滇南荒田铅锌矿床地质特征及找矿标志

荒田铅矿矿床位于华南褶皱系、扬子准地台和唐古拉-昌都-兰坪-思茅褶皱系三大Ⅰ级构造单元衔接过渡部位,属红河断裂北侧的回新-虾洞-荒田-大冷山铅锌银多金属成矿带南东部。矿区主要出露下二叠统茅口组(P1m)、玄武岩组(P1β)、上三叠统火把冲组(T3h),为一套海相碳酸盐岩-基性火山喷发-沉积岩组合。铅锌矿体赋存于玄武质、灰质角砾岩中并受角砾岩性和厚度控制,富厚部位常在褶皱(背斜)的两翼和向斜的槽部。玄武质、灰质角砾岩层和次级褶皱的两翼、槽部是重要的找矿标志。矿床成因属"火山沉积-岩浆期后热液+后期构造改造富集"。     

西宁市建筑施工垃圾的管理及对策研究

近年来我国建筑业发展迅速,西宁市城市化进程导致新建、改建、扩建、拆除工程随处可见,因而在施工阶段产生大量的建筑施工垃圾。如果对这些垃圾不进行有效分析和管理,产量加剧会更加严重,对环境污染也会更严重。本文针对西宁建筑施工垃圾管理现状,提出相对应的有效对策。     

基于马尔科夫分析的航材消耗预测

准确预测航材消耗量对保障飞行意义重大,航材消耗的特点符合马尔科夫过程。马尔科夫预测是关于事件发生概率预测的方法,通过示例分析表明在样本数据符合条件的情况下运用马尔科夫分析对航材消耗进行预测能够取得较好的预测效果。     

RMS5000产品可靠性信息管理系统设计

RMS5000产品可靠性信息管理系统是一套基于产品可靠性设计理念的信息管理系统,该系统从可靠性设计流程标准化、潜在缺陷分析、可靠性应力分析、可靠性测试流程标准化、设计评审审核、经验数据累计等几方面为用户提供了一整套技术有效、管理科学的可靠性技术体系。该系统旨在规范可靠性设计和测试流程、提高可靠性设计效率和产品质量。本文通过图文相结合的方式向用户全面阐述了该系统的设计理念、设计架构、产品开组成以及技术特点。希望能通过本文的介绍,向大家展示一个基于可靠性的产品开发体系模型,给设计人员在产品可靠性设计方面提供更多的帮助。     

Fundamentals and Exchange Rate Prediction Revisited

This paper measures latent fundamental exchange rates with independent component‐based rates constructed from a cross‐section of exchange rates and then uses their deviations from exchange rates to forecast. Empirical results indicate that the independent component‐based model and its Taylor rule and purchasing power parity augmented models are superior to the random walk in predicting exchange rates. These results are robust to several scenarios and are likely to be observed if the U.S. sources and the recursive scheme are applied. Our results reveal that information regarding the third moment of exchange rate changes is helpful to explain exchange rate movements.     

中国制造业并购目标的财务特征及预测

并购目标的选择关系到并购事件的成败,制造业是中国经济的重要组成部分,也是并购发生最频繁的行业,识别制造业目标公司的财务特征并对其进行预测具有很大的理论及现实意义。以2013年中国A股市场制造业并购目标为研究对象,选取代表企业各方面能力的20个财务变量,通过单因素方差分析将其与股本规模类似的制造业非目标公司和非制造业并购目标进行比较,实证分析结果表明制造业目标公司具有负债水平高、收益水平低、每股获利能力强、现金短缺、股权集中度低等特点。另外,制造业目标公司和非制造业目标公司在财务特征上不存在显著差异。采用多重共线性诊断并构建Logit回归模型以预测制造业并购目标,模型总体准确率为70.4%,表明对制造业并购目标进行预测在很大程度上是可行的。     

“中国梦”实现过程的中国经济总量和人民生活水平预测

文章分析了“中国梦”实现的三个阶段中,中国经济总量和人民生活水平将会发生的变化。文章预测：按实际汇率换算,2021年中国经济总量将超越美国成为世界第一经济大国,此时中国的人民生活水平将达到当时的世界平均水平;2049年中国的人民生活水平将达到世界高收入国家90%的生活水平;2065年中国的人民生活水平将达到最发达国家的生活水平,进而全面实现几代中国人梦寐以求的中华民族的伟大复兴。     

开采急倾斜煤层沉陷预测实例研究

沉陷预测是煤炭项目环境影响评价中最核心内容之一,其准确性直接关系到生态保护措施的有效性及可行性。急倾斜煤层由于采深变化大、顶底板条件不同,且没有实测的移动变形参数,采用常规概率积分法预测沉陷范围可能出现的偏差较大。以贵州省仁怀保利煤矿环评为实例,依据不同开采时段,分别采用深度积分及等价工作面方法预测沉陷范围。采用该方法对急倾斜煤层作沉陷预测更加符合实际,为其他类似矿井对环境影响评价的沉陷预测提供了参考。     

基于BG/NBD模型的电信客户终身价值测度研究

在技术水平不断提升以及产品服务趋于同质化的环境下,我国移动通信企业开始由基础网络建设向专业化的增值业务转型,在激烈的市场竞争中,及时准确的挖掘高价值客户,提供个性化的功能业务,提升客户价值,成为各通信企业获取新竞争优势的有效途径之一。在BG/NBD模型的基础上,通过捕捉客户随机交易行为,构建了客户终身价值测度模型,结合电信运营商的客户数据进行客户终身价值预测研究,结果发现,模型在客户整体层面和个体层面均能有效且准确的预测客户终身价值。     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.