56 research outputs found
Machine learning-based investigation of the association between CMEs and filaments
YesIn this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The Support Vector Machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the NGDC filament catalogue and the SOHO/LASCO CME catalogue are processed to associate filaments with CMEs. In the previous studies which classified CMEs into gradual and impulsive CMEs, the associations were refined based on CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data- mining system has been created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that could have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance
Stellar structure and compact objects before 1940: Towards relativistic astrophysics
Since the mid-1920s, different strands of research used stars as "physics
laboratories" for investigating the nature of matter under extreme densities
and pressures, impossible to realize on Earth. To trace this process this paper
is following the evolution of the concept of a dense core in stars, which was
important both for an understanding of stellar evolution and as a testing
ground for the fast-evolving field of nuclear physics. In spite of the divide
between physicists and astrophysicists, some key actors working in the
cross-fertilized soil of overlapping but different scientific cultures
formulated models and tentative theories that gradually evolved into more
realistic and structured astrophysical objects. These investigations culminated
in the first contact with general relativity in 1939, when J. Robert
Oppenheimer and his students George Volkoff and Hartland Snyder systematically
applied the theory to the dense core of a collapsing neutron star. This
pioneering application of Einstein's theory to an astrophysical compact object
can be regarded as a milestone in the path eventually leading to the emergence
of relativistic astrophysics in the early 1960s.Comment: 83 pages, 4 figures, submitted to the European Physical Journal
Developing a predictive modelling capacity for a climate change-vulnerable blanket bog habitat: Assessing 1961-1990 baseline relationships
Aim: Understanding the spatial distribution of high priority habitats and
developing predictive models using climate and environmental variables to
replicate these distributions are desirable conservation goals. The aim of this
study was to model and elucidate the contributions of climate and topography to
the distribution of a priority blanket bog habitat in Ireland, and to examine how
this might inform the development of a climate change predictive capacity for
peat-lands in Ireland.
Methods: Ten climatic and two topographic variables were recorded for grid
cells with a spatial resolution of 1010 km, covering 87% of the mainland
land surface of Ireland. Presence-absence data were matched to these variables
and generalised linear models (GLMs) fitted to identify the main climatic and
terrain predictor variables for occurrence of the habitat. Candidate predictor
variables were screened for collinearity, and the accuracy of the final fitted GLM
was evaluated using fourfold cross-validation based on the area under the curve
(AUC) derived from a receiver operating characteristic (ROC) plot. The GLM
predicted habitat occurrence probability maps were mapped against the actual
distributions using GIS techniques.
Results: Despite the apparent parsimony of the initial GLM using only climatic
variables, further testing indicated collinearity among temperature and precipitation
variables for example. Subsequent elimination of the collinear variables and
inclusion of elevation data produced an excellent performance based on the AUC
scores of the final GLM. Mean annual temperature and total mean annual
precipitation in combination with elevation range were the most powerful
explanatory variable group among those explored for the presence of blanket
bog habitat.
Main conclusions: The results confirm that this habitat distribution in general
can be modelled well using the non-collinear climatic and terrain variables tested
at the grid resolution used. Mapping the GLM-predicted distribution to the
observed distribution produced useful results in replicating the projected
occurrence of the habitat distribution over an extensive area. The methods
developed will usefully inform future climate change predictive modelling for
Irelan
Leishmania major: identification of stage-specific antigens and antigens shared by promastigotes and amastigotes
Surface mechanical attrition treatment of commercially pure titanium by electromagnetic vibration
- …