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Register a new userBayesian Estimation of the Hydroxyl Radical Diffusion Coefficient at Low Temperature and High Pressure from Atomistic Molecular Dynamics
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The hydroxyl radical is the primary reactive oxygen species produced by the radiolysis of water, and is a significant source of radiation damage to living organisms. Mobility of the hydroxyl radical at low temperatures and/or high pressures is hence a potentially important factor in determining the challenges facing psychrophilic and/or barophilic organisms in high-radiation environments (e.g., ice-interface or undersea environments in which radiative heating is a potential heat and energy source). Here, we estimate the diffusion coefficient for the hydroxyl radical in aqueous solution, using a hierarchical Bayesian model based on atomistic molecular dynamics trajectories in TIP4P/2005 water over a range of temperatures and pressures.
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Usually microscopic electrostatic field around ions is neglected when the ionization energy is concerned. The ionization energy is considered to be equal to that of a separate atom (molecule). Here the energy of the electrostatic field around ions is taken into account. It is shown that the energy of this field contributes to decrease in the effective ionization energy. The effective ionization energy may turn to zero at some critical concentration of delocalized electrons. This leads to a complete ionization of the atoms (molecules). Concrete calculations were performed for oxygen molecular gas.
X-ray diffraction and Raman scattering measurements, and first-principles calculations are performed to search for the formation of NaCl-hydrogen compound. When NaCl and H$_{2}$ mixture is laser-heated to above 1500 K at pressures exceeding 40 GPa, we observed the formation of NaClH$_{textit{x}}$ with $textit{P}$6$_{3}$/$textit{mmc}$ structure which accommodates H$_{2}$ molecules in the interstitial sites of NaCl lattice forming ABAC stacking. Upon the decrease of pressure at 300 K, NaClH$_textit{x}$ remains stable down to 17 GPa. Our calculations suggest the observed NaClH$_{textit{x}}$ is NaCl(H$_{2}$). Besides, a hydrogen-richer phase NaCl(H$_{2}$)$_{4}$ is predicted to become stable at pressures above 40 GPa.
In epidemiological modelling, the instantaneous reproduction number, $R_t$, is important to understand the transmission dynamics of infectious diseases. Current $R_t$ estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of $R_t$. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for $R_t$ estimation, resulting in the state-of-the-art DAR$_t$ system for $R_t$ estimation. With DAR$_t$, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and $R_t$; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DAR$_t$ through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.
The Epidemic Type Aftershock Sequence (ETAS) model is one of the most widely-used approaches to seismic forecasting. However most studies of ETAS use point estimates for the model parameters, which ignores the inherent uncertainty that arises from estimating these from historical earthquake catalogs, resulting in misleadingly optimistic forecasts. In contrast, Bayesian statistics allows parameter uncertainty to be explicitly represented, and fed into the forecast distribution. Despite its growing popularity in seismology, the application of Bayesian statistics to the ETAS model has been limited by the complex nature of the resulting posterior distribution which makes it infeasible to apply on catalogs containing more than a few hundred earthquakes. To combat this, we develop a new framework for estimating the ETAS model in a fully Bayesian manner, which can be efficiently scaled up to large catalogs containing thousands of earthquakes. We also provide easy-to-use software which implements our method.
We use the diffusion quantum Monte Carlo to revisit the enthalpy-pressure phase diagram of the various products from the different proposed decompositions of H$_2$S at pressures above 150~GPa. Our results entails a revision of the ground-state enthalpy-pressure phase diagram. Specifically, we find that the C2/c HS$_2$ structure is persistent up to 440~GPa before undergoing a phase transition into the C2/m phase. Contrary to density functional theory, our calculations suggest that the C2/m phase of HS is more stable than the I4$_1$/amd HS structure over the whole pressure range from 150 to 400 GPa. Moreover, we predict that the Im-3m phase is the most likely candidate for H$_3$S, which is consistent with recent experimental x-ray diffraction measurements.
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