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Methodological issues in the assessment and analysis of sexual risk behavior I: Item content and scaling Annals of Behavioral Medicine, 10 56 Shapiro, M F, Morton, S C, McCaffrey, D F, Senter tt, J W, Fleishman, J A, Perlman, J F, et al (1999) Variations in the care of HIV-infected adults in the United States: Results from the HIV Cost and Services Utilization Study Journal of the American Medical Association, 281, 2305 2315 Sikkema, K J, & Kelly, J A (1996) Behavioral medicine interventions can improve the quality-of-life and health of persons with HIV disease Annals of Behavioral Medicine 18, 40 48 Sikkema, K J, Kelly, J A, Winett, R A, Solomon, L J, Cargill, V A, Roffman, R A, et al (2000).

(c) 0.091, 0.91

Outcomes of a randomized community-level HIV prevention intervention for women living in 18 low-income housing developments American Journal of Public Health, 90, 57 63 St Lawrence, J S, Bras eld, T L, Jefferson, K W, Alleyne, E, O Bannon, R E, & Shirley, A (1995) Cognitive-behavioral intervention to reduce African American adolescents risk for HIV infection Journal of Consulting and Clinical Psychology, 63, 221 237..

Signal-to-Gaussian-Noise Ratio on PCM Repeatered Lines Regenerative Repeaters PCM System Enhancements 6.7.1 Enhancements to DS1 6.7.2 Enhancements to E1 Higher-Order PCM Multiplex Systems 6.8.1 Introduction 6.8.2 Stuf ng and Justi cation 6.8.3 North American Higher-Level Multiplex 6.8.4 European E1 Digital Hierarchy Long-Distance PCM Transmission 6.9.1 Transmission Limitations 6.9.2 Jitter and Wander 6.9.3 Distortion 6.9.4 Thermal Noise 6.9.5 Crosstalk Digital Loop Carrier 6.10.1 New Versions of DSL Digital Switching 6.11.1 Advantages and Issues of Digital Switching 6.11.2 Approaches to PCM Switching 6.11.3 Review of Some Digital Switching Concepts Digital Network 6.12.1 Introduction 6.12.2 Technical Requirements of the Digital Network 6.12.3 Digital Network Performance Requirements Review Exercises References

ust as weblogs make it easy to post your writing online for the entire world to see, photo blogs help photographers share their images with an online audience.

This is, for example, the result of training eSensor for Case2 with all the data available The evolution of the fuzzy rule base is provided for Case4 in Figure 148 One can see that the number of fuzzy rules is growing but also reducing (due to, for example, ageing and a shift of the concept as described in 2) Due to the large number of input variables (features), especially for Case2 and Case3 (47 inputs!) and propylene (23 inputs!), the antecedent (linguistic) part of the fuzzy rules becomes very long and therefore dif cult to understand The high dimensionality of the inputs and input output data space leads also to computational problems Note that in the rule base shown above, some fuzzy sets from different rules have values that are very close in a particular dimension (eg.

N 1

RS(Ti) is a subset of RS(TCj), and WS(Ti) is a subset of WS(TCj)

, in dimension x1, rule 1 and rule 2 have a very close de nition of the fuzzy sets: IF(x1is close to 4200 /4205) This is also seen in Figure 149 In order to increase the interpretability of the rule base, such very close fuzzy sets can be merged by using one of the focal points (the one that represents a younger rule) to represent the similar fuzzy sets Note that the approach proposed in Victor and Dourado (2004) and applied to eTS for online fuzzy rule base and fuzzy sets simpli cation based on arithmetic averaging suffers from the following problems: (1) The averaged result may be infeasible; and (2) the nonlinearity of the input output relation leads to introducing an error.

The approach described in more detail in 2 does not suffer from these limitations and leads to simpler and more accurate models as illustrated in this section Looking into the consequent part of the rules, the absolute values of some parameters on one dimension (feature) are signi cantly higher than the parameters on the other dimensions (features) These features therefore have more impact on the output of the rules and are therefore more important..

The Amazon Basin is a huge source of particulate and gaseous emissions, from which secondary aerosols can be produced. Apart from the natural biogenic emissions, anthropogenic activities like pasture clearing res and deforestation emit large quantities of smoke and dust into the atmosphere. Therefore large differences in aerosol concentrations and composition between the wet and dry season were observed [103]. On the other hand, the basin receives seasonally variable amounts of sea-salt aerosols and Saharan dust due to long-range transport [104].