Spin labile conducting metallopolymers : a new architecture for hybrid multifunctional materials

The synthesis of 3-ethynylthienyl- (2.07), 3-ethynylterthienyl- (2.19) substituted qsal [qsalH = N-(8-quinolyl)salicylaldimine] and 3,3' -diethynyl-2,2' -bithienyl bridging bisqsal (5.06) ligands are described along with the preparation and characterization of eight cationic iron(III) complexes containing these ligands with a selection of counteranions [(2.07) with: SCN- (2.08), PF6- (2.09), and CI04- (2.10); (2.19) with PF6 - (2.20); (5.06) with: cr (5.07), SeN- (5.08), PF6- (5.09), and CI04- (5.10)]. Spin-crossover is observed in the solid state for (2.08) - (2.10) and (5.07) - (5.10), including a ve ry rare S = 5/2 to 3/2 spin-crossover in complex (2.09). The unusal reduction of complex (2.10) produces a high-spin iron(I1) complex (2.12). Six iron(II) complexes that are derived from thienyl analogues of bispicen [bispicen = bis(2-pyridylmethyl)-diamine] [2,5-thienyl substituents = H- (3.11), Phenyl- (3.12), 2- thienyl (3.13) or N-phenyl-2-pyridinalimine ligands [2,5-phenyl substituents = diphenyl (3.23), di(2-thienyl) (3.24), 4-phenyl substituent = 3-thienyl (3.25)] are reported Complexes (3.11), (3.23) and (3.25) display thermal spin-crossover in the solid state and (3.12) remains high-spin at all temperatures. Complex (3.13) rearranges to form an iron(II) complex (3.14) with temperature dependent magnetic properties be s t described as a one-dimensional ferromagnetic chain, with interchain antiferromagnetic interactions and/or ZFS dominant at low temperatures. Magnetic succeptibility and Mossbauer data for complex (3.24) display a temperature dependent mixture of spin isomers. The preparation and characterization of two cobalt(II) complexes containing 3- ethynylthienyl- (4.04) and 3-ethynylterhienyl- (4.06) substituted bipyridine ligands [(4.05): [Co(dbsqh(4.04)]; (4.07): [Co(dbsq)2(4.06)]] [dbsq = 3,5-dbsq=3,5-di-tert-butylI ,2-semiquinonate] are reported. Complexes (4.05) and (4.07) exhibit thermal valence tautomerism in the solid state and in solution. Self assembly of complex (2.10) into polymeric spheres (6.11) afforded the first spincrossover, polydisperse, micro- to nanoscale material of its kind. . Complexes (2.20), (3.24) and (4.07) also form polymers through electrochemical synthesis to produce hybrid metaUopolymer films (6.12), (6.15) and (6.16), respectively. The films have been characterized by EDX, FT-IR and UV-Vis spectroscopy. Variable-temperature magnetic susceptibility measurements demonstrate that spin lability is operative in the polymers and conductivity measurements confirm the electron transport properties. Polymer (6.15) has a persistent oxidized state that shows a significant decrease in electrical resistance.

Dynamic Performance Evaluation of Canadian Fixed-Income Funds using Markov Regime Switching Models

This thesis examines the performance of Canadian fixed-income mutual funds in the context of an unobservable market factor that affects mutual fund returns. We use various selection and timing models augmented with univariate and multivariate regime-switching structures. These models assume a joint distribution of an unobservable latent variable and fund returns. The fund sample comprises six Canadian value-weighted portfolios with different investing objectives from 1980 to 2011. These are the Canadian fixed-income funds, the Canadian inflation protected fixed-income funds, the Canadian long-term fixed-income funds, the Canadian money market funds, the Canadian short-term fixed-income funds and the high yield fixed-income funds. We find strong evidence that more than one state variable is necessary to explain the dynamics of the returns on Canadian fixed-income funds. For instance, Canadian fixed-income funds clearly show that there are two regimes that can be identified with a turning point during the mid-eighties. This structural break corresponds to an increase in the Canadian bond index from its low values in the early 1980s to its current high values. Other fixed-income funds results show latent state variables that mimic the behaviour of the general economic activity. Generally, we report that Canadian bond fund alphas are negative. In other words, fund managers do not add value through their selection abilities. We find evidence that Canadian fixed-income fund portfolio managers are successful market timers who shift portfolio weights between risky and riskless financial assets according to expected market conditions. Conversely, Canadian inflation protected funds, Canadian long-term fixed-income funds and Canadian money market funds have no market timing ability. We conclude that these managers generally do not have positive performance by actively managing their portfolios. We also report that the Canadian fixed-income fund portfolios perform asymmetrically under different economic regimes. In particular, these portfolio managers demonstrate poorer selection skills during recessions. Finally, we demonstrate that the multivariate regime-switching model is superior to univariate models given the dynamic market conditions and the correlation between fund portfolios.

The Order of Canada Investiture Ceremony Program (Selection of Pages)

A selection of pages from the program for the Order of Canada Investiture Ceremony in 2003 when Dorothy Wetherald Rungeling was a recipient.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

La vivienda como variable dependiente de un eje industrial en formación hacia nuevas ciudades [por] Cecilia Andrade Cedeño

Tesis (Maestría en Ciencias para la Planificación de Asentamientos Humanos) U.A.N.L.

El costo variable de la atención odontológica : institución docente-asistencial Monterrey, Nuevo León [por] Hortencia Rodríguez Avella

Tesis (Maestría en Salud Pública con Esp. en Odontología Social) U.A.N.L.

Fatiga de metales con cargas de amplitud variable por el método de Strain-Life [por] Javier de Jesús García Villarreal

Tesis (Maestría en Ciencias) U.A.N.L.

Administración de la compensación fija y variable por Francisco Javier Escobar Ramos

Tesis (Maestría en Ciencias de la Administración con Especialidad en Relaciones Industriales) U.A.N.L.

Aplicación del control estadístico a la variable crítica temperatura en una empresa acerera.

Tesis (Master en Administración y de Negocios con Especialidad en Producción y Calidad) UANL, 2009.

Latent Variable Models for Stochastic Discount Factors.

Latent variable models in finance originate both from asset pricing theory and time series analysis. These two strands of literature appeal to two different concepts of latent structures, which are both useful to reduce the dimension of a statistical model specified for a multivariate time series of asset prices. In the CAPM or APT beta pricing models, the dimension reduction is cross-sectional in nature, while in time-series state-space models, dimension is reduced longitudinally by assuming conditional independence between consecutive returns, given a small number of state variables. In this paper, we use the concept of Stochastic Discount Factor (SDF) or pricing kernel as a unifying principle to integrate these two concepts of latent variables. Beta pricing relations amount to characterize the factors as a basis of a vectorial space for the SDF. The coefficients of the SDF with respect to the factors are specified as deterministic functions of some state variables which summarize their dynamics. In beta pricing models, it is often said that only the factorial risk is compensated since the remaining idiosyncratic risk is diversifiable. Implicitly, this argument can be interpreted as a conditional cross-sectional factor structure, that is, a conditional independence between contemporaneous returns of a large number of assets, given a small number of factors, like in standard Factor Analysis. We provide this unifying analysis in the context of conditional equilibrium beta pricing as well as asset pricing with stochastic volatility, stochastic interest rates and other state variables. We address the general issue of econometric specifications of dynamic asset pricing models, which cover the modern literature on conditionally heteroskedastic factor models as well as equilibrium-based asset pricing models with an intertemporal specification of preferences and market fundamentals. We interpret various instantaneous causality relationships between state variables and market fundamentals as leverage effects and discuss their central role relative to the validity of standard CAPM-like stock pricing and preference-free option pricing.

An Eigenfunction Approach for Volatility Modeling.

In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming that the loading function is ad hoc (e.g., exponential or affine), we assume that it is a linear combination of the eigenfunctions of the conditional expectation (resp. infinitesimal generator) operator associated to the state variable in discrete (resp. continuous) time. Special examples are the popular log-normal and square-root models where the eigenfunctions are the Hermite and Laguerre polynomials respectively. The eigenfunction approach has at least six advantages: i) it is general since any square integrable function may be written as a linear combination of the eigenfunctions; ii) the orthogonality of the eigenfunctions leads to the traditional interpretations of the linear principal components analysis; iii) the implied dynamics of the variance and squared return processes are ARMA and, hence, simple for forecasting and inference purposes; (iv) more importantly, this generates fat tails for the variance and returns processes; v) in contrast to popular models, the variance of the variance is a flexible function of the variance; vi) these models are closed under temporal aggregation.

Dealing with Moral Hazard and Adverse Selection Simultaneously

Although Insurers Face Adverse Selection and Moral Hazard When They Set Insurance Contracts, These Two Types of Asymmetrical Information Have Been Given Separate Treatments Sofar in the Economic Literature. This Paper Is a First Attempt to Integrate Both Problems Into a Single Model. We Show How It Is Possible to Use Time in Order to Achieve a First-Best Allocation of Risks When Both Problems Are Present Simultaneously.