Terminal synchrones in the tail of comet 1965f

Position and velocity measurements of six synchrone emissions in tail of comet Ikeya-Sek

A three-month oscillation in the longitude of Jupiter's red spot

Periodic oscillations in longitude of Great Red Spot in Jupiter atmospher

Publications of the NASA space biology program for 1980 - 1984

A listing of 562 publications supported by the NASA Space Biology Program for the years 1980 to 1984 is presented. References are arranged under the headings which are plant gravitational research, animal gravitational research, and general. Keyword title indexes and a principal investigator listing are also included

Space medicine research publications: 1983-1984

A list of publications supported by the Space Medicine Program, Office of Space Science and Applications is given. Included are publications entered into the Life Sciences Bibliographic Database by The George Washington University as of October 1, 1984

Recent measures of the latitude and longitude of jupiter's red spot

Latitude and longitude of Jupiter red spot measured from photographic plate

Latitude and longitude measurements of Jovian features in 1967-68

Photographic measurements of latitude and longitude of Jovian feature

On the terms violating the custodial symmetry in multi-Higgs-doublet models

We prove that a generic multi-Higgs-doublet model (NHDM) generally must contain terms in the potential that violate the custodial symmetry. This is done by showing that the O(4) violating terms of the NHDM potential cannot be excluded by imposing a symmetry on the NHDM Lagrangian. Hence we expect higher-order corrections to necessarily introduce such terms. We also note, in the case of custodially symmetric Higgs-quark couplings, that vacuum alignment will lead to up-down mass degeneration; this is not true if the vacua are not aligned.Comment: 16 pages, 1 figure. Title and abstract are modified, conclusions remain the same. Section on Yukawa couplings is extended. Published versio

A Computational Study of the Distribution of Particles in a Lab-Scale CFB Boiler

When two-fluid modeling is used to predict riser flows there have been difficulties in predicting the solids hold up in risers represented by the correct pressure drop profile. A way of encountering this inherent problem in current Eulerian-Eulerian CFD modeling is to approximate the actual particle size distribution by using more particle phases instead of the current practice of using one mean diameter. For the lab-scale CFB investigated, CFD simulations show that a mal-distribution occurs in the CFB; the larger particles are retained in the riser, whereas the intermediate and small particles are distributed both in the return leg and the riser. Simulations using an altered particle size distribution, i.e. a larger amount of large particles, show significant improvements in the pressure profile in the bottom part of the riser

Silicon-based three-dimensional microstructures for radiation dosimetry in hadrontherapy

In this work, we propose a solid-state-detector for use in radiation microdosimetry. This device improves the performance of existing dosimeters using customized 3D-cylindrical microstructures etched inside silicon. The microdosimeter consists of an array of micro-sensors that have 3D-cylindrical electrodes of 15 μm diameter and a depth of 5 μm within a silicon membrane, resulting in a well-defined micrometric radiation sensitive volume. These microdetectors have been characterized using an 241Am source to assess their performance as radiation detectors in a high-LET environment. This letter demonstrates the capability of this microdetector to be used to measure dose and LET in hadrontherapy centers for treatment plan verification as part of their patient-specific quality control program

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications