Predicting Big Bang Deuterium

We present new upper and lower bounds to the primordial abundances of deuterium and helium-3 based on observational data from the solar system and the interstellar medium. Independent of any model for the primordial production of the elements we find (at the 95\% C.L.): $1.5 \times 10^{-5} \le (D/H)_P \le 10.0 \times 10^{-5}$ and $(^3He/H)_P \le 2.6\times 10^{-5}$. When combined with the predictions of standard big bang nucleosynthesis, these constraints lead to a 95\% C.L. bound on the primordial abundance of deuterium: $(D/H)_{best} = (3.5^{+2.7}_{-1.8})\times 10^{-5}$. Measurements of deuterium absorption in the spectra of high redshift QSOs will directly test this prediction. The implications of this prediction for the primordial abundances of helium-4 and lithium-7 are discussed, as well as those for the universal density of baryons.Comment: Revised version of paper to reflect comments of the referee and reply to suggestions of Copi, Schramm, and Turner regarding the overall analysis and treatment of chemical evolution of D and He-3. Best-fit D/H abundance changes from (2.3 + 3.0 - 1.0)x10^{-5} to (3.5 +2.7 - 1.8) x10^{-5}. See also hep-ph/950531

JMASM36: Nine Pseudo R^2 Indices for Binary Logistic Regression Models (SPSS)

This syntax program is an applied complement to Veall and Zimmermann (1994), Menard (2000), and Smith and McKenna (2013) and produces nine pseudo R2 indices, not readily accessible in statistical software such as SPSS, which are used to describe the results from binary logistic regression analyses

Logistic Regression Under Sparse Data Conditions

The impact of sparse data conditions was examined among one or more predictor variables in logistic regression and assessed the effectiveness of the Firth (1993) procedure in reducing potential parameter estimation bias. Results indicated sparseness in binary predictors introduces bias that is substantial with small sample sizes, and the Firth procedure can effectively correct this bias

Environmental Risks and Agricultural Commodities

As natural disasters are expected to increase both in severity and frequency, their impact on agricultural commodities and the food supply chain is likely to rise in tandem. We use a unique data set of natural disasters that occurred globally between 1970 and 2015 and study their impact on the price and price volatility of eight globally traded agricultural commodities that account for more than 75% of the food supply in the world. Our results show that price and the price volatility of commodities are impacted around the occurrence of a natural disaster in producing countries. In addition, we find that the affected country’s production shares, the total damage caused by a natural disaster relative to the affected country’s GDP, and the demand pressure for the commodity have an impact on the magnitude of price and price volatility changes of a given commodity around the occurrence of a natural disaster

Evaluation of a Brazilian fuel alcohol yeast strain for Scotch whisky fermentations

Traditionally, distilling companies in Scotland have employed a very limited number of yeast strains in the production of alcohol for Scotch whiskies. Recent changes such as the decline in availability of brewers’ yeast as a secondary yeast strain and the availability of yeast in different formats (e.g., dried and cream yeast as alternatives to compressed yeast) have promoted interest in alternative Scotch whisky distilling yeasts. In previous work, we investigated different strains of yeasts, specifically Brazilian yeasts which had been isolated from and used in fuel alcohol distilleries. One of the Brazilian yeasts (CAT 1) showed a comparable fermentation performance and superior stress tolerance compared with a standard commercial Scotch whisky distilling yeast (M Type). The Brazilian CAT 1 yeast isolate was further assessed in laboratory scale fermentations and subsequent new make spirit was subjected to sensory analyses. The spirits produced using the Brazilian strain had acceptable flavour profiles and exhibited no sensory characteristics that were atypical of Scotch whisky new make spirit. This study highlights the potential of exploiting yeast biodiversity in traditional Scotch whisky distillery fermentation processes

Molecular Star Formation Rate Indicators in Galaxies

We derive a physical model for the observed relations between star formation rate (SFR) and molecular line (CO and HCN) emission in galaxies, and show how these observed relations are reflective of the underlying star formation law. We do this by combining 3D non-LTE radiative transfer calculations with hydrodynamic simulations of isolated disk galaxies and galaxy mergers. We demonstrate that the observed SFR-molecular line relations are driven by the relationship between molecular line emission and gas density, and anchored by the index of the underlying Schmidt law controlling the SFR in the galaxy. Lines with low critical densities (e.g. CO J=1-0) are typically thermalized and trace the gas density faithfully. In these cases, the SFR will be related to line luminosity with an index similar to the Schmidt law index. Lines with high critical densities greater than the mean density of most of the emitting clouds in a galaxy (e.g. CO J=3-2, HCN J=1-0) will have only a small amount of thermalized gas, and consequently a superlinear relationship between molecular line luminosity and mean gas density. This results in a SFR-line luminosity index less than the Schmidt index for high critical density tracers. One observational consequence of this is a significant redistribution of light from the small pockets of dense, thermalized gas to diffuse gas along the line of sight, and prodigious emission from subthermally excited gas. At the highest star formation rates, the SFR-Lmol slope tends to the Schmidt index, regardless of the molecular transition. The fundamental relation is the Kennicutt-Schmidt law, rather than the relation between SFR and molecular line luminosity. We use these results to make imminently testable predictions for the SFR-molecular line relations of unobserved transitions.Comment: ApJ Accepted - Results remain same as previous version. Content clarified with Referee's comment

On the Stock Market's Reaction to Major Railroad Accidents

This study examines the impact of train accidents on the stock price performance of the involved railroad companies. We employ a sample of 26 accidents involving trains operated by publicly traded U.S. and Canadian railroad companies between January 1993 and December 2003. Event study methodology is used to measure the abnormal performance of the involved railroad firms to these accidents. In addition, a series of univariate tests and cross-sectional regression analysis is employed to determine the factors that drive the abnormal returns for the firms in the sample. The magnitude of the initial price decline appears to be driven by various characteristics of both the firm and the accident itself. Specifically, there is strong evidence that suggests that one of the main determinants of the abnormal returns is expected legal liability claims against the railroads. Abnormal performance is negatively related to firm size and the number of injuries and fatalities resulting from the accident. In addition, accidents that result in hazardous material spills cause significantly larger stock price drops in the days following the event. Finally, investors appear to differentiate between accident causes. Accidents caused by reckless or illegal behavior on behalf of one or more of the railroad company's employees result in particularly large price declines. Accidents caused by mechanical failures or signal malfunctions, on the other hand, only cause small stock price drops

On the Stock Market's Reaction to Major Railroad Accidents

This study examines the impact of train accidents on the stock price performance of the involved railroad companies. We employ a sample of 26 accidents involving trains operated by publicly traded U.S. and Canadian railroad companies between January 1993 and December 2003. Event study methodology is used to measure the abnormal performance of the involved railroad firms to these accidents. In addition, a series of univariate tests and cross-sectional regression analysis is employed to determine the factors that drive the abnormal returns for the firms in the sample. The magnitude of the initial price decline appears to be driven by various characteristics of both the firm and the accident itself. Specifically, there is strong evidence that suggests that one of the main determinants of the abnormal returns is expected legal liability claims against the railroads. Abnormal performance is negatively related to firm size and the number of injuries and fatalities resulting from the accident. In addition, accidents that result in hazardous material spills cause significantly larger stock price drops in the days following the event. Finally, investors appear to differentiate between accident causes. Accidents caused by reckless or illegal behavior on behalf of one or more of the railroad company's employees result in particularly large price declines. Accidents caused by mechanical failures or signal malfunctions, on the other hand, only cause small stock price drops