Disorder Phenomena in Quantum Solids with Vacancies

Journal of Low Temperature Physics - We have investigated disorder phenomena in solid 4He near megting in presence of vacancies by means of Quantum Monte Carlo (MC) simulations using the Shadow...     

Fracture of glass in tensile and bending tests

To fully characterize the mechanical behavior of ceramics, it is useful to study their response to both tensile and bending tests. In this investigation, a quantitative comparison is made between tensile and bending results from (circularly) cylindrical glass rods of borosilicate glass and of fused silica. These experimental results show that in each material, the Weibull exponentm is approximately the same for both types of test. The fractured samples clearly showed that all failures initiated at surface-located flaws. The stress level at which the probability of fracture in tension equals 50 Pct was determined; the maximum tensile stress for 50 Pct probability of fracture in bending was also determined. The ratio of these two stresses was predicted previously from simple, theoretical considerations based upon the applicability of the Weibull function and on the distribution of defects in the specimens. This theoretical result is inconsistent with the experimental observations in view of the fractography. Therefore, it is concluded that failure in the materials tested is not satisfactorily described by Weibull statistics.     

Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Linear mixture model applied to Amazonian vegetation classification

Many research projects require accurate delineation of different secondary succession (SS) stages over large regions/subregions of the Amazon basin. However, the complexity of vegetation stand structure, abundant vegetation species, and the smooth transition between different SS stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier (MLC). Most of the time, classification distinguishes only between forest/non-forest. It has been difficult to accurately distinguish stages of SS. In this paper, a linear mixture model (LMM) approach is applied to classify successional and mature forests using Thematic Mapper (TM) imagery in the Rondônia region of the Brazilian Amazon. Three endmembers (i.e., shade, soil, and green vegetation or GV) were identified based on the image itself and a constrained least-squares solution was used to unmix the image. This study indicates that the LMM approach is a promising method for distinguishing successional and mature forests in the Amazon basin using TM data. It improved vegetation classification accuracy over that of the MLC. Initial, intermediate, and advanced successional and mature forests were classified with overall accuracy of 78.2% using a threshold method on the ratio of shade to GV fractions, a 7.4% increase over the MLC. The GV and shade fractions are sensitive to the change of vegetation stand structures and better capture biophysical structure information.     

Mapping of Trellises Associated with General Encoders onto High-Performance VLSI Architectures

A rate 1/n binary generic convolutional encoder is a shift-register circuit where the inputs are information bits and the outputs are blocks of n bits generated as linear combinations on the appropriate shift register contents. The decoding of the outputs of a convolutional encoder can be carried out by the well-known Viterbi algorithm. The communication pattern of the Viterbi Algorithm is given as a graph, called trellis, associated to the state diagram of the corresponding encoder. In this paper we present a methodology that permits the efficient mapping of the Viterbi algorithm onto a column of an arbitrary number of processors. This is done through the representation of the data flow by using mathematical operators which present an inmediate hardware projection. A single operator string has been obtained to represent a generic encoder through the study of the data flow of free-forward encoders and feed-back encoders. The formal model developed is employed for the partitioning of the computations among an arbitrary number of processors in such a way that the data are recirculated opimizing the use of the processors and the communications. As a result, we obtain a highly regular and modular architecture suitable for VLSI implementation.     

Mathematical modelling of the alcoholic fermentation of glucose by Zymomonas mobilis mobilis

The kinetics of alcoholic fermentation of a strain of Zymomonas mobilis, isolated from sugarcane juice, has been studied with the objective of determining the constansts of a non-structured mathematical model that represents the fermentation process. Assays in batch and in continuous culture have been carried out with different initial concentrations of glucose. The final concentrations of glucose, ethanol and biomass were determined. The following kinetic parameters were obtained: μ_max, 0·5 h^?1; K_s, 4·64 g dm^?3; P_max, 106 g dm^?3; Y_x/s, 0·0265 g g^?1; m, 1·4 g g^?1 h^?1; α, 17·38 g g^?1; β, 0·69 g g^?1 h^?1.     

Systemic lupus erythematosus in three ethnic groups: II. Features predictive of disease activity early in its course. LUMINA Study Group. Lupus in minority populations, nature versus nurture

OBJECTIVE: To determine the factors associated with disease activity in patients with recent-onset (< or =5 years) systemic lupus erythematosus (SLE) who were of Hispanic, African-American, or Caucasian ethnicity. METHODS: Incident and prevalent cases of SLE, as defined by the American College of Rheumatology criteria for SLE, among the 3 ethnic groups were identified in Alabama (The University of Alabama at Birmingham) and Texas (The University of Texas-Houston Health Science Center and The University of Texas Medical Branch at Galveston). Variables from the sociodemographic, clinical, immunologic, immunogenetic, behavioral, and psychological domains were obtained using validated instruments. Disease activity was ascertained with the Systemic Lupus Activity Measure (SLAM). Stepwise domain regressions with SLAM score as the dependent variable were performed. Final ethnic-specific and overall regression models were obtained by entering variables that were retained in the domain regressions. RESULTS: SLAM scores at study entry were higher in the African Americans (mean +/- SD 12.6 +/- 6.9) and Hispanics (11.0 +/- 6.2) than in the Caucasians (8.5 +/- 3.7) (P < or = 0.001). The final overall regression model (R2 = 28%) for higher SLAM score included the following variables: African-American ethnicity, lack of private health insurance, abrupt disease onset, presence of anti-Ro antibodies, absence of HLA-DRB1*0301, higher levels of helplessness, and abnormal illness-related behaviors. CONCLUSION: Socioeconomic, immunologic, immunogenetic, behavioral, and psychological variables were all predictive of disease activity early in the course of SLE, irrespective of ethnic group. However, there remain ethnic group differences in disease activity that were not explained by these factors.     

Hull-form stochastic optimization via computational-cost reduction methods

In this paper, analytical functions for the estimation of the temperature-dependent behaviors of poorly and highly dispersed graphene oxide reinforced nanocomposite (GORNC) materials are studied in the framework of a machine learning-based approach. The validity of the presented models is shown comparing the results achieved from this modeling with those reported in the open literature. Also, the application of the obtained functions in solving the thermal buckling problem of beams constructed from such nanocomposites is demonstrated based on an energy-based method incorporated with a shear deformable beam hypothesis. The verification of the results indicates that the presented mechanical model can approximate the buckling behaviors of nanocomposite beams with remarkable precision. It can be realized from the results that the temperature plays an indispensable role in the determination of the buckling load which can be endured by the nanocomposite structure.

     

Effects of ZrO2 precursors on the synthesis of V-ZrSiO4 solid solutions by the sol-gel method

The preparation of V-ZrSiO₄ solid solutions starting from different ZrO₂ precursors by using sol-gel methods is reported. The starting materials were hydrolysed and the dried gels were fired at a temperature between 500 and 900 °C with soaking times of 12h. The organic character of zirconia precursors was stronger, i.e. the starting material had more carbon atoms, a higher temperature was necessary to make the first crystalline phase appear (ZrO₂(tetragonal)) and the temperature range for the whole phase transformation was narrower. In all dried gel samples the presence of infrared bands which might be associated with either Si-O-Zr or Si-O-V was not observed. On the other hand, some bands could be attributed to a silica network and ZrO₈ groups. The main steps in V-ZrSiO₄ solid solutions were confirmed. ZrO₂(tetragonal) is crystallized on heating from an amorphous sample. The ZrO₂(tetragonal) ZrO₂(monoclinic) phase transformation then occurs and immediately afterwards the zircon formation begins. Finer textures in samples were obtained from polymeric gels rather than for colloidal gel samples, as seen from the scanning electron micrographs.