Defining housing market areas using commuting and migration algorithms. Catalonia (Spain) as an applied case study

In the literature on housing market areas, different approaches can be found to defining them, for example, using travel-to-work areas and, more recently, making use of migration data. Here we propose a simple exercise to shed light on which approach performs better. Using regional data from Catalonia, Spain, we have computed housing market areas with both commuting data and migration data. In order to decide which procedure shows superior performance, we have looked at uniformity of prices within areas. The main finding is that commuting algorithms present more homogeneous areas in terms of housing prices.

The role of fast atom bombardment mass spectroscopy (FABMS) in cluster characterization

Fast atom bombardment mass spectroscopy has been used to study a large number of cationic phosphine-containing transition-metal-gold clusters, which ranged in mass from 1000 to 4000. Many of these clusters have been previously characterized and were examined in order to test the usefulness of the FABMS technique. Results showed that FABMS is excellent in giving the correct molecular formula and when combined with NMR, IR, and microanalysis gave a reliable characterization for cationic clusters¹. Recently FABMS has become one of the techniques employed as routine in cluster characterization2,3 and also is an effective tool for the structure analysis of large biomolecules4. Some results in the present work reinforce the importance of these data in the characterization of clusters in the absence of crystals with quality for X-ray analysis.

Cómo aplicar un cluster jerárquico en SPSS

L'anàlisi de conglomerats o cluster és una tècnica multivariant que busca agrupar elements o variables tractant d'aconseguir la màxima homogeneïtat en cada grup i la major diferència entre ells, mitjançant una estructura jerarquitzada per poder decidir quin nivell jeràrquic és el més apropiat per establir la classificació. El programa SPSS disposa de tres tipus d'anàlisi de conglomerats: l'anàlisi de conglomerats jeràrquic, bietàpic i de K mitjanes. Aplicarem el mètode jeràrquic com el més idoni per determinar el nombre òptim de conglomerats existent en les dades i el contingut dels mateixos per al nostre cas pràctic.

A Fair and Secure Cluster Formation Process for Ad Hoc Networks

An efficient approach for organizing large ad hoc networks is to divide the nodesinto multiple clusters and designate, for each cluster, a clusterhead which is responsible forholding intercluster control information. The role of a clusterhead entails rights and duties.On the one hand, it has a dominant position in front of the others because it manages theconnectivity and has access to other node¿s sensitive information. But on the other hand, theclusterhead role also has some associated costs. Hence, in order to prevent malicious nodesfrom taking control of the group in a fraudulent way and avoid selfish attacks from suitablenodes, the clusterhead needs to be elected in a secure way. In this paper we present a novelsolution that guarantees the clusterhead is elected in a cheat-proof manner.

Electrocatalytic oxidation of methanol by the [Ru3O(OAc)6(py)2(CH3OH)]3+cluster: improving the metal-ligand electron transfer by accessing the higher oxidation states of a multicentered system

The [Ru3O(Ac)6(py)2(CH3OH)]+ cluster provides an effective electrocatalytic species for the oxidation of methanol under mild conditions. This complex exhibits characteristic electrochemical waves at -1.02, 0.15 and 1.18 V, associated with the Ru3III,II,II/Ru3III,III,II/Ru 3III,III,III /Ru3IV,III,III successive redox couples, respectively. Above 1.7 V, formation of two RuIV centers enhances the 2-electron oxidation of the methanol ligand yielding formaldehyde, in agreement with the theoretical evolution of the HOMO levels as a function of the oxidation states. This work illustrates an important strategy to improve the efficiency of the oxidation catalysis, by using a multicentered redox catalyst and accessing its multiple higher oxidation states.

MCMC Analysis Of Classical Time Series Algorithms

Identification of order of an Autoregressive Moving Average Model (ARMA) by the usual graphical method is subjective. Hence, there is a need of developing a technique to identify the order without employing the graphical investigation of series autocorrelations. To avoid subjectivity, this thesis focuses on determining the order of the Autoregressive Moving Average Model using Reversible Jump Markov Chain Monte Carlo (RJMCMC). The RJMCMC selects the model from a set of the models suggested by better fitting, standard deviation errors and the frequency of accepted data. Together with deep analysis of the classical Box-Jenkins modeling methodology the integration with MCMC algorithms has been focused through parameter estimation and model fitting of ARMA models. This helps to verify how well the MCMC algorithms can treat the ARMA models, by comparing the results with graphical method. It has been seen that the MCMC produced better results than the classical time series approach.

CHEMOSTAT, UM SOFTWARE GRATUITO PARA ANÁLISE EXPLORATÓRIA DE DADOS MULTIVARIADOS

The objective of this work was to develop a free access exploratory data analysis software application for academic use that is easy to install and can be handled without user-level programming due to extensive use of chemometrics and its association with applications that require purchased licenses or routines. The developed software, called Chemostat, employs Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), intervals Principal Component Analysis (iPCA), as well as correction methods, data transformation and outlier detection. The data can be imported from the clipboard, text files, ASCII or FT-IR Perkin-Elmer “.sp” files. It generates a variety of charts and tables that allow the analysis of results that can be exported in several formats. The main features of the software were tested using midinfrared and near-infrared spectra in vegetable oils and digital images obtained from different types of commercial diesel. In order to validate the software results, the same sets of data were analyzed using Matlab© and the results in both applications matched in various combinations. In addition to the desktop version, the reuse of algorithms allowed an online version to be provided that offers a unique experience on the web. Both applications are available in English.

Cluster structure of atomic nuclei

Kirjallisuusarvostelu

Activation of supply relationships : a study of main providers in the Turku logistics cluster

Företag inom industri och handel väljer allt oftare att låta ett logistikföretag sköta stora delar av sina logistiska processer. Logistikföretagen i sin tur överlåter utförandet av enskilda tjänster, som t.ex. olika typer av transport, till olika samarbetspartners inom branschen. I avhandlingen studeras hur logistikföretag går till väga då de väljer vilka av deras samarbetspartners som ska engageras för att delta i utförandet av ett logistiktjänstepaket, en arbetsprocess som här kallas aktivering. Fokus ligger på aktiveringens innehåll och de faktorer som inverkar på hur den går till och vilka samarbetsparter som kommer att engageras. Arbetet bygger på nätverksansatsen för studier av företagsrelationer på industriella marknader. Aktiveringsprocessen uppfattas som en rätt ordinär, rutinmässig verksamhet i företaget, men den kan också förväntas inverka på hur företagets samarbetsnätverk utvecklas över tiden, genom att vissa relationer förstärks medan andra försvagas. I den empi riska undersökningen deltog 29 logistikföretag i Åboregionen som utgående från ett diskussionsunderlag fick berätta om hur de går till väga vid aktivering.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

Adaptation of "Niagara Rosada" grape must to winemaking by partial cluster dehydration

This study aimed to verify the influence of partial dehydration of "Niagara Rosada" grape clusters in physicochemical quality of the pre- fermentation must. In Brazil, during the winemaking process it is common to need to adjust the grape must when the physicochemical characteristics of the raw material are insufficient to produce wines in accordance with the Brazilian legislation for classification of beverages, which establishes the minimum alcohol content of 8.6 % for the beverage to be considered wine. Therefore, given that the reduction in the water content of grape berries allows the concentration of chemical compounds present in its composition, especially the concentration of total soluble solids, we proceeded with the treatments that were formed by the combination of two temperatures (T1-37.1ºC and T2-22.9 ºC) two air speeds (S1: 1.79 m s-1 and S2: 3.21 m s-1) and a control (T0) that has not gone through the dehydration treatment. Analysis of pH, Total Titratable Acidity (TTA) were performed in mEq L-1, Total Soluble Solids (TSS) in ºBrix, water content on a dry basis and Concentration of Phenolic Compounds (CPC) in mg of gallic acid per 100g of must. The average comparison test identified statistically significant modifications for the adaptation of must for winemaking purposes, having the treatment with 22.9 ºC and air speed of 1.79 m s-1 shown the largest increase in the concentration of total soluble solids, followed by the second best result for concentration of phenolic compounds.