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

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Equation of state of a hot-and-dense quark gluon plasma: lattice simulations at real μB\mu_B 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 $\mu_B>0$, 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 $\mu_B/T=3$.Comment: 7 pages, 3 figure

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 $\mu_B/T=2.7$, 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 $\mu_B$ 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

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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