Sensibility analysis of the pearl model for pesticide leaching in the State of Mato Grosso do Sul, Brazil

For an accurate use of pesticide leaching models it is necessary to assess the sensitivity of input parameters. The aim of this work was to carry out sensitivity analysis of the pesticide leaching model PEARL for contrasting soil types of Dourados river watershed in the state of Mato Grosso do Sul, Brazil. Sensitivity analysis was done by carrying out many simulations with different input parameters and calculating their influence on the output values. The approach used was called one-at-a-time sensitivity analysis, which consists in varying independently input parameters one at a time and keeping all others constant with the standard scenario. Sensitivity analysis was automated using SESAN tool that was linked to the PEARL model. Results have shown that only soil characteristics influenced the simulated water flux resulting in none variation of this variable for scenarios with different pesticides and same soil. All input parameters that showed the greatest sensitivity with regard to leached pesticide are related to soil and pesticide properties. Sensitivity of all input parameters was scenario dependent, confirming the need of using more than one standard scenario for sensitivity analysis of pesticide leaching models.

Be-Fe coupled method for the analysis of 3D elastodynamic problems in the time domain

By coupling the Boundary Element Method (BEM) and the Finite Element Method (FEM) an algorithm that combines the advantages of both numerical processes is developed. The main aim of the work concerns the time domain analysis of general three-dimensional wave propagation problems in elastic media. In addition, mathematical and numerical aspects of the related BE-, FE- and BE/FE-formulations are discussed. The coupling algorithm allows investigations of elastodynamic problems with a BE- and a FE-subdomain. In order to observe the performance of the coupling algorithm two problems are solved and their results compared to other numerical solutions.

Pressure and time dependence of the cardiopulmonary reflex response in patients with hypertensive cardiomyopathy

The first minutes of the time course of cardiopulmonary reflex control evoked by lower body negative pressure (LBNP) in patients with hypertensive cardiomyopathy have not been investigated in detail. We studied 15 hypertensive patients with left ventricular dysfunction (LVD) and 15 matched normal controls to observe the time course response of the forearm vascular resistance (FVR) during 3 min of LBNP at -10, -15, and -40 mmHg in unloading the cardiopulmonary receptors. Analysis of the average of 3-min intervals of FVR showed a blunted response of the LVD patients at -10 mmHg (P = 0.03), but a similar response in both groups at -15 and -40 mmHg. However, using a minute-to-minute analysis of the FVR at -15 and -40 mmHg, we observed a similar response in both groups at the 1st min, but a marked decrease of FVR in the LVD group at the 3rd min of LBNP at -15 mmHg (P = 0.017), and -40 mmHg (P = 0.004). Plasma norepinephrine levels were analyzed as another neurohumoral measurement of cardiopulmonary receptor response to LBNP, and showed a blunted response in the LVD group at -10 (P = 0.013), -15 (P = 0.032) and -40 mmHg (P = 0.004). We concluded that the cardiopulmonary reflex response in patients with hypertensive cardiomyopathy is blunted at lower levels of LBNP. However, at higher levels, the cardiopulmonary reflex has a normal initial response that decreases progressively with time. As a consequence of the time-dependent response, the cardiopulmonary reflex response should be measured over small intervals of time in clinical studies.

The role of Ca2+/calmodulin-dependent protein kinase II and calcineurin in TNF-α-induced myocardial hypertrophy

We investigated whether Ca2+/calmodulin-dependent kinase II (CaMKII) and calcineurin (CaN) are involved in myocardial hypertrophy induced by tumor necrosis factor α (TNF-α). The cardiomyocytes of neonatal Wistar rats (1-2 days old) were cultured and stimulated by TNF-α (100 μg/L), and Ca2+ signal transduction was blocked by several antagonists, including BAPTA (4 µM), KN-93 (0.2 µM) and cyclosporin A (CsA, 0.2 µM). Protein content, protein synthesis, cardiomyocyte volumes, [Ca2+]i transients, CaMKIIδB and CaN were evaluated by the Lowry method, [³H]-leucine incorporation, a computerized image analysis system, a Till imaging system, and Western blot analysis, respectively. TNF-α induced a significant increase in protein content in a dose-dependent manner from 10 µg/L (53.56 µg protein/well) to 100 μg/L (72.18 µg protein/well), and in a time-dependent manner from 12 h (37.42 µg protein/well) to 72 h (42.81 µg protein/well). TNF-α (100 μg/L) significantly increased the amplitude of spontaneous [Ca2+]i transients, the total protein content, cell size, and [³H]-leucine incorporation in cultured cardiomyocytes, which was abolished by 4 µM BAPTA, an intracellular Ca2+ chelator. The increases in protein content, cell size and [³H]-leucine incorporation were abolished by 0.2 µM KN-93 or 0.2 µM CsA. TNF-α increased the expression of CaMKIIδB by 35.21% and that of CaN by 22.22% compared to control. These effects were abolished by 4 µM BAPTA, which itself had no effect. These results suggest that TNF-α induces increases in [Ca2+]i, CaMKIIδB and CaN and promotes cardiac hypertrophy. Therefore, we hypothesize that the Ca2+/CaMKII- and CaN-dependent signaling pathways are involved in myocardial hypertrophy induced by TNF-α.

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis

The point source method for inverse scattering in the time domain

Many recent inverse scattering techniques have been designed for single frequency scattered fields in the frequency domain. In practice, however, the data is collected in the time domain. Frequency domain inverse scattering algorithms obviously apply to time-harmonic scattering, or nearly time-harmonic scattering, through application of the Fourier transform. Fourier transform techniques can also be applied to non-time-harmonic scattering from pulses. Our goal here is twofold: first, to establish conditions on the time-dependent waves that provide a correspondence between time domain and frequency domain inverse scattering via Fourier transforms without recourse to the conventional limiting amplitude principle; secondly, we apply the analysis in the first part of this work toward the extension of a particular scattering technique, namely the point source method, to scattering from the requisite pulses. Numerical examples illustrate the method and suggest that reconstructions from admissible pulses deliver superior reconstructions compared to straight averaging of multi-frequency data. Copyright (C) 2006 John Wiley & Sons, Ltd.

Social networks: evolving graphs with memory dependent edges

The plethora, and mass take up, of digital communication tech- nologies has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the ex- istence or otherwise of certain infinite products and series involving age dependent model parameters. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

A time-domain probe method for three-dimensional rough surface reconstructions

The task of this paper is to develop a Time-Domain Probe Method for the reconstruction of impenetrable scatterers. The basic idea of the method is to use pulses in the time domain and the time-dependent response of the scatterer to reconstruct its location and shape. The method is based on the basic causality principle of timedependent scattering. The method is independent of the boundary condition and is applicable for limited aperture scattering data. In particular, we discuss the reconstruction of the shape of a rough surface in three dimensions from time-domain measurements of the scattered field. In practise, measurement data is collected where the incident field is given by a pulse. We formulate the time-domain fieeld reconstruction problem equivalently via frequency-domain integral equations or via a retarded boundary integral equation based on results of Bamberger, Ha-Duong, Lubich. In contrast to pure frequency domain methods here we use a time-domain characterization of the unknown shape for its reconstruction. Our paper will describe the Time-Domain Probe Method and relate it to previous frequency-domain approaches on sampling and probe methods by Colton, Kirsch, Ikehata, Potthast, Luke, Sylvester et al. The approach significantly extends recent work of Chandler-Wilde and Lines (2005) and Luke and Potthast (2006) on the timedomain point source method. We provide a complete convergence analysis for the method for the rough surface scattering case and provide numerical simulations and examples.

Modelling the time-evolution of phytoplankton size spectra from satellite remote sensing

A dynamic size-structured model is developed for phytoplankton and nutrients in the oceanic mixed layer and applied to extract phytoplankton biomass at discrete size fractions from remotely sensed, ocean-colour data. General relationships between cell size and biophysical processes (such as sinking, grazing, and primary production) of phytoplankton were included in the model through a bottom–up approach. Time-dependent, mixed-layer depth was used as a forcing variable, and a sequential data-assimilation scheme was implemented to derive model trajectories. From a given time-series, the method produces estimates of size-structured biomass at every observation, so estimates seasonal succession of individual phytoplankton size, derived here from remote sensing for the first time. From these estimates, normalized phytoplankton biomass size spectra over a period of 9 years were calculated for one location in the North Atlantic. Further analysis demonstrated that strong relationships exist between the seasonal trends of the estimated size spectra and the mixed-layer depth, nutrient biomass, and total chlorophyll. The results contain useful information on the time-dependent biomass flux in the pelagic ecosystem.

Wear of Ball Attachments After 1 to 8 Years of Clinical Use: A Qualitative Analysis

The purpose of this study was to analyze and compare scanning electron microscopy (SEM) observations of ball attachments that had been worn by patients during three periods of clinical use. One hundred forty-four specimens of ball anchor attachments (gold alloy matrix and titanium patrix) were studied by SEM after periods of approximately 1, 3.5, and 8 years of clinical use. Twenty new attachment components were examined as controls. SEM images revealed signs of mechanical wear for the ball attachments studied. The surfaces of the titanium patrix were associated primarily with roughening after short-term use, whereas surfaces of the gold alloy matrix showed wear, roughening, and loss of microscopic material in the form of flakes. Severe mechanical wear on both surfaces was noted after longer periods of use. The mechanical changes were not correlated with patient-mediated observations regarding the time-dependent retentive efficacy of the attachments. One year of clinical wear appeared to have limited effect on the ball attachment tested. Conversely, longer periods of use led to marked modifications in shape of the matrix and patrix components. Int J Prosthodont 2011;24:270-272.

Non-stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil

This work is an assessment of frequency of extreme values (EVs) of daily rainfall in the city of Sao Paulo. Brazil, over the period 1933-2005, based on the peaks-over-threshold (POT) and Generalized Pareto Distribution (GPD) approach. Usually. a GPD model is fitted to a sample of POT Values Selected With a constant threshold. However. in this work we use time-dependent thresholds, composed of relatively large p quantities (for example p of 0.97) of daily rainfall amounts computed from all available data. Samples of POT values were extracted with several Values of p. Four different GPD models (GPD-1, GPD-2, GPD-3. and GDP-4) were fitted to each one of these samples by the maximum likelihood (ML) method. The shape parameter was assumed constant for the four models, but time-varying covariates were incorporated into scale parameter of GPD-2. GPD-3, and GPD-4, describing annual cycle in GPD-2. linear trend in GPD-3, and both annual cycle and linear trend in GPD-4. The GPD-1 with constant scale and shape parameters is the simplest model. For identification of the best model among the four models WC used rescaled Akaike Information Criterion (AIC) with second-order bias correction. This criterion isolates GPD-3 as the best model, i.e. the one with positive linear trend in the scale parameter. The slope of this trend is significant compared to the null hypothesis of no trend, for about 98% confidence level. The non-parametric Mann-Kendall test also showed presence of positive trend in the annual frequency of excess over high thresholds. with p-value being virtually zero. Therefore. there is strong evidence that high quantiles of daily rainfall in the city of Sao Paulo have been increasing in magnitude and frequency over time. For example. 0.99 quantiles of daily rainfall amount have increased by about 40 mm between 1933 and 2005. Copyright (C) 2008 Royal Meteorological Society

Detailed analysis of the charge transfer complex N,N-dimethylaniline-SO(2) by Raman spectroscopy and density functional theory calculations

Although the amine sulfur dioxide chemistry was well characterized in the past both experimentally and theoretically, no systematic Raman spectroscopic study describes the interaction between N,N-dimethylaniline (DMA) and sulfur dioxide (SO(2)). The formation of a deep red oil by the reaction of SO(2) with DMA is an evidence of the charge transfer (CT) nature of the DMA-SO(2) interaction. The DMA -SO(2) normal Raman spectrum shows the appearance of two intense bands at 1110 and 1151 cm(-1), which are enhanced when resonance is approached. These bands are assigned to nu(s)(SO(2)) and nu(phi-N) vibrational modes, respectively, confirming the interaction between SO(2) and the amine via the nitrogen atom. The dimethyl group steric effect favors the interaction of SO(2) with the ring pi electrons, which gives rise to a pi-pi* low-energy CT electronic transition, as confirmed by time-dependent density functional theory (TDDFT) calculations. In addition, the calculated Raman DMA-SO(2) spectrum at the B3LYP/6-311++g(3df,3pd) level shows good agreement with the experimental results (vibrational wavenumbers and relative intensities), allowing a complete assignment of the vibrational modes. A better understanding of the intermolecular interactions in this model system can be extremely useful in designing new materials to absorb, detect, or even quantify SO(2). Copyright (C) 2009 John Wiley & Sons, Ltd.

Imperfectly credible disinflation under endogenous time-ependent pricing

The real effects of an imperfectly credible disinflation depend critically on the extent of price rigidity. Therefore, the study of how policymakers’ credibility affects the outcome of an announced disinflation should not be dissociated from the analysis of the determinants of the frequency of price adjustments. In this paper we examine how credibility affects the outcome of a disinflation in a model with endogenous timedependent pricing rules. Both the initial degree of price ridigity, calculated optimally, and, more notably, the changes in contract length during disinflation play an important role in the explanation of the effects of imperfect credibility. We initially evaluate the costs of disinflation in a setup where credibility is exogenous, and then allow agents to use Bayes rule to update beliefs about the “type” of monetary authority that they face. In both cases, the interaction between the endogeneity of time-dependent rules and imperfect credibility increases the output costs of disinflation, but the pattern of the output path is more realistic in the case with learning.

Analysis of the Influence of Sparkout Time on Grinding Using Several Lubrication/Cooling Methods

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