Determinação de teor de biodieseis de algodão e pinhão manso em misturas com diesel usando espectroscopia no infravermelho médio e quadrados mínimos parciais

Biodiesel is a renewable fuel derived from vegetable oils or animal fats, which can be a total or partial substitute for diesel. Since 2005, this fuel was introduced in the Brazilian energy matrix through Law 11.097 that determines the percentage of biodiesel added to diesel oil as well as monitoring the insertion of this fuel in market. The National Agency of Petroleum, Natural Gas and Biofuels (ANP) establish the obligation of adding 7% (v/v) of biodiesel to diesel commercialized in the country, making crucial the analytical control of this content. Therefore, in this study were developed and validated methodologies based on the use of Mid Infrared Spectroscopy (MIR) and Multivariate Calibration by Partial Least Squares (PLS) to quantify the methyl and ethyl biodiesels content of cotton and jatropha in binary blends with diesel at concentration range from 1.00 to 30.00% (v/v), since this is the range specified in standard ABNT NBR 15568. The biodiesels were produced from two routes, using ethanol or methanol, and evaluated according to the parameters: oxidative stability, water content, kinematic viscosity and density, presenting results according to ANP Resolution No. 45/2014. The built PLS models were validated on the basis of ASTM E1655-05 for Infrared Spectroscopy and Multivariate Calibration and ABNT NBR 15568, with satisfactory results due to RMSEP (Root Mean Square Error of Prediction) values below 0.08% (<0.1%), correlation coefficients (R) above 0.9997 and the absence of systematic error (bias). Therefore, the methodologies developed can be a promising alternative in the quality control of this fuel.

Estrutura produtiva da agropecuária, ocupação e pobreza no estado de Minas Gerais no ano de 2010

This thesis aimed to contribute to the discussion about the relationship between agricultural production structure, occupation and poverty in Brazil, specifically in the state of Minas Gerais, in 2010. The issue of employment is becoming increasingly challenging in the face of ongoing modernization process in agriculture, capital intensive and labor saver looking levels ever higher production and productivity. The productive inclusion can be an effective way to exit from poverty (the work is often the only asset of the poor). In this sense, we sought to investigate what activities or groups of activities occupied a larger number of people and generated higher yields and can potentially have contributed to a lower incidence of poverty. The basis for primary data was the 2010 Population Census (microdata). To achieve the objectives we used descriptive analysis, Pearson correlation coefficients and quantile regressions. Among the main findings highlight that agriculture occupied more and generated higher overall income than ranching presented more precarious, despite having lower average incomes and income percentile values, greater heterogeneity and instability, as well as higher proportions of poor. Overall, commodities showed greater formalization and lower poor proportions. In the case of agriculture, commodities activities occupied less, generated lower mass income and middle-income (although income percentiles slightly larger and more informality) and had lower poverty indicators than non-commodity (more heterogeneous rents). In livestock, commodities had higher percentages of occupation, income (although middle-income values and percentiles slightly smaller), and smaller proportions of poor than non-commodity (more heterogenous). In terms of occupation and income stood out the farming activities unspecified (non-commodity), the coffee growing and cattle (commodities). The cultivation of coffee and cattle had the lowest poverty indicators. agricultural production diversification indicators showed positive correlations with the occupation in activities not commodities (only), but also with the proportion of poor, indigent and concentration of income. In addition, the occupation in not commodities showed positive correlations with poverty indicators. It is noteworthy that the occupations in soybeans, coffee and fruit showed negative correlation coefficients with the indicators of poverty, indigence and gini. Finally, among the agricultural activities, there was to go to occupied in agricultural activities not commodities for commodity would be 'more equalizer' (decreasing coefficients over the distribution of income) than for cattle. The occupation in livestock (mostly non-commodity) would generate greater impact on the lower income deciles, but their coefficients grow back in the last deciles, which shows its most perverse character. Among the activities that would affect more strongly the lower deciles and less the higher deciles stand out pig farming, poultry, citrus cultivation, coffee and sugar cane. The cattle and the cultivation of soy, had the highest rates, but they grow back in the last deciles, which shows a more wicked character.

To what extent does the Health Professions Admission Test-Ireland predict performance in early undergraduate tests of communication and clinical skills? – An observational cohort study

Background: Internationally, tests of general mental ability are used in the selection of medical students. Examples include the Medical College Admission Test, Undergraduate Medicine and Health Sciences Admission Test and the UK Clinical Aptitude Test. The most widely used measure of their efficacy is predictive validity.A new tool, the Health Professions Admission Test- Ireland (HPAT-Ireland), was introduced in 2009. Traditionally, selection to Irish undergraduate medical schools relied on academic achievement. Since 2009, Irish and EU applicants are selected on a combination of their secondary school academic record (measured predominately by the Leaving Certificate Examination) and HPAT-Ireland score. This is the first study to report on the predictive validity of the HPAT-Ireland for early undergraduate assessments of communication and clinical skills. Method. Students enrolled at two Irish medical schools in 2009 were followed up for two years. Data collected were gender, HPAT-Ireland total and subsection scores; Leaving Certificate Examination plus HPAT-Ireland combined score, Year 1 Objective Structured Clinical Examination (OSCE) scores (Total score, communication and clinical subtest scores), Year 1 Multiple Choice Questions and Year 2 OSCE and subset scores. We report descriptive statistics, Pearson correlation coefficients and Multiple linear regression models. Results: Data were available for 312 students. In Year 1 none of the selection criteria were significantly related to student OSCE performance. The Leaving Certificate Examination and Leaving Certificate plus HPAT-Ireland combined scores correlated with MCQ marks.In Year 2 a series of significant correlations emerged between the HPAT-Ireland and subsections thereof with OSCE Communication Z-scores; OSCE Clinical Z-scores; and Total OSCE Z-scores. However on multiple regression only the relationship between Total OSCE Score and the Total HPAT-Ireland score remained significant; albeit the predictive power was modest. Conclusion: We found that none of our selection criteria strongly predict clinical and communication skills. The HPAT- Ireland appears to measures ability in domains different to those assessed by the Leaving Certificate Examination. While some significant associations did emerge in Year 2 between HPAT Ireland and total OSCE scores further evaluation is required to establish if this pattern continues during the senior years of the medical course.

Knowledge Modeling for The Outcome of Brain Stereotactic Radiosurgery

Purpose: To build a model that will predict the survival time for patients that were treated with stereotactic radiosurgery for brain metastases using support vector machine (SVM) regression.

Methods and Materials: This study utilized data from 481 patients, which were equally divided into training and validation datasets randomly. The SVM model used a Gaussian RBF function, along with various parameters, such as the size of the epsilon insensitive region and the cost parameter (C) that are used to control the amount of error tolerated by the model. The predictor variables for the SVM model consisted of the actual survival time of the patient, the number of brain metastases, the graded prognostic assessment (GPA) and Karnofsky Performance Scale (KPS) scores, prescription dose, and the largest planning target volume (PTV). The response of the model is the survival time of the patient. The resulting survival time predictions were analyzed against the actual survival times by single parameter classification and two-parameter classification. The predicted mean survival times within each classification were compared with the actual values to obtain the confidence interval associated with the model’s predictions. In addition to visualizing the data on plots using the means and error bars, the correlation coefficients between the actual and predicted means of the survival times were calculated during each step of the classification.

Results: The number of metastases and KPS scores, were consistently shown to be the strongest predictors in the single parameter classification, and were subsequently used as first classifiers in the two-parameter classification. When the survival times were analyzed with the number of metastases as the first classifier, the best correlation was obtained for patients with 3 metastases, while patients with 4 or 5 metastases had significantly worse results. When the KPS score was used as the first classifier, patients with a KPS score of 60 and 90/100 had similar strong correlation results. These mixed results are likely due to the limited data available for patients with more than 3 metastases or KPS scores of 60 or less.

Conclusions: The number of metastases and the KPS score both showed to be strong predictors of patient survival time. The model was less accurate for patients with more metastases and certain KPS scores due to the lack of training data.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Geological reference standards (JLK-1,JSD-1, JSD-2, JSD-3,JMS-1, JMS-2) measured by Itrax-XRF core scanner in different exposure times

X-ray fluorescence (XRF) core-scanning is a fast and nondestructive technique to assess elemental variations of unprocessed sediments. However, although the exposure time of XRF-scanning directly affects the scanning counts and total measurement time, only a few studies have considered the influence of exposure time during the scan. How to select an optimal exposure time to achieve reliable results and reduce the total measurement time is an important issue. To address this question, six geological reference materials from the Geological Survey of Japan (JLK-1, JMS-1, JMS-2, JSD-1, JSD-2, and JSD-3) were scanned by the Itrax-XRF core scanner using the Mo- and the Cr-tube with different exposure times to allow a comparison of scanning counts with absolute concentrations. The regression lines and correlation coefficients of elements that are generally used in paleoenvironmental studies were examined for the different exposure times and X-ray tubes. The results show that for those elements with relatively high concentrations or high detectability, the correlation coefficients are higher than 0.90 for all exposure times. In contrast, for the low detectability or low concentration elements, the correlation coefficients are relatively low, and improve little with increased exposure time. Therefore, we suggest that the influence of different exposure times is insignificant for the accuracy of the measurements. Thus, caution must be taken when interpreting the results of elements with low detectability, even when the exposure times are long and scanning counts are reasonably high.

Hydrogen isotope compositions of terrestrial plant-wax n-alkanes analysed on a transect of marine surface sediment samples from 1°N to 28°S off southwest Africa

A transect of marine surface sediment samples from 1° N to 28° S off southwest Africa was analysed to verify the application of hydrogen isotope compositions of terrestrial plant-wax n-alkanes preserved in ocean sediments as a proxy for continental hydrological conditions. Conditions on the adjacent continent range from humid evergreen forests to deciduous forests, wood- and shrub land and further to arid grasslands and deserts. The hydrogen isotope values for the dominant n-alkane homologues (C29, C31 and C33) vary from -123 per mil to -141 per mil VSMOW and correlate with the modelled hydrogen isotope composition of mean annual and growing season precipitation of postulated continental source areas (r up to 0.8, p < 0.01). The apparent hydrogen isotope fractionation between alkanes and mean annual precipitation is remarkably uniform (-109 per mil on average, Sigma <= 5 per mil, n = 27). Potentially, effects of aridity on the apparent hydrogen isotope fractionation are concealed by the contribution of different plants (C3 dicotyledons vs C4 grasses). Thus, isotope ratios of leaf wax n-alkanes preserved in ocean margin sediments in these and similar tropical regions may be directly converted to dD ratios of ancient precipitation by employing a constant hydrogen isotope fractionation.

Sea surface temperature reconstruction based on planktonic foraminifera of surface sediments and sediment cores off Iberia

Quantitative and qualitative analyses of planktonic foraminiferal assemblages from 134 core-top sediment samples collected along the western Iberian margin were used to assess the latitudinal and longitudinal changes in surface water conditions and to calibrate a Sea Surface Temperature (SST) transfer function for this seasonal coastal upwelling region. Q-mode factor analysis performed on relative abundances yielded three factors that explain 96% of the total variance: factor 1 (50%) is exclusively defined by Globigerina bulloides, the most abundant and widespread species, and reflects the modern seasonal (May to September) coastal upwelling areas; factor 2 (32%) is dominated by Neogloboquadrina pachyderma (dextral) and Globorotalia inflata and seems to be associated with the Portugal Current, the descending branch of the North Atlantic Drift; factor 3 (14%) is defined by the tropical-sub-tropical species Globigerinoides ruber (white), Globigerinoides trilobus trilobus, and G. inflata and mirrors the influence of the winter-time eastern branch of the Azores Current. In conjunction with satellite-derived SST for summer and winter seasons integrated over an 18 year period the regional foraminiferal data set is used to calibrate a SST transfer function using Imbrie & Kipp, MAT and SIMMAX(ndw) techniques. Similar predicted errors (RMSEP), correlation coefficients, and residuals' deviation from SST estimated for both techniques were observed for both seasons. All techniques appear to underestimate SST off the southern Iberia margin, an area mainly occupied by warm waters where upwelling occurs only occasionally, and overestimate SST on the northern part of the west coast of the Iberia margin, where cold waters are present nearly all year round. The comparison of these regional calibrations with former Atlantic and North Atlantic calibrations for two cores, one of which is influenced by upwelling, reveals that the regional one attests more robust paleo-SSTs than for the other approaches.

Multivariate Appoximations to Portfolio Return Distribution

This article proposes a three-step procedure to estimate portfolio return distributions under the multivariate Gram-Charlier (MGC) distribution. The method combines quasi maximum likelihood (QML) estimation for conditional means and variances and the method of moments (MM) estimation for the rest of the density parameters, including the correlation coefficients. The procedure involves consistent estimates even under density misspecification and solves the so-called ‘curse of dimensionality’ of multivariate modelling. Furthermore, the use of a MGC distribution represents a flexible and general approximation to the true distribution of portfolio returns and accounts for all its empirical regularities. An application of such procedure is performed for a portfolio composed of three European indices as an illustration. The MM estimation of the MGC (MGC-MM) is compared with the traditional maximum likelihood of both the MGC and multivariate Student’s t (benchmark) densities. A simulation on Value-at-Risk (VaR) performance for an equally weighted portfolio at 1% and 5% confidence indicates that the MGC-MM method provides reasonable approximations to the true empirical VaR. Therefore, the procedure seems to be a useful tool for risk managers and practitioners.

Climate change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea

During the 1980s, the North Sea plankton community underwent a well-documented ecosystem regime shift, including both spatial changes (northward species range shifts) and temporal changes (increases in the total abundances of warmer water species). This regime shift has been attributed to climate change. Plankton provide a link between climate and higher trophic-level organisms, which can forage on large spatial and temporal scales. It is therefore important to understand not only whether climate change affects purely spatial or temporal aspects of plankton dynamics, but also whether it affects spatiotemporal aspects such as metapopulation synchrony. If plankton synchrony is altered, higher trophic-level feeding patterns may be modified. A second motivation for investigating changes in synchrony is that the possibility of such alterations has been examined for few organisms, in spite of the fact that synchrony is ubiquitous and of major importance in ecology. This study uses correlation coefficients and spectral analysis to investigate whether synchrony changed between the periods 1959–1980 and 1989–2010. Twenty-three plankton taxa, sea surface temperature (SST), and wind speed were examined. Results revealed that synchrony in SST and plankton was altered. Changes were idiosyncratic, and were not explained by changes in abundance. Changes in the synchrony of Calanus helgolandicus and Para-pseudocalanus spp appeared to be driven by changes in SST synchrony. This study is one of few to document alterations of synchrony and climate-change impacts on synchrony. We discuss why climate-change impacts on synchrony may well be more common and consequential than previously recognized.

Climate change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea

During the 1980s, the North Sea plankton community underwent a well-documented ecosystem regime shift, including both spatial changes (northward species range shifts) and temporal changes (increases in the total abundances of warmer water species). This regime shift has been attributed to climate change. Plankton provide a link between climate and higher trophic-level organisms, which can forage on large spatial and temporal scales. It is therefore important to understand not only whether climate change affects purely spatial or temporal aspects of plankton dynamics, but also whether it affects spatiotemporal aspects such as metapopulation synchrony. If plankton synchrony is altered, higher trophic-level feeding patterns may be modified. A second motivation for investigating changes in synchrony is that the possibility of such alterations has been examined for few organisms, in spite of the fact that synchrony is ubiquitous and of major importance in ecology. This study uses correlation coefficients and spectral analysis to investigate whether synchrony changed between the periods 1959–1980 and 1989–2010. Twenty-three plankton taxa, sea surface temperature (SST), and wind speed were examined. Results revealed that synchrony in SST and plankton was altered. Changes were idiosyncratic, and were not explained by changes in abundance. Changes in the synchrony of Calanus helgolandicus and Para-pseudocalanus spp appeared to be driven by changes in SST synchrony. This study is one of few to document alterations of synchrony and climate-change impacts on synchrony. We discuss why climate-change impacts on synchrony may well be more common and consequential than previously recognized.

Incomplete DJH rearrangements as a novel tumor target for minimal residual disease quantitation in multiple myeloma using real-time PCR.

The hypervariable regions of immunoglobulin heavy-chain (IgH) rearrangements provide a specific tumor marker in multiple myeloma (MM). Recently, real-time PCR assays have been developed in order to quantify the number of tumor cells after treatment. However, these strategies are hampered by the presence of somatic hypermutation (SH) in VDJH rearrangements from multiple myeloma (MM) patients, which causes mismatches between primers and/or probes and the target, leading to a nonaccurate quantification of tumor cells. Our group has recently described a 60% incidence of incomplete DJH rearrangements in MM patients, with no or very low rates of SH. In this study, we compare the efficiency of a real-time PCR approach for the analysis of both complete and incomplete IgH rearrangements in eight MM patients using only three JH consensus probes. We were able to design an allele-specific oligonucleotide for both the complete and incomplete rearrangement in all patients. DJH rearrangements fulfilled the criteria of effectiveness for real-time PCR in all samples (ie no unspecific amplification, detection of less than 10 tumor cells within 10(5) polyclonal background and correlation coefficients of standard curves higher than 0.98). By contrast, only three out of eight VDJH rearrangements fulfilled these criteria. Further analyses showed that the remaining five VDJH rearrangements carried three or more somatic mutations in the probe and primer sites, leading to a dramatic decrease in the melting temperature. These results support the use of incomplete DJH rearrangements instead of complete somatically mutated VDJH rearrangements for investigation of minimal residual disease in multiple myeloma.

Credibilidade não linear para as metas de inflação no Brasil

O objetivo deste artigo é analisar a credibilidade do regime de metas de inflação no Brasil e propor um novo método de mensurá-la. Para tanto revisamos alguns índices de credibilidade anteriormente propostos para o país. Uma limitação comum a esses índices é o pressuposto de uma relação linear da credibilidade com os desvios da meta central de inflação. Neste sentido, é proposto um novo índice no qual a credibilidade depende de forma não linear dos afastamentos da meta e calculado o índice para o período de 2001 a 2012. Em um estudo comparativo, utilizando variáveis relacionadas à credibilidade, encontramos coeficientes de correlação maiores para o novo índice. Também se identificou uma alta credibilidade da política monetá- ria para o Brasil nos últimos anos.

'Movie Recommendation Systems Using An Artificial Immune System'

We apply the Artificial Immune System (AIS)technology to the collaborative Filtering (CF)technology when we build the movie recommendation system. Two different affinity measure algorithms of AIS, Kendall tau and Weighted Kappa, are used to calculate the correlation coefficients for this movie recommendation system. From the testing we think that Weighted Kappa is more suitable than Kendall tau for movie problems.

'On Affinity Measures for Artificial Immune System Movie Recommenders'

Abstract. We combine Artificial Immune Systems (AIS) technology with Collaborative Filtering (CF) and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin ([3], [4], [5]). Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendall's Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.