Non-Perturbative QCD Treatment of High-Energy Hadron-Hadron Scattering

Total cross-sections and logarithmic slopes of the elastic scattering cross-sections for different hadronic processes are calculated in the framework of the model of the stochastic vacuum. The relevant parameters of this model, a correlation length and the gluon condensate, are determined from scattering data, and found to be in very good agreement with values coming from completely different sources of information. A parameter-free relation is given between total cross-sections and slope parameters, which is shown to be remarkably valid up to the highest energies for which data exist.Comment: 60 pages, Heidelberg preprin

Observational Constraints on Chaplygin Quartessence: Background Results

We derive the constraints set by several experiments on the quartessence Chaplygin model (QCM). In this scenario, a single fluid component drives the Universe from a nonrelativistic matter-dominated phase to an accelerated expansion phase behaving, first, like dark matter and in a more recent epoch like dark energy. We consider current data from SNIa experiments, statistics of gravitational lensing, FR IIb radio galaxies, and x-ray gas mass fraction in galaxy clusters. We investigate the constraints from this data set on flat Chaplygin quartessence cosmologies. The observables considered here are dependent essentially on the background geometry, and not on the specific form of the QCM fluctuations. We obtain the confidence region on the two parameters of the model from a combined analysis of all the above tests. We find that the best-fit occurs close to the $\Lambda$CDM limit ($\alpha=0$). The standard Chaplygin quartessence ($\alpha=1$) is also allowed by the data, but only at the $\sim2\sigma$ level.Comment: Replaced to match the published version, references update

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of me-teorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric ap-proach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assump-tion is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive es-timation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covari-ance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomia