An Informatics Technical Note on Interaction of DNA - Graphene Chemical Sensor System as Reaction-Diffusion Wave-Based Computing System in Ionised Gaseous environments and their Applications Using Theoretical Studies and Scientific Computation Overview

The physics of plasmas encompasses basic problems from the universe and has assured us of promises in diverse applications to be implemented in a wider range of scientific and engineering domains, linked to most of the evolved and evolving fundamental problems. Substantial part of this domain could be described by R–D mechanisms involving two or more species (reaction–diffusion mechanisms). These could further account for the simultaneous non-linear effects of heating, diffusion and other related losses. We mention here that in laboratory scale experiments, a suitable combination of these processes is of vital importance and very much decisive to investigate and compute the net behaviour of plasmas under consideration. Plasmas are being used in the revolution of information processing, so we considered in this technical note a simple framework to discuss and pave the way for better formalisms and Informatics, dealing with diverse domains of science and technologies. The challenging and fascinating aspects of plasma physics is that it requires a great deal of insight in formulating the relevant design problems, which in turn require ingenuity and flexibility in choosing a particular set of mathematical (and/or experimental) tools to implement them.

A new biomimetic sensor based on molecularly imprinted polymers for highly sensitive and selective determination of hexazinone herbicide

A new selective sensor based on molecularly imprinted polymers (MIPs) was developed for the determination of hexazinone (HXZ) in environmental samples. MIPs were synthesized using a non-covalent approach, and selection of the monomers employed in the polymerization reaction was carried out by molecular modeling. Three functional monomers with high (2-vinylpyridine (MP17)) and intermediate (methacrylic acid (MP12) and acrylamide (MP5)) energies of binding to the template (HXZ) were selected for preparation of the MIPs, in order to conduct comparative studies and validate the theoretical data. For sensor construction, carbon pastes were modified with each MIP or NIP (non-imprinted polymer), and HXZ determination was performed using differential pulse adsorptive cathodic stripping voltammetry (DPAdCSV). All parameters affecting the sensor response were optimized. In HCl at pH 2.5, the sensor prepared with MP17 (5% w/w in the paste) showed a dynamic linear range between 1.9 × 10−11 and 1.1 × 10−10 mol L−1, and a detection limit of 2.6 × 10−12 mol L−1, under the following conditions: accumulation time of 200 s at a potential of −0.5V, scan rate of 50 mVs−1, pulse amplitude of 60 mV, and pulse width of 50 ms. The sensor was selective in the presence of other similar compounds, and was successfully applied to the analysis of HXZ in river water samples.

Avaliação do uso de técnicas digitais para caracterização de área degradada por empréstimo de solo: máxima verossimilhança x modelo linear de mistura espectral

Pós-graduação em Engenharia Civil - FEIS

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

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

Classification of histological images based on the stationary wavelet transform

Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels.

Sensitive luminescent paper-based sensor for the determination of gaseous hydrogen sulfide

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

Genetic analysis of the temperament of Nellore cattle using linear and threshold models

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A virtual experiment for the detection of specific features in the frequency response of a coupled nonlinear and linear oscillator

This paper presents an investigation into some practical issues that may be present in a real experiment, when trying to validate the theoretical frequency response curve of a two degree-of-freedom nonlinear system consisting of coupled linear and nonlinear oscillators. Some specific features, such as detached resonance curves, have been theoretically predicted in multi degree-of-freedom nonlinear oscillators, when subject to harmonic excitation, and the system parameters have been shown to be fundamental in achieving such features. When based on a simplified model, approximate analytical expression for the frequency response curves may be derived, which may be validated by the numerical solutions. In a real experiment, however, the practical achievability of such features was previously shown to be greatly affected by small disturbances induced by gravity and inertia, which led to some solutions becoming unstable which had been predicted to be stable. In this work a practical system configuration is proposed where such effects are reduced so that the previous limitations are overcome. A virtual experiment is carried out where a detailed multi-body model of the oscillator is assembled and the effects on the system response are investigated.

Evaluation of glucose biosensors based on Prussian Blue and lyophilised, crystalline and cross-linked glucose oxidases (CLEC (R))

Glucose biosensors based on lyophilised, crystalline and cross-linked glucose oxidase (GOx, CLEC(R)) and commercially available lyophilised GOx immobilised on top of glassy carbon electrodes modified with electrodeposited Prussian Blue are critically compared. Two procedures were carried out for preparing the biosensors: (1) deposition of one layer of adsorbed GOx dissolved in an aqueous solution followed by deposition of two layers of low molecular weight Nafion(R) dissolved in 90% ethanol, and (2) deposition of two layers of a mixture of GOx with Nafion dissolved in 90% ethanol. The performance of the biosensors was evaluated in terms of linear response range for hydrogen peroxide and glucose, detection limit, and susceptibility to some common interfering species (ascorbic acid, acetaminophen and uric acid). The operational stability of the biosensors was evaluated by applying a steady potential of -50 mV versus Ag/AgCl to the glucose biosensor and injecting standard solutions of hydrogen peroxide and glucose (50 muM and 1.0 mM, respectively, in phosphate buffer) for at least 5 h in a flow-injection system. Scanning electron microscopy was used for visualisation of the Prussian Blue redox catalyst and in the presence of the different GOx preparations on the electrode surface. (C) 2001 Elsevier B.V. B.V. All rights reserved.

Caracterização experimental de sistemas mecânicos com comportamento não-linear

Pós-graduação em Engenharia Mecânica - FEIS

GIS-based analytical tools for transport planning: spatial regression models for transportation demand forecast

Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.

Contribuição metodológica para o diagnóstico da dinâmica erosiva linear e seu prognóstico evolutivo visando subsidiar projetos de recuperação

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

Avaliação da qualidade térmica de espaços de transição do tipo linear externo

Pós-graduação em Arquitetura e Urbanismo - FAAC

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.