Combinatorial search of superconductivity in Fe-B composition spreads

We have fabricated Fe-B thin film composition spreads in search of possible superconducting phases following a theoretical prediction by Kolmogorov et al.^1 Co-sputtering was used to deposit spreads covering a large compositional region of the Fe-B binary phase diagram. A trace of superconducting phase was found in the nanocrystalline part of the spread, where the film undergoes a metal to insulator transition as a function of composition in a region with the average composition of FeB_2. The resistance drop occurs at 4K, and a diamagnetic signal has also been detected at the same temperature. The superconductivity is suppressible in the magnetic field up to 2 Tesla.Comment: 11 pages, 4 figure

Giant Magnetostriction in Annealed Co\u3csub\u3e1-x\u3c/sub\u3eFe\u3csub\u3ex\u3c/sub\u3e Thin-Films

Chemical and structural heterogeneity and the resulting interaction of coexisting phases can lead to extraordinary behaviours in oxides, as observed in piezoelectric materials at morphotropic phase boundaries and relaxor ferroelectrics. However, such phenomena are rare in metallic alloys. Here we show that, by tuning the presence of structural heterogeneity in textured Co1−xFex thin films, effective magnetostriction λeff as large as 260 p.p.m. can be achieved at low-saturation field of ~10 mT. Assuming λ100 is the dominant component, this number translates to an upper limit of magnetostriction ofλ100≈5λeff \u3e1,000 p.p.m. Microstructural analyses of Co1−xFex films indicate that maximal magnetostriction occurs at compositions near the (fcc+bcc)/bcc phase boundary and originates from precipitation of an equilibrium Co-rich fcc phase embedded in a Fe-rich bcc matrix. The results indicate that the recently proposed heterogeneous magnetostriction mechanism can be used to guide exploration of compounds with unusual magnetoelastic properties

Anomalous magnetoresistance in the spinel superconductor LiTi2O4

LiTi2O4 is a unique compound in that it is the only known spinel oxide superconductor. The lack of high quality single crystals has thus far prevented systematic investigations of its transport properties. Here we report a careful study of transport and tunnelling spectroscopy in epitaxial LiTi2O4 thin films. An unusual magnetoresistance is observed which changes from nearly isotropic negative to prominently anisotropic positive as the temperature is decreased. We present evidence that shows that the negative magnetoresistance likely stems from the suppression of local spin fluctuations or spin-orbit scattering centres. The positive magnetoresistance suggests the presence of an orbital-related state, also supported by the fact that the superconducting energy gap decreases as a quadratic function of magnetic field. These observations indicate that the spin-orbital fluctuations play an important role in LiTi2O4 in a manner similar to high-temperature superconductors

Giant magnetostriction in annealed Co1 − xFex thin-films

Chemical and structural heterogeneity and the resulting interaction of coexisting phases can lead to extraordinary behaviours in oxides, as observed in piezoelectric materials at morphotropic phase boundaries and relaxor ferroelectrics. However, such phenomena are rare in metallic alloys. Here we show that, by tuning the presence of structural heterogeneity in textured Co[subscript 1 − x]Fe[subscript x] thin films, effective magnetostriction λ[subscript eff] as large as 260 p.p.m. can be achieved at low-saturation field of ~10 mT. Assuming λ[subscript 100] is the dominant component, this number translates to an upper limit of magnetostriction of λ[subscript 100] ≈ 5λ[subscript eff] > 1,000 p.p.m. Microstructural analyses of Co[subscript 1 − x]Fe[subscript x] films indicate that maximal magnetostriction occurs at compositions near the (fcc + bcc)/bcc phase boundary and originates from precipitation of an equilibrium Co-rich fcc phase embedded in a Fe-rich bcc matrix. The results indicate that the recently proposed heterogeneous magnetostriction mechanism can be used to guide exploration of compounds with unusual magnetoelastic properties.Keywords: Fabrication, FE-GA, Multilayers, Microstructure, Similar magnetic Alloys, Extrinsic magnetostriction, TB-FE, Structurally heterogeneous mode

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of a water yield model for southwestern rangelands

Evaluation of a water yield model developed by Williams and LaSeur, with modifications suggested by Hawkins, on southwestern watersheds. Calibration and validation of the model were accomplished using 1963 to 1970 data from three selected subwatersheds of the Walnut Gulch Experimental Watershed. An International Mathematical and Statistical Library Program was used to calibrate the model on a portion of data, to determine the initial parameters. Validation of the model was accomplished by using the second portion of the watershed data and the initial parameters determined from the calibration for prediction. Results show that model overpredicted discharge on all three subwatersheds by varying amounts, with little consistancy in the amount of variation between the measured and predicted discharge explained. Stratification of storms by season, on one watershed, to try and account for the overestimation of discharge, showed a 10 percent increase in the variation of discharge explained. Model still overpredicted the discharge but not as greatly.hydrology collectio