Urban runoff pollution is commonly considered the nation’s number one water quality problem. Stormwater issues have increasingly become a key consideration in land use planning and development over the last several years in San Diego County. The San Diego Regional Water Quality Control Board (Board) first approved San Diego’s Municipal Stormwater Permit in 1990 (Order No. 90-42) and renewed the permit in 2001 (Order No. 2001-01), which required all jurisdictions to develop and implement a stormwater program. On January 24, 2007, the Board adopted the revised Municipal Stormwater Permit (Regional Water Quality Control Board (RWQCB) Order No. R9-2007-0001). The revised permit contains standards and requirements which are intended to further reduce the pollution that enters local streams, creeks, bays and beaches. San Diego County jurisdictions are mandated by the permit to regulate new and existing development and redevelopments (that add or increase impervious cover by 5,000 sq. ft.) to comply with stormwater requirements.
As part of the revised Municipal Stormwater Permit, San Diego jurisdictions must initially encourage developments to incorporate minimal Low Impact Development (LID) techniques into Priority Development Projects by January 2008. During this initial phase the LID Handbook will serve as the guidance structure for these LID techniques and the initial LID projects that will be monitored as LID standards and criteria are being developed in the region. San Diego jurisdictions will collectively establish feasibility and applicability criteria and develop specific LID requirements over the next couple years. Once these specific criteria and requirements have been established and accepted by the Board, the jurisdictions will have one year to incorporate the new LID requirements into their local codes and ordinances. Therefore, by the year 2010, the County and other local jurisdictions will each have an updated stormwater program with a comprehensive list of BMPs, including the new LID standards and criteria.
Bankruptcy prediction has been routinely studied by academics, practitioners, and regulators. The well-known prediction models include discriminant analysis model (Altman, 1968), Merton model (Merton, 1974; Vassalou and Xing, 2004), logit model (Ohlson, 1980), and probit model (Zmijewski, 1984), to name only a few. The common principal of these approaches is that the models are developed using single-period data of firms. Shumway (2001) pointed out that such prediction processes are static in nature, since they ignore the changing characteristics of firms through time. In order to avoid possible loss of prediction power due to using static models, Shumway (2001) and Chava and Jarrow (2004) proposed a discrete-time hazard model (DHM) using multiple-period data for bankruptcy prediction. Their novel model applies the idea of survival analysis (Cox and Oakes, 1984), and has the advantage of using all available information of firms to build up a prediction system so that each firm’s bankruptcy risk at each time point can be determined. Thus DHM is a dynamic forecasting model.
The performance of bankruptcy prediction models was mainly assessed in the literature by performing prediction-oriented tests; see for example the above references. Recently, Hillegeist et al. (2004) proposed a different approach for doing it. They compared the information content about credit risk provided by out-of-sample values of probability of bankruptcy (PB) based on Merton model, Z-Score based on discriminant analysis model, and O-Score based on logit model. Their results show that PB based on Merton model provides significantly more information than Z-Score and O-Score. In contrast, Agarwal and Taffler (2008) pointed out that there is little difference between PB based on Merton model and Z-Score, in terms of predictive ability and information content. The results in Hillegeist et al. (2004) and Agarwal and Taffler (2008) were developed by comparing “static” bankruptcy prediction models.
Temperature has a profound effect on the eye. The ocular surface has to cope physiologically with the imposed thermal stress of an environment that may change by more than 60 C? which could dramatically affect cellular mechanisms. For example the thickness of the cornea is dependent on the endothelial cells which line the back side of that tissue and the enzyme systems within these cells that control the corneal thickness of the cornea are very temperature dependent, blood flow, which responds to temperature, may be affected adversely in these widely varying conditions.
However, temperature monitoring of the interior of the human eye in vivo is not possible, and the engineering models that have been developed have not included the tissue material properties or interfacial behavior between different adjacent tissue that would allow predictions of the heat flow to be made with needed accuracy (Enrique, 2002; Sluzalec Jr., 1985; Scott, 1988; Lagendijk, 1982; Horven 1975;Fujishima 1996; Mapstone1, Mapstone2 and Mapstone3, 1968; Beuerman, 1978).
