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Modern health worries: Deriving two measurement invariant short scales for cross-cultural research with Ant Colony Optimization
(2019-02-07)
Worries about possible harmful effects of new technologies (modern health worries) have intensely been investigated in the last decade. However, the comparability of translated self-report measures across countries is often problematic. This study aimed to overcome this problem by developing psychometrically sound brief versions of the widely used 25-item Modern Health Worries Scale (MHWS) suitable for multi-country use. Based on data of overall 5,176 individuals from four European countries (England, Germany, Hungary, ...
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Impacts of Climate Change on the Water Availability, Seasonality and Extremes in the Upper Indus Basin (UIB)
(2020-02-11)
Projecting future hydrology for the mountainous, highly glaciated upper Indus basin (UIB) is a challenging task because of uncertainties in future climate projections and issues with the coverage and quality of available reference climatic data and hydrological modelling approaches. This study attempts to address these issues by utilizing the semi-distributed hydrological model “Soil and water assessment tool” (SWAT) with new climate datasets and better spatial and altitudinal representation as well as a wider range ...
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Vesicle motion during sustained exocytosis in chromaffin cells
(PLoS, 2015)
Chromaffin cells release catecholamines by exocytosis, a process that includes vesicle docking, priming and fusion. Although all these steps have been intensively studied, some aspects of their mechanisms, particularly those regarding vesicle transport to the active sites situated at the membrane, are still unclear. In this work, we show that it is possible to extract information on vesicle motion in Chromaffin cells from the combination of Langevin simulations and amperometric measurements. We developed a numerical ...
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A structural topic model approach to scientific reorientation of economics and chemistry after German reunification
(2020-08-05)
The detection of differences or similarities in large numbers of scientific publications is an open problem in scientometric research. In this paper we therefore develop and apply a machine learning approach based on structural topic modelling in combination with cosine similarity and a linear regression framework in order to identify differences in dissertation titles written at East and West German universities before and after German reunification. German reunification and its surrounding time period is used because ...
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Recent Developments in the Field of Modified Patankar-Runge-Kutta-methods
(2021-12-14)
Modified Patankar-Runge-Kutta (MPRK) schemes are numerical one-step methods for the solution of positive and conservative production-destruction systems (PDS). They adapt explicit Runge-Kutta schemes in a way to ensure positivity and conservation of the numerical approximation irrespective of the chosen time step size. Due to nonlinear relationships between the next and current iterate, the stability analysis for such schemes is lacking. In this work, we introduce a strategy to analyze the MPRK22(α)-schemes in the ...
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Validation and generalizability of machine learning prediction models on attrition in longitudinal studies
(2022-02-07)
Attrition in longitudinal studies is a major threat to the representativeness of the data and the generalizability of the findings. Typical approaches to address systematic nonresponse are either expensive and unsatisfactory (e.g., oversampling) or rely on the unrealistic assumption of data missing at random (e.g., multiple imputation). Thus, models that effectively predict who most likely drops out in subsequent occasions might offer the opportunity to take countermeasures (e.g., incentives). With the current study, ...
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Dietary Quality of Women of Reproductive Age in Low-Income Settings: A Cross-Sectional Study in Kyrgyzstan
(2022-01-11)
Dietary diversity and adequate nutrient intake are essential for conducting a healthy life. However, women in low-income settings often face difficulties in ensuring dietary quality. This research assessed relationships between the dietary diversity, nutrient adequacy, and socio-economic factors among women of reproductive age (WRA) in Kyrgyzstan. A cross-sectional study was undertaken in four locations, including two rural and two urban areas in the north and south of Kyrgyzstan. A survey with pre-coded and open-ended ...
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Effects of mucilage concentration at different water contents on mechanical stability and elasticity in a loamy and a sandy soil
(2021-10-26)
Mucilage released by plant roots affects hydrological and mechanical properties of the rhizosphere. The aim of this study was to disentangle the effects of the factors mucilage and soil moisture on a range of soil mechanical parameters in a sand and a loam. Both substrates were homogenised and filled into cylinders at bulk densities (ρb) of 1.26 and 1.47 g cm−³ for loam and sand, respectively. Chia seed (Salvia hispanica L.) mucilage concentrations of 0, 0.02, 0.2 and 2 g dry mucilage kg−¹ dry soil were tested at ...
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Synthesis, Structure and Cu-Phenylacetylide Coordination of an Unsymmetrically Substituted Bulky dppf-Analog
(2021-12-03)
The donor properties of a set of bulky ferrocene based bisphosphanes (Fe(C₅H₄PMes₂)₂ and (C₅H₄PMes₂)Fe(C₅H₄PᵗBu₂ with Mes= mesityl and ᵗBu=tert-butyl) were probed by exploring the NMR parameters of the corresponding selenophosphoranes amended by cyclovoltammetry. The ligand properties were explored in the complexation of copper phenylacetylide which is relevant as intermediate in the Cu(I) catalyzed CO₂ addition to phenylacetylene. Owing to the poor solubility of the resulting complexes their characterization was ...
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CLeaR: An adaptive continual learning framework for regression tasks
(2021-07-16)
Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This ...