The Importance of Chemical Data for the Study of Interstellar Shocks

The importance of chemical data for the study of interstellar shocks

Astrophysics and Space Science ◽

10.1007/bf00627340 ◽

1995 ◽

Vol 233 (1-2) ◽

pp. 125-137

Author(s):

Evelyne Roueff ◽

Guillaume Pineau Des For�ts ◽

David R. Flower

Keyword(s):

Chemical Data ◽

Interstellar Shocks

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Freeze-dried dispersions for automated SEM analysis of individual submicron airborne particulates

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100122447 ◽

1992 ◽

Vol 50 (1) ◽

pp. 408-409

Author(s):

Karen A. Katrinak ◽

David W. Brekke ◽

John P. Hurley

Keyword(s):

Air Pollutants ◽

Size Range ◽

Sem Analysis ◽

Individual Particle ◽

Chemical Data ◽

Particle Analysis ◽

Bulk Analysis ◽

Airborne Particulates ◽

Freeze Dried ◽

Individual Particle Analysis

Individual-particle analysis is well established as an alternative to bulk analysis of airborne particulates. It yields size and chemical data on a particle-by-particle basis, information that is critical in predicting the behavior of air pollutants. Individual-particle analysis is especially important for particles with diameter < 1 μm, because particles in this size range have a disproportionately large effect on atmospheric visibility and health.

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CARBON DIOXIDE, HYDROGRAPHIC, AND CHEMICAL DATA OBTAINED DURING THE R/V RONALD H. BROWN REPEAT HYDROGRAPHY CRUISE IN THE ATLANTIC OCEAN:CLIVAR CO2 SECTION A16S_2005 (11 JANUARY - 24 FEBRUARY, 2005)

Carbon Dioxide Information Analysis Center (CDIAC) Datasets ◽

10.3334/cdiac/otg.ndp087 ◽

2006 ◽

Cited By ~ 4

Author(s):

R. Wanninkhof, ◽

S.C. Doney, ◽

E. Peltola, ◽

R.D. Castle, ◽

F.J. Millero, ◽

...

Keyword(s):

Carbon Dioxide ◽

Chemical Data ◽

Repeat Hydrography

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Carbon Dioxide, Hydrographic, and Chemical Data Obtained in the South Pacific Ocean (WOCE Section P16A/P17A, P17E/P19S, and P19C, R/V Knorr, October 1992-April 1993)

Carbon Dioxide Information Analysis Center (CDIAC) Datasets ◽

10.3334/cdiac/otg.ndp065 ◽

1998 ◽

Cited By ~ 4

Author(s):

S. Rubin, ◽

J.G. Goddard, ◽

D.W. Chipman, ◽

T. Takahashi, ◽

S.C. Sutherland, ◽

...

Keyword(s):

Carbon Dioxide ◽

Pacific Ocean ◽

South Pacific ◽

Chemical Data ◽

The South ◽

South Pacific Ocean

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Carbon Dioxide, Hydrographic, and Chemical Data Obtained During the R/V Malcolm Baldrige South Atlantic OACES/CO2 Cruise, Legs 1 and 2, WOCE Section A16S (11 July - 02 September, 1991)

Carbon Dioxide Information Analysis Center ◽

10.3334/cdiac/otg.woce_oaces91_a16s ◽

1991 ◽

Cited By ~ 3

Author(s):

R. WANNINKHOF, ◽

J. HENDEE,

Keyword(s):

Carbon Dioxide ◽

South Atlantic ◽

Chemical Data ◽

Malcolm Baldrige

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Carbon Dioxide, Hydrographic, and Chemical Data Obtained During the R/V Mirai Repeat Hydrography Cruise in the Pacific Ocean: CLIVAR CO2 Section P10_2005 (25 May - 2 July, 2005)

Carbon Dioxide Information Analysis Center ◽

10.3334/cdiac/otg.clivar_p10_2005 ◽

2005 ◽

Cited By ~ 3

Author(s):

M. FUKASAWA, ◽

T. KAWANO, ◽

A. MURATA, ◽

F. SHIBATA, ◽

M. KITADA, ◽

...

Keyword(s):

Carbon Dioxide ◽

Pacific Ocean ◽

Chemical Data ◽

The Pacific ◽

The Pacific Ocean ◽

Repeat Hydrography

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Carbon Dioxide, Hydrographic, and Chemical Data Obtained During the R/V Melville Cruise in the Pacific Ocean on CLIVAR Repeat Hydrography Sections P02_2004 (15 June - 27 August, 2004)

Carbon Dioxide Information Analysis Center ◽

10.3334/cdiac/otg.clivar_p02_2004 ◽

2004 ◽

Cited By ~ 2

Author(s):

R. FEELY, ◽

A. DICKSON, ◽

D. HANSELL, ◽

C. CARLSON,

Keyword(s):

Carbon Dioxide ◽

Pacific Ocean ◽

Chemical Data ◽

The Pacific ◽

The Pacific Ocean ◽

Repeat Hydrography

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Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

10.26434/chemrxiv.12800348 ◽

2020 ◽

Author(s):

Vadim V. Korolev ◽

Artem Mitrofanov ◽

Kirill Karpov ◽

Valery Tkachenko

Keyword(s):

Neural Network ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Natural Science ◽

Therapeutic Agent ◽

Semantic Relations ◽

Chemical Data ◽

Processing Methods ◽

Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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