Farm households and nonfarm activities in Ethiopia: does clustering influence entry and exit?

This article examines how clustering affects the entry and exit decisions of farm households into and from nonfarm enterprises in rural Ethiopia. We find that the existence of clusters of microenterprises in the same district increases the likelihood of a rural household to start a nonfarm enterprise. Similarly, clustering of big manufacturing firms in the same zone is found to increase the likelihood of farm households to start a nonfarm enterprise. Nonfarm enterprises operating in clusters are also found to have a lower probability of exit than those operating outside of clusters. The study further investigates the impact of entry and exit into and from nonfarm enterprises on farm household's well‐being using as indicators total household income, the food security status of a household, and the household's ability to raise enough money in case of emergency. Using propensity score matching to account for selection bias, we find that entry into nonfarm enterprises significantly increases household's income and food security status. Exit from nonfarm enterprises, on the other hand, is found to significantly reduce household's income.     

中国技术市场运行效率：动态演进规律及空间差异特征

基于Malmquist指数法测算2006-2015年中国技术市场运行效率,并利用GeoDa软件对内地30省（市、自治区）的技术市场运行效率值进行空间聚类分析。研究发现：中国技术市场整体运行效率变动呈现上升趋势,在产出不变的情况下,技术市场运行效率每年平均提高7.2%;效率整体上升源于综合技术效率增加,而效率上升幅度不大主要是受到技术变动的拖累;总体上的Moran′s I表明,各省（市、自治区）技术市场的运行效率之间呈现空间负相关关系,聚类分析结果表明东部和东北经济发达地区的空间集聚度较高,而西部地区呈现效率低值集聚;在空间变化特征上,技术市场空间集中度均不一致,其中,经济发展水平低的地区技术市场集中度远低于经济发展水平高的地区,自2012年后技术市场发展渐趋一致。     

A high-frequency analysis of price resolution and pricing barriers in equities on the adoption of a new currency

We use ultra high frequency (trade by trade) data to demonstrate that equity price clustering and pricing predictability around psychologically important prices in Greece switches away from drachma-focused with the introduction of the euro, but does not immediately switch to euro-clustering. The change in trader price focus around the euro introduction addresses an open debate in the clustering literature on whether the presence of clustering is a bias related to the current prices or anchoring to past prices. Our findings of a decline in drachma clustering, but lack of switch to euro effects supports the case for clustering being a trading feature that is slow to transfer to new pricing regimes. A key advantage of the ultra high frequency dataset is we are also able to demonstrate the presence of psychological pricing barriers related to each currency that are not detectable in daily data.     

United States digital divide: State level analysis of spatial clustering and multivariate determinants of ICT utilization

This study analyzes factors associated with availability and utilization of information and communication technologies (ICTs) for U.S. states. We construct an exploratory conceptual model of technology utilization in which eight dependent ICT availability and utilization factors are posited to be associated with fourteen independent socio-economic, demographic, innovation, social capital, and societal openness factors. Technology utilization variables are spatially analyzed to determine extent of agglomeration, and regression residuals are examined to eliminate spatial bias. Findings indicate social capital, education, societal openness, urbanization, and ethnicities are significantly associated with ICT utilization. We suggest important implications for policymakers at state and federal levels.     

Literature-related discovery (LRD): Potential treatments for cataracts

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.This paper describes the second medical problem we addressed (cataract) using ODS LRD; the first problem addressed was Raynaud's Phenomenon (RP), and was described in the third paper of this Special Issue. Cataract was selected because it is ubiquitous globally, appears intractable to all forms of treatment other than surgical removal of cataracts, and is a major cause of blindness in many developing countries.The ODS LRD study had three objectives: a) identify non-drug non-surgical treatments that would 1) help prevent cataracts, or 2) reduce the progression rate of cataracts, or 3) stop the progression of cataracts, or 4) maybe even reverse the progression of cataracts; b) demonstrate that we could solve an ODS LRD problem with no prior knowledge of any results or prior work (unlike the case with the RP problem); c) determine whether large time savings in the discovery process were possible relative to the time required for conducting the RP study. To that end, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, as a substitute for the manually-intensive process used in the RP study to restrict potential discoveries to selected semantic classes. We also used additional semantic filtering to identify potential discovery within the selected semantic classes.All these goals were achieved. As will be shown, we generated large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. We identified many non-drug non-surgical treatments that may be able to reduce or even stop the progression rate of cataracts. Time, and much testing, will determine whether this is possible. Finally, the methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-Related Discovery (LRD): Potential treatments for Parkinson's Disease

Literature-Related Discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous two papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP) and to cataracts.Parkinson's Disease (PD) is a progressive neurodegenerative disorder, affecting approximately 1% of individuals older than 60 years, and is characterized by resting tremor, rigidity, bradykinesia, and postural instability. We selected the subject of PD because of its global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery.Our first goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of PD. Our second goal was to demonstrate that we could again solve an ODS problem (using LRD) with no prior knowledge of any results or prior work (unlike the case of the RP problem). As in the ‘cataract’ example, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. Our third goal was to generate large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. The discovery generation methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

生产性服务业集群化发展动因研究

对生产性服务业概念及其特征进行了界定,分析了生产性服务业集群化的原因,总结归纳了生产性服务业集群的5种模式。同时对上海和北京的生产性服务业集群进行了调研,并给出了政府促进集群发展的政策建议。     

Chinese science and technology — Structure and infrastructure

This paper identifies and analyzes the science and technology core competencies of China, based on a sampling of approximately half of the total Chinese publication output in the Science Citation Index/ Social Science Citation Index (SCI/SSCI) [SCI. Certain data included herein are derived from the Science Citation Index/Social Science Citation Index prepared by the Thomson Scientific®, Inc. (Thomson®), Philadelphia, Pennsylvania, USA: © Copyright Thomson Scientific® 2006. All rights reserved. [1]] for 2005. Aggregate China publication and citation bibliometrics were obtained and a hierarchical research taxonomy, based on document clustering, was generated. Additionally, bibliometrics and thematic trends were tracked over the past two decades.The key findings were that China's output of research articles has significantly expanded in the last decade. In terms of sheer numbers of research articles, especially in cuting-edge technologies, such as nanotechnology and energetic materials, it is among the leaders. Compared to the USA, the bulk of China's articles focus on the physical and engineering sciences, while the USA articles (compared to China) focus on medical, social, and psychological sciences.     

中国南北方汉族居住区宗族聚居的地域差异

中国古代的宗族聚居"古代北盛于南,近世南盛于北",其南北易势的转折点在宋代.自宋以后,无论从族田、祠堂、族谱及宗族组织和宗族活动上看,都是如此.     

基于灰色聚类的物流中心选址

随着物流中心选址在物流系统中的重要性越来越大。而传统的方法都有其不可避免的缺陷，考虑到物流中心选址的灰色特性。本文重点介绍了灰色白化权函数聚类评价方法。讨论了灰色白化权函数聚类方法在物流中心选址中的应用．最后对结论进行了分析。