Assessing the suitability of the minimum capture size and protection regimes in the gooseneck barnacle shellfishery

The suitability of a total-length-based, minimum capture-size and different protection regimes was investigated for the gooseneck barnacle Pollicipes pollicipes shellfishery in N Spain. For this analysis, individuals that were collected from 10 sites under different fishery protection regimes (permanently open, seasonally closed, and permanently closed) were used. First, we applied a non-parametric regression model to explore the relationship between the capitulum Rostro-Tergum (RT) size and the Total Length (TL). Important heteroskedastic disturbances were detected for this relationship, demon- strating a high variability of TL with respect to RT. This result substantiates the unsuitability of a TL-based minimum size by means of a mathematical model. Due to these disturbances, an alternative growth- based minimum capture size of 26.3 mm RT (23 mm RC) was estimated using the first derivative of a Kernel-based non-parametric regression model for the relationship between RT and dry weight. For this purpose, data from the permanently protected area were used to avoid bias due to the fishery. Second, the size-frequency distribution similarity was computed using a MDS analysis for the studied sites to evaluate the effectiveness of the protection regimes. The results of this analysis indicated a positive effect of the permanent protection, while the effect of the seasonal closure was not detected. This result needs to be interpreted with caution because the current harvesting based on a potentially unsuitable mini- mum capture size may dampen the efficacy of the seasonal protection regime.

Microbial production of potent phenolic-antioxidants through solid state fermentation

The agroindustrial residues including plant tissues rich in polyphenols were explored for microbial production of potent phenolics under solid state fermentation processes. The fungal strains capable of hydrolyzing tannin-rich materials were isolated from Mexican semidesert zones. These microorganisms have been employed to release potent phenolic antioxidants during the solid state fermentation of different materials (pomegranate peels, pecan nut shells, creosote bush and tar bush). This chapter includes the critical parameters for antioxidants production from selective microbes. Technical aspects of the microbial fermentation of antioxidants have also been discussed.

Search for very high energy Gamma radiation from the Starburst Galaxy IC 342

This thesis describes a search for very high energy (VHE) gamma-ray emission from the starburst galaxy IC 342. The analysis was based on data from the 2003 — 2004 observing season recorded using the Whipple 10-metre imaging atmospheric Cherenkov telescope located on Mount Hopkins in southern Arizona. IC 342 may be classed as a non-blazar type galaxy and to date only a few such galaxies (M 87, Cen A, M 82 and NGC 253) have been detected as VHE gamma-ray sources. Analysis of approximately 24 hours of good quality IC 342 data, consisting entirely of ON/OFF observations, was carried out using a number of methods (standard Supercuts, optimised Supercuts, scaled optimised Supercuts and the multivariate kernel analysis technique). No evidence for TeV gamma-ray emission from IC 342 was found. The significance was 0.6 a with a nominal rate of 0.04 ± 0.06 gamma rays per minute. The flux upper limit above 600 GeV (at 99.9 % confidence) was determined to be 5.5 x 10-8 m-2 s-1, corresponding to 8 % of the Crab Nebula flux in the same energy range.

Sparse nonlinear discriminants

Kernel-Functions, Machine Learning, Least Squares, Speech Recognition, Classification, Regression

Discriminative classifiers for speaker recognition

Speaker Recognition, Speaker Verification, Sparse Kernel Logistic Regression, Support Vector Machine

User-friendly parallel computations with econometric examples

This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.

Parametric and semiparametric estimation of sample selection models: an empirical application to the female labour force in Portugal

This comment corrects the errors in the estimation process that appear in Martins (2001). The first error is in the parametric probit estimation, as the previously presented results do not maximize the log-likelihood function. In the global maximum more variables become significant. As for the semiparametric estimation method, the kernel function used in Martins (2001) can take on both positive and negative values, which implies that the participation probability estimates may be outside the interval [0,1]. We have solved the problem by applying local smoothing in the kernel estimation, as suggested by Klein and Spady (1993).

Cat(0) and Cat (-1) dimensions of torsion free

We show that a particular free-by-cyclic group has CAT(0) dimension equal to 2, but CAT(-1) dimension equal to 3. We also classify the minimal proper 2-dimensional CAT(0) actions of this group; they correspond, up to scaling, to a 1-parameter family of locally CAT(0) piecewise Euclidean metrics on a fixed presentation complex for the group. This information is used to produce an infinite family of 2-dimensional hyperbolic groups, which do not act properly by isometries on any proper CAT(0) metric space of dimension 2. This family includes a free-by-cyclic group with free kernel of rank 6.

Generating trees for permutations avoiding generalized patterns

We construct generating trees with with one, two, and three labels for some classes of permutations avoiding generalized patterns of length 3 and 4. These trees are built by adding at each level an entry to the right end of the permutation, which allows us to incorporate the adjacency condition about some entries in an occurrence of a generalized pattern. We use these trees to find functional equations for the generating functions enumerating these classes of permutations with respect to different parameters. In several cases we solve them using the kernel method and some ideas of Bousquet-Mélou [2]. We obtain refinements of known enumerative results and find new ones.

Convergence of the mass-transport steepest descent scheme for the sub-critical Patlak-Keller-Segel model

Variational steepest descent approximation schemes for the modified Patlak-Keller-Segel equation with a logarithmic interaction kernel in any dimension are considered. We prove the convergence of the suitably interpolated in time implicit Euler scheme, defined in terms of the Euclidean Wasserstein distance, associated to this equation for sub-critical masses. As a consequence, we recover the recent result about the global in time existence of weak-solutions to the modified Patlak-Keller-Segel equation for the logarithmic interaction kernel in any dimension in the sub-critical case. Moreover, we show how this method performs numerically in one dimension. In this particular case, this numerical scheme corresponds to a standard implicit Euler method for the pseudo-inverse of the cumulative distribution function. We demonstrate its capabilities to reproduce easily without the need of mesh-refinement the blow-up of solutions for super-critical masses.

Labor productivity in Spain: 1977-2002

This study examines the evolution of labor productivity across Spanish regions during the period from 1977 to 2002. By applying the kernel technique, we estimate the effects of the Transition process on labor productivity and its main sources. We find that Spanish regions experienced a major convergence process in labor productivity and in human capital in the 1977-1993 period. We also pinpoint the existence of a transition co-movement between labor productivity and human capital. Conversely, the dynamics of investment in physical capital seem unrelated to the transition dynamics of labor productivity. The lack of co-evolution can be addressed as one of the causes of the current slowdown in productivity. Classification-JEL: J24, N34, N940, O18, O52, R10

Estimation of dynamic latent variable models using simulated nonparametric moments

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

Invariants de matrius Hadamard en MAGMA

L'objectiu d'aquest projecte ha estat generalitzar i integrar la funcionalitat de dos projectes anteriors que ampliaven el tractament que oferia el Magma respecte a les matrius de Hadamard. Hem implementat funcions genèriques que permeten construir noves matrius Hadamard de qualsevol mida per a cada rang i dimensió de nucli, i així ampliar la seva base de dades. També hem optimitzat la funció que calcula el nucli, i hem desenvolupat funcions que calculen la invariant Symmetric Hamming Distance Enumerator (SH-DE) proposada per Kai-Tai Fang i Gennian Gei que és més sensible per a la detecció de la no equivalència de les matrius Hadamard.

Determinació de diferents compostos bioactius en ingredients naturals per a l'elaboració de bombons funcionals amb beneficis cardiovasculars

Durant les darreres dècades, i degut, principalment, a un canvi en els hàbits alimentaris, hi ha hagut un augment a nivell mundial de malalties cròniques (l’obesitat, malalties cardiovasculars, etc.). En els països mediterranis hi ha menys incidència d’aquestes malalties i sembla ser que això es deu a l’anomenada dieta mediterrània. La dieta mediterrània es caracteritza per una combinació d’oli d’oliva com a grassa principal, verdures, hortalisses i fruites en abundància, lleguminoses, fruits secs, formatges i iogurt, peix, pa, pasta, cereals i els seus derivats i un consum moderat de vi i carns. Aquest model alimentari, ric en tocoferols, fitosterols i fitoestanols que ajuden a reduir el contingut de colesterol en sang, fa que en les poblacions mediterrànies hi hagi menys incidència de malalties cardiovasculars. Aquests compostos inhibeixen el deteriorament oxidatiu dels olis, actuen com agent antipolimerització per olis de fregir. Tenen capacitat de reduir els nivells de colesterol, evitant la incidència de malalties cardiovasculars. Els fitoesterols y fitoestanols es poden trobar en forma lliure o esterificada amb àcids grassos, àcids fenòlics i glucosa. Els objectius d’ aquest treball han estat, primer en el desenvolupament de mètodes d'anàlisi ràpids, fiables i robusts dels tocoferols, fitoesterols i fitoestanols i la seva aplicació en fruits sec, oli de segó, oli de pinyol de raïm i productes que els continguin. El primer mètode va estar basat en la cromatografía líquida (HPLC-DAD) amb extracció en fase sòlida (SPE) com tècnica alternativa a la saponificació para la determinació de fitoesterols lliures. Aquest mètode va estar aplicada a mostres de bombons que contenia fitoesterols. El segon mètode va estar basat en la cromatografia de gasos (GCFID) amb aponificació i SPE per quantificar fitoesterols i fitoestanols lliures, esterificats i totals. En els documents annexos es descriuen a profunditat els mètodes desenvolupats.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.