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As stated earlier, what makes this model different from ordinary regression is the possibility of having latent variables as DVs and predictors, as well as the possibility of DVs predicting other DVs The latter occurs with nonzero elements in B When all elements in B are zero, no DVs are predicted by other DVs, and the only coef cients needed are in ; these give weights for the IVs in predicting the DVs In such a case the model is a set of regression equations, albeit possibly with latent variables But when B has nonzero elements, certain DVs are predicted by other DVs, and the model is no longer just regression-like In the example, there are two nonzero elements in B (a xed 1 and a free parameter, *), so this is not just a regression model.

In more complex models (so-called nonrecursive models), free parameters exist in some symmetric elements of B, such as the 1,2 and the 2,1 elements (which here would indicate that V1 predicts V2 while V2 also predicts V1) Carefully compare the diagram in Figure 242 with this matrix equation The 5 1 vector of values to the left of the equal sign, the eta ( ) vector, is a vector of DVs listed in the following order, OKDRINK2 (V1), OKSMOKE2 (V2), TEACH_SC (V4), FAMILY_S (V5), and ACCEPT_RISK (F2) The beta matrix (B) is a 5 5 matrix of regression coef cients among the DVs..

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increases, the diode-string trigger network dissipates current whereas the capacitor-trigger network does not dissipate leakage current. RF simulation shows that the Third Order Intermodulation Product (3rd OIP) was also shown to have a larger negative value for the capacitor-triggered networks compared to the forward-bias voltage diode-trigger network. From the TLP I V characteristics, the capacitor-trigger network Darlington bipolar ESD power clamp was shown to have a turn-on voltage lower than 4 V, whereas the diodetriggered Darlington maintained over 10 V. Although the trigger conditions were modulated by the trigger network type, the TLP current-to-failure was approximately equal in all cases. Hence the trigger network in uenced the RF and loading effects and the trigger voltage but had minor in uence on the net ESD robustness of the bipolar power clamp network. In this comparison, it is noted that the capacitance loading effect of the capacitor-trigger network is higher than that of the diode-triggered network; this impacts the insertion loss RF characteristic and the loading effect. In the case of a ESD power clamp, this capacitive loading effect is not an issue. It was noted by Ma and Li [17,18] that the capacitor-triggered ESD network can be distributed within a network forming a L C transmission line, reducing the capacitive impact to a signal pin. As an ESD design practice, the utilization of a capacitor-triggered bipolar ESD network provides the following advantages for low leakage, good linearity, and ESD protection capability. The loading effect of the capacitance can be addressed by the distribution of the network and utilization of the distributed load circuit design techniques.

Ronald R. Yager has worked in the area of fuzzy sets and related disciplines of computational intelligence for over 25 years. He has published over 500 papers and 15 books. He was the recipient of the IEEE Computational Intelligence Society Pioneer award in fuzzy systems. Dr. Yager is a fellow of the IEEE, the New York Academy of Sciences, and the Fuzzy Systems Association. He was given an award by the Polish Academy of Sciences for his contributions. He served at the National Science Foundation as program director in the Information Sciences program. He was a NASA/Stanford visiting fellow and a research associate at the University of California Berkeley. He has been a lecturer at NATO Advanced Study Institutes. He received his undergraduate degree from the City College of New York and his PhD from the Polytechnic University of New York. Currently, he is director of the Machine Intelligence Institute and Professor of Information and Decision Technologies at Iona College. He is editor and chief of the International Journal of Intelligent Systems. He serves on the editorial board of a number of journals, including the IEEE Transactions on Fuzzy Systems, Neural Networks, Data Mining and Knowledge Discovery, IEEE Intelligent Systems, Fuzzy Sets and Systems, the Journal of Approximate Reasoning, and the International Journal of General Systems. In addition to his pioneering work in the area of fuzzy logic, he has made fundamental contributions to decision making under uncertainty and the fusion of information.

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Table 291 GMO speci c PCR methods for GMO analysis cPCR: conventional PCR; RTi-PCR: realtime PCR; dc-PCR: double competitive PCR; qc-PCR: quantitative competitive PCR Modi ed from Hernandez et al.

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