基于WSR的区域排水权分配影响因素研究

为了进一步减少防洪损失、解决区域排水纠纷问题,在界定排水权内涵的基础上,研究得出排水权具有强制性、排他性、可交易性、可变性和紧迫性。利用WSR系统方法论构建了排水权分配的WSR分析框架,认为排水权的分配受物理因素、事理因素和人理因素的影响,并分析了多种因素在分配过程中的角色,进一步构建了排水权分配影响因素的指标体系。研究有利于实现排水权有效配置驱动因素的系统化、层次化梳理,为实现排水权的合理分配与防洪决策的进一步优化提供有效依据,对于保障防洪安全、实现社会公平与区域均衡发展具有重要的支撑作用。     

盐城市水资源承载状态预警研究

在分析水资源承载状态预警概念的基础上,引入"三类六级"预警思想,采用可变模糊法和层次分析法的思想,构建盐城市水资源承载状态综合预警指标体系,评价盐城市2015年、2020年和2030年的水资源承载状态,并结合各年份承载状态变化趋势,对未来年份进行预警。结果表明:盐城市2015年水资源处于临界超载的状态,但随着"关于做好建立全国水资源承载能力监测预警机制工作的通知"等一系列措施的实施,2020年、2030年承载状态将逐步好转,到2030年将处于可载的状态。     

Financial development,economic growth and convergence clubs

To study the role of financial development in economic growth, we apply an array of convergence tests designed to capture nonlinear transitional dynamics to real outputs per capita. Strong evidence of multiple convergence clubs is observed, implying that the clubs are formed based on the initial level of real output per capita and average growth rate. Our empirical results show that the stage of economic growth of each country plays an important role for the composition of the convergence clubs. Furthermore, financial development emerges to be a significant determinant, albeit plays differently in the economic growth of each convergence club.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Worktime control and work stress: the moderating effect of self-comparisons and social comparisons

Abstract

Perceived work-related stress has increased notably in recent years, reducing individuals’ well-being and increasing organizations’ and economies’ costs. This study focuses on worktime control as a key approach to reducing work stress, as the extant research on its effects reports inconsistent results. The study argues that comparisons play a major role in how work stress, conceptualized as effort-reward imbalance, occurs. Ordinary least squares is used to test hypotheses with a sample of 1721 employees from Germany to determine whether employees’ prior worktime control and employees’ referents’ worktime control affect the relationship between worktime control and work stress. Results show that (1) worktime control is negatively associated with the experience of work stress, (2) employees’ prior worktime control moderates the relationship between worktime control and work stress, and (3) there is no moderating effect of employees’ referents’ worktime control. The results are discussed in light of gift-exchange theory and related empirical findings.     

Operational excellence in a green supply chain for environmental management: A case study

Nowadays, organizations have started to become more conscious about the environment in their supply chain operations. The greening process has guided supply chain practices into new ways of thinking according to green standards. The assessment of the performance of green supply chain management (GSCM) requires a holistic view for the whole supply chain. In this context, given that becoming green in the operational side of activities is essential, the performance assessment of operational activities also requires a holistic view to be taken. In this paper, an attempt has been made to improve the performance of GSCM by examining and evaluating the green operational excellence of a hot dip galvanizing company. The framework includes several green operational excellence key criteria, namely, quality management, efficiency management, green production/manufacturing, eco‐packaging, and green design. First, the weights of the criteria and the respective measurements were found by fuzzy analytic network process. Then, the overall operational performance score was found by a weighted scoring method. Finally, both managerial and theoretical implications were suggested according to the outcomes and findings of the case study.     

Market drivers of sustainability and sustainability learning capabilities: The moderating role of sustainability control systems

This study examines (i) the impact of market drivers of sustainability on the adoption of sustainability learning capabilities and (ii) the moderating role of sustainability control systems (SCS) on the relationship between market drivers of sustainability and sustainability learning capabilities. Drawing on the levers of control framework, stakeholder theory and organisational learning literature, survey data were collected from 175 large scale local and multinational companies operating in Sri Lanka. Findings reveal that market drivers of sustainability have a significant positive impact on sustainability learning capabilities. Whereas the interactive use of SCS shows a positive moderating impact, the diagnostic use of SCS shows a negative impact. The study enhances our understanding of (i) the influence of market drivers of sustainability on the adoption of sustainability learning capabilities and (ii) the use of SCS in enabling sustainability learning capabilities. The study reveals novel insights for managers responding to changing market drivers of sustainability, on how to (re)align different uses of SCS to enable sustainability learning capabilities.     

基于粒子群算法下的交叉口信号配时优化

为了合理地优化交叉口信号配时,通过分析交叉口的评价指标,建立了以车辆的平均延误、停车次数最小、交叉口整体通行能力最大、各相位有效绿灯时间和交叉口周期时长作为约束条件的数学模型。并通过改进前人研究基础上的动态加权系数,将多目标的非线性优化问题转化为单一目标的非线性规划问题,为了得出更科学稳定的解,提出了改善粒子群算法系统稳定性的2种方法,并将其与粒子群算法结合起来。然后以Matlab为求解工具,结合临邑市某一交叉口实例进行求解分析。最后的结果表明,在使用改进后的粒子群算法进行优化后交叉口通行能力较之现状提升了9%,延误下降了28%,停车次数下降了9%,且各项优化结果均优于Webster,改进后的算法在程序中运行300代,到216代才开始收敛,而未改进的算法稳定性较差,优化结果和收敛曲线则随着实验次数的变化而变化,最后的结论证明了该算法和模型的可靠性。     

融合反距离加权和矩阵填充的位置指纹库构建

为解决位置指纹定位在离线阶段构建位置指纹库时耗费的人力和时间成本较大，构建指纹库效率低和利用空间插值法构建的指纹库精度不高的问题，提出了一种融合反距离加权和矩阵填充的位置指纹库构建算法。该算法仅需人工采集定位区域内少量参考点的接收信号强度值用作信标点指纹信息，结合反距离加权算法特性计算出次信标点指纹信息，根据位置指纹库数据矩阵的低秩性，应用奇异值阈值矩阵填充算法构建出位置指纹数据库。仿真实验结果表明，所提算法有效降低了矩阵填充算法构建位置指纹库所需的人工和时间成本，构建出的位置指纹库定位性能优于反距离加权和克里金空间插值法，接近传统人工采集法，显著地提高了位置指纹库的构建效率。     

一种基于Radon变换的微表情识别算法

微表情是人们处在一些与平时生活环境不同的高强度环境下试图控制和掩饰的情感表现，也是一种不曾意识到的瞬时脸部表情,持续时间短，强度弱。为了提高其准确率，提出了基于Radon变换的微表情识别算法。首先，对数据库中的视频序列进行灰度归一化、尺寸归一化和二维主成分分析法（Two-dimensional Principal Component Analysis，2DPCA）降维预处理，使用光流法对降维后图像提取运动特征；然后使用Radon变换算法对光流图像进行处理，得到对应微表情的特征值和特征图像；最后使用支持向量机进行微表情分类识别。实验结果表明，使用Radon变换后得到的微表情特征图像得到了较好的识别效果，在微表情数据集CASME和CASMEⅡ上识别率分别为81.48%和82.17%，通过与选取的其他方法对比说明了该方法具有更好的识别性能。