Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning

Deployment in hazardous environments requires robots to understand the risks associated with their actions and movements to prevent accidents. Despite its importance, these risks are not explicitly modeled by currently deployed locomotion controllers for legged robots. In this work, we propose a risk sensitive locomotion training method employing distributional reinforcement learning to consider safety explicitly. Instead of relying on a value expectation, we estimate the complete value distribution to account for uncertainty in the robot's interaction with the environment. The value distribution is consumed by a risk metric to extract risk sensitive value estimates. These are integrated into Proximal Policy Optimization (PPO) to derive our method, Distributional Proximal Policy Optimization (DPPO). The risk preference, ranging from risk-averse to risk-seeking, can be controlled by a single parameter, which enables to adjust the robot's behavior dynamically. Importantly, our approach removes the need for additional reward function tuning to achieve risk sensitivity. We show emergent risk sensitive locomotion behavior in simulation and on the quadrupedal robot ANYmal

The Implementation of Scenarios using DSGE Models

The new generation of dynamic stochastic general equilibrium (DSGE) models seems particularly suited for conducting scenario analysis. These models formalise the behaviour of economic agents on the basis of explicit micro-foundations. As a result, they appear less prone to the Lucas critique than traditional macroeconometric models. DSGE models provide researchers with powerful tools, which allow for the design of a broad range of scenarios and can tackle a large range of issues, while at the same time offering an appealing structural interpretation of the scenario specification and simulation results. This paper provides illustrations of some of the modelling issues that often arise when implementing scenarios using DSGE models in the context of projection exercises or policy analysis. These issues reflect the sensitivity of DSGE model-based analysis to scenario assumptions, which in more traditional models are apparently less critical, such as, for example, scenario event anticipation and duration, as well as treatment of monetary and fiscal policy rules.Business fluctuations, monetary policy, fiscal policy, forecasting and simulation

Design Model of an Ecosystem for Resilient and Sustainable Value Creation of SMEs in Single and Small Batch Production

Today's markets are increasingly dynamic, not only due to shorter product development times and fast changing customer requirements but also unforeseen events. Contemporary crises and wars disrupt entire supply chains and can have existential consequences for manufacturing companies. In these times of uncertainty, it is essential for SMEs to have a resilient business orientation while at the same time fulfil the sustainability aspects demanded by their stakeholders. This paper provides a design model for an ecosystem for a resilient and sustainable value creation of SMEs in single and small batch production to increase competitiveness and to gain a better response to market dynamics. The developed model comprises the elements of ecosystem strategy, configuration and coordination. An adequate partner matching and the underlying business model complement the approach. The model is intended to assist practitioners as a reference framework in developing and managing ecosystems for value creation

Interdisciplinary fracture network characterization in the crystalline basement: a case study from the Southern Odenwald, SW Germany

The crystalline basement is considered a ubiquitous and almost inexhaustible source of geothermal energy in the Upper Rhine Graben (URG) and other regions worldwide. The hydraulic properties of the basement, which are one of the key factors in the productivity of geothermal power plants, are primarily controlled by hydraulically active faults and fractures. While the most accurate in situ information about the general fracture network is obtained from image logs of deep boreholes, such data are generally sparse and costly and thus often not openly accessible. To circumvent this problem, an outcrop analogue study was conducted with interdisciplinary geoscientific methods in the Tromm Granite, located in the southern Odenwald at the northeastern margin of the URG. Using light detection and ranging (lidar) scanning, the key characteristics of the fracture network were extracted in a total of five outcrops; these were additionally complemented by lineament analysis of two different digital elevation models (DEMs). Based on this, discrete fracture network (DFN) models were developed to calculate equivalent permeability tensors under assumed reservoir conditions. The influences of different parameters, such as fracture orientation, density, aperture and mineralization, were investigated. In addition, extensive gravity and radon measurements were carried out in the study area, allowing fault zones with naturally increased porosity and permeability to be mapped. Gravity anomalies served as input data for a stochastic density inversion, through which areas of potentially increased open porosity were identified. A laterally heterogeneous fracture network characterizes the Tromm Granite, with the highest natural permeabilities expected at the pluton margin, due to the influence of large shear and fault zones

Nuclear Magnetic Resonance Solution Structure and Functional Behavior of the Human Proton Channel

The human voltage-gated proton channel [Hv1(1) or VSDO(2)] plays an important role in the human innate immune system. Its structure differs considerably from those of other cation channels. It is built solely of a voltage-sensing domain and thus lacks the central pore domain, which is essential for other cation channels. Here, we determined the solution structure of an N- and C-terminally truncated human Hv1 (Δ-Hv1) in the resting state by nuclear magnetic resonance (NMR) spectroscopy. Δ-Hv1 comprises the typical voltage-sensing antiparallel four-helix bundle (S1–S4) preceded by an amphipathic helix (S0). The solution structure corresponds to an intermediate state between resting and activated forms of voltage-sensing domains. Furthermore, Zn2+-induced closing of proton channel Δ-Hv1 was studied with two-dimensional NMR spectroscopy, which showed that characteristic large scale dynamics of open Δ-Hv1 are absent in the closed state of the channel. Additionally, pH titration studies demonstrated that a higher H+ concentration is required for the protonation of side chains in the Zn2+-induced closed state than in the open state. These observations demonstrate both structural and dynamical changes involved in the process of voltage gating of the Hv1 channel and, in the future, may help to explain the unique properties of unidirectional conductance and the exceptional ion selectivity of the channel

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on a library of 28,294 lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy

Persistent bone impairment despite long-term control of hyperprolactinemia and hypogonadism in men and women with prolactinomas.

While prolactinoma patients have high bone turnover, current data are inconclusive when it comes to determining whether correction of hyperprolactinemia and associated hypogandism improves osteodensitometric data in men and women over the long term. In a large cohort of including 40 men and 60 women, we studied the long-term impact of prolactinoma treatment on bone mineral density (BMD) in men versus women, assessed adverse effects of a primary surgical or medical approach, and evaluated data for risk factors for impaired BMD at last follow-up using multivariate regression analyses. Median duration of follow-up was 79 months (range 13-408 months). Our data indicate that the prevalence of impaired BMD remained significantly higher in men (37%) than in women (7%, p < 0.001), despite the fact that hyperprolactinemia and hypogonadism are under control in the majority of men. We found that persistent hyperprolactinemia and male sex were independent risk factors for long-term bone impairment. Currently, osteoporosis prevention and treatment focus primarily on women, yet special attention to bone loss in men with prolactinomas is advised. Bone impairment as "end organ" reflects the full range of the disease and could become a surrogate marker for the severity of long-lasting hyperprolactinemia and associated hypogonadism

Multi-scale structural dataset of a crystalline reservoir analogue (Northern Odenwald)

For an accurate multi-scale property modelling of fractured crystalline geothermal reservoirs, an enhanced characterisation of the geometrical features and variability of the fracture network properties is an essential prerequisite. By the combination of regional digital elevation model analysis and local outcrop investigation, detailed insight into the 3D architecture of faults and fracture networks allows the quantification of structural parameters (fracture dimension, orientation, clustering and spacing). The structural dataset presented here contains the regional DEM interpretation at two resolutions (25 m and 1 m) of the Northern Odenwald and the LiDAR and GIS structural interpretation of 5 profiles acquired in the Mainzer Berg quarry between Darmstadt and Dieburg. This quarry exhibits the fracture network affecting a granodioritic pluton. Fracture length, orientation, dip, and fracture density and intensity are calculated for each profile. On GIS 2D datasets extracted from top and side views of the profiles, a clustering and spacing analysis between digitised items is performed and compared to the orientation of artificial scanlines. Power-law parametrisation is extracted from the length distribution of 2D and 3D datasets, with a, b coefficients. This multi-scale parametrisation of the fracture network can be used to construct near-surface discrete fracture network models. The dataset is a supplement to another publication (Bossennec al. 2021) that presents the structural organisation of crystalline rocks from the analogue of Mainzer Berg in the Northern Odenwald.V1.

Multiscale Characterisation of Fracture Patterns of a Crystalline Reservoir Analogue

For an accurate multiscale property modelling of fractured crystalline geothermal reservoirs, an enhanced characterisation of the geometrical features and variability of the fracture network properties is an essential prerequisite. Combining regional digital elevation model analysis and local outcrop investigation, the study comprises the characterisation of the fracture pattern of a crystalline reservoir analogue in the Northern Odenwald, with LiDAR and GIS structural interpretation. This approach provides insights into the 3D architecture of the fault and fracture network, its clustering, and its connectivity. Mapped discontinuities show a homogeneous length distribution, which follows a power law with a −2.03 scaling factor. The connectivity of the fracture network is heterogenous, due to a fault control at the hectometric scale. Clustering is marked by long sub-vertical fractures at the outcrop scale, and strongly enhance heterogeneity around weathered fracture and fault corridors. The multi-variable dataset created within this study can be used as input data for accurate discrete fracture networks and fluid-flow modelling of reservoirs of similar type