Desenvolvimento de modelo matemático de otimização logística para o transporte multimodal de safras agrícolas pelo corredor centro-oeste

Este artigo apresenta um modelo matemático de otimização logística para o transporte multimodal de safras agrícolas pelo corredor Centro-Oeste. Tal ferramenta foi desenvolvida no contexto de três amplos projetos de pesquisa financiados pela FINEP e executados por um grupo de universidades. O modelo, conhecido genericamente como Modelo de fluxo de Custo Mínimo Multiproduto, considera a otimização de fluxos em rede, para os produtos açúcar, álcool, milho, soja, óleo de soja, farelo de soja e trigo. O modelo proposto para estimativa dos fluxos inter-regionais mostrou-se uma ferramenta factível para fins de avaliação do potencial de utilização da multimodalidade. A análise destes resultados gera importantes subsídios para a seleção dos locais com potencial para instalação de mecanismos e equipamentos de transferência de cargas, além de auxiliar no dimensionamento dessas infraestruturas. Também é um resultado importante do ferramental desenvolvido a identificação das zonas de cargas que apresentam potencial captável pelas ferrovias, hidrovias e dutovias, ou seja, possibilita a identificação das regiões que revelam potencial para uso da multimodalidade.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Nonlinear optical characterizations of dibenzoylmethane in solution

This work reports on the two-photon absorption (2PA) cross-section and first hyperpolarizability of dibenzoylmethane solutions using femtosecond Z-scan and hyper-Rayleigh scattering techniques. The 2PA spectrum, spanning the wavelength range from 460 to 740 nm, presents a band centered at 510 nm, with a cross-section value estimated as 37 GM at this wavelength. Owing to the molecular symmetry, this band is not observed in the linear absorption spectrum. The sum-over-state approach was adopted to evaluate various spectroscopic parameters. Experimental and theoretical values of the first hyperpolarizability values were estimated in ethanol and DMSO solutions.

Theoretical studies of Membrane Proteins : Properties, Prediction Methods and Genome-wide analysis

Membrane proteins are a large and important class of proteins. They are responsible for several of the key functions in a living cell, e.g. transport of nutrients and ions, cell-cell signaling, and cell-cell adhesion. Despite their importance it has not been possible to study their structure and organization in much detail because of the difficulty to obtain 3D structures. In this thesis theoretical studies of membrane protein sequences and structures have been carried out by analyzing existing experimental data. The data comes from several sources including sequence databases, genome sequencing projects, and 3D structures. Prediction of the membrane spanning regions by hydrophobicity analysis is a key technique used in several of the studies. A novel method for this is also presented and compared to other methods. The primary questions addressed in the thesis are: What properties are common to all membrane proteins? What is the overall architecture of a membrane protein? What properties govern the integration into the membrane? How many membrane proteins are there and how are they distributed in different organisms? Several of the findings have now been backed up by experiments. An analysis of the large family of G-protein coupled receptors pinpoints differences in length and amino acid composition of loops between proteins with and without a signal peptide and also differences between extra- and intracellular loops. Known 3D structures of membrane proteins have been studied in terms of hydrophobicity, distribution of secondary structure and amino acid types, position specific residue variability, and differences between loops and membrane spanning regions. An analysis of several fully and partially sequenced genomes from eukaryotes, prokaryotes, and archaea has been carried out. Several differences in the membrane protein content between organisms were found, the most important being the total number of membrane proteins and the distribution of membrane proteins with a given number of transmembrane segments. Of the properties that were found to be similar in all organisms, the most obvious is the bias in the distribution of positive charges between the extra- and intracellular loops. Finally, an analysis of homologues to membrane proteins with known topology uncovered two related, multi-spanning proteins with opposite predicted orientations. The predicted topologies were verified experimentally, providing a first example of "divergent topology evolution".