宫腔积脓1例临床分析

目的探讨绝经妇女宫腔积脓的原因、诊断及治疗方法。方法对牡丹江医学院红旗医院收治的1例绝经妇女宫腔积脓的临床表现及辅助检查进行回顾性分析。结果引起绝经妇女宫腔积脓的原因与老年性阴道炎和子宫内膜炎、绝经后不及时取出节育器及合并子宫内膜癌有关。临床表现为腹痛、发热、阴道排液(阴道出血),经过详细的询问病史、妇科检查、适当的实验室检查可明确诊断。结论绝经妇女宫腔积脓病程进展缓慢,临床表现不典型,易误诊,有时可引起严重的并发症,应针对原因进行预防,早期诊断,及时治疗。     

活动性肺结核误诊4例患者的原因分析

目的探讨活动性肺结核误诊的临床原因。方法选取我中心误诊为活动性肺结核的患者4例作为研究对象,对其临床资料进行回顾性分析。结果患者就诊原因多为咳嗽咳痰、痰带血、咯血、低热、恶心呕吐且食欲下降、疲乏无力、气短、胸痛、肝功能异常或者黄疸、贫血加重、淋巴结增大以及行影像学检查未出现改变或者出现恶化现象。误诊原因主要是基层医务工作者综合素质较低所致。结论为降低误诊率,需增强疾控中心与基层医务人员的专业技术水平和道德素质,以提升诊断水平。     

Video mining: Measuring visual information using automatic methods

Marketers are becoming increasingly reliant on videos to market their products and services. However, there is no standard set of measures of visual information that can be applied to large datasets. This paper proposes two standard measures that can be automatically obtained from videos: visual variation and video content. The paper tests the measures on crowdfunding videos from a leading online crowdfunding website, and shows that the proposed measures have explanatory power on the funding outcomes of the projects. These measures can be effectively implemented and used for large datasets. Further, researchers can apply these measures to other sets of visual information, and marketers could use the research to guide their video design and improve their video marketing effectiveness.     

我国农村产业融合发展的效率评价

[目的]为了进一步提升农村产业融合发展效率,提升农业质量效益和竞争力、增强农村发展活力、促进农民持续增收,[方法]文章根据我国农村产业融合发展的投入产出指标数据,利用DEA方法对我国31个省(自治区、直辖市)农村产业融合效率进行了科学的分析与测算,[结果]研究表明,我国农村产业融发展的总体效率偏低。从综合效率看,仅有天津等7个省(自治区、直辖市)的综合效率值高于0. 5;从纯技术效率看,仅有江苏等8个省(自治区、直辖市)的纯技术效率高于0. 5;从规模效率看,西藏、甘肃、青海3个省(自治区)的规模效率低于0. 5。提升农村产业融合发展效率仍有较大的空间。[结论]应从进一步加大对农村产业融合发展的资金投入力度、着力优化农村产业融合发展的资金投入结构、建立农村产业融合发展效率考评制度等方面提升农村产业融合发展效率,促进农村产业融合快速发展。     

股权激励、反收购条款与企业创新

股权激励和反收购措施是公司内外部治理机制的重要方式.文章以2006—2017年沪深两市上市公司为研究样本,将股权激励、反收购条款和企业创新纳入统一分析框架中,考察两种公司治理机制对企业创新的综合影响.研究发现:股权激励显著提升企业创新产出水平,但反收购条款负向调节股权激励对企业创新的促进作用,控制内生性后结论依然成立.进一步分情景分样本进行分析发现,股权激励的创新效果相对稳定,但反收购条款的负向调节作用明显受外界环境和条件的影响,主要体现在市场竞争程度低、股权分散程度低及高管风险偏好程度低的企业中.本文的研究结论对理解股权激励的实施效果及反收购条款的公司治理作用具有一定的参考意义.     

The Short-Selling Hypothesis of Weekend Effect and T + 1 Trading Mechanism

Asia-Pacific Financial Markets - Utilizing a difference-in-difference regression model, we conduct cross-sectional and time-series analysis to explore effect of short sales on the weekend effect in...     

Economic Impacts of the Possible China–US Trade War

This article uses a multi-country global general equilibrium (GE) model to numerically simulate the effects of possible China–US trade wars. We introduce an endogenous trade imbalance structure with trade cost into the model which helps to explore both tariff and non-tariff trade war effects. Our simulation results show that China will be significantly hurt by the China–US trade war, but negative impacts are affordable. The US can gain under unilateral sanction measures to China, but will lose if China takes retaliation measures. Comparing the effects under mutual trade war, China will lose more than the US. Introducing non-tariff barrier trade wars will intensify the negative effects, and comparatively negative effects to China are larger than to the US. Mexico’s involvement in trade war with the US will strengthen the negative effects and comparatively hurt the US more. Under non-cooperative and cooperative Nash bargaining equilibrium, the US can gain more than China in trade war negotiation, which means the US has stronger bargaining power than China. Additionally, trade wars between China and the US will hurt most countries and the world especially in GDP and manufacturing employment, but benefit their welfare and trade.     

The evolution of competition in innovation resource: a theoretical study based on Lotka–Volterra model

This research uses the Lotka–Volterra model to analyse the competition of innovation resource between two enterprises and studies the dynamic effects of environmental changes through the change of model parameters. The research finds that there are three possible results of the competition in innovation resource. That comprises ‘crowding out effect’, ‘unstable equilibrium’ and ‘stable equilibrium’. The results of competitive evolution are determined by enterprises’ interaction parameters. However, the natural growth rates, the initial resource possessions of both enterprises and the amount of regional innovation resource have a significant impact on the evolution of competition.