Radiation enhanced diffusion of B in crystalline Ge

In this work we propose an alternative methodology to study B diffusion in crystalline Ge. We enhance B diffusion by means of passing implants in such a way to increase the point-defects distribution through the sample, well above the equilibrium value. A comparison between B diffusion occurring under implantation with different ions or after post-implantation annealing allowed to discern any possible role of ionization effects on B diffusion. Indeed, B diffusion is demonstrated to occur through a point-defect-mediated mechanism. The diffusion mechanism is hence discussed. These results are a key point for a full comprehension of the B diffusion in Ge.     

Carlos Albizu-Miranda (1920-1984): Obituary.

Carlos Albizu-Miranda died in Houston, Texas on October 6, 1984, several weeks after heart surgery. Albizu-Miranda was one of the early and continuing leaders of Puerto Rican psychology, and his death was a significant loss to Puerto Rican and American psychology. Puerto Rican psychology, as well as all of psychology, was enriched by his work and life. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

EWR(R)无头轧制和工字轮式卷取机组

在加热炉之后对钢坯进行焊接(EWR（R）)的无头轧制工艺和棒卷生产用的工字轮式卷取机组，能够消除钢坯间隔时间、降低废品率、减少轧机停机时间、消除冷床上的短捧材、减少切头切尾、消耗件的寿命长、易于车间管理和“定制”盘卷质量。工字轮式卷取机组能够以无扭方法将热轧圆钢、方钢和扁钢卷成高度紧凑的／大质量卷材，以及在不进行中间离线操作的情况下，直接提供给下游冷加工生产线。     

Chemical changes in nitrogenous compounds during fermentation of sour doughs and bread doughs

Summary The evolution of sour doughs (SD), prepared using a wheat bran extract as the starting material, and their respective bread doughs (BD) has been investigated by determining the chemical changes which take place in 0.85 mol/L NaCl-soluble nitrogen compounds during fermentation. The quantitative changes determined are: total nitrogen content (TN), primary amine nitrogen (AN) and-amine nitrogen of free amino acids (AAN) in the total soluble fraction (SN), protein (PSN) and non-protein (NPSN) subfractions. During fermentation,-amine nitrogen of the free amino acids (AAN_SN) and peptides (AN_NPSN-AAN_SN), increase in SD and decrease in BD. Changes are larger and faster in peptide nitrogen and even larger during the third and fifth steps. The total protein content (TN_PSN) decreases in both SD and BD. Results are discussed in relation to the quality of the bread obtained in each case.

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Chemische Veranderungen der Stickstoffverbindungen aus Sauerteigen und den damit hergestellten Brotteigen

Zusammenfassung Es wird die Entwicklung von Sauerteigen mit Weizenkleiextrakt als Starter und ihren entsprechenden Brotteigen durch Bestimmung der chemischen Veranderungen der in 0,85 mol/L NaCl1 slichen Stickstoffverbindungen wahrend der Gärung untersucht. Die quantitativen Veränderungen des Gesamt-Stickstoffgehalts (TN), des primären Aminostickstoffs (AN) und des -Aminostickstoffs der freien Aminosauren (AAN) in der loslichen Fraktion (SN) und in den Proteinen (PSN) und Nicht-Proteinen (NPNS) der Subfraktionen. Wahrend der Gärung steigt der Anteil des-Aminostickstoffs in den freien Aminosäuren (AAN_SN) und Peptiden (AN_NPSN-AAN_SN) in den Sauerteigen (SD) an und vermindert sich in den Brotteigen (BD). Die Veränderungen sind hoher und schneller beim Peptidstickstoff und größer wahrend der dritten und funften Stufe sowohl beim Sauer- als auch beim Brotteig; in beiden nimmt der Proteingehalt (TN_PSN) ab. Die Ergebnisse werden bezogen auf die Qualität der hergestellten Brote besprochen.

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Paper presented at the 7th World Congress of Food Science and Technology, Singapore (Singapore), 28 September – 2 October 1987     

A memetic algorithm for evolutionary prototype selection: A scaling up approach

Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. Evolutionary algorithms have been used recently for prototype selection showing good results. However, due to the complexity of this problem when the size of the databases increases, the behaviour of evolutionary algorithms could deteriorate considerably because of a lack of convergence. This additional problem is known as the scaling up problem.

Memetic algorithms are approaches for heuristic searches in optimization problems that combine a population-based algorithm with a local search. In this paper, we propose a model of memetic algorithm that incorporates an ad hoc local search specifically designed for optimizing the properties of prototype selection problem with the aim of tackling the scaling up problem. In order to check its performance, we have carried out an empirical study including a comparison between our proposal and previous evolutionary and non-evolutionary approaches studied in the literature.

The results have been contrasted with the use of non-parametric statistical procedures and show that our approach outperforms previously studied methods, especially when the database scales up.     

User acceptance model of open source software

The development and implementation of open source software (OSS) is one of the most current topics within the academic, business and political environments. Traditionally, research in OSS has focused on identifying individual personal motives for participating in the development of an OSS project, analyzing specific OSS solutions, or the OSS movement, itself. Nevertheless, user acceptance towards this type of technology has received very little attention. For this reason, the main purpose of the current study is to identify the variables and factors that have a direct effect on individual attitude towards OSS adoption. Therefore, we have developed a technological acceptance model on behalf of the users towards a solution based on OSS. For this development, we have considered the technology acceptance model. Findings show that OSS is a viable solution for information management for organizations.     

Symbolic signatures for deformable shapes

Recognizing classes of objects from their shape is an unsolved problem in machine vision that entails the ability of a computer system to represent and generalize complex geometrical information on the basis of a finite amount of prior data. A practical approach to this problem is particularly difficult to implement, not only because the shape variability of relevant object classes is generally large, but also because standard sensing devices used to capture the real world only provide a partial view of a scene, so there is partial information pertaining to the objects of interest. In this work, we develop an algorithmic framework for recognizing classes of deformable shapes from range data. The basic idea of our component-based approach is to generalize existing surface representations that have proven effective in recognizing specific 3D objects to the problem of object classes using our newly introduced symbolic-signature representation that is robust to deformations, as opposed to a numeric representation that is often tied to a specific shape. Based on this approach, we present a system that is capable of recognizing and classifying a variety of object shape classes from range data. We demonstrate our system in a series of large-scale experiments that were motivated by specific applications in scene analysis and medical diagnosis.     

Toward an optimal ensemble of kernel-based approximations with engineering applications

This paper presents a general approach toward the optimal selection and ensemble (weighted average) of kernel-based approximations to address the issue of model selection. That is, depending on the problem under consideration and loss function, a particular modeling scheme may outperform the others, and, in general, it is not known a priori which one should be selected. The surrogates for the ensemble are chosen based on their performance, favoring non-dominated models, while the weights are adaptive and inversely proportional to estimates of the local prediction variance of the individual surrogates. Using both well-known analytical test functions and, in the surrogate-based modeling of a field scale alkali-surfactant-polymer enhanced oil recovery process, the ensemble of surrogates, in general, outperformed the best individual surrogate and provided among the best predictions throughout the domains of interest. This work was supported in part by the Fondo Nacional de Ciencia, Tecnología e Innovación (FONACIT), Venezuela under Grant F-2005000210. N. Q. Author also acknowledges that this material is based upon work supported by National Science Foundation under Grant DDM-423280.     

On the choice of the best imputation methods for missing values considering three groups of classification methods

In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on a classification task with twenty-three classification methods and fourteen different imputation approaches to missing values treatment that are presented and analyzed. The analysis involves a group-based approach, in which we distinguish between three different categories of classification methods. Each category behaves differently, and the evidence obtained shows that the use of determined missing values imputation methods could improve the accuracy obtained for these methods. In this study, the convenience of using imputation methods for preprocessing data sets with missing values is stated. The analysis suggests that the use of particular imputation methods conditioned to the groups is required.