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In: Alfha.net / Sektion Bauingenieurwesen: 1. [Erster] Erfahrungsaustausch : Absolventen des Fachbereichs Bauingenieurwesens berichten. 13. Oktober 2006. S. 16-17 Der Umbau des Aachener Buschtunnels im Rahmen des europäischen Hochgeschwindigkeitsschienennetzes wird im sogenannten "Neuen Österreichischen Tunnelbauverfahren (NOeT)", also ein Ausbau in Kalotte, Strosse und Sohle durchgeführt.
Recently, SHARP corporation has developed the world’s first “Plasma Cluster Ions (PCI)” air purification technology, which uses plasma discharge to generate cluster ions. The new plasma cluster device releases into the air positive and negative ions, which are harmless to humans and are able to decompose and deactivate airborne substances by chemical reactions. A lot of phenomenological tests of the PCI air purification technology on microbial cells have been conducted. And, in most cases, it has been shown that PCI demonstrate strongly pronounced killing effect. Although, the particular mechanisms of PCI action are still not evident. We studied variations in resistance to PCI among gram-positive airborne microorganisms, as well as some dose-dependent, spatial, cultural and biochemical properties of PCI action in respect of Staphylococcus spp, Enterococcus spp, Micrococcus spp.
Recently, SHARP corporation has developed the world’s first "Plasma Cluster Ions® (PCI)" air purification technology, which uses plasma discharge to generate cluster ions. The new Plasma Cluster Device releases positive and negative ions into the air, which are harmless to humans and are able to decompose and deactivate airborne substances by chemical reactions. In the past, phenomenological tests on the efficacy of the PCI air purification technology on microbial cells have been conducted. In most cases, it has been shown that PCI demonstrated strongly pronounced killing effects on microorganisms. However, the particular mechanisms of PCI action still have to be uncovered.
Plasma-Spritzen
(1991)
The Ministry of Science and Research in North Rhine-Westphalia created eight platforms of excellence, one in the research area „Energy and Environment“ in 2002 at ACUAS. This platform concentrates the research and development of 13 professors in Jülich and Aachen and of two scientific institutes with different topics: – NOWUM-Energy with emphasis on efficient and economic energy conversion – The Solar Institute Jülich – SIJ – being the largest research institute in the field of renewables at a University of Applied Sciences in Germany With this platform each possible energy conversion – nuclear, fossil, renewable- can be dealt with to help solving the two most important problems of mankind, energy and potable water. At the CSE are presented the historical development, some research results and the combined master studies in „Energy Systems“ and „Nuclear Applications“
The optical properties of the thin metalized polymer films that are projected for solar sails are assumed to be affected by the erosive effects of the space environment. Their degradation behavior in the real space environment, however, is to a considerable degree indefinite, because initial ground test results are controversial and relevant inspace tests have not been made so far. The standard optical solar sail models that are currently used for trajectory design do not take optical degradation into account, hence its potential effects on trajectory design have not been investigated so far. Nevertheless, optical degradation is important for high-fidelity solar sail mission design, because it decreases both the magnitude of the solar radiation pressure force acting on the sail and also the sail control authority. Therefore, we propose a simple parametric optical solar sail degradation model that describes the variation of the sail film’s optical coefficients with time, depending on the sail film’s environmental history, i.e., the radiation dose. The primary intention of our model is not to describe the exact behavior of specific film-coating combinations in the real space environment, but to provide a more general parametric framework for describing the general optical degradation behavior of solar sails. Using our model, the effects of different optical degradation behaviors on trajectory design are investigated for various exemplary missions.
This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.
Praktische Untersuchungen zum zeitlichen Übertragungs- und Bündelfehlerverhalten der IEEE 802.15.4
(2006)
Bitcoin is a cryptocurrency and is considered a high-risk asset
class whose price changes are difficult to predict. Current research focusses
on daily price movements with a limited number of predictors. The paper at
hand aims at identifying measurable indicators for Bitcoin price movement s
and the development of a suitable forecasting model for hourly changes. The
paper provides three research contributions. First, a set of significant
indicators for predicting the Bitcoin price is identified. Second, the results of
a trained Long Short-term Memory (LSTM) neural network that predicts
price changes on an hourly basis is presented and compared with other
algorithms. Third, the results foster discussions of the applicability of neural
nets for stock price predictions. In total, 47 input features for a period of
over 10 months could be retrieved to train a neural net that predicts the
Bitcoin price movements with an error rate of 3.52 %.