Laboratory-scale liquefiers for natural gas: A design and assessment study

Exact knowledge of natural gas composition is essential in custody transfer to determine the energy content of the delivery. However, for liquefied natural gas (LNG), a reliable composition determination is difficult. Here, we describe the design of a laboratory-scale reference liquefier that enables the validation and calibration of optical spectroscopy sensors by providing them with a sample of metrologically traceable composition. Hence, it is crucial to avoid fractionation of the sample during liquefaction. This is realized by supercritical liquefaction of a reference gas mixture in conjunction with a special vapor–liquid-equilibrium (VLE) cell. As this is a demanding high-pressure application, low-pressure condensation as liquefaction method was also assessed. Through experimental investigations and VLE calculations, preservation of the composition of the produced liquid sample during condensation was studied. We conclude that under optimized conditions, validation, and calibration measurements of optical sensors can be performed on condensed liquids, which, however, needs further confirmation.     

A non-invasive form finding method with application to metal forming

Inverse form finding aims in determining the optimal material configuration of a workpiece for a specific forming process. A gradient- and parameter-free (nodal-based) form finding approach has recently been developed, which can be coupled non-invasively as a black box to arbitrary finite element software. Additionally the algorithm is independent from the constitutive behavior. Consequently, the user has not to struggle with the underlying optimization theory behind. Benchmark tests showed already that the approach works robustly and efficiently. This contribution demonstrates that the optimization algorithm is also applicable to more sophisticated forming processes including orthotropic large strain plasticity, combined hardening and frictional contact. A cup deep drawing process with solid-shell elements and a combined deep drawing and upsetting process to form a functional component with external teeth are investigated.     

Training echo state networks for rotation-invariant bone marrow cell classification

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

     

Das IT-Sicherheitsgesetz

Datenschutz und Datensicherheit - DuD - Die öffentliche Sicherheit zählt zu den wichtigsten Aufgaben der Innenpolitik. Dazu gehören der Schutz der Bürgerinnen und Bürger...     

Multi-agent programming contest 2017: lampe team description

In this paper we describe our participation in the 2017 edition of the Multi-Agent Programming Contest as team ‘lampe’. Our strategy was implemented in C++; it uses a centralised organisation of agents and evaluates different strategies based on an internal simulation of the future game state. Strategies are generated using handwritten heuristics in a randomised fashion, also relying on the internal simulation.     

Design of an Ultrafast G Protein Switch Based on a Mouse Melanopsin Variant

The primary goal of optogenetics is the light-controlled noninvasive and specific manipulation of various cellular processes. Herein, we present a hybrid strategy for targeted protein engineering combining computational techniques with electrophysiological and UV/visible spectroscopic experiments. We validated our concept for channelrhodopsin-2 and applied it to modify the less-well-studied vertebrate opsin melanopsin. Melanopsin is a promising optogenetic tool that functions as a selective molecular light switch for G protein-coupled receptor pathways. Thus, we constructed a model of the melanopsin G_q protein complex and predicted an absorption maximum shift of the Y211F variant. This variant displays a narrow blue-shifted action spectrum and twofold faster deactivation kinetics compared to wild-type melanopsin on G protein-coupled inward rectifying K⁺ (GIRK) channels in HEK293 cells. Furthermore, we verified the in vivo activity and optogenetic potential for the variant in mice. Thus, we propose that our developed concept will be generally applicable to designing optogenetic tools.