Consumer acceptance of high pressure processed beef-based chilled ready meals: The mediating role of food-related lifestyle factors

The aim of this research was to investigate the effects of high pressure processing (HPP) on consumer acceptance for chilled ready meals manufactured using a low-value beef cut. Three hundred consumers evaluated chilled ready meals subjected to 4 pressure treatments and a non-treated control monadically on a 9-point scale for liking for beef tenderness and juiciness, overall flavour, overall liking, and purchase intent. Data were also collected on consumers' food consumption patterns, their attitudes towards food by means of the reduced food-related lifestyle (FRL) instrument, and socio-demographics. The results indicated that a pressure treatment of 200 MPa was acceptable to most consumers. K-means cluster analysis identified 4 consumer groups with similar preferences, and the optimal pressure treatments acceptable to specific consumer groups were identified for those firms that would wish to target attitudinally differentiated consumer segments

Environmental factors, spatial variation, and specific requirements of Chironomidae in Mediterranean reference streams

Chironomidae spatial distribution was investigated at 63 near-pristine sites in 22 catchments of the Iberian Mediterranean coast. We used partial redundancy analysis to study Chironomidae community responses to a number of environmental factors acting at several spatial scales. The percentage of variation explained by local factors (23.3%) was higher than that explained by geographical (8.5%) or regional factors(8%). Catchment area, longitude, pH, % siliceous rocks in the catchment, and altitude were the best predictors of Chironomidae assemblages. We used a k-means cluster analysis to classified sites into 3 major groups based on Chironomidae assemblages. These groups were explained mainly by longitudinal zonation and geographical position, and were defined as 1) siliceous headwater streams, 2) mid-altitude streams with small catchment areas, and 3) medium-sized calcareous streams. Distinct species assemblages with associated indicator taxa were established for each stream category using IndVal analysis. Species responses to previously identified key environmental variables were determined, and optima and tolerances were established by weighted average regression. Distinct ecological requirements were observed among genera and among species of the same genus. Some genera were restricted to headwater systems (e.g., Diamesa), whereas others (e.g., Eukiefferiella) had wider ecological preferences but with distinct distributions among congenerics. In the present period of climate change, optima and tolerances of species might be a useful tool to predict responses of different species to changes in significant environmental variables, such as temperature and hydrology.

Bioinformática: interfaz web para estudiar el efecto de diferentes condiciones sobre la expresión de los genes

El trabajo realizado se divide en dos bloques bien diferenciados, ambos relacionados con el análisis de microarrays. El primer bloque consiste en agrupar las condiciones muestrales de todos los genes en grupos o clústers. Estas agrupaciones se obtienen al aplicar directamente sobre la microarray los siguientes algoritmos de agrupación: SOM,PAM,SOTA,HC y al aplicar sobre la microarray escalada con PC y MDS los siguientes algoritmos: SOM,PAM,SOTA,HC y K-MEANS. El segundo bloque consiste en realizar una búsqueda de genes basada en los intervalos de confianza de cada clúster de la agrupación activa. Las condiciones de búsqueda ajustadas por el usuario se validan para cada clúster respecto el valor basal 0 y respecto el resto de clústers, para estas validaciones se usan los intervalos de confianza. Estos dos bloques se integran en una aplicación web ya existente, el applet PCOPGene, alojada en el servidor: http://revolutionresearch.uab.es.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Clustering of grape yield maps to delineate site-specific management zones

Zonal management in vineyards requires the prior delineation of stable yield zones within the parcel. Among the different methodologies used for zone delineation, cluster analysis of yield data from several years is one of the possibilities cited in scientific literature. However, there exist reasonable doubts concerning the cluster algorithm to be used and the number of zones that have to be delineated within a field. In this paper two different cluster algorithms have been compared (k-means and fuzzy c-means) using the grape yield data corresponding to three successive years (2002, 2003 and 2004), for a ‘Pinot Noir’ vineyard parcel. Final choice of the most recommendable algorithm has been linked to obtaining a stable pattern of spatial yield distribution and to allowing for the delineation of compact and average sized areas. The general recommendation is to use reclassified maps of two clusters or yield classes (low yield zone and high yield zone) and, consequently, the site-specific vineyard management should be based on the prior delineation of just two different zones or sub-parcels. The two tested algorithms are good options for this purpose. However, the fuzzy c-means algorithm allows for a better zoning of the parcel, forming more compact areas and with more equilibrated zonal differences over time.

Mejora diagnóstica de hepatopatías de afectación difusa mediante técnicas de inteligencia artificial

The automatic diagnostic discrimination is an application of artificial intelligence techniques that can solve clinical cases based on imaging. Diffuse liver diseases are diseases of wide prominence in the population and insidious course, yet early in its progression. Early and effective diagnosis is necessary because many of these diseases progress to cirrhosis and liver cancer. The usual technique of choice for accurate diagnosis is liver biopsy, an invasive and not without incompatibilities one. It is proposed in this project an alternative non-invasive and free of contraindications method based on liver ultrasonography. The images are digitized and then analyzed using statistical techniques and analysis of texture. The results are validated from the pathology report. Finally, we apply artificial intelligence techniques as Fuzzy k-Means or Support Vector Machines and compare its significance to the analysis Statistics and the report of the clinician. The results show that this technique is significantly valid and a promising alternative as a noninvasive diagnostic chronic liver disease from diffuse involvement. Artificial Intelligence classifying techniques significantly improve the diagnosing discrimination compared to other statistics.

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Searching patterns in painting images with computer vision techniques

This paper presents a pattern recognition method focused on paintings images. The purpose is construct a system able to recognize authors or art styles based on common elements of his work (here called patterns). The method is based on comparing images that contain the same or similar patterns. It uses different computer vision techniques, like SIFT and SURF, to describe the patterns in descriptors, K-Means to classify and simplify these descriptors, and RANSAC to determine and detect good results. The method are good to find patterns of known images but not so good if they are not.

In conspectu prope totius urbis: an application of different visual methods at the ager Tarraconensis landscape

We present in this paper the results of the application of several visual methods on a group of locations, dated between VI and I centuries BC, of the ager Tarraconensis (Tarragona, Spain) a Hinterland of the roman colony of Tarraco. The difficulty in interpreting the diverse results in a combined way has been resolved by means of the use of statistical methods, such as Principal Components Analysis (PCA) and K-means clustering analysis. These methods have allowed us to carry out site classifications in function of the landscape's visual structure that contains them and of the visual relationships that could be given among them.

Estudi per a una interfície d'ajuda a daltònics en la interpretació de mapes de metro

En aquest projecte fem un estudi de diferents mètodes per a la segmentació i extracció de línies de mapes de metro com a suport per a daltònics. Hem aplicat dos mètodes amb intervenció de l’usuari i cinc mètodes automàtics on fem servir K-means per a la segmentació de color i Hough per a l’extracció de línies. Dels mètodes amb intervenció obtenim millors resultats amb un mètode d’assignació aproximada del color, i entre els autoàatics tenim com a millor una solució ad-hoc sense paràmetres aplicada sobre l’espai RGB. D’acord amb els resultats experimentals, aquests mètodes ens permeten fer una bona segmentació i extracció de les línies de metro.

ParallelKnoppix - Rapid deployment of a linux cluster for MPI parallel processing using non-dedicated computers

This note describes ParallelKnoppix, a bootable CD that allows creation of a Linux cluster in very little time. An experienced user can create a cluster ready to execute MPI programs in less than 10 minutes. The computers used may be heterogeneous machines, of the IA-32 architecture. When the cluster is shut down, all machines except one are in their original state, and the last can be returned to its original state by deleting a directory. The system thus provides a means of using non-dedicated computers to create a cluster. An example session is documented.

Ab initio absorption spectrum of NiO combining molecular dynamics with the embedded cluster approach in a discrete reaction field

We developed a procedure that combines three complementary computational methodologies to improve the theoretical description of the electronic structure of nickel oxide. The starting point is a Car-Parrinello molecular dynamics simulation to incorporate vibrorotational degrees of freedom into the material model. By means ofcomplete active space self-consistent field second-order perturbation theory (CASPT2) calculations on embedded clusters extracted from the resulting trajectory, we describe localized spectroscopic phenomena on NiO with an efficient treatment of electron correlation. The inclusion of thermal motion into the theoretical description allowsus to study electronic transitions that, otherwise, would be dipole forbidden in the ideal structure and results in a natural reproduction of the band broadening. Moreover, we improved the embedded cluster model by incorporating self-consistently at the complete active space self-consistent field (CASSCF) level a discrete (or direct) reaction field (DRF) in the cluster surroundings. The DRF approach offers an efficient treatment ofelectric response effects of the crystalline embedding to the electronic transitions localized in the cluster. We offer accurate theoretical estimates of the absorption spectrum and the density of states around the Fermi level of NiO, and a comprehensive explanation of the source of the broadening and the relaxation of the charge transferstates due to the adaptation of the environment

Ab initio cluster-model study of the on-top chemisorption of F and Cl on Si(111) and Ge(111) surfaces

The interaction of atomic F and Cl with Si4H9 and Ge4H9 cluster models has been studied by using ab initio pseudopotentials and basis sets of increasing complexity. The results show that the effect of d orbitals is important in order to reproduce the experimental findings. However, the use of polarization functions in the atoms which are directly involved in the chemisorption bond leads to results which are very close to those obtained using extended basis sets. The local nature of the chemisorption bond is also interpreted by means of a Mulliken population analysis. For F-Si4H9 and Cl-Si4H9 the present results are in good agreement with previous ab initio all-electron calculations, and for the chemisorption of Cl on Si(111) and Ge(111) surfaces, good agreement is found with respect to the available experimental results as well as with previous slab calculations based on the local-density-functional formalism.

Evidence for oxygen-island formation on Al(111): Cluster-model theory and x-ray photoelectron spectroscopy

The O 1s x-ray photoelectron spectroscopy spectrum for Al(111)/O at 300 K shows two components whose behavior as a function of time and variation of detection angle are consistent with either (a) a surface species represented by the higher binding-energy (BE) component and a subsurface species represented by the lower BE component, or (b) small close-packed oxygen islands with the interior atoms represented by the lower BE component and the perimeter atoms by the higher BE component. We have modeled both situations using ab initio Hartree-Fock wave functions for clusters of Al and O atoms. For an O atom in a threefold site, it was found that a below-surface position gave a higher O 1s BE than an above-surface position, incompatible with interpretation (a). This change in the O 1s BE could arise because the bond for O to Al may have a more covalent character when the O is below the surface than when it is above the surface. We present evidence consistent with this view. An O adatom island with all the O atoms in threefold sites gives calculated O 1s BE's which are significantly higher for the perimeter O atoms. Further, the results for an isolated O island without the Al substrate present also give higher BE¿s for the perimeter atoms. Both these results are consistent with interpretation (b). Published scanning-tunneling-microscopy data supports the suggestion that the chemisorbed state consists of small, close-packed islands, whereas the presence of two vibrational modes in high-resolution electron-energy-loss spectroscopy data has been interpreted as representing surface and subsurface oxygen atoms. In light of the present results, we suggest that a vibrational interpretation in terms of interior and perimeter adatoms should be considered.