology. Once at the utility, the streamed data must be processed for billing and monitoring of the grid. Though, given the large volume of the data (e.g. billions of meters), usual processing may be costly both in time and resources, fact that motivates a search for lower complexity processing. In this paper we apply dimensionality reduction via random projection to obtain a reduced version (sketch) of Meters' original data, thus increasing the processing throughput of the utility. Using real smart meters measurements, we show that processing using sketches sized 50% smaller than original data can achieve a 2% average relative error while presenting greater data rates.