Influence of dielectric thickness and electrode structure on the ion wind generation by micro fabricated plasma actuators

Surface dielectric barrier discharges are investigated in order to explore the combined effects of barrier thickness and microstructure of the exposed electrode on the ion wind generation. Actuators with straight and structured high voltage electrodes with characteristic sizes of 200 and 250 µm and dielectric thicknesses of 0.5, 1 and 2 mm are compared. It is observed that: i) actuator efficiency of ion wind generation strongly depends on the applied voltage amplitude; ii) operation voltage depends on the dielectric thickness logarithmically; iii) electrode microstructure slightly increases the dynamic pressure (few percent in maximum), however the effect decreases with thicker dielectrics and smaller electrode structures; iv) the pattern of the most intensive discharge parts as well as the dielectric erosion repeats the regular structure of the electrodes down to 200 µm. Several identical samples are tested during different days to estimate the impact of the air humidity and the degradation of the dielectric. The microscale precision of the sample manufacture was accomplished by a commercial facility for printed circuit boards. © 2020 The Author(s). Published by IOP Publishing Ltd

Influence of dielectric thickness and electrode structure on the ion wind generation by micro fabricated plasma actuators

Abstract Surface dielectric barrier discharges are investigated in order to explore the combined effects of barrier thickness and microstructure of the exposed electrode on the ion wind generation. Actuators with straight and structured high voltage electrodes with characteristic sizes of 200 and 250 µm and dielectric thicknesses of 0.5, 1 and 2 mm are compared. It is observed that: i) actuator efficiency of ion wind generation strongly depends on the applied voltage amplitude; ii) operation voltage depends on the dielectric thickness logarithmically; iii) electrode microstructure slightly increases the dynamic pressure (few percent in maximum), however the effect decreases with thicker dielectrics and smaller electrode structures; iv) the pattern of the most intensive discharge parts as well as the dielectric erosion repeats the regular structure of the electrodes down to 200 µm. Several identical samples are tested during different days to estimate the impact of the air humidity and the degradation of the dielectric. The microscale precision of the sample manufacture was accomplished by a commercial facility for printed circuit boards

Data Mining the Brain to Decode the Mind

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience

Improving 1-year mortality prediction in ACS patients using machine learning.

The Global Registry of Acute Coronary Events (GRACE) score is an established clinical risk stratification tool for patients with acute coronary syndromes (ACS). We developed and internally validated a model for 1-year all-cause mortality prediction in ACS patients. Between 2009 and 2012, 2'168 ACS patients were enrolled into the Swiss SPUM-ACS Cohort. Biomarkers were determined in 1'892 patients and follow-up was achieved in 95.8% of patients. 1-year all-cause mortality was 4.3% (n = 80). In our analysis we consider all linear models using combinations of 8 out of 56 variables to predict 1-year all-cause mortality and to derive a variable ranking. 1.3% of 1'420'494'075 models outperformed the GRACE 2.0 Score. The SPUM-ACS Score includes age, plasma glucose, NT-proBNP, left ventricular ejection fraction (LVEF), Killip class, history of peripheral artery disease (PAD), malignancy, and cardio-pulmonary resuscitation. For predicting 1-year mortality after ACS, the SPUM-ACS Score outperformed the GRACE 2.0 Score which achieves a 5-fold cross-validated AUC of 0.81 (95% CI 0.78-0.84). Ranking individual features according to their importance across all multivariate models revealed age, trimethylamine N-oxide, creatinine, history of PAD or malignancy, LVEF, and haemoglobin as the most relevant variables for predicting 1-year mortality. The variable ranking and the selection for the SPUM-ACS Score highlight the relevance of age, markers of heart failure, and comorbidities for prediction of all-cause death. Before application, this score needs to be externally validated and refined in larger cohorts. NCT01000701