突发公共卫生事件的 应急医疗资源配置研究——基于网格化管理和大数据平台

应急医疗资源是应对突发公共卫生事件最重要的物质基础和保障。在对前人研究进行总结的基础上,分析了目前我国在突发公共卫生事件时应急医疗资源调配机制的不足和网格化管理应用到资源配置中的可行性和优越性,以此将大数据平台和网格化管理模式引入到应急医疗资源的配置研究中。在对应急医疗资源的配置过程进行网格化模型设计的基础上,根据我国应急医疗资源配置的需要和特点,确定了网格系统中的基本单元,即网格划分,并选择五层沙漏模型作为突发公共卫生事件时应急医疗资源配置的网格模型架构,以期完善我国的应急资源管理体系,进一步提高我国在突发公共卫生事件时应急医疗资源的调配和管理水平。     

基于大数据的计算机网络信息安全防护措施

大数据是基于云计算技术开发的一种集成技术,可以通过分析、计算和处理大量数据信息来实现这些数据的价值。然而,随着大数据时代的到来,现有的计算机网络面临着新的挑战,特别是网络安全问题逐渐浮现,计算机网络带来的安全隐患导致用户个人的隐私受到了威胁,所以有必要加强网络信息安全工作的部署,以尽早遏制这些安全风险,并确保计算机网络的安全运行。     

大数据背景下医院财务管理的创新路径探索

财务管理在每个单位中发挥着至关重要的作用,同样财务管理是医院管理工作中的重要组成部分,其与医院的发展密切相关。当前,在大数据时代下医院管理模式缺乏创新,已经跟不上信息化时代的发展潮流。为了能够更好地适应大数据时代的发展趋势,医院财务管理应朝着数字化、自动化和智能化的方向发展。     

基于云计算与物联网技术的数据挖掘

在科学技术快速发展的今天,信息通信行业已经与我们的日常生活完全融为一体,并且推动了整个社会的发展,所以在科学技术快速进步的过程中,信息通信相关技术也在不断发展,其中,基于云计算的物联网发展越来越受到人们的重视。论文对基于云计算的物联网数据挖掘模式进行分析及探讨,从而为互联网数据挖掘技术发展提供相应的参考。     

云数据中心网络虚拟化技术的实现路径

当今社会,随着科学技术的迅猛发展,信息技术实现了持续快速发展。在大环境的影响下,云计算与网络虚拟化技术应运而生。该技术的产生和发展促使云数据中心网络的变革与发展成为新一轮技术革命的核心内容之一。当前,IT行业对数据中心网络的需求越来越大。基于此,论文论述了云数据中心网络虚拟化技术的实现路径。     

基于战略视角的财务状况质量分析——以SY重工为例

了解和掌握企业的整体状况及发展质量是利益相关者保护其权益的关键,也是市场经济健康发展的必然要求.财务数据一方面能够体现企业进行的经济业务活动,另一方面又能检验企业经济活动的执行情况.因此,有效分析企业财务状况质量对综合评价企业的质量具有重要意义.文章从战略视角出发,参考SY重工2019年财务报告数据,对其发展战略及管理质量进行了评价.     

Network based evidence of the financial impact of Covid-19 pandemic

How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analyzing the top 50 S&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.     

Do stock market returns predict changes to output? Evidence from a nonlinear panel data model

Recent empirical work suggests a predictive relationship between stock returns and output growth. We employ quarterly data from a panel of 27 countries to test whether stock returns as useful in predicting growth. Unlike previous research, our approach allows for the possible non-linear effect of recessions on the growth-return relationship. There is strong evidence to suggest that a linear model would be misspecified and provide potentially misleading inference. Using a switching regression approach, we find evidence that returns are most useful in predicting growth when the economy is in recession.First version received: November 2002/Final version received: April 2003This paper benefited greatly from discussions with Kalvinder Shields, Mark Harris, Pete Summers, and Vance Martin. Two anonymous referees provided useful comments on an earlier version of the paper for which we are grateful. The usual disclaimer applies to any errors and omissions. Funding from The University of Melbourne greatly assisted in the completion of this paper.     

Economic Growth and Transboundary Pollution in Europe: An Empirical Analysis

The existing empirical evidence suggests that environmental Kuznets curvesonly exist for pollutants with semi-local and medium term impacts.Ansuategi and Perrings (2000) have considered the behavioral basis for thecorrelation observed between different spatial incidence of environmentaldegradation and the relation between economic growth and environmentalquality. They show that self-interested planners following a Nash-typestrategy tend to address environmental effects sequentially: addressingthose with the most immediate costs first, and those whose costs aredisplaced in space later. This paper tests such behavioral basis in thecontext of sulphur dioxide emissions in Europe.     

An Econometric Estimation of Industrial Water Demand in France

This study investigates the structure of industrial water demand byestimating the derived demand for water on a sample of industrialestablishments located in the south-west of France. Productiontechnologies are represented by short-term variable cost functions andapproximated by a translog form. Industrial water use is modeled ashaving three components: the quantity of water bought to a waterutility, the quantity of autonomous water and the quantity of watertreated prior to use. We include in this framework water effluents,which are considered as a by-product of the production process, emittedby firms. Each of the three water components is treated as a separateinput and all are estimated as a system of simultaneous equations. Themodel is estimated on a sample of 51 industrial plants in the Girondedistrict observed from 1994 to 1996 using Seemingly Unrelated Regression(SUR) and Feasible Generalized Least Squares (FGLS). Results ofestimations show that industrial firms are sensitive to water priceinputs. Network water elasticity is estimated at –0.29. It variesfrom –0.10 to –0.79 according to the type of industryconsidered. Autonomous water price elasticity is not significant.Elasticity for treated water is evaluated at –1.42 at themean-sample and varies from –0.90 to –2.21 according to theindustry considered.