Nonperturbative aspects of the two-dimensional massive gauged Thirring model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for a standard model-like Higgs boson in the mu(+)mu(-) and e(+)e(-) decay channels at the LHC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Light-by-light scattering with intact protons at the LHC: from standard model to new physics

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

Bayesian age and growth analysis of the shortfin mako shark Isurus oxyrinchus in the Western South Atlantic Ocean using a flexible model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An MILP model for the plug-in electric vehicle charging coordination problem in electrical distribution systems

A new mixed-integer linear programming (MILP) model is proposed to represent the plug-in electric vehicles (PEVs) charging coordination problem in electrical distribution systems. The proposed model defines the optimal charging schedule for each division of the considered period of time that minimizes the total energy costs. Moreover, priority charging criteria is taken into account. The steady-state operation of the electrical distribution system, as well as the PEV batteries charging is mathematically represented; furthermore, constraints related to limits of voltage, current and power generation are included. The proposed mathematical model was applied in an electrical distribution system used in the specialized literature and the results show that the model can be used in the solution of the PEVs charging problem.

Yang-Lee zeros of the two- and three-state Potts model defined on π3 Feynman diagrams

We present both analytical and numerical results on the position of partition function zeros on the complex magnetic field plane of the q=2 state (Ising) and the q=3 state Potts model defined on phi(3) Feynman diagrams (thin random graphs). Our analytic results are based on the ideas of destructive interference of coexisting phases and low temperature expansions. For the case of the Ising model, an argument based on a symmetry of the saddle point equations leads us to a nonperturbative proof that the Yang-Lee zeros are located on the unit circle, although no circle theorem is known in this case of random graphs. For the q=3 state Potts model, our perturbative results indicate that the Yang-Lee zeros lie outside the unit circle. Both analytic results are confirmed by finite lattice numerical calculations.

Aeroelastic behavior of a wing with a magnetorheological hysteretic model to the damping

This paper shows the application of a hysteretic model for the Magnetorheological Damper (MRD) placed in the plunge degree-of-freedom of aeroelastic model of a wing. This hysteretic MRD model was developed by the researchers of the French Aerospace Lab. (ONERA) and describe, with a very good precision, the hysteretic behavior of the MRD. The aeroelastic model used in this paper do not have structural nonlinearities, the only nonlinearities showed in the model, are in the unsteady flow equations and are the same proposed by Theodorsen and Wagner in their unsteady aerodynamics theory; and the nonlinearity introduced by the hysteretic model used. The main objective of this paper is show the mathematical modeling of the problem and the equations that describes the aeroelastic response of our problem; and the gain obtained with the introduction of this hysteretic model in the equations with respect to other models that do not show the this behavior, through of pictures that represents the time response and Phase diagrams. These pictures are obtained using flow velocities before and after the flutter velocity. Finally, an open-loop control was made to show the effect of the MRD in the aeroelastic behavior.

The FGM bivariate lifetime copula model: a bayesian approach

In this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated via a simulation study and a real dataset.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway-Maxwell-Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.

Effective carrying capacity and analytical solution of a particular case of the Richards-like two-species population dynamics model

We consider a generalized two-species population dynamic model and analytically solve it for the amensalism and commensalism ecological interactions. These two-species models can be simplified to a one-species model with a time dependent extrinsic growth factor. With a one-species model with an effective carrying capacity one is able to retrieve the steady state solutions of the previous one-species model. The equivalence obtained between the effective carrying capacity and the extrinsic growth factor is complete only for a particular case, the Gompertz model. Here we unveil important aspects of sigmoid growth curves, which are relevant to growth processes and population dynamics. (C) 2011 Elsevier B.V. All rights reserved.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

Predicting Growth of Panicum maximum: An Adaptation of the CROPGRO-Perennial Forage Model

Warm-season grasses are economically important for cattle production in tropical regions and tools to aid in management and research on these forages would be highly beneficial both in research and the industry. This research was conducted to adapt the CROPGRO-Perennial Forage model to simulate growth of the tropical species guineagrass (Panicum maximum Jacq. cv. 'Tanzania') and to describe model adaptation for this species. To develop the CROPGRO parameters for this species, we began with values and relationships reported in the literature. Some parameters and relationships were calibrated by comparison with observed growth, development, dry matter accumulation, and partitioning during a 17-mo experiment with Tanzania guineagrass in Piracicaba, SP, Brazil. Compared with starting parameters for palisadegrass [Brachiaria brizantha (A. Rich.) Stapf. cv. 'Xaraes'], dormancy effects of the perennial forage model had to be minimized, partitioning to storage tissue or root decreased, and partitioning to leaf and stem increased to provide for more leaf and stem growth and less root. Parameters affecting specific leaf area and senescence of plant tissues were improved. After these changes were made to the model, biomass accumulation was better simulated, mean predicted herbage yield was 6576 kg ha(-1), averaged across 11 regrowth cycles of 35 (summer) or 63 d (winter), with a RMSE of 494 kg ha(-1) (Willmott's index of agreement d = 0.985, simulated/observed ratio = 1.014). The model also gave good predictions against an independent data set, with similar RMSE, ratio, and d. The results of the adaptation suggest that the CROPGRO model is an efficient tool to integrate physiological aspects of guineagrass and can be used to simulate growth.

Framing the difficulties resulting from implementing a Participatory Management Model in a public hospital

Objective: This study aims to address difficulties reported by the nursing team during the process of changing the management model in a public hospital in Brazil. Methods: This qualitative study used thematic content analysis as proposed by Bardin, and data were analyzed using the theoretical framework of Bolman and Deal. Results: The vertical implementation of Participatory Management contradicted its underlying philosophy and thereby negatively influenced employee acceptance of the change. The decentralized structure of the Participatory Management Model was implemented but shared decision-making was only partially utilized. Despite facilitation of the communication process within the unit, more significant difficulties arose from lack of communication inter-unit. Values and principals need to be shared by teams, however, that will happens only if managers restructure accountabilities changing job descriptions of all team members. Conclusion: Innovative management models that depart from the premise of decentralized decision-making and increased communication encourage accountability, increased motivation and satisfaction, and contribute to improving the quality of care. The contribution of the study is that it describes the complexity of implementing an innovative management model, examines dissent and intentionally acknowledges the difficulties faced by employees in the organization.