Probing the dependence of the properties of cellulose acetates and their films on the degree of biopolymer substitution: use of solvatochromic indicators and thermal analysis

Although cellulose acetates, CAs, are extensively employed there is scant information about the systematic dependence of their properties on their degree of substitution, DS; this is the subject of the present work. Nine CAs samples, DS from 0.83 to 3.0 were synthesized; their films were prepared. The following solvatochromic probes have been employed in order to determine the empirical polarity, E (T)(33); ""acidity, alpha""; ""basicity, beta"", and ""dipolarity/polarizability, pi*"" of the casted films: 2,6-dichloro-4-(2,4,6-triphenyl-pyridinium-1-yl) phenolate, WB; 4-nitroaniline; 4-nitroanisole; 4-nitro-N,N-dimethylaniline; 2,6-diphenyl-4-(2,4,6-triphenyl-pyridinium-1-yl)phenolate, RB. Additionally, two systems, ethanol plus ethyl acetate (EtOH-EtAc), and cellulose plus cellulose triacetate, CTA, were employed as models for CAs of different DS. Regarding the model systems, the following was observed: (i) For EtOH-EtAc, the dependence of all solvatochromic parameters on the ""equivalent-DS"" of the binary mixture was non-linear because of preferential solvation; (ii) The dependence of E (T)(33) on equivalent DS of the cellulose-CTA films is linear, but the slope is smaller than that of the corresponding plot for CAs. This is attributed to the more efficient hydrogen bonding in the model system, a conclusion corroborated by IR measurements. The dependence of solvatochromic parameters of CAs on their DS is described by the simple equations; a consequence of the substitution of the OH by the ester group. The thermal properties of bulk CAs samples were investigated by DSC and TGA; their dependence on DS is described by simple equations. The relevance of these data to the processing and applications of CAs is briefly discussed.

Detecting Major Genes Controlling Robustness of Chicken Body Weight Using Double Generalized Linear Models

Detecting both the majors genes that control the phenotypic mean and those controlling phenotypic variance has been raised in quantitative trait loci analysis. In order to mapping both kinds of genes, we applied the idea of the classic Haley-Knott regression to double generalized linear models. We performed both kinds of quantitative trait loci detection for a Red Jungle Fowl x White Leghorn F2 intercross using double generalized linear models. It is shown that double generalized linear model is a proper and efficient approach for localizing variance-controlling genes. We compared two models with or without fixed sex effect and prefer including the sex effect in order to reduce the residual variances. We found that different genes might take effect on the body weight at different time as the chicken grows.

Characterization of residential chimney conditions for flue gas flow measurements

A literature survey and a theoretical study were performed to characterize residential chimney conditions for flue gas flow measurements. The focus is on Pitot-static probes to give sufficient basis for the development and calibration of a velocity pressure averaging probe suitable for the continuous dynamic (i.e. non steady state) measurement of the low flow velocities present in residential chimneys. The flow conditions do not meet the requirements set in ISO 10780 and ISO 3966 for Pitot-static probe measurements, and the methods and their uncertainties are not valid. The flow velocities in residential chimneys from a heating boiler under normal operating condi-tions are shown to be so low that they in some conditions result in voiding the assumptions of non-viscous fluid justifying the use of the quadratic Bernoulli equation. A non-linear Reynolds number dependent calibration coefficient that is correcting for the viscous effects is needed to avoid significant measurement errors. The wide range of flow velocity during normal boiler operation also results in the flow type changing from laminar, across the laminar to turbulent transition region, to fully turbulent flow, resulting in significant changes of the velocity profile during dynamic measurements. In addition, the short duct lengths (and changes of flow direction and duct shape) used in practice are shown to result in that the measurements are done in the hydrodynamic entrance region where the flow velocity profiles most likely are neither symmetrical nor fully developed. A measurement method insensitive to velocity profile changes is thus needed, if the flow velocity profile cannot otherwise be determined or predicted with reasonable accuracy for the whole measurement range. Because of particulate matter and condensing fluids in the flue gas it is beneficial if the probe can be constructed so that it can easily be taken out for cleaning, and equipped with a locking mechanism to always ensure the same alignment in the duct without affecting the calibration. The literature implies that there may be a significant time lag in the measurements of low flow rates due to viscous effects in the internal impact pressure passages of Pitot probes, and the significance in the discussed application should be studied experimentally. The measured differential pressures from Pitot-static probes in residential chimney flows are so low that the calibration and given uncertainties of commercially available pressure transducers are not adequate. The pressure transducers should be calibrated specifically for the application, preferably in combination with the probe, and the significance of all different error sources should be investigated carefully. Care should be taken also with the temperature measurement, e.g. with averaging of several sensors, as significant temperature gradients may be present in flue gas ducts.

Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Strategisk utveckling och ekonomistyrning : Ett livscykelperspektiv

This dissertation synthesizes previous research and develops a model for the study of strategic development, strategic congruence and management control. The model is used to analyze a longitudinal case study of the Swedish engineering company Atlas Copco. Employing contingency theory, the study confirms that long-term survival of a company requires adaption to contingencies. Three levels of strategy are examined: corporate, business and functional. Previous research suggests that consistency between these levels (strategic congruence) is necessary for a company to be competitive. The dissertation challenges this proposition by using a life-cycle perspective and analyzes strategic congruence in the different phases of a life cycle. It also studies management control from a life-cycle perspective. In this context, two types of management control are examined: formal and informal. From a longitudinal perspective, the study further discusses how these types interact during organizational life cycles. The dissertation shows that strategic development is more complex than previous studies have indicated. It is a long, complex and non-linear process, the results of which cannot always be predicted. Previous models for strategy and management control are based on simple relationships and rarely take into account the fact that companies often go through different phases of strategic development. The case study shows that strategic incongruence may occur at times during organizational life cycles. Furthermore, the use of management control varies over time. In the maturity phase, formal control is in focus, while the use of informal control has a bigger role in both the introduction and decline phases. Research on strategy and management control has intensified in recent years. Still there is a gap regarding the coordination of complex corporate structures. The present study contributes with further knowledge on how companies manage long-term strategic development. Few studies deal with more than two levels of strategy. Moreover, the present study addresses the need to understand strategic congruence from a life-cycle perspective. This is particularly relevant in practice, when management in large companies face difficult issues for which they expect business research to assist them in the decision-making process.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Two essays on education and governament policies

Whether human capital increases or decreases wage uncertainty is an open ques- tion from an empirical standpoint. Yet, most policy prescriptions regarding human capital formation are based on models that impose riskiness on this type of invest- ment. We slightly deviate from the rest of the literature by allowing for non-linear income taxes in a two period model. This enables us to derive prescriptions that are robust to the risk characteristics of human capital: savings should be discouraged, human capital investments encouraged and both types of investment driven to an e¢ cient level from an aggregate perspective. These prescriptions are also robust to what choices are observed, even though the policy instruments used to implement them are not.

Detectando não-linearidades nos retornos dos fundos multimercados

Estudos recentes apontam que diversas estratégias implementadas em hedge funds geram retornos com características não lineares. Seguindo as sugestões encontradas no paper de Agarwal e Naik (2004), este trabalho mostra que uma série de hedge funds dentro da indústria de fundos de investimentos no Brasil apresenta retornos que se assemelham ao de uma estratégia em opções de compra e venda no índice de mercado Bovespa. Partindo de um modelo de fatores, introduzimos um índice referenciado no retorno sobre opções de modo que tal fator possa explicar melhor que os tradicionais fatores de risco a característica não linear dos retornos dos fundos de investimento.

Estimating the term structure of volatility and fixed income derivative pricing

Estimating the parameters of the instantaneous spot interest rate process is of crucial importance for pricing fixed income derivative securities. This paper presents an estimation for the parameters of the Gaussian interest rate model for pricing fixed income derivatives based on the term structure of volatility. We estimate the term structure of volatility for US treasury rates for the period 1983 - 1995, based on a history of yield curves. We estimate both conditional and first differences term structures of volatility and subsequently estimate the implied parameters of the Gaussian model with non-linear least squares estimation. Results for bond options illustrate the effects of differing parameters in pricing.

Previsão de retornos intradiários através de regressões usando funções-núcleo

As contribuições deste artigo são duas. A primeira, um método de avaliação de regressões não lineares para a previsão de retornos intradiários de ações no mercado brasileiro é discutido e aplicado, com o objetivo de maximizar o retorno de um portfólio simulado de compras e vendas. A segunda, regressões usando funções-núcleo associadas ao particionamento da amostra por vizinhos mais próximos são realizadas. Algumas variáveis independentes utilizadas são indicadores técnicos, cujos parâmetros são otimizados dentro da amostra de estimação. Os resultados alcançados são positivos e superam, em uma análise quartil a quartil, os resultados produzidos por um modelo benchmark de autorregressão linear

Crime and punishment with habit formation

Moral concepts affect crime supply. This idea is modelled assuming that illegal activities is habit forming. We introduce habits in a intertemporal general equilibrium framework to illegal activities and compare its outcomes with a model without habit formation. The findings are that habit and crime presents a non linear relationship that hinges upon the level of capital and habit formation. It is possible to show that while the effect of habit on crime is negative for low levels o habit formation it becomes positive as habits goes up. Secondly habit reduces the marginal effect of illegal activities return on crime. Finally, the effect of habit on crime depends positively on the amount of capital. This could explain the relationship between size of cities and illegal activity.

Consumo de bens duráveis e poupança em uma nova trajetória de comportamento do consumidor brasileiro

O trabalho avalia a dinâmica descrita pelo consumo de bens duráveis e poupança dos consumidores brasileiros entre setembro de 2005 e abril de 2011 e contribui com a literatura ao utilizar como ferramenta de análise um modelo autoregressivo com valor limite endógeno e dados qualitativos da pesquisa Sondagem de Expectativas do Consumidor Brasileiro, da FGV. Indicadores qualitativos para essas duas variáveis foram calculados e a metodologia proposta permitiu investigar, simultaneamente, a linearidade e estacionaridade de suas trajetórias. Os resultados sugerem, em ambos os casos, uma dinâmica não-linear com raiz unitária parcial. Adicionalmente, a estacionaridade constatada a partir de um valor limite estimado de 3,3 pontos percentuais para o Indicador de Compras de Bens Duráveis e de 3,6 pontos percentuais para o Indicador de Poupança permitem classificar seus históricos com indícios de saturação da capacidade de poupança e consumo dos indivíduos.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual horizons. The data to be used consists of metal-commodity prices in a monthly frequency from 1957 to 2012 from the International Financial Statistics of the IMF on individual metal series. We will also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009) , which are available for download. Regarding short- and long-run comovement, we will apply the techniques and the tests proposed in the common-feature literature to build parsimonious VARs, which possibly entail quasi-structural relationships between different commodity prices and/or between a given commodity price and its potential demand determinants. These parsimonious VARs will be later used as forecasting models to be combined to yield metal-commodity prices optimal forecasts. Regarding out-of-sample forecasts, we will use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates to forecast the returns and prices of metal commodities. With the forecasts of a large number of models (N large) and a large number of time periods (T large), we will apply the techniques put forth by the common-feature literature on forecast combinations. The main contribution of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding forecasting, we show that models incorporating (short-run) commoncycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation. Still, in most cases, forecast combination techniques outperform individual models.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900-2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009). We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts - including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.