Determinantes da fraude financeira : a relação entre os aspectos de racionalidade e a desonestidade

Dissertação (mestrado)—Universidade de Brasília, Universidade Federal da Paraíba, Universidade Federal do Rio Grande do Norte, Programa Multiinstitucional e Inter-regional de Pós-Graduação em Ciências Contábeis, 2016.

Sistema inteligente para estimar a porosidade em sedimentos a partir da análise de sinais GPR

This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)

Determinantes del precio del derecho de emisión europeo

[ES] Diversos estudios han investigado sobre los posibles determinantes del precio del derecho de emisión europeo. En este trabajo de fin de grado se pretende analizar qué factores influyen en el precio de este producto financiero y de qué manera lo hacen, además de comprobar posibles cambios en el funcionamiento del mercado. La metodología utilizada para llevar a cabo este análisis se basa principalmente en el modelo de regresión lineal general. A diferencia de otros estudios existentes, la muestra utilizada va desde 2008 hasta 2015, por lo que incluye la segunda fase (2008-2012) de este mercado de derechos de emisión y la tercera (2013-2015), lo que permite analizar las posibles diferencias de funcionamiento del mercado entre ambas fases. Los resultados obtenidos sostienen la existencia de este cambio estructural de manera que en la segunda fase los factores más influyentes son el gas natural y el petróleo, mientras que en la tercera fase el comportamiento del mercado cambia drásticamente de forma que el carbón parece ser el factor más influyente.

Geochemical Assessment and Separation of Source Waters in the Upper Boulder River Watershed Near Boulder, MT

Environmental samples were collected at three surface water sites between 5/21/2011 and 11/21/2014 along the Upper Boulder River near Boulder Montana. The sites were located at Bernice (within the mountain block), near the High Ore drainage (near the mountain block/basin transition), and at the USGS Gauging Station near Boulder, Montana (within the basin). The parameters measured in the field were SC, temperature, and alkalinity with occasional pH measurements. We collected samples for anions, cations, and stable isotopes in the catchment. We identified endmembers by sampling snow and groundwater and determined from available data an approximate endmember for rain, snow, and groundwater. We used temporal and spatial variations of water chemistry and isotopes to generate an endmember mixing model. Groundwater was found to always be an important contributor to river flow and could increase by nearly an order of magnitude during large snowmelt events. This resulted in groundwater comprising ~20% of total river flow during snowmelt at all sites. At peak snowmelt we observed that near surface water contributions to the river were from a mixture of rain and snow. Soil water, though not sampled, was hypothesized to be an important part of the hydrologic story. If so, the endmember contributions determined in this study may be different. Groundwater may have the highest variation depending on water chemistry of shallow soil water.

A Microdata Analysis Approach to Transport Infrastructure Maintenance

Maintenance of transport infrastructure assets is widely advocated as the key in minimizing current and future costs of the transportation network. While effective maintenance decisions are often a result of engineering skills and practical knowledge, efficient decisions must also account for the net result over an asset's life-cycle. One essential aspect in the long term perspective of transport infrastructure maintenance is to proactively estimate maintenance needs. In dealing with immediate maintenance actions, support tools that can prioritize potential maintenance candidates are important to obtain an efficient maintenance strategy. This dissertation consists of five individual research papers presenting a microdata analysis approach to transport infrastructure maintenance. Microdata analysis is a multidisciplinary field in which large quantities of data is collected, analyzed, and interpreted to improve decision-making. Increased access to transport infrastructure data enables a deeper understanding of causal effects and a possibility to make predictions of future outcomes. The microdata analysis approach covers the complete process from data collection to actual decisions and is therefore well suited for the task of improving efficiency in transport infrastructure maintenance. Statistical modeling was the selected analysis method in this dissertation and provided solutions to the different problems presented in each of the five papers. In Paper I, a time-to-event model was used to estimate remaining road pavement lifetimes in Sweden. In Paper II, an extension of the model in Paper I assessed the impact of latent variables on road lifetimes; displaying the sections in a road network that are weaker due to e.g. subsoil conditions or undetected heavy traffic. The study in Paper III incorporated a probabilistic parametric distribution as a representation of road lifetimes into an equation for the marginal cost of road wear. Differentiated road wear marginal costs for heavy and light vehicles are an important information basis for decisions regarding vehicle miles traveled (VMT) taxation policies. In Paper IV, a distribution based clustering method was used to distinguish between road segments that are deteriorating and road segments that have a stationary road condition. Within railway networks, temporary speed restrictions are often imposed because of maintenance and must be addressed in order to keep punctuality. The study in Paper V evaluated the empirical effect on running time of speed restrictions on a Norwegian railway line using a generalized linear mixed model.

The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.

Impedancias basales registradas durante la denervación renal por radiofrecuencia en pacientes con hipertensión arterial resistente

Objetivo: Determinar diferencias en las impedancias basales registradas durante los procedimientos de denervación renal por radiofrecuencia de los pacientes sometidos a este procedimiento en la Fundación Cardioinfantil de Bogotá durante los años 2012 a 2015. Materiales y métodos: Estudio observacional, analítico de corte retrospectivo, donde se analizaron todas las impedancias basales medidas durante los procedimientos de denervación renal, buscando diferencias significativas entre los segmentos de las arterias intervenidas, estratificados en proximal, medio distal y superior, lateral, inferior u ostial. Con seguimiento a los pacientes a tres, seis y doce meses en cuanto a presión arterial de consultorio. Resultados: Se evaluaron 150 puntos de denervación renal exitosos, correspondientes a 23 arterias renales de 11 procedimientos. La mediana de edad fue 56 años. Al realizar un modelo de regresión lineal no se encontró ninguna diferencia estadísticamente significativa entre las impedancias de ninguno de los segmentos de las arterias ni sitios anatómicos. Se documentó disminución de presión arterial sistólica a tres meses, seis meses y doce meses de 14 mmHg (RIQ 10-33mmHg), 21 mmHg (RIQ 12-42mmHg) y 19 mmHg (RIQ 11-42 mmHg) respectivamente

Choques al hogar, trabajo Infantil y acumulación de capital humano : evidencia para Colombia

En este trabajo se estima el efecto que tienen distintos choques a los hogares sobre el logro académico de los niños. Mediante un modelo de regresión lineal, se encuentra un efecto adverso de la presencia de choques sobre el puntaje de la prueba Saber 11. Además, los resultados sugieren que el trabajo infantil es un mecanismo por el cual los choques afectan negativamente la acumulación de capital humano. Al explorar efectos heterogéneos por sexo y edad, las estimaciones indican que el efecto de los choques es guiado por los hombres y los adolescentes mayores a 14 años.

Comportamiento metabólico, fisiológico y electrocardiográfico en trabajadores expuestos a altas temperaturas en una empresa líder en la explotación y coquización del carbón en Boyacá, Colombia, 2016

Objetivo: Establecer la relación entre la exposición ocupacional a altas temperaturas o sobrecarga térmica con el comportamiento fisiológico, metabólico y electrocardiográfico. Métodos: estudio de corte transversal, donde se incluyeron dos grupos (expuesto y no expuesto a altas temperaturas) en una empresa minera, en el departamento de Boyacá, Colombia, en el año 2016. El número de participantes fue de 160 trabajadores del género masculino, grupo expuesto (n=86) y grupo no expuesto (n=74). La exposición ocupacional a sobrecarga térmica se evaluó con el índice de temperatura de globo y bulbo húmedo (TGBH), el comportamiento fisiológico con el índice de costo cardiaco relativo (ICCR) con mediciones de frecuencia cardiaca (FC), el comportamiento metabólico con la determinación del colesterol total (CT), colesterol de alta densidad (C-HDL), colesterol de baja densidad (C-LDL), triglicéridos (TG) y glicemia basal (GL). Las alteraciones electrocardiográficas con la toma de Electrocardiograma de 12 derivaciones. También fueron evaluadas variables antropométricas, tensión arterial, hábitos y antecedentes de enfermedad cardiovascular en ambos grupos. Resultados: incrementos significativos del ICCR (p<0.001) y la carga física (p<0.001) fueron encontrados en los trabajadores expuestos a altas temperaturas. Los índices lipídicos y glicemia, así como los antecedentes personales cardiovasculares, IMC, consumo de cigarrillo y consumo de alcohol, no mostraron significancia. El antecedente familiar de ACV (p=0.043) y el EKG alterado (p=0.011) mostraron una asociación significativa con la exposición a altas temperaturas. El modelo de regresión lineal múltiple explicó la relación entre el incremento del ICCR y la exposición a altas temperaturas (β=4,213, IC 95%: 1.57,6.85) ajustado por variables fisiológicas y electrocardiográficas. Conclusiones: La exposición ocupacional a altas temperaturas, presenta asociación con las alteraciones cardiovasculares a nivel fisiológico y electrocardiográfico, aumentando el ICCR y la carga física de trabajo (GE trabajo).

Solastalgia y conductas pro-ambientales en un conflicto socioambiental por la construcción de una hidroeléctrica

Introducción: La construcción de megaproyectos hídricos implica una reconfiguración territorial donde se ven afectadas las fuentes de agua dulce, la biodiversidad terrestre y acuática, y los asentamientos humanos que colindan con dichas construcciones. Objetivo: estimar la asociación entre las conductas proambientales con la solastalgia entre las personas que se encuentran ejerciendo un proceso de resistencia social contra la Central Hidrosogamoso en el departamento de Santander, Colombia. Metodología: se utilizó un diseño de estudio transversal en el que se entrevistaron integrantes y no integrantes de grupos ambientalistas de las zonas de influencia del proyecto. Se realizó un análisis descriptivo de las variables sociodemográficas de los dos grupos de comparación presentando frecuencias absolutas y relativas y diferencias significativas por medio de la prueba ji cuadrado, exacta de Fisher y U de Mann Whitney. Se utilizó un modelo de regresión lineal múltiple en el que la variable dependiente fue el puntaje de solastalgia y las variables independientes fueron las escalas de las conductas proambientales: altruismo, austeridad, equidad, conducta ecológica, deliberación, indignación y aprecio por lo natural, además, se ajustó por algunas variables sociodemográficas de interés. Resultados: los grupos comparados presentaron diferencias importantes en cuanto a la zona de procedencia, condiciones económicas y organización social. El incremento de 5 puntos en la escala del sentimiento de indignación incrementó 0.98 la escala de solastalgia (IC95%: 0.19; 1.78). Las personas sin pareja estable tuvieron 3.02 puntos menos de solastalgia comparadas con personas casadas o en unión libre (IC95%: -4.96; -1.44), mientras que aquellas con alto nivel educativo obtuvieron 2.02 puntos menos que las personas con primaria y bachillerato (IC95%: -3.99; -0.06). Un modelo alterno mostró que no pertenecer a un grupo ambientalista disminuye en 2.29 puntos la solastalgia, comparado con pertenecer a un grupo (IC95%: -4.31; -0.28),. Conclusión: posiblemente las motivaciones por las cuales los actores involucrados se resisten a las transformaciones territoriales ocasionadas por la construcción de las represas son más un reflejo de la condición socioeconómica que de la preocupación de los actores por el daño del medio ambiente y además, esta resistencia es un fenómeno que se limita a aquellos que están afectados directamente en el área de influencia del proyecto

Variation in myogenic differentiation 1 mRNA abundance is associated with beef tenderness in Nelore cattle.

The myogenic differentiation 1 gene (MYOD1) has a key role in skeletal muscle differentiation and composition through its regulation of the expression of several muscle-specific genes. We first used a general linear mixed model approach to evaluate the association of MYOD1 expression levels on individual beef tenderness phenotypes. MYOD1 mRNA levels measured by quantitative polymerase chain reactions in 136 Nelore steers were significantly associated (P ? 0.01) with Warner?Bratzler shear force, measured on the longissimus dorsi muscle after 7 and 14 days of beef aging. Transcript abundance for the muscle regulatory gene MYOD1 was lower in animals with more tender beef. We also performed a coexpression network analysis using whole transcriptome sequence data generated from 30 samples of longissimus muscle tissue to identify genes that are potentially regulated by MYOD1. The effect of MYOD1 gene expression on beef tenderness may emerge from its function as an activator of muscle-specific gene transcription such as for the serum response factor (C-fos serum response element-binding transcription factor) gene (SRF), which determines muscle tissue development, composition, growth and maturation.

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP models include convergence and mixing difficulties. This paper proposes an algorithm based on the Pólya urn scheme that extends the Gibbs sampling algorithms to non-conjugate models with normal base measures and exponential family likelihoods. The algorithm proceeds by making Laplace approximations to the likelihood function, thereby reducing the procedure to that of conjugate normal MDP models. To ensure the validity of the stationary distribution in the non-conjugate case, the proposals are accepted or rejected by a Metropolis-Hastings step. In the special case where the data are normally distributed, the algorithm is identical to the Gibbs sampler.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.