高职院校教师工作压力源研究综述

通过对CNKI数据库中涉及高职院校教师工作压力源文献的统计分析表明：高职院校教师承担的工作压力源多达24项，其中主要包括工作负荷大、学生学习成绩差、教师职业发展受限、学校组织管理科学性不高、工资待遇低、科研论著要求高和社会支持较少等。从实施主体来看，缓解高职院校教师工作压力的对策可分为政府、学校和教师个体三类主体。现有研究在以下领域还需加强：提高对高职教师工作压力源的关注度，把握高职教育的办学特点，重视对缓解工作压力措施的可行性研究。     

Bailout uncertainty in a microfounded general equilibrium model of the financial system

This paper develops a micro-founded general equilibrium model of the financial system composed of ultimate borrowers, ultimate lenders and financial intermediaries. The model is used to investigate the impact of uncertainty about the likelihood of governmental bailouts on leverage, interest rates, the volume of defaults and the real economy. The distinction between risk and uncertainty is implemented by applying the multiple priors framework to beliefs about the probability of bailout.Results of the analysis include: (i) An unanticipated increase in bailout uncertainty raises interest rates, the volume of defaults in both the real and financial sectors and may lead to a total drying up of credit markets. (ii) Lower exante bailout uncertainty is conducive to higher leverage, which in turn raises moral hazard and makes the economy more vulnerable to expost increases in bailout uncertainty. (iii) Bailout uncertainty affects the likelihood of bubbles, the amplitude of booms and busts as well as the banking and the credit spreads. (iv) Higher bailout uncertainty is associated with higher returns’ variability in diversified portfolios and higher systemic risks, (v) Pre-crisis expansionary monetary policy reinforces those effects by inducing higher aggregate leverage levels. (vi) The larger the change in bailout uncertainty and the change in aversion to this uncertainty, the stronger the pre-crisis buildup and the deeper the ensuing crisis.A central policy implication of the analysis is that the vaguest is bailout policy prior to a crisis, the lower is the magnitude of investments destroyed or missed due to errors in evaluating bailout and other intervention policies. On the other hand, the clearer is bailout policy upon the eruption of a crisis, the smaller the contraction of credit and the destruction of investment activity.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Wetland mitigation banking: Negotiations with stakeholders in a zone of ecological-economic viability

Wetland mitigation banking (WMB) is an organizational form that attempts to balance the ecological goals of wetland conservation and the economic goals of development with the aim of improving the implementation of wetland offsetting. Given the resulting tension, it is important to understand how the way stakeholders employ the WMB regulatory framework affects the goal of No Net Loss of wetlands. In this study, we interviewed WMB stakeholders in Florida in the United States to identify their strategies during negotiations around different aspects of defining wetland mitigation credits (e.g. service areas, types of credit and credit release schedules). Using the approach of New Institutional Economics, we found that within a framework of well-defined rules that nonetheless allow flexibility, WMB enables a field of action for negotiating within a zone of ecological-economic viability – in part due to the stakeholders’ interest in maintaining a good reputation in this field. Outside of this zone of viability a wetland mitigation bank proposal collapses for economic or ecological reasons.     

Financial components of FDI and choice of capital structure: an analysis for 30 OECD countries

Even though the choice of capital structure depends on the three different financial components of foreign direct investment (FDI), previous research has regarded FDI as unidimensional rather than multidimensional. This study addresses new findings in the FDI area and investigates the relevant determinants of capital structure in 30 OECD countries from 2006 to 2014 within the framework of a simultaneous equation model. Our primary findings reveal that each component has its own deterministic features driven by relevant policy variables and risks in the market. While an increase or decrease in equity capital shows the ability of the host country to attract new investments, the subsequent components are mostly used to adjust the equity capital investment exposure.     

Philanthropic giving of China’s hotel firms: The roles of state ownership,corporate misconduct and executive remuneration

This study utilizes value enhancement theory, agency cost theory and crisis management theory to scrutinize the philanthropic giving (PG) of China’s listed hotel companies from 2003 to 2018. The results of dynamic panel regression tests reveal that state ownership (company character), corporate misconduct (crisis management determinant) and executive remuneration (agency cost determinant) are influential factors in hotel firms’ PG, but none of the value enhancement determinants can explain PG. Specifically, state ownership is negatively correlated with PG, reflecting low PG in government dominated hotel firms in China. PG is negatively associated with executive remuneration, indicating that hotel managers care more about self-interest than social responsibility. PG is also positively related to corporate misconduct, suggesting that hotel firms use philanthropy to manage a crisis. Finally, the lagged PG has a positive effect on PG, implying that PG is intended to avoid damage to social image and reputation of China’s hotel firms.     

SVHC第23批新增4项物质测试方法研究

本文建立了同时测定欧盟高关注物质(SVHC)第23批新增的4种物质[1-乙烯基咪唑、-甲基咪唑、4-羟基苯甲酸丁酯、双(乙酰丙酮酸)二丁基锡的气相色谱质谱联用仪(GC-MS)]的检测方法。将样品用二氯甲烷超声后,采用选择离子监控模式(SIM)进行分析样品中的待测物。实验结果表明,4种物质在50 mg/L范围内线性关系良好,线性相关系数为0.995以上;样品加标回收率为94%110%,相对标准偏差(RSD)小于7%;方法检出限分别为2 mg/kg、10mg/kg、0.8 mg/kg、0.6 mg/kg,解决了欧盟新增加的4种SVHC测试问题。     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

基于SVM的房地产财务决策风险预测及敏感性分析

本文以房地产上市公司的财务数据为参考依据,结合相关性分析,筛选并建立影响财务风险的指标体系;利用支持向量机建立财务风险分析预测模型;并使用灰色关联理论对指标进行敏感性分析,从而得到影响财务风险最敏感的指标。     

对江苏省电力负荷进行回归预测

本文主要根据2001-2011年江苏省用电量样本数据,建立了江苏省电力负荷与人均GDP、工业化以及人口数之间的多元回归预测方程,并预测了江苏省2014-2020年总用电量数据,在此基础上提出了相应的建议。