Literature-related discovery (LRD): Potential treatments for Raynaud's Phenomenon

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.Raynaud's Phenomenon (RP) is a condition in which small arteries, most commonly in fingers and toes, contract and cause the skin to turn pale or a patchy red to blue. We selected the subject of RP for analysis by LRD because of RP's global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our main 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 RP. Our secondary goal was to compare our ODS LRD approach to the RP problem with other investigators who have addressed the RP problem since Swanson's pioneering 1986 ODS LRD paper on potential RP treatments [D.R. Swanson, Fish oil, Raynauds syndrome, and undiscovered public knowledge, Perspectives in Biology and Medicine 30 (1) (1986) 7-18].We used Medline from 1965-1985 to identify potential discovery for RP. We differ from all the other authors who have addressed this problem in two major respects: we make no numerically-based filtering assumptions, and we generate substantial potential discovery (∼ 130 potential discoveries). Further, we believe our reported results are the tip of the iceberg. Much more potential discovery is possible with an adequately resourced study using the lessons learned from this demonstration study and the other demonstration studies that follow in this Special Issue.     

基于GARCH模型的认购权证与认沽权证波动率比较研究

波动性是经济和金融研究的热点问题。本文分别采用无条件波动度量方法和条件波动模型对我国权证市场上具有代表性的六支权证的波动性进行估计,得出以下几个结果:1、六支权证基本上都存在不同程度的波动聚类现象。2、认沽权证的市场有效性弱于认购权证。3、认购权证的波动持续性大于认沽权证,说明认沽权证投机性更强,风险更大。4、认购权证的风险收益补偿大概是认沽权证的6倍。最后,结合本文研究,将给广大投资者一些投资建议。     

Factor models for large and incomplete data sets with unknown group structure

Most economic applications rely on a large number of time series, which typically have a remarkable clustering structure and they are available over different spans. To handle these databases, we combined the expectation–maximization (EM) algorithm outlined by Stock and Watson (JBES, 2002) and the estimation algorithm for large factor models with an unknown number of group structures and unknown membership described by Ando and Bai (JAE, 2016; JASA, 2017) . Several Monte Carlo experiments demonstrated the good performance of the proposed method at determining the correct number of clusters, providing the appropriate number of group-specific factors, identifying error-free group membership, and obtaining accurate estimates of unobserved missing data. In addition, we found that our proposed method performed substantially better than the standard EM algorithm when the data had a grouped factor structure. Using the Federal Reserve Economic Data FRED-QD, our method detected two distinct groups of macroeconomic indicators comprising the real activity indicators and nominal indicators. Thus, we demonstrated the usefulness of our group-specific factor model for studies of business cycle chronology and for forecasting purposes.     

A dual-clustering algorithm for a robust medical grid partition problem considering patient referral

The medical grid including hospitals at all levels is a new hierarchical diagnosis and treatment system. It is assumed to provide health services for residents in a certain area, allowing free referral of patients, so as to effectively utilize medical resources. Therefore, from the perspective of the government, the key issue is how to divide the medical grid in a robust and balanced manner. In this paper, various deterministic factors, such as hospital level, location and department, as well as uncertain factors, including patient distribution or population density, are considered in decision-making. To solve this problem, a dual-clustering algorithm based on K-means and K-medoids (DCKK) is developed with local search methods to minimize the average patient waiting and travelling time. The experimental results show that DCKK algorithm can generate better and more robust grid partition solutions than the existing mainstream algorithms in different scenarios. In addition, the rules between the number of medical grids and the number of patients, as well as the hospital sharing between medical grids, are also studied. Finally, a real medical grid partition case of Ji'nan, China, with forty hospitals in four urban areas, is studied, and five medical grids are recommended.     

基于城市轨道交通网络的广告营销站点选择研究

城市轨道交通网络背景下的广告营销已成为一种流行的产品推广方式，快速、准确筛选出传播范围广、影响力高的站点，运营方和投资商对其投入大量广告可以产生较高的宣传力度和回报收益。影响力最大化的提出，为解决站点筛选提供了一种新的思路，首先提出一种融合节点结构信息和网络拓扑结构信息的网络表示学习模型，并将其用于学习网络节点的特征向量；进一步将网络节点的特征向量输入聚类算法中有效筛选候选种子集，同时结合贪婪策略从候选种子集中筛选出种子节点集合，进而有效提升网络节点影响力最大化问题求解效果。以武汉地铁网络为例进行验证，结果表明所提方法在传播范围上优于现有的影响力最大化方法，筛选高影响力站点具备合理性。