Synthesis of Intermediates by Rhodium-Catalyzed Hydroformylation

The reaction of olefins with carbon monoxide and hydrogen to give aldehydes is referred to as hydroformylation (oxo reaction). As catalyst for this reaction rhodium is about three to four orders of magnitude more active than the more commonly employed cobalt. With special rhodium compounds, e.g. di-ν-chlorobis(ν-1,5-cyclooctadiene)dirhodium [RhCl(C₈H₁₂)]₂, in the presence of chiral phosphanes, even asymmetric hydroformylations can be achieved; however, the enantiomeric purity of the products (20–30%) is not high enough for industrial-scale syntheses.     

From numbers to narratives: Preservice teachers experiences’ with mathematics anxiety and mathematics teaching anxiety

This paper presents qualitative and quantitative approaches to exploring teachers’ experiences of mathematics anxiety (for learning and doing mathematics) and mathematics teaching anxiety (for instructing others in mathematics), the relationship between these types of anxiety and test/evaluation anxiety, and the impacts of anxiety on experiences in teacher education. Findings indicate that mathematics anxiety and mathematics teaching anxiety may be similar (i.e., that preservice teachers perceive a logical continuity and cumulative effect of their experiences of mathematics anxiety as learners in K–12 classrooms that impacts their work as teachers in future K–12 classrooms). Further, anxiety is not limited to occurring in evaluative settings, but when anxiety is triggered by thoughts of evaluation, preservice teachers may be affected by worrying about their own as well as their students' performances. The implications for preservice experiences within a teacher education program and for impacting future students are discussed.     

Noisy Hamiltonian Monte Carlo for Doubly Intractable Distributions

Hamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician’s toolbox as an alternative sampling method in settings when standard Metropolis–Hastings is inefficient. HMC generates a Markov chain on an augmented state space with transitions based on a deterministic differential flow derived from Hamiltonian mechanics. In practice, the evolution of Hamiltonian systems cannot be solved analytically, requiring numerical integration schemes. Under numerical integration, the resulting approximate solution no longer preserves the measure of the target distribution, therefore an accept–reject step is used to correct the bias. For doubly intractable distributions—such as posterior distributions based on Gibbs random fields—HMC suffers from some computational difficulties: computation of gradients in the differential flow and computation of the accept–reject proposals poses difficulty. In this article, we study the behavior of HMC when these quantities are replaced by Monte Carlo estimates. Supplemental codes for implementing methods used in the article are available online.