Open Access Days 2018 – “Varieties of Open Access” (Graz, September 24–26, 2018)

Vom 24. bis 26. September 2018 fanden die Open-Access-Tage in Graz statt. Gastgeber für die 12. Ausgabe der Konferenz war die Technische Universität Graz. Die Open-Access-Tage haben sich in den letzten Jahren als fixer Termin für alle an Open Access Interessierte im deutschsprachigen Raum etabliert. Das diesjährige Thema der Konferenz war „Vielfalt von Open Access“. Die Konferenz hatte ein dementsprechend vielfältiges Programm und über 300 Expertinnen und Experten im Bereich Open Access nutzten die Chance spannende Vorträge zu hören, neue Entwicklungen zu erfahren, an kreativen Workshops teilzunehmen und sich mit KollegInnen auszutauschen. Zahlreiche Vortragende aus dem In- und Ausland spannten den inhaltlichen Bogen von Open Educational Resources bis zu Open Science.  The Open Access Days took place in Graz from September 24 to 26, 2018. The Graz University of Technology hosted the 12th edition of the conference. The Open Access Days have become an established event for all those interested in Open Access in German-speaking countries. This year’s theme of the conference was “Varieties of Open Access”. The conference had a correspondingly varied programme and more than 300 experts in the field of Open Access took the opportunity to listen to exciting lectures, learn about new developments, take part in creative workshops and exchange ideas with colleagues. Numerous speakers from Germany and abroad spanned the spectrum from Open Educational Resources to Open Science

Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson's Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54\% to 86.88\%.Comment: ICMI2017 (oral session

Electro-Chemical Modelling of Laser Structured Electrodes

A simulation study performed in the scope of the project RealLi! is presented. One of the project’s main goals is to improve NMC811 and graphite electrode cycling capacities at high C-rates. The rapid charging and discharging capability of batteries is improved using laser ablation to introduce structures into the surface of the electrode composite layers. Due to improved transport kinetics, this not only improves the electrochemical properties in the high-current range, but also homogenizes and accelerates the electrolyte wetting during production as a side effect. This is particularly advantageous in thick-film electrodes for providing high energy densities. This study supports the laser structuring process of battery electrodes [1][2] via a virtual optimisation, based on electro-chemical battery models. The electrodes are structured by ultrafast laser ablation, with parallel channels being introduced along the electrode surface. This modification enables an easier electrolyte penetration, a reduced charge transfer resistance, and shortened lithium-ion transport pathways which finally leads to a reduced diffusion overpotential at high C-rates. The geometrical parameters of this process (pitch distance, width, and cross-sectional shape of laser-generated micro-channels) and their impact on cell performance are virtually optimised by simulations. The simulations are based on a homogenised multi-scale model, applied in 2D/3D macroscopic cuts, coupled with 1D microscopic particle cuts. The 2D/3D macroscopic electrolyte transport equations are common concentrated electrolyte equations. The microscopic particle transport equations are either a set of non-linear Fick’s Diffusion equations [3] that are used to describe spherical symmetric NMC811 materials or a set of Cahn-Hilliard equations [4] that consistently describe the phase separating nature of graphite anodes in cylindrically symmetric particles. The underlying numerical method is an implicit-multi-scale finite-element-method [3] that allows for a flexible implementation of such models. The first results of this ongoing project will be presented along with the overall structure of the method and its implementation. The results include geometrical as well as electro-chemical parameter variations and their respective sensitivity analysis. Furthermore, in the discussed electrode geometry the possible anisotropic structure of an electrode (due to particle shape and distribution) has a bigger impact than in unstructured electrodes. The improved transport pathways along the channels, therefore, imply the necessity of a more thorough homogenisation than it is usually done, for example in a Newman-Model approach. A long-term goal of this work is to enable a significant increase in areal energy density, i.e., the use of thicker electrode films and the use of advanced high energy materials in battery electrodes. [1]3D silicon/graphite composite electrodes for high-energy lithium-ion batteries, W. Pfleging et.al., Electrochimica Acta, Volume 317, 2019, Pages 502-508, J Power Sources 145 (5), 2345-2356 [2]Recent progress in laser texturing of battery materials: a review of tuning electrochemical performances, related material development, and prospects for large-scale manufacturing,W. Pfleging,International Journal of Extreme Manufacturing, Vol 3, 2020 [3]Derivation of a multi-scale battery model and its high-performance computing implementation, F. Pichler, Doctoral Thesis, Graz, 2018 [4]Phase Transformation Dynamics in Porous Battery Electrodes, R. Ferguson, M. Z. Bazant, Electrochimica Acta, Volume 146, Pages 89-97, 2014 Figure

Modelling and Optimisation of Laser-Structured Battery Electrodes

An electrochemical multi-scale model framework for the simulation of arbitrarily three-dimensional structured electrodes for lithium-ion batteries is presented. For the parameterisation, the electrodes are structured via laser ablation, and the model is fit to four different, experimentally electrochemically tested cells. The parameterised model is used to optimise the parameters of three different pattern designs, namely linear, gridwise, and pinhole geometries. The simulations are performed via a finite element implementation in two and three dimensions. The presented model is well suited to depict the experimental cells, and the virtual optimisation delivers optimal geometrical parameters for different C-rates based on the respective discharge capacities. These virtually optimised cells will help in the reduction of prototyping cost and speed up production process parameterisation

The AST/ALT (De-Ritis) ratio: a novel marker for critical limb ischemia in peripheral arterial occlusive disease patients

The aspartat aminotransferase (AST)/alanin aminotransferase (ALT) (De-Ritis) ratio (AAR) is an easily applicable blood test. An elevated AAR on the one hand has been associated with an increase in nonalcoholic fatty liver disease (NAFLD). NAFLD on the other hand is associated with an increase in cardiovascular disease, all-cause mortality, and diabetes. As the AAR is also elevated in case of muscular damage, we investigated AAR and its association with critical limb ischemia (CLI) in peripheral arterial occlusive disease (PAOD) patients. In our cross-sectional study, we included 1782 PAOD patients treated at our institution from 2005 to 2010. Patients with chronic alcohol consumption (>20 g/day) were excluded. AAR was calculated and the cohort was categorized into tertiles according to the AAR. An optimal cut-off value for the continuous AAR was calculated by applying a receiver operating curve analysis to discriminate between CLI and non-CLI. In our cohort, occurrence of CLI significantly increased with an elevation in AAR. As an optimal cut-off value, an AAR of 1.67 (sensitivity 34.1%, specificity 81.0%) was identified. Two groups were categorized, 1st group containing 1385 patients (AAR < 1.67) and a 2nd group with 397 patients (AAR > 1.67). CLI was more frequent in AAR > 1.67 patients (166 [41.9%]) compared to AAR < 1.67 patients (329 [23.8%]) (P < 0.001), as was prior myocardial infarction (28 [7.1%] vs 54 [3.9%], P = 0.01). Regarding inflammatory parameters, C-reactive protein (median 8.1 mg/L [2.9–28.23] vs median 4.3 mg/L [2.0–11.5]) and fibrinogen (median 427.5 mg/dL [344.25–530.0] vs 388.0 mg/dL [327.0–493.0]) also significantly differed in the 2 patient groups (both P < 0.001). Finally, an AAR > 1.67 was associated with an odds ratio (OR) of 2.0 (95% confidence interval [CI] 1.7–2.3) for CLI even after adjustment for other well-established vascular risk factors. An increased AAR is significantly associated with patients at high risk for CLI and other cardiovascular endpoints. The AAR is a broadly available and cheap marker, which might be useful to highlight patients at high risk for vascular endpoints