Binary Collision Dynamics and Numerical Evaluation of Dilute Gas Transport Properties for Potentials with Multiple Extrema

Prediction of gaseous transport properties requires calculation of Chapman-Enskog collision integrals which depend on all possible binary collision trajectories. The interparticle potential is required as input, and for a variety of applications involving monatomic gases the Hulburt-Hirschfelder potential is useful since it is determined entirely from spectroscopic information and can accomodate the long-range maxima and minima found in many systems. Hulburt-Hirschfelder potentials are classified into five distinct types according to their qualitative binary collision dynamics, which in general can be quite complex and can exhibit “double orbiting”, i.e., a pair of orbiting impact parameters for a single energy of collision. The collision integral program of O\u27Hara and Smith has been revised extensively to accomodate all physical cases of the Hulburt-Hirschfelder potential, and the required numerical methods are described and justified. The revised program substantially extends the range of potentials for which collision integrals can be calculated

Role of Experienced Pain in the Assessment of Fear of Pain: A Predictive Validity Study of the Fear of Pain Questionnaire - III

Psycholog

Transport Properties of Ground State Nitrogen Atoms

Transport properties of dilute monatomic gases depend on the two body atom-atom interaction potential. When two ground state (4S) nitrogen atoms interact, they can follow any of four potential energy curves corresponding to the N2 molecule; the X 1Σ+g, A3Σ+u, 5Σ+g, and 7Σ+u curves. Transport collision integrals for the 1Σ+g and 3Σ+u states have been calculated by representing the potentials for these states with the Hulburt-Hirschfelder potential. The5Σ+g state has a large local maximum which requires changes in the computational procedure used previously; a modified Hulburt-Hirschfelder potential has been used to represent the potential for this state. Collision integrals for the 7Σ+u state have been obtained by direct use of a recent theoretical potential for this state. The collision integrals are compared with results obtained in previous studies

A model for the atomic-scale structure of a dense, nonequilibrium fluid: the homogeneous cooling state of granular fluids

It is shown that the equilibrium Generalized Mean Spherical Model of fluid structure may be extended to nonequilibrium states with equation of state information used in equilibrium replaced by an exact condition on the two-body distribution function. The model is applied to the homogeneous cooling state of granular fluids and upon comparison to molecular dynamics simulations is found to provide an accurate picture of the pair distribution function.Comment: 29 pages, 11 figures Revision corrects formatting of the figure

Effects of Intronic and Exonic Polymorphisms of Paraoxonase 1 (PON1) Gene on Serum PON1 Activity in a Korean Population

Paraoxonase 1 (PON1) hydrolyzes a number of toxic organophosphorous compounds and reduces lipid peroxide accumulation, and PON1 genetic polymorphisms in the coding region modulate serum PON1 activity. In this study, we investigated the association between 3 polymorphisms of PON1 located in intron 5 (17899insdelTT and 17974CT) and exon 6 (192QR) and serum PON1 activity. The genetic polymorphisms and serum activity of PON1 were analyzed in 153 healthy Koreans by using a direct sequencing assay and spectrophotometric method, respectively. A significant linkage disequilibrium (LD) was observed between all tested single nucleotide polymorphisms, with the strongest LD observed between 17899insdelTT and 192QR (D' = 0.984). The 17899insdelTT, 17974CT and 192QR genetic polymorphisms were associated with significant differences in serum paraoxonase activity. In multiple regression analyses, smoking, triglyceride level, high-density lipoprotein (HDL) level, and the 17899insdelTT and 192QR genetic polymorphisms were significant determinants of serum paraoxonase activity, while age, smoking, triglyceride level, HDL level, and the 192QR genetic polymorphism were significant determinants of serum arylesterase activity. These results suggest that although the 192QR genetic polymorphism in the coding region of PON1 is primarily associated with serum PON1 activity, the intronic polymorphisms are also involved in serum PON1 activity, and this association may be mediated by LD

Hidden Voices of Black Men

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66982/2/10.1177_002193479402500102.pd

Simplified Models for LHC New Physics Searches

This document proposes a collection of simplified models relevant to the design of new-physics searches at the LHC and the characterization of their results. Both ATLAS and CMS have already presented some results in terms of simplified models, and we encourage them to continue and expand this effort, which supplements both signature-based results and benchmark model interpretations. A simplified model is defined by an effective Lagrangian describing the interactions of a small number of new particles. Simplified models can equally well be described by a small number of masses and cross-sections. These parameters are directly related to collider physics observables, making simplified models a particularly effective framework for evaluating searches and a useful starting point for characterizing positive signals of new physics. This document serves as an official summary of the results from the "Topologies for Early LHC Searches" workshop, held at SLAC in September of 2010, the purpose of which was to develop a set of representative models that can be used to cover all relevant phase space in experimental searches. Particular emphasis is placed on searches relevant for the first ~50-500 pb-1 of data and those motivated by supersymmetric models. This note largely summarizes material posted at http://lhcnewphysics.org/, which includes simplified model definitions, Monte Carlo material, and supporting contacts within the theory community. We also comment on future developments that may be useful as more data is gathered and analyzed by the experiments.Comment: 40 pages, 2 figures. This document is the official summary of results from "Topologies for Early LHC Searches" workshop (SLAC, September 2010). Supplementary material can be found at http://lhcnewphysics.or

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