氡气测量在煤矿地质上的应用

针对目前氡气测量的现状,分析了氡气测量在煤矿上应用的地质基础和氡气的运移机制,探讨了氡气异常与不同地质现象的关系。证明氡气测量可在煤矿生产中发挥重要的作用。     

穿越采空区桥隧工程危害效应分析及对策

青岛-银川高速公路的康家沟大桥与庙梁隧道下伏康家沟煤矿采空区,为确保桥隧工程的安全,基于MIDAS/GTS有限元程序构建了FLAC3D三维计算模型,对分期采矿形成地下采空区进行注浆处治,对桥隧工程施工过程中地表变形响应、隧道初衬结构、桥梁和墩台的变形及受力特征等进行数值模拟分析,揭示了不同工况条件下的地表变形程度,确定了地表沉陷盆地特征,对隧道初衬结构和桥梁墩台进行了变形和受力关键部位的预测分析,并提出了有效工程对策.研究成果对指导类似桥隧工程设计和施工有重要指导意义.     

菏泽地区持续开发利用黄河水资源研究

在分析黄河水资源开发利用现状和发展趋势的基础上,对黄河来水量进行了预测,并提出了菏泽地区扩大引黄的可行性和对策措施     

一类高新技术公司风险预测指标分析及评估方法

以管理顾问及咨询公司为背景,来探讨高新技术公司的风险.在分析可能产生的风险基础上,建立风险预测指标体系,并给出风险评估的方法.     

受断层影响地表移动规律的研究

在建立了地表移动观测站及现场观测的基础上,对云盖山煤矿地表移动观测站观测资料进行系统的计算和分析,得出了该区域地质采矿条件下地表移动变形规律及有关参数。并研究分析了该区域在断层影响下地表移动和变形规律的特殊性及其形成原因。为本矿区及类似条件下“三下“采煤技术的研究提供了可靠的理论依据。     

笕桥机场12.6冬季雷雨的分析

雷雨对飞行安全影响很大,一直为航空气象预报所重视．但冬季雷雨极为少见,事先特征也不明显,很容易漏报,给飞行安全带来很大的影响．1997年12月6日,笕桥机场发生了一次漏报的冬季雷雨,这是一个十分难得的个案材料．本文利用天气系统、探空资料、物理量、数值预报产品,对本次过程进行探讨,归纳总结其发生发展的特征,重点说明数值预报产品对预报系统性强对流天气的强度和落区有很好的参考启迪作用.     

基于LSTM神经网络的短期轨道预报

针对基于动力学模型的轨道预报方法对卫星自主轨道预报与大量非合作目标轨道预报中存在建模成本过高和缺少目标空间环境信息的问题,提出一种基于误差数据驱动的神经网络轨道预报方法.该方法在解析法动力学模型的基础上,使用长短期记忆神经网络对历史轨道预报的误差进行学习,预测未来短期动力学模型的预报误差,以此对预报结果进行修正.选用A...     

Geopolitical risk and global financial cycle: Some forecasting experiments

In this study, we investigate the connection between geopolitical risk (GPR) and global financial cycle (GFCy) as well as whether the former has predictive value for the out-of-sample predictability of the latter. We utilize both the historical and recent GPR data and their variants, namely, GPR act covering all “acts” that constitute GPR such as war, nuclear invasion and terrorism, and GPR threat, which represents threats of these acts. We construct a predictive model that accommodates the salient features of the predicted and predictor series while the forecast evaluation is conducted for both in-sample and out-of-sample periods. Our findings reveal that a rise in GPR discourages investments in risky assets and by implication worsens GFCy. The impact is more severe after the global financial crisis (gfc), and the GPR threat exerts more adverse effect on GFCy compared with GPR act regardless of whether historical GPR or recent GPR is used. Meanwhile, the predictive model of GFCy that accommodates the GPR data outperforms the benchmark model that ignores it both in the in-sample and out-of-sample estimates albeit with improved forecast performance during the post-gfc period and at a longer forecast horizon. However, the recent GPR data, which are broader in scope, offer better forecast accuracy than the historical GPR data. Additional analyses involving the vulnerability of global economic conditions reveal similar outcomes as GFCy.     

Forecasting the duration of short‐term deflation episodes

The paper proposes a simulation‐based approach to multistep probabilistic forecasting, applied for predicting the probability and duration of negative inflation. The essence of this approach is in counting runs simulated from a multivariate distribution representing the probabilistic forecasts, which enters the negative inflation regime. The marginal distributions of forecasts are estimated using the series of past forecast errors, and the joint distribution is obtained by a multivariate copula approach. This technique is applied for estimating the probability of negative inflation in China and its expected duration, with the marginal distributions computed by fitting weighted skew‐normal and two‐piece normal distributions to autoregressive moving average ex post forecast errors and using the multivariate Student t copula.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.