Evaluation of corrosion expansion of reinforced concrete specimen using fiber optical Brillouin sensing technique

This paper investigated the evaluation of the concrete damage degree due to steel bar corrosion for reinforced concrete structures. Brillouin optical fiber time domain analysis (BOTDA) sensors were developed to monitor the steel bar corrosion expansion strain. Electrochemical accelerating experimental results showed the sensors could be used for early detection and the lifelong monitoring. The damage factor was proposed to quantitatively evaluate the concrete damage degree before initial cracking and during the development of cracks. Finite element analysis was performed on concrete specimens to map the monitoring results with the damage factor, which supported the capability of the damage factor

Wetting of cylindrical droplet on heterogeneous and cylindrical solid substrate

By methods of thermodynamics, wetting of cylindrical droplet on heterogeneous and smooth but chemically non-deformable cylindrical outer surfaces is investigated in this paper. For the three-phase system, we suppose the solid substrate is composed of two types of materials. Using Gibbs's method of dividing surface, the system can be separated into six segments. On the assumption that the temperature and chemical potential are constant, a generalized Cassie-Baxter equation is derived taking the line tension effects into consideration. This generalized Cassie-Baxter equation is discussed based on some assumptions

Intravenous pretreatment with emulsified isoflurane preconditioning protects kidneys against ischemia/reperfusion injury in rats

Ordinal logistic regression models have been developed for analysis of epidemiological studies. However, the adequacy of such models for adjustment has so far received little attention. In this article, we reviewed the most important ordinal regression models and common approaches used to verify goodness-of-fit, using R or Stata programs. We performed formal and graphical analyses to compare ordinal models using data sets on health conditions from the National Health and Nutrition Examination Survey (NHANES II).Los modelos de regresión logística ordinal vienen aplicándose con éxito en el análisis de estudios epidemiológicos. Sin embargo, la verificación de la adecuación de cada modelo ha recibido atención limitada. El artículo presenta un breve análisis de los principales modelos de regresión logística ordinal y las estrategias para ajustes, las técnicas de verificación de calidad de ajuste, así como los comandos para ejecución en los softwares R y Stata. La metodología es ilustrada con la aplicación de los datos del Second Nacional Health and Nutrition Examination Survey (NHANES II), el conocido análisis de salud y nutrición.Os modelos de regressão logística ordinal vêm sendo aplicados com sucesso na análise de estudos epidemiológicos. Entretanto, a verificação da adequação de cada modelo tem recebido atenção limitada. O artigo apresenta uma breve análise dos principais modelos de regressão logística ordinal e as estratégias para ajuste s, as técnicas de verificação de qualidade do ajuste, bem como os comandos para execução nos softwares R e Stata. A metodologia é ilustrada com aplicação dos dados do Second National Health and Nutrition Examination Survey (NHANES II), o conhecido levantamento de saúde e nutrição

An Optimized Real-Time PCR to Avoid Species-/Tissue-Associated Inhibition for H5N1 Detection in Ferret and Monkey Tissues

The real-time PCR diagnostics for avian influenza virus H5N1 in tissue specimens are often suboptimal, since naturally occurring PCR inhibitors present in samples, or unanticipated match of primer to unsequenced species' genome. With the principal aim of optimizing the SYBR Green real-time PCR method for detecting H5N1 in ferret and monkey (Chinese rhesus macaque) tissue specimens, we screened various H5N1 gene-specific primer pairs and tested their ability to sensitively and specifically detect H5N1 transcripts in the infected animal tissues, then we assessed RNA yield and quality by comparing Ct values obtained from the standard Trizol method, and four commonly used RNA isolation kits with small modifications, including Roche High Pure, Ambion RNAqueous, BioMIGA EZgene, and Qiagen RNeasy. The results indicated that a single primer pair exhibited high specificity and sensitivity for H5N1 transcripts in ferret and monkey tissues. Each of the four kits and Trizol reagent produced high-quality RNA and removed all or nearly all PCR inhibitors. No statistically significant differences were found between the Ct values from the isolation methods. So the optimized SYBR Green real-time PCR could avoid species- or tissue-associated PCR inhibition in detecting H5N1 in ferret and monkey tissues, including lung and small intestine

Aperture Diffraction for Compact Snapshot Spectral Imaging

We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter sensor, requiring no additional physical footprint compared to common RGB cameras. Then we introduce a new optical design that each point in the object space is multiplexed to discrete encoding locations on the mosaic filter sensor by diffraction-based spatial-spectral projection engineering generated from the orthogonal mask. The orthogonal projection is uniformly accepted to obtain a weakly calibration-dependent data form to enhance modulation robustness. Meanwhile, the Cascade Shift-Shuffle Spectral Transformer (CSST) with strong perception of the diffraction degeneration is designed to solve a sparsity-constrained inverse problem, realizing the volume reconstruction from 2D measurements with Large amount of aliasing. Our system is evaluated by elaborating the imaging optical theory and reconstruction algorithm with demonstrating the experimental imaging under a single exposure. Ultimately, we achieve the sub-super-pixel spatial resolution and high spectral resolution imaging. The code will be available at: https://github.com/Krito-ex/CSST.Comment: accepted by International Conference on Computer Vision (ICCV) 202

Current Status of the Chinese National Twin Registry

The Chinese National Twin Registry is the first and largest population-based twin registry in China. It was established in 2001. The primary goal of this program is the establishment of a population-based twin registry of 45,000 twin pairs from several regions representing north, south, urban, and rural areas in China. A secondary goal is to study genetic contributions to complex diseases, and to test associations of candidate genes with related phenotypes. Seven thousand, four hundred and twenty-three twin pairs have been enrolled in the registry in which 1613 pairs have undergone detailed questionnaire assessments and physical examination. Based on the baseline registry, a twin cohort was established. Continued research includes studies on intermediate phenotypes of cardiovascular and cerebrovascular diseases and psychological studies in adult twins, studies on growth and development in adolescent twins, and so forth. The current state and future plans for the Chinese National Twin Registry will be discussed in this article.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000243216600009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Genetics & HeredityObstetrics & GynecologySCI(E)PubMed17ARTICLE6747-752

Incremental learning-based visual tracking with weighted discriminative dictionaries

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out