A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components.

This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as 'recognition of degradation patterns in the database' and 'degradation prediction of new samples without any kind of irradiation'. The technique can be used under two different approaches called 'pure data driven' and 'model based'. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component.

[Prevalence of SARS-CoV-2 coronavirus infection in patients and professional staff at a medium or long-stay hospital in Spain]. / Prevalencia de infección por coronavirus SARS-CoV-2 en pacientes y profesionales de un hospital de media y larga estancia en España.

BACKGROUND AND GOALS: The aim of the study is to know the prevalence of SARS-CoV-2 infection in patients and professional staff of a medium or long-stay hospital during the peak period of the pandemic in Spain, spring 2020. MATERIAL AND METHODS: At the end of February 2020, we developed at the hospital a strategy to diagnose the SARS-CoV-2 infection consisting of complementing the realization of PCR tests at real time with a quick technique of lateral flow immunochromatography to detect IgG and IgM antibodies against the virus. We also developed a protocol to realize those diagnostic tests and considered an infection (current or past) a positive result in any of the above tests. We included 524 participants in the study (230 patients and 294 hospital staff), and divided them into hospital patients and Hemodialysis outpatients. Furthermore, we divided the hospital staff into healthcare and non-healthcare staff. The documented period was from March, 20th to April, 21st, 2020. RESULTS: 26 out of 230 patients tested positive in any of the diagnostic techniques (PCR, antibodies IgG, IgM) with a 11.30% prevalence. According to patients groups, we got a 14.38% prevalence in hospital patients vs. 5.95% in outpatients, with a significantly higher risk in admitted patients after adjustment for age and gender (OR=3,309, 95%CI: 1,154-9,495). 24 out of 294 hospital staff tested positive in any of the diagnostic techniques, with a 8.16% prevalence. According to the groups, we got a 8.91% prevalence in healthcare staff vs. 4.26% in non-healthcare staff. Thus, we do not see any statistically significant differences between hospital staff and patients as far as prevalence is concerned (P=0,391), (OR=2,200, 95%CI: 0,500-9,689). CONCLUSIONS: The result of the study was a quite low prevalence rate of SARS-CoV-2 infection, in both patients and hospital staff, being the hospital patients' prevalence rate higher than the outpatients', and the healthcare staff higher than the non-healthcare's. Combining PCR tests (gold standard) with antibodies tests proved useful as a diagnostic strategy.

Prevalencia de infección por coronavirus SARS-CoV-2 en pacientes y profesionales de un hospital de media y larga estancia en España / Prevalence of SARS-CoV-2 coronavirus infection in patients and professional staff at a medium or long-stay hospital in Spain

ANTECEDENTES Y OBJETIVO: El objetivo de este estudio fue conocer la prevalencia de la infección por SARS-CoV-2 en pacientes y profesionales de un hospital de media y larga estancia en el periodo del pico de la pandemia en España en la primavera de 2020. MATERIAL Y MÉTODOS: A finales de febrero del 2020, se diseñó en el hospital una estrategia para el diagnóstico de la infección por SARS-CoV-2 consistente en complementar la realización de PCR a tiempo real con una técnica rápida de inmunocromatografía de flujo lateral para la detección de anticuerpos IgG e IgM frente al virus. Se protocolizó la realización de dichas pruebas diagnósticas y se consideró como infección (actual o pasada) un resultado positivo de alguna de ellas. Se incluyeron en el estudio a 524 participantes (230 pacientes y 294 profesionales). Los pacientes se agruparon en ingresados y en ambulatorios para terapia de hemodiálisis. Los trabajadores se agruparon en asistenciales y no asistenciales. El periodo que se documenta es el comprendido entre el 20 de marzo y el 21 de abril del 2020. RESULTADOS: En 26 de los 230 pacientes el resultado fue positivo en alguna de las técnicas, con una prevalencia del 11,30%. Por grupos, en ingresados fue del 14,38% frente al 5,95% de los ambulatorios (p = 0,055), siendo significativamente superior el riesgo en pacientes ingresados tras ajustar por sexo y edad (OR = 3,309; IC del 95%: 1,154-9,495). En 24 de los 294 profesionales el resultado fue positivo en alguna de las técnicas, con una prevalencia del 8,16%. Por grupos, en asistenciales fue del 8,91% frente al 4,26% de los no asistenciales (p = 0,391), OR ajustada = 2,502 (IC del 95%: 0,559-11,202). CONCLUSIONES: Se ha encontrado una tasa de prevalencia baja frente a SARS-CoV-2 tanto en pacientes como en profesionales. La prevalencia en pacientes hospitalizados es mayor que en ambulatorios, también es superior la prevalencia de sanitarios asistenciales respecto a los no asistenciales

BACKGROUND AND GOALS: The aim of the study is to know the prevalence of SARS-CoV-2 infection in patients and professional staff of a medium or long-stay hospital during the peak period of the pandemic in Spain, spring 2020. MATERIAL AND METHODS: At the end of February 2020, we developed at the hospital a strategy to diagnose the SARS-CoV-2 infection consisting of complementing the realization of PCR tests at real time with a quick technique of lateral flow immunochromatography to detect IgG and IgM antibodies against the virus. We also developed a protocol to realize those diagnostic tests and considered an infection (current or past) a positive result in any of the above tests. We included 524 participants in the study (230 patients and 294 hospital staff), and divided them into hospital patients and Hemodialysis outpatients. Furthermore, we divided the hospital staff into healthcare and non-healthcare staff. The documented period was from March, 20th to April, 21st, 2020. RESULTS: 26 out of 230 patients tested positive in any of the diagnostic techniques (PCR, antibodies IgG, IgM) with a 11.30% prevalence. According to patients groups, we got a 14.38% prevalence in hospital patients vs. 5.95% in outpatients, with a significantly higher risk in admitted patients after adjustment for age and gender (OR=3,309, 95%CI: 1,154-9,495). 24 out of 294 hospital staff tested positive in any of the diagnostic techniques, with a 8.16% prevalence. According to the groups, we got a 8.91% prevalence in healthcare staff vs. 4.26% in non-healthcare staff. Thus, we do not see any statistically significant differences between hospital staff and patients as far as prevalence is concerned (P=0,391), (OR=2,200, 95%CI: 0,500-9,689). CONCLUSIONS: The result of the study was a quite low prevalence rate of SARS-CoV-2 infection, in both patients and hospital staff, being the hospital patients' prevalence rate higher than the outpatients', and the healthcare staff higher than the non-healthcare's. Combining PCR tests (gold standard) with antibodies tests proved useful as a diagnostic strategy

Impact of Gamma Radiation on Dynamic RDSON Characteristics in AlGaN/GaN Power HEMTs.

GaN high-electron-mobility transistors (HEMTs) are promising next-generation devices in the power electronics field which can coexist with silicon semiconductors, mainly in some radiation-intensive environments, such as power space converters, where high frequencies and voltages are also needed. Its wide band gap (WBG), large breakdown electric field, and thermal stability improve actual silicon performances. However, at the moment, GaN HEMT technology suffers from some reliability issues, one of the more relevant of which is the dynamic on-state resistance (RON_dyn) regarding power switching converter applications. In this study, we focused on the drain-to-source on-resistance (RDSON) characteristics under 60Co gamma radiation of two different commercial power GaN HEMT structures. Different bias conditions were applied to both structures during irradiation and some static measurements, such as threshold voltage and leakage currents, were performed. Additionally, dynamic resistance was measured to obtain practical information about device trapping under radiation during switching mode, and how trapping in the device is affected by gamma radiation. The experimental results showed a high dependence on the HEMT structure and the bias condition applied during irradiation. Specifically, a free current collapse structure showed great stability until 3.7 Mrad(Si), unlike the other structure tested, which showed high degradation of the parameters measured. The changes were demonstrated to be due to trapping effects generated or enhanced by gamma radiation. These new results obtained about RON_dyn will help elucidate trap behaviors in switching transistors.

On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation.

The effects of ionizing radiation on field-programmable gate arrays (FPGAs) have been investigated in depth during the last decades. The impact of these effects is typically evaluated on implementations which have a deterministic behavior. In this article, two well-known true-random number generators (TRNGs) based on sampling jittery signals have been exposed to a Co-60 radiation source as in the standard tests for space conditions. The effects of the accumulated dose on these TRNGs, an in particular, its repercussion over their randomness quality (e.g., entropy or linear complexity), have been evaluated by using two National Institute of Standards and Technology (NIST) statistical test suites. The obtained results clearly show how the degradation of the statistical properties of these TRNGs increases with the accumulated dose. It is also notable that the deterioration of the TRNG (non-deterministic component) appears before that the degradation of the deterministic elements in the FPGA, which compromises the integrated circuit lifetime.

Rotura de la arteria renal en un posoperatorio complicado de angioplastia por estenosis renal / Renal artery rupture during complicated recovery from angioplasty to treat renal stenosis

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