非均匀介质下的可调光流控器件及其生化应用

 探讨了非均匀介质下不同光流控器件及其生化应用。在微流控芯片上，主要从2个方面开发和实现各类具有光波导、透镜、细胞计数、化学检测等功能的新器件。首先，在液液非均匀介质下，通过调节沟道内液体流速，控制液体间的对流扩散来实现液体在微腔内的渐变折射率分布和阶跃分布，从而得到其特殊的光学特性，例如光束分离、弯曲、自聚焦等。其次，在固体非均匀介质下，利用特殊的微流结构与液体相结合，实现更加灵敏可调的新型探测手段。这些新型技术手段分别在生物传感、能源生产、细胞探测及海水检测等诸多应用中可发挥关键性作用。     

一个典型弹性梁方程涉及第一特征值的正解

运用锥理论和不动点指数方法,在与相应的线性算子第一特征值有关的条件下,获得了一个典型弹性梁方程正解的存在性,改进了相关文献的结论.     

2005～2017年快速城市化背景下洱海流域不透水表面时空格局分析

近年来随着我国经济建设的不断加快以及城市化的快速发展,工业和人口的大规模聚集造成了不透水表面的快速扩张,这对城市生态环境产生了严重的影响。作为城市化水平的关键指示因子,不透水表面已经被广泛应用在城市生态环境评估中。为了定量的分析洱海流域不透水表面拓展的时空动态特征规律,本文以大理市所在的洱海流域为研究对象,利用2005年至2017年的大理市Landsat 5、Landsat 8遥感影像,提取归一化水体指数(MNDWI)和归一化差值不透水表面指数(NDISI)对洱海流域的城市、道路进行空间分析并提取出不透水表面信息进行空间格局分析。研究结果表明：2005~2017年间,随着城市化的快速发展,洱海流域的植被覆盖率和水体等自然地物覆盖率处于下降状态,而不透水覆盖率从11.6%上升到19.8%,呈现快速持续上升趋势。由洱海水质和其流域内不透水覆盖率线性拟合结果可以得到两者具有一定的负相关性。可以预见洱海水质未来发展中随着不透水覆盖率的增长而下降。因此,在城市发展的同时,应该注重对洱海的综合治理,不能任水质自行调节。本研究结果为洱海流域内城市规划管理和流域(水)环境治理提供参考信息,具有重要的现实意义。     

Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data

Given the confirmed effectiveness of the survey‐based consumer sentiment index (CSI) as a leading indicator of real economic conditions, the CSI is actively used in making policy judgments and decisions in many countries. However, although the CSI offers qualitative information for presenting current conditions and predicting a household's future economic activity, the survey‐based method has several limitations. In this context, we extracted sentiment information from online economic news articles and demonstrated that the Korean cases are a good illustration of applying a text mining technique when generating a CSI using sentiment analysis. By applying a simple sentiment analysis based on the lexicon approach, this paper confirmed that news articles can be an effective source for generating an economic indicator in Korea. Even though cross‐national comparative research results are suited better than national‐level data to generalize and verify the method used in this study, international comparisons are quite challenging to draw due to the necessary linguistic preprocessing. We hope to encourage further cross‐national comparative research to apply the approach proposed in this study.     

互联网新闻媒体报道对我国股市的影响分析

互联网新闻媒体作为投资者的重要信息来源,对投资者的投资决策具有重要影响,同时也会影响股票市场的具体表现.为研究互联网媒体上发布的新闻报道对股票市场的影响,本文通过互联网搜索引擎采集了与股票市场相关的大量新闻报道,从新闻媒体关注度和情感指标两个方面分析其对我国股票市场的影响.通过对2012年1月1日至2016年4月30日之间上证综指的表现进行实证检验,发现互联网新闻媒体的关注度和情感指标对我国股票市场的运行均有一定的影响,而舆情指数一方面不受新闻媒体报道数量的影响,另一方面衡量了情感指标的相对影响,是量化新闻媒体报道情感的较佳指标.同时,我们的实证显示众多新闻媒体报道的网站来源中,52.8%的来源中已经包含了99.3%的新闻报道,是对股票市场产生影响的重要来源.通过互联网新闻媒体关注度和情感指标对股票市场影响的研究,可以加强对股票市场走势的分析与预测,为投资者提供投资建议.     

草原退化过程中植物群落的结构异质性分析——以家庭牧场为例

以内蒙古呼伦贝尔草原某家庭牧场作为研究尺度,以卧羊圈为中心沿辐射状将该家庭牧场划分为重牧区、中牧区、轻牧区和无牧区(围栏内),同时与村落自由放牧区(即过牧区)进行比较,开展植物群落结构空间异质性研究。结果表明:该家庭牧场植物群落种类较为丰富,多年生草本植物居多,一年生草本植物随放牧强度的减弱,比例也随之增加;轻牧条件下群落地上生物量最高,适度的放牧干扰有助于植物干物质的积累;轻牧区禾本科植物的重要值较大,说明轻度放牧干扰有利于群落发生恢复演替;重牧区和轻牧区Simpson集中性概率指数、Shannon-Wiener指数、均匀度指数高于过牧区,重牧区和轻牧区植物群落由于竞争达成了较稳定状态,群落较为集中,空间异质性高。     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.