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Atom probe tomography (APT) is a powerful technique to characterize buried three-dimensional nanostructures in a variety of materials. Accurate characterization of those nanometer-scale clusters and precipitates is of great scientific significance to understand the structure–property relationships and the microstructural evolution. The current widely used cluster analysis method, a variant of the density-based spatial clustering of applications with noise algorithm, can only accurately extract clusters of the same atomic density, neglecting several experimental realities, such as density variations within and between clusters and the nonuniformity of the atomic density in the APT reconstruction itself (e.g., crystallographic poles and other field evaporation artifacts). This clustering method relies heavily on multiple input parameters, but ideal selection of those parameters is challenging and oftentimes ambiguous. In this study, we utilize a well-known cluster analysis algorithm, called ordering points to identify the clustering structures, and an automatic cluster extraction algorithm to analyze clusters of varying atomic density in APT data. This approach requires only one free parameter, and other inputs can be estimated or bounded based on physical parameters, such as the lattice parameter and solute concentration. The effectiveness of this method is demonstrated by application to several small-scale model datasets and a real APT dataset obtained from an oxide-dispersion strengthened ferritic alloy specimen.
This research aims to explore the submerged landscapes of the Pilbara of western Australia, using predictive archaeological modelling, airborne LiDAR, marine acoustics, coring and diver survey. It includes excavation and geophysical investigation of a submerged shell midden in Denmark to establish guidelines for the underwater discovery of such sites elsewhere.
A gravity survey was conducted on the Windmill Islands, East Antarctica, during the 2004–05 summer season. The aim of the study was to investigate the subsurface geology of the Windmill Islands area. Ninety-seven gravity stations were established. Additionally, 49 observations from a survey in 1993–94 were re-reduced and merged with the 2004–05 data. A three-dimensional subsurface model was constructed from the merged gravity dataset to determine the subsurface geology of the Windmill Islands. The main country rock in the Windmill Islands is a Garnet-bearing Granite Gneiss. A relatively dense intrusive charnockite unit, the Ardery Charnockite, generates the dominant gravity high of the study area and has been modelled to extend to depths of 7–13 km. It has moderate to steep contacts against the surrounding Garnet-bearing Granite Gneiss. The Ardery Charnockite surrounds a less dense granite pluton, the Ford Granite, which is modelled to a depth of 6–12 km and creates a localized gravity low. This granitic pluton extends at depth towards the east. The modelling process has also shown that Mitchell Peninsula is linked to the adjacent Law Dome ice cap by an ‘ice ramp’ of approximately 100 m thickness.
Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks. Statistical analyses of FRN data with such models ignore the censored and ranked nature of the data and could potentially result in misleading statistical inference. To investigate this possibility, we compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the FRN scheme, and therefore accommodate the ranked and censored nature of the data. We show analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals. We also compare these different likelihoods in a data analysis of several adolescent social networks. For some of these networks, the parameter estimates from the binary and FRN likelihoods lead to different conclusions, indicating the importance of analyzing FRN data with a method that accounts for the FRN survey design.
The activities of hunter-gatherers are often captured in rockshelters, but here the authors present a study of a riverside settlement outside one, with a rich sequence from 1300 BC to AD 800. Thanks to frequent flooding, periods of occupation were sealed and could be examined in situ. The phytolith and faunal record, especially fish, chronicle changing climate and patterns of subsistence, emphasising that the story here is no predictable one-way journey from hunter-gatherer to farmer. Right up to the period of the famous nineteenth-century rock paintings in the surrounding Maloti-Drakensberg region, adaptation was dynamic and historically contingent.
Walnuts contain a number of potentially neuroprotective compounds like vitamin E, folate, melatonin, several antioxidative polyphenols and significant amounts of n-3 α-linolenic fatty acid. The present study sought to determine the effect of walnuts on verbal and non-verbal reasoning, memory and mood. A total of sixty-four college students were randomly assigned to two treatment sequences in a crossover fashion: walnuts–placebo or placebo–walnuts. Baseline data were collected for non-verbal reasoning, verbal reasoning, memory and mood states. Data were collected again after 8 weeks of intervention. After 6 weeks of washout, the intervention groups followed the diets in reverse order. Data were collected once more at the end of the 8-week intervention period. No significant increases were detected for mood, non-verbal reasoning or memory on the walnut-supplemented diet. However, inferential verbal reasoning increased significantly by 11·2 %, indicating a medium effect size (P = 0·009; d = 0·567). In young, healthy, normal adults, walnuts do not appear to improve memory, mood or non-verbal reasoning abilities. However, walnuts may have the ability to increase inferential reasoning.
UK Nirex has supported a programme of work to develop models describing the postclosure evolution of intermediate-level waste packages with the objectives of:
• providing support and justification for the parameters and representations used in performance assessment models;
• informing future model development and packaging advice.
Scenarios for the potential evolution of a waste package were developed and modelled taking explicit account of waste package heterogeneity and the time-dependence of the physical and chemical characteristics of the system. The modelling work highlighted the treatment of organic complexants and the representation of physical containment as two areas in which the impacts of time dependence and package scale heterogeneity might be particularly significant.
A subsequent study of the impact of organic complexants emphasised the importance of heterogeneity in package inventory in determining the radionuclide release from the near field.
The degree of containment afforded by the wasteform and the waste container has been investigated as part of a study to develop a preliminary understanding of the mixing scales within the repository. The study suggests that the most important control on the release of radionuclides from the waste packages is the integrity of the waste encapsulation grout. Interactions between neighbouring packages are to be expected, but the degree to which homogeneous (well mixed) conditions develop may be limited in both time and space.