Validação de modelos de estimativa de produtividade de milho, feijão, arroz e trigo no Brasil.

A Embrapa Informática Agropecuária produz anualmente uma vasta gama de informação tanto bibliográfica, de natureza técnico-científica, quanto referencial. No entanto, as mudanças ocorridas na sociedade devido aos impactos das novas tecnologias de informação e de comunicação exigem da Empresa Brasileira de Pesquisa Agropecuária (Embrapa) novos procedimentos para organização da informação disponível, de forma que resulte em efetiva transferência de tecnologia. Some-se a isso do fato de que a Embrapa Transferência de Tecnologia gerencia, hoje, uma vasta carteira de clientes, desde produtores rurais a cooperativas, que demandam continuamente por novas informações e tecnologias para garantir a evolução de seus negócios. Através da integração de modelos de simulação de crescimento e de desenvolvimento de culturas, bases de dados de clima e de solo, prognósticos de tempo e de clima, técnicas de análise regional e ferramentas de geoprocessamento, acoplados a um serviço eficiente de monitoramento agrometeorológico, é possível produzir um eficiente sistema de monitoramento da produtividade das culturas em escala local e regional.bitstream/item/32638/1/Doc110-1.pd

Análise de homogeneidade de temperatura mínima mensal para o Estado de São Paulo.

Neste trabalho, o pacote Climatol-R foi aplicado a 122 séries mensais de temperatura mínima, cobrindo o interior do estado de São Paulo para os anos 1940 até 2012.bitstream/item/144640/1/BolPesq41.pd

Inflationary Models Driven by Adiabatic Matter Creation

The flat inflationary dust universe with matter creation proposed by Prigogine and coworkers is generalized and its dynamical properties are reexamined. It is shown that the starting point of these models depends critically on a dimensionless parameter $\Sigma$, closely related to the matter creation rate $\psi$. For $\Sigma$ bigger or smaller than unity flat universes can emerge, respectively, either like a Big-Bang FRW singularity or as a Minkowski space-time at $t=-\infty$. The case $\Sigma=1$ corresponds to a de Sitter-type solution, a fixed point in the phase diagram of the system, supported by the matter creation process. The curvature effects have also been investigated. The inflating de Sitter is a universal attractor for all expanding solutions regardless of the initial conditions as well as of the curvature parameter.Comment: 25 pages, 2 figures(available from the authors), uses LATE

Variationally Mimetic Operator Networks

In recent years operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input functions that describe material properties, forcing functions and boundary data to the solution of a PDE. This work describes a new architecture for operator networks that mimics the form of the numerical solution obtained from an approximate variational or weak formulation of the problem. The application of these ideas to a generic elliptic PDE leads to a variationally mimetic operator network (VarMiON). Like the conventional Deep Operator Network (DeepONet) the VarMiON is also composed of a sub-network that constructs the basis functions for the output and another that constructs the coefficients for these basis functions. However, in contrast to the DeepONet, the architecture of these sub-networks in the VarMiON is precisely determined. An analysis of the error in the VarMiON solution reveals that it contains contributions from the error in the training data, the training error, the quadrature error in sampling input and output functions, and a "covering error" that measures the distance between the test input functions and the nearest functions in the training dataset. It also depends on the stability constants for the exact solution operator and its VarMiON approximation. The application of the VarMiON to a canonical elliptic PDE and a nonlinear PDE reveals that for approximately the same number of network parameters, on average the VarMiON incurs smaller errors than a standard DeepONet and a recently proposed multiple-input operator network (MIONet). Further, its performance is more robust to variations in input functions, the techniques used to sample the input and output functions, the techniques used to construct the basis functions, and the number of input functions.Comment: 49 pages, 18 figures, 1 Appendi

Self-tuning to the Hopf bifurcation in fluctuating systems

The problem of self-tuning a system to the Hopf bifurcation in the presence of noise and periodic external forcing is discussed. We find that the response of the system has a non-monotonic dependence on the noise-strength, and displays an amplified response which is more pronounced for weaker signals. The observed effect is to be distinguished from stochastic resonance. For the feedback we have studied, the unforced self-tuned Hopf oscillator in the presence of fluctuations exhibits sharp peaks in its spectrum. The implications of our general results are briefly discussed in the context of sound detection by the inner ear.Comment: 37 pages, 7 figures (8 figure files

Rice yield estimation based on weather conditions and on technological level of production systems in Brazil.

The objective of this work was to evaluate an estimation system for rice yield in Brazil, based on simple agrometeorological models and on the technological level of production systems. This estimation system incorporates the conceptual basis proposed by Doorenbos & Kassam for potential and attainable yields with empirical adjusts for maximum yield and crop sensitivity to water deficit, considering five categories of rice yield. Rice yield was estimated from 2000/2001 to 2007/2008, and compared to IBGE yield data. Regression analyses between model estimates and data from IBGE surveys resulted in significant coefficients of determination, with less dispersion in the South than in the North and Northeast regions of the country. Index of model efficiency (E1') ranged from 0.01 in the lower yield classes to 0.45 in higher ones, and mean absolute error ranged from 58 to 250 kg ha-1, respectively