High-order mixed finite elements for an energy-based model of the polarization process in ferroelectric materials

An energy-based model of the ferroelectric polarization process is presented in the current contribution. In an energy-based setting, dielectric displacement and strain (or displacement) are the primary independent unknowns. As an internal variable, the remanent polarization vector is chosen. The model is then governed by two constitutive functions: the free energy function and the dissipation function. Choices for both functions are given. As the dissipation function for rate-independent response is non-differentiable, it is proposed to regularize the problem. Then, a variational equation can be posed, which is subsequently discretized using conforming finite elements for each quantity. We point out which kind of continuity is needed for each field (displacement, dielectric displacement and remanent polarization) is necessary to obtain a conforming method, and provide corresponding finite elements. The elements are chosen such that Gauss' law of zero charges is satisfied exactly. The discretized variational equations are solved for all unknowns at once in a single Newton iteration. We present numerical examples gained in the open source software package Netgen/NGSolve

Safer_RAIN: A DEM-based hierarchical filling-&-spilling algorithm for pluvial flood hazard assessment and mapping across large urban areas

The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g., cloudbursts, rainstorms, heavy rainfall, hail, heavy snow), combined with the high population density and concentration of assets, makes urban areas particularly vulnerable to pluvial flooding. Hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modeling is resource intensive and therefore scarcely suitable for performing consistent hazard assessments across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential of fast-processing DEM-based methods, such as the Hierarchical Filling-&-Spilling or Puddle-to-Puddle Dynamic Filling-&-Spilling Algorithms (abbreviated herein as HFSAs). We develop a fast-processing HFSA, named Safer_RAIN, that enables mapping of pluvial flooding in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. We present the first applications of the algorithm to two case studies in Northern Italy. Safer_RAIN output is compared against ground evidence and detailed output from a two-dimensional (2D) hydrologic and hydraulic numerical model (overall index of agreement between Safer_RAIN and 2D benchmark model: sensitivity and specificity up to 71% and 99%, respectively), highlighting potential and limitations of the proposed algorithm for identifying pluvial flood-hazard hotspots across large urban environments

Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum

We introduce a new measure of bivariate jointness to assess the degree of inclusion dependency between pairs of explanatory variables in Bayesian Model Averaging analysis. Building on the discussion concerning appropriate statistics to assess covariate inclusion dependency in this context, a set of desirable properties for bivariate jointness measures is proposed. We show that none of the proposed measures so far meets all these criteria and an alternative measure is presented which fulfils all of them. Our measure corresponds to a regularised version of the Yule’s Q association coefficient, obtained by combining the original measure with a Jeffreys prior to avoid problems in the case of zero counts. We provide an empirical illustration using cross-country data on economic growth and its determinants

Unveiling covariate inclusion structures in economic growth regressions using latent class analysis

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian Model Averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model pace formed by linear regression models reveals interesting patterns of complementarity and substitutabiliy across economic growth determinants

A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation

Background: The aim of this study was to investigate and model the interactions between a range of risk and protective factors for suicidal ideation using general population data collected during the critical phase of the COVID-19 pandemic. Methods: Bayesian network analyses were applied to cross-sectional data collected 1 month after the COVID-19 lockdown measures were implemented in Austria and the United Kingdom. In nationally representative samples (n = 1,005 Austria; n = 1,006 UK), sociodemographic features and a multi-domain battery of health, wellbeing and quality of life (QOL) measures were completed. Predictive accuracy was examined using the area under the curve (AUC) within-sample (country) and out-of-sample. Results: The AUC of the Bayesian network models were ≥ 0.84 within-sample and ≥0.79 out-of-sample, explaining close to 50% of variability in suicidal ideation. In total, 15 interrelated risk and protective factors were identified. Seven of these factors were replicated in both countries: depressive symptoms, loneliness, anxiety symptoms, self-efficacy, resilience, QOL physical health, and QOL living environment. Conclusions: Bayesian network models had high predictive accuracy. Several psychosocial risk and protective factors have complex interrelationships that influence suicidal ideation. It is possible to predict suicidal risk with high accuracy using this information

Nationaler Energie- und Klimaplan (NEKP) für Österreich - Wissenschaftliche Bewertung der in der Konsultation 2023 vorgeschlagenen Maßnahmen [National Energy and Climate Plan (NEKP) for Austria - Scientific assessment of the measures proposed in the 2023 consultation]

Um den globalen Klimawandel zu bremsen, seine Auswirkungen abzumildern und eine nach-haltige Zukunft für junge und zukünftige Generationen zu gestalten, sind internationale Koor-dination sowie umfassende nationale Umsetzungspläne für Klimamaßnahmen unerlässlich. Vor diesem Hintergrund hat das Bundesministerium für Klimaschutz, Umwelt, Energie, Mobi-lität, Innovation und Technologie (BMK) nach Einbindung der relevanten anderen österreichi-schen Bundesministerien Ende Juni 2023 den Entwurf eines integrierten nationalen Energie- und Klimaplans (NEKP) für Österreich (Periode 2021-2030) vorgelegt. Dieser Entwurf stand im Sommer 2023 zur Kommentierung offen, um eine breite Beteiligung von öffentlichen und privaten Institutionen und Personen sicherzustellen. In order to slow down global climate change, mitigate its effects and shape a sustainable future for young and future generations, international coordination and comprehensive national implementation plans for climate measures are essential. Against this background, the Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology (BMK), after involving the relevant other Austrian federal ministries, presented the draft of an integrated national energy and climate plan (NEKP) for Austria at the end of June 2023 ( Period 2021-2030). This draft was open for comment in summer 2023 to ensure broad participation from public and private institutions and individuals

The role of renal hypoperfusion in development of renal microcirculatory dysfunction in endotoxemic rats

To study the role of renal hypoperfusion in development of renal microcirculatory dysfunction in endotoxemic rats. Rats were randomized into four groups: a sham group (n = 6), a lipopolysaccharide (LPS) group (n = 6), a group in which LPS administration was followed by immediate fluid resuscitation which prevented the drop of renal blood flow (EARLY group) (n = 6), and a group in which LPS administration was followed by delayed (i.e., a 2-h delay) fluid resuscitation (LATE group) (n = 6). Renal blood flow was measured using a transit-time ultrasound flow probe. Microvascular perfusion and oxygenation distributions in the renal cortex were assessed using laser speckle imaging and phosphorimetry, respectively. Interleukin (IL)-6, IL-10, and tumor necrosis factor (TNF)-α were measured as markers of systemic inflammation. Furthermore, renal tissue samples were stained for leukocyte infiltration and inducible nitric oxide synthase (iNOS) expression in the kidney. LPS infusion worsened both microvascular perfusion and oxygenation distributions. Fluid resuscitation improved perfusion histograms but not oxygenation histograms. Improvement of microvascular perfusion was more pronounced in the EARLY group compared with the LATE group. Serum cytokine levels decreased in the resuscitated groups, with no difference between the EARLY and LATE groups. However, iNOS expression and leukocyte infiltration in glomeruli were lower in the EARLY group compared with the LATE group. In our model, prevention of endotoxemia-induced systemic hypotension by immediate fluid resuscitation (EARLY group) did not prevent systemic inflammatory activation (IL-6, IL-10, TNF-α) but did reduce renal inflammation (iNOS expression and glomerular leukocyte infiltration). However, it could not prevent reduced renal microvascular oxygenatio