Results of the Jordan River midden excavation

The Jordan River Midden is a large shell midden situated on the west bank of the Jordan River approximately 17km NNW of Hobart. It is 37km from the mouth of the Derwent River. The lower Jordan River cuts through Jurassic dolerite on the eastern bank and Tertiary basalt on the western bank. Several quarry sites have been reported within a 6.5km radius of JRM1

Constraining Model Uncertainty in Plasma Equation-of-State Models with a Physics-Constrained Gaussian Process

Equation-of-state (EOS) models underpin numerical simulations at the core of research in high energy density physics, inertial confinement fusion, laboratory astrophysics, and elsewhere. In these applications EOS models are needed that span ranges of thermodynamic variables that far exceed the ranges where data are available, making uncertainty quantification (UQ) of EOS models a significant concern. Model uncertainty, arising from the choice of functional form assumed for the EOS, is a major challenge to UQ studies for EOS that is usually neglected in favor of parameteric and data uncertainties which are easier to capture without violating the physical constraints on EOSs. In this work we introduce a new statistical EOS construction that naturally captures model uncertainty while automatically obeying the thermodynamic consistency constraint. We apply the model to existing data for $B_4C$\ to place an upper bound on the uncertainty in the EOS and Hugoniot, and show that the neglect of thermodynamic constraints overestimates the uncertainty by factors of several when data are available and underestimates when extrapolating to regions where they are not. We discuss extensions to this approach, and the role of GP-based models in accelerating simulation and experimental studies, defining portable uncertainty-aware EOS tables, and enabling uncertainty-aware downstream tasks.Comment: 11 pages, 5 figure

Learning thermodynamically constrained equations of state with uncertainty

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in the EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon, using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty reduces by considering the thermodynamic constraints.Comment: 26 pages, 7 figure

Variation in bioactive content in broccoli (Brassica oleracea var. italica) grown under conventional and organic production systems

This is the accepted version of the following article: Valverde, J., Reilly, K., Villacreces, S., Gaffney, M., Grant, J. and Brunton, N. (2015), Variation in bioactive content in broccoli (Brassica oleracea var. italica) grown under conventional and organic production systems. J. Sci. Food Agric., 95: 1163–1171, which has been published in final form at http://dx.doi.org/10.1002/jsfa.6804peer-reviewedBACKGROUND Broccoli and other cruciferous vegetables contain a number of bioactive compounds, in particular glucosinolates and polyphenols, which are proposed to confer health benefits to the consumer. Demand for organic crops is at least partly based on a perception that organic crops may contain higher levels of bioactive compounds; however, insufficient research has been carried out to either support or refute such claims. RESULTS In this study we examined the effect of conventional, organic, and mixed cultivation practices on the content of total phenolics, total flavonoids, and total and individual glucosinolates in two varieties of broccoli grown over 2 years in a split-plot factorial systems comparison trial. Levels of total phenolics and total flavonoids showed a significant year-on-year variation but were not significantly different between organic and conventional production systems. In contrast, levels of the indolyl glucosinolates glucobrassicin and neoglucobrassicin were significantly higher (P < 0.05) under fully organic compared to fully conventional management. CONCLUSION Organic cultivation practices resulted in significantly higher levels of glucobrassicin and neoglucobrassicin in broccoli florets; however, other investigated compounds were unaffected by production practices.The Irish Department of Agriculture, Fisheries and Food (FIRM 06/NITAFRC6) is gratefully acknowledged for financial suppor

Maximizing value of genetic sequence data requires an enabling environment and urgency

Severe price spikes of the major grain commodities and rapid expansion of cultivated area in the past two decades are symptoms of a severely stressed global food supply. Scientific discovery and improved agricultural productivity are needed and are enabled by unencumbered access to, and use of, genetic sequence data. In the same way the world witnessed rapid development of vaccines for COVID-19, genetic sequence data afford enormous opportunities to improve crop production. In addition to an enabling regulatory environment that allowed for the sharing of genetic sequence data, robust funding fostered the rapid development of coronavirus diagnostics and COVID-19 vaccines. A similar level of commitment, collaboration, and cooperation is needed for agriculture