Particle Characteristics’ Influence on FLASH Sintering of Potassium Sodium Niobate: A Relationship with Conduction Mechanisms

The considerable decrease in temperature and time makes FLASH sintering a more sustainable alternative for materials processing. FLASH also becomes relevant if volatile elements are part of the material to be processed, as in alkali-based piezoelectrics like the promising lead-free K_0.5Na_0.5NbO₃ (KNN). Due to the volatile nature of K and Na, KNN is difficult to process by conventional sintering. Although some studies have been undertaken, much remains to be understood to properly engineer the FLASH sintering process of KNN. In this work, the effect of FLASH temperature, T_F, is studied as a function of the particle size and impurity content of KNN powders. Differences are demonstrated: while the particle size and impurity degree markedly influence T_F, they do not significantly affect the densification and grain growth processes. The conductivity of KNN FLASH-sintered ceramics and KNN single crystals (SCs) is compared to elucidate the role of particles’ surface conduction. When particles’ surfaces are not present, as in the case of SCs, the FLASH process requires higher temperatures and conductivity values. These results have implications in understanding FLASH sintering towards a more sustainable processing of lead-free piezoelectrics.     

Implementation of High-Dimensional Feature Map for Segmentation of MR Images

A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (≥3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.     

Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy

In this paper, segmentation of blood vessels from colour retinal images using a novel clustering algorithm with a partial supervision strategy is proposed. The proposed clustering algorithm, which is a RAdius based Clustering ALgorithm (RACAL), uses a distance based principle to map the distributions of the data by utilising the premise that clusters are determined by a distance parameter, without having to specify the number of clusters. Additionally, the proposed clustering algorithm is enhanced with a partial supervision strategy and it is demonstrated that it is able to segment blood vessels of small diameters and low contrasts. Results are compared with those from the KNN classifier and show that the proposed RACAL performs better than the KNN in case of abnormal images as it succeeds in segmenting small and low contrast blood vessels, while it achieves comparable results for normal images. For automation process, RACAL can be used as a classifier and results show that it performs better than the KNN classifier in both normal and abnormal images.     

Hepatic gene expression discriminates responders and nonresponders in treatment of chronic hepatitis C viral infection

BACKGROUND & AIMS: Pegylated interferon (IFN)-alpha plus ribavirin is the most effective treatment of chronic hepatitis C but has unpleasant side effects and high costs. A large proportion of patients do not respond to therapy for reasons that are unclear. We used gene expression profiling to investigate the molecular basis for treatment failure. METHODS: Expression profiling was performed on percutaneous needle liver biopsy specimens taken before therapy. Gene expression levels were compared among 15 nonresponder, 16 responder, and 20 normal liver biopsy specimens. Differential gene expression was confirmed using real-time polymerase chain reaction. RESULTS: We identified 18 genes whose expression differed significantly between all responders and all nonresponders (P < .005). Many of these 18 genes are IFN sensitive and 2 (ISG15/USP18) are linked in a novel IFN-regulatory pathway, suggesting a possible rationale for treatment resistance. Using a number of independent classifier analyses, an 8-gene subset accurately predicted treatment response for 30 of 31 patients. The classifier analyses were applicable to patients with genotype 1 infection and were not correlated with viral load, disease activity, or fibrosis. CONCLUSIONS: Hepatic gene expression profiling identified consistent differences in patients who subsequently fail treatment with pegylated IFN-alpha plus ribavirin: up-regulation of a specific set of IFN-responsive genes predicts nonresponse to exogenous therapy. These data may be of use in predicting clinical responses to treatment.     

A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies

Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method. Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry. Indeed, NIRS is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Moreover, NIRS can be implemented during pharmaceutical development, in production for process monitoring or in quality control laboratories.This review focuses on chemometric techniques and pharmaceutical NIRS applications. The following topics are covered: qualitative analyses, quantitative methods and on-line applications. Theoretical and practical aspects are described with pharmaceutical examples of NIRS applications.     

Machine learning in cardiac CT: Basic concepts and contemporary data

Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular medicine. Owing to the growing body of literature validating both the diagnostic performance as well as the prognostic implications of anatomic and physiologic findings, coronary computed tomography angiography (CCTA) is now a well-established non-invasive modality for the assessment of cardiovascular disease. ML has been increasingly utilized to optimize performance as well as extract data from CCTA as well as non-contrast enhanced cardiac CT scans. The purpose of this review is to describe the contemporary state of ML based algorithms applied to cardiac CT, as well as to provide clinicians with an understanding of its benefits and associated limitations.     

音乐个性化推荐算法RR-UBPMF的研究

大规模隐式反馈数据的使用是推荐系统中的研究热点和难点问题。针对隐式反馈数据高噪声和缺少负反馈的特点,以音乐推荐为背景,在研究概率矩阵分解模型（PMF）的基础上提出了一种直接优化排名倒数（RR）的概率矩阵分解模型（RR-PMF）。通过与User-based KNN算法相结合提出了RR-UBPMF算法,并利用交叉最小二乘法（ALS）进行优化学习。在last.fm数据集上的实验结果表明,该算法在准确率（Precision）、尤其是在标准化折算累加值（NDCG）等评价指标上表现出极大的优势,能够明显提高预测准确性,并且具有良好的可拓展性。     

基于SIFT算法的图像特征点提取和匹配研究

目的:把SIFT算法应用在牙齿模型图像上,检测牙齿图像的特征点?方法:首先采用高斯差分算子DoG搜索整个图像的尺度和位置信息,从而确定具有代表性尺度?方向的特征点?基于其稳定性选择关键点,得到一个详细的模型以确定每个候选点的合适位置和范围?基于局部图像梯度方向信息将方向矢量和关键点对应起来?在选定范围内的每个关键点周边区域测量局部图像梯度,并采用KNN算法进行特征匹配?结果:通过大量的实验和与其他特征提取方法相比较,该方法能有效地检测牙齿模型图像的特征,并为牙齿模型三维重建提供有效的参数?结论:在口腔医学牙齿模型图像中应用SIFT算法,有助于牙齿模型的三维重建?     

基于智能视觉技术的白及饮片真伪快速辨识方法研究

目的 利用电子眼和色差仪分别对白及Bletillastriata及其伪品进行辨识,旨在建立白及真伪的快速辨识方法。方法 首先基于《中国药典》、地方标准和HPLC指纹图谱对多采集来源的134批白及及其伪品样品进行综合鉴别,以确定标杆辨识信息（Y）。然后基于电子眼和色差仪分别获取上述样品的智能视觉信息（X）。最后利用Matlab建立Y=F(X)模型,即分别建立白及真伪二分类（白及、非白及）和四分类（白及、天麻Gastrodiaelata、玉竹Polygonatumodoratum、黄花白及Bletilla ochracea）的主成分分析-判别分析（principal component analysis-discriminant analysis,PCA-DA）、偏最小二乘-判别分析（partial least squares-discriminant analysis,PLS-DA）、最小二乘-支持向量机（least squares-support vector machine,LS-SVM）和K最近邻（K nearest neighbor,KNN）辨识模型并验证。结果 经留一法交互验证...