CITRICULTURE ECONOMIC AND FINANCIAL EVALUATION UNDER CONDITIONS OF UNCERTAINTY

ABSTRACT The citriculture consists in several environmental risks, as weather changes and pests, and also consists in considerable financial risk, mainly due to the period ofreturn on the initial investment. This study was motivated by the need to assess the risks of a business activity such as citriculture. Our objective was to build a stochastic simulation model to achieve the economic and financial analysis of an orange producer in the Midwest region of the state of Sao Paulo, under conditions of uncertainty. The parameters used were the Net Present Value (NPV), the Modified Internal Rate of Return(MIRR), and the Discounted Payback. To evaluate the risk conditions we built a probabilistic model of pseudorandom numbers generated with Monte Carlo method. The results showed that the activity analyzed provides a risk of 42.8% to reach a NPV negative; however, the yield assessed by MIRR was 7.7%, higher than the yield from the reapplication of the positive cash flows. The financial investment pays itself after the fourteenth year of activity.

Comparing N = 1 effect size indices in presence of autocorrelation

Generalization from single-case designs can be achieved by means of replicating individual studies across different experimental units and settings. When replications are available, their findings can be summarized using effect size measurements and integrated through meta-analyses. Several procedures are available for quantifying the magnitude of treatment"s effect in N = 1 designs and some of them are studied in the current paper. Monte Carlo simulations were employed to generate different data patterns (trend, level change, slope change). The experimental conditions simulated were defined by the degrees of serial dependence and phases" length. Out of all the effect size indices studied, the Percent of nonoverlapping data and standardized mean difference proved to be less affected by autocorrelation and perform better for shorter data series. The regression-based procedures proposed specifically for single-case designs did not differentiate between data patterns as well as simpler indices.

Randomization tests for ABAB designs: Comparing-data-division-specific and common distributions

Monte Carlo simulations were used to generate data for ABAB designs of different lengths. The points of change in phase are randomly determined before gathering behaviour measurements, which allows the use of a randomization test as an analytic technique. Data simulation and analysis can be based either on data-division-specific or on common distributions. Following one method or another affects the results obtained after the randomization test has been applied. Therefore, the goal of the study was to examine these effects in more detail. The discrepancies in these approaches are obvious when data with zero treatment effect are considered and such approaches have implications for statistical power studies. Data-division-specific distributions provide more detailed information about the performance of the statistical technique.

Monotonic continuous-time random walks with drift and stochastic reset events

In this paper we consider a stochastic process that may experience random reset events which suddenly bring the system to the starting value and analyze the relevant statistical magnitudes. We focus our attention on monotonic continuous-time random walks with a constant drift: The process increases between the reset events, either by the effect of the random jumps, or by the action of the deterministic drift. As a result of all these combined factors interesting properties emerge, like the existence (for any drift strength) of a stationary transition probability density function, or the faculty of the model to reproduce power-law-like behavior. General formulas for two extreme statistics, the survival probability, and the mean exit time, are also derived. To corroborate in an independent way the results of the paper, Monte Carlo methods were used. These numerical estimations are in full agreement with the analytical predictions.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Radial dose function of a 90Sr 90Y seed in water and A150: Comment on Calibration and characterization of beta-particle sources for intravascular brachytherapy

Intravascular brachytherapy with beta sources has become a useful technique to prevent restenosis after cardiovascular intervention. In particular, the Beta-Cath high-dose-rate system, manufactured by Novoste Corporation, is a commercially available 90Sr 90Y source for intravascular brachytherapy that is achieving widespread use. Its dosimetric characterization has attracted considerable attention in recent years. Unfortunately, the short ranges of the emitted beta particles and the associated large dose gradients make experimental measurements particularly difficult. This circumstance has motivated the appearance of a number of papers addressing the characterization of this source by means of Monte Carlo simulation techniques.

Molecular dynamics simulation of the spherical electrical double layer of a soft nanoparticle. Effect of the surface and counterion valence

Molecular dynamics simulations were performed to study the ion and water distribution around a spherical charged nanoparticle. A soft nanoparticle model was designed using a set of hydrophobic interaction sites distributed in six concentric spherical layers. In order to simulate the effect of charged functionalyzed groups on the nanoparticle surface, a set of charged sites were distributed in the outer layer. Four charged nanoparticle models, from a surface charge value of −0.035 Cm−2 to − 0.28 Cm−2, were studied in NaCl and CaCl2 salt solutions at 1 M and 0.1 M concentrations to evaluate the effect of the surface charge, counterion valence, and concentration of added salt. We obtain that Na + and Ca2 + ions enter inside the soft nanoparticle. Monovalent ions are more accumulated inside the nanoparticle surface, whereas divalent ions are more accumulated just in the plane of the nanoparticle surface sites. The increasing of the the salt concentration has little effect on the internalization of counterions, but significantly reduces the number of water molecules that enter inside the nanoparticle. The manner of distributing the surface charge in the nanoparticle (uniformly over all surface sites or discretely over a limited set of randomly selected sites) considerably affects the distribution of counterions in the proximities of the nanoparticle surface.

Testing reciprocity in social interactions: A comparison between the Directional consistency and Skew symmetry statistics

In the present work we focus on two indices that quantify directionality and skew-symmetrical patterns in social interactions as measures of social reciprocity: the Directional consistency (DC) and Skew symmetry indices. Although both indices enable researchers to describe social groups, most studies require statistical inferential tests. The main aims of the present study are: firstly, to propose an overall statistical technique for testing null hypotheses regarding social reciprocity in behavioral studies, using the DC and Skew symmetry statistics (Φ) at group level; and secondly, to compare both statistics in order to allow researchers to choose the optimal measure depending on the conditions. In order to allow researchers to make statistical decisions, statistical significance for both statistics has been estimated by means of a Monte Carlo simulation. Furthermore, this study will enable researchers to choose the optimal observational conditions for carrying out their research, as the power of the statistical tests has been estimated.

The effect of skewness and kurtosis on the Kenward-Roger approximation when group distributions differ

This study examined the independent effect of skewness and kurtosis on the robustness of the linear mixed model (LMM), with the Kenward-Roger (KR) procedure, when group distributions are different, sample sizes are small, and sphericity cannot be assumed. Methods: A Monte Carlo simulation study considering a split-plot design involving three groups and four repeated measures was performed. Results: The results showed that when group distributions are different, the effect of skewness on KR robustness is greater than that of kurtosis for the corresponding values. Furthermore, the pairings of skewness and kurtosis with group size were found to be relevant variables when applying this procedure. Conclusions: With sample sizes of 45 and 60, KR is a suitable option for analyzing data when the distributions are: (a) mesokurtic and not highly or extremely skewed, and (b) symmetric with different degrees of kurtosis. With total sample sizes of 30, it is adequate when group sizes are equal and the distributions are: (a) mesokurtic and slightly or moderately skewed, and sphericity is assumed; and (b) symmetric with a moderate or high/extreme violation of kurtosis. Alternative analyses should be considered when the distributions are highly or extremely skewed and samples sizes are small.

Considerações radiodosimétricas da braquiterapia ocular com iodo-125 e rutênio/ródio-106

OBJETIVO: Analisar, por meio de um modelo computacional da região ocular, as características da distribuição da dose utilizando placas contendo iodo-125 e rutênio/ródio-106. MATERIAIS E MÉTODOS: Foi utilizado um modelo computacional de voxels da região ocular incluindo os diversos tecidos, com a placa posicionada sobre a esclera. O código Monte Carlo foi utilizado para simular a irradiação. A distribuição da dose é apresentada por curvas de isodoses. RESULTADOS: As simulações computacionais apresentam a distribuição da dose no interior do bulbo e nas estruturas externas. Os resultados permitem comparar a distribuição espacial das doses geradas por partículas beta e por fótons. As simulações mostram que a aplicação de sementes de iodo-125 implica alta dose no cristalino, enquanto o rutênio/ródio-106 produz alta dose na superfície da esclera. CONCLUSÃO: A dose no cristalino depende da espessura do tumor, da posição e do diâmetro da placa, e do radionuclídeo utilizado. No presente estudo, a fonte de rutênio/ródio-106 é recomendada para tumores de dimensões reduzidas. A irradiação com iodo-125 gera doses maiores no cristalino do que a irradiação com rutênio/ródio-106. O valor máximo de dose no cristalino corresponde a 12,75% do valor máximo de dose com iodo-125 e apenas 0,005% para rutênio/ródio-106.

Eye lens monitoring for interventional radiology personnel: dosemeters, calibration and practical aspects of H p (3) monitoring. A 2015 review.

A thorough literature review about the current situation on the implementation of eye lens monitoring has been performed in order to provide recommendations regarding dosemeter types, calibration procedures and practical aspects of eye lens monitoring for interventional radiology personnel. Most relevant data and recommendations from about 100 papers have been analysed and classified in the following topics: challenges of today in eye lens monitoring; conversion coefficients, phantoms and calibration procedures for eye lens dose evaluation; correction factors and dosemeters for eye lens dose measurements; dosemeter position and influence of protective devices. The major findings of the review can be summarised as follows: the recommended operational quantity for the eye lens monitoring is H p (3). At present, several dosemeters are available for eye lens monitoring and calibration procedures are being developed. However, in practice, very often, alternative methods are used to assess the dose to the eye lens. A summary of correction factors found in the literature for the assessment of the eye lens dose is provided. These factors can give an estimation of the eye lens dose when alternative methods, such as the use of a whole body dosemeter, are used. A wide range of values is found, thus indicating the large uncertainty associated with these simplified methods. Reduction factors from most common protective devices obtained experimentally and using Monte Carlo calculations are presented. The paper concludes that the use of a dosemeter placed at collar level outside the lead apron can provide a useful first estimate of the eye lens exposure. However, for workplaces with estimated annual equivalent dose to the eye lens close to the dose limit, specific eye lens monitoring should be performed. Finally, training of the involved medical staff on the risks of ionising radiation for the eye lens and on the correct use of protective systems is strongly recommended.

Simulação computacional de um feixe de fótons de 6 MV em diferentes meios heterogêneos utilizando o código PENELOPE

OBJETIVO: Utilizar o código PENELOPE e desenvolver geometrias onde estão presentes heterogeneidades para simular o comportamento do feixe de fótons nessas condições. MATERIAIS E MÉTODOS: Foram feitas simulações do comportamento da radiação ionizante para o caso homogêneo, apenas água, e para os casos heterogêneos, com diferentes materiais. Consideraram-se geometrias cúbicas para os fantomas e geometrias em forma de paralelepípedos para as heterogeneidades com a seguinte composição: tecido simulador de osso e pulmão, seguindo recomendações da International Commission on Radiological Protection, e titânio, alumínio e prata. Definiram-se, como parâmetros de entrada: a energia e o tipo de partícula da fonte, 6 MV de fótons; a distância fonte-superfície de 100 cm; e o campo de radiação de 10x 10 cm². RESULTADOS: Obtiveram-se curvas de percentual de dose em profundidade para todos os casos. Observou-se que em materiais com densidade eletrônica alta, como a prata, a dose absorvida é maior em relação à dose absorvida no fantoma homogêneo, enquanto no tecido simulador de pulmão a dose é menor. CONCLUSÃO: Os resultados obtidos demonstram a importância de se considerar heterogeneidades nos algoritmos dos sistemas de planejamento usados no cálculo da distribuição de dose nos pacientes, evitando-se sub ou superdosagem dos tecidos próximos às heterogeneidades.

Uncertainty evaluation in activity measurements using ionization chambers

Pressurized re-entrant (or 4 pi) ionization chambers (ICs) connected to current-measuring electronics are used for activity measurements of photon emitting radionuclides and some beta emitters in the fields of metrology and nuclear medicine. As a secondary method, these instruments need to be calibrated with appropriate activity standards from primary or direct standardization. The use of these instruments over 50 years has been well described in numerous publications, such as the Monographie BIPM-4 and the special issue of Metrologia on radionuclide metrology (Ratel 2007 Metrologia 44 S7-16, Schrader1997 Activity Measurements With Ionization Chambers (Monographie BIPM-4) Schrader 2007 Metrologia 44 S53-66, Cox et al 2007 Measurement Modelling of the International Reference System (SIR) for Gamma-Emitting Radionuclides (Monographie BIPM-7)). The present work describes the principles of activity measurements, calibrations, and impurity corrections using pressurized ionization chambers in the first part and the uncertainty analysis illustrated with example uncertainty budgets from routine source-calibration as well as from an international reference system (SIR) measurement in the second part.

Análise da distribuição espacial de dose absorvida em próton terapia ocular

OBJETIVO: Propõe-se avaliar os perfis de dose em profundidade e as distribuições espaciais de dose para protocolos de radioterapia ocular por prótons, a partir de simulações computacionais em código nuclear e modelo de olho discretizado em voxels. MATERIAIS E MÉTODOS: As ferramentas computacionais empregadas foram o código Geant4 (GEometry ANd Tracking) Toolkit e o SISCODES (Sistema Computacional para Dosimetria em Radioterapia). O Geant4 é um pacote de software livre, utilizado para simular a passagem de partículas nucleares com carga elétrica através da matéria, pelo método de Monte Carlo. Foram executadas simulações computacionais reprodutivas de radioterapia por próton baseada em instalações pré-existentes. RESULTADOS: Os dados das simulações foram integrados ao modelo de olho através do código SISCODES, para geração das distribuições espaciais de doses. Perfis de dose em profundidade reproduzindo o pico de Bragg puro e modulado são apresentados. Importantes aspectos do planejamento radioterápico com prótons são abordados, como material absorvedor, modulação, dimensões do colimador, energia incidente do próton e produção de isodoses. CONCLUSÃO: Conclui-se que a terapia por prótons, quando adequadamente modulada e direcionada, pode reproduzir condições ideais de deposição de dose em neoplasias oculares.