LED-Induced Fluorescence System for Tea Classification and Quality Assessment

A fluorescence system is developed by using several light emitting diodes (LEDs) with different wavelengths as excitation light sources. The fluorescence detection head consists of multi LED light sources and a multimode fiber for fluorescence collection, where the LEDs and the corresponding filters can be easily chosen to get appropriate excitation wavelengths for different applications. By analyzing fluorescence spectra with the principal component analysis method, the system is utilized in the classification of four types of green tea beverages and two types of black tea beverages. Qualities of the Xihu Longjing tea leaves of different grades, as well as the corresponding liquid tea samples, are studied to further investigate the ability and application of the system in the evaluation of classification/quality of tea and other foods

Wear Behaviors of a Ti-Based Bulk Metallic Glass at Elevated Temperatures

Bulk metallic glasses (BMGs) often offer excellent physical, chemical, and mechanical properties such as high strength, high hardness, and good wear/corrosion resistance, stemming from their unique atomic configuration. These properties enable them to be a potential engineering material in a range of industrial applications. However, the wear behaviors must be considered in structural applications. Here, the wear tests of a TiZrNiCuBe bulk metallic glass at high temperatures were carried out. As the testing temperature increases, the wear rate of the studied BMG sample gradually decreases and the sample surface becomes smoother. Meanwhile, a higher applied normal load causes a higher wear rate. The wear mechanism evolves from the abrasive to adhesive mode with increase in the testing temperature. The results obtained here could shed more insights into the deformation mechanism of BMGs and thus extend their industrial uses in high-temperature environments

Effect of Pu-erh tea pomace on the composition and diversity of cecum microflora in Chahua chicken No. 2

Pu-erh tea pomace (PTP), a solid substance after extracting functional substances or steeping tea, is rich in crude protein, and crude fiber, and could be used as considerable bioactive substances in animal production. However, its application as poultry feed and its role in regulating the characteristics of gut microorganisms is unclear. The present study investigated the effects of PTP on growth performance and gut microbes of chicken. A total of 144 Chahua chickens No. 2 were individually housed and divided into three groups which were fed diets containing 0% (CK), 1% PTP (T1), and 2% PTP (T2), respectively. The serum and cecum contents were collected after slaughter for analysis. The results indicated that growth performance and carcass traits were not affected by the PTP content. Serum total triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) levels in the T1 and T2 groups were significantly lower than in the CK group (p < 0.05). The gut microbiota α-diversity in the T2 group was significantly lower than in the CK group (p < 0.05). Based on partial least squares-discriminant analysis (PLS-DA), we observed significant segregation in gut bacterial communities among the groups. At the phylum level, Bacteroidetes and Firmicutes were dominant in the cecum, occupying about 85% of the cecum flora. The relative abundance of Bacteroidetes tended to increase. At the genus level, the relative abundance of Bacteroides is the highest in the CK、T1 and T2 groups. The relative abundances of Bacteroides and Prevotellaceae_UCG-001 microorganisms in the T2 group were significantly higher than in the CK group (p < 0.05). However, the relative abundance of CHKCI001 microorganisms in the T2 group was significantly lower compared to the CK group (p < 0.05). TG content was significantly positively correlated with CHKCI001 relative abundance, and significantly negatively correlated with Prevotellaceae_UCG-001 relative abundance (p < 0.05). Moreover, the LDL-C content was significantly positively correlated with CHKCI001 relative abundance (p < 0.05). In conclusion, PTP could decrease the cholesterol levels in the blood by improving the composition of gut microbiota, which provides a reference for the application of PTP in the poultry industry

A protocol of Chinese expert consensuses for the management of health risk in the general public

IntroductionNon-communicable diseases (NCDs) represent the leading cause of mortality and disability worldwide. Robust evidence has demonstrated that modifiable lifestyle factors such as unhealthy diet, smoking, alcohol consumption and physical inactivity are the primary causes of NCDs. Although a series of guidelines for the management of NCDs have been published in China, these guidelines mainly focus on clinical practice targeting clinicians rather than the general population, and the evidence for NCD prevention based on modifiable lifestyle factors has been disorganized. Therefore, comprehensive and evidence-based guidance for the risk management of major NCDs for the general Chinese population is urgently needed. To achieve this overarching aim, we plan to develop a series of expert consensuses covering 15 major NCDs on health risk management for the general Chinese population. The objectives of these consensuses are (1) to identify and recommend suitable risk assessment methods for the Chinese population; and (2) to make recommendations for the prevention of major NCDs by integrating the current best evidence and experts’ opinions.Methods and analysisFor each expert consensus, we will establish a consensus working group comprising 40–50 members. Consensus questions will be formulated by integrating literature reviews, expert opinions, and an online survey. Systematic reviews will be considered as the primary evidence sources. We will conduct new systematic reviews if there are no eligible systematic reviews, the methodological quality is low, or the existing systematic reviews have been published for more than 3 years. We will evaluate the quality of evidence and make recommendations according to the GRADE approach. The consensuses will be reported according to the Reporting Items for Practice Guidelines in Healthcare (RIGHT)

Entrepreneurship, dynamic externality, and urban growth in the United States

Includes bibliographical references (pages [248]-271).It is the principal task of this study to examine what factors determine urban growth. Using a novel data set from the U.S. Census Bureau's County Business Patterns for the period 1974–1997, this study attempts to add a new dimension to existing work on geographic concentration and entrepreneurship, and to improve our understanding of the determinants of urban growth. To this author's knowledge, there have been no quantitative efforts to establish the systematic relationship between entrepreneurship and dynamic externality and urban growth. Traditionally, urban economic analysis has focused on economies of agglomeration while ignoring entrepreneurship. Recently, broader theories of entrepreneurship have stressed the important role of entrepreneurship in economic growth. However, few urban researchers have given entrepreneurship empirical attention, owing partly to a lack of appropriate data and partly to a lack of appropriate measurement. This study utilizes hand-collecting data on the 10 largest two-digit industries and five high-tech manufacturing industries in 317 metropolitan areas. A standard production framework, modified to incorporate entrepreneurship, guides the econometric analysis of this study. The empirical analysis confirms the importance of entrepreneurship in urban growth, as entrepreneurship was found to promote urban employment growth. Human capital was positively related to urban employment and productivity growth. Generally, industrial specialization hurt both employment growth and productivity growth. Jacobs's diversity was found to play an ambiguous role in generating employment and productivity growth. In particular, Jacobs's diversity hurt employment growth in the declining industries. However, industrial specialization spurs employment growth in the growing industries. Geography, right-to-work laws, and interurban knowledge spillovers proved important as well. These results are robust to different specifications, different measures, and different levels of analysis. The findings of this study suggest that entrepreneurship, education, pro-business policy, and geographic advantages play an important role in urban growth, whereas neither a highly specialized nor a diversified metropolitan employment structure can guarantee its future growth.Ph.D. (Doctor of Philosophy

Heterogeneous cellular automata subway station personnel evacuation model based on cooperative behavior

In order to study the influence of cooperative behavior in the evacuation process of subway station personnel, and considering the heterogeneity of evacuees, the heterogeneous cellular automata method is adopted to establish a human evacuation model of subway station under cooperative behavior based on the floor field model. In the research process, the evacuated persons are divided into two types, which are seeking cooperation and accepting cooperation. Then, the effects of different cooperative behavior probability ratios of seeking cooperative personnel on evacuation efficiency, evacuation process, and evacuation bottleneck are analyzed through simulation. The result shows that cooperative behavior can effectively improve evacuation efficiency of the subway station, but it is limited by cooperative probability and the proportion of people seeking cooperation; Cooperative behavior plays a role in the whole evacuation process, which is mainly reflected in the later stage of evacuation and will promote the gathering of evacuees. The higher the probability of cooperation, the shorter the evacuation bottleneck formation time, the duration, and overall evacuation time, which will help improve the emergency safety of subway stations

Heterogeneous cellular automata subway station personnel evacuation model based on cooperative behavior

In order to study the influence of cooperative behavior in the evacuation process of subway station personnel, and considering the heterogeneity of evacuees, the heterogeneous cellular automata method is adopted to establish a human evacuation model of subway station under cooperative behavior based on the floor field model. In the research process, the evacuated persons are divided into two types, which are seeking cooperation and accepting cooperation. Then, the effects of different cooperative behavior probability ratios of seeking cooperative personnel on evacuation efficiency, evacuation process, and evacuation bottleneck are analyzed through simulation. The result shows that cooperative behavior can effectively improve evacuation efficiency of the subway station, but it is limited by cooperative probability and the proportion of people seeking cooperation; Cooperative behavior plays a role in the whole evacuation process, which is mainly reflected in the later stage of evacuation and will promote the gathering of evacuees. The higher the probability of cooperation, the shorter the evacuation bottleneck formation time, the duration, and overall evacuation time, which will help improve the emergency safety of subway stations

Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles

This study is to analyze the influence of visibility in a foggy weather environment on the accuracy of machine vision obstacle detection in assisted driving. We present a foggy day imaging model and analyze the image characteristics, then we set up the faster region convolutional neural network (Faster R-CNN) as the basic network for target detection in the simulation experiment and use Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data for network detection and classification training. PreScan software is used to build weather and traffic scenes based on a foggy imaging model, and we study object detection of machine vision in four types of weather condition—clear (no fog), light fog, medium fog, and heavy fog—by simulation experiment. The experimental results show that the detection recall is 91.55%, 85.21%, 72.54~64.79%, and 57.75% respectively in no fog, light fog, medium fog, and heavy fog environments. Then we used real scenes in medium fog and heavy fog environment to verify the simulation experiment. Through this study, we can determine the influence of bad weather on the detection results of machine vision, and hence we can improve the safety of assisted driving through further research