基于随机p-鲁棒优化的国家血液战略储备库选址-库存模型

以往的救灾实践对建立国家血液战略储备体系提出了迫切要求。国家血液战略储备库的建设问题亟待解决。由于血液产品特性以及应急血液保障特性的存在,使得国家血液战略储备库的选址决策具有一定的复杂性。本文将问题定位为选址-库存问题。首先,以应急条件下血液保障及时度最高为目标,构建了一个不确定环境下考虑多情景、多血型、多阶段、带提前期、有容量限制、日常随机需求、有预算约束及协同定位的国家血液战略储备库选址-库存模型。同时,为了规避应急条件下的不确定风险,进一步构建了国家血液战略储备库选址-库存问题的随机p-鲁棒优化模型。该模型为离散非线性混合整数规划模型,难以快速精确求解。故基于模型性质,设计了相应的遗传算法。最后,设计了两组算例验证模型与算法的有效性。其中,第1组算例基于我国大陆地区31个省级血液中心与省级行政区的数据,并根据不同预算值给出6个算例,得到了国家血液战略储备库的选址-库存决策方案。第2组算例为6个不同规模的模拟算例,用来测试不同规模下的算法性能。算例结果表明：遗传算法的性能更好;鲁棒解与确定性模型最优值相差不大（最大差距≤1.08%）,可降低不确定性导致的风险。实践中,可对本文所建模型稍作改进,应用于具有类似特征的易腐品（药品、粮食等）应急物资储备库选址-库存决策。     

关系型贷款、市场结构与小额贷款公司使命漂移

我国小额贷款公司发展极为迅速,但小微企业融资难问题依然严峻,小额贷款公司存在着显著的使命漂移.对于微型金融机构的使命漂移问题,大量的研究主要以财务可持续性为视角.本文结合关系型贷款理论和市场结构理论并构建理论模型,以一个新的视角分析我国小额贷款公司的使命漂移问题,证明了开展关系型贷款和提升小额贷款市场的竞争程度均有利于小额贷款公司发放更多的小额贷款,增加小微企业等融资主体的信贷可得性,从而避免使命漂移.进一步的,通过对一个代表性城市小额贷款公司的相关数据分析,认为小额贷款公司使命漂移的内在原因是关系型贷款技术运用不足和小额贷款市场竞争程度较低.     

Modified genetic algorithm-based clustering for probability density functions

This article modifies two internal validity measures and applies them to evaluate the quality of clustering for probability density functions (pdfs). Based on these measures, we propose a new modified genetic algorithm called GA-CDF to establish the suitable clusters for pdfs. The proposed algorithm is tested by four numerical examples including two synthetic data sets and two real data sets. These examples illustrate the superiority of proposed algorithm over some existing algorithms in evaluating the internal or external validity measures. It demonstrates the feasibility and applicability of the GA-CDF for practical problems in data mining.     

Threshold Estimation for Stochastic Processes with Small Noise

Consider a process satisfying a stochastic differential equation with unknown drift parameter, and suppose that discrete observations are given. It is known that a simple least squares estimator (LSE) can be consistent but numerically unstable in the sense of large standard deviations under finite samples when the noise process has jumps. We propose a filter to cut large shocks from data and construct the same LSE from data selected by the filter. The proposed estimator can be asymptotically equivalent to the usual LSE, whose asymptotic distribution strongly depends on the noise process. However, in numerical study, it looked asymptotically normal in an example where filter was chosen suitably, and the noise was a Lévy process. We will try to justify this phenomenon mathematically, under certain restricted assumptions.     

Case–control genome-wide joint association study using semiparametric empirical model and approximate Bayes factor

We propose a semiparametric approach for the analysis of case–control genome-wide association study. Parametric components are used to model both the conditional distribution of the case status given the covariates and the distribution of genotype counts, whereas the distribution of the covariates are modelled nonparametrically. This yields a direct and joint modelling of the case status, covariates and genotype counts, and gives a better understanding of the disease mechanism and results in more reliable conclusions. Side information, such as the disease prevalence, can be conveniently incorporated into the model by an empirical likelihood approach and leads to more efficient estimates and a powerful test in the detection of disease-associated SNPs. Profiling is used to eliminate a nuisance nonparametric component, and the resulting profile empirical likelihood estimates are shown to be consistent and asymptotically normal. For the hypothesis test on disease association, we apply the approximate Bayes factor (ABF) which is computationally simple and most desirable in genome-wide association studies where hundreds of thousands to a million genetic markers are tested. We treat the approximate Bayes factor as a hybrid Bayes factor which replaces the full data by the maximum likelihood estimates of the parameters of interest in the full model and derive it under a general setting. The deviation from Hardy–Weinberg Equilibrium (HWE) is also taken into account and the ABF for HWE using cases is shown to provide evidence of association between a disease and a genetic marker. Simulation studies and an application are further provided to illustrate the utility of the proposed methodology.     

Mathematische aspekte zu einer methode zur numerischen bestimmung individueller thermodynamischer aktivitätskoeffizienten einzelner ionensorten in konzentrierten elektrolytlösungen

The mathematical problems of the – in an communication [3] described – principle for the calculation of individual thermodynamic activity coefficients of single ionic species in concentrated electrolyte solutions are specified. It is the Newtonian approximation method that makes possible the evaluation of the constants b ₁,…b ₄ in the concentration function (0.1) for the product of the activity coefficients.

The efficiency of the method is represented by the example of the activity coefficients of pure and of – with other electrolytes – mixed solutions of NaCIO₄. The individual activity coefficients of the single ionic species are evaluated for several electrolytes of the concentration range from m = 0 to m = 10 mole/kg and published at another place [3, 17, 18].     

储层建筑结构要素分析及在剩余油挖潜中应用

储层非均质性研究是油田开发后期剩余油挖潜、提高采收率的关键。以河流沉积学理论为指导、以现代沉积、露头调查为依据，利用密井网条件下丰富的测井资料和储层建筑结构层次分析方法对孤岛油田馆陶组上段储层进行了研究。研究认为，孤岛油田馆陶组上段储层可划分为十个层次，并将储层精细解剖到第七层次，识别出主河道、废弃河道、决口水道等七种建筑结构要素。在储层精细对比的基础上，总结出以单一河道砂体追溯为核心建立单砂体建筑结构模型的方法，并探讨了这种地质结构模型对于油田开发挖潜的重要意义。     

粗集与神经网络相结合的股票价格预测模型

粗集和神经网络结合反映了人类智能的定性和定量、清晰和隐含、串行和并行相互交叉混合的常规思维机理。本文建立这样一种混合杂交模型用于股票价格波动趋势的预测,通过粗集对数据的二维约简预处理消除了样本中的噪声和冗余,在提高神经网络预测精度的同时降低了学习负担。为了获得最优的预测精度,本文还利用遗传算法进行属性离散化和网络学习。通过对上证综指的实证研究表明,这种混合杂交模型的性能明显优于BP和GA神经网络模型。     

基于遗传算法的概率准则组合证券模拟求解

针对概率准则意义下的组合证券投资模型,采用随机模拟技术和遗传算法相结合的思 想, 设计出求解算法, 并用Matlab 语言实现. 求解算法适用于证券收益率服从任意分布的情 况, 甚至不考虑证券收益率分布, 用实际数据进行模拟和优化. 实例证明, 该算法有很好的 收敛性及较高的计算效率.     

章炳麟的《成均图》及“音转理论”

文章通过分析<成均图>,并与孔广森的"对转"学说相比较,进一步探讨、评价了章炳麟的"音转理论".