Fully nonlinear excitations of non-Abelian plasma

We investigate fully nonlinear, non-Abelian excitations of quark-antiquark plasma, using relativistic fluid theory in cold plasma approximation. There are mainly three important nonlinearities, coming from various sources such as non-Abelian interactions of Yang-Mills (YM) fields, Wong's color dynamics and plasma nonlinearity, in our model. By neglecting nonlinearities due to plasma and color dynamics we get back the earlier results of Blaizot {\it et. al.}, Phys. Rev. Lett. 72, 3317 (1994). Similarly, by neglecting YM fields nonlinearity and plasma nonlinearity, it reduces to the model of Gupta {\it et. al.}, Phys. Lett. B498, 223 (2005). Thus we have the most general non-Abelian mode of quark-gluon plasma (QGP). Further, our model resembles the problem of propagation of laser beam through relativistic plasma, Physica 9D, 96 (1983). in the absence of all non-Abelian interactions.Comment: 8 pages, 2 figures, articl

Strongly Coupled Quark Gluon Plasma (SCQGP)

We propose that the reason for the non-ideal behavior seen in lattice simulation of quark gluon plasma (QGP) and relativistic heavy ion collisions (URHICs) experiments is that the QGP near T_c and above is strongly coupled plasma (SCP), i.e., strongly coupled quark gluon plasma (SCQGP). It is remarkable that the widely used equation of state (EoS) of SCP in QED (quantum electrodynamics) very nicely fits lattice results on all QGP systems, with proper modifications to include color degrees of freedom and running coupling constant. Results on pressure in pure gauge, 2-flavors and 3-flavors QGP, are all can be explained by treating QGP as SCQGP as demonstated here.Energy density and speed of sound are also presented for all three systems. We further extend the model to systems with finite quark mass and a reasonably good fit to lattice results are obtained for (2+1)-flavors and 4-flavors QGP. Hence it is the first unified model, namely SCQGP, to explain the non-ideal QGP seen in lattice simulations with just two system dependent parameters.Comment: Revised with corrections and new results, Latex file (11 pages), postscript file of 7 figure

Does the Boltzmann principle need a dynamical correction?

In an attempt to derive thermodynamics from classical mechanics, an approximate expression for the equilibrium temperature of a finite system has been derived [M. Bianucci, R. Mannella, B. J. West, and P. Grigolini, Phys. Rev. E 51, 3002 (1995)] which differs from the one that follows from the Boltzmann principle S = k log (Omega(E)) via the thermodynamic relation 1/T= dS/dE by additional terms of "dynamical" character, which are argued to correct and generalize the Boltzmann principle for small systems (here Omega(E) is the area of the constant-energy surface). In the present work, the underlying definition of temperature in the Fokker-Planck formalism of Bianucci et al. is investigated and shown to coincide with an approximate form of the equipartition temperature. Its exact form, however, is strictly related to the "volume" entropy S = k log (Phi(E)) via the thermodynamic relation above for systems of any number of degrees of freedom (Phi(E) is the phase space volume enclosed by the constant-energy surface). This observation explains and clarifies the numerical results of Bianucci et al. and shows that a dynamical correction for either the temperature or the entropy is unnecessary, at least within the class of systems considered by those authors. Explicit analytical and numerical results for a particle coupled to a small chain (N~10) of quartic oscillators are also provided to further illustrate these facts.Comment: REVTeX 4, 10 pages, 2 figures. Accepted to J. Stat. Phy

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

High cervical intraspinal enterogenous cyst

A case of histologically verified ventro-laterally placed enterogenous cyst in the upper cervical region is reported