基于ＮＰ准则的属性关联度量及门限确定方法

在目标空域密集环境下,属性辅助运动状态数据关联是目前公认的解决数据关联性能严重下降问题的潜在有效途径。从Neyman Pearson准则出发,在分析经典运动状态信息关联度量及门限确定方法合理性的基础上,给出属性辅助数据关联中属性度量及对应门限的确定方法。仿真结果表明,与惯用的固定门限相比,该方法确定的度量及门限具有稳定的关联性能。     

镇保隧道左幅进口洞口段变形及坍方处治方案

镇保隧道左幅进口进洞前后，洞口边仰坡变形开裂、洞内右侧初期支护大范围变形侵界、浅埋段地表沉降开裂滑塌及大坍方，文章阐述了如何对以上情况进行处理。     

国有企业人力资源管理中存在的问题及对策

21世纪人力资源管理面临着越来越大的挑战,现在国有企业人力资源管理中存在一些问题,如：人力资源管理意识淡薄、领导重视不够等,针对这些问题提出对策,加强国有企业人力资源管理,提高管理水平。     

Research on catalysis of sodium-metallochlorophylls in Ni/MH battery

The effects of sodium-metallochlorophylls including sodium-iron chlorophyllin, sodium-copper chlorophyllin and sodium-magnesium chlorophyllin on performance of Ni/MH battery were investigated. The results show that sodium-iron chlorophyllin can effectively activate the gases produced during charge in Ni/MH battery, therefore reducing the reduction potential of oxygen and the oxidation potential of hydrogen, and the increased speed of inner pressure of battery is decreased significantly. With the aid of DMol3 software, the activation process of oxygen and hydrogen by sodiummetallochlorophylls was simulated and analyzed. It was found that the catalysis behavior is in agreement with the experimental results, and the activation process also gets a reasonable explanation from the calculated results.     

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.     

A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility

In a conditional predictive ability test framework, we investigate whether market factors influence the relative conditional predictive ability of realized measures (RMs) and implied volatility (IV), which is able to examine the asynchronism in their forecasting accuracy, and further analyze their unconditional forecasting performance for volatility forecast. Our results show that the asynchronism can be detected significantly and is strongly related to certain market factors, and the comparison between RMs and IV on average forecast performance is more efficient than previous studies. Finally, we use the factors to extend the empirical similarity (ES) approach for combination of forecasts derived from RMs and IV.     

Engineering of multiferroic BiFeO3 grain boundaries with head-to-head polarization configurations

Confined low dimensional charges with high density such as two-dimensional electron gas (2DEG) at interfaces and charged domain walls in ferroelectrics show gre...     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Germline-specific dgcr8 knockout in zebrafish using a BACK approach

Zebrafish is an important model to study developmental biology and human diseases. However, an effective approach to achieve spatial and temporal gene knockout in zebrafish has not been well established. In this study, we have developed a new approach, namely bacterial artificial chromosome-rescue-based knockout (BACK), to achieve conditional gene knockout in zebrafish using the Cre/loxP system. We have successfully deleted the DiGeorge syndrome critical region gene 8 (dgcr8) in zebrafish germ line and demonstrated that the maternal-zygotic dgcr8 (MZdgcr8) embryos exhibit MZdicer-like phenotypes with morphological defects which could be rescued by miR-430, indicating that canonical microRNAs play critical role in early development. Our findings establish that Cre/loxP-mediated tissue-specific gene knockout could be achieved using this BACK strategy and that canonical microRNAs play important roles in early embryonic development in zebrafish.