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It is called the best linear unbiased estimator 5 is concluded by introducing recursive linear least squares estimators These update the value of the estimates of the parameters with each additional observation Two different versions of this estimator are presented in Section 518 and Section 519, respectively The first is an ordinary least squares estimator, that is, its weighting matrix is the identity matrix The second is suitable for tracking time varying parameters since the weighting matrix is chosen so that observations influence the estimates more as they are more recent In the final chapter, 6, we explain principles and use of iterative numerical function optimization methods relevant to the estimators or experimental designs described in this book The estimators require either the log-likelihood function to be maximized or the least squares criterion to be minimized.

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To supply appropriate terms for Mm, MWg, Mu and Mig for the particular two phase conditions that occur in the annulus, a flow regime map has been constructed from observations and physical intuition gained from the experimental study. Previous work, as discussed by Dempster and Abouhadra (3) has established that the exit flow characteristics are predominately independent of the inlet liquid flowrate. This feature has also been observed for the flow regimes, as indicated in the work of Abouhadra (4) and shown in Fig. 13.3. It can be seen in this figure that the flow regimes have similar distributions for approximately the same non-dimensional gas flowrate J*g and for different liquid flow rates, J*w- These results suggest that it may be possible to create a flow regime map that is independent of the inlet liquid flowrate. The independence of the liquid flowrate has only been established using the gross features of the flow. To develop a model further it has been assumed that the local flow regime at any point in the annulus can be completely described by local conditions independent of the liquid flowrate.

The experimental designs require the CramBrRao lower bound matrix to be optimized in the sense of a chosen optimality criterion These optimization problems have in common that their solution is typically not closed-form and has to be computed iteratively After a short introduction in Section 61, key notions in numerical function optimization are introduced in Section 62 These are: the objective function, which is the function to be optimized; the gradient vector, which is the vector of first-order partial derivatives of the objective function; and the Hessian matrix, which is the matrix of second-order derivatives with respect to the independent variables In the optimization problems in this book, the objective functions are the log-likelihood function or the least squares criterion as a function of the parameters, or the optimality criterion for the experimental design as a function of free experimental variables.

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COMPONENT ANALYSIS (ALLOWED OR FORBIDDEN )

Furthermore, the concept ascent (descent) direction of a vector is defined Finally, the concepts exact observations, reference log-likelihood function, and reference least squares criterion are introduced in Section 62 These facilitate software testing Section 63 is devoted to the steepest descent (ascent) method This is a general function minimization (maximization) method It is not specialized to optimizing least squares.

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Specialized apparatus featuring an array of electrodes is required for pulsed eld gel electrophoresis. The shape or geometry of this array differs between particular types of pulsed eld electrophoresis experiments (Figures 5.39 5.42). A unique electric eld orientated in a particular direction is achieved by varying which electrodes carry current, for how long and in what direction. Flow of current to the electrodes and pulse-times are electronically controlled allowing selection of optimized separation conditions. Agarose gels are poured and electrophoresed in a at-bed, horizontal format as shown in Figure 5.37 and the gel may be arranged in a variety of orientations relative to the array of electrodes. In orthogonal eld alternating gel electrophoresis (Figure 5.38), pairs of electrodes are arranged at 90 relative to each other, resulting in creation of an inhomogeneous electrical eld. This inhomogeneity has the disadvantage that lanes are distorted outwards at the bottom of the gel. In contour-clamped homogeneous electric eld gel electrophoresis (Figure 5.39), point electrodes are arranged hexagonally around the gel. This results in a homogeneous electrical eld in all lanes of the gel. Crossed- eld or rotating-gel electrophoresis (Figure 5.40) takes advantage of a constant electrical eld with the gel is held on a rotating platform. This is limited to separation of DNA >50 kb since smaller molecules require tp less than one second which is

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