Four Years of Sediment and Phosphorus Monitoring in the Kraichbach River Using Large-Volume Samplers

Various sampling strategies come into operation to monitor water quality in rivers. Most frequently, grab samples are taken, but they are not suitable for recording the highly dynamic transport of solids and solid-bound pollutants. Composite samples reduce the influence of input and transport dynamics and are better suited to determine the annual river loads. Large-volume samplers (LVSs) produce both a composite sample over a long period of time and an amount of solids which allows for further analyses. In the small sub-catchment area of the Kraichbach river in Baden-Wuerttemberg (Germany) two LVSs have been installed to sample the river flow. The concentration of solids and phosphorus in the supernatant water and the settled sediment in the sampler have been determined and mean concentrations have been derived. Annual river loads were calculated in combination with discharge data from the nearby gauging station. Two sampling strategies of the LVS were tested and compared. For the first strategy, the LVS was used to collect quasi-continuous composite samples throughout the year, whereas, with the second strategy, samples were taken specifically for different flow conditions (low, mean and high flow). This study compares the advantages and constraints of both strategies. Results indicate that the first strategy is better suited to determine annual river loads. Quasi-continuous large-volume composite sampling is recommended for further monitoring campaigns.

Deep learning for historical books: classification of printing technology for digitized images

Printing technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized collections for this task. To overcome them and to achieve good performance, shallow networks and appropriate sampling strategies needed to be combined. We also show how class activation maps (CAM) can be used to analyze the results.

Relationship between Representation of Violence Against Women in TV Drama Serials and Reactivity of Viewers

Purpose: The present study aims to explore the role of viewer’s reactivity of entertainment and empathy in their perception of violence against women among viewers of Lahore, Pakistan. The supplementary aim was also formulated to gain more clear insight which is the role of viewers’ gender in viewer’s reactivity and perception of violence against women. Design/Methodology/Approach Quantitative method was used in the study. Sample was consisted of 500 viewers of the thirty dramas serials of Geo, ARY Digital and Hum TV which presented violence against women. The participants were drawn through purposive and snowball sampling strategies. The sample has 233 male participants and 267 female participants. Findings: The findings indicated that viewer’s reactivity of both enjoyment and empathy correlated with perception of violence against women. Gender differences were found only for the one sub scale of perception (i.e., domination). Implications/Originality/Value: The findings highlighted that perception of violence against women was more inclined towards active violence being portrayed by the entertainment channels and chances to imitate the same behavior in society cannot be ruled out.

Accounting for spatial sampling patterns in Bayesian phylogeography

Statistical phylogeography provides useful tools to characterize and quantify the spread of organisms during the course of evolution. Analyzing georeferenced genetic data often relies on the assumption that samples are preferentially collected in densely populated areas of the habitat. Deviation from this assumption negatively impacts the inference of the spatial and demographic dynamics. This issue is pervasive in phylogeography. It affects analyses that approximate the habitat as a set of discrete demes as well as those that treat it as a continuum. The present study introduces a Bayesian modeling approach that explicitly accommodates for spatial sampling strategies. An original inference technique, based on recent advances in statistical computing, is then described that is most suited to modeling data where sequences are preferentially collected at certain locations, independently of the outcome of the evolutionary process. The analysis of georeferenced genetic sequences from the West Nile virus in North America along with simulated data shows how assumptions about spatial sampling may impact our understanding of the forces shaping biodiversity across time and space.

WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications

ABSTRACTThe weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE. Key features of the new WESTPA 2.0 software enhance efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation datasets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and “binless” WE strategies.

A Mega-Analysis of Personality Prediction: Robustness and Boundary Conditions

Decades of studies identify prospective associations between personality characteristics and life outcomes. However, previous investigations of personality characteristic-outcome associations have not taken a principled approach to sampling strategies to ensure the robustness of personality-outcome associations. In a preregistered study, we test whether and for whom personality-outcome associations are robust against selection bias using prospective associations between 14 personality characteristics and 14 health, social, education/work, and societal outcomes across eight different person- and study-level moderators using individual participant data from 171,395 individuals across 10 longitudinal panel studies in a mega-analytic framework with propensity score matching. Two findings emerged: First, personality characteristics remain robustly associated with later life outcomes. Second, the effects generalize, as there are few moderators of personality-outcome associations. In sum, personality characteristics are robustly associated with later life outcomes with few moderated associations. We discuss how these findings can inform studies of personality-outcome associations.