Abstinence from masturbation and hypersexuality

Despite the lack of evidence for negative health effects of masturbation, abstinence from masturbation is frequently recommended as a strategy to improve one’s sexual self-regulation. We adopted a framework of perceived problems with pornography to collect first hints about whether abstinence from masturbation stems from a psychological and behavioral “addiction” or conflicting attitudes. In an online questionnaire survey recruited via a non-thematic Reddit thread (n = 1063), most participants reported that they had tried to be abstinent from masturbation. As visible from zero-order correlations and multiple linear regression, motivation for abstinence was mostly associated with attitudinal correlates, specifically the perception of masturbation as unhealthy. While there were associations with hypersexuality, no significant correlation with behavioral markers such as maximum number of orgasms was found. Higher abstinence motivation was related to a higher perceived impact of masturbation, conservatism, and religiosity and to lower trust in science. We argue that research on abstinence from masturbation can enrich the understanding of whether and how average frequencies of healthy behavior are pathologized

Undermining or defending Democracy? The Consequences of Distrust for Democratic Attitudes and Participation

We can observe a well-documented decline of trust levels in Western societies: from the reputation of political representatives as being "not trustworthy" to the rise of anti-system-oriented populist parties. Yet the implications of different forms of distrust for a society and democratic institutions have been theorized in conflicting ways so far. In order to illuminate existing inconsistencies in social and democratic theory, this article addresses two research questions: What are the implications of different manifestations of distrust for the acceptance of democracy and democratic institutions? How do different forms of distrust affect the motivation to become engaged in democratic decision-making and in civil society institutions? Taking empirical evidence from 25 focus groups in Germany, our findings show that growing social divisions affect the role distrust plays for political interest representation of social groups and for the acceptance of liberal representative democracy

Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT

The Wald, likelihood ratio, score, and the recently proposed gradient statistics can be used to assess a broad range of hypotheses in item response theory models, for instance, to check the overall model fit or to detect differential item functioning. We introduce new methods for power analysis and sample size planning that can be applied when marginal maximum likelihood estimation is used. This allows the application to a variety of IRT models, which are commonly used in practice, e.g., in large-scale educational assessments. An analytical method utilizes the asymptotic distributions of the statistics under alternative hypotheses. We also provide a sampling-based approach for applications where the analytical approach is computationally infeasible. This can be the case with 20 or more items, since the computational load increases exponentially with the number of items. We performed extensive simulation studies in three practically relevant settings, i.e., testing a Rasch model against a 2PL model, testing for differential item functioning, and testing a partial credit model against a generalized partial credit model. The observed distributions of the test statistics and the power of the tests agreed well with the predictions by the proposed methods in sufficiently large samples. We provide an openly accessible R package that implements the methods for user-supplied hypotheses

Characterisation and Comparison of Material Parameters of 3D-Printable Absorbing Materials.

We compared different commercially available materials that are 3D-printable for their suitability for making microwave absorbers by means of additive manufacturing, i.e., 3D printing. For this, we determined their complex permittivity, and, if applicable, the complex permeability. They are responsible for the RF losses within the material and, therefore, determine its usefulness as an absorber material. Further, we made SEM (scanning electron microscope) images of material samples showing the filling materials that have been used to achieve absorbing properties

Investigation of fluorine-based release agents for structural adhesive bonding of carbon fibre reinforced plastics

Abstract Peel plies can be used during the manufacture of fibre-reinforced plastics for two reasons: to protect the surface during transport and storing the parts as well as during subsequent work steps, such as adhesive bonding, the removal of the peel ply can result in bondable surface with required surface characteristics. However, the use of peel plies is not straightforward. It can be difficult to remove peel plies and a surface produced by a peel ply is altered in terms of roughness and elemental composition. In the present work, the influence of fluorine-based release agents on adhesive joining of carbon fibre reinforced composites is investigated. Within the scope of the screening, 14 fluorine-based release agents—ETFE release films, PTFE coated glass fabrics as well as fabrics made of PTFE fibres—were investigated. Preliminary studies (Meer, in: Deutscher Luft- und Raumfahrtkongress 2014, Augsburg, 2015) have shown that ETFE films have advantages in terms of adhesion. The study covers a number of aspects: the determination of the tear strength of the release agent by peel test; the determination of the element composition (XPS) and surface characteristics (SEM) before and after atmospheric pressure plasma pre-treatment, characterization the topology and the characterization of the adhesive strength by centrifugal adhesion test

Sample Size in Natural Language Processing within Healthcare Research

Sample size calculation is an essential step in most data-based disciplines. Large enough samples ensure representativeness of the population and determine the precision of estimates. This is true for most quantitative studies, including those that employ machine learning methods, such as natural language processing, where free-text is used to generate predictions and classify instances of text. Within the healthcare domain, the lack of sufficient corpora of previously collected data can be a limiting factor when determining sample sizes for new studies. This paper tries to address the issue by making recommendations on sample sizes for text classification tasks in the healthcare domain. Models trained on the MIMIC-III database of critical care records from Beth Israel Deaconess Medical Center were used to classify documents as having or not having Unspecified Essential Hypertension, the most common diagnosis code in the database. Simulations were performed using various classifiers on different sample sizes and class proportions. This was repeated for a comparatively less common diagnosis code within the database of diabetes mellitus without mention of complication. Smaller sample sizes resulted in better results when using a K-nearest neighbours classifier, whereas larger sample sizes provided better results with support vector machines and BERT models. Overall, a sample size larger than 1000 was sufficient to provide decent performance metrics. The simulations conducted within this study provide guidelines that can be used as recommendations for selecting appropriate sample sizes and class proportions, and for predicting expected performance, when building classifiers for textual healthcare data. The methodology used here can be modified for sample size estimates calculations with other datasets.Comment: Submitted to Journal of Biomedical Informatic