62,534 research outputs found
Omnidirectionally Bending to the Normal in epsilon-near-Zero Metamaterials
Contrary to conventional wisdom that light bends away from the normal at the
interface when it passes from high to low refractive index media, here we
demonstrate an exotic phenomenon that the direction of electromagnetic power
bends towards the normal when light is incident from arbitrary high refractive
index medium to \epsilon-near-zero metamaterial. Moreover, the direction of the
transmitted beam is close to the normal for all angles of incidence. In other
words, the electromagnetic power coming from different directions in air or
arbitrary high refractive index medium can be redirected to the direction
almost parallel to the normal upon entering the \epsilon-near-zero
metamaterial. This phenomenon is counterintuitive to the behavior described by
conventional Snell's law and resulted from the interplay between
\epsilon-near-zero and material loss. This property has potential applications
in communications to increase acceptance angle and energy delivery without
using optical lenses and mechanical gimbals
A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data
Background: A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity
information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.
Results: In this work, a Bayesian neural network approach is employed to analyze ion intensity information present in 13878 different MS/MS spectra. The influence of a library of 35 features on peptide fragmentation is examined under different proton mobility conditions. Useful rules
involved in peptide fragmentation are found and subsets of features which have significant influence on fragmentation pathway of peptides are characterised. An intensity model is built based on the selected features and the model can make an accurate prediction of the intensity patterns for given MS/MS spectra. The predictions include not only the mean values of spectra intensity but also the
variances that can be used to tolerate noises and system biases within experimental MS/MS spectra.
Conclusion: The intensity patterns of fragmentation spectra are informative and can be used to analyze the influence of various characteristics of fragmented peptides on their fragmentation pathway. The features with significant influence can be used in turn to predict spectra intensities. Such information can help develop more reliable algorithms for peptide and protein identification
Scalable quantum information processing with atomic ensembles and flying photons
We present a scheme for scalable quantum information processing (QIP) with
atomic ensembles and flying photons. Using the Rydberg blockade, we encode the
qubits in the collective atomic states, which could be manipulated fast and
easily due to the enhanced interaction, in comparison to the single-atom case.
We demonstrate that our proposed gating could be applied to generation of
two-dimensional cluster states for measurement-based quantum computation.
Moreover, the atomic ensembles also function as quantum repeaters useful for
long distance quantum state transfer. We show the possibility of our scheme to
work in bad cavity or in weak coupling regime, which could much relax the
experimental requirement. The efficient coherent operations on the ensemble
qubits enable our scheme to be switchable between quantum computation and
quantum communication using atomic ensembles.Comment: 8 pages, 7 figure
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