金佛山4种植物群落下表土炭屑特征初步分析

通过对比分析金佛山4种植被下表土炭屑形态和大小,揭示出森林群落与草本群落表土炭屑存在着较明显的差异.从炭屑形态上来区分:亚高山草甸表土中炭屑较薄,大部分为长方形或长条形,且其纵向边缘平直,横向断口一端相对平直,另一端较参差;森林群落下表土炭屑都较厚,多为近圆形或矩形,边缘参差不齐.炭屑大小上主要表现为:森林群落表土炭屑较小,平均L/W值为1.56±0.05,且L/W值大于3的炭屑不超过10%;亚高山草甸群落表土炭屑L/W值为1.77±0.10,且仍有超过10%的炭屑L/W值大于3.     

求解亚式期权定价问题的迎风差分方法

期权理论的核心是期权定价问题.研究连续取样的算术平均亚式期权定价问题的差分方法,根据问题所满足的偏微分方程终边值问题,构造出一种隐式的迎风差分格式,论证了差分解的惟一存在性和绝对稳定性,并给出差分解在离散L2范数下的误差估计.数值计算表明本文数值方法是一种高效和收敛的近似方法.     

基于瑞利分布的计量型序贯网图检验

针对瞄准发射系统中着落点位置通常服从瑞利分布的事实,研究了计量型检验问题,建立了基于瑞利分布的序贯网图检验.采用搜索和迭代的计算机程序,可以得到计量型序贯网图检验方案.通过实例将序贯网图与计量型定数检验和计量型序贯概率比检验进行比较.结果显示,计量型序贯网图检验在减小最大样本量方面有更好的效果,更适用于高成本的抽样检验.     

An efficient algorithm to generate candidates in discovering frequent episodes

0 IntroductionData mining is widely used in many research fieldssuch as decision supporting systems[1], bio-informationanalysis[2]and knowledge engineering[3-5]. Most data col-lected from scientific experiments or telecommunicationnetworks have inherent sequential nature inthemand canthus be abstractly viewed as a sequence of events . Onebasic problemin miningsuchevent sequencesis discoveryof recurrent combinations of events , which are calledepisodes. Once frequent episodes are discovered,rul…     

法律智能合约平台模型的研究与设计

现有的智能合约技术本质上只是一段链上代码,而真正的智能合约应是法律合约的数字化,可以实现合约条款的自动执行.为此,介绍了法律智能合约的重要科技,包括法律考量、预言机、事件模型等基础设施,在分析相关机构工作的基础上提出了法律智能合约的5个标准开发步骤,通过对法律流程标准的研究提出了设计智能合约模版的方法.为解决智能合约模...     

大事件建筑规划设计与城市空间协同发展研究

在日趋激烈的全球竞争环境下,大事件营销已成为城市提升竞争力的重要战略工具,大事件建筑也由此应运而生。文章提出了大事件建筑的明确概念,分析了大事件建筑对城市空间结构优化的意义,指出大事件建筑无论在规划选址、建筑设计、后续运营等环节中,都必须坚持“建筑-城市”协同观,并对南京奥体中心案例进行了重点分析。     

基于图像特征的双目测距系统的研究

针对双目视觉测距中测量误差大、图像信息单一、实时性差等问题,提出一种基于ORB(oriented fast and rotated brief)特征的双目测距方法。对视频帧进行中值滤波处理,提取图像ORB特征,通过实验选出匹配效果最好的汉明距离。对筛选后的匹配点进行RANSAC(random sample consensus)模型估计,去除误匹配,分析视差和真实距离的模型关系,构建最优的测距模型并在实验平台上进行验证。结果表明:所提方法比其他双目测距方法具有测距精确、运行速度快、鲁棒性强的优势,能够实时显示图中特征的距离信息。     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

The Information Content of Equity Block Trades on the Warsaw Stock Exchange: Conventional and Bootstrap Approaches

This paper focuses on the Polish stock market by analysing the information content of 95 equity block trade transactions executed on shares of companies constituting the WIG20 index. A normalized conventional approach and a bootstrap approach are used to draw inferences. These approaches make use of a multivariate regression model with two explanatory variables: a market return and a dummy variable for the event. Resampling allows construction of an empirical distribution of the normalized test statistic. The outcomes obtained from the application of a normalized conventional approach as well as a bootstrap approach are in line and confirm that equity block trade transactions carry an important signal to investors. Significant abnormal positive (negative) returns are associated with the execution of the equity block trades, the prices of which are higher (lower) than the closing prices 2 days before the execution of the equity block trade transactions. Copyright © 2015 John Wiley & Sons, Ltd.     

决策网络计划中决策单元结构及优化方法拓展

为解决决策网络计划中随机样本空间变化而产生的模型表达上的困难,提出经拓展能够描述多个样本空间的决策单元结构.将随机规划理论引入决策网络计划的优化中,建立了新的考虑期望成本与风险等综合因素的数学模型,并通过算例实现了对模型的求解.计算结果表明:经拓展后的决策单元结构及相应的优化模型能够更为有效地解决不同样本空间下的决策问题,具有较高的理论意义与实用价值.