Rapid Wolff–Kishner reductions in a silicon carbide microreactor

Wolff–Kishner reductions are performed in a novel silicon carbide microreactor. Greatly reduced reaction times and safer operation are achieved, giving high yields without requiring a large excess of hydrazine. The corrosion resistance of silicon carbide avoids the problematic reactor compatibility issues that arise when Wolff–Kishner reductions are done in glass or stainless steel reactors. With only nitrogen gas and water as by-products, this opens the possibility of performing selective, large scale ketone reductions without the generation of hazardous waste streams.Novartis-MIT Center for Continuous ManufacturingNatural Sciences and Engineering Research Council of Canada (post-doctoral fellowship

An Efficient Chemoenzymatic Approach towards the Synthesis of Rugulactone

Rugulactone is a natural product isolated from the plant Cryptocarya rugulosa. It has shown very important biological activity as an inhibitor of the nuclear factor κB (NF-κB) activation pathway. A new chemoenzymatic approach towards the synthesis of rugulactone is presented here. The chirality, induced to the key intermediate by a stereoselective enzymatic reduction utilizing NADPH-dependent ketoreductase, is described in detail

Metaheuristics Application on a Financial Forecasting Problem

EDDIE is a Genetic Programming (GP) tool, which is used to tackle problems in the field of financial forecasting. The novelty of EDDIE is in its grammar, which allows the GP to look in the space of technical analysis indicators, instead of using pre-specified ones, as it normally happens in the literature. The advantage of this is that EDDIE is not constrained to use pre-specified indicators; instead, thanks to its grammar, it can choose any indicators within a pre-defined range, leading to new solutions that might have never been discovered before. However, a disadvantage of the above approach is that the algorithm's search space is dramatically larger, and as a result good solutions can sometimes be missed due to ineffective search. This paper presents an attempt to deal with this issue by applying to the GP three different meta-heuristics, namely Simulated Annealing, Tabu Search, and Guided Local Search. Results show that the algorithm's performance significantly improves, thus making the combination of Genetic Programming and meta-heuristics an effective financial forecasting approach. © 2013 IEEE