45 research outputs found
Process reliability and reproducibility of pneumomechanical and electrohydraulic forming processes
A sufficiently high process reliability and reproducibility is mandatory if a high-speed
forming process is to be used in industrial production. A great deal of basic research work
into pneumo-mechanical and electrohydraulic forming has been successfully performed in
different institutions in the past. There, the focus has been more on process related
correlations, such as the influence and interaction of different parameters on the course
and result of those processes. The aspects of reliability and reproducibility have not been
examined to a sufficient extent. Hence, in the case of pneumo-mechanical forming,
insufficient investigations have been conducted into the effect that key parameters like the
kinetic energy level, the filling height of the working media or the conditions inside the
acceleration tube have on the reproducibility and course of the process. For
electrohydraulic forming, the repeatability has worsened on occasions up to now. To
improve the forming results and, in particular, the reputability of the process, it is
necessary to examine the tool parameters associated with the electrodes and the working
media. That is why research of this type is currently ongoing at the LUF. One important
issue here is examining the options that exist for visualising the way the spark takes hold
in the discharge chamber
Predictive Analytics Supporting Labor Market Success: A Career Explorer for Job Seekers and Workforce Professionals in Michigan
Career Explorer provides customized career exploration tools for workforce development staff and job seekers in Michigan. There are separate Career Explorer modules for mediated staff services and self-service by job seekers. The system was developed by the Michigan Center for Data and Analytics in collaboration with the W.E. Upjohn Institute for Employment Research and Michigan Works! Southwest. It was funded by the U.S. Department of Labor’s Office of Workforce Investment and the Schmidt Futures foundation’s Data for the American Dream (D4AD) project. In this paper, we describe specifications of the models behind the frontline-staff-mediated version of Career Explorer, which are based on program administrative data, applying data-science methods for predictive analytics. We also describe the self-service Career Explorer, which provides customized labor market information based on published Bureau of Labor Statistics data. Career Explorer became an active feature of Michigan’s online reemployment-services system in June 2021
Constructing Delaunay triangulations along space-filling curves
Incremental construction con BRIO using a space-filling curve order for insertion is a popular algorithm for constructing Delaunay triangulations. So far, it has only been analyzed for the case that a worst-case optimal point location data structure is used which is often avoided in implementations. In this paper, we analyze its running time for the more typical case that points are located by walking. We show that in the worst-case the algorithm needs quadratic time, but that this can only happen in degenerate cases. We show that the algorithm runs in O(n logn) time under realistic assumptions. Furthermore, we show that it runs in expected linear time for many random point distributions. This research was supported by the Deutsche Forschungsgemeinschaft within the European graduate program ’Combinatorics, Geometry, and Computation’ (No. GRK 588/2) and by the Netherlands’ Organisation for Scientific Research (NWO) under BRICKS/FOCUS grant number 642.065.503 and project no. 639.022.707
The disruption of GDP-fucose de novo biosynthesis suggests the presence of a novel fucose-containing glycoconjugate in <i>Plasmodium</i> asexual blood stages
Glycosylation is an important posttranslational protein
modification in all eukaryotes. Besides
glycosylphosphatidylinositol (GPI) anchors and N-glycosylation,
O-fucosylation has been recently reported in key sporozoite
proteins of the malaria parasite. Previous analyses showed the
presence of GDP-fucose (GDP-Fuc), the precursor for all
fucosylation reactions, in the blood stages of Plasmodium
falciparum. The GDP-Fuc de novo pathway, which requires the
action of GDP-mannose 4,6-dehydratase (GMD) and GDP-L-fucose
synthase (FS), is conserved in the parasite genome, but the
importance of fucose metabolism for the parasite is unknown. To
functionally characterize the pathway we generated a PfGMD
mutant and analyzed its phenotype. Although the labelling by the
fucose-binding Ulex europaeus agglutinin I (UEA-I) was
completely abrogated, GDP-Fuc was still detected in the mutant.
This unexpected result suggests the presence of an alternative
mechanism for maintaining GDP-Fuc in the parasite. Furthermore,
PfGMD null mutant exhibited normal growth and invasion rates,
revealing that the GDP-Fuc de novo metabolic pathway is not
essential for the development in culture of the malaria parasite
during the asexual blood stages. Nonetheless, the function of
this metabolic route and the GDP-Fuc pool that is generated
during this stage may be important for gametocytogenesis and
sporogonic development in the mosquito
Approachability in Stackelberg Stochastic Games with Vector Costs
The notion of approachability was introduced by Blackwell [1] in the context
of vector-valued repeated games. The famous Blackwell's approachability theorem
prescribes a strategy for approachability, i.e., for `steering' the average
cost of a given agent towards a given target set, irrespective of the
strategies of the other agents. In this paper, motivated by the multi-objective
optimization/decision making problems in dynamically changing environments, we
address the approachability problem in Stackelberg stochastic games with vector
valued cost functions. We make two main contributions. Firstly, we give a
simple and computationally tractable strategy for approachability for
Stackelberg stochastic games along the lines of Blackwell's. Secondly, we give
a reinforcement learning algorithm for learning the approachable strategy when
the transition kernel is unknown. We also recover as a by-product Blackwell's
necessary and sufficient condition for approachability for convex sets in this
set up and thus a complete characterization. We also give sufficient conditions
for non-convex sets.Comment: 18 Pages, Submitted to Dynamic Games and Application
Toward Self-Referential Autonomous Learning of Object and Situation Models
Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach