Effects of transformation processes in 'jubraka' agroforestry systems of the Nuba Mountains, Sudan, on nutrient fluxes

Intensification processes in homegardens of the Nuba Mountains, Sudan, raise concerns about strongly positive carbon (C) and nutrient balances which are expected to lead to substantial element losses from these agroecosystems, in particular via soil gaseous emissions. Therefore, this thesis aimed at the quantification of C, nitrogen (N), phosphorus (P) and potassium (K) input and output fluxes with a special focus on soil gaseous losses, and the calculation of respective element balances. A further focus in this thesis was rainfall, a valuable resource for rain-fed agriculture in the Nuba Mountains. To minimize negative consequences of the high variability of rainfall, risk reducing mechanisms were developed by rain-fed farmers that may lose their efficacy in the course of climate change effects predicted for East Africa. Therefore, the second objective of this study was to examine possible changes in rainfall amounts during the last 60 years and to provide reliable risk and probability statements of rainfall-induced events of agricultural importance to rain-fed farmers in the Nuba Mountains. Soil gaseous emissions of C (in form of CO2) and N (in form of NH3 and N2O) of two traditional and two intensified homegardens were determined with a portable dynamic closed chamber system. For C gaseous emission rates reached their peak at the onset of the rainy season (2,325 g CO2-C ha-1 h-1 in an intensified garden type) and for N during the rainy season (16 g NH3-N ha-1 h-1 and 11.3 g N2O-N ha-1 h-1, in a traditional garden type). Data indicated cumulative annual emissions of 5,893 kg CO2-C ha-1, 37 kg NH3-N ha-1, and 16 kg N2O-N ha-1. For the assessment of the long-term productivity of the two types of homegardens and the identification of pathways of substantial element losses, a C and nutrient budget approach was used. In three traditional and three intensified homegardens observation plots were selected. The following variables were quantified on each plot between June and December in 2010: soil amendments, irrigation, biomass removal, symbiotic N2 fixation, C fixation by photosynthesis, atmospheric wet and dry deposition, leaching and soil gaseous emissions. Annual balances for C and nutrients amounted to -21 kg C ha-1, -70 kg N ha-1, 9 kg P ha-1 and -117 kg K ha-1 in intensified homegardens and to -1,722 kg C ha-1, -167 kg N ha-1, -9 kg P ha-1 and -74 kg K ha-1 in traditional homegardens. For the analysis of rainfall data, the INSTAT+ software allowed to aggregate long-term daily rainfall records from the Kadugli and Rashad weather stations into daily, monthly and annual intervals and to calculate rainfall-induced events of agricultural importance. Subsequently, these calculated values and events were checked for possible monotonic trends by Mann-Kendall tests. Over the period from 1970 to 2009, annual rainfall did not change significantly for either station. However, during this period an increase of low rainfall events coinciding with a decline in the number of medium daily rainfall events was observed in Rashad. Furthermore, the availability of daily rainfall data enabled frequency and conditional probability calculations that showed either no statistically significant changes or trends resulting only in minor changes of probabilities.

Maximum Entropy Discrimination

We present a general framework for discriminative estimation based on the maximum entropy principle and its extensions. All calculations involve distributions over structures and/or parameters rather than specific settings and reduce to relative entropy projections. This holds even when the data is not separable within the chosen parametric class, in the context of anomaly detection rather than classification, or when the labels in the training set are uncertain or incomplete. Support vector machines are naturally subsumed under this class and we provide several extensions. We are also able to estimate exactly and efficiently discriminative distributions over tree structures of class-conditional models within this framework. Preliminary experimental results are indicative of the potential in these techniques.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

Possible solution of some essential zero problems in compositional data analysis

One of the tantalising remaining problems in compositional data analysis lies in how to deal with data sets in which there are components which are essential zeros. By an essential zero we mean a component which is truly zero, not something recorded as zero simply because the experimental design or the measuring instrument has not been sufficiently sensitive to detect a trace of the part. Such essential zeros occur in many compositional situations, such as household budget patterns, time budgets, palaeontological zonation studies, ecological abundance studies. Devices such as nonzero replacement and amalgamation are almost invariably ad hoc and unsuccessful in such situations. From consideration of such examples it seems sensible to build up a model in two stages, the first determining where the zeros will occur and the second how the unit available is distributed among the non-zero parts. In this paper we suggest two such models, an independent binomial conditional logistic normal model and a hierarchical dependent binomial conditional logistic normal model. The compositional data in such modelling consist of an incidence matrix and a conditional compositional matrix. Interesting statistical problems arise, such as the question of estimability of parameters, the nature of the computational process for the estimation of both the incidence and compositional parameters caused by the complexity of the subcompositional structure, the formation of meaningful hypotheses, and the devising of suitable testing methodology within a lattice of such essential zero-compositional hypotheses. The methodology is illustrated by application to both simulated and real compositional data

Modelling structural zeros in compositional data

This analysis was stimulated by the real data analysis problem of household expenditure data. The full dataset contains expenditure data for a sample of 1224 households. The expenditure is broken down at 2 hierarchical levels: 9 major levels (e.g. housing, food, utilities etc.) and 92 minor levels. There are also 5 factors and 5 covariates at the household level. Not surprisingly, there are a small number of zeros at the major level, but many zeros at the minor level. The question is how best to model the zeros. Clearly, models that try to add a small amount to the zero terms are not appropriate in general as at least some of the zeros are clearly structural, e.g. alcohol/tobacco for households that are teetotal. The key question then is how to build suitable conditional models. For example, is the sub-composition of spending excluding alcohol/tobacco similar for teetotal and non-teetotal households? In other words, we are looking for sub-compositional independence. Also, what determines whether a household is teetotal? Can we assume that it is independent of the composition? In general, whether teetotal will clearly depend on the household level variables, so we need to be able to model this dependence. The other tricky question is that with zeros on more than one component, we need to be able to model dependence and independence of zeros on the different components. Lastly, while some zeros are structural, others may not be, for example, for expenditure on durables, it may be chance as to whether a particular household spends money on durables within the sample period. This would clearly be distinguishable if we had longitudinal data, but may still be distinguishable by looking at the distribution, on the assumption that random zeros will usually be for situations where any non-zero expenditure is not small. While this analysis is based on around economic data, the ideas carry over to many other situations, including geological data, where minerals may be missing for structural reasons (similar to alcohol), or missing because they occur only in random regions which may be missed in a sample (similar to the durables)

Discrete and continuous compositions

This paper examines a dataset which is modeled well by the Poisson-Log Normal process and by this process mixed with Log Normal data, which are both turned into compositions. This generates compositional data that has zeros without any need for conditional models or assuming that there is missing or censored data that needs adjustment. It also enables us to model dependence on covariates and within the composition

A new distribution on the simplex containing the Dirichlet family

The Dirichlet family owes its privileged status within simplex distributions to easyness of interpretation and good mathematical properties. In particular, we recall fundamental properties for the analysis of compositional data such as closure under amalgamation and subcomposition. From a probabilistic point of view, it is characterised (uniquely) by a variety of independence relationships which makes it indisputably the reference model for expressing the non trivial idea of substantial independence for compositions. Indeed, its well known inadequacy as a general model for compositional data stems from such an independence structure together with the poorness of its parametrisation. In this paper a new class of distributions (called Flexible Dirichlet) capable of handling various dependence structures and containing the Dirichlet as a special case is presented. The new model exhibits a considerably richer parametrisation which, for example, allows to model the means and (part of) the variance-covariance matrix separately. Moreover, such a model preserves some good mathematical properties of the Dirichlet, i.e. closure under amalgamation and subcomposition with new parameters simply related to the parent composition parameters. Furthermore, the joint and conditional distributions of subcompositions and relative totals can be expressed as simple mixtures of two Flexible Dirichlet distributions. The basis generating the Flexible Dirichlet, though keeping compositional invariance, shows a dependence structure which allows various forms of partitional dependence to be contemplated by the model (e.g. non-neutrality, subcompositional dependence and subcompositional non-invariance), independence cases being identified by suitable parameter configurations. In particular, within this model substantial independence among subsets of components of the composition naturally occurs when the subsets have a Dirichlet distribution

La cooperación estratégica de Centros Comerciales

El presente trabajo desarrolla el concepto de cooperación empresarial aplicado al sector económico de Centros Comerciales. Se parte del supuesto que tanto los seres vivos como las organizaciones sociales son esencialmente cooperantes. Se confirma tal apreciación por los postulados de la biología y las ciencias económicas a través de los conceptos del apoyo mutuo, la sociobiología, las teorías de los costos de transacción y la agencia, entre otras. Así mismo, se aborda las diferentes escuelas de estrategia, su relación con la gestión organizacional y sus implicaciones y alcances con la cooperación interfirmas. Seguidamente se analizan los diferentes tipos de cooperación, las principales variables a tener en cuenta en la selección de las formas cooperativas. Posteriormente se hace un análisis a la luz del caso Colombiano de las diferentes experiencias colaborativas de la mediana y gran empresa. En la parte final del proyecto se presenta un diagnóstico del sector de Centro Comerciales donde se analizan sus principales indicadores tales como truput, tráfico, inversión de mercadeo entre otros. Así mismo, se presentan los resultados de una investigación aplicada a los gerentes de los Centros Comerciales para medir el grado de acogida que puede tener una iniciativa colaborativa. Todo esto con el propósito de formular un modelo de colaboración estratégica que se aplicara al sector, en su análisis se presentan sus factores condicionantes, su estructura organizativa, sus ejes estratégicos y su agenda de prioridades.

Uso de modelos econométricos en aseguramiento de portafolios

El aseguramiento de portafolio trae consigo unos costos de transacción asociados que son reconocidos por la teoría financiera pero que no han sido objeto de estudio de muchas aproximaciones empíricas. Mediante modelos econométricos de series de tiempo se puede pronosticar el número de rebalanceos necesarios para mantener un portafolio asegurado, así como el tiempo que debe transcurrir entre cada uno de estos. Para tal fin se usan modelos de Datos de Cuenta de Poisson Autorregresivos (ACP) modificados para captar las características de la serie y modelos de Duración Autorregresivos (ACD). Los modelos capturan la autocorrelación de las series y pronostican adecuadamente el costo de transacción asociado a los rebalanceos.

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Exam 99 pdf

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Factores asociados a infección por Acinetobacter baumannii en una Unidad de Cuidados Intensivos en Bogotá D.C.

INTRODUCCIÓN: Acinetobacter baumannii es un cocobacilo gram negativo, oportunista, de baja virulencia. En los últimos años, se ha convertido en responsable del aumento de la incidencia de infecciones en las Unidades de Cuidado Intensivo (UCI), que se caracterizan por multiresistencia a antibióticos de amplio espectro. METODOLOGÍA: Estudio de Casos y Controles Pareado, razón 1:4, en tres cohortes de brotes por A. baumannii 2006-2010 de un Hospital Universitario. Como medida de asociación se calculó el Odd Ratio con una confiabilidad del 95%, utilizando regresión logística condicional. RESULTADOS: Se identificaron 3 brotes en el periodo 2006-2010, de los cuales se obtuvo una muestra de 14 casos y 56 controles. En el análisis multivariado se encontró asociación estadísticamente significativa entre la infección/colonización por A. baumannii y el presentar algún estado de inmunosupresión (OR=15.45; IC95%=1.12-212.44) y el tener catéter venoso central en un tiempo superior a diez días (OR=13.74; IC95%=1.25-151.44). No se encontró asociación estadísticamente significativa entre infección/colonización y mortalidad. De 14 casos, 13 presentaron aislamientos de multiresistentes, 9 son de origen respiratorio, 2 hemocultivos y 3 de origen abdominal. La mortalidad en los casos no está asociada a procesos de inmunosupresión, bacteremias e infecciones/colonizaciones respiratorias. CONCLUSIONES: La infección/colonización por A. baumannii se asoció a estado de inmunosupresión del paciente y el tener catéter venoso central por más de 10 días, que se correlaciona con la intervención invasiva, frecuente en las Unidades de Cuidados Intensivos. No fue posible establecer diferenciación clara entre infección y colonización, y su asociación con la mortalidad de los pacientes.

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Análisis de las críticas sobre asistencialismo estatal en la lucha contra la pobreza durante la presidencia de Álvaro Uribe Vélez (2002 - 2010), a través del Programa Familias en Acción

Durante el gobierno de Álvaro Uribe Vélez hubo un gran crecimiento económico en Colombia; sin embargo, al final de su mandato había más de 20 millones de personas en condición de pobreza, es decir casi la mitad de su población. Como los problemas que trata de resolver la política pública no tienen soluciones absolutas, cada gobierno debe apostarle a diferentes soluciones. Dado los buenos resultados en las primeras evaluaciones del programa de transferencias condicionadas Familias en Acción, el gobierno de Uribe aumentó su cobertura y número de beneficiados. Sin embargo, dicho fortalecimiento fue criticado por diferentes personalidades públicas del país por considerarlo asistencialista, clientelista, proclive al uso electoral, entre otros. La presente disertación, analiza dichas críticas y las posibles razones que llevaron al gobierno de Uribe a fortalecer y ampliar el programa. Al analizar una política pública, lo importante no es alabar o desvirtuar sus alcances, sino resaltar los aportes positivos y sus debilidades para continuar con lo que está dando resultados y tomar las medidas pertinentes respecto a los nuevos problemas que se presentan. Dadas las dimensiones de la pobreza en Colombia, este tema debe ser una prioridad de todos los ciudadanos; su solución no puede quedar en manos del gobierno o del mercado, sino que debe haber un debate serio al respecto.

Exercise sheet 6 pdf

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