Interim report on transient heat-transfer measurements of catalytic recombination in a step-function flow of atomic oxygen.

"AFOSR 3203. Contract no. AF 49(638)-782."

The Lateral Trigger Probability function for the Ultra-High Energy Cosmic Ray showers detected by the Pierre Auger Observatory

In this paper we introduce the concept of Lateral Trigger Probability (LTP) function, i.e., the probability for an Extensive Air Shower (EAS) to trigger an individual detector of a ground based array as a function of distance to the shower axis, taking into account energy, mass and direction of the primary cosmic ray. We apply this concept to the surface array of the Pierre Auger Observatory consisting of a 1.5 km spaced grid of about 1600 water Cherenkov stations. Using Monte Carlo simulations of ultra-high energy showers the LTP functions are derived for energies in the range between 10(17) and 10(19) eV and zenith angles up to 65 degrees. A parametrization combining a step function with an exponential is found to reproduce them very well in the considered range of energies and zenith angles. The LTP functions can also be obtained from data using events simultaneously observed by the fluorescence and the surface detector of the Pierre Auger Observatory (hybrid events). We validate the Monte Carlo results showing how LTP functions from data are in good agreement with simulations.

On the Approximation of the Generalized Cut Function of Degree p+1 By Smooth Sigmoid Functions

We introduce a modification of the familiar cut function by replacing the linear part in its definition by a polynomial of degree p + 1 obtaining thus a sigmoid function called generalized cut function of degree p + 1 (GCFP). We then study the uniform approximation of the (GCFP) by smooth sigmoid functions such as the logistic and the shifted logistic functions. The limiting case of the interval-valued Heaviside step function is also discussed which imposes the use of Hausdorff metric. Numerical examples are presented using CAS MATHEMATICA.

Pitfalls driven by the sole use of local updates in dynamical

The recent claim that the exit probability (EP) of a slightly modified version of the Sznadj model is a continuous function of the initial magnetization is questioned. This result has been obtained analytically and confirmed by Monte Carlo simulations, simultaneously and independently by two different groups (EPL, 82 (2008) 18006; 18007). It stands at odds with an earlier result which yielded a step function for the EP (Europhys. Lett., 70 (2005) 705). The dispute is investigated by proving that the continuous shape of the EP is a direct outcome of a mean-field treatment for the analytical result. As such, it is most likely to be caused by finite-size effects in the simulations. The improbable alternative would be a signature of the irrelevance of fluctuations in this system. Indeed, evidence is provided in support of the stepwise shape as going beyond the mean-field level. These findings yield new insight in the physics of one-dimensional systems with respect to the validity of a true equilibrium state when using solely local update rules. The suitability and the significance to perform numerical simulations in those cases is discussed. To conclude, a great deal of caution is required when applying updates rules to describe any system especially social systems. Copyright (C) EPLA, 2011

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

A total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed. Test-day milk yields were classified into 44 weekly classes of DIM. The contemporary groups were defined as herd-year-week of test-day. The model included direct additive genetic, permanent environmental and residual effects as random and fixed effects of contemporary group and age of cow at calving as covariable, linear and quadratic effects. Mean trends were modeled by a cubic regression on orthogonal polynomials of DIM. Additive genetic and permanent environmental random effects were estimated by random regression on orthogonal Legendre polynomials. Residual variances were modeled using third to seventh-order variance functions or a step function with 1, 6,13,17 and 44 variance classes. Results from Akaike`s and Schwarz`s Bayesian information criterion suggested that a model considering a 7th-order Legendre polynomial for additive effect, a 12th-order polynomial for permanent environment effect and a step function with 6 classes for residual variances, fitted best. However, a parsimonious model, with a 6th-order Legendre polynomial for additive effects and a 7th-order polynomial for permanent environmental effects, yielded very similar genetic parameter estimates. (C) 2008 Elsevier B.V. All rights reserved.

Determination of activation energy distributions for chemisorption of oxygen on carbon: an improved approach

The present paper proposes an approach to obtaining the activation energy distribution for chemisorption of oxygen onto carbon surfaces, while simultaneously allowing for the activation energy dependence of the pre-exponential factor of the rate constant. Prior studies in this area have considered this factor to be uniform, thereby biasing estimated distributions. The results show that the derived activation energy distribution is not sensitive to the chemisorption mechanism because of the step function like property of the coverage. The activation energy distribution is essentially uniform for some carbons, and has two or possibly more discrete stages, suggestive of at least two types of sites, each with its own uniform distribution. The pre-exponential factors of the reactions are determined directly from the experimental data, and are found not to be constant as assumed in earlier work, but correlated with the activation energy. The latter results empirically follow an exponential function, supporting some earlier statistical and experimental work. The activation energy distribution obtained in the present paper permits improved correlation of chemisorption data in comparison to earlier studies. (C) 2000 Elsevier Science Ltd. All rights reserved.

A Log-linear model of grain size influence on the geochemistry of sediments

Sediment composition is mainly controlled by the nature of the source rock(s), and chemical (weathering) and physical processes (mechanical crushing, abrasion, hydrodynamic sorting) during alteration and transport. Although the factors controlling these processes are conceptually well understood, detailed quantification of compositional changes induced by a single process are rare, as are examples where the effects of several processes can be distinguished. The present study was designed to characterize the role of mechanical crushing and sorting in the absence of chemical weathering. Twenty sediment samples were taken from Alpine glaciers that erode almost pure granitoid lithologies. For each sample, 11 grain-size fractions from granules to clay (ø grades &-1 to &9) were separated, and each fraction was analysed for its chemical composition.The presence of clear steps in the box-plots of all parts (in adequate ilr and clr scales) against ø is assumed to be explained by typical crystal size ranges for the relevant mineral phases. These scatter plots and the biplot suggest a splitting of the full grain size range into three groups: coarser than ø=4 (comparatively rich in SiO2, Na2O, K2O, Al2O3, and dominated by “felsic” minerals like quartz and feldspar), finer than ø=8 (comparatively rich in TiO2, MnO, MgO, Fe2O3, mostly related to “mafic” sheet silicates like biotite and chlorite), and intermediate grains sizes (4≤ø &8; comparatively rich in P2O5 and CaO, related to apatite, some feldspar).To further test the absence of chemical weathering, the observed compositions were regressed against three explanatory variables: a trend on grain size in ø scale, a step function for ø≥4, and another for ø≥8. The original hypothesis was that the trend could be identified with weathering effects, whereas each step function would highlight those minerals with biggest characteristic size at its lower end. Results suggest that this assumption is reasonable for the step function, but that besides weathering some other factors (different mechanical behavior of minerals) have also an important contribution to the trend.Key words: sediment, geochemistry, grain size, regression, step function

Symmetries and fixed point stability of stochastic differential equations modeling self-organized criticality

A stochastic nonlinear partial differential equation is constructed for two different models exhibiting self-organized criticality: the Bak-Tang-Wiesenfeld (BTW) sandpile model [Phys. Rev. Lett. 59, 381 (1987); Phys. Rev. A 38, 364 (1988)] and the Zhang model [Phys. Rev. Lett. 63, 470 (1989)]. The dynamic renormalization group (DRG) enables one to compute the critical exponents. However, the nontrivial stable fixed point of the DRG transformation is unreachable for the original parameters of the models. We introduce an alternative regularization of the step function involved in the threshold condition, which breaks the symmetry of the BTW model. Although the symmetry properties of the two models are different, it is shown that they both belong to the same universality class. In this case the DRG procedure leads to a symmetric behavior for both models, restoring the broken symmetry, and makes accessible the nontrivial fixed point. This technique could also be applied to other problems with threshold dynamics.

Temporal variability of soil CO2 emission after conventional and reduced tillage described by an exponential decay in time model

To study Assessing the impact of tillage practices on soil carbon losses dependents it is necessary to describe the temporal variability of soil CO2 emission after tillage. It has been argued that large amounts of CO2 emitted after tillage may serve as an indicator for longer-term changes in soil carbon stocks. Here we present a two-step function model based on soil temperature and soil moisture including an exponential decay in time component that is efficient in fitting intermediate-term emission after disk plow followed by a leveling harrow (conventional), and chisel plow coupled with a roller for clod breaking (reduced) tillage. Emission after reduced tillage was described using a non-linear estimator with determination coefficient (R²) as high as 0.98. Results indicate that when emission after tillage is addressed it is important to consider an exponential decay in time in order to predict the impact of tillage in short-term emissions.

A Log-linear model of grain size influence on the geochemistry of sediments

Sediment composition is mainly controlled by the nature of the source rock(s), and chemical (weathering) and physical processes (mechanical crushing, abrasion, hydrodynamic sorting) during alteration and transport. Although the factors controlling these processes are conceptually well understood, detailed quantification of compositional changes induced by a single process are rare, as are examples where the effects of several processes can be distinguished. The present study was designed to characterize the role of mechanical crushing and sorting in the absence of chemical weathering. Twenty sediment samples were taken from Alpine glaciers that erode almost pure granitoid lithologies. For each sample, 11 grain-size fractions from granules to clay (ø grades <-1 to >9) were separated, and each fraction was analysed for its chemical composition. The presence of clear steps in the box-plots of all parts (in adequate ilr and clr scales) against ø is assumed to be explained by typical crystal size ranges for the relevant mineral phases. These scatter plots and the biplot suggest a splitting of the full grain size range into three groups: coarser than ø=4 (comparatively rich in SiO2, Na2O, K2O, Al2O3, and dominated by “felsic” minerals like quartz and feldspar), finer than ø=8 (comparatively rich in TiO2, MnO, MgO, Fe2O3, mostly related to “mafic” sheet silicates like biotite and chlorite), and intermediate grains sizes (4≤ø <8; comparatively rich in P2O5 and CaO, related to apatite, some feldspar). To further test the absence of chemical weathering, the observed compositions were regressed against three explanatory variables: a trend on grain size in ø scale, a step function for ø≥4, and another for ø≥8. The original hypothesis was that the trend could be identified with weathering effects, whereas each step function would highlight those minerals with biggest characteristic size at its lower end. Results suggest that this assumption is reasonable for the step function, but that besides weathering some other factors (different mechanical behavior of minerals) have also an important contribution to the trend. Key words: sediment, geochemistry, grain size, regression, step function

Temporal variability of soil CO2 emission after conventional and reduced tillage described by an exponential decay in time model

A quantificação do impacto das práticas de preparo sobre as perdas de carbono do solo é dependente da habilidade de se descrever a variabilidade temporal da emissão de CO2 do solo após preparo. Tem sido sugerido que as grandes quantidades de CO2 emitido após o preparo do solo podem servir como um indicador das modificações nos estoques de carbono do solo em longo termo. Neste trabalho é apresentado um modelo de duas partes baseado na temperatura e na umidade do solo e que inclui um termo exponencial decrescente do tempo que é eficiente no ajuste das emissões intermediárias após preparo: arado de disco seguido de uma passagem com a grade niveladora (convencional) e escarificador de arrasto seguido da passagem com rolo destorroador (reduzido). As emissões após o preparo do solo são descritas utilizando-se estimativa não linear com um coeficiente de determinação (R²) tão alto quanto 0.98 após preparo reduzido. Os resultados indicam que nas previsões da emissão de CO2 após o preparo do solo é importante considerar um termo exponencial decrescente no tempo após preparo.

Random regressions models to describe the genetic variation of milk yield over multiple parities in Buffaloes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Utilização de modelos de regressão aleatória para produção de leite no dia do controle, com diferentes estruturas de variâncias residuais

Foram utilizados quatorze modelos de regressão aleatória, para ajustar 86.598 dados de produção de leite no dia do controle de 2.155 primeiras lactações de vacas Caracu, truncadas aos 305 dias. Os modelos incluíram os efeitos fixos de grupo contemporâneo e a covariável idade da vaca ao parto. Uma regressão ortogonal de ordem cúbica foi usada para modelar a trajetória média da população. Os efeitos genéticos aditivos e de ambiente permanente foram modelados por meio de regressões aleatórias, usando polinômios ortogonais de Legendre, de ordens cúbicas. Diferentes estruturas de variâncias residuais foram testadas e consideradas por meio de classes contendo 1, 10, 15 e 43 variâncias residuais e de funções de variâncias (FV) usando polinômios ordinários e ortogonais, cujas ordens variaram de quadrática até sêxtupla. Os modelos foram comparados usando o teste da razão de verossimilhança, o Critério de Informação de Akaike e o Critério de Informação Bayesiano de Schwar. Os testes indicaram que, quanto maior a ordem da função de variâncias, melhor o ajuste. Dos polinômios ordinários, a função de sexta ordem foi superior. Os modelos com classes de variâncias residuais foram aparentemente superiores àqueles com funções de variância. O modelo com homogeneidade de variâncias foi inadequado. O modelo com 15 classes heterogêneas foi o que melhor ajustou às variâncias residuais, entretanto, os parâmetros genéticos estimados foram muito próximos para os modelos com 10, 15 ou 43 classes de variâncias ou com FV de sexta ordem.