1 468例类风湿关节炎患者终点事件发生情况队列研究

目的 采用队列研究方法,随访安徽中医药大学第一附属医院类风湿关节炎（rheumatoid arthritis,RA）出院患者终点事件发生情况,探讨其终点事件发生的相关因素。方法 纳入安徽中医药大学第一附属医院2012年1月至2016年6月期间诊断为RA患者的病例,调取并记录RA患者的一般资料,采用电话随访方式,随访并记录出院患者中医药使用情况（根据RA患者服药时间分为非暴露组、低暴露组、中暴露组和高暴露组）、终点事件发生情况,调取随访成功RA患者首次入院的实验室指标,采用多元Logistic回归分析法分析各因素与RA患者终点事件发生的相关性。结果 共纳入1 812例RA患者,随访成功1 468例,随访终点事件有80例。全因死亡的影响因素是中医药暴露强度,年龄、抗环瓜氨酸多肽抗体能够增加病死率;再发入院的影响因素是中医药暴露强度,血沉、超氧化物歧化酶、血小板可以增加再发入院的发生率;关节外病变的影响因素是中医药暴露强度、改善病情的抗风湿药,年龄、抗环瓜氨酸多肽抗体会增加关节外病变的风险;手术治疗的影响因素是中医药暴露强度。结论 中医药干预是RA患者终点事件发生的相关因素,且干预强度越大,终点事件发生率越低。     

大数据，认知与计划经济——与何大安教授商榷

数据是明晰的,客观的,而信息本质上是主观的,隐含的和实践的,数据被个体理解之后才会转化成“他的”信息。人们在使用数据之前先会判断该数据的重要性,这种判断因人因时因地而异,而大数据本身并不能替个人做出这样的判断。信息分散在无数人的头脑中,不能被某个头脑或超级计算机集中掌握。个体确定目标后,才能决定把什么样的数据作为手段来实现自己的目标,否则他会在海量的数据面前不知所措。数据是人们行动的产物。作为“历史”,数据本身不能告诉人们未来他们应该做什么。另外,没有市场的运行,甚至数据本身都不可能产生。正确的进路应该是“让个体的知识得以充分利用”,而不是何大安教授提出的“改善个体的认知”。何大安教授预设的“智慧大脑”类似于计划经济中的“中央计划委员会”,他对“普通大脑”的认知和“智慧大脑”的认知趋同的欲求,也正如中央计划委员会试图让普通大众服从于它的指令。正是市场对分散信息的分散利用使个体在固有的认知局限的情况下也有可能实现自己的目标,而大数据不能扮演同样的角色。何大安教授有关认知与大数据的讨论仍属于新古典经济学的“最大化”范式,他没意识到市场是一个互动（交换）过程,最大化范式并不适用。     

股权分置改革有效改善了公司绩效吗？——基于双重差分模型的估计

本文基于2004年到2008年上市公司样本数据,研究股权分置改革的政策有效性。考虑到股权分置改革的分批逐步推进特性,本文运用双重差分方法进行分析。研究结果发现,股权分置改革显著改善了公司绩效,总资产收益率提高了2．19％,并且股改具有一定的持续效应,股改当年总资产收益率提高了1．14％,股改后第一年进一步提高1．69％,但第二年开始股改政策效应减弱,在第三年甚至变为显著为负的影响。本文的研究对于认识股权分置改革的有效性及其长期影响有重要意义。     

China's new regulatory regime tailored for the sharing economy: The case of Uber under Chinese local government regulation in comparison to the EU,US, and the UK

Although Uber's arrival in China has resulted in disruptive competition for incumbent taxi companies, it offers an attractive alternative in China's supply-demand-imbalanced urban passenger transport system. China's regulatory regime for Uber has evolved in three stages: from the regulatory vacuum prior to 2015 to its official legalization in 2015–2016, and the enactment of numerous local regulations in 2016, with specific and more demanding requirements for Uber. This policy is a part of the Chinese approach to the gradual liberalization of the urban passenger transport market. Policymakers should consider ‘fair competition’ as the guiding principle to balance the interests of sharing firms and incumbent service providers, as well as between different sharing firms. The core value of this principle lies in the benefits it provides for consumers and the way it engenders a pro-competitive market environment. The labor protection arrangements for sharing firms’ laborers should be more flexible and diversified. In order to recognize whether an Uber-Driver is an employee or independent contractor, a new standard taking into account a range of factors should be established through collective negotiations between the participants of the sharing economy, and dialogues between members of the judiciary, academics, and the policymakers. Further, consumer protection law and personal data protection provisions should apply when sharing firms misuse their distinctive algorithmic management model to compete unfairly to the detriment of consumers and other users. Ex ante regulatory measures designed to protect the personal data of users should be introduced for deployment in the context of the sharing economy. When enforcing these rules, a balance should be struck ensuring free data flow that is essential to sharing firms’ innovation and competition, and the need to ensure the level of data security required to underpin a well-functioning sharing society.     

From fragile to smart consumers: Shifting paradigm for the digital era

The use of digital technologies, functioning thanks to data processing, has been conquering many sectors of the world economy and it is possible that, in the near future, only a few markets will still be excluded from this industrial revolution. Therefore, even if one chose unreasonably to disregard the many innovations that the digital economy has brought about, its development seems quite inexorable, although it is true that this new stage in human progress raises some concerns. In particular, many worry about the millions of passive and powerless digital consumers who, facing a few huge and influential companies without any education or awareness, could succumb and find themselves poorer, victimized, and manipulated. The paper proposes to react to this state of affairs without further fueling the fear of the digital revolution and without the thought that regulation can be used only as a shield to protect fragile digital consumers. Rather, by taking inspiration from some regulatory actions undertaken by the European Union, the paper bears in mind that regulation can be used as a sword in the hands of consumers to finally assign them a lead role in digital markets. New rules to empower consumers and to make them take autonomous and independent decisions as to the management of their personal data as well as to the merits of digital firms can be envisaged. After all, one of the cultural roots of Western societies is that every individual should be enabled to be faber ipsius fortunae.     

Application of Enhanced Point Estimators to a Sample of In Vivo CT‐derived Facial Soft Tissue Thicknesses

Facial approximations based on facial soft tissue depth measurement tables often utilize the arithmetic mean as a central tendency estimator. Stephan et al. (J Forensic Sci 2013;58:1439) suggest that the shorth and 75‐shormax statistics are better suited to describe the central tendency of non‐normal soft tissue depth data, while also accommodating normal distributions. The shorth, 75‐shormax, arithmetic mean, and other central tendency estimators were evaluated using a CT ‐derived facial soft tissue depth dataset. Differences between arithmetic mean and shorth mean for the tissue depths examined ranged from 0 mm to +2.3 mm (average 0.6 mm). Differences between the arithmetic mean plus one standard deviation (to approximate the same data points covered by the 75‐shormax) and 75‐shormax values ranged from ?0.8 mm to +0.7 mm (average 0.2 mm). The results of this research suggest that few practical differences exist across the central tendency point estimators for the evaluated soft tissue depth dataset.     

Forensic Analysis of Digital Dynamic Signatures: New Methods for Data Treatment and Feature Evaluation

This study explored digital dynamic signatures containing quantifiable dynamic data. The change in data content and nature necessitates the development of new data treatment approaches. A SignPad Omega digitizing tablet was used to assess measurement reproducibility, as well as within‐writer variation and the occurrence of correctly simulated features. Measurement reproducibility was found to be high except for pressure information. Within‐writer variation was found to be higher between days than on a same day. Occurrence of correct simulation was low for features such as signature size, trajectory length, and total signature time. Feature discrimination factors combining within‐writer variability and the occurrence of correctly simulated features were computed and show that signature size, trajectory length, and signature time are the features that perform the best for discriminating genuine from simulated signatures. A final experiment indicates that dynamic information can be used to create connections between simulation cases.     

Using Cluster Analysis and ICP‐MS to Identify Groups of Ecstasy Tablets in Sao Paulo State,Brazil

The variations found in the elemental composition in ecstasy samples result in spectral profiles with useful information for data analysis, and cluster analysis of these profiles can help uncover different categories of the drug. We provide a cluster analysis of ecstasy tablets based on their elemental composition. Twenty‐five elements were determined by ICP‐MS in tablets apprehended by Sao Paulo's State Police, Brazil. We employ the K‐means clustering algorithm along with C4.5 decision tree to help us interpret the clustering results. We found a better number of two clusters within the data, which can refer to the approximated number of sources of the drug which supply the cities of seizures. The C4.5 model was capable of differentiating the ecstasy samples from the two clusters with high prediction accuracy using the leave‐one‐out cross‐validation. The model used only Nd, Ni, and Pb concentration values in the classification of the samples.