Relative Contribution Of Parameters Research Articles

Simple Modeling and Analysis of Total Ionizing Dose Effects on Radio-Frequency Low-Noise Amplifiers

In this study, the degradation characteristics of radio frequency (RF)-low-noise amplifiers (LNA) due to a total ionizing dose (TID) is investigated. As a device-under-test (DUT), sample LNAs were prepared using silicon–germanium (SiGe) heterojunction bipolar transistors (HBTs) as core elements. The LNA was based on a cascode stage with emitter degeneration for narrowband applications. By using a simplified small-signal model of a SiGe HBT, design equations such as gain, impedance matching, and noise figure (NF) were derived for analyzing TID-induced degradations in the circuit-level performance. To study radiation effects in circuits, the SiGe-RF-LNAs fabricated in a commercial 350 nm SiGe technology were exposed to 10-keV X-rays to a total ionizing dose of up to 3 Mrad(SiO2). The TID-induced performance changes of the LNA were modeled by applying degradation to device parameters. In the modeling process, new parameter values after irradiation were estimated based on information in the literature, without direct measurements of SiGe HBTs used in the LNA chip. As a result, the relative contributions of parameters on the circuit metrics were compared, identifying dominant parameters for degradation modeling. For the TID effects on input matching (S11) and NF, the base resistance (RB) and the base-to-emitter capacitance (Cπ) of the input transistor were mostly responsible, whereas the transconductances (gm) played a key role in the output matching (S22) and gain (S21). To validate the proposed approach, it has been applied to a different LNA in the literature and the modeling results predicted the TID-induced degradations within reasonable ranges.

Prediction of atrial fibrillation by 12-lead electrocardiogram parameters in patients without structural heart disease

Abstract Background Recently, the analysis of electrocardiogram (ECG) waveform by artificial intelligence has been reported to pick out those who have atrial fibrillation (AF) or have a high potential of developing AF, which, however, cannot explain the mechanisms or algorisms for the prediction from its nature. Purpose The purpose of this study is to conduct a comprehensive analysis to investigate the difference of weighting in predicting capability for AF among hundreds of automatically-measured ECG parameters using a single ECG at sinus rhythm. Methods and results Out of Shinken Database 2010–2017 (n=19170), 12825 patients were extracted, where those with ECG showing AF rhythm at the initial visit (including all persistent/permanent AF and a part of paroxysmal AF) and those with structural heart diseases were excluded. Out of 639 automatically-measured ECG parameters in MUSE data management system (GE Healthcare, USA), 438 were used. [Analysis 1] A predicting model for paroxysmal AF were determined by logistic regression analysis (Total, n=12825; paroxysmal AF, n=1138), showing a high predictive capability (AUC = 0.780, p<0.001). In this model, the relative contribution of ECG parameters (by coefficient of determination) according to the time phase were P:72.4%, QRS:32.7%, and ST-T:13.7%, respectively (Figure A). [Analysis 2] Excluding AF at baseline, a predicting model for new-developed AF were determined by Cox regression analysis (Total, n=11687; new-developed AF, n=87), showing a high predictive capability (AUC = 0.887, p<0.001). In this model, the relative contribution of parameters (by log likelihood) according to the time phase were P:40.8%, QRS:42.5%, and ST-T:24.9%, respectively (Figure B). Conclusions We determined ECG parameters that potentially contribute to picking up existing AF or predicting future development of AF, where the measurement of P wave strongly contributed in the former whereas all time phases were similarly important in the latter. Weighting of parameters to predict AF Funding Acknowledgement Type of funding source: Private hospital(s). Main funding source(s): Self funding of the institute

On the ability of RegCM4 regional climate model to simulate surface solar radiation patterns over Europe: an assessment using satellite-based observations

Abstract. In this work, we assess the ability of RegCM4 regional climate model to simulate surface solar radiation (SSR) patterns over Europe. A decadal RegCM4 run (2000–2009) was implemented and evaluated against satellite-based observations from the Satellite Application Facility on Climate Monitoring (CM SAF), showing that the model simulates adequately the SSR patterns over the region. The SSR bias between RegCM4 and CM SAF is +1.5 % for MFG (Meteosat First Generation) and +3.3 % for MSG (Meteosat Second Generation) observations. The relative contribution of parameters that determine the transmission of solar radiation within the atmosphere to the deviation appearing between RegCM4 and CM SAF SSR is also examined. Cloud macrophysical and microphysical properties such as cloud fractional cover (CFC), cloud optical thickness (COT) and cloud effective radius (Re) from RegCM4 are evaluated against data from CM SAF. Generally, RegCM4 underestimates CFC by 24.3 % and Re for liquid/ice clouds by 36.1 %/28.3 % and overestimates COT by 4.3 %. The same procedure is repeated for aerosol optical properties such as aerosol optical depth (AOD), asymmetry factor (ASY) and single-scattering albedo (SSA), as well as other parameters, including surface broadband albedo (ALB) and water vapor amount (WV), using data from MACv1 aerosol climatology, from CERES satellite sensors and from ERA-Interim reanalysis. It is shown here that the good agreement between RegCM4 and satellite-based SSR observations can be partially attributed to counteracting effects among the above mentioned parameters. The potential contribution of each parameter to the RegCM4–CM SAF SSR deviations is estimated with the combined use of the aforementioned data and a radiative transfer model (SBDART). CFC, COT and AOD are the major determinants of these deviations on a monthly basis; however, the other parameters also play an important role for specific regions and seasons. Overall, for the European domain, CFC, COT and AOD are the most important factors, since their underestimations and overestimations by RegCM4 cause an annual RegCM4–CM SAF SSR absolute deviation of 8.4, 3.8 and 4.5 %, respectively.