Prediction of soil shear strength in agricultural and natural environments of the Brazilian Cerrado

The objective of this work was to develop uni- and multivariate models to predict maximum soil shear strength (τmax) under different normal stresses (σn), water contents (U), and soil managements. The study was carried out in a Rhodic Haplustox under Cerrado (control area) and under no-tillage and conventional tillage systems. Undisturbed soil samples were taken in the 0.00-0.05 m layer and subjected to increasing U and σn, in shear strength tests. The uni- and multivariate models - respectively τmax=10(a+bU) and τmax=10(a+bU+cσn) - were significant in all three soil management systems evaluated and they satisfactorily explain the relationship between U, σn, and τmax. The soil under Cerrado has the highest shear strength (τ) estimated with the univariate model, regardless of the soil water content, whereas the soil under conventional tillage shows the highest values with the multivariate model, which were associated to the lowest water contents at the soil consistency limits in this management system.

Determinação de misturas de sulfametoxazol e trimetoprima por espectroscopia eletrônica multivariada

In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate models is seriously affected by spectral changes induced by pH variations, a fact that acquires a great significance in the analysis of real samples (pharmaceuticals) that contain chemical additives.

Caracterização físico-química de queijo prato por espectroscopia no infravermelho e regressão de mínimos quadrados parciais

In this work an analytical methodology for the determination of relevant physicochemical parameters of prato cheese is reported, using infrared spectroscopy (DRIFT) and partial least squares regression (PLS). Several multivariate models were developed, using different spectral regions and preprocessing routines. In general, good precision and accuracy was observed for all studied parameters (fat, protein, moisture, total solids, ashes and pH) with standard deviations comparable with those provided by the conventional methodologies. The implantation of this multivariate routine involves significant analytical advantages, including reduction of cost and time of analysis, minimization of human errors, and elimination of chemical residues.

Previsão das propriedades físicas do papel kraft por espectroscopia no infravermelho próximo (NIR) e regressão por mínimos quadrados parciais (PLS)

The main objective of the present work is represented by the characterization of the physical properties of industrial kraft paper (i.e. transversal and longitudinal tear resistance, transversal traction resistance, bursting or crack resistance, longitudinal and transversal compression resistance (SCT (Compressive Strength Tester) and compression resistance (RCT-Ring Crush Test)) by near infrared spectroscopy associated to partial least squares regression. Several multivariate models were developed, many of them with high prevision capacity. In general, low prevision errors were observed and regression coefficients that are comparable with those provided by conventional standard methodologies.

Um método verde, rápido e simples para determinar o valor energético de farinhas e cereais matinais

The Energy Value (EV) corresponds to the sum of the energetic contributions from food macronutrients (proteins, carbohydrates and fats) and is required on the labels of pre-packaged foods. The determinations of these parameters are based on distinct analytical procedures, each one being time-consuming, laborious and producing residues. This work presents multivariate models to determine the EV contents of industrialized foods for human consumption by using X-ray fluorescence spectra of samples with known parameters, determined through conventional methods. The proposed method is an alternative to conventional analytical methods and does not require any reagent, given the demands of the "green chemistry".

Determinação de constituintes químicos em madeira de eucalipto por Pi-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte

Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.

Association of adult weight gain and nonalcoholic fatty liver in a cross-sectional study in Wan Song Community, China

Our objective was to examine associations of adult weight gain and nonalcoholic fatty liver disease (NAFLD). Cross-sectional interview data from 844 residents in Wan Song Community from October 2009 to April 2010 were analyzed in multivariate logistic regression models to examine odds ratios (OR) and 95% confidence intervals (CI) between NAFLD and weight change from age 20. Questionnaires, physical examinations, laboratory examinations, and ultrasonographic examination of the liver were carried out. Maximum rate of weight gain, body mass index, waist circumference, waist-to-hip ratio, systolic blood pressure, diastolic blood pressure, fasting blood glucose, cholesterol, triglycerides, uric acid, and alanine transaminase were higher in the NAFLD group than in the control group. HDL-C in the NAFLD group was lower than in the control group. As weight gain increased (measured as the difference between current weight and weight at age 20 years), the OR of NAFLD increased in multivariate models. NAFLD OR rose with increasing weight gain as follows: OR (95%CI) for NAFLD associated with weight gain of 20+ kg compared to stable weight (change <5 kg) was 4.23 (2.49-7.09). Significantly increased NAFLD OR were observed even for weight gains of 5-9.9 kg. For the “age 20 to highest lifetime weight” metric, the OR of NAFLD also increased as weight gain increased. For the “age 20 to highest lifetime weight” metric and the “age 20 to current weight” metric, insulin resistance index (HOMA-IR) increased as weight gain increased (P<0.001). In a stepwise multivariate regression analysis, significant association was observed between adult weight gain and NAFLD (OR=1.027, 95%CI=1.002-1.055, P=0.025). We conclude that adult weight gain is strongly associated with NAFLD.

Development and Validation of Predictive Models of Cardiac Mortality and Transplantation in Resynchronization Therapy

AbstractBackground:30-40% of cardiac resynchronization therapy cases do not achieve favorable outcomes.Objective:This study aimed to develop predictive models for the combined endpoint of cardiac death and transplantation (Tx) at different stages of cardiac resynchronization therapy (CRT).Methods:Prospective observational study of 116 patients aged 64.8 ± 11.1 years, 68.1% of whom had functional class (FC) III and 31.9% had ambulatory class IV. Clinical, electrocardiographic and echocardiographic variables were assessed by using Cox regression and Kaplan-Meier curves.Results:The cardiac mortality/Tx rate was 16.3% during the follow-up period of 34.0 ± 17.9 months. Prior to implantation, right ventricular dysfunction (RVD), ejection fraction < 25% and use of high doses of diuretics (HDD) increased the risk of cardiac death and Tx by 3.9-, 4.8-, and 5.9-fold, respectively. In the first year after CRT, RVD, HDD and hospitalization due to congestive heart failure increased the risk of death at hazard ratios of 3.5, 5.3, and 12.5, respectively. In the second year after CRT, RVD and FC III/IV were significant risk factors of mortality in the multivariate Cox model. The accuracy rates of the models were 84.6% at preimplantation, 93% in the first year after CRT, and 90.5% in the second year after CRT. The models were validated by bootstrapping.Conclusion:We developed predictive models of cardiac death and Tx at different stages of CRT based on the analysis of simple and easily obtainable clinical and echocardiographic variables. The models showed good accuracy and adjustment, were validated internally, and are useful in the selection, monitoring and counseling of patients indicated for CRT.

The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix (Lepidoptera, Arctiidae)

Species' geographic ranges are usually considered as basic units in macroecology and biogeography, yet it is still difficult to measure them accurately for many reasons. About 20 years ago, researchers started using local data on species' occurrences to estimate broad scale ranges, thereby establishing the niche modeling approach. However, there are still many problems in model evaluation and application, and one of the solutions is to find a consensus solution among models derived from different mathematical and statistical models for niche modeling, climatic projections and variable combination, all of which are sources of uncertainty during niche modeling. In this paper, we discuss this approach of ensemble forecasting and propose that it can be divided into three phases with increasing levels of complexity. Phase I is the simple combination of maps to achieve a consensual and hopefully conservative solution. In Phase II, differences among the maps used are described by multivariate analyses, and Phase III consists of the quantitative evaluation of the relative magnitude of uncertainties from different sources and their mapping. To illustrate these developments, we analyzed the occurrence data of the tiger moth, Utetheisa ornatrix (Lepidoptera, Arctiidae), a Neotropical moth species, and modeled its geographic range in current and future climates.

Determination of sulfur in diesel using ATR/FTIR and multivariate calibration

The aim of this present work was to provide a more fast, simple and less expensive to analyze sulfur content in diesel samples than by the standard methods currently used. Thus, samples of diesel fuel with sulfur concentrations varying from 400 and 2500 mgkg-1 were analyzed by two methodologies: X-ray fluorescence, according to ASTM D4294 and by Fourier transform infrared spectrometry (FTIR). The spectral data obtained from FTIR were used to build multivariate calibration models by partial least squares (PLS). Four models were built in three different ways: 1) a model using the full spectra (665 to 4000 cm-1), 2) two models using some specific spectrum regions and 3) a model with variable selected by classic method of variable selection stepwise. The model obtained by variable selection stepwise and the model built with region spectra between 665 and 856 cm-1 and 1145 and 2717 cm-1 showed better results in the determination of sulfur content.

Soil penetration resistance analysis by multivariate and geostatistical methods

The penetration resistance (PR) is a soil attribute that allows identifies areas with restrictions due to compaction, which results in mechanical impedance for root growth and reduced crop yield. The aim of this study was to characterize the PR of an agricultural soil by geostatistical and multivariate analysis. Sampling was done randomly in 90 points up to 0.60 m depth. It was determined spatial distribution models of PR, and defined areas with mechanical impedance for roots growth. The PR showed a random distribution to 0.55 and 0.60 m depth. PR in other depths analyzed showed spatial dependence, with adjustments to exponential and spherical models. The cluster analysis that considered sampling points allowed establishing areas with compaction problem identified in the maps by kriging interpolation. The analysis with main components identified three soil layers, where the middle layer showed the highest values of PR.

Multivariate study and regression analysis of gluten-free granola

This study developed a gluten-free granola and evaluated it during storage with the application of multivariate and regression analysis of the sensory and instrumental parameters. The physicochemical, sensory, and nutritional characteristics of a product containing quinoa, amaranth and linseed were evaluated. The crude protein and lipid contents ranged from 97.49 and 122.72 g kg-1 of food, respectively. The polyunsaturated/saturated, and n-6:n-3 fatty acid ratios ranged from 2.82 and 2.59:1, respectively. Granola had the best alpha-linolenic acid content, nutritional indices in the lipid fraction, and mineral content. There were good hygienic and sanitary conditions during storage; probably due to the low water activity of the formulation, which contributed to inhibit microbial growth. The sensory attributes ranged from 'like very much' to 'like slightly', and the regression models were highly fitted and correlated during the storage period. A reduction in the sensory attribute levels and in the product physical stabilisation was verified by principal component analysis. The use of the affective test acceptance and instrumental analysis combined with statistical methods allowed us to obtain promising results about the characteristics of gluten-free granola.

ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS

The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.

Geographical patterns of proportionate mortality for the most common causes of death in Brazil

Mortality due to chronic diseases has been increasing in all regions of Brazil with corresponding decreases in mortality from infectious diseases. The geographical variation in proportionate mortality for chronic diseases for 17 Brazilian state capitals for the year 1985 and their association with socio-economic variables and infectious disease was studied. Calculations were made of correlation coefficients of proportionate mortality for adults of 30 years or above due to ischaemic heart disease, stroke and cancer of the lung, the breast and stomach with 3 socio-economic variables, race, and mortality due to infectious disease. Linear regression analysis included as independent variables the % of illiteracy, % of whites, % of houses with piped water, mean income, age group, sex, and % of deaths caused by infectious disease. The dependent variables were the % of deaths due to each one of the chronic diseases studied by age-sex group. Chronic diseases were an important cause of death in all regions of Brazil. Ischaemic heart diseases, stroke and malignant neoplasms accounted for more than 34% of the mortality in each of the 17 capitals studied. Proportionate cause-specific mortality varied markedly among state capitals. Ranges were 6.3-19.5% for ischaemic heart diseases, 8.3-25.4% for stroke, 2.3-10.4% for infections and 12.2-21.5% for malignant neoplasm. Infectious disease mortality had the highest (p < 0.001) correlation with all the four socio-economic variables studied and ischaemic heart disease showed the second highest correlation (p < 0.05). Higher socio-economic level was related to a lower % of infectious diseases and a higher % of ischaemic heart diseases. Mortality due to breast cancer and stroke was not associated with socio-economic variables. Multivariate linear regression models explained 59% of the variance among state capitals for mortality due to ischaemic heart disease, 50% for stroke, 28% for lung cancer, 24% for breast cancer and 40% for stomach cancer. There were major differences in the proportionate mortality due to chronic diseases among the capitals which could not be accounted for by the social and environmental factors and by the mortality due to infectious disease.