A political end to a pioneering career: Marianne Beth and the psychology of religion

Although forgotten in both Religionswissenschaft (the Science of Religion) and psychology, Marianne Beth (1880-1984), initially trained as a lawyer and already in 1928 called a “leading European woman”, must be considered as one of the female pioneers of these fields. She has been active especially in the psychology of religion, a field in which she, together with her husband Karl Beth, founded a research institute, an international organization and a journal. In 1932, the Beths organized in Vienna (where Karl was a professor) the largest conference ever in the history of the psychology of religion. Because of her Jewish descent, Marianne Beth fled to the USA when Austria was annexed by Nazi Germany in 1938. This brought an abrupt end to her career as researcher and writer. The article reconstructs Marianne Beth’s path into psychology, analyzes some of her work and puts her achievements in an international perspective

Approximation of multi-variable signals and systems : a tensor decomposition approach

Signals that evolve over multiple variables or indices occur in all fields of science and engineering. Measurements of the distribution of temperature across the globe during a certain period of time are an example of such a signal. Multivariable systems describe the evolution of signals over a spatial-temporal domain. The mathematical equations involved in such a description are called a model and this model dictates which values the signals can obtain as a function of time and space. In an industrial production setting, such mathematical models may be used to monitor the process or determine the control action required to reach a certain set-point. Since their evolution is over both space and time, multi-variable systems are described by Partial Differential Equations (PDEs). Generally, it is not the signals or systems themselves one is interested in, but the information they carry. The main numerical tools to extract system trajectories from the PDE description are Finite Element (FE) methods. FE models allow simulation of the model via a discretization scheme. The main problem with FE models is their complexity, which leads to large simulation time, making them not suitable for applications such as on-line monitoring of the process or model-based control design. Model reduction techniques aim to derive lowcomplexity replacement models from complex process models, in the setting of this work, from FE models. The approximations are achieved by projection on lower-dimensional subspaces of the signals and their dynamic laws. This work considers the computation of empirical projection spaces for signals and systems evolving over multi-dimensional domains. Formally, signal approximation may be viewed as a low-rank approximation problem. Whenever the signal under consideration is a function of multiple variables, low-rank approximations can be obtained via multi-linear functionals, tensors. It has been explained in this work that approximation of multi-variable systems also boils down to low-rank approximation problems.The first problem under consideration was that of finding low-rank approximations to tensors. For order-2 tensors, matrices, this problem is well understood. Generalization of these results to higher-order tensors is not straightforward. Finding tensor decompositions that allow suitable approximations after truncation is an active area of research. In this work a concept of rank for tensors, referred to as multi-linear or modal rank, has been considered. A new method has been defined to obtain modal rank decompositions to tensors, referred to as Tensor Singular Value Decomposition (TSVD). Properties of the TSVD that reflect its sparsity structure have been derived and low-rank approximation error bounds have been obtained for certain specific cases. An adaptation of the TSVD method has been proposed that may give better approximation results when not all modal directions are approximated. A numerical algorithm has been presented for the computation of the (dedicated) TSVD, which with a small adaptation can also be used to compute successive rank-one approximation to tensors. Finally, a simulation example has been included which demonstrates the methods proposed in this work and compares them to a well-known existing method. The concepts that were introduced and discussed with regard to signal approximation have been used in a system approximation context.We have considered the well-known model reduction method of Proper Orthogonal Decompositions (POD). We have shown how the basis functions inferred from the TSVD can be used to define projection spaces in POD. This adaptation is both a generalization and a restriction. It is a generalization because it allows POD to be used in a scalable fashion for problems with an arbitrary number of dependent and independent variables. However, it is also a restriction, since the projection spaces require a Cartesian product structure of the domain. The model reduction method that is thus obtained has been demonstrated on a benchmark example from chemical engineering. This application shows that the method is indeed feasible, and that the accuracy is comparable to existing methods for this example. In the final part of the thesis the problem of reconstruction and approximation of multi-dimensional signals was considered. Specifically, the problem of sampling and signal reconstruction for multi-variable signals with non-uniformly distributed sensors on a Cartesian domain has been considered. The central question of this chapter was that of finding a reconstruction of the original signal from its samples. A specific reconstruction map has been examined and conditions for exact reconstruction have been presented. In case that exact reconstruction was not possible, we have derived an expression for the reconstruction error

Critical Success Factors of Continuous Practices in a DevOps Context

Context: Software companies try to achieve adaptive near to real-time software delivery and apply continuous practices in a DevOps context. While continuous practices may create new business opportunities, continuous practices also present new challenges. Objective: This study aims to aid in adopting continuous practices and performance improvements by increasing our understanding of these practices in a DevOps context. Method: By conducting a systematic literature review we identified critical success factors on continuous practices and grouped the found factors. This led to the construction of our initial framework. We started to validate the critical success factors in this framework in a DevOps context by conducting a first pilot interview. Results: We developed an initial framework of critical success factors and conducted a pilot interview to make a first step to validate the framework. Some factors were confirmed and clarified i.e., enriched, on the basis of the retrieved information. In future work we will strive at further validation of the framework. Conclusions: We took a first step to validate our framework and retrieved valuable information, which is promising to take the next steps for further development of the framework

Modeling as Scientific Reasoning—The Role of Abductive Reasoning for Modeling Competence

While the hypothetico-deductive approach, which includes inductive and deductive reasoning, is largely recognized in scientific reasoning, there is not much focus on abductive reasoning. Abductive reasoning describes the theory-based attempt of explaining a phenomenon by a cause. By integrating abductive reasoning into a framework for modeling competence, we strengthen the idea of modeling being a key practice of science. The framework for modeling competence theoretically describes competence levels structuring the modeling process into model construction and model application. The aim of this theoretical paper is to extend the framework for modeling competence by including abductive reasoning, with impact on the whole modeling process. Abductive reasoning can be understood as knowledge expanding in the process of model construction. In combination with deductive reasoning in model application, such inferences might enrich modeling processes. Abductive reasoning to explain a phenomenon from the best fitting guess is important for model construction and may foster the deduction of hypotheses from the model and further testing them empirically. Recent studies and examples of learners’ performance in modeling processes support abductive reasoning being a part of modeling competence within scientific reasoning. The extended framework can be used for teaching and learning to foster scientific reasoning competences within modeling processes.Peer Reviewe

Reasoning on Controversial Science Issues in Science Education and Science Communication

The ability to make evidence-based decisions, and hence to reason on questions concerning scientific and societal aspects, is a crucial goal in science education and science communication. However, science denial poses a constant challenge for society and education. Controversial science issues (CSI) encompass scientific knowledge rejected by the public as well as socioscientific issues, i.e., societal issues grounded in science that are frequently applied to science education. Generating evidence-based justifications for claims is central in scientific and informal reasoning. This study aims to describe attitudes and their justifications within the argumentations of a random online sample (N = 398) when reasoning informally on selected CSI. Following a deductive-inductive approach and qualitative content analysis of written open-ended answers, we identified five types of justifications based on a fine-grained category system. The results suggest a topic-specificity of justifications referring to specific scientific data, while justifications appealing to authorities tend to be common across topics. Subjective, and therefore normative, justifications were slightly related to conspiracy ideation and a general rejection of the scientific consensus. The category system could be applied to other CSI topics to help clarify the relation between scientific and informal reasoning in science education and communication.Peer Reviewe

Pre-service Biology Teachers’ Responses to First-Hand Anomalous Data During Modelling Processes

In this research project we investigate the role of responses to anomalous data during modelling processes. Modelling is seen as a comprehensive practice that encompasses various aspects of scientific thinking; hence, it is an important style of scientific thinking, especially if analysed from a process-based perspective. Therefore, it provides the opportunity to understand the role of anomalous data on scientific thinking from a broader perspective. We analysed how pre-service biology teachers (N = 11) reacted to self-generated anomalous data during modelling processes induced by investigating a water black box. The videotaped and transcribed modelling processes were analysed using qualitative content analysis. If anomalous data were recognised, a majority of explanations were based on methodical issues. This finding supports results from previous studies investigating responses to first-hand anomalous data. Furthermore, we found four response patterns to anomalous data during modelling processes: no recognition, no explanation, methodical explanation, and model-related explanation. Besides, our study indicates by trend a systematic relation between response patterns to anomalous data and modelling strategies. Consequently, the improvement of responses to anomalous data could be a promising way to foster modelling competencies. We are convinced that an integrated approach to anomalous data and modelling could lead to deeper insights into the role of data in scientific thinking processes