Impact experimentation and the microgravity environment: An overview

Impact is an ubiguitous physical process in the solar system. It occurs on all solid bodies and operates over a spectrum of scales. Understanding impact phenomena is therefore paramount in constraining and underpinning a large number of research efforts into fundamental problems in planetary geology. Gravity is an important parameter in impact processes. The advantages of microgravity experimentation are discussed

Melt production in large-scale impact events: Planetary observations and implications

Differences in scaling relationships for crater formation and the generation of impact melt should lead to a variety of observable features and phenomena. These relationships infer that the volume of the transient cavity (and final crater) relative to the volume of impact melt (and the depth to which melting occurs) decreases as the effects of gravity and impact velocity increase. Since planetary gravity and impact velocity are variables in the calculation of cavity and impact-melt volumes, the implications of the model calculation will vary between planetary bodies. Details of the model calculations of impact-melt generation as a function of impact and target physical conditions were provided elsewhere, as were attempts to validate the model through ground-truth data on melt volumes, shock attenuation, and morphology from terrestrial impact craters

Melt production in large-scale impact events: Calculations of impact-melt volumes and crater scaling

Along with an apparent convergence in estimates of impact-melt volumes produced during planetary impact events, intensive efforts at deriving scaling relationships for crater dimensions have also yielded results. It is now possible to examine a variety of phenomena associated with impact-melt production during large cratering events and apply them to planetary problems. This contribution describes a method of combining calculations of impact-melt production with crater scaling to investigate the relationship between the two

Melt production in large-scale impact events: Implications and observations at terrestrial craters

The volume of impact melt relative to the volume of the transient cavity increases with the size of the impact event. Here, we use the impact of chondrite into granite at 15, 25, and 50 km s(sup -1) to model impact-melt volumes at terrestrial craters in crystalline targets and explore the implications for terrestrial craters. Figures are presented that illustrate the relationships between melt volume and final crater diameter D(sub R) for observed terrestrial craters in crystalline targets; also included are model curves for the three different impact velocities. One implication of the increase in melt volumes with increasing crater size is that the depth of melting will also increase. This requires that shock effects occurring at the base of the cavity in simple craters and in the uplifted peaks of central structures at complex craters record progressively higher pressures with increasing crater size, up to a maximum of partial melting (approx. 45 GPa). Higher pressures cannot be recorded in the parautochthonous rocks of the cavity floor as they will be represented by impact melt, which will not remain in place. We have estimated maximum recorded pressures from a review of the literature, using such observations as planar features in quartz and feldspar, diaplectic glasses of feldspar and quartz, and partial fusion and vesiculation, as calibrated with estimates of the pressures required for their formation. Erosion complicates the picture by removing the surficial (most highly shocked) rocks in uplifted structures, thereby reducing the maximum shock pressures observed. In addition, the range of pressures that can be recorded is limited. Nevertheless, the data define a trend to higher recorded pressures with crater diameter, which is consistent with the implications of the model. A second implication is that, as the limit of melting intersects the base of the cavity, central topographic peaks will be modified in appearance and ultimately will not occur. That is, the peak will first develop a central depression, due to the flow of low-strength melted materials, when the melt volume begins to intersect the transient-cavity base

Sharing large data collections between mobile peers

New directions in the provision of end-user computing experiences mean that we need to determine the best way to share data between small mobile computing devices. Partitioning large structures so that they can be shared efficiently provides a basis for data-intensive applications on such platforms. In conjunction with such an approach, dictionary-based compression techniques provide additional benefits and help to prolong battery life

A comparison of alternative strategies for choosing control populations in observational studies.

Various approaches have been used to select control groups in observational studies: (1) from within the intervention area; (2) from a convenience sample, or randomly chosen areas; (3) from areas matched on area-level characteristics; and (4) nationally. The consequences of the decision are rarely assessed but, as we show, it can have complex impacts on confounding at both the area and individual levels. We began by reanalyzing data collected for an evaluation of a rapid response service on rates of unplanned hospital admission. Balance on observed individual-level variables was better with external than local controls, after matching. Further, when important prognostic variables were omitted from the matching algorithm, imbalances on those variables were also minimized using external controls. Treatment effects varied markedly depending on the choice of control area, but in the case study the variation was minimal after adjusting for the characteristics of areas. We used simulations to assess relative bias and means-squared error, as this could not be done in the case study. A particular feature of the simulations was unexplained variation in the outcome between areas. We found that the likely impact of unexplained variation for hospital admissions dwarfed the benefits of better balance on individual-level variables, leading us to prefer local controls in this instance. In other scenarios, in which there was less unexplained variation in the outcome between areas, bias and mean-squared error were optimized using external controls. We identify some general considerations relevant to the choice of control population in observational studies

Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.

For a continuous treatment, the generalised propensity score (GPS) is defined as the conditional density of the treatment, given covariates. GPS adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose-response function assumes correct specification of both the GPS and the outcome-treatment relationship. This paper introduces a machine learning method, the 'Super Learner', to address model selection in this context. In the two-stage estimation approach proposed, the Super Learner selects a GPS and then a dose-response function conditional on the GPS, as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the GPS and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for patients with acute traumatic brain injury. With parametric models for the outcome, we find that dose-response curves differ according to choice of specification. With the Super Learner approach to both regression and the GPS, we find that transfer time does not have a statistically significant marginal effect on the outcomes

Estimating causal effects : considering three alternatives to difference-in-differences estimation

Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tariffs programme. The conclusions of the original DiD analysis are sensitive to the choice of approach. We conduct a Monte Carlo simulation study that investigates these methods’ performance. While DiD produces unbiased estimates when the parallel trends assumption holds, the alternative approaches provide less biased estimates of treatment effects when it is violated. In these cases, the LDV approach produces the most efficient and least biased estimates