信息不对称下的逆向供应链激励契约设计

文章运用委托一代理理论,研究了一个包含单一供应商和单一零售商的逆向供应链系统,讨论了逆向选择下此系统中零售商的激励扭曲问题,并求出了该情形下供应商用于协调逆向供应链的最优契约。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

Calibration of deterministic NWP forecasts and its impact on verification

Deterministic forecasts (as opposed to ensemble or probabilistic forecasts) issued by numerical weather prediction (NWP) models require post-processing. Such corrective procedure can be viewed as a form of calibration. It is well known that, based on different objective functions, e.g., minimizing the mean square error or the mean absolute error, the calibrated forecasts have different impacts on verification. In this regard, this paper investigates how a calibration directive can affect various aspects of forecast quality outlined in the Murphy–Winkler distribution-oriented verification framework. It is argued that the correlation coefficient is the best measure for the potential performance of NWP forecast verification when linear calibration is involved, because (1) it is not affected by the directive of linear calibration, (2) it can be used to compute the skill score of the linearly calibrated forecasts, and (3) it can avoid the potential deficiency of using squared error to rank forecasts. Since no single error metric can fully represent all aspects of forecast quality, forecasters need to understand the trade-offs between different calibration strategies. To echo the increasing need to bridge atmospheric sciences, renewable energy engineering, and power system engineering, as to move toward the grand goal of carbon neutrality, this paper first provides a brief introduction to solar forecasting, and then revolves its discussion around a solar forecasting case study, such that the readers of this journal can gain further understanding on the subject and thus potentially contribute to it.     

Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques

Emergency Departments (EDs) can better manage activities and resources and anticipate overcrowding through accurate estimations of waiting times. However, the complex nature of EDs imposes a challenge on waiting time prediction. In this paper, we test various machine learning techniques, using predictive analytics, applied to two large datasets from real EDs. We evaluate the predictive ability of Lasso, Random Forest, Support Vector Regression, Artificial Neural Network, and the Ensemble Method, using different error metrics and computational times. To improve the prediction accuracy, new queue-based variables, that capture the current state of the ED, are defined as additional predictors. The results show that the Ensemble Method is the most effective at predicting waiting times. In terms of both accuracy and computational efficiency, Random Forest is a reasonable trade-off. The results have significant practical implications for EDs and hospitals, suggesting that a real-time performance monitoring system that supports operational decision-making is possible.     

基于IAHP的流程重组预警指标体系设计

根据业务流程重组预警的实际需要,结合业务流程重组评价指标,构建了涵盖流程效率、资源利用率、流程成本及顾客满意度四个方面的业务流程重组预警的指标体系。最后用改进的层次分析法(IAHP法),对该指标体系进行了评价,得出了各指标对业务流程重组影响的权重。     

物业税的开征对房地产市场的调控作用

从长期看,房价会随着经济的增长而增长。但我国房价近年来每年以30%～50%的速度增长肯定是有问题的。高房价成了目前中国社会各种矛盾的焦点之一。政府可以通过法律与制度来调整目前国内房地产市场严重的利益不均衡。而税收政策就是其中不可或缺的工具。文中从物业税设置的必要性、可行性等方面加以论述,讨论了物业税开征对房价的调控作用。     

基于时间序列的BP神经网络预测鄂尔多斯市的GDP

国内生产总值(GDP)是制定经济发展战略、规划、年度计划以及各种宏观经济政策的重要依据,研究GDP预测方法具有重要的现实意义。在对鄂尔多斯市GDP数据序列分析的基础上,考虑到BP神经网络可以通过简单非线性函数的复合能够实现对于复杂函数的映射,本文采用BP神经网络方法来预测鄂尔多斯市的GDP,取得了较好的预测效果。     

我国非制造业上市公司财务失败预警模型的实证研究

财务失败预警模型在世界范围内的应用已经很广泛了,但其在我国却仍处于研究探索阶段.笔者对目前在实践中最常用的Altman非制造业上市公司财务失败预警模型(Z3模型)在我国上市公司中的应用进行了实证研究,并对其研究过程及结果进行了分析、总结和评价,最后根据其在我国的实际应用中所存在的问题提出了一些修改建议并通过具体的实例研究对修改结果做了进一步的验证。     

语域预测在听力教学中的应用

语域理论是系统功能语言学中的一个重要理论。系统功能语言学在语境下研究语域,语域和语境是互为预测关系。将语域预测应用到大学英语听力教学可以提高学生的语言意识和元语言能力,从而可以提高学生的听力理解水平。     

基于偏离度分析法的江苏省旅游就业潜力预测

当前，旅游业的就业功能受到广泛关注和普遍重视，预测和把握旅游就业潜力对促进一个国家和地区旅游业的发展乃至国民经济的发展意义重大。在借鉴已有相关研究成果的基础上，以江苏省为例，运用偏离度分析法对其旅游就业潜力进行了具体测算，以期对江苏省旅游就业工作的发展有所裨益。