Physical and biogeochemical variables from the NOAA-GFDL Earth System Model 2M experiments, and previously published observation-based datasets, used for the study 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'.
Link, A. James; Carson, Drew V.; So, Larry; Cheung-Lee, Wai Ling
Abstract:
This entry encompasses the raw NMR spectra used to determine the structure of the lasso peptide achromonodin-1. Within one file are included the five following spectra: COSY, TOCSY, NOESY (150 ms mixing time), NOESY (700 ms mixing time), and C,H HSQC. The file requires Mestrenova software to read. These spectra were used to develop the 3D structure models of achromonodin-1 that are deposited at the protein data bank (PDB) as entry 8SVB.
Physical and biogeochemical variables from the NOAA-GFDL Earth System Model 2M experiments (pre-processed), previously published observation-based datasets, and code to reproduce figures from these datasets, used for the study 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'.
Large-eddy simulations were employed over half-ice and half-water surfaces, with varying surface temperatures, wind speeds, directions, as to test if the atmospheric interaction with the heterogeneous surface can be predicted via a heterogeneity Richardson number. This dataset was used to determine that surface heat fluxes over ice, water, and the aggregate surface seem to be captured reasonably well by the wind direction and the heterogeneity Richardson number, but the mean wind and turbulent kinetic energy (TKE) profiles were not, suggesting that not only the difference in stability between the two surface, but also the individual stabilities over each surface influence the dynamics.
Recently an improved confinement regime, characterized by reduced turbulent fluctuations has been observed in the Large Helical Device upon the injection of boron powder into the plasma (Nespoli et al 2022 Nat. Phys.18 350–56). In this article, we report in more detail the experimental observations of increased plasma temperature and the decrease of turbulent fluctuations across the plasma cross section, on an extended database. In particular, we compare powders of different materials (B, C, BN), finding similar temperature improvement and turbulence response for the three cases. Modeling of the powder penetration into the plasma and of neoclassical electric field and fluxes support the interpretation of the experimental results. Additionally, we report evidence of the temperature improvement increasing with powder injection rates and decreasing for both increasing density and heating power. Though, plasma turbulence response varies depending on the initial conditions of the plasma, making it difficult to draw an inclusive description of the phenomenon.
Chatbots, or artificial agents that can carry on text conversations, are increasingly used as social companions: as friends, mentors, or significant others. Because they have become widely available only recently, there is still relatively little research on their psychological impact on people. To study how human-chatbot interaction can impact social health, relationships with family and friends, and self-esteem, we conducted a study of people who have relationships with companion chatbots and people who do not. Frequent users indicated that they received social health benefits. The perceived benefit was significantly correlated with people’s perceptions of the chatbot as having consciousness, agency, experience, and, especially, human likeness. People who did not have a relationship with a chatbot reported a more neutral to negative view, judging that if they were to use it, they might suffer harm. Yet even in the non-user group, perceived benefits positively correlated with perceived consciousness, agency, experience, and human likeness. It may be that with careful development, humanlike artificial intelligence can have a net positive effect on society, aiding mental health by supplying a reliable social interaction that improves, rather than replaces, people’s interactions with each other.
Griffies, Stephen M; Beadling, Rebecca L; Krasting, John P; Hurlin, William J
Abstract:
This output was produced in coordination with the Southern Ocean Freshwater release model experiments Initiative (SOFIA) and is the Tier 1 experiment where freshwater is delivered in a spatially and temporally uniform pattern at the surface of the ocean at sea surface temperature in a 1-degree latitude band extending from Antarctica’s coastline. The total additional freshwater flux imposed as a monthly freshwater flux entering the ocean is 0.1 Sv. Users are referred to the methods section of Beadling et al. (2022) for additional details on the meltwater implementation in CM4 and ESM4. The datasets in this collection contain model output from the coupled global climate model, CM4, and Earth System Model, ESM4, both developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The ocean_monthly_z and ocean_annual_z output are provided as z depth levels in meters as opposed to the models native hybrid vertical ocean coordinate which consists of z* (quasi-geopotential) coordinates in the upper ocean through the mixed layer, transitioning to isopycnal (referenced to 2000 dbar) in the ocean interior. Please see README for further details.
This dataset contains example input files, training data sets and potential files related to the publication "First-principles-based Machine Learning Models for Phase Behavior and Transport Properties of CO2." by Mathur et al (2023). In this work, we developed machine learning models for CO2 based on different exchange-correlation DFT functionals. We assessed their performance on liquid densities, vapor-liquid equilibrium and transport properties.
Mondal, Shanka Subhra; Webb, Taylor; Cohen, Jonathan
Abstract:
A dataset of Raven’s Progressive Matrices (RPM)-like problems using realistically rendered
3D shapes, based on source code from CLEVR (a popular visual-question-answering dataset) (Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., & Girshick, R. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2901-2910)).
