Abstract
Disentangling innovation from growth is unrealistic in the present times. Also, anticipating the future behavior of innovative firms is relevant to the entire innovation ecosystem; and assessing the persistence of innovation and appraising the role of factors affecting ongoing innovation activities in firms is essential. This chapter discusses a very important subject related to the concept of innovation persistence in relation to structural innovation characteristics of firms, with a focus on technological regimes, to better understand if there is change in innivation continuity accordingly to the technological intensity embedded in the sector. The empirical research is based on data from CIS database, comprising 3237 firms which present in the 2014 and 2018 waves. We analyze the innovative persistence behavior of these firms regarding proxies like firm dimension, innovation activities, types of innovation, government funding, and more importantly, technological regimes. To do this, we applied binary logistic regression for developing a model which can forecast the drivers of innovation persistency propensity. The presented study shows that some very important results are achieved. Besides demonstrating innovative persistency in 75% of science-based firms, the findings confirm that firms in high-tech and science-based industries are more prone to continue innovating and, as a result, this consistency in innovation will generate virtuous cycles of innovation. Furthermore, our data shows that complex innovators are more likely to persist than single innovators, proving the existence of complementarities among the innovation types.

Abstract
Pulse oximeters are medical devices used to assess peripheral arterial oxygen saturation (SpO(2)) noninvasively. In contrast, the gold standard requires arterial blood to be drawn to measure the arterial oxygen saturation (SaO(2)). Devices currently on the market measure SpO(2) with lower accuracy in populations with darker skin tones. Pulse oximetry inaccuracies can yield episodes of hidden hypoxemia (HH), with SpO(2) >= 88%, but SaO(2) < 88%. HH can result in less treatment and increased mortality. Despite being flawed, pulse oximeters remain ubiquitously used; debiasing models could alleviate the downstream repercussions of HH. To our knowledge, this is the first study to propose such models. Experiments were conducted using the MIMIC-IV dataset. The cohort includes patients admitted to the Intensive Care Unit with paired (SaO(2), SpO(2)) measurements captured within 10min of each other. We built a XGBoost regression predicting SaO(2) from SpO(2), patient demographics, physiological data, and treatment information. We used an asymmetric mean squared error as the loss function to minimize falsely elevated predicted values. The model achieved R-2 = 67.6% among Black patients; frequency of HH episodes was partially mitigated. Respiratory function was most predictive of SaO(2); race-ethnicity was not a top predictor. This singlecenter study shows that SpO(2) corrections can be achieved with Machine Learning. In future, model validation will be performed on additional patient cohorts featuring diverse settings.

Abstract
Silicon carbide (SiC) switching devices have an enormous influence on power electronic systems, entitled to extraordinary outcomes attained in low switching and conduction losses. The research work exploration is to develop and analyze the interleaved SiC full-bridge converter. It is subjected to analyze the performance of silicon carbide (SiC) module-based converter design which can offer a power of 42kW. Power conversion is done between DC/AC by using a standard 1200V single-phase SiC module from Semikron [1]. The SiC-MOSFETs are controlled by an adequate galvanically isolated gate driver circuit. Several gate drivers' functionalities are added in the converter design for optimized performance and safe operation. The features include split turn-on/turn-off outputs, desaturation and active miller-clamp. The DC-link capacitors are designed to cancel the input ripple current and stabilizing the source voltage. The interleaving (180° phase-shift between the legs) helps to reduce ripple currents both, in the input capacitor as well as at the output. At the output coupled inductors are providing suppression of transverse currents between the interleaved legs. The coupled inductors help to reduce the size of the filter in the case of DC-DC or DC-AC grid-tie inverters. © 2023 IEEE.

Abstract

Abstract
&lt;p&gt;A novel ionisation detector, previously deployed on meteorological radiosonde flights, has demonstrated responsivity to X-rays and gamma radiation, and additionally, is thought to be sensitive to ionising radiation from cosmic rays. The PiN detector, composed of a 1x1x0.8 cm&lt;sup&gt;3 &lt;/sup&gt;CsI(Tl) microscintillator coupled to a PiN photodiode, was deployed on the NRP Sagres sailing vessel on a cruise in the Atlantic between Portugal and the Azores in 2021. The instrument can determine both the count rate and energy of incoming ionising radiation particles.&lt;/p&gt;&lt;p&gt;The instrument was operational during the voyage in November 2021 when a coronal mass ejection event induced a sudden decrease in the observed cosmic ray intensity, known as a Forbush decrease. We present data recorded by the ionisation detector during this period, to characterise the instrument&amp;#8217;s ability to detect cosmic ray events, and we compare the performance with neutron monitoring stations Oulu in Finland, and Dourbes in Belgium. As the PiN detector provides spectral and count rate data, it is possible to group events by their energy, and investigate the count rates of specific energy regimes. This approach is useful as many sources &amp;#8211; including high and low energy ionising radiation from cosmic rays &amp;#8211; contribute to the background energy spectrum. As a result, more meaningful comparisons and relationships can be established with the neutron monitoring stations.&lt;/p&gt;

Abstract