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The Asplenium coenobiale complex is distributed in Eastern Asia and Southeast Asia with its distribution center in southwestern China. In this study, we carried out a detailed morphological, cytological, and phylogenetic study by adding two samples from Danxia landform in Guangdong. The sequences of five chloroplast markers and one nuclear marker of the A. coenobiale complex were analyzed with maximum likelihood and Bayesian inference, respectively. The morphological and phylogenetic analyses support the recognition of a new species (A. danxiaense K.W.Xu sp. nov.) of the A. coenobiale complex from a cave of Danxia mountain, Guangdong province, southern China. This new species can be distinguished from A. coenobiale and A. pulcherrimum by having scales narrowly triangular to lanceolate, apex ending in a short apical tail, basal basiscopic pinnule usually largest, fertile segment scarce, and exospore length usually more than 50 μm and shows significant molecular differences from other species in this complex. A detailed description and illustrations are presented.
"High-aluminous coal" is an important coal kind and widely distributed in North China in age of Permo-Carboniferous period. To explore their occurrence state, a total of 15 harmful elements (Li, Ga, In, Cd, Cr, Pb, Be, Mn, Zn, Ag, Co, Ni, Cu, Ba and U) in the No.9 coal and No.11 coal collected from Pingshuo mining district were determined by inductively coupled plasma mass spectrometry (ICP-MS) and scanning electron microscope with energy spectrum (SEM-EDX). The results showed that the content of Li, Ga, In, Pb, Ag and U were all exceed the world hard coal. In view of the result of clustering analysis within trace elements, it was found that Co, Ni, Zn, Cu, Ag and Cr were mainly associated with sulfide minerals due to their common sulfophilic property. Manganese was mainly occurred in carbonate minerals, while Ba, Cd and U were mainly associated with total minerals. In addition, Pb was related to sulfides and Be is mainly distributed in clay minerals. The enrichment of such harmful elements in Pingshuo coal was caused by the combined effect of transgression and input of terrestrial materials in the peat accumulation stage. Li, Ga, In and Ag have reached the harmful grade.
We derive the collision term in the Boltzmann equation using the equation of motion for the Wigner function of massive spin-1/2 particles. To next-to-lowest order in h, it contains a nonlocal contribution, which is responsible for the conversion of orbital into spin angular momentum. In a proper choice of pseudogauge, the antisymmetric part of the energy-momentum tensor arises solely from this nonlocal contribution. We show that the collision term vanishes in global equilibrium and that the spin potential is, then, equal to the thermal vorticity. In the nonrelativistic limit, the equations of motion for the energy-momentum and spin tensors reduce to the well-known form for hydrodynamics for micropolar fluids.
Using combined data from the Relativistic Heavy Ion and Large Hadron Colliders, we constrain the shear and bulk viscosities of quark-gluon plasma (QGP) at temperatures of ∼150–350 MeV. We use Bayesian inference to translate experimental and theoretical uncertainties into probabilistic constraints for the viscosities. With Bayesian model averaging we propagate an estimate of the model uncertainty generated by the transition from hydrodynamics to hadron transport in the plasma’s final evolution stage, providing the most reliable phenomenological constraints to date on the QGP viscosities.
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.
J/ψ suppression has long been considered a sensitive signature of the formation of the Quark-Gluon Plasma (QGP) in relativistic heavy-ion collisions. In this letter, we present the first measurement of inclusive J/ψ production at mid-rapidity through the dimuon decay channel in Au+Au collisions at √sNN = 200 GeV with the STAR experiment. These measurements became possible after the installation of the Muon Telescope Detector was completed in 2014. The J/ψ yields are measured in a wide transverse momentum (pT) range of 0.15 GeV/c to 12 GeV/c from central to peripheral collisions. They extend the kinematic reach of previous measurements at RHIC with improved precision. In the 0-10% most central collisions, the J/ψ yield is suppressed by a factor of approximately 3 for pT > 5 GeV/c relative to that in p + p collisions scaled by the number of binary nucleon-nucleon collisions. The J/ψ nuclear modification factor displays little dependence on pT in all centrality bins. Model calculations can qualitatively describe the data, providing further evidence for the color-screening effect experienced by J/ψ mesons in the QGP.
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Ф). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective “EoS-meter” in detecting the nature of the QCD transition.
Clustering of cardiovascular risk factors and carotid intima-media thickness : the USE-IMT study
(2017)
Background: The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation.
Methods: Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group.
Results: Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women.
Conclusion: Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters.