论《集注分类东坡诗》的历史阐释

《王状元集百家注分类东坡先生诗》中注者众多,且距离苏轼生活的年代较近。这些注者根据苏诗与当代史实密切相关的特点,运用孟子提出的"以意逆志"与"知人论世"相结合的诗歌阐释方法,考证与苏诗相关的大小事件,展现诗歌创作的背景,揭示引发诗歌创作的事因,从而解释全篇之意。对使用"赋"体、"比兴"体、典故等不同表现方法的诗句,都能以"本事"为根据,准确地解释其意义,对后来的苏诗注有重要的启发。     

论网络语境下当代青年的爱国主义教育

随着互联网时代的到来,网络语境下当代青年的爱国主义教育缺失日渐凸显,在部分青年群体中存在潜在的网络极端化爱国主义舆论倾向。分析发现:从问题的出现或偶然性事件的刺激到多数个体发表的评价性意见,再到"群体极化"现象的出现,最终极端化爱国主义舆论在互联网上广泛传播是网络极端化爱国主义舆论的基本形成机制。如此网络极端化爱国主义舆论不可避免地对国家、社会、制度和青年自身产生一系列的危害。网络语境下当代青年的爱国主义引导是新时期爱国主义教育的重要课题:应该正视青年的爱国情感,加强爱国主义教育;立足祖国长远利益,培养青年的理性爱国情感;增强舆论引导,减少青年非理性情绪的蔓延,对青年的网络极端化爱国主义舆论进行积极有效的引导。     

基于案例的水体污染公共安全事件诱发过程分析

把握水体污染公共安全事件诱发过程和阶段是合理预警与应对的前提。以松花江水体污染公共安全事件为例,采用案例研究,归纳分析水体污染公共安全事件诱发过程的六个阶段：常态水体污染阶段,水体污染积累阶段,水体污染警告阶段,水体污染人工干预阶段,水体污染危害潜伏阶段,触发事件阶段。     

Linearity of regression for non-adjacent record values

Let X₁,X₂,… be a sequence of iid random variables having a continuous distribution; by R₁,R₂,… denote the corresponding record values. All the distributions allowing linearity of regressions either E(R_m+k|R_m) or E(R_m|R_m+k) are identified.     

大型医院血液库存系统订货点决策的仿真研究

血液是一种典型的易变质物品。本文以国内某大型医院的血液库存系统为对象,依照其运作流程,基于该系统运行的历史数据建立了一个离散事件系统仿真模型对这一系统进行研究,得到了该系统的最优订货点。本文的研究有助于血液库存管理措施的制定与改进,对其它易变质物品库存系统的管理也有一定的借鉴作用。     

影响川南浅丘区田坎玉米产量的气候条件分析

利用积分回归方法分析气候因子对川南浅丘区田坎玉米产量的影响。结果表明 ,日照、温度及降雨日数是影响田坎玉米产量的主要因子。苗期的高温 ,生育中期光照不足及灌浆成熟期的雨日偏多均不利于提高田坎玉米产量     

浅议会计的发展方向——事项法会计

与价值法会计相比,事项法会计的优点在于信息容量大;能够满足不同使用者对会计信息"个性化"需求;能够增强会计信息的及时性、客观性;能够提高会计信息的决策相关性.计算机和网络技术的发展为事项法会计的发展提供了技术支持,事项法会计必将取代价值法会计,引领会计未来的发展方向.     

A Goodness-of-Fit Test for Location-Scale Max-Stable Distributions

In this article, a technique based on the sample correlation coefficient to construct goodness-of-fit tests for max-stable distributions with unknown location and scale parameters and finite second moment is proposed. Specific details to test for the Gumbel distribution are given, including critical values for small sample sizes as well as approximate critical values for larger sample sizes by using normal quantiles. A comparison by Monte Carlo simulation shows that the proposed test for the Gumbel hypothesis is substantially more powerful than some other known tests against some alternative distributions with positive skewness coefficient.     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

Extreme Quantile Estimation for Autoregressive Models

ABSTRACT

A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time series data based on extreme value theory. We build the connection between the second-order conditions for the autoregression coefficients and for the conditional quantile functions, and establish the asymptotic properties of the proposed estimator. The finite sample performance of the proposed method is illustrated through a simulation study and the analysis of U.S. retail gasoline price.