283 research outputs found
Airflow Model Testing to Determine the Distribution of Hot Gas Flow and O/F Ratio Across the Space Shuttle Main Engine Main Injector Assembly
Engine 0209, the certification engine for the new Phase 2+ Hot Gas Manifold (HGM), showed severe deterioration of the Main Combustion Chamber (MCC) liner during hot fire tests. One theory on the cause of the damage held that uneven local distribution of the fuel rich hot gas flow through the main injector assembly was producing regions of high oxidizer/fuel (O/F) ratio near the wall of the MCC liner. Airflow testing was proposed to measure the local hot gas flow rates through individual injector elements. The airflow tests were conducted using full scale, geometrically correct models of both the current Phase 2 and the new Phase 2+ HGMs. Different main injector flow shield configurations were tested for each HGM to ascertain their effect on the pressure levels and distribution of hot gas flow. Instrumentation located on the primary faceplate of the main injector measured hot gas flow through selected injector elements. These data were combined with information from the current space shuttle main engine (SSME) power balances to produce maps of pressure, hot gas flow rate, and O/F ratio near the main injector primary plate. The O/F distributions were compared for the different injector and HGM configurations
Clustering through post inhibitory rebound in synaptically coupled neurons
Post inhibitory rebound is a nonlinear phenomenon present in a variety of nerve cells. Following a period of hyper-polarization this effect allows a neuron to fire a spike or packet of spikes before returning to rest. It is an important mechanism underlying central pattern generation for heartbeat, swimming and other motor patterns in many neuronal systems. In this paper we consider how networks of neurons, which do not intrinsically oscillate, may make use of inhibitory synaptic connections to generate large scale coherent rhythms in the form of cluster states. We distinguish between two cases i) where the rebound mechanism is due to anode break excitation and ii) where rebound is due to a slow T-type calcium current. In the former case we use a geometric analysis of a McKean type model to obtain expressions for the number of clusters in terms of the speed and strength of synaptic coupling. Results are found to be in good qualitative agreement with numerical simulations of the more detailed Hodgkin-Huxley model. In the second case we consider a particular firing rate model of a neuron with a slow calcium current that admits to an exact analysis. Once again existence regions for cluster states are explicitly calculated. Both mechanisms are shown to prefer globally synchronous states for slow synapses as long as the strength of coupling is sufficiently large. With a decrease in the duration of synaptic inhibition both systems are found to break into clusters. A major difference between the two mechanisms for cluster generation is that anode break excitation can support clusters with several groups, whilst slow T-type calcium currents predominantly give rise to clusters of just two (anti-synchronous) populations
Pharmacological Effects of Active Compounds on Neurodegenerative Disease with Gastrodia and Uncaria Decoction, a Commonly Used Poststroke Decoction
published_or_final_versio
Equation of state of a hot-and-dense quark gluon plasma: lattice simulations at real vs. extrapolations
The equation of state of the quark gluon plasma is a key ingredient of heavy
ion phenomenology. In addition to the traditional Taylor method, several novel
approximation schemes have been proposed with the aim of calculating it at
finite baryon density. In order to gain a pragmatic understanding of the limits
of these schemes, we compare them to direct results at , using
reweighting techniques free from an overlap problem. We use 2stout improved
staggered fermions with 8 time-slices and cover the entire RHIC BES range in
the baryochemical potential, up to .Comment: 7 pages, 3 figure
Continuum extrapolated high order baryon fluctuations
Fluctuations play a key role in the study of QCD phases. Lattice QCD is a
valuable tool to calculate them, but going to high orders is challenging. Up to
the fourth order, continuum results are available since 2015. We present the
first continuum results for sixth order baryon fluctuations for temperatures
between MeV, and eighth order at MeV in a fixed volume.
We show that for MeV, relevant for criticality search, finite
volume effects are under control. Our results are in sharp contrast with well
known results in the literature obtained at finite lattice spacing.Comment: 5 pages, 2 figures (main text) + 5 pages, 7 figures (supplemental
material
Coherence Resonance and Noise-Induced Synchronization in Globally Coupled Hodgkin-Huxley Neurons
The coherence resonance (CR) of globally coupled Hodgkin-Huxley neurons is
studied. When the neurons are set in the subthreshold regime near the firing
threshold, the additive noise induces limit cycles. The coherence of the system
is optimized by the noise. A bell-shaped curve is found for the peak height of
power spectra of the spike train, being significantly different from a
monotonic behavior for the single neuron. The coupling of the network can
enhance CR in two different ways. In particular, when the coupling is strong
enough, the synchronization of the system is induced and optimized by the
noise. This synchronization leads to a high and wide plateau in the local
measure of coherence curve. The local-noise-induced limit cycle can evolve to a
refined spatiotemporal order through the dynamical optimization among the
autonomous oscillation of an individual neuron, the coupling of the network,
and the local noise.Comment: five pages, five figure
Lattice simulations of the QCD chiral transition at real μB
Most lattice studies of hot and dense QCD matter rely on extrapolation from
zero or imaginary chemical potentials. The ill-posedness of numerical analytic
continuation puts severe limitations on the reliability of such methods. We
studied the QCD chiral transition at finite real baryon density with the more
direct sign reweighting approach. We simulate up to a baryochemical
potential-temperature ratio of , covering the RHIC Beam Energy
Scan range, and penetrating the region where methods based on analytic
continuation are unpredictive.This opens up a new window to study QCD matter at
finite from first principles.Comment: 10 pages, 3 figures; Contribution to the XXXIII International
(ONLINE) Workshop on High Energy Physics "Hard Problems of Hadron Physics:
Non-Perturbative QCD & Related Quests"; Based on 2108.09213 [hep-lat]. arXiv
admin note: substantial text overlap with arXiv:2112.0213
Zika Virus Infection in Dexamethasone-immunosuppressed Mice Demonstrating Disseminated Infection with Multi-organ Involvement Including Orchitis Effectively Treated by Recombinant Type I Interferons
published_or_final_versio
Implementing fuzzy-based artificial intelligence approach for location of damage in structures
Modal parameters are functions of the
physical characteristics of a structure and they are very
sensitive to damage. Therefore, any alterations in the
physical features can change the vibration parameters of a
structure. Modal data such as natural frequencies and mode
shapes are easy to acquire from the measurements of
structural behavior. One method of structural damage
identification is to apply natural frequency. Natural
frequencies represent the global behaviors of a structure
and are not too sensitive when detecting the damage in
structures and cannot offer spatial information about
structural changes, and thus, their application is considered
as challenging. On the other hand, a mode shape is a
vibrational deformation of a system and it represents the
relative displacement of all parts of a structure and can
provide spatial information as well as give a significant
indication of the damage occurring in a structure. In this
present research, an intelligent hybrid approach, namely
adaptive neuro-fuzzy inference system (ANFIS), as a
fuzzy-based artificial intelligence approach was developed
and applied due to its ability to recognize patterns, strong
computational features, and capability of locating defects
in a scaled girder bridge using direct modal parameters.
The experimental analysis and numerical simulations of a
steel girder bridge provided mode shape parameter datasets
under different positions and sizes of faults in the structure.
The results demonstrated the effectiveness of this method
and provided acceptable precision even when the input
datasets contained errors or were corrupted with a certain
level of noise
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