Estimation of gross land-use change and its uncertainty using a Bayesian data assimilation approach

We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/

Inference of spatial heterogeneity in surface fluxes from eddy covariance data: a case study from a subarctic mire ecosystem

Horizontal heterogeneity causes difficulties in the eddy covariance technique for measuring surface fluxes, related to both advection and the confounding of temporal and spatial variability. Our aim here was to address this problem, using statistical modelling and footprint analysis, applied to a case study of fluxes of sensible heat and methane in a subarctic mire. We applied a new method to infer the spatial heterogeneity in fluxes of sensible heat and methane from a subarctic ecosystem in northern Sweden, where there were clear differences in surface types within the landscape. We inferred the flux from each of these surface types, using a Bayesian approach to estimate the parameters of a hierarchical model which includes coefficients for the different surface types. The approach is based on the variation in the flux observed at a single eddy covariance tower as the footprint changes over time. The method has applications wherever spatial heterogeneity is a concern in the interpretation of eddy covariance fluxes

Replicating financial market dynamics with a simple self-organized critical lattice model

We explore a simple lattice field model intended to describe statistical properties of high frequency financial markets. The model is relevant in the cross-disciplinary area of econophysics. Its signature feature is the emergence of a self-organized critical state. This implies scale invariance of the model, without tuning parameters. Prominent results of our simulation are time series of gains, prices, volatility, and gains frequency distributions, which all compare favorably to features of historical market data. Applying a standard GARCH(1,1) fit to the lattice model gives results that are almost indistinguishable from historical NASDAQ data.Comment: 20 pages, 33 figure

Challenges in Scaling Up Greenhouse Gas Fluxes: Experience From the UK Greenhouse Gas Emissions and Feedbacks Program

The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at the national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long-term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short-term, point observations to estimate large-scale annual totals, and quantify the uncertainty associated with this upscaling. Here, we review some approaches to this problem and synthesize the work in the recent UK Greenhouse Gas Emissions and Feedbacks Program, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near-continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterize the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates

Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models

Ecological models are used to quantify processes over large regions. When the model is nonlinear and input variables are spatially averaged, the regional mean will be in error. A formula for estimating the upscaling error can be derived from Taylor expansion of the model (Bresler and Dagan 1988). We test this for simple models under three different input distributions (Gaussian, exponential, lognormal). In several cases the formula is exact, in others it provides a reasonable approximation. We then study models for emissions of methane, ammonia, and nitrous oxide across the UK. We scale from 1 × 1 km to 32 × 32 km. The UK-average upscaling errors are −12%, −48% and −3%, well estimated using the formula. The formula is a useful tool for modellers desiring to correct upscaling error for their application. Calculation of second-order partial derivatives of model output is required, for which we provide R-code

Risk perception by food handlers in the tourism sector

The objective of this study was to evaluate the perception of risk by food handlers in the food services of the tourism sector. One structured methodological instrument was used to analyze the risk perception of 108 food handlers from 19 establishments in a tourist region in the State of São Paulo, Brazil. The highest score was related to the subject “Integrated pest control” and the lowest level was related to “Eating soft-cooked eggs”. Differences (p < 0.05) were observed among the levels of risk perception, making it possible to form three sub-groups concerning the questions. The central topic of the first sub-group was safety aspects in food production (I), the second related to sanitation operations (II) and the third to integrated pest control (III). Sub-groups II and III presented the greatest level of perceived risk. Differences (p < 0.05) between the level of risk perception and the socio-demographic variables were identified. Women showed greater risk perception compared to men. The results can provide important information for public and private programs, improving development of institutional strategies directed at food safety.TÍTULO PT: Percepção de risco por manipuladores de alimentos do setor de turismoO objetivo deste estudo foi avaliar a percepção de risco de manipuladores de alimentos em serviços de alimentação do setor de turismo. Um instrumento metodológico estruturado foi aplicado para avaliar a percepção de risco de 108 manipuladores em 19 estabelecimentos de uma região turística no Estado de São Paulo, Brasil. O maior escore médio entre os níveis de risco percebido foi relacionado ao tema “Controle Integrado de Pragas” e o menor nível a “Comer ovos de gema mole”. Diferenças (p < 0,05) foram observadas entre os níveis de percepção de risco, possibilitando a formação de três subgrupos em relação ao conjunto de questões. O tema central do primeiro subgrupo está relacionado aos aspectos de segurança na produção de alimentos (I), o segundo, as operações de higiene (II) e o terceiro ao controle integrado de pragas (III). Os subgrupos II e III apresentaram maior nível de risco percebido. Foram identificadas diferenças (p < 0,05) entre o nível de risco percebido e as variáveis sociodemográficas. As mulheres apresentaram maior nível de risco percebido do que os homens. Os resultados podem fornecer informações importantes para programas públicos e privados, visando o desenvolvimento de estratégias institucionais direcionadas à segurança dos alimentos

Hierarchical contribution of individual lifestyle factors and their interactions on adenomatous and serrated polyp risk.

BACKGROUND Individual colorectal polyp risk factors are well characterized; however, insights into their pathway-specific interactions are scarce. We aimed to identify the impact of individual risk factors and their joint effects on adenomatous (AP) and serrated polyp (SP) risk. METHODS We collected information on 363 lifestyle and metabolic parameters from 1597 colonoscopy participants, resulting in over 521,000 data points. We used multivariate statistics and machine-learning approaches to assess associations of single variables and their interactions with AP and SP risk. RESULTS Individual factors and their interactions showed common and polyp subtype-specific effects. Abdominal obesity, high body mass index (BMI), metabolic syndrome, and red meat consumption globally increased polyp risk. Age, gender, and western diet associated with AP risk, while smoking was associated with SP risk. CRC family history was associated with advanced adenomas and diabetes with sessile serrated lesions. Regarding lifestyle factor interactions, no lifestyle or dietary adjustments mitigated the adverse smoking effect on SP risk, whereas its negative effect was exacerbated by alcohol in the conventional pathway. The adverse effect of red meat on SP risk was not ameliorated by any factor, but was further exacerbated by western diet along the conventional pathway. No modification of any factor reduced the negative impact of metabolic syndrome on AP risk, whereas increased fatless fish or meat substitutes' intake mitigated its effect on SP risk. CONCLUSIONS Individual risk factors and their interactions for polyp formation along the adenomatous and serrated pathways are strongly heterogeneous. Our findings may facilitate tailored lifestyle recommendations and contribute to a better understanding of how risk factor combinations impact colorectal carcinogenesis

Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT makes increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park, CA, US