Modifying the stochastic model to mitigate GPS systematic errors in relative positioning

The GPS observables are subject to several errors. Among them, the systematic ones have great impact, because they degrade the accuracy of the accomplished positioning. These errors are those related, mainly, to GPS satellites orbits, multipath and atmospheric effects. Lately, a method has been suggested to mitigate these errors: the semiparametric model and the penalised least squares technique (PLS). In this method, the errors are modeled as functions varying smoothly in time. It is like to change the stochastic model, in which the errors functions are incorporated, the results obtained are similar to those in which the functional model is changed. As a result, the ambiguities and the station coordinates are estimated with better reliability and accuracy than the conventional least square method (CLS). In general, the solution requires a shorter data interval, minimizing costs. The method performance was analyzed in two experiments, using data from single frequency receivers. The first one was accomplished with a short baseline, where the main error was the multipath. In the second experiment, a baseline of 102 km was used. In this case, the predominant errors were due to the ionosphere and troposphere refraction. In the first experiment, using 5 minutes of data collection, the largest coordinates discrepancies in relation to the ground truth reached 1.6 cm and 3.3 cm in h coordinate for PLS and the CLS, respectively, in the second one, also using 5 minutes of data, the discrepancies were 27 cm in h for the PLS and 175 cm in h for the CLS. In these tests, it was also possible to verify a considerable improvement in the ambiguities resolution using the PLS in relation to the CLS, with a reduced data collection time interval. © Springer-Verlag Berlin Heidelberg 2007.

Brazilian solar water heating systems evaluation

The Brazilian National Electricity Conservation Program - PROCEL - runs regular surveys in the electric-energy-consumption market. These studies are used as valuable data to better plan the actions of this program. These data also evaluate the program's performance by identifying the level of penetration of the most efficient electric equipment within the residential sector. PROCEL's main lines of action is to promote and make available the most efficient technologies. Based on the results from the latest survey, it is estimated that 24% of the electric-energy consumption of the residential sector is used by electric shower devices, which instantaneously heat the water that flows through them, normally using an electric resistance of 5 kW. These are an important factor in a country where electric-heating devices are present in about 73% of Brazilian households. Keeping that in mind, the purpose of this work is to present the main results of the Brazilian Solar-Water-Heating-Systems Evaluation, finished in 2010, where 535 installations were visited and more than 50 researchers from different universities participated in the project. Moreover, seven Brazilian cities were selected to be studied. The information was collected from field research and statistically treated. The collected information focused on the adequacy of the project to the household, installation, operation and life cycle of the systems, as well as the users' satisfaction level. Technical questionnaires were developed to summarize all the required information, such as a Web site designed to organize and manage the data collected and a Matlab application that performed the dimensioning and F-chart systems evaluation. Quality indicators were created through a full system monitoring, with thermographic analysis and evaluation of shading influence at the system's efficiency, using the Ecotect software.

Photo-Fenton degradation kinetics of low ciprofloxacin concentration using different iron sources and pH

The aim of the present study was to compare the degradation kinetics of low (1 mg L-1) and high (25 mg L-1) concentrations of ciprofloxacin (CIP) aiming to decrease the concentration of additives and evaluate the pH limitation by the use of low iron concentrations and organic ligands. A parameterized kinetic model was satisfactorily fitted to the experimental data in order to study the performance of photo-Fenton process with specific iron sources (iron citrate, iron oxalate, iron nitrate) under different pH medium (2.5, 4.5, 6.5). The process modeling allowed selecting those process conditions (iron source, additives concentrations and pH medium) which maximize the two performance parameters related to the global equilibrium conversion and kinetic rate of the process. For the high CIP concentration, degradation was very influenced by the iron source, resulting in much lower efficiency with iron nitrate. At pH 4.5, highest TOC removal (0.87) was achieved in the presence of iron citrate, while similar CIP conversions were obtained with oxalate and citrate (0.98 after 10 min). For the low CIP concentration, much higher conversion was observed in the presence of citrate or oxalate in relation to iron nitrate up to pH 4.5. This behavior denotes the importance of complexation also at low dosages. Appropriate additives load (320 μM H 2O2; 6 μM Fe) resulted in a CIP conversion of 0.96 after10 min reaction with citrate up to pH 4.5. © 2013 Elsevier B.V. All rights reserved.

Spin-resolved local density of states for an Anderson adatom in a ferromagnetic island

In this work, we investigate theoretically the spin-resolved local density of states (SR-LDOS) of a ferromagnetic (FM) island hybridized with an adatom, which is described by the Single Impurity Anderson Model (SIAM). Our results are comparable with Scanning Tunneling Microscope (STM) experimental data. © 2012 Springer Science+Business Media, LLC.

Abordagens de descoberta de conhecimento em bases de dados aplicadas ao Cadastro Territorial Multifinalitário

Pós-graduação em Ciências Cartográficas - FCT

Um estudo sobre os conhecimentos geométricos adquiridos por alunos do ensino médio

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fatores facilitadores e inibidores no uso das comunidades de prática: um estudo de caso em uma empresa alemã

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

RAMSY: Ratio Analysis of Mass Spectrometry to Improve Compound Identification

The complexity of biological samples poses a major challenge for reliable compound identification in mass spectrometry (MS). The presence of interfering compounds that cause additional peaks in the spectrum can make interpretation and assignment difficult. To overcome this issue, new approaches are needed to reduce complexity and simplify spectral interpretation. Recently, focused on unknown metabolite identification, we presented a new approach, RANSY (ratio analysis of nuclear magnetic resonance spectroscopy; Anal. Chem. 2011, 83, 7616-7623), which extracts the signals related to the same metabolite based on peak intensity ratios. On the basis of this concept, we present the ratio analysis of mass spectrometry (RAMSY) method, which facilitates improved compound identification in complex MS spectra. RAMSY works on the principle that, under a given set of experimental conditions, the abundance/intensity ratios between the mass fragments from the same metabolite are relatively constant. Therefore, the quotients of average peak ratios and their standard deviations, generated using a small set of MS spectra from the same ion chromatogram, efficiently allow the statistical recovery of the metabolite peaks and facilitate reliable identification. RAMSY was applied to both gas chromatography/MS and liquid chromatography tandem MS (LC-MS/MS) data to demonstrate its utility. The performance of RAMSY is typically better than the results from correlation methods. RAMSY promises to improve unknown metabolite identification for MS users in metabolomics or other fields.

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Competência em informação na construção da inteligência competitiva nas organizações: o caso da empresa Mizumo

Pós-graduação em Ciência da Informação - FFC

Tipos documentais e fluxos de informação como subsídios para o processo decisório em ambientes organizacionais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)