Impulsübertrag und Strömungsverhä1tnisse in einem runden Wind-Wasser Kanal

In der vorliegenden Arbeit werden verschiedene Methoden zur Eichung von Hitzdrahtanemometern für die Geschwindig- keitsmessung in Wasser beschrieben. Es wurde ein Hitzdrahtanemometer mit einer Temperaturkompensation aus Pt-100 Fühlern und die Elektronik dazu aufgebaut. Dieses Pt-100 Hitzdrahtanemometer hat gegenüber den üblichen, zur Messung von Profilen i benutzten Anemometern, den Vorteil geringerer Schmutzempfindlichkeit. Zur Bestimmung von u* wurde das Abklingen einer stationären Strömung benutzt. Es wird eine Beziehung Windgeschwindigkeit - u* und Wassergeschwindigkeit - u* angegeben. Profilmessungen mit einer DISA-Sonde ergaben ein Bild der Wasserströmung im ringförmigen Kanal. Die Strömungsverhältnisse sind kompliziert, da die Zentrifugalkräfte eine Rolle spielen. Die Berechnungen mit dem Boxmodell sind ein Ansatz zur Beschreibung des Problems. Es zeigt sich dabei, obwohl von sehr vereinfachenden Bedingungen ausgegangen wird (laminare Strömung,Vernachlässigung der Trägheitsterme), daß allein durch die Zentrifugalkräfte bereits eine Art Grenzschicht ausgebildet wird

Tracing and quantifying groundwater inflow into lakes using a simple method for radon-222 analysis

Due to its high activities in groundwater, the radionuclide <sup>222</sup>Rn is a sensitive natural tracer to detect and quantify groundwater inflow into lakes, provided the comparatively low activities in the lakes can be measured accurately. Here we present a simple method for radon measurements in the low-level range down to 3 Bq m<sup>−3</sup>, appropriate for groundwater-influenced lakes, together with a concept to derive inflow rates from the radon budget in lakes. The analytical method is based on a commercially available radon detector and combines the advantages of established procedures with regard to efficient sampling and sensitive analysis. Large volume (12 l) water samples are taken in the field and analyzed in the laboratory by equilibration with a closed air loop and alpha spectrometry of radon in the gas phase. After successful laboratory tests, the method has been applied to a small dredging lake without surface in- or outflow in order to estimate the groundwater contribution to the hydrological budget. The inflow rate calculated from a <sup>222</sup>Rn balance for the lake is around 530 m³ per day, which is comparable to the results of previous studies. In addition to the inflow rate, the vertical and horizontal radon distribution in the lake provides information on the spatial distribution of groundwater inflow to the lake. The simple measurement and sampling technique encourages further use of radon to examine groundwater-lake water interaction

Tracing and quantifying groundwater inflow into lakes using radon-222

International audienceDue to its high activities in groundwater, the radionuclide 222Rn is a sensitive natural tracer to detect and quantify groundwater inflow into lakes, provided the comparatively low activities in the lakes can be measured accurately. Here we present a simple method for radon measurements in the low-level range down to 3 Bq m?3, appropriate for groundwater-influenced lakes, together with a concept to derive inflow rates from the radon budget in lakes. The analytical method is based on a commercially available radon detector and combines the advantages of established procedures with regard to efficient sampling and sensitive analysis. Large volume (12 l) water samples are taken in the field and analyzed in the laboratory by equilibration with a closed air loop and alpha spectrometry of radon in the gas phase. After successful laboratory tests, the method has been applied to a small dredging lake without surface in- or outflow in order to estimate the groundwater contribution to the hydrological budget. The inflow rate calculated from a 222Rn balance for the lake is around 530 m3 per day, which is comparable to the results of previous studies. In addition to the inflow rate, the vertical and horizontal radon distribution in the lake provides information on the spatial distribution of groundwater inflow to the lake. The simple measurement and sampling technique encourages further use of radon to examine groundwater-lake interaction

Visual cues combined with treadmill training to improve gait performance in Parkinson's disease: a pilot randomized controlled trial

Objective: To evaluate the effects of visual cues combined with treadmill training on gait performance in patients with Parkinson's disease and to compare the strategy with pure treadmill training. Design: Pilot, exploratory, non-blinded, randomized controlled trial. Setting: University Hospital of Munich, Germany. Subjects: Twenty-three outpatients with Parkinson's disease (Hoehn and Yahr stage II-IV). Interventions: Patients received 12 training sessions within five weeks of either visual cues combined with treadmill training (n = 12) or pure treadmill training (n = 11). Main measures: Outcome measures were gait speed, stride length and cadence recorded on the treadmill. Functional tests included the Timed Up and Go Test, the Unified Parkinson's Disease Rating Scale and the Freezing of gait-questionnaire. Assessments were conducted at baseline, after the training period and at two months follow-up. Results: After the training period (n = 20), gait speed and stride length had increased in both groups (p <= 0.05). Patients receiving the combined training scored better in the Timed Up and Go Test compared with the patients receiving pure treadmill training (p <= 0.05). At two months follow-up (n = 13), patients who underwent the combined training sustained better results in gait speed and stride length (p <= 0.05) and sustained the improvement in the Timed Up and Go Test (p <= 0.05). Conclusions: This pilot study suggests that visual cues combined with treadmill training have more beneficial effects on gait than pure treadmill training in patients with a moderate stage of Parkinson's disease. A large-scale study with longer follow-up is required

Atmospheric observation-based global SF6 emissions - comparison of top-down and bottom-up estimates

Emissions of sulphur hexafluoride (SF6), one of the strongest greenhouse gases on a per molecule basis, are targeted to be collectively reduced under the Kyoto Protocol. Because of its long atmospheric lifetime (≈3000 years), the accumulation of SF6 in the atmosphere is a direct measure of its global emissions. Examination of our extended data set of globally distributed high-precision SF6 observations shows an increase in SF6 abundance from near zero in the 1970s to a global mean of 6.7 ppt by the end of 2008. In-depth evaluation of our long-term data records shows that the global source of SF6 decreased after 1995, most likely due to SF6 emission reductions in industrialised countries, but increased again after 1998. By subtracting those emissions reported by Annex I countries to the United Nations Framework Convention of Climatic Change (UNFCCC) from our observation-inferred SF6 source leaves a surprisingly large gap of more than 70–80% of non-reported SF6 emissions in the last decade

Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System

We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. At a system power consumption of 5.6W, we measure a total energy consumption of 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (93.7${\pm}$0.7)% at (14.0${\pm}$1.0)% false positives. The system is directly applicable to edge inference applications due to its small size, power envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC to be operated reliably outside a specialized lab setting. In future applications, the system allows for a combination of conventional machine learning layers with online learning in spiking neural networks on a single neuromorphic platform

Deep metagenome and metatranscriptome analyses of microbial communities affiliated with an industrial biogas fermenter, a cow rumen, and elephant feces reveal major differences in carbohydrate hydrolysis strategies

Additional file 4. Compressed rar file containing the bins generated from the biogas fermenter metagenome, part 4 of 4

S(C)ENTINEL - monitoring automated vehicles with olfactory reliability displays

Overreliance in technology is safety-critical and it is assumed that this could have been a main cause of severe accidents with automated vehicles. To ease the complex task of per- manently monitoring vehicle behavior in the driving en- vironment, researchers have proposed to implement relia- bility/uncertainty displays. Such displays allow to estimate whether or not an upcoming intervention is likely. However, presenting uncertainty just adds more visual workload on drivers, who might also be engaged in secondary tasks. We suggest to use olfactory displays as a potential solution to communicate system uncertainty and conducted a user study (N=25) in a high-fidelity driving simulator. Results of the ex- periment (conditions: no reliability display, purely visual reliability display, and visual-olfactory reliability display) comping both objective (task performance) and subjective (technology acceptance model, trust scales, semi-structured interviews) measures suggest that olfactory notifications could become a valuable extension for calibrating trust in automated vehicles