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Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP)
(2022-09-26)
The Hierarchical Taxonomy of Psychopathology (HiTOP) organizes phenotypes of mental disorder based on empirical covariation, offering a comprehensive organizational framework from narrow symptoms to broader patterns of psychopathology. We argue that established self-report measures of psychopathology from the pre-HiTOP era should be systematically integrated into HiTOP to foster cumulative research and further the understanding of psychopathology structure. Hence, in this study, we mapped 92 established psychopathology ...
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Predictors of post-stroke depression: Validation of established risk factors and introduction of a dynamic perspective in two longitudinal studies
(2023-02-13)
Introduction: Cerebral insults lead in many cases not only to cognitive impairment but also to disturbed emotionality. After stroke, one in three survivors develops a depression which impacts quality of life and rehabilitation. Meta-analyses have identified five main predictors of post-stroke depression (PSD): history of mental disorder, stroke severity, physical disability, cognitive impairment, and social support. However, these five established variables have never been conjointly investigated in a sample of stroke ...
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Motiviert begonnen, erfolgreich beendet?
(2021-03-01)
Die Bedeutung der Studienwahlmotivation für den Studien- und Berufserfolg von Lehrkräften wird vielfach betont, wurde bislang jedoch nicht hinreichend untersucht. So ist zwar bekannt, dass die Studienwahlmotivation mit leistungsrelevanten Merkmalen wie der Nutzung von Lernstrategien in Verbindung steht, unklar ist jedoch, inwiefern die Studienwahlmotivation darüber hinaus auch Leistungsmaße beeinflusst. Im vorliegenden Beitrag wird die prognostische Validität der Studienwahlmotivation auf zwei Leistungsmaße geprüft: ...
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Research topic flows in co-authorship networks
(2022-10-11)
In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure ...
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Consumers' body image expressions: Reflection of a Snow White or an Evil Queen
(2023-03-17)
Introduction: The aim of this paper is to explore how minimal-self impacts the body image, projecting it as a reflection of one's approach toward their health and mental well-being.
Methods: The study takes qualitative data from two countries India and Germany and draws on a qualitative study of 20 individuals who are involved in some kind of physical activity for a long time. This paper examines the body image perspectives from Grimms Brothers fairytale characters showcasing fit and healthy perspectives on Snow ...
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Is the perceptual disfluency effect moderated by working memory capacity? Direct replication of Lehmann et al. (2016)
(2023-12-30)
According to an aptitude-treatment interaction experiment (Lehmann et al., Metacognition and Learning, 11, 89–105, 2016, N = 47, published in Metacognition and Learning), perceptually disfluent texts facilitated retention and comprehension performance (but not transfer performance) only for learners with higher working memory capacity (WMC). No effects of WMC for a fluent text were found (albeit theoretically, fluency may be more advantageous for learners with lower WMC). The findings of our (pre-registered) direct ...
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Covariational reasoning in Bayesian situations
(2024-01-27)
Previous studies on Bayesian situations, in which probabilistic information is used to update the probability of a hypothesis, have often focused on the calculation of a posterior probability. We argue that for an in-depth understanding of Bayesian situations, it is (apart from mere calculation) also necessary to be able to evaluate the effect of changes of parameters in the Bayesian situation and the consequences, e.g., for the posterior probability. Thus, by understanding Bayes’ formula as a function, the concept ...
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On the Performance of Malleable APGAS Programs and Batch Job Schedulers
(2024-01-18)
Malleability—the ability for applications to dynamically adjust their resource allocations at runtime—presents great potential to enhance the efficiency and resource utilization of modern supercomputers. However, applications are rarely capable of growing and shrinking their number of nodes at runtime, and batch job schedulers provide only rudimentary support for such features. While numerous approaches have been proposed to enable application malleability, these typically focus on iterative computations and require ...
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Development and Validation of a German Interpersonal Sensitivities Circumplex (ISC-G)
(2022-11-09)
The Interpersonal Sensitivities Circumplex (ISC) assesses individual differences in sensitivities to aversive interpersonal behaviors. In this research (total N = 1,519), we developed and validated a German adaptation of the ISC (ISC-G) and extended the nomological net of interpersonal sensitivity as a construct. Using the structural summary method, we investigated associations with self- and informant reports of adaptive (interpersonal) personality traits as well as self-reported personality functioning, maladaptive ...
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Data‑driven prediction of the surface layer state in hard‑turning for optimization of component quality
(2024-01-30)
Data-driven approaches are an effective solution for modeling problems in machining. To increase the service life of hardturned components, it is important to quantify the correlation between the cutting parameters such as feed rate, cutting speed and depth of cut and the near-surface properties. For obtaining high-quality models with small data sets, different data-driven approaches are investigated in this contribution. Additionally, models that enable uncertainty quantification are crucial for effective decision-making ...