Universidade de Pernambuco

About: Universidade de Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Artificial neural network. The organization has 6147 authors who have published 6948 publications receiving 73648 citations.

Large Enhancement of Critical Current in Superconducting Devices by Gate Voltage

Abstract: Significant control over the properties of a high-carrier density superconductor via an applied electric field has been considered infeasible due to screening of the field over atomic length scales. Here, we demonstrate an enhancement of up to 30% in critical current in a back-gate tunable NbN micro- and nano superconducting bridges. Our suggested plausible mechanism of this enhancement in critical current based on surface nucleation and pinning of Abrikosov vortices is consistent with expectations and observations for type-II superconductor films with thicknesses comparable to their coherence length. Furthermore, we demonstrate an applied electric field-dependent infinite electroresistance and hysteretic resistance. Our work presents an electric field driven enhancement in the superconducting property in type-II superconductors which is a crucial step toward the understanding of field-effects on the fundamental properties of a superconductor and its exploitation for logic and memory applications in a superconductor-based low-dissipation digital computing paradigm.

Zika in Pernambuco: rewriting the first outbreak

Abstract: A Zika virus epidemic was registered in 2015 in Northeast Brazil In the State of Pernambuco, thousands of classical cases transpired, and in the following months, neurological disturbances in adults and microcephaly in newborns emerged as complications After the peak of the epidemic, the official system reported only four cases of Zika virus but over 100,000 cases of dengue virus The vigilance system was unable to retrospectively estimate cases or to issue an alert to officially notified cases with possible inconsistence concerning specific arbovirosis diagnoses

Prevalence of central obesity in a large sample of adolescents from public schools in Recife, Brazil

Abstract: OBJECTIVE: To determine the prevalence and association of central obesity (CO) and hypertension and its associations with alcohol intake, smoking and physical activity in adolescents. SUBJECTS AND METHODS: Cross sectional study in 1,824 students from 29 public schools in Recife. RESULTS: 89.6% were normal weight, 6.7% overweight and 3.7% obese; 77.2% were normotensive, 5.9% prehypertensive and 16.9% hypertensive; CO was 10.2% when the 90th percentile was used as cutoff and 25.2% when the 75th percentile was used. There was a higher likelihood of central obesity among students aged 18 to 20 years, smoking and alcohol intake. The probability of hypertension increases if the subject is male, has a waist circumference (WC) > 90, WC > 75 and does not practice physical activity. CONCLUSIONS: A high prevalence of CO and hypertension was found in adolescents. CO was more frequent in students aged 18 to 20 years, smokers and with alcohol intake and hypertension was associated with male, CO and no physical activity.

IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

Abstract: In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall and sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.