Icing mitigation by mems-fabricated surface dielectric barrier discharge

Avoiding ice accumulation on aerodynamic components is of enormous importance to flight safety. Novel approaches utilizing surface dielectric barrier discharges (SDBDs) are expected to be more efficient and effective than conventional solutions for preventing ice accretion on aerodynamic components. In this work, the realization of SDBDs based on thin-film substrates by means of micro-electro-mechanical-systems (MEMS) technology is presented. The anti-icing performance of the MEMS SDBDs is presented and compared to SDBDs manufactured by printed circuit board (PCB) technology. It was observed that the 35 µm thick electrodes of the PCB SDBDs favor surface icing with an initial accumulation of supercooled water droplets at the electrode impact edges. This effect was not observed for 0.3 µm thick MEMS-fabricated electrodes indicating a clear advantage for MEMS-technology SDBDs for anti-icing applications. Titanium was identified as the most suitable material for MEMS electrodes. In addition, an optimization of the MEMS-SDBDs with respect to the dielectric materials as well as SDBD design is discussed

Icing Mitigation by MEMS-Fabricated Surface Dielectric Barrier Discharge

Avoiding ice accumulation on aerodynamic components is of enormous importance to flight safety. Novel approaches utilizing surface dielectric barrier discharges (SDBDs) are expected to be more efficient and effective than conventional solutions for preventing ice accretion on aerodynamic components. In this work, the realization of SDBDs based on thin-film substrates by means of micro-electro-mechanical-systems (MEMS) technology is presented. The anti-icing performance of the MEMS SDBDs is presented and compared to SDBDs manufactured by printed circuit board (PCB) technology. It was observed that the 35 μm thick electrodes of the PCB SDBDs favor surface icing with an initial accumulation of supercooled water droplets at the electrode impact edges. This effect was not observed for 0.3 μm thick MEMS-fabricated electrodes indicating a clear advantage for MEMS-technology SDBDs for anti-icing applications. Titanium was identified as the most suitable material for MEMS electrodes. In addition, an optimization of the MEMS-SDBDs with respect to the dielectric materials as well as SDBD design is discussed

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

BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS 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. CLINICAL TRIAL REGISTRATION NCT01000701