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We discuss two models of radiative neutrino mass generation. The first model features one–loop Zee model with Z{sub 4} symmetry. The second model is the two–loop neutrino mass model with singly- and doubly-charged scalars. These two models fit neutrino oscillation data well and predict some interesting rates for lepton flavor violation processes.
Quantitative risk estimates from exposure to ionizing radiation are dominated by analysis of the one-time exposures received by the Japanese survivors at Hiroshima and Nagasaki. Three recent epidemiologic studies suggest that the risk from protracted exposure is no lower, and in fact may be higher, than from single exposures. There is near-universal acceptance that epidemiologic data demonstrates an excess risk of delayed cancer incidence above a dose of 0.1 sievert (Sv), which, for the average American, is equivalent to 40 years of unavoidable exposure from natural background radiation. Model fits, both parametric and nonparametric, to the atomic-bomb data support a linear no-threshold model, below 0.1 Sv. On the basis of biologic arguments, the scientific establishment in the United States and many other countries accepts this dose-model down to zero-dose, but there is spirited dissent. The dissent may be irrelevant for developed countries, given the increase in medical diagnostic radiation that has occurred in recent decades; a sizeable percentage of this population will receive cumulative doses from the medical profession in excess of 0.1 Sv, making talk of a threshold or other sublinear response below that dose moot for future releases from nuclear facilities or a dirty bomb. The risks from both medical diagnostic doses and nuclear accident doses can be computed using the linear dose-response model, with uncertainties assigned below 0.1 Sv in a way that captures alternative scientific hypotheses. Then, the important debate over low-level radiation exposures, namely planning for accident response and weighing benefits and risks of technologies, can proceed with less distraction. One of the biggest paradoxes in the low-level radiation debate is that an individual risk can be a minor concern, while the societal risk-the total delayed cancers in an exposed population-can be of major concern.
We investigate the Sun–Earth dynamics of a set of eight well observed solar coronal mass ejections (CMEs) using data from the Solar Terrestrial Relations Observatory spacecraft. We seek to quantify the extent to which momentum coupling between these CMEs and the ambient solar wind (i.e., the aerodynamic drag) influences their dynamics. To this end, we use results from a 3D flux rope model fit to the CME data. We find that solar wind aerodynamic drag adequately accounts for the dynamics of the fastest CME in our sample. For the relatively slower CMEs, we find that drag-based models initiated below heliocentric distances ranging from 15 to 50 R{sub ⊙} cannot account for the observed CME trajectories. This is at variance with the general perception that the dynamics of slow CMEs are influenced primarily by solar wind drag from a few R{sub ⊙} onwards. Several slow CMEs propagate at roughly constant speeds above 15–50 R{sub ⊙}. Drag-based models initiated above these heights therefore require negligible aerodynamic drag to explain their observed trajectories.
We present a joint analysis of the cosmic microwave background (CMB) lensing power spectra measured from the Data Release 6 of the Atacama Cosmology Telescope (ACT) and Planck PR4, cross-correlations between the ACT and Planck lensing reconstruction and galaxy clustering from unWISE, and the unWISE clustering auto-spectrum. We obtain 1.5% constraints on the matter density fluctuations at late times parametrized by the best constrained parameter combination 𝑆$^{3x2pt}_{8}$ ≡ 𝜎 8 (Ω 𝑚 /0.3) 0.4 = 0.815 ± 0.012. The commonly used 𝑆 8 ≡ 𝜎 8 (Ω 𝑚 /0.3) 0.5 parameter is constrained to 𝑆 8 = 0.816 ± 0.015. In combination with baryon acoustic oscillation (BAO) measurements we find 𝜎 8 = 0.815 ± 0.012. We also present sound-horizon-independent estimates of the present day Hubble rate of 𝐻 0 = 66.4$^{+3.2}_{−3.7}$ km s −1 Mpc −1 from our large scale structure data alone and 𝐻 0 = 64.3$^{+2.1}_{−2.4}$ km s −1 Mpc −1 in combination with uncalibrated supernovae from Pantheon+. Using parametric estimates of the evolution of matter density fluctuations, we place constraints on cosmic structure in a range of high redshifts typically inaccessible with cross-correlation analyses. Combining lensing cross- and autocorrelations, we derive a 3.3% constraint on the integrated matter density fluctuations above 𝑧 = 2.4, one of the tightest constraints in this redshift range and fully consistent with a Λ cold dark matter (ΛCDM) model fit to the primary CMB from Planck. Finally, combining with primary CMB observations and using the extended low redshift coverage of these combined datasets we derive constraints on a variety of extensions to the ΛCDM model including massive neutrinos, spatial curvature, and dark energy. We find in flat ΛCDM ∑𝑚 𝜈 < 0.12 eV at 95% confidence using the large scale structure data, BAO measurements from Sloan Digital Sky Survey, and primary CMB observations.
We construct and verify a statistical method to nowcast influenza activity from a time-series of the frequency of reports concerning influenza related topics. Such reports are published electronically by both public health organizations as well as newspapers/media sources, and thus can be harvested easily via web crawlers. Since media reports are timely, whereas reports from public health organization are delayed by at least two weeks, using timely, open-source data to compensate for the lag in %E2%80%9Cofficial%E2%80%9D reports can be useful. We use morbidity data from networks of sentinel physicians (both the Center of Disease Control's ILINet and France's Sentinelles network) as the gold standard of influenza-like illness (ILI) activity. The time-series of media reports is obtained from HealthMap (http://healthmap.org). We find that the time-series of media reports shows some correlation ( 0.5) with ILI activity; further, this can be leveraged into an autoregressive moving average model with exogenous inputs (ARMAX model) to nowcast ILI activity. We find that the ARMAX models have more predictive skill compared to autoregressive (AR) models fitted to ILI data i.e., it is possible to exploit the information content in the open-source data. We also find that when the open-source data are non-informative, the ARMAX models reproduce the performance of AR models. The statistical models are tested on data from the 2009 swine-flu outbreak as well as the mild 2011-2012 influenza season in the U.S.A.
Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.
We study the broadband spectral energy distribution (SED) of the prototypical low-mass active galactic nucleus (AGN) in NGC 4395. We jointly model the optical through mid-IR SED with a combination of galaxy and AGN light, and find that on arcsecond scales the AGN dominates at most wavelengths. However, there is still some ambiguity about emission from the galaxy, owing partially to the strong short-term variability of the black hole. We investigate the use of smooth- and clumpy-torus models in order to disentangle the nuclear IR emission, as well as exploring the use of poloidal wind emission to account for the blue spectral slope observed in the near-IR. Even when simultaneously fitting the full optical–IR spectral range, we find that degeneracies still remain in the best-fit models. We conclude that high-spatial-resolution and wider-wavelength coverage with the JWST is needed to understand the mid-IR emission in this complex, highly variable object, which is the best nearby example to provide a blueprint to finding other low-mass AGNs via their mid-IR emission in the future.
Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).
The chemical pulping of biomass involves the recycling of calcium through the calcination of lime mud, which is mostly comprised of calcium carbonate (CaCO 3 ). Lime mud decomposes under elevated temperatures to generate calcium oxide (CaO) and carbon dioxide (CO 2 ), the kinetics of which are strongly influenced by the CO 2 partial pressure and temperature. Oxy-fuel combustion and electrified lime kilns for lime mud calcination are intriguing methods to decarbonize this highly polluting operation within the biomass pulping industry. However, the high CO 2 concentration in oxy-fuel and electrified calcination processes alters the kinetics and overall reactivity of lime mud. For the first time, a model-fitting method is used to determine the kinetic parameters for lime mud calcination under a wide range of temperatures (550 °C–1250 °C) and under different concentrations of CO2 (0 %, 15 %, 50 %, and 90 %). A kinetic model is developed that accurately predicts the reaction rates as a function of temperature and CO 2 concentration. The apparent activation of energy for lime mud calcination is elevated under a high CO 2 environment. Relative to inert gas (N 2 , Ar), the temperature window for calcination is much smaller under high CO 2 environments. The presence of Na in lime mud does not seem to affect calcination under a high CO 2 environment. Finally, particle size variation does not have a significant effect on calcination under a high CO 2 environment.
The Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) reactivation mission has completed its third year of surveying the sky in the thermal infrared for near-Earth asteroids and comets. NEOWISE collects simultaneous observations at 3.4 and 4.6 μ m of solar system objects passing through its field of regard. These data allow for the determination of total thermal emission from bodies in the inner solar system, and thus the sizes of these objects. In this paper, we present thermal model fits of asteroid diameters for 170 NEOs and 6110 Main Belt asteroids (MBAs) detected during the third year of the survey, as well as the associated optical geometric albedos. We compare our results with previous thermal model results from NEOWISE for overlapping sample sets, as well as diameters determined through other independent methods, and find that our diameter measurements for NEOs agree to within 26% (1 σ ) of previously measured values. Diameters for the MBAs are within 17% (1 σ ). This brings the total number of unique near-Earth objects characterized by the NEOWISE survey to 541, surpassing the number observed during the fully cryogenic mission in 2010.
Exoplanet WASP-14b is a highly irradiated, transiting hot Jupiter. Joshi et al. calculate an equilibrium temperature (T {sub eq}) of 1866 K for zero albedo and reemission from the entire planet, a mass of 7.3 ± 0.5 Jupiter masses (M {sub J}), and a radius of 1.28 ± 0.08 Jupiter radii (R {sub J}). Its mean density of 4.6 g cm{sup -3} is one of the highest known for planets with periods less than three days. We obtained three secondary eclipse light curves with the Spitzer Space Telescope. The eclipse depths from the best jointly fit model are 0.224% ± 0.018% at 4.5 μm and 0.181% ± 0.022% at 8.0 μm. The corresponding brightness temperatures are 2212 ± 94 K and 1590 ± 116 K. A slight ambiguity between systematic models suggests a conservative 3.6 μm eclipse depth of 0.19% ± 0.01% and brightness temperature of 2242 ± 55 K. Although extremely irradiated, WASP-14b does not show any distinct evidence of a thermal inversion. In addition, the present data nominally favor models with day-night energy redistribution less than ∼30%. The current data are generally consistent with oxygen-rich as well as carbon-rich compositions, although an oxygen-rich composition provides a marginally better fit. We confirm a significant eccentricity of e = 0.087 ± 0.002 and refine other orbital parameters.
Here we compare models of supernova (SN) neutrino emission with the Kamiokande II data on SN 1987A using the Bayesian approach. These models are taken from simulations and are representative of current one-dimensional SN models. We find that models with a brief accretion phase of neutrino emission are the most favored. This result is not affected by varying the overall flux normalization or considering neutrino oscillations. We also check the compatibility of the best-fit models with the data.
Abstract Thermoelastic properties of mantle candidate minerals are essential to our understanding of geophysical phenomena, geochemistry, and geodynamic evolutions of the silicate Earth. However, the lower-mantle mineralogy remains much debated due to the lack of single-crystal elastic moduli (Cij) and aggregate sound velocities of (Al,Fe)-bearing bridgmanite, the most abundant mineral of the planet, at the lower mantle pressure-temperature (P-T) conditions. Here we report single-crystal Cij of (Al,Fe)-bearing bridgmanite, Mg0.88Fe0.1Al0.14Si0.90O3 (Fe10-Al14-Bgm) with Fe3+/ΣFe = ~0.65, up to ~82 GPa using X-ray diffraction (XRD), Brillouin light scattering (BLS), and impulsive stimulated light scattering (ISLS) measurements in diamond-anvil cells (DACs). Two crystal platelets with orientations of (–0.50, 0.05, –0.86) and (0.65, –0.59, 0.48), that are sensitive to deriving all nine Cij, are used for compressional and shear wave velocity (νP and νS) measurements as a function of azimuthal angles over 200° at each experimental pressure. Our results show that all Cij of singe-crystal Fe10-Al14-Bgm increase monotonically with pressure with small uncertainties of 1–2% (±1σ), except C55 and C23, which have uncertainties of 3–4%. Using the third-order Eulerian finite-strain equations to model the elasticity data yields the aggregate adiabatic bulk and shear moduli and respective pressure derivatives at the reference pressure of 25 GPa: KS = 326 ± 4 GPa, µ = 211 ± 2 GPa, KS′ = 3.32 ± 0.04, and µ′ = 1.66 ± 0.02 GPa. The high-pressure aggregate νS and νP of Fe10-Al14-Bgm are 2.6–3.5% and 3.1–4.7% lower than those of MgSiO3 bridgmanite end-member, respectively. These data are used with literature reports on bridgmanite with different Fe and Al contents to quantitatively evaluate pressure and compositional effects on their elastic properties. Comparing with one-dimensional seismic profiles, our modeled velocity profiles of major lower-mantle mineral assemblages at relevant P-T suggest that the lower mantle could likely consist of about 89 vol% (Al,Fe)-bearing bridgmanite. After considering uncertainties, our best-fit model is still indistinguishable from pyrolitic or chondritic models.
As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly important. Uncertainty quantification (UQ) provides important information about the ability of a learned model to make sound predictions, often with respect to individual test cases. However, most UQ methods for ML are themselves data-driven and therefore susceptible to the same knowledge gaps as the models themselves. Specifically, UQ helps to identify points near decision boundaries where the models fit the data poorly, yet predictions can score as certain for points that are under-represented by the training data and thus out-of-distribution (OOD). One method for evaluating the quality of both ML models and their associated uncertainty estimates is out-of-distribution detection (OODD). We combine OODD with UQ to provide insights into the reliability of the individual predictions made by an ML model.
2-(1'-Pyrenyl)-4,4,5,5-tetramethyl-4,5-dihydro-1H-imidazole-3-oxide-1-oxyl (PyrNN) was reacted with M(hfac){sub 2} (M = Mn(II) and Co(II), hfac = hexafluoracetylacetonate) to give two isostructural ML{sub 2} stoichiometry M(hfac){sub 2}(PyrNN){sub 2} complexes and a ML stoichiometry one-dimensional (1-D) polymer chain complex [Mn(hfac){sub 2}(PyrNN)]. The ML{sub 2} complexes have similar crystal structures with monoclinic unit cells, in which one NO unit from each PyrNN ligand is bonded to the transition metal on cis vertices of a distorted octahedron. The major magnetic interactions are intracomplex metal-to-radical exchange (J), and intermolecular exchange across a close contact between the uncoordinated NO units (J'). For M = Mn(II) an approximate chain model fit gives g = 2.0, J = (-)125 cm{sup -1} and J' = (-)49 cm{sup -1}; for M = Co(II), g = 2.4, J = (-)180 cm{sup -1}, and J' = (-)70 cm{sup -1}. Hybrid density functional theory (DFT) computations modeling the intermolecular exchange by using only the radical units across the close contact are in good accord with the estimated values of J'. The chain type complex structure shows solvent incorporation for overall structure [Mn(hfac){sub 2}(PyrNN)]{sub n} {center_dot} 0.5(CHCl{sub 3}) {center_dot} 0.5(C{sub 7}H{sub 16}). Both NO groups of the PyrNN ligand are complexed to form helical chains, with very strong metal to radical antiferromagnetic exchange that gives overall ferrimagnetic behavior.
Isopiestic vapor pressures were measured at 298.15 K for aqueous NaNO3 + Eu(NO3)3 solutions, using NaCl(aq) as the reference standard. Measurements were made for both binary (single salt) solutions and for ternary solutions of the following NaNO3 ionic strength fractions: 0.05995, 0.08749, 0.16084, 0.27709, and 0.36313 over the water activity range 0.8951 = aw = 0.9832. (These ionic strength fractions correspond to NaNO3 molality fractions 0.27675, 0.36519, 0.53489, 0.69695, and 0.77381, respectively.) The results, and those of other studies for the two pure aqueous solutions, were used to determine the Pitzer model parameters for aqueous Eu(NO3)3 for molalities up to 3 mol kg–1 and the two ternary (mixture) parameters ?Eu,Na = 0.367 ± 0.0035 and ?Eu,Na,NO3 = -0.0743 ± 0.0014. Some deviations of the measurements from the fitted model, of the order of +0.0075 in the osmotic coefficient, were noted for mixtures containing less than about 1 mol kg–1 total NO3–. The use of the mixture parameters in the Pitzer model yields predicted trace activity coefficients of Eu3+ in 1 mol kg–1 aqueous NaNO3 almost a factor of 2 greater than if they are omitted.
Radon is a naturally occurring radioactive gas that poses a serious health risk as the primary cause of lung cancer in non-smokers. Despite the well-known adverse association with health outcomes, current radon exposure assessments are limited to county-level or average-level estimates, which fail to capture regional variability. This study uses Machine Learning models, including Random Forest (RF) and Quantile Regression Forest (QRF), to estimate the indoor radon concentrations at the ZCTA (Zip code tabulation area)-level and characterize uncertainties in model estimates. Incorporating geological, meteorological, and building-specific data, the models aim to improve radon risk assessment by capturing mean exposure, variability, and extreme concentration levels. Processed radon test data (n = 718,111) were analyzed using average, variability, and quantile prediction methods. Models that estimate the average radon exposure at the ZCTA-level can yield promising model-fit results, but they do not capture the underlying variability of indoor radon exposure within a ZCTA. We utilize volatility analyses to identify characteristics indicative of high variability of indoor radon exposure. We also show that a QRF model can be used to estimate upper quantiles of residential radon exposure, thereby uncovering localized areas of elevated exposure that were not apparent in mean estimates. The results highlighted the need for a deep characterization of exposure risk and show that regions with moderate average exposure levels could still harbor extreme outliers with implications for evaluating health risks. Utilizing multiple radon exposure models allows for a deeper characterization of radon risk within a geographic area and can better identify high-risk areas. The results from this study provide a foundation for developing mitigation strategies and examining associations between radon exposure and health outcomes at fine scales. Future research should extend the geographic scope and incorporate additional environmental risk factors to establish a comprehensive framework for risk assessment.
We simulate the chemistry of infrared dark clouds (IRDCs) with a model in which the physical conditions are homogeneous and time independent. The chemistry is solved as a function of time with three networks: one purely gas phase, one that includes accretion and desorption, and one, the complete gas-grain network, that includes surface chemistry in addition. We compare our results with observed molecular abundances for two representative IRDCs-IRDC013.90-1 and IRDC321.73-1-using the molecular species N{sub 2}H{sup +}, HC{sub 3}N, HNC, HCO{sup +}, HCN, C{sub 2}H, NH{sub 3}, and CS. IRDC013.90-1 is a cold IRDC, with a temperature below 20 K, while IRDC321.73-1 is somewhat warmer, in the range 20-30 K. We find that the complete gas-grain model fits the data very well, but that the goodness of fit is not sharply peaked at a particular temperature. Surface processes are important for the explanation of the high gas-phase abundance of N{sub 2}H{sup +} in IRDC321.73-1. The general success of the zero-dimensional model in reproducing single-dish observations of our limited sample of eight species shows that it is probably sufficient for an explanation of this type of data. To build and justify more complicated models, including spatial temperature and density structure, contraction, and heating, we require high-resolution interferometric data.