A study on factors affecting the degradation of magnesium and a magnesium-yttrium alloy for biomedical applications.

Controlling degradation of magnesium or its alloys in physiological saline solutions is essential for their potential applications in clinically viable implants. Rapid degradation of magnesium-based materials reduces the mechanical properties of implants prematurely and severely increases alkalinity of the local environment. Therefore, the objective of this study is to investigate the effects of three interactive factors on magnesium degradation, specifically, the addition of yttrium to form a magnesium-yttrium alloy versus pure magnesium, the metallic versus oxide surfaces, and the presence versus absence of physiological salt ions in the immersion solution. In the immersion solution of phosphate buffered saline (PBS), the magnesium-yttrium alloy with metallic surface degraded the slowest, followed by pure magnesium with metallic or oxide surfaces, and the magnesium-yttrium alloy with oxide surface degraded the fastest. However, in deionized (DI) water, the degradation rate showed a different trend. Specifically, pure magnesium with metallic or oxide surfaces degraded the slowest, followed by the magnesium-yttrium alloy with oxide surface, and the magnesium-yttrium alloy with metallic surface degraded the fastest. Interestingly, only magnesium-yttrium alloy with metallic surface degraded slower in PBS than in DI water, while all the other samples degraded faster in PBS than in DI water. Clearly, the results showed that the alloy composition, presence or absence of surface oxide layer, and presence or absence of physiological salt ions in the immersion solution all influenced the degradation rate and mode. Moreover, these three factors showed statistically significant interactions. This study revealed the complex interrelationships among these factors and their respective contributions to degradation for the first time. The results of this study not only improved our understanding of magnesium degradation in physiological environment, but also presented the key factors to consider in order to satisfy the degradation requirements for next-generation biodegradable implants and devices

Doubly stochastic continuous-time hidden Markov approach for analyzing genome tiling arrays

Microarrays have been developed that tile the entire nonrepetitive genomes of many different organisms, allowing for the unbiased mapping of active transcription regions or protein binding sites across the entire genome. These tiling array experiments produce massive correlated data sets that have many experimental artifacts, presenting many challenges to researchers that require innovative analysis methods and efficient computational algorithms. This paper presents a doubly stochastic latent variable analysis method for transcript discovery and protein binding region localization using tiling array data. This model is unique in that it considers actual genomic distance between probes. Additionally, the model is designed to be robust to cross-hybridized and nonresponsive probes, which can often lead to false-positive results in microarray experiments. We apply our model to a transcript finding data set to illustrate the consistency of our method. Additionally, we apply our method to a spike-in experiment that can be used as a benchmark data set for researchers interested in developing and comparing future tiling array methods. The results indicate that our method is very powerful, accurate and can be used on a single sample and without control experiments, thus defraying some of the overhead cost of conducting experiments on tiling arrays.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS248 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Do Racial Disparities Exist During Pretrial Decisionmaking? Evidence From North Carolina

Racial Disparities in the Criminal Justice System are well documented in that minority defendants are over-represented compared with white defendants. The present authors argue that it is crucial to study the pretrial stages because they are a pivotal point in the criminal justice process continuum and racial disparities may begin to take root at an early stage of the process. We find some evidence of racial disparities in pretrial decisionmaking. The type of bond assigned differs by race. Black defendants who were unable to post bond spent more days in jail, compared to white counterparts. However, race is not a significant predictor of bond amount in the regression analysis, indicating that racial disparities may not be as pronounced as some advocates believe in terms of bond amounts set by judges. We acknowledge that the findings are limited due to small sample size and cautions should be taken when generalizing the findings

Thermodynamics and kinetics of the Mg65Cu25Y10 bulk metallic glass forming liquid

The thermodynamics and kinetics of the bulk metallic glass forming Mg65Cu25Y10 liquid were investigated using differential scanning calorimetry and three-point beam bending. The experiments lead to the determination of the thermodynamic functions as well as the viscosity of the supercooled liquid. The viscosity shows a temperature dependence, which is consistent with that of a strong glass similar to Zr–Ti–Cu–Ni–Be bulk metallic glasses or sodium silicate glasses. This contrasts with more fragile conventional metallic glass formers or pure metals. The relatively weak temperature dependence of the thermodynamic functions of the supercooled liquid is related to these sluggish kinetics in the supercooled liquid. Entropy, viscosity, and kinetic glass transition are compared in the frameworks of the fragility concept and the Adam–Gibbs theory. Strong liquid behavior retards the formation of crystals kinetically and thermodynamically

Efficient Embedded Speech Recognition for Very Large Vocabulary Mandarin Car-Navigation Systems

Automatic speech recognition (ASR) for a very large vocabulary of isolated words is a difficult task on a resource-limited embedded device. This paper presents a novel fast decoding algorithm for a Mandarin speech recognition system which can simultaneously process hundreds of thousands of items and maintain high recognition accuracy. The proposed algorithm constructs a semi-tree search network based on Mandarin pronunciation rules, to avoid duplicate syllable matching and save redundant memory. Based on a two-stage fixed-width beam-search baseline system, the algorithm employs a variable beam-width pruning strategy and a frame-synchronous word-level pruning strategy to significantly reduce recognition time. This algorithm is aimed at an in-car navigation system in China and simulated on a standard PC workstation. The experimental results show that the proposed method reduces recognition time by nearly 6-fold and memory size nearly 2- fold compared to the baseline system, and causes less than 1% accuracy degradation for a 200,000 word recognition task

Homogenous Ensemble Phonotactic Language Recognition Based on SVM Supervector Reconstruction

Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR system. Phonotactic SLR subsystems may vary in the acoustic features vectors or include multiple language-specific phone recognizers and different acoustic models. These methods achieve good performance but usually compute at high computational cost. In this paper, a new diversification for phonotactic language recognition systems is proposed using vector space models by support vector machine (SVM) supervector reconstruction (SSR). In this architecture, the subsystems share the same feature extraction, decoding, and N-gram counting preprocessing steps, but model in a different vector space by using the SSR algorithm without significant additional computation. We term this a homogeneous ensemble phonotactic language recognition (HEPLR) system. The system integrates three different SVM supervector reconstruction algorithms, including relative SVM supervector reconstruction, functional SVM supervector reconstruction, and perturbing SVM supervector reconstruction. All of the algorithms are incorporated using a linear discriminant analysis-maximum mutual information (LDA-MMI) backend for improving language recognition evaluation (LRE) accuracy. Evaluated on the National Institute of Standards and Technology (NIST) LRE 2009 task, the proposed HEPLR system achieves better performance than a baseline phone recognition-vector space modeling (PR-VSM) system with minimal extra computational cost. The performance of the HEPLR system yields 1.39%, 3.63%, and 14.79% equal error rate (EER), representing 6.06%, 10.15%, and 10.53% relative improvements over the baseline system, respectively, for the 30-, 10-, and 3-s test conditions

The role of matrix cracks and fibre/matrix debonding on the stress transfer between fibre and matrix in a single fibre fragmentation test

The single fibre fragmentation test is commonly used to characterise the fibre/matrix interface. During fragmentation, the stored energy is released resulting in matrix cracking and/or fibre/matrix debonding. Axisymmetric finite element models were formulated to study the impact of matrix cracks and fibre/matrix debonding on the effective stress transfer efficiency (EST) and stress transfer length (STL). At high strains, plastic deformation in the matrix dominated the stress transfer mechanism. The combination of matrix cracking and plasticity reduced the EST and increased STL. For experimental validation, three resins were formulated and the fragmentation of an unsized and uncoupled E-glass fibre examined as a function of matrix properties. Fibre failure was always accompanied by matrix cracking and debonding. With the stiff resin, debonding, transverse matrix cracking and conical crack initiation were observed. With a lower modulus and lower yield strength resin the transverse matrix crack length decreased while that of the conical crack increased. (C) 2011 Elsevier Ltd. All rights reserved

Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance