Controlled-dispersion flow analysis : Flow-injection analysis applied to clinical chemistry

Mechanisation came early to clinical chemistry and has passed through a number of phases. Selective multichannel machines, usually discrete analysers with their associated mechanical complexities, have become popular; the simplicity and reliability of flow analysers has been lost. Flow injection offers new opportunities to develop simple selective machines. Sample waste is avoided in the controlled-dispersion flow analyser; the slug of sample is picked up by a probe, the volume being metered by the peristaltic pump driven by a stepping motor under computer control. Reagent waste is avoided by a similar system and use of merging zones. Very economical operation is thus possible and acceptable precision is attained. Various features of the technique, including prolonged incubation, use of kinetic methods to minimise the need for blanks, and application of different detectors are discussed in the context of clinical assays. Anomalous behaviour of particulate matter in flow streams and the changing shape of sample slugs in stationary streams are described. The application of flow-injection systems in clinical chemistry is extremely promising.     

pKas of the conjugate acids of N-heterocyclic carbenes in water

pK(a) values of 19.8-28.2 are reported for the conjugate acids of a large series of NHCs in water. The effects of ring size, N-substituent and C(4)-C(5) saturation on pK(a) are discussed.     

Covering arrays of higher strength from permutation vectors

A covering array CA(N;t,k,v) is an N × k array such that every N × t sub‐array contains all t‐tuples from v symbols at least once, where t is the strength of the array. Covering arrays are used to generate software test suites to cover all t‐sets of component interactions. We introduce a combinatorial technique for their construction, focussing on covering arrays of strength 3 and 4. With a computer search, covering arrays with improved parameters have been found. © 2005 Wiley Periodicals, Inc. J Combin Designs 14: 202–213, 2006     

A characterization of fuzzy subgroups

An ordinary subgroup of a group G is (1) a subset of G, (2) closed under the group operation. In a fuzzy subgroup it is precisely these two notions that lose their deterministic character. A fuzzy subgroup μ of a group (G,·) associates with each group element a number, the larger the number the more certainly that element belongs to the fuzzy subgroup. The closure property is captured by the inequality μ(x · y)?T(μ(x), μ(y)). In A. Rosenfeld's original definition, T was the function ‘minimum’. However, any t-norm T provides a meaningful generalization of the closure property. Two classes of fuzzy subgroups are investigated. The fuzzy subgroups in one class are subgroup generated, those in the other are function generated. Each fuzzy subgroup in these classes satisfies the above inequality with T given by T(a, b) = max(a + b ?1, 0). While the two classes look different, each fuzzy subgroup in either is isomorphic to one in the other. It is shown that a fuzzy subgroup satisfies the above inequality with $\text{T =}\text{‘minimum’}$ if and only if it is subgroup generated of a very special type. Finally, these notions are applied to some abstract pattern recognition problems.     

Temperature dependence of self-diffusion in adamantane

Radiotracer experiments show that self-diffusion in the rotator crystalline phase of adamantane proceeds by a vacancy mechanism. The results are in good agreement with those determined from NMR experiments.