Color Dependence on Thickness in Topaz Crystal from Brazil

It is well known that crystals of topaz from the Eastern Brazilian Pegmatite Province may turn blue by the irradiation with Co-60 gamma rays followed by heat treatment. Also, it is known that the sensation of color changes with the thickness of these crystals. The dependence of the color, given by 1931 CIE chromaticity coordinates, with the thickness of the crystal was analyzed. The absorbance used in the calculation of these coordinates was given by the sum of Gaussian lines. The parameters of these lines were determined through the decomposition of the optical absorption spectra in the ultraviolet and visible regions. The decomposition revealed several lines, whose assignment was made considering studies in spodumene and beryl crystals and highly accurate quantum mechanical calculations. The transmittance becomes very narrow with increasing thickness, and the CIE chromaticity coordinates converge to the borderline of the CIE Chromaticity Diagram at the wavelength of maximum transmittance. Furthermore, the purity of color increases with increasing thickness, and the dominant wavelength reaches the wavelength of maximum transmittance.

Educational levels and the functional dependence of ischemic stroke survivors

We evaluated the functional dependence of stroke survivors from the Study of Stroke Mortality and Morbidity, using the Rankin Scale. Out of 355 ischemic stroke survivors (with a mean age of 67.9 years), 40% had some functional dependence at 28 days and 34.4% had some functional dependence at 6 months. Most predictors of physical dependence were identified at 28 days. These predictors were: low levels of education [illiterate vs. >= 8 years of education, multivariate odds ratio (OR) = 3.7; 95% confidence interval (95%CI): 1.60-8.54] and anatomical stroke location (total anterior circulation infarct, OR = 16.9; 95%CI: 2.93-97.49). Low levels of education and ischemic brain injury influenced functional dependence in these stroke survivors. Our findings reinforce the necessity of developing strategies for the rehabilitation of stroke patients, more especially in formulating specific strategies for care and treatment of stroke survivors with low socioeconomic status.

Dependence of brittle-to-ductile transition on crystallographic direction in diamond turning of single-crystal silicon

The objective of this paper is to show the dependence relationship between the crystallographic orientations upon brittle-to-ductile transition during diamond turning of monocrystalline silicon. Cutting tests were performed using a -5 degrees rake angle round nose diamond tool at different machining scales. At the micrometre level, the feedrate was kept constant at 2.5 micrometres per revolution (mu m/r), and the depth of cut was varied from 1 to 5 mu m. At the submicrometre level, the depth of cut was kept constant at 500 nm and the feedrate varied from 5 to 10 mu m/r. At the micrometre level, the uncut shoulder generated with an interrupted cutting test procedure provided a quantitative measurement of the ductile-to-brittle transition. Results show that the critical chip thickness in silicon for ductile material removal reaches a maximum of 285 nm in the [100] direction and a minimum of 115 nm in the [110] direction, when the depth of cut was 5 mu m. It was found that when a submicrometre depth of cut was applied, microcracks were revealed in the [110] direction, which is the softer direction in silicon. Micro Raman spectroscopy was used to estimate surface residual stress after machining. Compressive residual stress in the range 142 MPa and smooth damage free surface finish was probed in the [100] direction for a depth of cut of 5 mu m, whereas residual stresses in the range 350 MPa and brittle damage was probed in the [110] direction for a depth of cut of 500 nm.

An analytic method to compute the stress dependence on the dimensions and its influence in the characteristics of triple gate devices

Triple-gate devices are considered a promising solution for sub-20 nm era. Strain engineering has also been recognized as an alternative due to the increase in the carriers mobility it propitiates. The simulation of strained devices has the major drawback of the stress non-uniformity, which cannot be easily considered in a device TCAD simulation without the coupled process simulation that is time consuming and cumbersome task. However, it is mandatory to have accurate device simulation, with good correlation with experimental results of strained devices, allowing for in-depth physical insight as well as prediction on the stress impact on the device electrical characteristics. This work proposes the use of an analytic function, based on the literature, to describe accurately the strain dependence on both channel length and fin width in order to simulate adequately strained triple-gate devices. The maximum transconductance and the threshold voltage are used as the key parameters to compare simulated and experimental data. The results show the agreement of the proposed analytic function with the experimental results. Also, an analysis on the threshold voltage variation is carried out, showing that the stress affects the dependence of the threshold voltage on the temperature. (C) 2011 Elsevier Ltd. All rights reserved.

The Dependence of Retention Time on Gate Length in UTBOX FBRAM With Different Source/Drain Junction Engineering

The floating-body-RAM sense margin and retention-time dependence on the gate length is investigated in UTBOX devices using BJT programming combined with a positive back bias (so-called V th feedback). It is shown that the sense margin and the retention time can be kept constant versus the gate length by using a positive back bias. Nevertheless, below a critical L, there is no room for optimization, and the memory performances suddenly drop. The mechanism behind this degradation is attributed to GIDL current amplification by the lateral bipolar transistor with a narrow base. The gate length can be further scaled using underlap junctions.

Dependence of Generation-Recombination Noise With Gate Voltage in FD SOI MOSFETs

A model for computing the generation-recombination noise due to traps within the semiconductor film of fully depleted silicon-on-insulator MOSFET transistors is presented. Dependence of the corner frequency of the Lorentzian spectra on the gate voltage is addressed in this paper, which is different to the constant behavior expected for bulk transistors. The shift in the corner frequency makes the characterization process easier. It helps to identify the energy position, capture cross sections, and densities of the traps. This characterization task is carried out considering noise measurements of two different candidate structures for single-transistor dynamic random access memory devices.

On the Origin of the Trigger-Angle Dependence of the Ridge Structure

The origin of the trigger-angle dependence of the ridge structure in two-hadron long-range correlations, as observed at RHIC, is discussed as due to an interplay between the elliptic flow caused by the initial state global geometry and flow produced by fluctuations.

Energy and system-size dependence of two- and four-particle nu(2) measurements in heavy-ion collisions at root S-NN=62.4 and 200 GeV and their implications on flow fluctuations and nonflow

We present STAR measurements of azimuthal anisotropy by means of the two- and four-particle cumulants nu(2) (nu(2){2} and nu(2){4}) for Au + Au and Cu + Cu collisions at center-of-mass energies root S-NN = 62.4 and 200 GeV. The difference between nu(2){2}(2) and nu(2){4}(2) is related to nu(2) fluctuations (sigma(nu 2)) and nonflow (delta(2)). We present an upper limit to sigma(nu 2)/nu 2. Following the assumption that eccentricity fluctuations sigma(epsilon) dominate nu(2) fluctuations nu(2)/sigma nu(2) approximate to epsilon/sigma epsilon we deduce the nonflow implied for several models of eccentricity fluctuations that would be required for consistency with nu(2){2} and nu(2){4}. We also present results on the ratio of nu(2) to eccentricity.

Polynomial and Poisson dependence in free Poisson algebras and free Poisson fields

We prove that any two Poisson dependent elements in a free Poisson algebra and a free Poisson field of characteristic zero are algebraically dependent, thus answering positively a question from Makar-Limanov and Umirbaev (2007) [8]. We apply this result to give a new proof of the tameness of automorphisms for free Poisson algebras of rank two (see Makar-Limanov and Umirbaev (2011) [9], Makar-Limanov et al. (2009) [10]). (C) 2011 Elsevier Inc. All rights reserved.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

Measurement and model calculation of the temperature dependence of ligand-to-metal energy transfer rates in lanthanide complexes

Temperature dependent transient curves of excited levels of a model Eu3+ complex have been measured for the first time. A coincidence between the temperature dependent rise time of the 5D0 emitting level and decay time of the 5D1 excited level in the [Eu(tta)3(H2O)2] complex has been found, which unambiguously proves the T1→5D1→5D0 sensitization pathway. A theoretical approach for the temperature dependent energy transfer rates has been successfully applied to the rationalization of the experimental data.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.

A partial dependence factorial analysis to deal with selection bias in observational studis

This thesis presents a creative and practical approach to dealing with the problem of selection bias. Selection bias may be the most important vexing problem in program evaluation or in any line of research that attempts to assert causality. Some of the greatest minds in economics and statistics have scrutinized the problem of selection bias, with the resulting approaches – Rubin’s Potential Outcome Approach(Rosenbaum and Rubin,1983; Rubin, 1991,2001,2004) or Heckman’s Selection model (Heckman, 1979) – being widely accepted and used as the best fixes. These solutions to the bias that arises in particular from self selection are imperfect, and many researchers, when feasible, reserve their strongest causal inference for data from experimental rather than observational studies. The innovative aspect of this thesis is to propose a data transformation that allows measuring and testing in an automatic and multivariate way the presence of selection bias. The approach involves the construction of a multi-dimensional conditional space of the X matrix in which the bias associated with the treatment assignment has been eliminated. Specifically, we propose the use of a partial dependence analysis of the X-space as a tool for investigating the dependence relationship between a set of observable pre-treatment categorical covariates X and a treatment indicator variable T, in order to obtain a measure of bias according to their dependence structure. The measure of selection bias is then expressed in terms of inertia due to the dependence between X and T that has been eliminated. Given the measure of selection bias, we propose a multivariate test of imbalance in order to check if the detected bias is significant, by using the asymptotical distribution of inertia due to T (Estadella et al. 2005) , and by preserving the multivariate nature of data. Further, we propose the use of a clustering procedure as a tool to find groups of comparable units on which estimate local causal effects, and the use of the multivariate test of imbalance as a stopping rule in choosing the best cluster solution set. The method is non parametric, it does not call for modeling the data, based on some underlying theory or assumption about the selection process, but instead it calls for using the existing variability within the data and letting the data to speak. The idea of proposing this multivariate approach to measure selection bias and test balance comes from the consideration that in applied research all aspects of multivariate balance, not represented in the univariate variable- by-variable summaries, are ignored. The first part contains an introduction to evaluation methods as part of public and private decision process and a review of the literature of evaluation methods. The attention is focused on Rubin Potential Outcome Approach, matching methods, and briefly on Heckman’s Selection Model. The second part focuses on some resulting limitations of conventional methods, with particular attention to the problem of how testing in the correct way balancing. The third part contains the original contribution proposed , a simulation study that allows to check the performance of the method for a given dependence setting and an application to a real data set. Finally, we discuss, conclude and explain our future perspectives.

Isospin dependence of the Ar+Ni fusion-evaporation cross section

The fundamental goal of this thesis is the determination of the isospin dependence of the Ar+Ni fusion-evaporation cross section. Three Ar isotope beams, with energies of about 13AMeV, have been accelerated and impinged onto isotopically enriched Ni targets, in order to produce Pd nuclei, with mass number varying from 92 to 104. The measurements have been performed by the high performance 4pi detector INDRA, coupled with the magnetic spectrometer VAMOS. Even if the results are very preliminary, the obtained fusion-evaporation cross sections behaviour gives a hint at the possible isospin dependence of the fusion-evaporation cross sections.