The Mechanism of Core-Collapse Supernova Explosions: A Status Report

We review the status of the current quest to understand the mechanism of core-collapse supernovae, if neutrino-driven. In the process, we discuss the spherical explosion paradigm and its problems, some results from our new suite of collapse calculations performed using a recently-developed 1D implicit, multi-group, Feautrier/tangent-ray, Boltzmann solver coupled to explicit predictor/corrector hydrodynamics, the basic energetics of supernova explosions, and the promise of multi-D radiation/hydro simulations to explain why the cores of massive stars explode.Comment: 10 pages, LaTeX, 4 JPEGs included. To be published in the proceedings to the ESO/MPA/MPE Workshop (an ESO Astrophysics Symposium) entitled "From Twilight to Highlight: The Physics of Supernovae," held in Garching bei M\"unchen, Germany, July 29-31, 2002, eds. Bruno Leibundgut and Wolfgang Hillebrandt (Springer-Verlag

Blame and the Humean Theory of Motivation

A classic, though basically neglected question about motivation arises when we attempt to account for blame’s nature—namely, does the recognition central to blame need help from an independent desire in order to motivate the blame-characteristic dispositions that arise in the blamer? Those who have attended to the question think the answer is yes. Hence, they adopt what I call a Humean Construal of blame on which blame is (a) a judgment that an individual S is blameworthy and (b) an independent desire about S not doing as they did or being as they are. This paper rejects arguments for the Humean Construal, illustrates deep failings of that view, and uses these considerations to support anti-Humean accounts of blame in particular and moral motivation more broadly

\u3ci\u3eIn silico\u3c/i\u3e Driven Metabolic Engineering Towards Enhancing Biofuel and Biochemical Production

The development of a secure and sustainable energy economy is likely to require the production of fuels and commodity chemicals in a renewable manner. There has been renewed interest in biological commodity chemical production recently, in particular focusing on non-edible feedstocks. The fields of metabolic engineering and synthetic biology have arisen in the past 20 years to address the challenge of chemical production from biological feedstocks. Metabolic modeling is a powerful tool for studying the metabolism of an organism and predicting the effects of metabolic engineering strategies. Various techniques have been developed for modeling cellular metabolism, with the underlying principle of mass balance driving the analysis. In this dissertation, two industrially relevant organisms were examined for their potential to produce biofuels. First, Saccharomyces cerevisiae was used to create biodiesel in the form of fatty acid ethyl esters (FAEEs) through expression of a heterologous acyl-transferase enzyme. Several genetic manipulations of lipid metabolic and / or degradation pathways were rationally chosen to enhance FAEE production, and then culture conditions were modified to enhance FAEE production further. The results were used to identify the rate-limiting step in FAEE production, and provide insight to further optimization of FAEE production. Next, Clostridium thermocellum, a cellulolytic thermophile with great potential for consolidated bioprocessing but a weakly understood metabolism, was investigated for enhanced ethanol production. To accomplish the analysis, two models were created for C. thermocellum metabolism. The core metabolic model was used with extensive fermentation data to elucidate kinetic bottlenecks hindering ethanol production. The genome scale metabolic model was constructed and tuned using extensive fermentation data as well, and the refined model was used to investigate complex cellular phenotypes with Flux Balance Analysis. The work presented within provide a platform for continued study of S. cerevisiae and C. thermocellum for the production of valuable biofuels and biochemicals

A Guideline for Increasing Efficiency of TEM/EDS Data Collection by Dwell Time Optimization

Composition analysis using energy dispersive spectroscopy (EDS) mapping in transmission electron microscopy (TEM) is crucial in the semiconductor industry for development of new products and enhancement of production yields. High quality EDS data relies on acquiring sufficient X-ray counts from the TEM sample. The amount of time that the electron beam interacts with the sample generating X-rays per pixel within the mapping area is known as dwell time, which is an EDS system parameter that governs optimum data acquisition. However, a systematic study to optimize this parameter has not been previously reported. An analytical expression was derived that enabled the prediction of a dwell time range that optimizes the total X-ray signal collected during the EDS data collection. Experimental results from multiple materials across several TEM/EDS systems confirmed the validity of the expression. The results of this study provide a guideline for increasing efficiency of TEM/EDS data collection from different materials using a variety of TEM/EDS systems through the optimization of EDS dwell time