Steady-state differential calorimeter measures gamma heating in reactor

Steady-state differential calorimeter, which displays good accuracy and reproducibility of results, is used to measure gamma heating in a reactor environment. The calorimeter has a long life expectancy since it is virtually unharmed by the reactor environment

Critical features in electromagnetic anomalies detected prior to the L'Aquila earthquake

Electromagnetic (EM) emissions in a wide frequency spectrum ranging from kHz to MHz are produced by opening cracks, which can be considered as the so-called precursors of general fracture. We emphasize that the MHz radiation appears earlier than the kHz in both laboratory and geophysical scale. An important challenge in this field of research is to distinguish characteristic epochs in the evolution of precursory EM activity and identify them with the equivalent last stages in the earthquake (EQ) preparation process. Recently, we proposed the following two epochs/stages model: (i) The second epoch, which includes the finally emerged strong impulsive kHz EM emission is due to the fracture of the high strength large asperities that are distributed along the activated fault sustaining the system. (ii) The first epoch, which includes the initially emerged MHz EM radiation is thought to be due to the fracture of a highly heterogeneous system that surrounds the family of asperities. A catastrophic EQ of magnitude Mw = 6.3 occurred on 06/04/2009 in central Italy. The majority of the damage occurred in the city of L'Aquila. Clear kHz - MHz EM anomalies have been detected prior to the L'Aquila EQ. Herein, we investigate the seismogenic origin of the detected MHz anomaly. The analysis in terms of intermittent dynamics of critical fluctuations reveals that the candidate EM precursor: (i) can be described in analogy with a thermal continuous phase transition; (ii) has anti-persistent behaviour. These features suggest that the emerged candidate precursor could be triggered by microfractures in the highly disordered system that surrounded the backbone of asperities of the activated fault. We introduce a criterion for an underlying strong critical behavior.Comment: 8 pages, 6 figure

Influence of different polishing materials in the material removal of steel samples

The quality of injection moulded polymer optic parts depends on the surface finish of the respective mould. In order to improve and control the surface finish of the mould it is important to be able to keep the material removal constant during the polishing process of these moulds. This will provide a tactical material removal therefore allowing a controlled correction of the mould’s surface geometry. The aim of this work is to study the influence of different polishing materials in the material removal rate and its reproducibility during the polishing process of hardened steel. Different polyurethane polishing materials with different fillers were tested. It was observed that the filler material of the polyurethane is crucial in order to obtain constant and reproducible results. Experiments were carried out with an industrial robot and the material removal’s depth value was compared

Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity

Functional brain networks are well described and estimated from data with Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance estimators. Comparing functional connectivity of subjects in two populations calls for comparing these estimated GGMs. Our goal is to identify differences in GGMs known to have similar structure. We characterize the uncertainty of differences with confidence intervals obtained using a parametric distribution on parameters of a sparse estimator. Sparse penalties enable statistical guarantees and interpretable models even in high-dimensional and low-sample settings. Characterizing the distributions of sparse models is inherently challenging as the penalties produce a biased estimator. Recent work invokes the sparsity assumptions to effectively remove the bias from a sparse estimator such as the lasso. These distributions can be used to give confidence intervals on edges in GGMs, and by extension their differences. However, in the case of comparing GGMs, these estimators do not make use of any assumed joint structure among the GGMs. Inspired by priors from brain functional connectivity we derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. This leads us to introduce the debiased multi-task fused lasso, whose distribution can be characterized in an efficient manner. We then show how the debiased lasso and multi-task fused lasso can be used to obtain confidence intervals on edge differences in GGMs. We validate the techniques proposed on a set of synthetic examples as well as neuro-imaging dataset created for the study of autism