Far-red luminescent ruthenium pyridylimine complexes; building blocks for multinuclear arrays

Ruthenium(II) pyridylimine complexes are explored for their potential as units that might be incorporated into electronic or photonic arrays. The complexes [Ru(bipy)2(L)][PF6]2 (1) and [Ru(tpy)(L)Cl][BF4] (2) with L = phenylpyridin-2-ylmethylene-amine are synthesized and fully characterised using X-ray diffraction analysis and (2D) NMR spectroscopy. 1 displays emission in the far-red area of the spectrum at room temperature. The emission is significantly shifted to longer wavelength with respect to [Ru(bpy)3]2+ indicating that the lowest MLCT state is localised on the pyridylimine ligand. 2 is non-emissive at room temperature and at 77 K.     

Optimization of a genetic algorithm for searching molecular conformer space

We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA--a meta-genetic algorithm--was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm.     

An enantioselective formal synthesis of berkelic acid

An enantioselective formal synthesis of berkelic acid is described. The key step involves a late-stage silyl enol ether addition to a benzannulated oxonium ion with subsequent spiroketalization leading to construction of the tetracyclic core. Thermodynamically controlled equilibration under acidic conditions affords the desired spiroketal configuration as a single diastereoisomer.     

Stabilization of CeO(2) nanoparticles in a CO(2) rich solvent

Here it is shown that the chemical nature of outer organic surfactant layers, used to stabilize inorganic nanoparticles (NPs), is a key factor controlling solubility in a mixed liquid CO(2)-heptane (10% vol) solvent.     

Facilitated modelling with discrete-event simulation: Reality or myth?

Is it possible for discrete-event simulation to be used in a facilitated workshop environment? Over the last decade there have been various attempts to use simulation in this way, but we argue here that none have been successful in achieving a fully facilitated mode where the model is both developed and used in the workshop. We attempt to use a discrete-event simulation in a facilitated mode as part of a lean improvement workshop in a hospital setting. The model was successfully developed and used within the 3 day period of the workshop. Although the intervention was successful, we still had to build the model in the ‘back-office’, meaning that a fully facilitated mode was not achieved. The paper concludes by discussing how fully facilitated modelling with discrete-event simulation might be made possible; the answer is more about changing mind-sets than about technological challenge.     

Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners

Pathology ordering by general practitioners (GPs) is a significant contributor to rising health care costs both in Australia and worldwide. A thorough understanding of the nature and patterns of pathology utilization is an essential requirement for effective decision support for pathology ordering. In this paper a novel methodology for integrating data mining and case-based reasoning for decision support for pathology ordering is proposed. It is demonstrated how this methodology can facilitate intelligent decision support that is both patient-oriented and deeply rooted in practical peer-group evidence. Comprehensive data collected by professional pathology companies provide a system-wide profile of patient-specific pathology requests by various GPs as opposed to that limited to an individual GP practice. Using the real data provided by XYZ Pathology Company in Australia that contain more than 1.5 million records of pathology requests by general practitioners (GPs), we illustrate how knowledge extracted from these data through data mining with Kohonen’s self-organizing maps constitutes the base that, with further assistance of modern data visualization tools and on-line processing interfaces, can provide “peer-group consensus” evidence support for solving new cases of pathology test ordering problem. The conclusion is that the formal methodology that integrates case-based reasoning principles which are inherently close to GPs’ daily practice, and data-driven computationally intensive knowledge discovery mechanisms which can be applied to massive amounts of the pathology requests data routinely available at professional pathology companies, can facilitate more informed evidential decision making by doctors in the area of pathology ordering.