Geographical externalities and the Pacifying Police Units Program in Rio de Janeiro

The paper quantifies the effects on violence and police activity of the Pacifying Police Unit program (UPP) in Rio de Janeiro and the possible geographical spillovers caused by this policy. This program consists of taking selected shantytowns controlled by criminals organizations back to the State. The strategy of the policy is to dislodge the criminals and then settle a permanent community-oriented police station in the slum. The installation of police units in these slums can generate geographical spillover effects to other regions of the State of Rio de Janeiro. We use the interrupted time series approach proposed by Gonzalez-Navarro (2013) to address effects of a police when there is contagion of the control group and we find that criminal outcomes decrease in areas of UPP and in areas near treated regions. Furthermore, we build a model which allows to perform counterfactuals of this policy and to estimate causal effects in other areas of the State of Rio de Janeiro outside the city.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Conversão catalítica de clorometano em hidrocarbonetos

Amorphous silica-alumina and modified by incipient impregnation of iron, nickel, zinc and chromium were synthetized in oxide and metal state and evaluated as catalysts for the chloromethane conversion reaction. With known techniques their textural properties were determined and dynamics techniques in programmed temperature were used to find the acid properties of the materials. A thermodynamic model was used to determine the adsorption and desorption capacity of chloromethane. Two types of reactions were studied. Firstly the chloromethane was catalytically converted to hydrocarbons (T = 300 450 oC e m = 300 mg) in a fixed bed reactor with controlled pressure and flow. Secondly the deactivation of the unmodified support was studied (at 300 °C and m=250 g) in a micro-adsorver provided of gravimetric monitoring. The metal content (2,5%) and the chloromethane percent of the reagent mixture (10% chloromethane in nitrogen) were fixed for all the tests. From the results the chloromethane conversion and selectivity of the gaseous products (H2, CH4, C3 and C4) were determined as well as the energy of desorption (75,2 KJ/mol for Ni/Al2O3-SiO2 to 684 KJ/mol for the Zn/Al2O3-SiO2 catalyst) considering the desorption rate as a temperature function. The presence of a metal on the support showed to have an important significance in the chloromethane condensation. The oxide class catalyst presented a better performance toward the production of hydrocarbons. Especial mention to the ZnO/Al2O3-SiO2 that, in a gas phase basis, produced C3 83 % max. and C4 63% max., respectively, in the temperature of 450 oC and 20 hours on stream. Hydrogen was produced exclusively in the FeO/Al2O3-SiO2 catalysts (15 % max., T = 550 oC and 5,6 h on stream) and Ni/SiO2-Al2O3 (75 % max., T = 400 oC and 21,6 h on stream). All the catalysts produced methane (10 à 92 %), except for Ni/Al2O3-SiO2 and CrO/Al2O3-SiO2. In the deactivation study two models were proposed: The parallel model, where the product production competes with coke formation; and the sequential model, where the coke formation competes with the product desorption dessorption step. With the mass balance equations and the mechanism proposed six parameters were determined. Two kinetic parameters: the hydrocarbon formation constant, 8,46 10-4 min-1, the coke formation, 1,46 10-1 min-1; three thermodynamic constants (the global, 0,003, the chloromethane adsorption 0,417 bar-1, the hydrocarbon adsorption 2,266 bar-1), and the activity exponent of the coke formation (1,516). The model was reasonable well fitted and presented a satisfactory behavior in relation with the proposed mechanism

Microbiological assay for ceftazidime injection

A simple, sensitive, and specific biodiffusion assay for the! antibacterial ceftazidime was developed using a strain of Staphylococcus epidermidis (ATCC 12228) as the test organism. Ceftazidime was measured in powder for injection at concentrations ranging from 100 to 400 mu g/mL. The calibration graph for ceftazidime was linear (r(2) = 1), and the method validation showed that it was precise (relative standard deviation = 0.415) and accurate. The results obtained by biodiffusion assay were statistically calculated by linear parallel model and by means of regression analysis and were verified using analysis of variance. It was concluded that the microbiological assay is satisfactory for in vitro quantification of the antibacterial activity of ceftazidime in pharmaceuticals.

Dimmable electronic ballast with high power-factor SEPIC preregulator, for multiple tubular fluorescent lamps

An electronic ballast for multiple tubular fluorescent lamps is presented in this paper. The proposed structure features high power-factor, dimming capability, and soft-switching to the semiconductor devices operated in high frequencies. A Zero-Current-Switching - Pulse-Width-Modulated (ZCS-PWM) SEPIC converter composes the rectifying stage, controlled by the instantaneous average input current technique, performing soft-commutations and high input power factor. Regarding the inverting stage, it is composed by a classical resonant Half-Bridge converter, associated to Series Parallel-Loaded Resonant (SPLR) filters. The dimming control technique employed in this Half-Bridge inverter is based on the phase-shift in the current processed through the sets of filter + lamp. In addition, experimental results are shown in order to validate the developed analysis.

Microbiological assay for the determination of azithromycin in ophthalmic solutions

The validation of a simple, sensitive and specific agar diffusion bioassay, applying cylinder-plate method, for the determination of the antibiotic azithromycin in ophthalmic solutions is described. Using a strain of Bacillus subtilis ATCC 9372 as the test organism, azithromycin at concentrations ranging from 50.0 to 200.0 μg·mL-1 could be measured in 1.666 7 mg·mL-1 ophthalmic solutions. A prospective validation of the method showed that the method was linear (r = 0.999 9) and precise (RSD = 0.70) and accurate (it measured the added quantities). The results obtained by bioassay method could be statistically calculated by linear parallel model and by means of regression analysis and verified using analysis of variance (ANOVA). We conclude that the microbiological assay is satisfactory for quantification of azithromycin in ophthalmic solutions.

Algoritmos para geração de referências, inicialização e proteção de um condicionador unificado de qualidade de energia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelagem de níveis freáticos no sistema Aquifero Bauru como ferramenta na gestão de recursos hídricos subterrâneos

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

Exergames: Fator motivacional para a prática de atividades físicas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Practical Large-Scale Spatio-Temporal Modeling of Particulate Matter Concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988-2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of PM10 for the full time period and PM2.5 for a subset of the period. For the earlier part of the period, 1988-1998, few PM2.5 monitors were operating, so we develop a simple extension to the model that represents PM2.5 conditionally on PM10 model predictions. In the epidemiological analysis, model predictions of PM10 are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space-time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.

Respiration rates of tropical Atlantic copepods from the Cape Verde Islands

Copepods were sampled at two sampling sites off the island of São Vicente, Cape Verde Archipelago, in spring (March/April) and early summer (May/June) of 2010. The two sampling sites were located in Mindelo Bay (16.90N, 25.01W; bottom depth 22 m) and around 8 km off the town of São Pedro (16.77N, 25.12W; bottom depth 800 m). Samples were collected on board the local fishing vessel 'Sinagoga' using a WP-2 net (Hydrobios, 0.26 m**2 mouth opening, 200 µm mesh size). The net was either applied as a driftnet, drifting for 10 min in 22 to 0 m depth below the surface, or it was towed vertically with a towing speed of 0.5 m/s**1. For stratified sampling, the net was deployed in repetitive hauls from 560 to 210 m, from 210 to 80 m, and from 80 to 0 m in March/April and from 600 to 300 m, 300 to 100 m, and 100 to 0 m in May/June. Additional depth-integrated hauls were conducted from 600-0 m or 500-0 m during both field campaigns. Respiration rates of epi- and mesopelagic calanoid copepods were measured in the land-based laboratory at the Instituto Nacional de Desenvolvimento das Pescas (INDP) in Mindelo. Oxygen consumption was measured non-invasively by optode respirometry at three different ambient temperatures (13, 18, and 23°C) with a 10-channel oxygen respirometer (Oxy-10 Mini, PreSens Precision Sensing GmbH, Regensburg, Germany). All experiments were run in darkness in temperature-controlled incubators (LMS Cooled Incubator Series 1A, Model 280) equipped with water baths to ensure constant temperatures throughout the experiments, tolerating a variation of ±1°C.

InfIuence of wave induced second-order forces in semisubmersible fowt mooring design

The main focus of this paper is on hydrodynamic modelling of a semisubmersible platform (which can support a 1.5MW wind turbine and is composed by three buoyant columns connected by bracings) with especial emphasis on the estimation of the wave drift components and their effects on the design of the mooring system. Indeed, with natural periods of drift around 60 seconds, accurate computation of the low-frequency second-order components is not a straightforward task. As methods usually adopted when dealing with the slow-drifts of deep-water moored systems, such as Newman?s approximation, have their errors increased by the relatively low resonant periods, and as the effects of depth cannot be ignored, the wave diffraction analysis must be based on full Quadratic Transfer Functions (QTF) computations. A discussion on the numerical aspects of performing such computations is presented, making use of the second-order module available with the seakeeping software WAMIT®. Finally, the paper also provides a preliminary verification of the accuracy of the numerical predictions based on the results obtained in a series of model tests with the structure fixed in bichromatic waves.

Glucose-Insulin regulator for type 1 diabetes using high order neural networks

In this paper a Glucose-Insulin regulator for Type 1 Diabetes using artificial neural networks (ANN) is proposed. This is done using a discrete recurrent high order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas? beta-cells behavior of a virtual patient. The ANN which reproduces and identifies the dynamical behavior system, is configured as series parallel and trained on line using the extended Kalman filter algorithm to achieve a quickly convergence identification in silico. The control objective is to regulate the glucose-insulin level under different glucose inputs and is based on a nonlinear neural block control law. A safety block is included between the control output signal and the virtual patient with type 1 diabetes mellitus. Simulations include a period of three days. Simulation results are compared during the overnight fasting period in Open-Loop (OL) versus Closed- Loop (CL). Tests in Semi-Closed-Loop (SCL) are made feedforward in order to give information to the control algorithm. We conclude the controller is able to drive the glucose to target in overnight periods and the feedforward is necessary to control the postprandial period.