Kalmag: a high spatio temporal model of the Geomagnetic field

2021 ◽  
Author(s):  
Julien Baerenzung ◽  
Matthias Holschneider
Keyword(s):  
Geomagnetic Field ◽  
Learning Algorithm ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Large Set ◽  
The Core ◽  
Resolution Model ◽  
Auto Regressive ◽  
Spatio Temporal ◽  
Marine Vessels

<p>We present a new high resolution model of the Geomagnetic field spanning the last 121 years. The model derives from a large set of data taken by low orbiting satellites, ground based observatories, marine vessels, airplane and during land surveys. It is obtained by combining a Kalman filter to a smoothing algorithm. Seven different magnetic sources are taken into account. Three of them are of internal origin. These are the core, the lithospheric  and the induced / residual ionospheric fields. The other four sources are of external origin. They are composed by a close, a remote and a fluctuating magnetospheric fields as well as a source associated with field aligned currents. The dynamical evolution of each source is prescribed by an auto regressive process of either first or second order, except for the lithospheric field which is assumed to be static. The parameters of the processes were estimated through a machine learning algorithm with a sample of data taken by the low orbiting satellites of the CHAMP and Swarm missions. In this presentation we will mostly focus on the rapid variations of the core field, and the small scale lithospheric field.  We will also discuss the nature of model uncertainties and the limitiations they imply.</p>

EDR signatures observed by MMS : a statistical study of dayside events found with machine learning

2021 ◽  
Author(s):  
Quentin Lenouvel ◽  
Vincent Génot ◽  
Philippe Garnier ◽  
Benoit Lavraud ◽  
Sergio Toledo
Keyword(s):  
Machine Learning ◽  
Statistical Study ◽  
Learning Algorithm ◽  
Small Scale ◽  
Diffusion Region ◽  
Machine Learning Algorithm ◽  
Physical Processes ◽  
The Core ◽  
Machine Learning Methods ◽  
Guide Field

<p>The understanding of magnetic reconnection's physical processes has considerably been improved thanks to the data of the Magnetopsheric Multiscale mission (MMS). However, a lot of work still has to be done to better characterize the core of the reconnection process : the electron diffusion region (EDR). We previously developed a machine learning algorithm to automatically detect EDR candidates, in order to increase the available list of events identified in the literature. However, identifying the parameters that are the most relevant to describe EDRs is complex, all the more that some of the small scale plasma/fields parameters show limitations in some configurations such as for low particle densities or large guide fields cases. In this study, we perform a statistical study of previously reported dayside EDRs as well as newly reported EDR candidates found using machine learning methods. We also show different single and multi-spacecraft parameters that can be used to better identify dayside EDRs in time series from MMS data recorded at the magnetopause. And finally we show an analysis of the link between the guide field and the strength of the energy conversion around each EDR.</p>

scholarly journals Characterization of dynamic evolution of the spatio-temporal variation of rain-field in Hong Kong

2015 ◽  
Vol 47 (2) ◽  
pp. 468-482 ◽  
Author(s):  
Peng Liu ◽  
Yeou-Koung Tung
Keyword(s):  
Hong Kong ◽  
Spatial Structure ◽  
Temporal Structure ◽  
Radar Data ◽  
Small Scale ◽  
Large Set ◽  
Time Space ◽  
Main Challenge ◽  
Geostatistical Approach ◽  
Spatio Temporal

A significant part of Hong Kong has hilly terrain with relatively short flow concentration time and, hence, is susceptible to the threat of flash floods and landslides during intense convective thunderstorms and tropical cyclones. For places like Hong Kong, a rainfall model that could adequately capture small-scale temporal and spatial variations would be highly desirable. The main challenge in rain-field modeling is to capture and describe the dynamic time-space evolution of the rainfall during rainstorm events. In this study, radar data with a high spatial (1 km2) and temporal (6 min) resolution of four rainstorm events in Hong Kong are analyzed. A geostatistical approach based on indicator variograms of rain-fields is used. The spatial structure of a rain-field is found to be highly anisotropic and should be adequately considered in the model. Variability of the spatial structure of a rain-field was described well by the main features of the variograms. Moreover, it is possible to identify whether multiple rainstorm centers exist by comparing the mean length and range. In order to establish reliable statistics on the spatial and temporal structure of rain-fields in Hong Kong, this approach could be applied to a large set of rainstorm events in this same region in the future.

Exploring Coronal Structures with SOHO

2000 ◽  
Vol 179 ◽  
pp. 403-406
Author(s):  
M. Karovska ◽  
B. Wood ◽  
J. Chen ◽  
J. Cook ◽  
R. Howard
Keyword(s):  
Solar Corona ◽  
Image Enhancement ◽  
Coronal Mass Ejections ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Low Contrast ◽  
Contrast Structures ◽  
Scale Structures ◽  
Small Scale Structures ◽  
Coronal Structures

AbstractWe applied advanced image enhancement techniques to explore in detail the characteristics of the small-scale structures and/or the low contrast structures in several Coronal Mass Ejections (CMEs) observed by SOHO. We highlight here the results from our studies of the morphology and dynamical evolution of CME structures in the solar corona using two instruments on board SOHO: LASCO and EIT.

scholarly journals mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Prabina Kumar Meher ◽  
Anil Rai ◽  
Atmakuri Ramakrishna Rao
Keyword(s):  
Random Forest ◽  
Cellular Localization ◽  
Learning Algorithm ◽  
Elastic Net ◽  
Messenger Rnas ◽  
Computational Tool ◽  
Temporal Gene Expression ◽  
Single Location ◽  
Spatio Temporal ◽  
Localization Prediction

Abstract Background Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs. Results The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1–6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools. Conclusions This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server “mLoc-mRNA” is accessible at http://cabgrid.res.in:8080/mlocmrna/. The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.

scholarly journals Geomagnetic Virtual Observatories: monitoring geomagnetic secular variation with the Swarm satellites

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Magnus D. Hammer ◽  
Grace A. Cox ◽  
William J. Brown ◽  
Ciarán D. Beggan ◽  
Christopher C. Finlay
Keyword(s):  
Time Series ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Time Windows ◽  
Low Earth Orbit ◽  
Data Selection ◽  
Spherical Harmonic Analysis ◽  
Core Field ◽  
The Core ◽  
Swarm Satellites

AbstractWe present geomagnetic main field and secular variation time series, at 300 equal-area distributed locations and at 490 km altitude, derived from magnetic field measurements collected by the three Swarm satellites. These Geomagnetic Virtual Observatory (GVO) series provide a convenient means to globally monitor and analyze long-term variations of the geomagnetic field from low-Earth orbit. The series are obtained by robust fits of local Cartesian potential field models to along-track and East–West sums and differences of Swarm satellite data collected within a radius of 700 km of the GVO locations during either 1-monthly or 4-monthly time windows. We describe two GVO data products: (1) ‘Observed Field’ GVO time series, where all observed sources contribute to the estimated values, without any data selection or correction, and (2) ‘Core Field’ GVO time series, where additional data selection is carried out, then de-noising schemes and epoch-by-epoch spherical harmonic analysis are applied to reduce contamination by magnetospheric and ionospheric signals. Secular variation series are provided as annual differences of the Core Field GVOs. We present examples of the resulting Swarm GVO series, assessing their quality through comparisons with ground observatories and geomagnetic field models. In benchmark comparisons with six high-quality mid-to-low latitude ground observatories we find the secular variation of the Core Field GVO field intensities, calculated using annual differences, agrees to an rms of 1.8 nT/yr and 1.2 nT/yr for the 1-monthly and 4-monthly versions, respectively. Regular sampling in space and time, and the availability of data error estimates, makes the GVO series well suited for users wishing to perform data assimilation studies of core dynamics, or to study long-period magnetospheric and ionospheric signals and their induced counterparts. The Swarm GVO time series will be regularly updated, approximately every four months, allowing ready access to the latest secular variation data from the Swarm satellites.

scholarly journals Dynamical Evolution of Globular Clusters After Core Collapse

1985 ◽  
Vol 113 ◽  
pp. 139-160 ◽  
Author(s):  
Douglas C. Heggie
Keyword(s):  
Surface Density ◽  
Globular Clusters ◽  
Stellar System ◽  
Dynamical Evolution ◽  
Theoretical Models ◽  
Numerical Calculations ◽  
Core Collapse ◽  
Density Profiles ◽  
The Core ◽  
Fokker Planck

This review describes work on the evolution of a stellar system during the phase which starts at the end of core collapse. It begins with an account of the models of Hénon, Goodman, and Inagaki and Lynden-Bell, as well as evaporative models, and modifications to these models which are needed in the core. Next, these models are related to more detailed numerical calculations of gaseous models, Fokker-Planck models, N-body calculations, etc., and some problems for further work in these directions are outlined. The review concludes with a discussion of the relation between theoretical models and observations of the surface density profiles and statistics of actual globular clusters.

scholarly journals Lin28, a major translation reprogramming factor, gains access to YB-1-packaged mRNA through its cold-shock domain

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Anastasiia Samsonova ◽  
Krystel El Hage ◽  
Bénédicte Desforges ◽  
Vandana Joshi ◽  
Marie-Jeanne Clément ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Cold Shock ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Binding Protein ◽  
Large Set ◽  
Core Component ◽  
The Core ◽  
Cold Shock Domain ◽  
Global Translation

AbstractThe RNA-binding protein Lin28 (Lin28a) is an important pluripotency factor that reprograms translation and promotes cancer progression. Although Lin28 blocks let-7 microRNA maturation, Lin28 also binds to a large set of cytoplasmic mRNAs directly. However, how Lin28 regulates the processing of many mRNAs to reprogram global translation remains unknown. We show here, using a structural and cellular approach, a mixing of Lin28 with YB-1 (YBX1) in the presence of mRNA owing to their cold-shock domain, a conserved β-barrel structure that binds to ssRNA cooperatively. In contrast, the other RNA binding-proteins without cold-shock domains tested, HuR, G3BP-1, FUS and LARP-6, did not mix with YB-1. Given that YB-1 is the core component of dormant mRNPs, a model in which Lin28 gains access to mRNPs through its co-association with YB-1 to mRNA may provide a means for Lin28 to reprogram translation. We anticipate that the translational plasticity provided by mRNPs may contribute to Lin28 functions in development and adaptation of cancer cells to an adverse environment.

scholarly journals Small-scale spatio-temporal characteristics of accumulation rates in western Dronning Maud Land, Antarctica

2008 ◽  
Vol 54 (185) ◽  
pp. 315-323 ◽  
Author(s):  
Helgard Anschütz ◽  
Daniel Steinhage ◽  
Olaf Eisen ◽  
Hans Oerter ◽  
Martin Horwath ◽  
...  
Keyword(s):  
Accumulation Rate ◽  
Temporal Variations ◽  
Spatial Variations ◽  
Small Scale ◽  
Accumulation Rates ◽  
Order Of Magnitude ◽  
Dronning Maud Land ◽  
Spatio Temporal ◽  
Ground Penetrating ◽  
Firn Core

AbstractSpatio-temporal variations of the recently determined accumulation rate are investigated using ground-penetrating radar (GPR) measurements and firn-core studies. The study area is located on Ritscherflya in western Dronning Maud Land, Antarctica, at an elevation range 1400–1560 m. Accumulation rates are derived from internal reflection horizons (IRHs), tracked with GPR, which are connected to a dated firn core. GPR-derived internal layer depths show small relief along a 22 km profile on an ice flowline. Average accumulation rates are about 190 kg m−2 a−1 (1980–2005) with spatial variability (1σ) of 5% along the GPR profile. The interannual variability obtained from four dated firn cores is one order of magnitude higher, showing 1σ standard deviations around 30%. Mean temporal variations of GPRderived accumulation rates are of the same magnitude or even higher than spatial variations. Temporal differences between 1980–90 and 1990–2005, obtained from two dated IRHs along the GPR profile, indicate temporally non-stationary processes, linked to spatial variations. Comparison with similarly obtained accumulation data from another coastal area in central Dronning Maud Land confirms this observation. Our results contribute to understanding spatio-temporal variations of the accumulation processes, necessary for the validation of satellite data (e.g. altimetry studies and gravity missions such as Gravity Recovery and Climate Experiment (GRACE)).

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