Clustering of electrical transmission systems based on network topology and stability

A proper understanding and modelling of the behaviour of heavily loaded large-scale electrical transmission systems is essential for a secure and uninterrupted operation. In this paper, we present methods to cluster electrical power networks based on different criteria into regions. These regions are useful for the efficient modelling of large transcontinental electricity networks, switching operation decisions or placement of redundant parts of the monitoring and control system. In alternating current electricity networks, power oscillations are normal, but they can become dangerous if they build up. The first approach uses the correlation between results of a stability assessment for these oscillations at every node for the cluster criterion. The second method concentrates on the network topology and uses spectral clustering on the network graph to create clusters where all nodes are interconnected. In this work, we also discuss the problem how to choose the right number of clusters and how the discussed clustering methods can be used for an efficient modelling of large electricity networks or in protection and control systems.     

Automatic model selection for high-dimensional survival analysis

Many different models for the analysis of high-dimensional survival data have been developed over the past years. While some of the models and implementations come with an internal parameter tuning automatism, others require the user to accurately adjust defaults, which often feels like a guessing game. Exhaustively trying out all model and parameter combinations will quickly become tedious or infeasible in computationally intensive settings, even if parallelization is employed. Therefore, we propose to use modern algorithm configuration techniques, e.g. iterated F-racing, to efficiently move through the model hypothesis space and to simultaneously configure algorithm classes and their respective hyperparameters. In our application we study four lung cancer microarray data sets. For these we configure a predictor based on five survival analysis algorithms in combination with eight feature selection filters. We parallelize the optimization and all comparison experiments with the BatchJobs and BatchExperiments R packages.     

On the Restricted Liu Estimator in the Logistic Regression Model

The logistic regression model is used when the response variables are dichotomous. In the presence of multicollinearity, the variance of the maximum likelihood estimator (MLE) becomes inflated. The Liu estimator for the linear regression model is proposed by Liu to remedy this problem. Urgan and Tez and Mansson et al. examined the Liu estimator (LE) for the logistic regression model. We introduced the restricted Liu estimator (RLE) for the logistic regression model. Moreover, a Monte Carlo simulation study is conducted for comparing the performances of the MLE, restricted maximum likelihood estimator (RMLE), LE, and RLE for the logistic regression model.     

Gestaltpädagogische Konzepte und Praxen von Beratung und Förderung sozialer und personaler Kompetenzen in der Aus- und Fortbildung von Erwachsenenbildnern und -bildnerinnen

The article describes pedagogical Gestalt concepts of consultancy and professional development of adult educators. The focus change of the adult educators from teachers to consultants is outlined at the beginning. The changed role demands concepts of consultancy and professional development that can reflect and strengthen the self and social competence of the actors. It is shown that the design pedagogical concepts are particularly suitable because the promotion of the conscious perception of one owns’ body, needs, feelings and imagination and their integration into the self image of the learning person (client) are in the center of such approach. The concept and practice of ‘role-playing’ as part of the course instructors’ further professional training related to the design pedagogical concepts are documented in details using case-studies. It is furthermore detailed where the learning opportunities lie, related to the perception, self reflection and enhancement of one owns’ action potential for teaching and consultancy activities. The summary emphasizes one of the most important axioms of the design approach: It is not about a somehow newly defined nice person but about the paradox of change through precise perception of the actual situation.     

Ausländer unter Tatverdacht

Findings on the quantity and quality of criminal behavior of immigrant groups differ very widely (partly depending on the source of data used) and interpretations differ according to theoretical standpoint. There is also controversy over the reasons why charges are more often dropped in cases against young non-Germans than in cases against German citizens. Data from prosecution service records were analysed (N = 860 suspects) to investigate whether differences could be found in the quality and structure of the offenses of which Germans and non-Germans were accused, or in the outcome of the investigations. Only marginal differences were found in the quality of offenses with which Germans and non-Germans were charged. The primary reason for the higher rate of charges being dropped against young foreigners was found to be lack of evidence. This need not, however, in any way be interpreted as meaning that young foreigners are more successful at hiding the evidence of their crimes. It could simply show that non-Germans are more often falsely accused of committing a crime.     

Multi-professional vocational rehabilitation group meetings with female clients: a qualitative study

Many women require vocational rehabilitation before they can return to work. The objective with the present study was to describe female clients' situation during the rehabilitation process, as it became apparent during the rehabilitation meetings with the various actors involved. The clients' diagnoses varied, but the majority was affected with musculoskeletal disorders. The meetings were audio-taped and transcribed verbatim, after which they were analysed by qualitative content analysis. The following themes emerged: Adaptation to the rehabilitation group; client's health status; the workplace's significance for rehabilitation; and the client's decision-point. Conflict between health and work was immensely important for rehabilitation. Differences in attitude were apparent during the rehabilitation meetings, as some clients were passive and exercised less influence on the planning, than the other more active clients did.     

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.     

A Monte Carlo simulation study on partially adaptive estimators of linear regression models

This paper presents a comprehensive comparison of well-known partially adaptive estimators (PAEs) in terms of efficiency in estimating regression parameters. The aim is to identify the best estimators of regression parameters when error terms follow from normal, Laplace, Student's t, normal mixture, lognormal and gamma distribution via the Monte Carlo simulation. In the results of the simulation, efficient PAEs are determined in the case of symmetric leptokurtic and skewed leptokurtic regression error data. Additionally, these estimators are also compared in terms of regression applications. Regarding these applications, using certain standard error estimators, it is shown that PAEs can reduce the standard error of the slope parameter estimate relative to ordinary least squares.