Emitted Power Of Jupiter Based On Cassini CIRS And VIMS Observations

The emitted power of Jupiter and its meridional distribution are determined from observations by the Composite Infrared Spectrometer (CIRS) and Visual and Infrared Spectrometer (VIMS) onboard Cassini during its flyby en route to Saturn in late 2000 and early 2001. Jupiter's global- average emitted power and effective temperature are measured to be 14.10+/-0.03 W/sq m and 125.57+/-0.07 K, respectively. On a global scale, Jupiter's 5-micron thermal emission contributes approx. 0.7+/-0.1 % to the total emitted power at the global scale, but it can reach approx. 1.9+/-0.6% at 15degN. The meridional distribution of emitted power shows a significant asymmetry between the two hemispheres with the emitted power in the northern hemisphere 3.0+/-0.3% larger than that in the southern hemisphere. Such an asymmetry shown in the Cassini epoch (2000-01) is not present during the Voyager epoch (1979). In addition, the global-average emitted power increased approx. 3.8+/-1.0% between the two epochs. The temporal variation of Jupiter's total emitted power is mainly due to the warming of atmospheric layers around the pressure level of 200 mbar. The temporal variation of emitted power was also discovered on Saturn (Li et al., 2010). Therefore, we suggest that the varying emitted power is a common phenomenon on the giant planets

Dynamic nuclear polarization and spin-diffusion in non-conducting solids

There has been much renewed interest in dynamic nuclear polarization (DNP), particularly in the context of solid state biomolecular NMR and more recently dissolution DNP techniques for liquids. This paper reviews the role of spin diffusion in polarizing nuclear spins and discusses the role of the spin diffusion barrier, before going on to discuss some recent results.Comment: submitted to Applied Magnetic Resonance. The article should appear in a special issue that is being published in connection with the DNP Symposium help in Nottingham in August 200

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Trammel, W. J., 1844- : Confederate Service Record, 1909.

This service record is an account of military actions during the American Civil War by veteran W. J. Trammel (1844- ), dated from 1909.All descriptive lists and service records in this United Confederate (Civil War) Veterans manuscript collection believed to be based out of Robert E. Lee Camp #158 of the United Confederate Veterans (Fort Worth, Tex.). United Confederate Veterans. R.E. Lee Camp No. 158 (Fort Worth, Tex.)The Southwest Collection Manuscript Record can be accessed at the following URL: http://www.lib.utexas.edu/taro/ttusw/00119/tsw-00119.html1 leaf ; 2 pdf pages

Decoding semantic relatedness and prediction from EEG: A classification method comparison

Data and analysis MATLAB scripts for "Decoding semantic relatedness and prediction from EEG: A classification model comparison". MATLAB scripts adapted from Bae & Luck, 2018 (https://osf.io/bpexa/). Original ERPCORE data can be found at: https://doi.org/10.18115/D5JW4