Detecção de cicatrizes de áreas queimadas baseada no modelo linear de mistura espectral e imagens índice de vegetação utilizando dados multitemporais do sensor MODIS/TERRA no estado do Mato Grosso, Amazônia brasileira

O objetivo desta pesquisa foi avaliar os dados do sensor MODIS para detectar e monitorar cicatrizes de áreas recém queimadas. Utilizamos imagens da reflectância de superfície do sensor MODIS: produto MOD09 (dia 5 de outubro) e produto MOD13A1 (meses de outubro e novembro). Foi avaliada também uma série temporal de um ano dos índices de vegetação (IV) EVI e NDVI (produto MOD13A1). Uma imagem do sensor ETM+ (dia 5 de outubro) foi utilizada como base para a delimitação dos polígonos amostrais e avaliação dos dados MODIS devido a sua melhor resolução espacial. A metodologia focou na aplicação do modelo linear de mistura espectral nas imagens reflectância para a geração das imagens fração sombra. Análises de regressão foram efetuadas para comparação entre o percentual de sombra derivado da imagem ETM+ e das imagens MODIS. As alterações multitemporais nas imagens IV foram avaliadas com base no teste de Tukey. Os resultados mostraram que a imagem fração sombra gerada a partir do produto MOD09 apresentou um R² = 0,66 (p < 0,01) em relação aos dados ETM+. Para as imagens do produto MOD13A1 não foram identificadas relações significativas. Os IV dentro dos mesmos polígonos apresentaram uma variação sazonal durante o ano. No entanto, não houve uma diminuição significativa dos valores destes índices nos meses onde foram observadas as cicatrizes de áreas recém queimadas. Portanto, o produto MOD09 mostrou-se mais eficiente que o produto MOD13A1 para a detecção de cicatrizes de áreas recém queimadas. A análise multitemporal dos IV sugeriu que não foi possível detectar este mesmo padrão na área de estudo.

Measurement of the top pair production cross-section in 8 TeV proton-proton collisions using kinematic information in the lepton+jets final state with ATLAS

A measurement is presented of the tt¯ inclusive production cross section in pp collisions at a center-of-mass energy of s√=8  TeV using data collected by the ATLAS detector at the CERN Large Hadron Collider. The measurement was performed in the lepton+jets final state using a data set corresponding to an integrated luminosity of 20.3  fb−1. The cross section was obtained using a likelihood discriminant fit and b-jet identification was used to improve the signal-to-background ratio. The inclusive tt¯ production cross section was measured to be 260±1(stat)+22−23(stat)±8(lumi)±4(beam)  pb assuming a top-quark mass of 172.5 GeV, in good agreement with the theoretical prediction of 253+13−15  pb. The tt¯→(e,μ)+jets production cross section in the fiducial region determined by the detector acceptance is also reported.

Impact performance prediction of injection-molded talc-filled polypropylene through thermomechanical environment assessment

Due to the fact that different injection molding conditions tailor the mechanical response of the thermoplastic material, such effect must be considered earlier in the product development process. The existing approaches implemented in different commercial software solutions are very limited in their capabilities to estimate the influence of processing conditions on the mechanical properties. Thus, the accuracy of predictive simulations could be improved. In this study, we demonstrate how to establish straightforward processing-impact property relationships of talc-filled injection-molded polypropylene disc-shaped parts by assessing the thermomechanical environment (TME). To investigate the relationship between impact properties and the key operative variables (flow rate, melt and mold temperature, and holding pressure), the design of experiments approach was applied to systematically vary the TME of molded samples. The TME is characterized on computer flow simulation outputsanddefined bytwo thermomechanical indices (TMI): the cooling index (CI; associated to the core features) and the thermo-stress index (TSI; related to the skin features). The TMI methodology coupled to an integrated simulation program has been developed as a tool to predict the impact response. The dynamic impact properties (peak force, peak energy, and puncture energy) were evaluated using instrumented falling weight impact tests and were all found to be similarly affected by the imposed TME. The most important molding parameters affecting the impact properties were found to be the processing temperatures (melt andmold). CI revealed greater importance for the impact response than TSI. The developed integrative tool provided truthful predictions for the envisaged impact properties.

Electronic transport across linear defects in graphene

We investigate the low-energy electronic transport across grain boundaries in graphene ribbons and infinite flakes. Using the recursive Green’s function method, we calculate the electronic transmission across different types of grain boundaries in graphene ribbons. We show results for the charge density distribution and the current flow along the ribbon. We study linear defects at various angles with the ribbon direction, as well as overlaps of two monolayer ribbon domains forming a bilayer region. For a class of extended defect lines with periodicity 3, an analytic approach is developed to study transport in infinite flakes. This class of extended grain boundaries is particularly interesting, since the K and K0 Dirac points are superposed.

Prediction of drug targets in human pathogens

The identification of new and druggable targets in bacteria is a critical endeavour in pharmaceutical research of novel antibiotics to fight infectious agents. The rapid emergence of resistant bacteria makes today's antibiotics more and more ineffective, consequently increasing the need for new pharmacological targets and novel classes of antibacterial drugs. A new model that combines the singular value decomposition technique with biological filters comprised of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of E. coli has been developed to predict potential drug targets in the Enterobacteriaceae family [1]. This model identified 99 potential target proteins amongst the studied bacterial family, exhibiting eight different functions that suggest that the disruption of the activities of these proteins is critical for cells. Out of these candidates, one was selected for target confirmation. To find target modulators, receptor-based pharmacophore hypotheses were built and used in the screening of a virtual library of compounds. Postscreening filters were based on physicochemical and topological similarity to known Gram-negative antibiotics and applied to the retrieved compounds. Screening hits passing all filters were docked into the proteins catalytic groove and 15 of the most promising compounds were purchased from their chemical vendors to be experimentally tested in vitro. To the best of our knowledge, this is the first attempt to rationalize the search of compounds to probe the relevance of this candidate as a new pharmacological target.

Short-term corneal changes with gas-permeable contact lens wear in keratoconus subjects: a comparison of two fitting approaches

Purpose: To evaluate changes in anterior corneal topography and higher-order aberrations (HOA) after 14-days of rigid gas-permeable (RGP) contact lens (CL) wear in keratoconus subjects comparing two different fitting approaches. Methods: Thirty-one keratoconus subjects (50 eyes) without previous history of CL wear were recruited for the study. Subjects were randomly fitted to either an apical-touch or three-pointtouch fitting approach. The lens’ back optic zone radius (BOZR) was 0.4 mm and 0.1 mm flatter than the first definite apical clearance lens, respectively. Differences between the baseline and post-CL wear for steepest, flattest and average corneal power (ACP) readings, central corneal astigmatism (CCA), maximum tangential curvature (KTag), anterior corneal surface asphericity, anterior corneal surface HOA and thinnest corneal thickness measured with Pentacam were compared. Results: A statistically significant flattening was found over time on the flattest and steepest simulated keratometry and ACP in apical-touch group (all p < 0.01). A statistically significant reduction in KTag was found in both groups after contact lens wear (all p < 0.05). Significant reduction was found over time in CCA (p = 0.001) and anterior corneal asphericity in both groups (p < 0.001). Thickness at the thinnest corneal point increased significantly after CL wear (p < 0.0001). Coma-like and total HOA root mean square (RMS) error were significantly reduced following CL wearing in both fitting approaches (all p < 0.05). Conclusion: Short-term rigid gas-permeable CL wear flattens the anterior cornea, increases the thinnest corneal thickness and reduces anterior surface HOA in keratoconus subjects. Apicaltouch was associated with greater corneal flattening in comparison to three-point-touch lens wear.

Time-variations of equivalent water heights'from Grace Mission and in-situ river stages in the Amazon basin

Gravity Recovery and Climate Experiment (GRACE) mission is dedicated to measuring temporal variations of the Earth's gravity field. In this study, the Stokes coefficients made available by Groupe de Recherche en Géodésie Spatiale (GRGS) at a 10-day interval were converted into equivalent water height (EWH) for a ~4-year period in the Amazon basin (from July-2002 to May-2006). The seasonal amplitudes of EWH signal are the largest on the surface of Earth and reach ~ 1250mm at that basin's center. Error budget represents ~130 mm of EWH, including formal errors on Stokes coefficient, leakage errors (12 ~ 21 mm) and spectrum truncation (10 ~ 15 mm). Comparison between in situ river level time series measured at 233 ground-based hydrometric stations (HS) in the Amazon basin and vertically-integrated EWH derived from GRACE is carried out in this paper. Although EWH and HS measure different water bodies, in most of the cases a high correlation (up to ~80%) is detected between the HS series and EWH series at the same site. This correlation allows adjusting linear relationships between in situ and GRACE-based series for the major tributaries of the Amazon river. The regression coefficients decrease from up to down stream along the rivers reaching the theoretical value 1 at the Amazon's mouth in the Atlantic Ocean. The variation of the regression coefficients versus the distance from estuary is analysed for the largest rivers in the basin. In a second step, a classification of the proportionality between in situ and GRACE time-series is proposed.

Wear of PEEK-Ti6Al4V systems under micro-abrasion and linear sliding conditions

Dissertação de mestrado integrado em Engenharia Biomédica

Desenvolvimento e otimização do projeto mecatrónico para um Tribómetro Linear

Dissertação de mestrado integrado em Engenharia Mecânica

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Retroanálise de parâmetros geomecânicos baseada em algoritmos de otimização

Dissertação de mestrado integrado em Engenharia Civil

A linear algebra approach to OLAP

Inspired by the relational algebra of data processing, this paper addresses the foundations of data analytical processing from a linear algebra perspective. The paper investigates, in particular, how aggregation operations such as cross tabulations and data cubes essential to quantitative analysis of data can be expressed solely in terms of matrix multiplication, transposition and the Khatri–Rao variant of the Kronecker product. The approach offers a basis for deriving an algebraic theory of data consolidation, handling the quantitative as well as qualitative sides of data science in a natural, elegant and typed way. It also shows potential for parallel analytical processing, as the parallelization theory of such matrix operations is well acknowledged.

Development and characterization of lipid-based nanosystems for controlled release of bioactive compounds

Tese de Doutoramento em Engenharia Química e Biológica.

Avaliação do impacto na circulação e risco de acidente em estradas em obras

Dissertação de mestrado integrado em Engenharia Civil

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.