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The available skid resistance, or friction, in a pavement surface is a vital parameter for functional evaluations of roads due to its relation with crashes. Therefore, highway administrations must collect friction data on their road network to provide safe roads to users. Additionally, a prediction model that can forecast the available skid resistance in each road segment is necessary for an efficient pavement management system (PMS). The aim of this paper is to develop a skid resistance prediction model for the bituminous pavements of the motorway network of federal state of Bavaria, in Germany, with information that is typically available in a PMS: the Annual Average Daily Traffic , the Annual Average Daily Heavy Traffic , and the number of lanes in each segment. Despite its simplicity, with 6410 road segments of 2 and 3 lanes of the Bavarian motorway network, the model achieves a determination coefficient (R2) of 0.405. If information about the surface layer material is added, R2 increases to 0.480. Consequently, apart from predicting the minimum available friction in each lane in a motorway, the study underlines the necessity that a PMS should contain the recommended elements and additional surface layer material, because the quality of the prediction improves. [ABSTRACT FROM AUTHOR]

It has been widely demonstrated that pre‐ and post‐migration experiences can be highly traumatic for refugees. The purpose of this study was to assess the mediating effects of religious coping and sense of coherence on the association of negative dysfunctional situation appraisal and mental well‐being among refugees. Paper surveys were completed by 600 people representing various refugee groups (mostly from Mashreq Countries) in German Bavaria, aged 18–65. Mediation analysis revealed that negative religious coping and sense of coherence singly and serially mediated the relationship between negative dysfunctional situation appraisal and well‐being, whereas positive religious coping did not have statistically significant relationships with these variables. The data obtained suggest that the negative religious coping style and sense of coherence are two important targets for future experimental studies, and interventions on them may bring the synergistic effect on improving mental well‐being in refugees. [ABSTRACT FROM AUTHOR]

Currently, numerous physician practices in industrial and emerging countries are being taken over by private equity firms and integrated into novel physician corporations. This involves private equity firms producing a global wealth chain (GWC) between their investors and the target asset, using offshore financial centres to facilitate tax‐avoiding reflux of capital. Moreover, they are opening up ambulatory health care as an asset for capital investment by overcoming previous market barriers to ambulatory health care via a legal construct. In this paper, we trace the spatial links of these finance‐side and sector‐specific corporate chains based on a capital flow analysis of private equity takeovers of Medical Care Centres (MCCs) in Bavaria, Germany. With our heuristics of a double‐layered GWC, which enables the extraction of value from the German health system, we contribute to the emerging GWC debate that aims to conceptualise the complex and often opaque spatialisations of financialisation processes. [ABSTRACT FROM AUTHOR]

The growing share of fluctuating renewable electricity production within the German energy system causes the increasing necessity for flexible consumers, producers, and storage technologies to balance supply and demand. District heating networks with combined heat and power units, Power-to-Heat applications, and thermal energy storage capacities can serve as one of these flexible options. In this context, a simulation model of the district heating network of the rural community Dollnstein, Germany, was built. With the residual load of different regional areas (Germany, Bavaria, Eichstätt, Dollnstein) it is investigated, how the heat generators can operate in an electricity market beneficial way. Two different control algorithms were evaluated: Due to a correlation between the residual loads and the CO2 emissions of the electricity mix, the CO2 savings achieved by this control algorithm are determined. Another way to operate electricity market beneficial is to consider the current CO2 emissions of each region. The main outcomes of this paper are, that there is a high potential for sector coupling by shifting the operation times of a CHP and a heat pump according to the residual load. The electricity demand of the heat pump can be met in terms of low CO2 emissions of the electricity mix, while the CHP can replace electricity with high CO2 emissions. These results can be improved, by considering not the residual load but the current CO2 emissions in the control algorithm. [ABSTRACT FROM AUTHOR]

In this article, we describe in detail three seismic measurement campaigns based on refraction methods that we conducted at different sites in Bavaria, Germany. The measured data is published as an open data set. The particularity of this data set lies in its available ground truth information about each measurement site. Acquiring seismic data from sites with ground truth information is important for validation of seismic inversion algorithms. Since near-surface seismic field data with ground truth information is rather limited, we anticipate this data set to be a valuable contribution to the research community. For the measurements, three sites have been selected: (1) a gravel pit with a ground water layer, (2) a site above a highway tunnel and (3) a surface over underground tubes. The measurements have been conducted using line arrays of geophones, the Geode Seismograph from Geometrics Inc. and hammer strikes as seismic source. To obtain inversion results a travel time tomography based on first-arrivals within the software SeisImager is used. The inversion results show that we are able to image the ground water layer in the gravel pit, the highway tunnel and partly features of underground tubes. Furthermore, the results coincide with available ground truth information about the measurement sites. This paper summarizes the measurement campaigns and the respective data sets obtained through these campaigns. The data have been published by the authors as an open data set under the license CC BY 4.0 on figshare to make it available to the research community for validation of seismic data processing and inversion techniques. [ABSTRACT FROM AUTHOR]

While the swabian and franconian karsts of southern Germany have yielded rich and important neogene vertebrate faunas, the middle to late Miocene transition is still extremely underrepresented. This period is however key for the understanding of the European mammal evolution. A microtoid rodent Microtocricetus M2 is described and commented in this paper. The fossil discovered in the karst filling Petersbuch 69 is regarded herein such as derived and might be thus the youngest fossil of the genus in Germany. [ABSTRACT FROM AUTHOR]

In early medieval times, the people of the Baiuvarii inhabited large parts of modern Bavaria (Germany). Among other evidence, about 10,000 graves dating from this time provide evidence of an exceptional level of settlement activity. These graves were arranged predominately in multiple parallel rows, so‐called Reihengräber, whereby the bodies were placed in stretched, supine positions. Burials that deviate from this custom are rarely found. For this reason, the excavation of a male skeleton at the cemetery of Aying‐Großhelfendorf (seventh to eighth centuries) attracted particular attention. The skeleton with label number 115 was found in a prone position and appears to have sustained four separate instances of sharp force trauma. The grave included a number of metal grave goods including a spatha and an iron shield boss. One focus of the study was on the macroscopic examination of the violence‐related lesions found on skeleton 115. They were located on the femur, the humerus, the frontal bone, and the maxilla on the right side of the body. The shape of the lesions indicates the insertion of a heavy, sharp, or bladed instrument. There is no evidence of healing detectable, leading to the conclusion that the individual was most likely involved in fight with fatal outcome. Subsequently, the victim was buried in a prone position alongside 17 other individuals in an otherwise regular graveyard. A possible interpretation of this difference in burial treatment is the fear of the victim's dependents of an unexpected or violent death or an expression of foreign origin. The aim of the paper is to present the most likely interpretation of the evidence in correlation with the anthropological and archaeological data. [ABSTRACT FROM AUTHOR]

• Soil reflectance composites were generated with Sentinel-2 L2A data. • A multi-channel CNN learning framework for soil organic carbon prediction is proposed. • A dual input deep learning architecture improves the predictions. • The framework produced results that can be interpreted. The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN's different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92). [ABSTRACT FROM AUTHOR]

After the new Coronavirus disease (COVID-19) emerged in the end of January 2020 in Germany, a large number of individuals suffered from severe symptoms and eventually needed intensive care in hospitals. Due to the rapid spread of the disease, the number of deceased individuals increased as well, which is a motivation to prevent as many new infections as possible. Therefore, the knowledge about the current evolution of the virus spread is crucial to predict its future behavior and to react with suitable interventions. In this paper, the evolution of the COVID-19 pandemic in Germany is forecasted by a network-based inference method, in which the interactions of individuals are taken into account using a contact matrix. Then the results are compared to the predictions without considering a contact matrix as well as to the logistic regression, which shows the advantage of incorporating the contact matrix. Furthermore, the basic reproduction number of the pandemic in Germany using a neural network approach is estimated and used for further predictions of the evolution of COVID-19 in Germany. In order to mathematically model the different compartments of the population in the considered regions, the classical SIR model is employed. In this work, we deploy the LASSO (Least Absolute Shrinkage and Selection Operator) for the unknown parameter estimation. Furthermore, we calculate and illustrate the MAPE (Mean Absolute Percentage Error) of the estimations to show the accuracy of the predictions. The results include model parameter estimation and model validation, as well as the outbreak forecasting using network-informed algorithms. Our findings show that the network-inference based approach outperforms the logistic regression as well as the neural network approach and the SIR model calibration without a contact network. Furthermore according to the results, the network-inference based approach is particularly suitable for short- to mid-term predictions, even when there is not much information about the new disease. Moreover, the predictions based on the estimation of the reproduction number in Germany can yield more reliable results with increasing the availability of data, but could not outperform the network-inference based algorithm. • The evolution of the COVID-19 pandemic in Germany and Bavaria state is forecasted. • The approach takes into account the interactions of individuals via a contact matrix. • The Mean Absolute Percentage Error (MAPE) of the estimation is calculated. • The prediction results are compared with the true data, NNs and logistic regression. • The predictions outperform the results of estimations without contact matrix. • The network-based approach is suitable for short- to mid-term predictions. • The predictions are informative for decision-makers to set protective measures. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) provide a promising solution for the maintenance and operation of bridges, thanks to their ability to mirror physical/structural conditions. A bridge DT generally consists of a geometric-semantic model whose creation, however, requires extensive manual effort. This paper presents an automated framework to generate the parametric model of bridges from their segmented point clouds. Following the concept of reverse engineering with parametric modeling, Parametric Prototype Models (PPMs) are proposed as tools to extract parameter values from point clouds. A local and global optimization problem is defined to adjust and assemble PPMs into an integrated model. The proposed approach has been validated by applying it to the point cloud of bridge components as well as point clouds captured from six concrete bridges in Bavaria, Germany. The results show that the proposed approach can generate the parametric model of bridges with a mean absolute error (MAE) of 8.71 cm. • A reverse engineering approach is proposed for the parametric modeling of bridges. • Parametric Prototype Models (PPMs) are introduced to describe bridge point clouds. • Local and global optimization problems are defined to adjust and assemble PPMs. • Metaheuristic optimization algorithms are utilized to derive parameter values. • The method is validated with the point cloud of six bridges in Bavaria, Germany. [ABSTRACT FROM AUTHOR]

• Long-distance heat transport reduce the cost of heat supply by 15% • Cost of geothermal heat supply is competitive with other heat technologies. • Economic performance of geothermal systems increases with high full-load hours. • Geothermal energy can transition from a local to a regional use. • Geothermal district heating systems contribute to the low-carbon heat supply. Geothermal energy can play an important role in decarbonizing the heating sector, however, a limiting factor is that heat demand in urban areas does not usually coincide spatially with geological settings favorable to the extraction of geothermal energy. Long-distance heat transport could enable the direct use of geothermal resources even in areas with low or no geothermal potential. This paper proposes the cost-optimal coordinated deployment of geothermal heating plants together with heat transport and distribution networks to simultaneously supply geothermal heat to multiple urban areas. To this end, a holistic approach comprising the mapping of geothermal potential for direct-use, the estimation of district heating potential, and a two-step optimization model to calculate cost-optimal large-scale geothermal district heating systems, is presented and applied to the Free State of Bavaria in Germany. As a result, heat supply costs can be reduced by 15% if fewer geothermal wells are drilled in more geologically favorable areas at greater distances from heat sinks. Calculated levelized costs of heat without local distribution networks of 33–39 €/MWh show that geothermal energy could transition from a local to a regional use if utilized in in scenarios with high full-load hours. The proposed methodology can be adapted to develop expansion strategies for deep geothermal energy in other similar regions worldwide. [ABSTRACT FROM AUTHOR]