The 25 most recently published documents
In tree-based adaptive mesh refinement (AMR) we store refinement trees in the cells of an unstructured coarse mesh. This lets us combine the speed and simpler management of structured refinement trees with the more flexible mesh generation of the unstructured coarse mesh. But this creates a conflict between performance and geometrical accuracy. If we favor speed we reduce the cells in our coarse mesh and hence reduce the accuracy of our geometrical representation. If we want more accurate results we generate a finer coarse mesh and lose performance by managing more cells in our unstructured coarse mesh. To mitigate this conflict we present the prototype of an geometry description which we implement in an already existing library. With this description we build geometry adapted hexahedral refinement trees, which also support high-order curved boundary cells. We also present examples on how to use this description. Moreover, we test the speedup of this new algorithm compared with coarse meshes with different geometrical errors.
Advocates of autonomous driving predict that the occupation of taxi driver could be made obsolete by shared autonomous vehicles (SAV) in the long term. Conducting interviews with German taxi drivers, we investigate how they perceive the changes caused by advancing automation for the future of their business. Our study contributes insights into how the work of taxi drivers could change given the advent of autonomous driving: While the task of driving could be taken over by SAVs for standard trips, taxi drivers are certain that other areas of their work such as providing supplementary services and assistance to passengers would constitute a limit to such forms of automation, but probably involving a shifting role for the taxi drivers, one which focuses on the sociality of the work. Our findings illustrate how taxi drivers see the future of their work, suggesting design implications for tools that take various forms of assistance into account, and demonstrating how important it is to consider taxi drivers in the co-design of future taxis and SAV services.
The epithelial sodium channel (ENaC) plays a key role in salt and water homeostasis in tetrapod vertebrates. There are four ENaC subunits (α, β, γ, δ), forming heterotrimeric αβγ- or δβγ-ENaCs. While the physiology of αβγ-ENaC is well understood, for decades the field has stalled with respect to δβγ-ENaC due to the lack of mammalian model organisms. The SCNN1D gene coding for δ-ENaC was previously believed to be absent in rodents, hindering studies using standard laboratory animals. We analysed all currently available rodent genomes and discovered that SCNN1D is present in rodents but was independently lost in five rodent lineages, including the Muridae (mice and rats). The independent loss of SCNN1D in rodent lineages may be constrained by phylogeny and taxon-specific adaptation to dry habitats, however habitat aridity does not provide a selection pressure for maintenance of SCNN1D across Rodentia. A fusion of two exons coding for a structurally flexible region in the extracellular domain of δ-ENaC appeared in the Hystricognathi (a group that includes guinea pigs). This conserved pattern evolved at least 41 Ma ago and represents a new autapomorphic feature for this clade. Exon fusion does not impair functionality of guinea pig (Cavia porcellus) δβγ-ENaC expressed in Xenopus oocytes. Electrophysiological characterisation at the whole-cell and single-channel level revealed conserved biophysical features and mechanisms controlling guinea pig αβγ- and δβγ-ENaC function as compared to human orthologues. Guinea pigs therefore represent commercially available mammalian model animals that will help shed light on the physiological function of δ-ENaC.
Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be more usable than two-factor authentication and just as secure. However, users currently obtain RBA's high security and usability benefits at the cost of exposing potentially sensitive personal data (e.g., IP address or browser information). This conflicts with user privacy and requires to consider user rights regarding the processing of personal data. We outline potential privacy challenges regarding different attacker models and propose improvements to balance privacy in RBA systems. To estimate the properties of the privacy-preserving RBA enhancements in practical environments, we evaluated a subset of them with long-term data from 780 users of a real-world online service. Our results show the potential to increase privacy in RBA solutions. However, it is limited to certain parameters that should guide RBA design to protect privacy. We outline research directions that need to be considered to achieve a widespread adoption of privacy preserving RBA with high user acceptance.
Components and Architecture for the Implementation of Technology-Driven Employee Data Protection
(2021)
Background & Objective: Due to the policy goals for sustainable energy production, renewable energy plants such as photovoltaics are increasingly in use. The energy production from solar radiation depends strongly on atmospheric conditions. As the weather mostly changes, electrical power generation fluctuates, making technical planning and control of power grids to a complex problem. Due to used materials (semiconductors e.g. silicon, gallium arsenide, cadmium telluride) the photovoltaic cells are spectrally selective. It means that only radiation of certain wavelengths converts into electrical energy. A material property called spectral response characterizes a certain degree of conversion of solar radiation into the electric current for each wavelength of solar light.
In the field of service robots, dealing with faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment due to insufficient knowledge for action execution. In our previous work [1], we have shown that such missing knowledge can be obtained through learning by experimentation. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. However we did not propose a suitable representation of the symbolic model. In this work we investigate such symbolic representation and evaluate its learning capability. The experimental analysis is performed on four use cases using four different learning paradigms. As a result, the symbolic representation together with the most suitable learning paradigm are identified.
Motivation: With the increasing penetration of distributed energy resources (DER) in power systems, the need for forecasts at the distribution level increases. Forecasts of distributed generation (DG) units and power flow forecasts of relevant distribution nodes are already required for capacity planning at the transmission level. In Germany, the upcoming redispatch 2.0 procedure will require forecasting at the distribution level. This paper evaluates the benefits of using advanced PV forecasts in predictive congestion management.
