美国中产阶级的“美国梦”研究——以《兔子,快跑》为例

"美国梦"的概念的出现,反映了美国人积极向上的精神,即人只要努力奋斗就能过上体面人的生活。围绕这种共识,约翰·厄普代克出版的《兔子,快跑》一书,生动地刻画了在美国梦理念下哈利·阿斯特罗姆的人生历程,折射了美国中产阶级面临的困惑与迷茫,并对美国梦进行了深刻的反思。     

瞬变电磁法在一四井煤矿采空区探测中的应用

采用瞬变电磁法(TEM)在河南省新密市境内探查一四井煤矿采空区积水分布情况、断层等构造的含导水性情况及二1煤底板的富水性情况,为矿井防治水工作的合理实施提供依据.     

Poisson方程各向异性一阶混合元格式

研究了Poisson方程在各向异性网格下的一阶混合元格式，在不引入传统投影算子的情况下，直接利用插值技巧得到了与以往文献相同的误差估计．     

用小波变换和奇异指数表征蔡氏混沌信号键带

计算了蔡氏电路混沌信号的离散二进小波变换及奇异指数，结果发现，当尺度2∧5上的小波变换结果的模大于某一阈值时，其所对应的信号段就是键带，在混沌信号的键带处，奇异指数限大的正值，而在非键带处，奇异指数取负值或小的正值。     

论国际商事仲裁中的公共政策保留

公共政策条款是<纽约公约>中关于拒绝承认和执行外国仲裁裁决的重要抗辩理由之一,也是各国法院保护其法律制度的一道安全阀.但由于公约以及各国的立法均未对公共政策作一个明确的界定,其在实践中便存在被各国滥用的危险.随着仲裁在国际商事争议解决中的作用和价值得到越来越多国家的重视,公共政策抗辩正在呈现窄化的趋势,这将有助于仲裁在更为自由和自治的基础上继续发展.     

New crystal structure of molecular complex 1-piperidine carboxylate-piperidinium-H<Subscript>2</Subscript>O studied by X-ray single crystal diffraction

Piperidine absorbs CO2 and H2O in air to form a molecular complex： piperidium-l-piperidinecarboxylate-H2O. The structure of the complex was characterized by X-ray single crystal diffraction. The crystal structure was determined to be triclinic, space group P1^-with a=0.648 6（8） nm, b=0.809 200） nm, c= 1.357 1（16） nm, a=96.96706）°, β =102.506（15）°,γ=104.202 05）°, Z=2. The complex is stabilized via five hydrogen bonds between the three components, N-O electrostatic interaction and O-O interaction （electron transfer） betweenl-piperidinecarboxylate and H2O. Due to electron transference of carbamate ion, the oxygen atom in water molecule is strongly negatively charged and the O-H bond is considerably shorter than that of the free molecule of water. The formation of the molecular complex is a reversible process and will decompose upon heating. The mechanism of formation and stabilization is further investigated herein.     

浅谈变电站自动化系统调试

文章从变电站现场调试工作前的准备到设备投运的整个工作程序和调试方法及注意事项进行了详细介绍,并说明了细节问题应注意的事项。     

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.     

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.     

NGDcrm:a numeric graph dependency-based conflict resolution method for knowledge graph

Knowledge graph(KG)conflict resolution is to solve knowledge conflicts problem in the con-struction of KG.Aiming at the problem of KG conflict resolution,a KG c...