Fuzzy-Analysis in a Generic Polymorphic Uncertainty Quantification Framework

In this thesis, a framework for generic uncertainty analysis is developed. The two basic uncertainty characteristics aleatoric and epistemic uncertainty are differentiated. Polymorphic uncertainty as the combination of these two characteristics is discussed. The main focus is on epistemic uncertainty, with fuzziness as an uncertainty model. Properties and classes of fuzzy quantities are discussed. Some information reduction measures to reduce a fuzzy quantity to a characteristic value, are briefly debated. Analysis approaches for aleatoric, epistemic and polymorphic uncertainty are discussed. For fuzzy analysis α-level-based and α-level-free methods are described. As a hybridization of both methods, non-flat α-level-optimization is proposed. For numerical uncertainty analysis, the framework PUQpy, which stands for “Polymorphic Uncertainty Quantification in Python” is introduced. The conception, structure, data structure, modules and design principles of PUQpy are documented. Sequential Weighted Sampling (SWS) is presented as an optimization algorithm for general purpose optimization, as well as for fuzzy analysis. Slice Sampling as a component of SWS is shown. Routines to update Pareto-fronts, which are required for optimization are benchmarked. Finally, PUQpy is used to analyze example problems as a proof of concept. In those problems analytical functions with uncertain parameters, characterized by fuzzy and polymorphic uncertainty, are examined

Weekly reports for R.V. Polarstern expedition PS103 (2016-12-16 - 2017-02-03, Cape Town - Punta Arenas), German and English version

Priming is arguably one of the key phenomena in contemporary social psychology. Recent retractions and failed replication attempts have led to a division in the field between proponents and skeptics and have reinforced the importance of confirming certain priming effects through replication. In this study, we describe the results of 2 preregistered replication attempts of 1 experiment by Förster and Denzler (2012). In both experiments, participants first processed letters either globally or locally, then were tested using a typicality rating task. Bayes factor hypothesis tests were conducted for both experiments: Experiment 1(N = 100) yielded an indecisive Bayes factor of 1.38, indicating that the in-lab data are 1.38 times more likely to have occurred under the null hypothesis than under the alternative. Experiment 2 (N = 908) yielded a Bayes factor of 10.84, indicating strong support for the null hypothesis that global priming does not affect participants' mean typicality ratings. The failure to replicate this priming effect challenges existing support for the GLOMOsys model

Rekursivität und Horizontalisierung - das kommerzielle Internet als Vorbild digitalisierter Arbeit

Ausgehend von zwei exemplarischen Fallstudien befasst sich der Beitrag mit digital gestützten Innovationen im Gebiet der Arbeitskontrolle und -steuerung. Diese zeichnen sich durch zwei Charakteristika aus: Erstens eine Horizontalisierung von Kontrolle, die sich insbesondere in Kommentierungs- und Ratingsystemen manifestiert, in denen sich ArbeitnehmerInnen gegenseitig bewerten müssen; und zweitens eine automatisierte Rekursivität, die sich in unmittelbaren Feedbacks auf der Basis eines ubiquitären digitalen Trackings des Arbeitsprozesses manifestiert. Basierend auf diesen empirischen Erkenntnissen lautet unsere Diagnose deshalb, dass man die Digitalisierung betrieblicher Herrschaft aus einer Perspektive verstehen muss, die an der Analyse des kommerziellen Internets geschult wurde. In einer Umkehr der klassischen Prosumenten-These zeigen wir auf, dass das kommerzielle Internet heute nicht nur nach dem Vorbild der Arbeit gestaltet ist, sondern die Arbeit nach dem Vorbild des kommerziellen Internets.Based on two exemplary case studies, this article addresses digital innovations in work control. These innovations are characterized in a twofold way: First, by a horizontalization of control, which manifests primarily in systems of commenting and rating in which employees have to evaluate each other. Second, by automated recursivity, which manifests primarily in immediate feedbacks from ubiquitous digital tracking of the labor process. Based on these empirical findings, we argue that the digitalization of work control has to be understood from a perspective that is instructed by the analysis of the commercial internet. By turning around the classical prosumer thesis, our conclusion is that nowadays the commercial internet is not so much structured after the example of work practices, but work is structured after the example of the commercial internet

Datenschutzrechtliche Rahmenbedingungen medizinischer Forschung

The European Data Protection Regulation applies since May 25th, 2018. It creates a uniform data protection legal framework within the EU. National and international medical research projects, regardless of whether they were started before or after the introduction of the GDPR, are obliged to follow this new regulation and implement it promptly. This raises various challenges for a large number of medical research projects. The University Medicine Greifswald commissioned this legal report, that was prepared by DIERKS+COMPANY. Two real-world research projects, the Baltic Fracture Competence Centre (BFCC) as well as the German Centre for Cardiovascular Research (DZHK) provide use cases, questions, and context for this legal report. It addresses questions regarding all steps of data processing. The report provides practical answers to a wide array of technical and organisational questions in the area of data protection-compliant processing of research data. A comprehensive guide to GDPR-compliant data processing has been developed, which both summarises the broad legal environment and provides specific assistance in the design and implementation of GDPR-compliant data management processes, including Informed Consent, Legal Consequences of Withdrawal, and Privacy by Design