A GIS-based empirical model for vegetation prediction in Lefka Ori, Crete

The aim of the study was to establish and verify a predictive vegetation model for plant community distribution in the alti-Mediterranean zone of the Lefka Ori massif, western Crete. Based on previous work three variables were identified as significant determinants of plant community distribution, namely altitude, slope angle and geomorphic landform. The response of four community types against these variables was tested using classification trees analysis in order to model community type occurrence. V-fold cross-validation plots were used to determine the length of the best fitting tree. The final 9node tree selected, classified correctly 92.5% of the samples. The results were used to provide decision rules for the construction of a spatial model for each community type. The model was implemented within a Geographical Information System (GIS) to predict the distribution of each community type in the study site. The evaluation of the model in the field using an error matrix gave an overall accuracy of 71%. The user's accuracy was higher for the Crepis-Cirsium (100%) and Telephium-Herniaria community type (66.7%) and relatively lower for the Peucedanum-Alyssum and Dianthus-Lomelosia community types (63.2% and 62.5%, respectively). Misclassification and field validation points to the need for improved geomorphological mapping and suggests the presence of transitional communities between existing community types.

Predicting functional gene links from phylogenetic-statistical analyses of whole genomes

An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one gene's presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks.

Design and analysis of climate model experiments for the efficient estimation of anthropogenic signals

Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings. The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.

Modelling oil absorption during post-frying cooling - I: model development

Several studies have highlighted the importance of the cooling period in oil absorption in deep-fat fried products. Specifically, it has been established that the largest proportion of oil which ends up into the food, is sucked into the porous crust region after the fried product is removed from the oil bath, stressing the importance of this time interval. The main objective of this paper was to develop a predictive mechanistic model that can be used to understand the principles behind post-frying cooling oil absorption kinetics, which can also help identifying the key parameters that affect the final oil intake by the fried product. The model was developed for two different geometries, an infinite slab and an infinite cylinder, and was divided into two main sub-models, one describing the immersion frying period itself and the other describing the post-frying cooling period. The immersion frying period was described by a transient moving-front model that considered the movement of the crust/core interface, whereas post-frying cooling oil absorption was considered to be a pressure driven flow mediated by capillary forces. A key element in the model was the hypothesis that oil suction would only begin once a positive pressure driving force had developed. The mechanistic model was based on measurable physical and thermal properties, and process parameters with no need of empirical data fitting, and can be used to study oil absorption in any deep-fat fried product that satisfies the assumptions made.

On the statistical modeling of persistence in total ozone anomalies

Geophysical time series sometimes exhibit serial correlations that are stronger than can be captured by the commonly used first‐order autoregressive model. In this study we demonstrate that a power law statistical model serves as a useful upper bound for the persistence of total ozone anomalies on monthly to interannual timescales. Such a model is usually characterized by the Hurst exponent. We show that the estimation of the Hurst exponent in time series of total ozone is sensitive to various choices made in the statistical analysis, especially whether and how the deterministic (including periodic) signals are filtered from the time series, and the frequency range over which the estimation is made. In particular, care must be taken to ensure that the estimate of the Hurst exponent accurately represents the low‐frequency limit of the spectrum, which is the part that is relevant to long‐term correlations and the uncertainty of estimated trends. Otherwise, spurious results can be obtained. Based on this analysis, and using an updated equivalent effective stratospheric chlorine (EESC) function, we predict that an increase in total ozone attributable to EESC should be detectable at the 95% confidence level by 2015 at the latest in southern midlatitudes, and by 2020–2025 at the latest over 30°–45°N, with the time to detection increasing rapidly with latitude north of this range.

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.

Developing a Model for Pedestrian Route Selection at Colby

Millions of unconscious calculations are made daily by pedestrians walking through the Colby College campus. I used ArcGIS to make a predictive spatial model that chose paths similar to those that are actually used by people on a regular basis. To make a viable model of how most travelers choose their way, I considered both the distance required and the type of traveling surface. I used an iterative process to develop a scheme for weighting travel costs which resulted in accurate least-cost paths to be predicted by ArcMap. The accuracy was confirmed when the calculated routes were compared to satellite photography and were found to overlap well-worn “shortcuts” taken between the paved paths throughout campus.

A model to estimate the US term structure of interest rates

The US term structure of interest rates plays a central role in fixed-income analysis. For example, estimating accurately the US term structure is a crucial step for those interested in analyzing Brazilian Brady bonds such as IDUs, DCBs, FLIRBs, EIs, etc. In this work we present a statistical model to estimate the US term structure of interest rates. We address in this report all major issues which drove us in the process of implementing the model developed, concentrating on important practical issues such as computational efficiency, robustness of the final implementation, the statistical properties of the final model, etc. Numerical examples are provided in order to illustrate the use of the model on a daily basis.

Probabilidade de financiamento por cartões de crédito híbridos

Esta pesquisa tem o objetivo de identificar as variáveis e sua influência na propensão à aquisição de crédito pessoal, propondo um modelo estatístico de propensão ao financiamento por cartões de crédito híbridos para maximização de contratação de crédito e otimização dos esforços de marketing. O estudo descritivo pode gerar insights para a compreensão da expansão do crédito ao consumo, sobretudo num contexto de escassez de opções de financiamento e limitação no canal de distribuição. Foram usados dados de uma base de clientes de uma instituição financeira com variáveis sócio demográficas e transacionai, e o modelo matemático foi seguido da validação de sua capacidade preditiva.

Robust statistical modeling of portfolios

Atypical points in the data may result in meaningless e±cient frontiers. This follows since portfolios constructed using classical estimates may re°ect neither the usual nor the unusual days patterns. On the other hand, portfolios constructed using robust approaches are able to capture just the dynamics of the usual days, which constitute the majority of the business days. In this paper we propose an statistical model and a robust estimation procedure to obtain an e±cient frontier which would take into account the behavior of both the usual and most of the atypical days. We show, using real data and simulations, that portfolios constructed in this way require less frequent rebalancing, and may yield higher expected returns for any risk level.

Cálculo do Value at Risk (VaR) para o Ibovespa, pós crise de 2008, por meio dos modelos de heterocedasticidade condicional (GARCH) e de volatilidade estocástica (Local Scale Model - LSM)

O objetivo deste estudo é propor a implementação de um modelo estatístico para cálculo da volatilidade, não difundido na literatura brasileira, o modelo de escala local (LSM), apresentando suas vantagens e desvantagens em relação aos modelos habitualmente utilizados para mensuração de risco. Para estimação dos parâmetros serão usadas as cotações diárias do Ibovespa, no período de janeiro de 2009 a dezembro de 2014, e para a aferição da acurácia empírica dos modelos serão realizados testes fora da amostra, comparando os VaR obtidos para o período de janeiro a dezembro de 2014. Foram introduzidas variáveis explicativas na tentativa de aprimorar os modelos e optou-se pelo correspondente americano do Ibovespa, o índice Dow Jones, por ter apresentado propriedades como: alta correlação, causalidade no sentido de Granger, e razão de log-verossimilhança significativa. Uma das inovações do modelo de escala local é não utilizar diretamente a variância, mas sim a sua recíproca, chamada de “precisão” da série, que segue uma espécie de passeio aleatório multiplicativo. O LSM captou todos os fatos estilizados das séries financeiras, e os resultados foram favoráveis a sua utilização, logo, o modelo torna-se uma alternativa de especificação eficiente e parcimoniosa para estimar e prever volatilidade, na medida em que possui apenas um parâmetro a ser estimado, o que representa uma mudança de paradigma em relação aos modelos de heterocedasticidade condicional.

Extração, caracterização e avaliação bioativa do extrato de Rumex acetosa

Studies show the great influence of free radicals and other oxidants as responsible for aging and degenerative diseases. On the other hand, the natural phenolic compounds has shown great as antioxidants to inhibit lipid peroxidation and lipoxygenase in vitro. Among these, is highlighted trans-resveratrol ( 3,5,4 `- trihydroxystilbene ) phenolic compound , characterized as a polyphenol stilbene class. The vegetables popularly known as "Azedinha" (Rumex Acetosa) has trans-resveratrol in its composition and from this, the present work aimed to study on the supercritical extraction and conventional extraction (Soxhlet and sequential) in roots of Rumex Acetosa, evaluating the efficiency of extractive processes, antioxidant activity, total phenolic content and quantification of trans-resveratrol contained in the extracts. Extractions using supercritical CO2 as solvent, addition of co-solvent (ethanol) and were conducted by the dynamic method in a fixed bed extractor. The trial met a 23 factorial design with three replications at the central point, with the variable reply process yield and concentration of trans-resveratrol and pressure as independent variables, temperature and concentration of co-solvent (% v/v). Yields ( mass of dry extract / mass of raw material used ) obtained from the supercritical extraction ranged from 0,8 to 7,63 % , and the best result was obtained at 250 bar and 90 °C using the co-solvent 15% ethanol (% v/v). The value was calculated for YCER a flow rate of 1,0 ± 0,17 g/min resulting in 0,0469 CO2 ( g solute / g solvent ). The results of the mass yield varied between conventional extractions 0,78 % ( hexane) and 9,97 % (ethanol). The statistical model generated from the data of the concentration of trans-resveratrol performed as meaningful and predictive for a 95% confidence. GC analysis on HPLC (High Performance Liquid Chromatography), transresveratrol was quantified in all extracts and concentration values ranged between 0,0033 and 0,42 ( mg / g extract) for supercritical extracts and between 0,449 and 17,046 (mg / g extract) to conventional extractions and therefore, the Soxhlet extraction with ethanol for more selective trans-resveratrol than the supercritical fluid. Evaluation of antioxidant (radical method to sequester 2,2- diphenyl-1- picryl - hydrazyl - DPPH) the supercritical extracts resulted in EC50 values (concentration effective to neutralize 50% of free radicals) of between 7,89 and 18,43 mg/mL , while resulting in a Soxhlet extraction with EC50 values in the range of 6,05 and 7,39 mg/mL. As for quantification of the phenolic compounds (Method Spectrophotometer Folin-Ciocalteau) the supercritical extracts resulted in values between 85,3 and 194,79 mg GAE / g extract, whereas values derived from the Soxhlet extract resulted in values between 178,5 and 237,8 mg GAE / g extract. The high antioxidant activity can not be attributed solely to the presence of phenolic compounds, but the presence of other antioxidants in the existing Rumex acetosa

Modelos estatísticos indicadores de comportamentos associados a bem-estar térmico para matrizes pesadas

O estudo de reprodutoras de frangos de corte é de grande importância para o País, uma vez que o frango de corte é um dos maiores itens agropecuários em exportação, sendo o Brasil o segundo maior exportador mundial dessa carne. O comportamento animal é conceituado como reflexo do efeito da interação de diversos fatores, entre os quais o ambiental. Dessa forma, o ambiente interno do galpão de produção constitui um dos elementos que fornecem indicações sobre o conforto térmico das aves. O comportamento de matrizes expressa, sob a forma de padrões específicos, a saúde e o bem-estar dessas aves. Este trabalho teve como objetivo a aplicação dos modelos estatísticos preditivos, por meio da construção de cenários, apresentando resultados do conforto animal perante diversas condições ambientais. O trabalho foi desenvolvido com dados coletados em ambiente controlado, utilizando a linhagem Hybro-PG®, submetida a diferentes níveis de temperatura ambiente, tipos padronizados de ração e idade. Foram procedidas as análises descritiva e exploratória e, posteriormente, a modelagem, utilizando as Equações de Estimação Generalizadas (EEG). A pesquisa permitiu o desenvolvimento de indicadores de bem-estar térmico por meio das equações dos modelos estatísticos de predição, sob os distintos cenários estudados.

Constrained optimization model for the design of an adaptive (X)over-bar chart

An economic-statistical model is developed for variable parameters (VP) (X) over bar charts in which all design parameters vary adaptively, that is, each of the design parameters (sample size, sampling interval and control-limit width) vary as a function of the most recent process information. The cost function due to controlling the process quality through a VP (X) over bar chart is derived. During the optimization of the cost function, constraints are imposed on the expected times to signal when the process is in and out of control. In this way, required statistical properties can be assured. Through a numerical example, the proposed economic-statistical design approach for VP (X) over bar charts is compared to the economic design for VP (X) over bar charts and to the economic-statistical and economic designs for fixed parameters (FP) (X) over bar charts in terms of the operating cost and the expected times to signal. From this example, it is possible to assess the benefits provided by the proposed model. Varying some input parameters, their effect on the optimal cost and on the optimal values of the design parameters was analysed.

Comparação, em mulheres inférteis, de teste de avaliação psicológica com a análise psicanalítica clássica

This study aims to compare a psychological evaluation test to classical psychoanalysis in infertile women. Two hundred women were submitted to the Psychological Evaluation Test (PET). The sum of the scores for the responses ranged from 15 to 60 points, with scores ≥ 30 points being defined as psycho-emotional maladjustment (cut-off point: median + 25%). For comparison, a psychologist submitted the patients to a psychological examination simultaneously, who was unaware of the PET results. From the 200 patients, 66 (33%) presented a test with ≥ 30 points (psycho-emotional maladjustment) and 134 (67%) a test with < 30 points (normal). Upon psychological examination, 105 (52.5%) presented an abnormal evaluation and 95 (47.5%) a normal evaluation. For the PET, statistical analysis showed 82% efficiency, 62% sensitivity, 98% positive predictive value, 99% specificity, 70% negative predictive value, likelihood ratio for a positive test result 62, and likelihood ratio for negative test result 0.38. The PET proved to be a useful clinical instrument, being of help for the selection of patients with psychological needs induced by infertility.