Dynamics of Liquid Water: Fluctuations and Collective Motions

1992 ◽  
pp. 130-138 ◽  
Author(s):  
I. Ohmine ◽  
H. Tanaka
Keyword(s):  
Liquid Water ◽  
Collective Motions

Ancient Mars probably too cold for liquid water

Nature ◽  
2014 ◽  
Author(s):  
Sid Perkins
Keyword(s):  
Liquid Water

Reciprocal Polarizable Embedding with a Transferable H2O Potential Function II: Application to (H2O)n Clusters & Liquid Water

2019 ◽  
Author(s):  
Asmus Ougaard Dohn ◽  
Elvar Jónsson ◽  
Hannes Jonsson
Keyword(s):  
Density Functional Theory ◽  
Liquid Water ◽  
Density Functional ◽  
Water Clusters ◽  
Theory Model ◽  
Coupling Scheme ◽  
Functional Theory ◽  
Total Polarization ◽  
Electrostatic Embedding ◽  
Polarizable Embedding

The manuscript analyzes the accuracy of our recently developed reciprocal polarizable embedding scheme, where a density functional theory model of the QM region is coupled to a dipole- and quadrupole polarizable water potential of the MM region. We present calculations of water clusters and liquid water where we analyze the energy, atomic forces and total polarization to demonstrate that artifacts in energy and polarization introduced by the QM/MM coupling are small and well-behaved. Furthermore, our methodology improves the consistency of the structure of optimized water hexamer geometries when compared to results obtained with models that neglect polarization. Additionally, the manuscript provides evidence that our coupling scheme eliminates artifacts in the structure of liquid water obtained with simpler electrostatic embedding models.

Lichens of the Oasis Molodyozhnyi and adjacent areas (Enderby Land, Antarctic)

2013 ◽  
Vol 47 ◽  
pp. 167-178 ◽  
Author(s):  
M. P. Andreev
Keyword(s):  
Liquid Water ◽  
Climate Conditions ◽  
Water Ice ◽  
Buellia Frigida ◽  
Antarctic Continent ◽  
Harsh Climate ◽  
The Antarctic ◽  
First Time ◽  
Lichen Flora

Lichen flora and vegetation in the vicinity of the Russian base «Molodyozhnaya» (Enderby Land, Antarctica) were investigated in 2010–2011 in details for the first time. About 500 specimens were collected in 100 localities in all available ecotopes. The lichen flora is the richest in the region and numbers 39 species (21 genera, 11 families). The studied vegetation is very poor and sparse, but typical for coastal oases of the Antarctic continent. The poorness is caused by the extremely harsh climate conditions, insufficient availability of liquid water, ice-free land, and high insolation levels. The dominant and most common lichens are Rinodina olivaceobrunnea, Amandinea punctata, Candelariella flava, Physcia caesia, Caloplaca tominii, Lecanora expectans, Caloplaca ammiospila, Lecidea cancriformis, Pseudephebe minuscula, Lecidella siplei, Umbilicaria decussata, Buellia frigida, Lecanora fuscobrunnea, Usnea sphacelata, Lepraria and Buellia spp.

Flow patterns of two-component liquid water-exxsol mixture in a horizontal channel

2019 ◽  
Vol 56 ◽  
pp. 7-15
Author(s):  
I.А. DAVLETSHIN ◽  
◽  
A.A. Valeev ◽  
N.I. Mikheev ◽  
A.A. Paereliy ◽  
...  
Keyword(s):  
Liquid Water ◽  
Flow Patterns ◽  
Horizontal Channel ◽  
Two Component

Understanding of social behaviour in human collective motions with non-trivial rule of control

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 84-86
Author(s):  
Keisuke Fujii
Keyword(s):  
Machine Learning ◽  
Team Sports ◽  
Machine Learning Techniques ◽  
Theoretical Modelling ◽  
Video Footage ◽  
Learning Techniques ◽  
Collective Motions ◽  
Tracking Movements ◽  
Or Team ◽  
Computational Ability

The coordination and movement of people in large crowds, during sports games or when socialising, seems readily explicable. Sometimes this occurs according to specific rules or instructions such as in a sport or game, at other times the motivations for movement may be more focused around an individual's needs or fears. Over the last decade, the computational ability to identify and track a given individual in video footage has increased. The conventional methods of how data is gathered and interpreted in biology rely on fitting statistical results to particular models or hypotheses. However, data from tracking movements in social groups or team sports are so complex as they cannot easily analyse the vast amounts of information and highly varied patterns. The author is an expert in human behaviour and machine learning who is based at the Graduate School of Informatics at Nagoya University. His challenge is to bridge the gap between rule-based theoretical modelling and data-driven modelling. He is employing machine learning techniques to attempt to solve this problem, as a visiting scientist in RIKEN Center for Advanced Intelligence Project.

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