Experimental Investigations on Electrical Plasma Conductivity in a Model Spark Gap for Surge Currents

In this experimental investigation the electrical conductivity of plasma is measured during surge current using potential probes. The measurements were carried out in a narrow gap arrangement based on spark gap technology. In order to investigate the electrical conductivity during surge this model is tested using 8/20 µs surge currents according to the IEC 62475. The measured behaviour of the electrical conductivity during surge and the uncertainty of these measurements are discusse

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

Benefits of climate modeling for actors along the food chain - reflections for further engagement between science and practice

In the agricultural practice in Europe climate aspects are still rarely influencing decision-making at farm level, other aspects as short- term economics and legislative constraints being more relevant. But also in Europe farmers are facing shifts in weather patterns with weather extremes, thus showing that there is a need for more information regarding climate change. In this regard models which are able to describe climate phenomena and possible options for (pre-)adaptation are becoming more and more valuable for the farming community. This is in particularly relevant for long -term investments like for livestock buildings or irrigation infrastructures, but also for the choice of crops and management practices and related machinery. At the same time agriculture and in particular the livestock sector is pointed out as an important GHG emitent, in particular for methane. With the Paris agreement, EU Member States are asked to present strategies on how to reduce their emissions. There still is little knowledge about costeffective measures to reduce emissions at national, and especially at regional and farm level. Here sophisticated, consolidated climate models, able to present possible pathways for emission reductions and in particular its costs can be a very helpful tool for the selection of cost-effective mitigation measures. But in order to have realistic model predictions that are accepted by practitioners, it is important that the scenario- building is done in cooperation with those actors which are in the end asked to base their decisions on them. For the actors along the food chain it is very important not only to get information regarding overall benefits and costs, but at operational level. Still too seldom climate models are used to provide sound information about structural effects induced by climate changes as well as by climate change policies. Another important aspect is the consistency of model outcomes - too often there is heterogeneity in the quantitative as well as in the qualitative model results affecting the trust in agricultural model ing, in particular if not sufficiently explained. Here MACSUR has already made great progress by aligning scenario definitions and consolidations within and between crop, livestock and trade models, but still much work is necessary to further enforce the dialogue with stakeholders. This is particularly true for possible pathways to reduce livestock emissions without affecting productivity negatively - or even better looking for synergies. Another aspect that should be looked at in more detail are organic soils under agriculture land use and climate and water optimised fertilisation strategies. Climate models cannot only help farmers and other actors along the food chain, including input and food industries as well as the retail sector to better consider climate aspects in their economic decisions, but are a very powerful tool for decision- makers and for future climate change policies. Here it will become even more relevant in future to address leakage effects

Optical Investigations on Plasma Temperature Estimation in a Model Spark Gap for Surge Currents

In this experimental investigation optical emission spectroscopy is used to characterize the radiation of the plasma in a spark gap during surge. Different approaches are used, compared and discussed in order to estimate plasma temperatures. The measurements were carried out in a narrow gap arrangement based on spark gap technology. This model is tested using 8/20 µs surge currents according to the IEC 62475 with amplitudes of 5 kA and 11 kA

Green Function Monte Carlo with Stochastic Reconfiguration

A new method for the stabilization of the sign problem in the Green Function Monte Carlo technique is proposed. The method is devised for real lattice Hamiltonians and is based on an iterative ''stochastic reconfiguration'' scheme which introduces some bias but allows a stable simulation with constant sign. The systematic reduction of this bias is in principle possible. The method is applied to the frustrated J1-J2 Heisenberg model, and tested against exact diagonalization data. Evidence of a finite spin gap for J2/J1 >~ 0.4 is found in the thermodynamic limit.Comment: 13 pages, RevTeX + 3 encapsulated postscript figure

Quantum simulations of the superfluid-insulator transition for two-dimensional, disordered, hard-core bosons

We introduce two novel quantum Monte Carlo methods and employ them to study the superfluid-insulator transition in a two-dimensional system of hard-core bosons. One of the methods is appropriate for zero temperature and is based upon Green's function Monte Carlo; the other is a finite-temperature world-line cluster algorithm. In each case we find that the dynamical exponent is consistent with the theoretical prediction of $z=2$ by Fisher and co-workers.Comment: Revtex, 10 pages, 3 figures (postscript files attached at end, separated by %%%%%% Fig # %%%%%, where # is 1-3). LA-UR-94-270

Anisotropic two-dimensional Heisenberg model by Schwinger-boson Gutzwiller projected method

Two-dimensional Heisenberg model with anisotropic couplings in the $x$ and $y$ directions ($J_x \neq J_y$) is considered. The model is first solved in the Schwinger-boson mean-field approximation. Then the solution is Gutzwiller projected to satisfy the local constraint that there is only one boson at each site. The energy and spin-spin correlation of the obtained wavefunction are calculated for systems with up to $20 \times 20$ sites by means of the variational Monte Carlo simulation. It is shown that the antiferromagnetic long-range order remains down to the one-dimensional limit.Comment: 15 pages RevTex3.0, 4 figures, available upon request, GWRVB8-9

The Use of Maxillary Sinus Imaging as a Tool in Human Identification

The presented research performs evaluations of maxillary sinus morphologies for human identification purpose. Due to the durability of maxillary sinuses, morphological analyses of the structure can prove to be extremely valuable and informative, even when parts of the skull are destroyed and dental records cannot be applied. This research is comprised of 4 studies evaluating maxillary sinus morphologies in order to build a comprehensive outlook on the methodological potentials. In total the used sample is comprised of right and left maxillary sinuses from 988 individuals divided into 12 populations. The morphologies are assessed by extracting the maxillary sinuses from radiographic and CT images and applying elliptic Fourier analyses on the structures. Morphological variability is investigated by converting the maxillary sinus morphology into multiple closed curves, and embedding them into a cartesian system. Principal component analyses on four components further simplifies the processing. The first two studies of this research are concerned with morphological uniqueness testing both in a simulated and real-life scenario to lay a comprehensive foundation for method applicability. Uniqueness testing is executed as a morphological ante- and postmortem comparison by calculating Euclidean and Mahalanobis distances. Euclidean correlation values from 0.000 in the simulated sample up to 0.002 in the real-life sample indicate maxillary sinus morphological uniqueness for each ante- and postmortem sinus morphology pair. Mahalanobis distances are used for visualisation. The third study is assessing the reproducibility of maxillary sinus morphological extraction by applying Cohen’s kappa values. The high kappa values in intra- and inter-observer reliability testing indicate high quality extraction and interpretation of morphologies, increasing the methodological confidence level. Finally, the last study is dedicated to understanding age-related changes in maxillary sinuses by calculating growth rates by population and by sex on Euclidean distances. All evaluations reveal quasi-linear and monotonously rising distances with growth rates varying among left and right sinuses and population. This research advances the potential of maxillary sinus morphologies for human identification and demonstrates its advantages over other paranasal identification methodologies. Therefore, this research acts as an essential first step toward using the proposed methodological framework in future forensic casework