General Electric

About: General Electric is a company organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Turbine & Signal. The organization has 76365 authors who have published 110557 publications receiving 1885108 citations. The organization is also known as: General Electric Company & GE.

Elastic fluid turbine

Abstract: The present invention relates to elastic fluid turbines, more specifically to turbine arrangements from which elastic fluid is tapped or extracted from the last stages of the turbine, that is, ahead of the last bucket wheel as regards the direction of flow through the turbine. Aside from arrangements...

q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans

Abstract: Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning.

Toward perfect antireflection coatings: numerical investigation.

Abstract: A perfect antireflection (AR) coating would remove completely the reflection from an interface between two media for all wavelengths, polarizations, and angles of incidence. The degree to which this can be achieved is investigated numerically. It is shown that wideband solutions can be found provided that layers can be deposited with refractive indices that are close to that of the low-index medium. Thus realistic solutions exist for interfaces between two solid media. Narrow-band high-angle AR solutions are also possible for polarized light and for unpolarized light in the vicinity of certain reststrahlen bands.

Spin-Peierls transitions in magnetic donor-acceptor compounds of tetrathiafulvalene (TTF) with bisdithiolene metal complexes

Abstract: The spin-Peierls transition is considered as a progressive spin-lattice dimerization occurring below a transition temperature in a system of one-dimensional antiferromagnetic Heisenberg chains. In the simplest theories, the transition is second order and the ground state is a singlet with a magnetic gap. The historical origins and theoretical development of the concept are examined. Magnetic susceptibility and EPR measurements on the $\ensuremath{\pi}$-donor-acceptor compounds TTF\ifmmode\cdot\else\textperiodcentered\fi{}$M{S}_{4}{C}_{4}{(\mathrm{C}{F}_{3})}_{4}$ ($M=\mathrm{Cu}, \mathrm{Au}$; TTF is tetrathiafulvalene) are reported. These compounds exhibit clearly the characteristics of the spin-Peierls transition in reasonably good agreement with a mean-field theory. The susceptibility of each compound has a broad maximum near 50 K, while the transitions occur at 12 and 2.1 K for $M=\mathrm{Cu} \mathrm{and} \mathrm{Au}$, respectively. EPR linewidth observations over a broad temperature range are examined. Areas for further experimental and theoretical work are indicated, and a critical comparison is made of related observations on other materials.