建设社会主义核心价值观应当加强宪法教育

党中央关于建设社会主义核心价值观的英明决策,需要具体的方案和手段予以实施。从国家和社会的整合意义看,宪法的功能就是为公民树立核心价值观体系,因此,宪法教育之于建设社会主义核心价值观具有显著的价值和功能。宪法教育可以为马克思主义指导思想和中国特色社会主义共同理想在中国公民心中扎根、为重建以社会主义荣辱观为基本内涵的社会信仰、为培养一代具有以爱国主义为核心的民族精神的合格公民以及为人们发扬以改革创新为核心的时代精神进行激励等方面发挥重大作用。故,建设社会主义核心价值观,应当加强宪法教育。     

跨国公司与民族地区的经济安全

自上世纪70年代末中国开始吸引外资以来,跨国公司的活动对中国经济的影响日益增强。对于民族地区吸引外资,目前尚存在诸多误区,主要是片面从数量上强调GDP,而忽视跨国公司利润的所有权问题,夸大跨国公司对民族地区的正效应,低估甚至忽视其对民族地区经济安全带来的威胁。应该从正、负两面考察跨国公司对民族经济的影响,客观地剖析两大利益主体之间的博弈,充分意识到跨国公司的进入对民族地区经济安全构成的威胁,并制定有效的应对措施。     

谈现代企业加强诚信管理的意义和途径

诚信管理是政府和企业在生产经营过程中,通过进行诫信制度建设,树立诚信理念,打造诚信品牌,以及对企业各部门、组织、管理者、员工加强诚信教育和管理监督的活动,使企业做到货真价实、讲究信誉、信守合同和绝不欺诈,以期达到赢得社会普遍信任与和谐发展的目的。现代诚信管理既包括企业内在的诚信经营管理,也包括政府有关部门对企业进行的外在诚信监督管理,二者有机结合可以打造诚信社会,为企业开展公平竞争和实现可持续发展创造良好的诚信市场环境。     

试论股份回购对上市公司财务管理的影响

指出股份回购是抵御外来收购、优化资本结构、提升公司股价、分配股利以及经营者股权激励等的有效手段,有助于优化资本结构、完善公司治理,分析了股份回购对上市公司资本结构、股票价格、股利政策、公司风险、财务指标等问题所产生的影响.     

基于Web的绿色制造系统

随着环境问题的日益严重，要求制造技术的发展必须与自然资源和环境相结合．由此提出了一个新的制造概念——绿色制造．本文对绿色制造的概念及其内容体系进行了论述，并就以web技术为支撑平台的绿色制造技术做了进一步地探讨．     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Summary.  We discuss the inversion of the gas profiles (ozone, NO₃, NO₂, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for a more comprehensive statistical inversion are presented. A direct inversion leads to a non-linear model with hundreds of parameters to be estimated. The problem is solved with an adaptive single-step Markov chain Monte Carlo algorithm. Another approach is to divide the problem into several non-linear smaller dimensional problems, to run parallel adaptive Markov chain Monte Carlo chains for them and to solve the gas profiles in repetitive linear steps. The effect of grid size is discussed, and we present how the prior regularization takes the grid size into account in a way that effectively leads to a grid-independent inversion.