10,277 research outputs found
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
Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies
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
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