TATRAS.at - Tax and Transfer Simulator for Austria: eine Mikrosimulationsplattform zu Reformen der bundesweiten Steuer- und Transferregelungen

Jedes Steuer-, und Transfersystem ist nur so gut wie seine Adaptierungsfähigkeit an die sich laufend ändernden Rahmenbedingungen. Dieser Überzeugung folgend wurde am Österreichischen Institut für Familienforschung an der Universität Wien die Mikrosimulationsplattform TATRAS.at entwickelt. Mit TATRAS.at können Umverteilungseffekte von Reformen in den Bereichen Einkommens- und Lohnsteuer, der Sozialversicherungsabgaben, der Lohnnebenkosten sowie der bundesweit gültigen Familientransfers vorab simuliert werden

AutoML for Log File Analysis (ALFA) in a Production Line System of Systems pointed towards Predictive Maintenance

Automated machine learning and predictive maintenance have both become prominent terms in recent years. Combining these two fields of research by conducting log analysis using automated machine learning techniques to fuel predictive maintenance algorithms holds multiple advantages, especially when applied in a production line setting. This approach can be used for multiple applications in the industry, e.g., in semiconductor, automotive, metal, and many other industrial applications to improve the maintenance and production costs and quality. In this paper, we investigate the possibility to create a predictive maintenance framework using only easily available log data based on a neural network framework for predictive maintenance tasks. We outline the advantages of the ALFA (AutoML for Log File Analysis) approach, which are high efficiency in combination with a low entry border for novices, among others. In a production line setting, one would also be able to cope with concept drift and even with data of a new quality in a gradual manner. In the presented production line context, we also show the superior performance of multiple neural networks over a comprehensive neural network in practice. The proposed software architecture allows not only for the automated adaption to concept drift and even data of new quality but also gives access to the current performance of the used neural networks

Mental disorders are no predictors to determine the duration of cannabis-based treatment for chronic pain

BackgroundChronic pain (CP), a complex biopsychosocial disorder with a global prevalence of up to 33%, can be treated by following multidisciplinary approaches that may include cannabis-based medicine (CBM). However, because CBM continues to be a new treatment, questions remain regarding the ideal duration for CBM and its psychosocial determinants, including mental comorbidities.MethodsIn a retrospective cross-sectional study involving 46 patients with CP (ICD-10 code F45.4-), three validated instruments—the German Pain Questionnaire, the Depression Anxiety Stress Scale (DASS), and the Marburg Questionnaire of Habitual WellBeing—were used to identify pain-specific psychosocial determinants and mental disorders. Descriptive analyses, a group differences analysis, and a logistic regression analysis were performed using SPSS.ResultsThe patients most frequently reported low back pain as the primary location of their CP, and in attributing the condition to tissue damage, most had largely adopted a somatic orientation in conceptualizing their illness. Most had experienced CP for more than 5 years (M = 5.13 years, SD = 1.41) and, as a consequence, faced significant restrictions in their everyday life and exhibited low subjective wellbeing (MFHW median = 4.00, N = 43, Q1: 2.00, Q3: 9.00, range: 0–20). Comorbidities among the patients included depression, (DASS-Depression, median: 11.50, Q1: 7.00, Q3: 16.25), anxiety (DASS-Anxiety, median: 4.50, Q1: 2.75, Q3: 8.00), and stress (DASS-Stress, median: 11.00, Q1: 7.00, Q3: 15.00). Between the two cannabis-based treatments with a course lasting either less or more than a year, the duration of treatment showed no between-group differences in terms of sociodemographic factors, pain-specific factors, conceptualizations of the illness, or mental disorders. Psychosocial determinants such as subjective wellbeing and mental comorbidities were not significant predictors of the duration of cannabis-based treatment.ConclusionWe found no evidence indicating that the benefits of short-term vs. long-term cannabis-based treatment can be predicted by mental comorbidities or psychosocial factors. However, because CBM may be included in approaches to treat CP, questions about the ideal duration of such treatment remain to be answered

Automotive Sensor Data. An Example Dataset from the AEGIS Big Data Project

This is an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected by using a BeagleBone single plate computer which has been developed at VIF to collect data for driving analytics. The BeagleBoard can be connected to the OBD2 interface of a vehicle to capture data from CAN bus and has been additionally equipped with further sensors (GPS, gyroscope, acceleration). The data in this research dataset was collected during 35 different trips conducted by one driver driving one vehicle in the Graz area in Austria

A Research Agenda for Vehicle Information Systems

As modern cars have transformed to computers on wheels, digitalization is an important driver of ser-vice and business innovation within the automotive domain. As a new class of information systems (IS), vehicle information systems (Vehicle IS) are enabled through the data generated by a plethora of different sensors within modern vehicles, meshed up with data from a variety of different other sources. Expecting the awareness on and the needs for Vehicle IS to steadily increase in the future - as a result of the continuing provision of driver assistance systems towards fully automation - we investigate existing literature on Vehicle IS published by the academic IS community. To get an overview on topics discussed so far as well as publication activity in general, we use the AIS Electronic Library as an indicator. We then provide a definition of the term ‘vehicle information system’ and give an overview of relevant research directions with a set of example research questions, which we deem important for the academic IS community to advance the state-of-the-art in designing Vehicle IS

Towards a privacy-preserving way of vehicle data sharing - A case for Blockchain technology?

Vehicle data is a valuable source for digital services, especially with a rising degree of driving automatization. Despite regulation on data protection has become stricter due to Europe’s GDPR we argue that the exchange of vehicle and driving data will massively increase. We therefore raise the question on what would be a privacy-preserving way of vehicle data exploitation? Blockchain technology could be an enabler, as it is associated with privacy-friendly concepts including transparency, trust, and decentralization. Hence, we launch the discussion on unsolved technical and non-technical issues and provide a concept for an Open Vehicle Data Platform, respecting the privacy of both the vehicle owner and driver using Blockchain technology

Efficacy and feasibility of pharmacoscopy-guided treatment for acute myeloid leukemia patients who have exhausted all registered therapeutic options.

Not available

Medical students’ attitudes and perceived competence regarding medical cannabis and its suggestibility

Abstract Introduction The global trend of legalizing medical cannabis (MC) is on the rise. In Germany, physicians have prescribed MC at the expense of health insurers since 2017. However, the teaching on MC has been scant in medical training. This study investigates medical students’ attitudes and perceived competence regarding MC and evaluates how varying materials (videos/articles) impact their opinions. Methods Fourth-year medical students were invited to participate in the cross-sectional study. During an online session, students viewed a video featuring a patient with somatoform pain discussing her medical history, plus one of four randomly assigned MC-related materials (each an article and a video depicting a positive or negative perspective on MC). Students’ opinions were measured at the beginning [T0] and the end of the course [T1] using a standardized questionnaire with a five-point Likert scale. We assessed the influence of the material on the students’ opinions using paired-sample t-tests. One-way analysis of variance and Tukey post-hoc tests were conducted to compare the four groups. Pearson correlations assessed correlations. Results 150 students participated in the course, the response rate being 75.3% [T0] and 72.7% [T1]. At T0, students felt a little competent regarding MC therapy (M = 1.80 ± 0.82). At T1, students in groups 1 (positive video) and 3 (positive article) rated themselves as more capable in managing MC therapy $$(\text{t}\left(28\right)=-3.816,\text{p}<0.001; \text{t}\left(23\right)=-4.153,\text{p}<0.001)$$ , and students in groups 3 (positive article) and 4 (negative article) felt more skilled in treating patients with chronic pain $$(\text{t}\left(23\right)=-2.251,\text{p}=0.034;\text{t}\left(30\right)=-2.034;\text{p}=0.051)$$ . Compared to the other groups, group 2 students (negative video) felt significantly less competent. They perceived cannabis as addictive, hazardous and unsuitable for medical prescription. Discussion This study showed that medical students lack knowledge and perceived competence in MC therapy. Material influences their opinions in different ways, and they seek more training on MC. This underlines that integrating MC education into medical curricula is crucial to address this knowledge gap

TATRAS.at: Tax and Transfer Simulator for Austria : Eine Mikrosimulationsplattform zu Reformen der bundesweiten Steuer- und Transferregelungen

Jedes Steuer-, und Transfersystem ist nur so gut wie seine Adaptierungsfähigkeit an die sich laufend ändernden Rahmenbedingungen. Dieser Überzeugung folgend wurde am Österreichischen Institut für Familienforschung an der Universität Wien die Mikrosimulationsplattform TATRAS.at entwickelt. Mit TATRAS.at können Umverteilungseffekte von Reformen in den Bereichen Einkommens- und Lohnsteuer, der Sozialversicherungsabgaben, der Lohnnebenkosten sowie der bundesweit gültigen Familientransfers vorab simuliert werden