公共场所出入口的新型导流柱设计

随着我国经济的飞速发展以及城市化的快速进展,城市公共场所也在逐步建设和完善,公共场所的人流量也在逐渐增长。目前常见的公共场所都具有大众性、服务性、人群聚集性等特性,因此维护这些拥挤公共区域的出入口安全、通畅也是越来越受到关注。传统的导流柱只能在正常情况下起到引流作用,但是在突发事件下不仅不能发挥出作用,反而会成为人们疏散的阻碍工具。文章设计的一种实用新型快速升降导流柱,属于导流技术领域。当处于正常情况的时候,可以根据场合的人流量对各个导流柱之间的连接方式进行调整,实现最合适的人群导流,当发生紧急意外情况时,导流柱能够快速收缩至地下,并亮起逃离指示灯,协助人群疏散。     

Exploring corporate governance research in accounting journals through latent semantic and topic analyses

The literature on corporate governance (CG) has been expanding at an unprecedented rate since major corporate scandals surfaced, such as Enron, WorldCom, and HealthSouth. Corresponding with accounting's important role in CG, accounting scholars increasingly have investigated CG in recent years, so the body of literature is growing. Although previous attempts have been made to summarize extant literature on CG via reviews, none of these attempts has utilized recent developments in text analyses and natural language processing. This study uses latent semantic and topic analyses to address this research gap by analysing abstracts from 1,399 articles in all accounting journals that the Australian Business Deans Council (ABDC) has rated A and A*. The ABDC journal list is widely recognized as a journal‐quality indicator across many universities worldwide. The analyses revealed 10 distinct research topics on CG in the ABDC's top accounting journals. The results presented include the five most representative articles for each topic, as distinguished by topic scores. This study carries important practice and policy implications, as it reveals major research streams and exhibits how researchers respond to various CG problems.     

Can users embed their user experience in user-generated images? Evidence from JD.com

Nowadays, massive user-generated images (UGIs) are posted online to convey users' experiences with specific brands or products. Thus, this visual information is precious, as it conveys users' actual and subjective feelings about brands and products. Because of the unprecedented quantity of images and the heterogeneity of their content, it is quite challenging for brand marketers and retailers to probe into subjective user experience in large-scale UGIs. To address this gap, this study aims to identify the connection between user experience and different image semantic features (i.e. centrality and richness) by using deep learning models. By employing objective data (8963 images) from JD.com and using deep learning algorithms (faster R-CNN), we found that users with positive user experience prefer to generate high-centrality and high-richness pictures. Our study enriches the relevant literature and provides valuable practical implications for brand marketers and e-commerce retailers. Based on findings of this work, relevant stakeholders can understand their users’ experience better from objective UGIs and devise corresponding recommendation and service strategies.     

What are the top five journals in economics? A new meta-ranking

We construct a meta-ranking of 277 economics journals based on 22 different rankings. The ranking incorporates bibliometric indicators from four different databases (Web of Science, Scopus, Google Scholar, and RePEc). We account for the different scaling of the bibliometric indicators by standardizing each ranking score. We run a principal component analysis to assign weights to each ranking. In our meta-ranking, the top five journals are given by: Quarterly Journal of Economics, Journal of Financial Economics, Journal of Economic Literature (JEL), Journal of Finance, and Econometrica. Additionally, leaving out the JEL as a survey journal and the finance journals in our top 10 list we confirm the perceived top five journals in the economics profession.     

Measuring the spatial price differences in China with regional price parity methods

The measurement of prices has been an important field in economics, and a spatial price index is very useful in comparing the standards of living and well-being across regions in a country. This paper intends to measure the regional price parities (RPPs) across different provincial areas in China with an urban sample of 140 goods and services in 2015 according to the framework of the International Comparison Program (ICP) methodology. The results show that the RPPs that were estimated with commonly used approaches, such as the Gini–Éltetö–Köves–Szulc (GEKS), Geary–Khamis (GK) and weighted country product dummy (WCPD), are only slightly different. The RPPs of three regions including Guangdong, Shanghai and Zhejiang are greater than 1 (with Beijing = 1), while the other 27 regions are all lower than 1, which represents price levels that are less than Beijing. In the extrapolation of the RPPs from 2000 to 2014, a significant descending trend is found for the standard deviation series of the RPPs over time that shows that the price differences across regions decreased during the extrapolating period. This finding provides evidence of a phenomenon of spatial price converging in China. Finally, a study on the deflation of provincial aggregates with the RPPs reveals that the spatial price adjustment will change the ranks or relative importance of different regions in the country. Especially, the measurement of income inequality proves that the Gini coefficients of provincial income deflated by the RPP^GEKS are all lower than the Gini coefficients of unadjusted incomes.     

Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.     

Signs of dependence and heavy tails in non-life insurance data

In this paper, we study data from the yearly reports the four major Swedish non-life insurers have sent to the Swedish Financial Supervisory Authority (FSA). We aim at finding marginal distributions of, and dependence between, losses on the five largest lines of business (LoBs) in order to create models for solvency capital requirement (SCR) calculation. We try to use data in an optimal way by sensibly defining an accounting year loss in terms of actuarial liability predictions and by pooling observations from several companies when possible to decrease the uncertainty about the underlying distributions and their parameters. We find that dependence between LoBs is weaker in our data than what is assumed in the Solvency II standard formula. We also find dependence between companies that may affect financial stability and must be taken into account when estimating loss distribution parameters. Moreover, we discuss under what circumstances an insurer is better (or worse) off using an internal model for SCR calculation, instead of the standard formula.     

Using semantic fingerprinting in finance

Researchers in finance and adjacent fields have increasingly been working with textual data, a common challenge being analysing the content of a text. Traditionally, this task has been approached through labour- and computation-intensive work with lists of words. In this article we compare word list analysis with an easy-to-implement and computationally efficient alternative called semantic fingerprinting. Using the prediction of stock return correlations as an illustration, we show semantic fingerprinting to produce superior results. We argue that semantic fingerprinting significantly reduces the barrier to entry for research involving textual content analysis, and we provide guidance on implementing this technique.