Assessment of Local Observation of Atomic Ordering in Alloys via the Radial Distribution Function: A Computational and Experimental Approach

As a powerful analytical technique, atom probe tomography (APT) has the capacity to acquire the spatial distribution of millions of atoms from a complex sample. However, extracting information at the Ångstrom-scale on atomic ordering remains a challenge due to the limits of the APT experiment and data analysis algorithms. The development of new computational tools enable visualization of the data and aid understanding of the physical phenomena such as disorder of complex crystalline structures. Here, we report progress towards this goal using two steps. We describe a computational approach to evaluate atomic ordering in the crystal structure by generating radial distribution functions (RDF). Atomic ordering is rendered as the Fractional Cumulative Radial Distribution Function (FCRDF) which allows for greater visibility of local compositions at short range in the structure. Further, we accommodate in the analysis additional parameters such as uncertainty in the atomic coordinates and the atomic abundance to ascertain short-range ordering in APT data sets. We applied the FCRDF analysis to synthetic and experimental APT data sets for Ni3Al. The ability to observe a signal of atomic ordering consistent with the known L12 crystal structure is heavily dependent on spatial uncertainty, irrespective of abundance. Detection of atomic ordering is subject to an upper limit of spatial uncertainty of atoms described with Gaussian distributions with a standard deviation of 1.3 Å. The FCRDF analysis was also applied to the APT data set for a six-component alloy, Al1.3CoCrCuFeNi. In this case, we are currently able to visualize elemental segregation at the nanoscale, though unambiguous identification of atomic ordering at the Ångstrom (nearest-neighbor) scale remains a goal.

Mapping of bioavailable strontium isotope ratios in France for archaeological provenance studies

Strontium isotope ratios (87Sr/86Sr) of archaeological samples (teeth and bones) can be used to track mobility and migration across geologically distinct landscapes. However, traditional interpolation algorithms and classification approaches used to generate Sr isoscapes are often limited in predicting multiscale 87Sr/86Sr patterning. Here we investigate the suitability of plant samples and soil leachates from the IRHUM database (www. irhumdatabase.com) to create a bioavailable 87Sr/86Sr map using a novel geostatistical framework. First, we generated an 87Sr/86Sr map by classifying 87Sr/86Sr values into five geologically representative isotope groups using cluster analysis. The isotope groups were then used as a covariate in kriging to integrate prior geological knowledge of Sr cycling with the information contained in the bioavailable dataset and enhance 87Sr/86Sr predictions. Our approach couples the strengths of classification and geostatistical methods to generate more accurate 87Sr/86Sr predictions (Root Mean Squared Error=0.0029) with an estimate of spatial uncertainty based on lithology and sample density. This bioavailable Sr isoscape is applicable for provenance studies in France, and the method is transferable to other areas with high sampling density. While our method is a step forward in generating accurate 87Sr/86Sr isoscapes, the remaining uncertainty also demonstrates that finemodelling of 87Sr/86Sr variability is challenging and requires more than geological maps for accurately predicting 87Sr/86Sr variations across the landscape. Future efforts should focus on increasing sampling density and developing predictive models to further quantify and predict the processes that lead to 87Sr/86Sr variability.

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making.

Mapping Autistic Wayfinding in Urban Environments

Needs and preferences in wayfinding tasks of people with autism spectrum disorders (ASD) have been a topic of ongoing discussion in the scientific literature over the last decades. While different tasks have revealed both autistic strengths (e.g., encoding and recall of route information) and weaknesses (e.g., understanding allocentric representations), ASD spatial behaviour is not fully understood yet. In this paper we focus on spatial uncertainty, which is the discrepancy between a-priori expectation and in-situ experience and thus a constant factor in ASD wayfinding tasks. As a matter of course, spatial uncertainty is inevitable, always resulting from a dynamic interaction of situational qualities (e.g., noise or smell). Nevertheless, mapping uncertainty and the underlying spatial patterns in an organized way might help users from the ASD spectrum to better prepare for the different levels of expectable uncertainty in route. We propose a framework of conceptualizing, measuring, and mapping spatial uncertainty from an autistic viewpoint. The discussion of this framework is based on a qualitative analysis of the spatial behaviour of B, a five-year-old child with ASD and nonverbal communication, in an urban environment. We compare the level of spatial uncertainty of the routes developed by B against the routes indicated by ourselves.

A History of Open Weather in New Zealand (HOWNZ): an open access 1-km resolution monthly 1910–2019 time-series of interpolated temperature and rainfall grids with associated uncertainty

Long time-series of weather grids are fundamental to understanding how weather affects environmental or ecological patterns and processes such as plant distributions, plant and animal phenology, wildfires, and hydrology. Ideally such weather grids should be openly available and be associated with uncertainties so that users can understand any data quality issues. We present a History of Open Weather in New Zealand (HOWNZ) that uses climatological aided natural neighbour interpolation to provide monthly 1-km resolution grids of total rainfall, mean air temperature, mean daily maximum air temperature, and mean daily minimum air temperature across New Zealand from 1910 to 2019. HOWNZ matches the best available temporal extent and spatial resolution of any open weather grids that include New Zealand, and is unique in providing associated spatial uncertainty in appropriate units of measurement. The HOWNZ weather and uncertainty grids capture the dynamic spatial and temporal nature of the monthly weather variables and the uncertainty associated with the interpolation. We also demonstrate how to quantify and visualise temporal trends across New Zealand that recognise the temporal and spatial variation of uncertainties in the HOWNZ data. The HOWNZ data is openly available at https://doi.org/10.7931/zmvz-xf30 (Etherington et al., 2021).

The Boreal–Arctic Wetland and Lake Dataset (BAWLD)

Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

Information technologies for creating spatiotemporal modems multiposition active-passive radar systems

The methods of synthesis of the directional diagram of active transmitting antenna arrays when receiving signals reflected from radar targets are considered. It is shown that when using multifrequency orthogonal coherent signals in the elements and addressable access at their reception it is possible to provide a small level of the side lobes of the spatial uncertainty function in a given sector of observation by selecting the type of intrapulse modulation of partial signals. Orthogonalization of antenna basis of transmitting and receiving antennas allows digital spectral-correlation processing of samples of aggregate signal from each target to solve the technological problem of multidimensional observation space in multiposition systems of coherent radiolocation when detecting, resolving, estimating coordinates and motion parameters of targets. The results of simulation modeling of spatio-temporal radar modems implemented according to the stated principles are given.

Experience-driven recalibration of learning from surprising events

Different contexts favor different patterns of adaptive learning. A surprising event that in one context would drive rapid belief updating might, in another context, be interpreted as a meaningless outlier. Here, across two experiments, we examined whether participants performing a target judgment task under spatial uncertainty (n=31, n=64) would spontaneously adapt their patterns of predictive gaze according to the informativeness or uninformativeness of surprising events in their current environment. Uninstructed predictive eye movements exhibited a form of metalearning in which event-by-event learning rates were modulated differently by surprise across contexts. Participants also appropriately readjusted their patterns of adaptive learning when the statistics of the environment underwent an unsignaled change. Although significant metalearning was observed in all conditions, performance was consistently superior in contexts in which surprising events reflected meaningful change, potentially reflecting a bias toward interpreting surprise as informative. Overall, our results demonstrate remarkable flexibility in contextually adaptive metalearning.

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue burning emissions and their uncertainty in North China Plain (NCP) using three widely used methods, including statistical-based, burned area-based, and fire radiative power-based methods. The impacts of biomass burning emissions on atmospheric carbon dioxide (CO2) were also examined by using a global chemical transport model (GEOS-Chem) simulation. The crop residue burning emissions were found to be high in June and followed by October, which is the harvest times for the main crops in NCP. The estimates of CO2 emission from crop residue burning exhibits large interannual variation from 2003 to 2019, with rapid growth from 2003 to 2012 and a remarkable decrease from 2013 to 2019, indicating the effects of air quality control plans in recent years. Through Monte Carlo simulation, the uncertainty of each estimation was quantified, ranging from 20% to 70% for CO2 emissions at the regional level. Concerning spatial uncertainty, it was found that the crop residue burning emissions were highly uncertain in small agricultural fire areas with the maximum changes of up to 140%. While in the areas with large agricultural fire, i.e., southern parts of NCP, the coefficient of variation mostly ranged from 30% to 100% at the gridded level. The changes in biomass burning emissions may lead to a change of surface CO2 concentration during the harvest times in NCP by more than 1.0 ppmv. The results of this study highlighted the significance of quantifying the uncertainty of biomass burning emissions in a modeling study, as the variations of crop residue burning emissions could affect the emission-driven increases in CO2 and air pollutants during summertime pollution events by a substantial fraction in this region.