Mean intrinsic tensile properties of stone groundwood fibers from softwood

Stone groundwood (SGW) is a fibrous matter commonly prepared in a high yield process, and mainly used for papermaking applications. In this work, the use of SGW fibers is explored as reinforcing element of polypropylene (PP) composites. Due to its chemical and superficial features, the use of coupling agents is needed for a good adhesion and stress transfer across the fiber-matrix interface. The intrinsic strength of the reinforcement is a key parameter to predict the mechanical properties of the composite and to perform an interface analysis. The main objective of the present work was the determination of the intrinsic tensile strength of stone groundwood fibers. Coupled and non-coupled PP composites from stone groundwood fibers were prepared. The influence of the surface morphology and the quality at interface on the final properties of the composite was analyzed and compared to that of fiberglass PP composites. The intrinsic tensile properties of stone groundwood fibers, as well as the fiber orientation factor and the interfacial shear strength of the current composites were determined

Prediction of subcutaneous fatty acid composition from ham of CLA fed boars and gilts using a NIRS fiber optic probe

The aim of this work was the use of NIR technology by direct application of a fiber optic probe on back fat to analyze the fatty acid composition of CLA fed boars and gilts. 265 animals were fed 3 different diets and the fatty acid profile of back fat from Gluteus medius was analyzed using gas chromatography and FT-NIR. Spectra were acquired using a Bruker Optics Matrix-F duplex spectrometer equipped with a fiber optic probe (IN-268-2). Oleic and stearic fatty acids were predicted accurately; myristic, vaccenic and linoleic fatty acids were predicted with lower accuracy, while palmitic and α-linolenic fatty acids were poorly predicted. The relative percentage of fatty acids and NIR spectra showed differences in fatty acid composition of back fat from pigs fed CLA which increased the relative percentage of SFA and PUFA while MUFA decreased. Results suggest that a NIR fiber optic probe can be used to predict total saturated and unsaturated fatty acid composition, as well as the percentage of stearic and oleic. NIR showed potential as a rapid and easily implemented method to discriminate carcasses from animals fed different diets.

Analytic approximation of matrix functions in Lp

"Vegeu el resum a l'inici del document del fitxer adjunt."

Linear Aggregation In The Social Accounting Matrix Framework

In economic literature, information deficiencies and computational complexities have traditionally been solved through the aggregation of agents and institutions. In inputoutput modelling, researchers have been interested in the aggregation problem since the beginning of 1950s. Extending the conventional input-output aggregation approach to the social accounting matrix (SAM) models may help to identify the effects caused by the information problems and data deficiencies that usually appear in the SAM framework. This paper develops the theory of aggregation and applies it to the social accounting matrix model of multipliers. First, we define the concept of linear aggregation in a SAM database context. Second, we define the aggregated partitioned matrices of multipliers which are characteristic of the SAM approach. Third, we extend the analysis to other related concepts, such as aggregation bias and consistency in aggregation. Finally, we provide an illustrative example that shows the effects of aggregating a social accounting matrix model.

Elaboració de membranes de fibra buida modificades amb nanopartícules metàl·liques per aplicacions catalítiques

Investigación producida a partir de una estancia en la Université Paul Sabatier, Toulouse III - CNRS, entre 2007 y 2009. Durante los últimos años la investigación centrada en nuevos materiales de tamaño nanoscòpico (nanopartículas, quantum dots, nanotubos de carbono,...) ha experimentado un crecimiento considerable debido a las especiales propiedades de los "nanoobjetos" con respecto a magnetismo, catálisis, conductividad eléctrica, etc ... Sin embargo, hoy en día todavía existen pocas aplicaciones de las nanopartículas en temas medioambientales. Uno de los motivos de esta situación es la posible toxicidad de los nanoobjetos, pero existe también una dificultad tecnológica dado que las nanopartículas tienden a agregarse y es muy difícil manipularlas sin que pierdan sus propiedades especiales. Así, aunque la preparación de materiales catalíticos nanoestructurados es muy interesante, es necesario definir nuevas estrategias para prepararlos. Este proyecto de investigación tiene como objetivo principal la preparación de nuevas membranas catalíticas con nanopartículas metálicas en el interior para aplicaciones de tratamiento de agua. La innovación principal de este proyecto consiste en que las nanopartículas no son introducidas en la matriz polimérica una vez preformadas sino que se hacen crecer en el interior de la matriz polimérica mediante una síntesis intermatricial. El único requisito es que la matriz polimérica contenga grupos funcionales capaces de interaccionar con los precursores de las nanopartículas. Una vez finalizado el proyecto se puede afirmar que se han logrado parte de los objetivos planteados inicialmente. Concreamente ha quedado demostrado que se pueden sintetizar nanopartículas metálicas de metales nobles (platino y paladio) en membranas de fibra hueca de micro- y ultrafiltración siguiendo dos metodologías diferentes: modificación fotoquímica de polímeros y deposición de multicapas de polielectrolitos. Los nuevos materiales son efectivos en la catálisis de reducción de un compuesto modelo (4-nitrofenol con borohidruro de sodio) y, en general, los resultados han sido satisfactorios. Sin embargo, se ha puesto de manifiesto que el uso de un reactivo que genera hidrógeno gas en contacto con la solución acuosa dificulta enormemente la implementación de la reacción catalítica al ser el medio de la membrana una matriz porosa. Así, como conclusión principal se puede decir que se han encontrado las limitaciones de esta aproximación y se sugieren dos posibilidades de continuidad: la utilización de las membranas sintetizadas en contactores gas-líquido o bien el estudio y optimización del sistema de membrana en configuración de membranas planas, un objetivo más asequible dada su menor complejidad. Esta investigación se ha realizado en el seno del “Laboratoire de Génie Chimique” de Toulouse y del Departamento de Química de la Michigan State University y ha sido posible gracias a un proyecto financiado por la “Agence National pour la Recherce” y al programa PERMEANT entre el CNRS y la NSF.

A survey addressing the fundamental matrix estimation problem

Epipolar geometry is a key point in computer vision and the fundamental matrix estimation is the only way to compute it. This article surveys several methods of fundamental matrix estimation which have been classified into linear methods, iterative methods and robust methods. All of these methods have been programmed and their accuracy analysed using real images. A summary, accompanied with experimental results, is given

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Protein phosphatase inhibition assays for okadaic acid detection in shellfish: matrix effects, applicability and comparison with LC-MS/MS analysis

The applicability of the protein phosphatase inhibition assay (PPIA) to the determination of okadaic acid (OA) and its acyl derivatives in shellfish samples has been investigated, using a recombinant PP2A and a commercial one. Mediterranean mussel, wedge clam, Pacific oyster and flat oyster have been chosen as model species. Shellfish matrix loading limits for the PPIA have been established, according to the shellfish species and the enzyme source. A synergistic inhibitory effect has been observed in the presence of OA and shellfish matrix, which has been overcome by the application of a correction factor (0.48). Finally, Mediterranean mussel samples obtained from Rı´a de Arousa during a DSP closure associated to Dinophysis acuminata, determined as positive by the mouse bioassay, have been analysed with the PPIAs. The OA equivalent contents provided by the PPIAs correlate satisfactorily with those obtained by liquid chromatography–tandem mass spectrometry (LC–MS/MS).

Universality class of fiber bundles with strong heterogeneities

We study the effect of strong heterogeneities on the fracture of disordered materials using a fiber bundle model. The bundle is composed of two subsets of fibers, i.e. a fraction 0 ≤ α ≤ 1 of fibers is unbreakable, while the remaining 1 - α fraction is characterized by a distribution of breaking thresholds. Assuming global load sharing, we show analytically that there exists a critical fraction of the components αc which separates two qualitatively diferent regimes of the system: below αc the burst size distribution is a power law with the usual exponent Ƭ= 5/2, while above αc the exponent switches to a lower value Ƭ = 9/4 and a cutoff function occurs with a diverging characteristic size. Analyzing the macroscopic response of the system we demonstrate that the transition is conditioned to disorder distributions where the constitutive curve has a single maximum and an inflexion point defining a novel universality class of breakdown phenomena

Avalanche dynamics of fiber bundle models

We present a detailed analytical and numerical study of the avalanche distributions of the continuous damage fiber bundle model CDFBM . Linearly elastic fibers undergo a series of partial failure events which give rise to a gradual degradation of their stiffness. We show that the model reproduces a wide range of mechanical behaviors. We find that macroscopic hardening and plastic responses are characterized by avalanche distributions, which exhibit an algebraic decay with exponents between 5/2 and 2 different from those observed in mean-field fiber bundle models. We also derive analytically the phase diagram of a family of CDFBM which covers a large variety of potential avalanche size distributions. Our results provide a unified view of the statistics of breaking avalanches in fiber bundle models

Stone-ground wood pulp-reinforced polypropylene composites: water uptake and thermal properties

Two of the drawbacks of using natural-based composites in industrial applications are thermal instability and water uptake capacity. In this work, mechanical wood pulp was used to reinforce polypropylene at a level of 20 to 50 wt. %. Composites were mixed by means of a Brabender internal mixer for both non-coupled and coupled formulations. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to determine the thermal properties of the composites. The water uptake behavior was evaluated by immersion of the composites in water until an equilibrium state was reached. Results of water absorption tests revealed that the amount of water absorption was clearly dependent upon the fiber content. The coupled composites showed lower water absorption compared to the uncoupled composites. The incorporation of mechanical wood pulp into the polypropylene matrix produced a clear nucleating effect by increasing the crystallinity degree of the polymer and also increasing the temperature of polymer degradation. The maximum degradation temperature for stone ground wood pulp–reinforced composites was in the range of 330 to 345 ºC

Impairment of mossy fiber long-term potentiation and associative learning in pituitary adenylate cyclase activating polypeptide type I receptor-deficient Mice

The pituitary adenylate cyclase activating polypeptide (PACAP) type I receptor (PAC1) is a G-protein-coupled receptor binding the strongly conserved neuropeptide PACAP with 1000-fold higher affinity than the related peptide vasoactive intestinal peptide. PAC1-mediated signaling has been implicated in neuronal differentiation and synaptic plasticity. To gain further insight into the biological significance of PAC1-mediated signaling in vivo, we generated two different mutant mouse strains, harboring either a complete or a forebrain-specific inactivation of PAC1. Mutants from both strains show a deficit in contextual fear conditioning, a hippocampus-dependent associative learning paradigm. In sharp contrast, amygdala-dependent cued fear conditioning remains intact. Interestingly, no deficits in other hippocampus-dependent tasks modeling declarative learning such as the Morris water maze or the social transmission of food preference are observed. At the cellular level, the deficit in hippocampus-dependent associative learning is accompanied by an impairment of mossy fiber long-term potentiation (LTP). Because the hippocampal expression of PAC1 is restricted to mossy fiber terminals, we conclude that presynaptic PAC1-mediated signaling at the mossy fiber synapse is involved in both LTP and hippocampus-dependent associative learning.