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Planning the air-terminations for a structure to be protected the use of the rolling-sphere method (electro-geometrical model) is the best way from the physics of lightning point-of-view. Therefore, international standards prefer this method. However, using the rolling-sphere method only results in possible point-of-strikes on a structure without giving information about the probability of strikes at the individual points compared to others.
Numerical avalanche dynamics models have become an essential part of snow engineering. Coupled with field observations and historical records, they are especially helpful in understanding avalanche flow in complex terrain. However, their application poses several new challenges to avalanche engineers. A detailed understanding of the avalanche phenomena is required to construct hazard scenarios which involve the careful specification of initial conditions (release zone location and dimensions) and definition of appropriate friction parameters. The interpretation of simulation results requires an understanding of the numerical solution schemes and easy to use visualization tools. We discuss these problems by presenting the computer model RAMMS, which was specially designed by the SLF as a practical tool for avalanche engineers. RAMMS solves the depth-averaged equations governing avalanche flow with accurate second-order numerical solution schemes. The model allows the specification of multiple release zones in three-dimensional terrain. Snow cover entrainment is considered. Furthermore, two different flow rheologies can be applied: the standard Voellmy–Salm (VS) approach or a random kinetic energy (RKE) model, which accounts for the random motion and inelastic interaction between snow granules. We present the governing differential equations, highlight some of the input and output features of RAMMS and then apply the models with entrainment to simulate two well-documented avalanche events recorded at the Vallée de la Sionne test site.
Oxorhenium(V) complexes [ReOX3(PPh3)2] (X = Cl, Br) react with phenylacetylene under formation of complexes with ylide-type ligands. Compounds of the compositions [ReOCl3(PPh3){C(Ph)C(H)(PPh3)}] (1), [ReOBr3(OPPh3){C(Ph)C(H)(PPh3)}] (2), and [ReOBr3(OPPh3){C(H)C(Ph)(PPh3)}] (3) were isolated and characterized by X-ray diffraction. They contain a ligand, which was formed by a nucleophilic attack of released PPh3 at coordinated phenylacetylene. The structures of the products show that there is no preferable position for this attack. Cleavage of the Re–C bond in 3 and dimerization of the organic ligand resulted in the formation of the [{(PPh3)(H)CC(Ph)}2]2+ cation, which crystallized as its [(ReOBr4)(OReO3)]2– salt.
In this contribution, we focus on the detection of toxic gases with living eukaryotic cells. A cell-based gas sensor system, able to measure the effects of direct exposure of gases to cells in real-time, was set up. Impedance data as well as oxygen consumption of Chinese hamster lung fibroblast cells (V79) were analysed upon exposure to carbon monoxide (CO). The CO (diluted in wet synthetic air) affects the cell respiration as indicated by an attenuated respiration signal after the CO exposure as well as an instant increase of the capacitive part of the impedance signal during the gas exposure.
Realization of a calorimetric gas sensor on polyimide foil for applications in aseptic food industry
(2010)
A calorimetric gas sensor is presented for the monitoring of gas-phase H2O2 at elevated temperature during sterilization processes in aseptic food industry. The sensor consists of two temperature-sensitive thin-film resistances built up on a polyimide foil with a thickness of 25 μm, which are passivated with a layer of SU-8 photo resist and catalytically activated with manganese(IV) oxide. Instead of an active heating structure, the calorimetric sensor utilizes the elevated temperature of an evaporated H2O2 aerosol. In an experimental set-up, the sensor has shown a sensitivity of 4.78 °C/(%v/v) in a H2O2 concentration range of 0 to 10% v/v at an evaporation temperature of 240 ∘C. Furthermore, the sensor possesses the same, unchanged sensor signal even at varied evaporation temperatures of the gas stream. The sensor characterization demonstrates the suitability of the calorimetric gas sensor for monitoring the efficiency of sterilization processes.
Quantifying and minimizing uncertainty is vital for simulating technically and economically successful geothermal reservoirs. To this end, we apply a stochastic modelling sequence, a Monte Carlo study, based on (i) creating an ensemble of possible realizations of a reservoir model, (ii) forward simulation of fluid flow and heat transport, and (iii) constraining post-processing using observed state variables. To generate the ensemble, we use the stochastic algorithm of Sequential Gaussian Simulation and test its potential fitting rock properties, such as thermal conductivity and permeability, of a synthetic reference model and—performing a corresponding forward simulation—state variables such as temperature. The ensemble yields probability distributions of rock properties and state variables at any location inside the reservoir. In addition, we perform a constraining post-processing in order to minimize the uncertainty of the obtained distributions by conditioning the ensemble to observed state variables, in this case temperature. This constraining post-processing works particularly well on systems dominated by fluid flow. The stochastic modelling sequence is applied to a large, steady-state 3-D heat flow model of a reservoir in The Hague, Netherlands. The spatial thermal conductivity distribution is simulated stochastically based on available logging data. Errors of bottom-hole temperatures provide thresholds for the constraining technique performed afterwards. This reduce the temperature uncertainty for the proposed target location significantly from 25 to 12 K (full distribution width) in a depth of 2300 m. Assuming a Gaussian shape of the temperature distribution, the standard deviation is 1.8 K. To allow a more comprehensive approach to quantify uncertainty, we also implement the stochastic simulation of boundary conditions and demonstrate this for the basal specific heat flow in the reservoir of The Hague. As expected, this results in a larger distribution width and hence, a larger, but more realistic uncertainty estimate. However, applying the constraining post-processing the uncertainty is again reduced to the level of the post-processing without stochastic boundary simulation. Thus, constraining post-processing is a suitable tool for reducing uncertainty estimates by observed state variables.
The purpose of the current study was to examine the reproducibility of fascicle length and pennation angle of gastrocnemius medialis while human walking. To the best of our knowledge, this is the first study of the reproducibility of fascicle length and pennation angle of gastrocnemius medialis in vivo during human gait. Twelve males performed 10 gait trials on a treadmill, in 2 separate days. B-mode ultrasonography, with the ultrasound probe firmly adjusted in the transverse and frontal planes using a special cast, was used to measure the fascicle length and the pennation angle of the gastrocnemius medialis (GM). A Vicon 624 system with three cameras operating at 120 Hz was also used to record the ankle and knee joint angles. The results showed that measurements of fascicle length and pennation angle showed high reproducibility during the gait cycle, both within the same day and between different days. Moreover, the root mean square differences between the repeated waveforms of both variables were very small, compared with their ranges (fascicle length: RMS = ∼3 mm, range: 38–63 mm; pennation angle: RMS = ∼1.5°, range: 22–32°). However, their reproducibility was lower compared to the joint angles. It was found that representative data have to be derived by a wide number of gait trials (fascicle length ∼six trials, pennation angle more than 10 trials), to assure the reliability of the fascicle length and pennation angle in human gait.
As the field strength and, therefore, the operational frequency in MRI is increased, the wavelength approaches the size of the human head/body, resulting in wave effects, which cause signal decreases and dropouts. Several multichannel approaches have been proposed to try to tackle these problems, including RF shimming, where each element in an array is driven by its own amplifier and modulated with a certain (constant) amplitude and phase relative to the other elements, and Transmit SENSE, where spatially tailored RF pulses are used. In this article, a relatively inexpensive and easy to use imaging scheme for 7 Tesla imaging is proposed to mitigate signal voids due to B1 field inhomogeneity. Two time-interleaved images are acquired using a different excitation mode for each. By forming virtual receive elements, both images are reconstructed together using GRAPPA to achieve a more homogeneous image, with only small SNR and SAR penalty in head and body imaging at 7 Tesla.
In this extended abstract we describe the robot programming and planning language READYLOG, a GOLOG dialect which was developed to support the decision making of robots acting in dynamic real-time domains like robotic soccer. The formal framework of READYLOG, which is based on the situation calculus, features imperative control structures like loops and procedures, allows for decision-theoretic planning, and accounts for a continuously changing world. We developed high-level controllers in READYLOG for our soccer robots in RoboCup’s Middle-size league, but also for service robots and for autonomous agents in interactive computer games.