Searching for losers

We investigate the following process: N people select b losers by flipping coins. The 0-party continues until there are less than b losers; then the 1-party has to find the other losers by the same process. The average time for this process is about long₂ N, but this result requires rather advanced methods. Furthermore, the average size of a binary tree associated to this process and the average number of coin flippings are computed. The method used in this article can be used to give asympotical solutions of a special type of bivariate recurrences. © 1993 John Wiley & Sons, Inc.     

3-D vertical ray shooting and 2-D point enclosure, range searching, and arc shooting amidst convex fat objects

We present a new data structure for a set of n convex simply-shaped fat objects in the plane, and use it to obtain efficient and rather simple solutions to several problems including (i) vertical ray shooting—preprocess a set of n non-intersecting convex simply-shaped flat objects in 3-space, whose xy-projections are fat, for efficient vertical ray shooting queries, (ii) point enclosure—preprocess a set C of n convex simply-shaped fat objects in the plane, so that the k objects containing a query point p can be reported efficiently, (iii) bounded-size range searching— preprocess a set C of n convex fat polygons, so that the k objects intersecting a “not-too-large” query polygon can be reported efficiently, and (iv) bounded-size segment shooting—preprocess a set C as in (iii), so that the first object (if exists) hit by a “not-too-long” oriented query segment can be found efficiently. For the first three problems we construct data structures of size O(λ_s(n)log³n), where s is the maximum number of intersections between the boundaries of the (xy-projections) of any pair of objects, and λ_s(n) is the maximum length of (n, s) Davenport-Schinzel sequences. The data structure for the fourth problem is of size O(λ_s(n)log²n). The query time in the first problem is O(log⁴n), the query time in the second and third problems is O(log³n + klog²n), and the query time in the fourth problem is O(log³n).

We also present a simple algorithm for computing a depth order for a set as in (i), that is based on the solution to the vertical ray shooting problem. (A depth order for , if exists, is a linear order of , such that, if K₁, K₂ and K₁ lies vertically above K₂, then K₁ precedes K₂.) Unlike the algorithm of Agarwal et al. (1995) that might output a false order when a depth order does not exist, the new algorithm is able to determine whether such an order exists, and it is often more efficient in practical situations than the former algorithm.     

Evolved gas analysis (EGA) of brick clays

Using a system based on non-dispersive infrared (NDIR) detectors, evolved gas analysis (EGA) was able to identify and quantify the principal volatiles produced by heating powdered samples of UK brick clays. From these results, atmospheric emissions likely to result from brick production can be predicted. In addition, EGA results for extruded brick clay test pieces are significantly different from those of powdered samples. Within an extruded brick clay body, evolved gases are contained within a pore system and evolved gas-solid phase reactions also occur. This EGA study provides further evidence on the nature of firing reactions within brick clay bodies. The qualitative and quantitative influence of heating rate — a key process condition in brick manufacture — on gas release is also outlined.Dedicated to Dr. Robert Mackenzie on the occasion of his 75th birthday