Current Productions Carnuntum, German Limes and Radiopast

[EN] The here presented three chosen projects mark out different techniques of production and their transmission of content. The differentiated impact on the public absorption of the transported content are described dependent to experiences with it in exhibitions and publications, and can be used to rectify future approaches of similar topics. In most of these productions, technical difficulties were observed and solved through extensive use of different tools and techniques to achieve a reasonable output and represent our current state of knowledge which we would like to share. The documentation of the production as well as the communication between the production and research team is indispensable to the sucsess of these media formats.[ES] Presento tres proyectos elegidos que delimitan técnicas diferentes de la producción y su transmisión de contenido. El impacto diferenciado en la absorción pública del contenido es descrito dependiente a experiencias con ello en exposiciones y publicaciones, y puede ser usado para rectificar futuros acercamientos de temas similares. En la mayor parte de estas producciones, las dificultades técnicas fueron estudiadas y solucionadas por el uso extenso de instrumentos diferentes y técnicas para conseguir una salida razonable y representar nuestro estado del conocimiento que nos gustaría compartir. La documentación de la producción así como la comunicación entre la producción y grupo de investigación es indispensable en estos formatos multimedia.The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007- 2013) under grant agreement n° 230679, under the action Marie Curie – People IAPP, with the Project entitled “Radiography of the past. Integrated non-destructive approaches to understand and valorise complex archaeological sitesHumer, F.; Gugl, C.; Pregesbauer, M.; Vermeulen, F.; Corsi, C.; Klein, M. (2011). Current Productions Carnuntum, German Limes and Radiopast. Virtual Archaeology Review. 2(4):131-137. https://doi.org/10.4995/var.2011.4569OJS13113724CORSI C., DE DAPPER M., DE PREZ S., VERMEULEN F. (2005). Geoarchaeological observations on the Roman town of Ammaia, Internet Archaeology 19.CORSI C., VERMEULEN F. (2007). Elementi per la ricostruzione del paesaggio urbano e suburbano della città romana di Ammaia in Lusitania, Lusitania, Archeologia Aerea 3: 13-30.DEPREZ S., DE DAPPER M. & DE JAEGER C. (2006), The water supply of the Roman town of Ammaia (Northeastern Alentejo, Portugal): a geoarchaeological case study, Publicações da Associação Portuguesa de Geomorfólogos 3: 109-133.MANTAS V. (2000). A sociedade luso-romano do município de Ammaia, in: Gorges J.-G. & Nogales Basarrate T. (Eds.), Sociedad y cultura en Lusitania romana, Mérida, Museo Nacional de Arte Romano: 391-420.http://www.carnuntum.co.at/ , http://www.carnuntum-db.at/, http://www.limes.co.at/, http://7reasons.at/http://www.limeswelten.net/, http://www.arctron.de/, http://7reasons.at

Using 18O/2H, 3H/3He, 85Kr and CFCs to determine mean residence times and water origin in the Grazer and Leibnitzer Feld groundwater bodies (Austria)

Two groundwater bodies, Grazer Feld and Leibnitzer Feld, with surface areas of 166 and 103 km2 respectively are characterised for the first time by measuring the combination of δ18O/δ2H, 3H/3He, 85Kr, CFC-11, CFC-12 and hydrochemistry in 34 monitoring wells in 2009/2010. The timescales of groundwater recharge have been characterised by 131 δ18O measurements of well and surface water sampled on a seasonal basis. Most monitoring wells show a seasonal variation or indicate variable contributions of the main river Mur (0–30%, max. 70%) and/or other rivers having their recharge areas in higher altitudes. Combined δ18O/δ2H-measurements indicate that 65–75% of groundwater recharge in the unusual wet year of 2009 was from precipitation in the summer based on values from the Graz meteorological station. Monitoring wells downstream of gravel pit lakes show a clear evaporation trend. A boron–nitrate differentiation plot shows more frequent boron-rich water in the more urbanised Grazer Feld and more frequent nitrate-rich water in the more agricultural used Leibnitzer Feld indicating that a some of the nitrate load in the Grazer Feld comes from urban sewer water. Several lumped parameter models based on tritium input data from Graz and monthly data from the river Mur (Spielfeld) since 1977 yield a Mean Residence Time (MRT) for the Mur-water itself between 3 and 4 years in this area. Data from δ18O, 3H/3He measurements at the Wagna lysimeter station supports the conclusion that 90% of the groundwaters in the Grazer Feld and 73% in the Leibnitzer Feld have MRTs of 20 m) with relative thicker unsaturated zones. The young MRT of groundwater from two monitoring wells in the Leibnitzer Feld was confirmed by 85Kr-measurements. Most CFC-11 and CFC-12 concentrations in the groundwater exceed the equilibration concentrations of modern concentrations in water and are therefore unsuitable for dating purposes. An enrichment factor up to 100 compared to atmospheric equilibrium concentrations and the obvious correlation of CFC-12 with SO4, Na, Cl and B in the ground waters of the Grazer Feld suggest that waste water in contact with CFC-containing material above and below ground is the source for the contamination. The dominance of very young groundwater (<5 years) indicates a recent origin of the contamination by nitrate and many other components observed in parts of the groundwater bodies. Rapid measures to reduce those sources are needed to mitigate against further deterioration of these waters

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

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-&amp;-Spilling or Puddle-to-Puddle Dynamic Filling-&amp;-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