Life Cycle Greenhouse Gas Emission Comparison of Steel Products with Other Materials

This paper outlines the background of Life-Cycle Inventory/ Life-Cycle Assessment (LCI/LCA) and reviews an undergraduate design project in progress at the University of Missouri - Rolla (UMR) comparing LCI/LCA of steel products with similar products produced from competing materials. GaBi 4 LCI/LCA software is being used to model LCI/LCA with a demonstration of the use of the software for a typical steelmaking operation.1 Future research utilizing the LCI/LCA methodology is being applied to compare the environmental impact of steel products to other alternative engineering materials. This work involves 13 undergraduate students working in four design teams under a FeMET design grant provided by the American Iron and Steel Institute (AISI) and the Association of Iron and Steel Technology (AIST) Foundation

Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland.

BackgroundAs successful malaria control programmes move towards elimination, they must identify residual transmission foci, target vector control to high-risk areas, focus on both asymptomatic and symptomatic infections, and manage importation risk. High spatial and temporal resolution maps of malaria risk can support all of these activities, but commonly available malaria maps are based on parasite rate, a poor metric for measuring malaria at extremely low prevalence. New approaches are required to provide case-based risk maps to countries seeking to identify remaining hotspots of transmission while managing the risk of transmission from imported cases.MethodsHousehold locations and travel histories of confirmed malaria patients during 2011 were recorded through routine surveillance by the Swaziland National Malaria Control Programme for the higher transmission months of January to April and the lower transmission months of May to December. Household locations for patients with no travel history to endemic areas were compared against a random set of background points sampled proportionate to population density with respect to a set of variables related to environment, population density, vector control, and distance to the locations of identified imported cases. Comparisons were made separately for the high and low transmission seasons. The Random Forests regression tree classification approach was used to generate maps predicting the probability of a locally acquired case at 100 m resolution across Swaziland for each season.ResultsResults indicated that case households during the high transmission season tended to be located in areas of lower elevation, closer to bodies of water, in more sparsely populated areas, with lower rainfall and warmer temperatures, and closer to imported cases than random background points (all p < 0.001). Similar differences were evident during the low transmission season. Maps from the fit models suggested better predictive ability during the high season. Both models proved useful at predicting the locations of local cases identified in 2012.ConclusionsThe high-resolution mapping approaches described here can help elimination programmes understand the epidemiology of a disappearing disease. Generating case-based risk maps at high spatial and temporal resolution will allow control programmes to direct interventions proactively according to evidence-based measures of risk and ensure that the impact of limited resources is maximized to achieve and maintain malaria elimination

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future

Limits to gauge coupling in the dark sector set by the non-observation of instanton-induced decay of Super-Heavy Dark Matter in the Pierre Auger Observatory data

We investigate instanton-induced decay processes of super-heavy dark matter particles $X$ produced during the inflationary epoch. Using data collected at the Pierre Auger Observatory we derive a bound on the reduced coupling constant of gauge interactions in the dark sector: $\alpha_X^{\rm eff} \lesssim 0.09$, for $10^{10} < M_X/{\rm GeV} < 10^{16}$. We show that this upper limit on $\alpha_X^{\rm eff}$ is complementary to that obtained from the non-observation of tensor modes in the cosmic microwave background

Limits to gauge coupling in the dark sector set by the non-observation of instanton-induced decay of Super-Heavy Dark Matter in the Pierre Auger Observatory data

We investigate instanton-induced decay processes of super-heavy dark matter particles $X$ produced during the inflationary epoch. Using data collected at the Pierre Auger Observatory we derive a bound on the reduced coupling constant of gauge interactions in the dark sector: $\alpha_X^{\rm eff} \lesssim 0.09$, for $10^{10} < M_X/{\rm GeV} < 10^{16}$. We show that this upper limit on $\alpha_X^{\rm eff}$ is complementary to that obtained from the non-observation of tensor modes in the cosmic microwave background

Limits to gauge coupling in the dark sector set by the non-observation of instanton-induced decay of Super-Heavy Dark Matter in the Pierre Auger Observatory data

We investigate instanton-induced decay processes of super-heavy dark matter particles $X$ produced during the inflationary epoch. Using data collected at the Pierre Auger Observatory we derive a bound on the reduced coupling constant of gauge interactions in the dark sector: $\alpha_X^{\rm eff} \lesssim 0.09$, for $10^{10} < M_X/{\rm GeV} < 10^{16}$. We show that this upper limit on $\alpha_X^{\rm eff}$ is complementary to that obtained from the non-observation of tensor modes in the cosmic microwave background