矿产资源规划的若干问题研究

矿产资源规划是实现资源可持续利用和矿业可持续发展的重要保证.矿产资源规划的编制是…项新的工作,许多问题还处在探索当中.作者结合参与区县级矿产资源规划工作的实际,对矿产资源规划编制工作中存在的若干问题进行了探讨,提出了一系列切实可行的解决办法和措施.在规划的过程中,应注意引导下面的几个问题,以有利于国家体系性规划的顺利进行.     

中国煤炭企业的产能过剩 ——程度测算、演变特征与影响因素

目前,中国煤炭企业产能过剩问题仍然严重,而准确衡量和判断煤炭企业产能过剩程度是科学化解产能过剩的前提。在传统产能利用率估计框架中,引入企业生产率异质性的影响,使用1998-2013年中国煤炭企业数据,对企业层面产能利用率进行测度,并检验其演变特征:2000年前,中国煤炭企业产能利用率逐年降低,2001-2008年呈现迅速上升趋势,随后产能利用率开始下降;煤炭企业产能利用率水平呈现显著的区域以及所有权差异,具有顺经济周期特性。进一步地,通过分析中国煤炭企业产能过剩的成因,发现:过度投资、政府补贴以及信贷扭曲是导致产能过剩的主要因素。因此,化解煤炭企业过剩产能,需要健全企业退出与转型机制、处理好政府与市场的关系,有效发挥市场资源配置作用。此外,要推动煤炭清洁高效的开发利用,形成安全高效清洁的煤炭供应保障体系。     

医疗卫生服务与农村居民幸福感的关系——基于CGSS 2013和CGSS 2015调查数据的实证分析

幸福是每个人的追求,也是整个人类社会所要实现的最终目标。利用中国综合社会调查2013年和2015年的数据,采用文献资料法、实证分析法等研究医疗卫生服务与农村居民幸福感之间的关系,通过Ordered Logit回归方法对中国综合社会调查2013年的数据进行分析后认为,政府医疗卫生服务对农村居民生活幸福感具有一定影响,其中充足性、公共性和便利性这三个维度对农村居民生活幸福感具有显著影响,尤其是便利性对改进农村居民生活幸福感影响较为显著。根据中国综合社会调查2015年数据的描述统计,得出医疗卫生服务在当前最需要加强的公共服务中排在第二位,医疗服务水平的提高具有渐进性与长期性的研究结果。针对实证分析结果以及两次调查的对比,提出加大对农村医疗卫生资源的投入力度,增强医疗卫生服务的公共性以及提高医疗卫生服务资源的便利性等对策,以期进一步促进农村医疗卫生服务改善,提高农村居民生活幸福感。     

江苏省基层全科医生队伍建设现状及思考

分析江苏省近年来基层卫生人才队伍现状和基层全科人才培养现状,发现通过三年制临床医学生的培养加快了面向农村、充实基层的全科医疗队伍的建设规模,并初见成效;但培养质量和服务能力的提升仍面临挑战。针对江苏省高校培养基层全科医学生的现状,探索协同院校教育助力“3+2”助理全科医师培养机制,围绕完善制度、健全组织和岗位职责,增强教学软实力,帮扶助理全科医师规范化培训社区基地建设,打造社区教学示范点等要点问题提出了一系列针对性的建议和举措。     

人道主义:高尔基与中国左翼作家的精神对话

高尔基以人民为本位的无产阶级人道主义迥异于以个人为本位的西方资产阶级人道主义。其人道主义涵纳的底层情怀、平民意识和人民性特质,引发了鲁迅、茅盾、夏衍、艾芜等中国左翼作家的精神共鸣与对话。这种共鸣与对话或表现为高尔基文学创作对左翼作家的直接影响,或彰显为高尔基与左翼作家精神层面的相互呼应。这些影响与呼应的存在,极大丰富了20世纪30、40年代的左翼文学世界,形成左翼文学创作中一道独特而亮丽的风景。     

欧洲学分转换与积累系统:发展历程、运行机制及启示

创立于1989年的欧洲学分转换与积累系统经历了萌芽、确立、推广和完善四个阶段,从仅具有转换单一功能发展到具有转换与积累双重功能,从只为高等教育服务拓展到为终身教育服务,在欧盟教育发展中扮演了重要角色。分析欧洲学分转换与积累系统的运行机制,剖析其关键要素、运行流程、证明文件等,对于中国区域学分银行的建设具有如下启示:加强法律政策保障、着眼组织保障、强化校际合作、开发系统平台和完善核心要素。     

Multiplicative Adjustment Method for Semiparametric Regression with Mixing Dependent Data

In this article, we extend a semiparametric regression estimator with multiplicative adjustment to time series context. The asymptotic theory and results from a simulation study are discussed. Theoretical results and numerical comparison show that, in the time series case, the semiparametric estimator is better than the traditional local polynomial estimator in a wide neighbourhood around the true regression function.     

Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression

The monitoring of process/product profiles is presently a growing and promising area of research in statistical process control. This study is aimed at developing monitoring schemes for nonlinear profiles with random effects. We utilize the technique of principal components analysis to analyze the covariance structure of the profiles and propose monitoring schemes based on principal component (PC) scores. The number of the PC scores used in constructing control charts is crucial to the detecting power. In the Phase I analysis of historical data, due to the dependency of the PC-scores, we adopt the usual Hotelling T ² chart to check the stability. For Phase II monitoring, we study individual PC-score control charts, a combined chart scheme that combines all the PC-score charts, and a T ² chart. Although an individual PC-score chart may be perfect for monitoring a particular mode of variation, a chart that can detect general shifts, such as the T ² chart and the combined chart scheme, is more feasible in practice. The performances of the schemes under study are evaluated in terms of the average run length.     

Semi-Parametric Density Estimation for Time-Series with Multiplicative Adjustment

In this article, we extend a class of semi-parametric density estimators to time-series context. The asymptotic theory and simulation study are discussed. Theoretical results and numerical comparison show that in the time-series case, the estimators in this class are better than, or at least competitive with, the traditional kernel density estimator in a broad class of densities.     

Empirical Likelihood Inference for the Parameter in Additive Partially Linear EV Models

In this article, we consider empirical likelihood inference for the parameter in the additive partially linear models when the linear covariate is measured with error. By correcting for attenuation, a corrected-attenuation empirical log-likelihood ratio statistic for the unknown parameter β, which is of primary interest, is suggested. We show that the proposed statistic is asymptotically standard chi-square distribution without requiring the undersmoothing of the nonparametric components, and hence it can be directly used to construct the confidence region for the parameter β. Some simulations indicate that, in terms of comparison between coverage probabilities and average lengths of the confidence intervals, the proposed method performs better than the profile-based least-squares method. We also give the maximum empirical likelihood estimator (MELE) for the unknown parameter β, and prove the MELE is asymptotically normal under some mild conditions.