Quark model predictions for the SU(6)-breaking ratio of the proton momentum distributions

The ratio between the anomalous magnetic moments of proton and neutron has been recently parametrized by the ratio of proton momentum fractions $M_{2}^{q_{val}}$. This ratio is evaluated using different constituent quark models, starting from the CQM density distributions and calculating the next-to leading order distributions. We show that this momentum fractions $M_{2}^{q_{val}}$ ratio is a sensitive test for SU(6)-breaking effects and therefore it is useful to distinguish among different CQMs. We investigate also the possibility of getting constraints on the formulation of quark structure models.Comment: 12 pages, 3 figure

A rigorous bound on quark distributions in the nucleon

I deduce an inequality between the helicity and the transversity distribution of a quark in a nucleon, at small energy scales. Then I establish, thanks to the positivity constraint, a rigorous bound on longitudinally polarized valence quark densities, which finds nontrivial applications to d-quarks. This, in turn, implies a bound for the distributions of the longitudinally polarized sea, which is probably not SU(3)-symmetric. Some model predictions and parametrizations of quark distributions are examined in the light of these results.Comment: Talk given at the QCD03 Conference, Montpellier, 2-9 July 200

Not all surveillance data are created equal—A multi‐method dynamic occupancy approach to determine rabies elimination from wildlife

1. A necessary component of elimination programmes for wildlife disease is effective surveillance. The ability to distinguish between disease freedom and non‐detection can mean the difference between a successful elimination campaign and new epizootics. Understanding the contribution of different surveillance methods helps to optimize and better allocate effort and develop more effective surveillance programmes. 2. We evaluated the probability of rabies virus elimination (disease freedom) in an enzootic area with active management using dynamic occupancy modelling of 10 years of raccoon rabies virus (RABV) surveillance data (2006–2015) collected from three states in the eastern United States. We estimated detection probability of RABV cases for each surveillance method (e.g. strange acting reports, roadkill, surveillance‐trapped animals, nuisance animals and public health samples) used by the USDA National Rabies Management Program. 3. Strange acting, found dead and public health animals were the most likely to detect RABV when it was present, and generally detectability was higher in fall– winter compared to spring–summer. Found dead animals in fall–winter had the highest detection at 0.33 (95% CI: 0.20, 0.48). Nuisance animals had the lowest detection probabilities (~0.02). 4. Areas with oral rabies vaccination (ORV) management had reduced occurrence probability compared to enzootic areas without ORV management. RABV occurrence was positively associated with deciduous and mixed forests and medium to high developed areas, which are also areas with higher raccoon (Procyon lotor) densities. By combining occupancy and detection estimates we can create a probability of elimination surface that can be updated seasonally to provide guidance on areas managed for wildlife disease. 5. Synthesis and applications. Wildlife disease surveillance is often comprised of a combination of targeted and convenience‐based methods. Using a multi‐method analytical approach allows us to compare the relative strengths of these methods, providing guidance on resource allocation for surveillance actions. Applying this multi‐method approach in conjunction with dynamic occupancy analyses better informs management decisions by understanding ecological drivers of disease occurrence

Fast computation by block permanents of cumulative distribution functions of order statistics from several populations

The joint cumulative distribution function for order statistics arising from several different populations is given in terms of the distribution function of the populations. The computational cost of the formula in the case of two populations is still exponential in the worst case, but it is a dramatic improvement compared to the general formula by Bapat and Beg. In the case when only the joint distribution function of a subset of the order statistics of fixed size is needed, the complexity is polynomial, for the case of two populations.Comment: 21 pages, 3 figure

ON THE EXPECTED VALUES OF SEQUENCES OF FUNCTIONS

We prove new extensions to lemmas about combinations of convergent sequences of distribution functions and absolutely continuous bounded functions. New lemma one, a generalized Helly theorem, allows computing the limit of the expected value of a sequence of functions with respect to a sequence of measures. Previously published results allow either the function or the measure to be a sequence, but not both. Lemma two allows computing the expected value of an absolutely continuous monotone function by integrating the probabilities of the inverse function values. Previous results were restricted to the identity function. Lemma three gives a computationally and analytically convenient form for the limit of the expected value of a sequence of functions of a sequence of random variables. This is a new result that follows directly from the first two lemmas. Although the lemmas resemble standard results and seem obviously true, we have found only similar looking and related but quite distinct results in the literature. We provide examples which highlight the value of the new results

Adjusting power for a baseline covariate in linear models

The analysis of covariance provides a common approach to adjusting for a baseline covariate in medical research. With Gaussian errors, adding random covariates does not change either the theory or the computations of general linear model data analysis. However, adding random covariates does change the theory and computation of power analysis. Many data analysts fail to fully account for this complication in planning a study. We present our results in five parts. (i) A review of published results helps document the importance of the problem and the limitations of available methods. (ii) A taxonomy for general linear multivariate models and hypotheses allows identifying a particular problem. (iii) We describe how random covariates introduce the need to consider quantiles and conditional values of power. (iv) We provide new exact and approximate methods for power analysis of a range of multivariate models with a Gaussian baseline covariate, for both small and large samples. The new results apply to the Hotelling-Lawley test and the four tests in the “univariate” approach to repeated measures (unadjusted, Huynh-Feldt, Geisser-Greenhouse, Box). The techniques allow rapid calculation and an interactive, graphical approach to sample size choice. (v) Calculating power for a clinical trial of a treatment for increasing bone density illustrates the new methods. We particularly recommend using quantile power with a new Satterthwaite-style approximation

An Assessment of Oral Health on the Pine Ridge Indian Reservation

An assessment on the oral health of 292 Oglala Lakota residents of the Pine Ridge Indian Reservation in South Dakota looks at dental issues, periodontal disease, oral lesions and need for dental care. The research was conducted by the University of Colorado, Center for Native Oral Health Research and funded by the W.K. Kellogg Foundation

Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes: J. L. JOHNSONET AL.

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes, and accounts for within cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least 6 clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Since small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach

Studies of parton thermalization at RHIC

We consider the evolution of a parton system which is formed in the central region just after a relativistic heavy ion collision. The parton consist of mostly gluons, minijets, which are produced by elastic scattering between constituent partons of the colliding nuclei. We assume the system can be described by a semi-classical Boltzmann transport equation, which we solve by means of the test particle Monte-Carlo method including retardation. The partons proliferate via secondary radiative $gg \to ggg$ processes until the thermalization is reached for some assumptions. The extended system is thermalized at about $t=1.6$ fm/$c$ with $T = 570$ MeV and stays in equilibrium for about 2 fm/$c$ with breaking temperature $T = 360$ MeV in the rapidity central region.Comment: 14 page