曝气生物滤池及其填料作用机理

介绍了曝气生物滤池及其填料的应用情况和研究进展,对曝气生物滤池反应动力学以及其填料作用机理等方面的研究进展进行了研究,概述了影响曝气生物滤池运行的主要因素,同时提出了今后曝气生物滤池的发展方向。     

Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl     

A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

We show that, for three common SARV models, fitting a minimummean square linear filter is equivalent to fitting a GARCH model.This suggests that GARCH models may be useful for filtering,forecasting, and parameter estimation in stochastic volatilitysettings. To investigate, we use simulations to evaluate howthe three SARV models and their associated GARCH filters performunder controlled conditions and then we use daily currency andequity index returns to evaluate how the models perform in arisk management application. Although the GARCH models produceless precise forecasts than the SARV models in the simulations,it is not clear that the performance differences are large enoughto be economically meaningful. Consistent with this view, wefind that the GARCH and SARV models perform comparably in testsof conditional value-at-risk estimates using the actual data.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

The effect of permanent product discounts and order coupons on purchase incidence,purchase quantity,and spending

This paper examines the influence of a permanent discount strategy on customer purchase behavior, i.e., purchase incidence in each week, purchase quantity (in units), and total order spending (in CNY). Permanent discounts are defined as discounts continuously provided by retailers. We identify two types of permanent discounts, namely, product-specific price discounts (PD) and order coupons (OD, which can be redeemed for a total order). We collect transactional data from a Chinese online retailer and empirically examine the effects of the two types of permanent discounts and customers’ expectations of PD and OD. We find nonlinear relationships between permanent discounts and customer purchase behavior. PDs negatively influence spending when they are lower than 19% but show a positive effect beyond this threshold, hence depicting a U-shaped relationship. They also affect purchase quantity positively but at a decreasing rate. Customer expectations of PD influence purchase incidence, spending, and purchase quantity following a U-shaped patter with a positive influence appearing when PD expectations are high than 31%, 27%, and 18% respectively. On the other hand, ODs influence spending and purchase quantity positively at an increasing rate. Customer expectations of OD influence purchase incidence, spending, and purchase quantity following a U-shaped relationship where the positive influence on purchase incidence shows beyond OD expectations of 426 CNY, and the positive effect appearing on spending and purchase quantity when these expectations are higher than 34 CNY. We also find that customer expectations of discounts interact with current discount levels in their influence on spending. Combining these results and considering that order coupons negatively affect the profit margin of the total basket, we suggest that retailers should offer order coupons with relatively low value but product-specific price discounts with high discount depth.     

The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms

The impact of the industrial and digital (information) revolutions has, undoubtedly, been substantial on practically all aspects of our society, life, firms and employment. Will the forthcoming AI revolution produce similar, far-reaching effects? By examining analogous inventions of the industrial, digital and AI revolutions, this article claims that the latter is on target and that it would bring extensive changes that will also affect all aspects of our society and life. In addition, its impact on firms and employment will be considerable, resulting in richly interconnected organizations with decision making based on the analysis and exploitation of “big” data and intensified, global competition among firms. People will be capable of buying goods and obtaining services from anywhere in the world using the Internet, and exploiting the unlimited, additional benefits that will open through the widespread usage of AI inventions. The paper concludes that significant competitive advantages will continue to accrue to those utilizing the Internet widely and willing to take entrepreneurial risks in order to turn innovative products/services into worldwide commercial success stories. The greatest challenge facing societies and firms would be utilizing the benefits of availing AI technologies, providing vast opportunities for both new products/services and immense productivity improvements while avoiding the dangers and disadvantages in terms of increased unemployment and greater wealth inequalities.     

数字普惠金融的创业效应与收入不平等:数字鸿沟还是数字红利？

数字普惠金融与实体经济之间的关系受到学界广泛关注,但其究竟是普惠的数字红利还是金融领域的数字鸿沟,这是数字普惠金融快速发展中亟需回答的重要理论与现实问题。文章通过匹配宏观统计数据和微观调查数据,实证研究了数字普惠金融的发展对收入不平等的影响,并从创业效应这一渠道进行了机制分析。研究发现：数字普惠金融的发展总体上改善了收入不平等状况,而数字普惠金融的创业效应是影响收入不平等的重要渠道之一。进一步的中介效应分析表明,数字普惠金融的创业效应对收入不平等的影响存在异质性：数字普惠金融通过促进生存型创业从而改善了收入不平等,而数字普惠金融对机会型创业的促进却在一定程度上扩大了收入不平等。文章的研究结论对于理解数字普惠金融发展的正外部性以及如何缓解收入不平等提供了新的视角。     

The significance of digital data systems for smart city policy

Our planet is gradually moving towards an urbanized world. Modern urban agglomerations tend to turn nowadays into advanced information hubs supporting a smart management of dynamic urban systems. The currently popular notion of ‘smart cities’ aims to provide a new perspective for sustainable and high-performance strategies of city stakeholders in our ‘urban century’. In this context, digital information technology provides a new tool for efficient and effective management and planning of urban space, inter alia in the field of transportation, environment, public facilities or advanced service provision to citizens. This paper aims to offer, first, a concise overview of the emerging opportunities of information and communication technology (ICT) for smart urban policy; digital technology in particular, appears to provide novel pathways for modern planning strategies in smart cities. Against this background, the paper sketches out the complex force field of global urbanisation phenomena and highlights the data and information needs for strategic planning of cities (using inter alia as a framework the so-called ‘urban piazza’ strategy framework). Secondly, various new decision support tools that are currently emerging and that offer a new promising scope for handling complex urban management issues (for instance, on accessibility, congestion, safety or sustainability) are briefly presented. And finally, the potential of such digital data systems for urban management and policy is concisely illustrated by means of some recent applications in the area of smartphone data systems. The paper concludes with a discussion of the challenges ahead for urban policy, inter alia by paying attention to institutional and governance aspects of ‘big digital data’ management in urban systems.     

Infrastructure investment on the margins of the market: The role of niche infrastructure providers in the UK

Across Europe, policymakers and market forces are striving to deploy next generation access (NGA) networks and ensure ubiquitous access to superfast broadband services. Due to scale economies and sunk costs, the roll-out of NGA is expected to be profitable only for large-scale providers and in densely populated areas. Nonetheless, alternative providers, such as utilities and local communities, have significantly contributed to NGA diffusion in many countries. Over the past five years, several small-scale initiatives have emerged in the UK, bringing fibre networks to urban and rural areas previously overlooked by either commercial or subsidised deployments. A multiple case study approach is employed here to explore the nature and the drivers of niche providers in the UK NGA market. All these initiatives are demand-driven and to follow a modular approach. Despite adopting different business models, they all rely on the resources inherited from past broadband initiatives and relationships with local partners. By investigating the strategies of niche providers in NGA market, this analysis sheds light on their contribution to bridging the digital divide in the UK and is presented as a preliminary assessment of their sustainability and potential growth.     

Modelling the implied volatility surface based on Shanghai 50ETF options

We develop a dynamic factor model to forecast the implied volatility surface (IVS) of Shanghai Stock Exchange 50ETF options. Based on the assumption that dynamic change in IVS is mean-reverting and Markovian, we use a state space model to capture the dynamics of IVS, and set the latent factors to be the Ornstein–Uhlenbeck processes. We obtain the optimal estimations of parameters using the Kalman filter algorithm. Empirical results show that our model performs better than the traditional IVS model in terms of fitting ability and prediction performance.