Compendio della vita del Ven ... fratello Antonio Alonso Bermejo ... del Terz' Ordine del... Padre San Francisco ... /

Mode of access: Internet.

Vida, virtudes, dones sobrenaturales y milagros de el venerable siervo de Dios el hermano Antonio Alonso Bermejo, natural de la villa de la Nava del Rey y fundador en ella de el Hospital de curacion de el arcangel San Miguel ... /

Mode of access: Internet.

[¿Pueden la sensibilidad y la movilidad servir por si solos de caractéres distintivos entre el reino animal y el vejetal (sic)?] : discurso leído ante el claustro de la Universidad Central, por el Licenciado en la Facultad de Medicina y Cirujía D. Antonio Alonso Cortés en el acto solemne de recibir la investidura de Doctor en la espresada Facultad.

Tesis Universidad Central, 1866.

Identificación de habitats potenciales, efectivos y conectividad para la fauna silvestre, Nandaime, Nicaragua

El presente estudio surge a partir de monitoreos previos en la zona en los cuales se determinaron muy pocas especies principalmente de Mamíferos, así que se propuso conocer si los hábitats en el parche tiene la capacidad para albergar suficientes especies y si el parche se encontraba aislado de otros para que se diera un flujo de animales desde otras partes del paisaje hasta el sitio de interés. Se identificaron los hábitats efectivos y potenciales y la existencia de conexión de ésta área con otra área natural (estudio de paisaje) que sirva de corredor a la fauna silvestre en la comunidad La Chipopa, Nandaime. Los tipos de hábitats se seleccionaron según criterios florísticos: formas de vida predominantes, composición y densidad de especies. Para el estudio de conectividad y fragmentación se realizó un análisis con imágenes de satélite para determinar las clases de cobertura de la zona y se utilizaron como firmas espectrales las clases de cobertura vegetal 2011 del Ministerio de Agropecuario y Forestal (MAGFOR), la herramienta V_LATE 1.1 y la herramienta Corridor Designer . Se determinaron siete tipos de hábitat. El hábitat de mayor tamaño fue el Área de árboles con poca lianas (AAPL) (3.25 ha) la cual representa el 76 % del área total. En registro bibliográfico se determinaron 270 especies en el paisaje, de éstas el 22 % no encontrarían hábitat dentro del área. El hábitat efectivo el cual alberga más especies (12 ) fue AAPL. Dentro de los hábitats potenciales se encuentran: AAP L (25 ) y Área de árboles con mucha infección por lianas (AAML) (12 ), sin embargo, se determinaron que 67 especies (32 %) prefieren cualquier hábitat como potencial dentro del mosaico. El análisis de estructura del paisaje indica que el área se divide en 17 clases de cobertura, de éstas, las áreas de tacotal cubren el 48% del total mientras que las áreas de cultivos y pastos cubren en 34%. Dentro de la matriz del paisaje el uso que se encuentra más fragmentado es el área de bosque cerrado ya que la distancia mínima que una especie debería de recorrer para encontrar esa clase es de 4,184 m, las clases de cobertura menos fragmentadas fueron las áreas agrícolas y pastos. Existe una relación entre los índices de diversidad de formas , dimensión fractal y proximidad en relación a los usos cultivos, pasto y bosque latifoliado cerrado. Dentro del paisaje se crean dos tipos de corredores uno se localiza al noroeste y otro al sureste, a pesar de formarse corredores el paisaje se encuentra muy fragmentado para permitir la conectividad de Nandarola con otros parches cercanos.

Using network mesures to test evolved NK-landscapes

In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.

Data analysis advances in marine science for fisheries management : supervised classification application

169 p. : il. col.

A review of travel time estimation and forecasting for advanced traveler information systems

Providing on line travel time information to commuters has become an important issue for Advanced Traveler Information Systems and Route Guidance Systems in the past years, due to the increasing traffic volume and congestion in the road networks. Travel time is one of the most useful traffic variables because it is more intuitive than other traffic variables such as flow, occupancy or density, and is useful for travelers in decision making. The aim of this paper is to present a global view of the literature on the modeling of travel time, introducing crucial concepts and giving a thorough classification of the existing tech- niques. Most of the attention will focus on travel time estimation and travel time prediction, which are generally not presented together. The main goals of these models, the study areas and methodologies used to carry out these tasks will be further explored and categorized.

Clasificadores supervisados para predecir la abstinencia a 12 meses en fumadores tratados con vareniclina

Tesis leida dentro del Master de "Ingeniería Computacional y Sistemas Inteligentes"

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions.

The Linear Ordering Problem Revisited

The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this problem. This paper studies a procedure to extract static information from an instance of the problem, and proposes a method to incorporate the obtained knowledge in order to improve the performance of local search-based algorithms. The procedure introduced identifies the positions where the indexes cannot generate local optima for the insert neighbourhood, and thus global optima solutions. This information is then used to propose a restricted insert neighbourhood that discards the insert operations which move indexes to positions where optimal solutions are not generated. In order to measure the efficiency of the proposed restricted insert neighbourhood system, two state-of-the-art algorithms for the LOP that include local search procedures have been modified. Conducted experiments confirm that the restricted versions of the algorithms outperform the classical designs systematically. The statistical test included in the experimentation reports significant differences in all the cases, which validates the efficiency of our proposal.

Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance

[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and learning (estimating) such distributions when the metric on permutations is the Cayley distance. We propose new methods for both operations, whose performance is shown through several experiments. We also introduce novel procedures to count and randomly generate permutations at a given Cayley distance both with and without certain structural restrictions. An application in the field of biology is given to motivate the interest of this model.

Sampling and learning the Mallows and Weighted Mallows models under the Hamming distance

[EN]In this paper we deal with distributions over permutation spaces. The Mallows model is the mode l in use. The associated distance for permutations is the Hamming distance.

Sampling and learning the Mallows model under the Ulam distance

[EN]In this paper we deal with probability distributions over permutation spaces. The Probability model in use is the Mallows model. The distance for permutations that the model uses in the Ulam distance.

An R package for permutations, Mallows and Generalized Mallows models

[EN]Probability models on permutations associate a probability value to each of the permutations on n items. This paper considers two popular probability models, the Mallows model and the Generalized Mallows model. We describe methods for making inference, sampling and learning such distributions, some of which are novel in the literature. This paper also describes operations for permutations, with special attention in those related with the Kendall and Cayley distances and the random generation of permutations. These operations are of key importance for the efficient computation of the operations on distributions. These algorithms are implemented in the associated R package. Moreover, the internal code is written in C++.

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.