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2 (b) Find the exponent in the probability Qn (Xn (Xn )2 ). You can leave the answer in parametric form.

Your deleted data can be recovered by someone who has the appropriate software and computer smarts. That s good news for you if you accidentally deleted the Great American Novel you are writing, but it is bad news if your computer is hacked into, if you donate your computer to a charity, or if you sell it on eBay. If your computer data is not properly destroyed, it could be recovered and used by criminals to commit identity theft.

, 249, 263 Mullen, B, 53, 72 Mullen, L, 212 Muller, H H, 598, 609 Muller, W E, 601, 608, 611 Muller-Bardoff, H, 189 Muller-Lissner, S A, 412 Mullins, D, 401, 411 Mullis, R, 388 Mullooly, J, 364, 498, 509 Mulrow, C, 611 Mulry, G, 242 Mulry, R P, 362 Mulvihill, C, 412 Mumma, G H, 273, 290 Munck, A, 75, 93 Munno, I, 92 Mu oz, P, 249, 264 Munoz, R, 385 Munuksela, L, 464 Murasko, D M K A, 78, 93, 489, 507 Murdoch, R N, 385 Murphy, A D, 48, 174, 186, 462 Murphy, D A, 227, 229, 242 Murphy, D B, 400, 402, 412 Murphy, G P, 271, 290 Murphy, L.

The ER equivalent schema for this database is depicted in Figure 12.18. This gure should be self-explanatory.

, 443, 462, 530, 540, 565, 569, 570, 574, 576, 578, 586, 587, 588 Murphy, N J, 550, 559 Murphy, R, 361, 546, 563 Murphy, S, 2, 6, 15, 21, 174, 186, 565 Murr, M M, 130, 139 Murray, C J L, 519, 541, 543 Murray, L, 527 Murray, M, 5, 22 Murray, N G, 165 Murray, S E, 174, 186 Murray, T, 268, 288, 382, 388 Murtagh, D R R, 332, 336 Murthy, K C, 120.

2.3.2. Substrate Dissolve 1 tablet (5 mg) of phosphatase substrate (p-nitrophenyl phosphate) per 5 ml of substrate buffer. 2.3.3. p-Nitrophenol for Standard Curve Prepare a 50 nmol/ml solution of p-nitrophenol by combining 50 l of 10 mol/ml p-nitrophenol standard solution with 9.95 ml of 0.02N NaOH. Prepare further dilutions as illustrated in the following table:

Algorithms C and D only give good assignments for weight vectors with specific properties, but they can be combined to give near-optimal algorithms for any weight vector How they are combined will depend on the relation between the parameters First, note that Algorithm C is optimal if c0 cR = 2nc1/(n 1) If 2c1 c0 < cR, then Algorithms A, C, and D can be combined to give a linear time algorithm with performance ratio 1 + 1/(4n 3), where n is the number of nodes in the cycle The algorithm is described below Given a weight vector w, compute = v V w(v) (n 1)wmax If 0, Algorithm D is used, with spectrum [0, c0 wmax] The span is at most c0wmax, which is within a constant of lower bound (52), so the assignment is optimal.

0/01

If instead > 0, Algorithm C is combined with either Algorithm A or D to derive an assignment Denote by f1 the assignment computed by Algorithm C for (G, w ) where w (v) = min{w(v), } This assignment has span at most cR Consider the remaining weight w after this assignment Clearly wmax = wmax We will denote by f2 the assignment for (G, w), and compute it in two different ways depending on a key property of w If there is a node v with w(v) = 0 at this stage, we have a bipartite graph left Then f2 is the assignment computed by Algorithm A for (G, w) This assignment has a span of at most c0wmax If all nodes have nonzero weight, then Algorithm D is used to compute f2, the assignment for (G, w).