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关键词： 脑梗死   住院费用   影响因素   路径分析  

摘要： 目的分析兰州市脑梗死出院病例住院费用情况及影响因素,为降低脑梗死病例住院费用提供理论依据。方法该研究数据来源于甘肃省卫生健康委员会卫生信息统计中心的网络直报数据,选取2016年1月1日至2020年12月31日甘肃省兰州市全部公立医院脑梗死出院病例的病案首页数据。采用SPSS 25.0和Amos 21.0软件分析数据,采用多元逐步回归分析和路径分析法对住院费用的影响因素进行分析。结果共纳入70882例脑梗死病例,病例住院总费用为64096.4661万元,例均住院费用均数为9042.70元,中位数为6127.22元;住院天数中位数为9 d。药品费(35.31%)、诊断费(28.25%)、治疗费(14.38%)是住院费用主要构成。2016—2020年脑梗死病例的例均住院总费用、药品费、诊断费、耗材费与年份呈负相关(rs值分别为-0.131、-0.247、-0.134、-0.160,P<0.01),治疗费、医疗服务费、护理费、康复费与年份呈正相关(rs值分别为0.133、0.136、0.094、0.103,P<0.01)。路径模型分析结果显示,药品费是住院费用首要影响因素,总效应值为0.685、其次是医院级别(0.191)、住院天数(0.187)。结论兰州市脑梗死住院病例的住院费用负担较重,建议相关部门加强脑梗死费用的精益化管理,进一步降低脑梗死病例药品费,同时落实分级诊疗制度、合理缩短住院天数。

关键词： 政府债务   居民消费   资产负债表   长期视角   取精用宏   经济复苏   高质量发展   关键问题  

摘要： 高质量发展背景下,保持资产与债务平衡至关重要。通过分析资产负债表,能够更加清晰地理解居民消费、企业投资、政府债务等关键问题,可以从中长期视角把脉经济发展。那么,在当前的经济复苏进程中,我国资产负债表状况如何?本期“特别关注”博采众论、取精用宏,以飨读者。

关键词： 无形资产   新会计制度   固定资产   事业单位   物质载体   采购   物质条件   生存与发展  

摘要： 一、前言固定资产就是事业单位在工作过程中需要用到的“物质载体”,这种物质载体的丰富性关乎事业单位的生存与发展。采购的各种有形、无形资产,这些资产是保证事业单位正常工作的重要物质条件[1]。事业单位一定要对新会计制度进行深入探讨,并且结合事业单位的实际情况,应用新型的固定资产管理模式。

关键词： 财务性股权投资   借款费用   资本化  

摘要： 财务性股权投资的借款费用是否可以资本化,在借款费用准则及其实施细则中并未明确规定,金融工具准则对相关资产的初始计量规定上存在前后矛盾的情况,目前针对此类投资的借款费用均进行费用化处理,计入当期损益。在我国金融市场逐渐完善并发展壮大的过程中,越来越多的企业将通过多种方式融资所取得的资金用于投资活动,如果仍将借款费用费用化将对企业的经营造成较大影响,基于这方面的考量,对财务性股权投资借款费用是否能够资本化进行分析并提出了相关建议。

关键词： 数字化转型   烟草行业   利润中心   现状   对策  

摘要： 随着企业的发展和会计人员从“核算型”向“战略型”转型,为进一步成为企业战略的深度践行者,从财务视角为管理者提供成熟的分析信息和决策依据,一是要选择一个能相对全面衡量企业经营水平的财务管理工具和配套的财务指标,二是要跟上国家数字化转型的脚步。在传统的成本核算基础上,企业为了更全面地评价管理水平,可以尝试运用利润中心建设的思路。

关键词： 费用构成   成本费用   组成分析   南水北调   北延应急供水工程  

摘要： 南水北调东线一期工程北延应急供水工程已于2019年5月通水。东线一期工程的成本费用直接影响着北延应急供水水价。在南水北调东线一期工程可行性研究成本费用组成、分类基础上,利用工程初步设计阶段投资,按照工程区段分析东线一期工程成本费用,并提出适时调整水价建议,为合理制定北延应急供水水价提供参考。

摘要： Pigeon pea is a drought-tolerant crop mainly grown by small-scale growers in arid and semi-arid regions mostly for income generation and enhancing food security. Pigeon pea is a very essential crop, particularly in destinations accustomed to drought. Nevertheless, its production remains low. As is with other small holder farmers, pigeon pea producers are often faced with resource-use inefficiency and high costs of inputs in production implying that the proper and efficient allocation of resources is vital to guarantee pigeon pea farmers attainment of additional benefits from their input. Hence, this study aimed at estimating the profit and technical efficiency, assessing profitability and examining factors influencing profit and technical efficiency of pigeon pea production in Machakos County, Kenya. The sample size was 346 respondents targeting pigeon pea farming households' population. This study relied on primary data collected using structured questionnaire administered to the farmers. Machakos County was purposively selected for the study. The inefficiency effect model and a Cobb-Douglas stochastic frontier production analysis method were utilized to estimate profit and technical efficiency and determine the factors that determine pigeon pea farmers' efficiency. Furthermore, thorough evaluation of profit level was made possible by gross margin analysis. According to the pigeon pea gross margin analysis, pigeon pea farmers in Machakos County had a gross margin of Kshs 3470.60 per acre. The sample pigeon pea farms had profit efficiency levels ranging from 0.11 to 0.9, while their technical efficiency levels ranged from 0.09 to 0.86, according to the findings. The estimated mean level of technical efficiency of the sample farmers, which was approximately 59%, demonstrates the likelihood of increasing the quantity of pigeon pea yield by 41%. Farmers can only achieve this by effectively utilizing the resources at their disposal. On the other hand, mean profit effici

摘要： In this study, I model the debated statements of Federal Reserve regarding transitory inflation in New Keynesian context and imperfect information about cost shock, where it is formulated as sum of unobserved persistent and transitory components. Specifically, I consider the case, in which policy maker and private agents are uninformed about the components and subject to a gradual recognition from observation using a Kalman filter. Policy maker solves the inference problem to learn the components, and, in turn, private agents rely on central bank speeches to learn policy maker's inferences. Then, based on policy maker's estimation, private agent's problem and, so New Keynesian relations are formulated. The responses with filtered variables incorporate progressive learning. In particular, when the true realization of the shock and its perception is persistent, the increase (decline) in inflation (output), therefore in interest rate is less compared to that of perfect information responses, creating a relatively desirable outcome for the policy maker. If instead shock is perceived as transitory and communicated as such, which was the case in Fed's statements, the response of inflation, hence the required policy response is significantly more pronounced. This leads me to conclude that it may not be the best response to announce that shock is transitory while it is not the case. Then, I continue studying welfare loss under discretionary policy when the estimation and the actual realization is persistent. I find that loss under imperfect information with an inferred persistent shock is smaller relative to that of perfect information when variance of the noise increases, and shock becomes less persistent. Lastly, I extend the model by introducing a signal on transitory component.

摘要： Cloud computing is emerging in the IT industry and is being established as the standard way of providing high quality applications and services, being able to scale when neces- sary and maintaining low latency. This high availability allows new users and devices to connect which may possibly cause some saturation in the network. The edge computing paradigm has recently emerged to address raising concerns on the ability of cloud computing infrastructures (and data links connecting end user devices to such infrastructures) to receive, process, and reply to operations generated by edge devices (e. g., sensors or other more sophisticated devices) in an expedited way. One way to mitigate this challenge, put forward by the edge computing paradigm, is to decompose the computations executed by applications in smaller components that can then be pushed into the devices (either on the cloud or edge) that are in close vicinity to the location of data generation and consumption. This allows to remove pressure from network links (by avoiding to move data) and can, in particular cases, speedup the production of results to be consumed in the edge. However, achieving this intuitive vision is cumbered by several challenges, in particular: 1. How to decompose the logic of a (large) computation into smaller units; 2. Understanding the requirements for the correct processing of such smaller units; 3. Define mechanisms to compose the results of the smaller computations; 4. Effectively pushing the computations to the locations where they will be conducted and ensuring their execution despite failures. In this thesis we will start to pave the way towards such a vision by studying how to decompose computations, classifying relevant properties of a subset of classes of these computations, and exploring the design space of the runtime support to execute smaller units in an integrated way across the cloud and edge. We will focus on settings with multiple cloud datacenters potentially with a f