Background:In children, the complications of severe acute respiratory syndrome coronavirus 2 infection occur less frequently than in adults but the characteristics of this disease in oncology patients are not well characterized. Methods: This was a retrospective study in patients younger than 18 years of age with coronavirus disease 2019 (COVID-19) and cancer diagnoses between April and September 2020. Demographic variables, laboratory, and radiologic findings and complications of each case were identified. A descriptive analysis was performed. Results: A total of 33 patients were identified; the median age was 10 years. Fifteen patients (42%) were in chemotherapy at the time of the infection diagnosis, in two patients the chemotherapy protocol was permanently suspended. The most common symptom was fever in 20 patients (60%). Seven patients (21.2%) showed mild pneumonia, four patients (12.1%) severe pneumonia, and three cases (9.0%) were classified as critical. In the evaluated cohort, five patients (15.1%) died, and in two of those, death was caused by COVID-19 infection. Conclusions: Children with an oncologic disease, the search for COVID cases should be oriented to patients with fever, including febrile neutropenia, the presence of respiratory symptoms, and the search for epidemiologic contact. A higher frequency of complications and mortality attributed to COVID-19, two in pediatric oncohematologic patients was found. Institutional strategies to detect the infection early and lower institutional infection are indicated.

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In this work, vibration analysis and Gaussian Processes techniques are used in useful life prognostics of ball bearings. The database is provided by The Prognostics Data Repository from NASA, and shows the failure evolution in ball bearings. The data basis also provides training and validation data sets for ball bearing useful life prediction. Several time and frequency characteristics are extracted from ball bearing vibration signals for trending analysis, and finally one of these is taken as input for the Gaussian process and describe, with a probabilistic strategy, the failure evolution system. No dimensionality reduction algorithm is used in this paper, only the evaluation of trends in failure evolution is taken for decision. This data basis was used in 2012 IEEE classification contest. Several participants used classification techniques based on time-frequency transformation and Artificial Intelligence algorithms but none of them used Gaussian Processes in a classification scheme. Although, the present work does not have the best results in classification it does show a major simplicity in formulation and implementation than most of the classification schemes.

An estimation method of the combustion chamber pressure, in an internal combustion engine, based on the processing of the vibration (acceleration) signal of the cylinder head, at constant speed and no load conditions is presented in this paper. The model is created based on the comparison of the vibration and pressure signals around the peaks of highest vibration, after a preprocessing and filtering of the signal using the most similar frequency bands between the sources. A polynomial regression is used between the selected data points to generate the resulting model relating pressure and vibration (and average rotational speed per cycle, calculated based on the vibration peaks). The model is tested with measurements from two spark ignited engine test benches: a single cylinder engine and a four-cylinder engine. The resulting model has very low computational cost and can provide a very accurate estimation of general shape and magnitude of the pressure trace, but does not reflect strongly cycle by cycle variations. Testing the Normalized Root Mean Square Error (NRMSE), where the best value is 100 % the single cylinder engine scores were 63.52 % and 20.02 % for the points before and after the vibration peak. For the four-cylinder engine those values were: 82.47 % and 28.27 % respectively.