The α-arylation of α-bromo- and α-chloroenones using palladium-catalysed cross-coupling

The palladium-catalysed cross-coupling reaction of various arylboronic acids with α-bromoenones and α-chloroenones offers an operationally simple approach to the synthesis of both cyclic and acyclic α-arylenones.     

On a zero equation model of turbulence

In this paper we study a widely used zero equation model of turbulence. The governing equations are derived by applying to the incompressible Navier-Stokes equations the Reynolds time averaging procedure. We achieve closure by employing the eddy viscosity concept. Using the Implicit Function Theorem we obtain an existence and uniquencess result. We also discuss the existence of nonsingular solutions. Finally, we present an algorithm for solving the modeled equations.     

A composite methodology for multistage degradation of polymers

Five different analytical schemes for examining isothermal and nonisothermal degradation of polymers were reviewed and found inadequate for describing multistage decomposition. The different schemes were experimentally tested using thermogravimetric analysis data for an ethylene-vinyl acetate (EVA) copolymer, which exhibited a well-behaved two-step decomposition process in a nitrogen environment. Based on these experimental and analytical findings, a generalized methodology was developed capable of describing the exhibited complex decomposition behavior of polymers by combining two or more kinetic mechanisms in a series or parallel arrangement. This composite of combinational methodology thus extends established analytical schemes for describing complex decomposition of polymers in a rational manner, incorporating both experimental and theoretical considerations.     

Pit your wits against young minds!

This column is a place for those bits of contagious mathematics that travel from person to person in the community, because they are so elegant, suprising, or appealing that one has an urge to pass them on. Contributions are most welcome.     

Consistency of a myopic Bayesian algorithm for one-dimensional global optimization

A sequential Bayesian method for finding the maximum of a function based on myopically minimizing the expected dispersion of conditional probabilities is described. It is shown by example that an algorithm that generates a dense set of observations need not converge to the correct answer for some priors on continuous functions on the unit interval. For the Brownian motion prior the myopic algorithm is consistent; for any continuous function, the conditional probabilities converge weakly to a point mass at the true maximum.