The purpose of this study was to evaluate the effect of four common types of mandatory state-level workplace safety regulations on injury severity rates during the period 1992 to 1997 for the manufacturing sector. The full Poisson regression model showed safety committee regulations to have a highly significant reducing effect on injury rates, chi 2 (1, n = 3286) = 10.1774, P = 0.0014. Safety program regulations were significant at the alpha = 0.10 level, chi 2 (1, n = 3286) = 3.5676, P = 0.0589. The effect of insurance carrier loss control regulations in the full model was nonsignificant. However, insurance carrier loss control regulations were highly significant (alpha = 0.01) in the final reduced model. Targeting initiatives were nonsignificant in both the full and reduced models (alpha = 0.05). The study results are important to state and federal agencies considering adopting workplace safety regulations that are similar to the four types evaluated in this study.
Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals as a potential and attractive route for drug substance development. In this study, we implemented graph neural networks to predict the formationof cocrystals using our first created API-co-formers interactions graph dataset. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting,and artificial neural networks). All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60, and 95.95% for GCN, GraphSAGE, and R-GCN respectively). Furthermore, R-GCN prevailed among the built graph-based models due to its capability to learn the topological structure of the graph from the additionally provided information (i.e., non-ionic andnon-covalent interactions or link information) between APIs and coformers.
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