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The development and analysis of three waveguides for the exposure of small biological in vitro samples to mobile communication signals at 900 MHz (GSM, Global System for Mobile Communications), 1.8 GHz (GSM), and 2 GHz (UMTS, Universal Mobile Telecommunications System) is presented. The waveguides were based on a fin-line concept and the chamber containing the samples bathed in extracellular solution was placed onto two fins with a slot in between, where the exposure field concentrates. Measures were taken to allow for patch clamp recordings during radiofrequency (RF) exposure. The necessary power for the achievement of the maximum desired specific absorption rate (SAR) of 20 W/kg (average over the mass of the solution) was approximately Pin = 50 mW, Pin = 19 mW, and Pin = 18 mW for the 900 MHz, 1800 MHz, and 2 GHz devices, respectively. At 20 W/kg, a slight RF-induced temperature elevation in the solution of no more than 0.3 °C was detected, while no thermal offsets due to the electromagnetic exposure could be detected at the lower SAR settings (2, 0.2, and 0.02 W/kg). A deviation of 10% from the intended solution volume yielded a calculated SAR deviation of 8% from the desired value. A maximum ±10% variation in the local SAR could occur when the position of the patch clamp electrode was altered within the area where the cells to be investigated were located.
Digital elevation models (DEMs), represent the three-dimensional terrain and are the basic input for numerical snow avalanche dynamics simulations. DEMs can be acquired using topographic maps or remote-sensing technologies, such as photogrammetry or lidar. Depending on the acquisition technique, different spatial resolutions and qualities are achieved. However, there is a lack of studies that investigate the sensitivity of snow avalanche simulation algorithms to the quality and resolution of DEMs. Here, we perform calculations using the numerical avalance dynamics model RAMMS, varying the quality and spatial resolution of the underlying DEMs, while holding the simulation parameters constant. We study both channelized and open-terrain avalanche tracks with variable roughness. To quantify the variance of these simulations, we use well-documented large-scale avalanche events from Davos, Switzerland (winter 2007/08), and from our large-scale avalanche test site, Valĺee de la Sionne (winter 2005/06). We find that the DEM resolution and quality is critical for modeled flow paths, run-out distances, deposits, velocities and impact pressures. Although a spatial resolution of ~25 m is sufficient for large-scale avalanche modeling, the DEM datasets must be checked carefully for anomalies and artifacts before using them for dynamics calculations.
We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
The importance of the availability of stored blood or blood cells, respectively, for urgent transfusion cannot be overestimated. Nowadays, blood storage becomes even more important since blood products are used for epidemiological studies, bio-technical research or banked for transfusion purposes. Thus blood samples must not only be processed, stored, and shipped to preserve their efficacy and safety, but also all parameters of storage must be recorded and reported for Quality Assurance. Therefore, blood banks and clinical research facilities are seeking more accurate, automated means for blood storage and blood processing.