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follows a standard normal distribution, where SE refers to the standard error of the coef cient as estimated from the data and reported by the software. Table 4.2 provides the coef cient estimate and the standard error as follows: b1 = 0.06696, and SE(b1 ) = 0.03223, giving us Z Wald = 0.06696 = 2.08 0.03223

Specifying drinking water guidelines for general observance is complicated by the fact that many countries have established their own drinking water guidelines For instance, in the USA the federal Environmental Protection Agency (EPA) sets the standards which are then implemented by the individual states Similarly, central standards have been established by the European Union, and these have then been translated into national regulations by individual EU Member States Although minor differences can be detected between quality standards in different jurisdictions, most suites of guidelines have converged on very similar maximum admissible concentrations (MACs) for safe human consumption In the vast majority of cases, these MACs closely match the guideline values published by the World Health Organization (WHO) The current WHO guidelines (WHO 2004) represent the closest approximation to truly worldwide standards for drinking water quality The WHO guidelines cover a range of waterborne pathogens (ie.

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The fuzzy model evolves continuously by being faced with incoming data to capture the essence of the system that undergoes changes Such changes could be either continuous or abrupt In the case of continuous changes, the model may adapt to such modi cations through the corresponding changes made to the parameters of the model In situations where we encounter more radical changes, the structure of the model needs to be modi ed in a structural manner In the context of fuzzy rule-based systems, the parametric changes can be quite directly translated into continuous updates of the parameters of the local models standing in the conclusions of the rules In the case of linear models, we can see here a recurrent format of the updates of the parameters that is well documented in the literature.

disease-causing microbes) and some 128 chemicals which may be found in drinking waters Table 74 summarizes some of the principal recommendations Fortunately, most groundwaters are relatively secure from gross contamination by pathogens However, in shallow gravel aquifers and in karst terrains, it is always prudent to assume that pathogen contamination is a possibility Also, where pumping wells or springs are inadequately engineered (Section 73) localized introduction of pathogens into the abstracted water is a frequent cause of apparent microbial infestation of groundwater A vast range of microbes (viruses, bacteria, protozoa, and helminths) have the potential to cause gastroenteritis, diarrhea, and dysentery In the surveillance of groundwater sources, microbial water quality is usually assessed simply by testing for the presence or absence of easily detected organisms which unequivocally indicate the presence of fecal contamination.

In the vast majority of cases, the organism which is tested for is the bacterium Escherichia coli (usually written simply as E coli), the presence of which provides conclusive.

The structural changes are more sophisticated and their realization is associated with the varying number of the rules In the presence of dynamically collected data, information granules move around, and become more re ned or generalized all of these effects are driven by the nature of the data being collected and their continuous changes In this study we will concentrate on this facet of evolvability of the fuzzy models We develop a general strategy, using which the number of rules (viz the number of fuzzy clusters) becomes modi ed dynamically (either reduced or extended) depending on the current dynamics of the data (phenomenon).

13

Many hot spots are emerging and multiple WLANs are being deployed within a small geographic vicinity such as of ce buildings, multitenant residential complexes, city downtown areas, and university campuses Different WLANs in a particular area may be deployed by different operators In such an environment with multiple WLANs coexisting, a growing interest is that WLAN providers may set up reciprocal agreements and coordinations so that mobile users may share the usage of multiple WLANs A direct bene t of resource sharing among multiple WLANs is the expansion of network coverage A WLAN usually suffers limited communication range By integrating different overlapping WLANs, WLAN providers can offer value-added inter-WLAN roaming services to subscribers who need wider roaming areas Meanwhile, mobile users can roam among multiple networks, enjoying the wide-area wireless access Another bene t of this integration is the cooperation of resource management among multiple WLANs.

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