scholarly journals Geoeffectiveness Prediction of CMEs

2021 ◽  
Vol 8 ◽  
Author(s):  
Diana Besliu-Ionescu ◽  
Marilena Mierla
Keyword(s):  
Position Angle ◽  
Angular Width ◽  
Training Set ◽  
Binary Variable ◽  
The Earth ◽  
Measured Position ◽  
Logistic Regression Method ◽  
Cycle 24 ◽  
Method Model ◽  
Selection Of

Coronal mass ejections (CMEs), the most important pieces of the puzzle that drive space weather, are continuously studied for their geomagnetic impact. We present here an update of a logistic regression method model, that attempts to forecast if a CME will arrive at the Earth and it will be associated with a geomagnetic storm defined by a minimum Dst value smaller than −30 nT. The model is run for a selection of CMEs listed in the LASCO catalogue during the solar cycle 24. It is trained on three fourths of these events and validated for the remaining one fourth. Based on five CME properties (the speed at 20 solar radii, the angular width, the acceleration, the measured position angle and the source position – binary variable) the model successfully predicted 98% of the events from the training set, and 98% of the events from the validation one.

Note on Selection of Coordinate Systems for Geodynamics

1975 ◽  
Vol 26 ◽  
pp. 395-407
Author(s):  
S. Henriksen
Keyword(s):  
Sea Level ◽  
The Other ◽  
Coordinate Systems ◽  
Mean Sea Level ◽  
The Earth ◽  
The One ◽  
Static Systems ◽  
Selection Of

The first question to be answered, in seeking coordinate systems for geodynamics, is: what is geodynamics? The answer is, of course, that geodynamics is that part of geophysics which is concerned with movements of the Earth, as opposed to geostatics which is the physics of the stationary Earth. But as far as we know, there is no stationary Earth – epur sic monere. So geodynamics is actually coextensive with geophysics, and coordinate systems suitable for the one should be suitable for the other. At the present time, there are not many coordinate systems, if any, that can be identified with a static Earth. Certainly the only coordinate of aeronomic (atmospheric) interest is the height, and this is usually either as geodynamic height or as pressure. In oceanology, the most important coordinate is depth, and this, like heights in the atmosphere, is expressed as metric depth from mean sea level, as geodynamic depth, or as pressure. Only for the earth do we find “static” systems in use, ana even here there is real question as to whether the systems are dynamic or static. So it would seem that our answer to the question, of what kind, of coordinate systems are we seeking, must be that we are looking for the same systems as are used in geophysics, and these systems are dynamic in nature already – that is, their definition involvestime.

Seasonal behavior of H component during solar cycle 24

2021 ◽  
Author(s):  
Yasmina Bouderba ◽  
Ener Aganou ◽  
Abdenaceur Lemgharbi
Keyword(s):  
Solar Activity ◽  
Solar Cycle ◽  
Horizontal Component ◽  
Low Latitude ◽  
Seasonal Behavior ◽  
The Earth ◽  
Earth Magnetic Field ◽  
Solar Cycle 24 ◽  
Minimum Solar Activity ◽  
Cycle 24

<p>In this work we will show the behavior of the horizontal component H of the Earth Magnetic Field (EMF) along the seasons during the period of solar cycle 24 lasting from 2009 to 2019. By means of  continuous measurements of geomagnetic components (X, Y) of the EMF, we compute the horizontal component H at the Earth’s surface. The data are recorded with a time resolution of one minute at Tamanrasset observatory in Algeria at the geographical coordinates of 22.79° North and 5.53° East. These data are available from the INTERMAGNET network. We find that the variation in amplitude of the hourly average of H component at low latitude changes from a season to another and it is greater at the maximum solar activity than at the minimum solar activity.</p><p><strong>Keywords:</strong> Solar cycle 24, Season, Horizontal component H. </p>

The Pre-Platonic Philosophers

2019 ◽  
pp. 56-77
Author(s):  
Filippo Del Lucchese
Keyword(s):  
The Other ◽  
Early Generation ◽  
Successful Result ◽  
Spontaneous Formation ◽  
Normal Life ◽  
The Earth ◽  
Forms Of Life ◽  
Selection Of

This chapter examines Empedocles’s idea of monstrosity in the early generation of life, when the earth spontaneously produces all sort of monstrous beings, only some of which will survive and generate viable forms of life. Empedocles intends to establish the norms of life on the process of generation and selection of monstrosities. Nature is not an artist that shapes normal life after many unsuccessful attempts. Empedocles rather sees Nature itself as the successful result of spontaneuous events that create limits and boundaries for viable life. The other major philosopher of the pre-Platonic period is Democritus. I explore his materialism and its relationship with necessity and chance. Atomists have been accused of paradoxically grounding their universe on both necessity and chance. I show that the paradox, however, is only such from the Aristotelian perspective, which aims at establishing teleology as the highest form of causality, in particular in the biological realm. Through the idea of monstrosity, Democritus grounds its atomism on the concept of the spontaneous formation of life. Beyond Empedocles, Democritus flattens even further the material ontology of nature, grounding it on the epigenetical production of normal and mostrous life alike. Through a reading of the agonistic process of life formation, monstrosity becomes the antidote to teleology.

Repeated selection of supplier, factors and results: the Czech Republic

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michal Plaček ◽  
Martin Schmidt ◽  
František Ochrana ◽  
Gabriela Vaceková ◽  
Jana Soukopová
Keyword(s):  
Recurrent Selection ◽  
Public Procurement ◽  
Ordinary Least Squares ◽  
Group Method ◽  
Strong Impact ◽  
Least Squares Regression ◽  
Content Type ◽  
The Public ◽  
Logistic Regression Method ◽  
Selection Of

Purpose The paper aims to deal with the analysis of the factor leading to the repeated selection of the specific supplier and the effect of this recurrent selection on overpricing of public contracts. Design/methodology/approach A mix of quantitative and qualitative methods is used to achieve this goal. To analyze the chances of obtaining repeated contracts, the logistic regression method is used. To analyze the factor of overpriced contracts, the classic ordinary least squares regression model is used. The focus group method is then used to explain the factors acting on the part of the contracting authorities. Findings The results show that the prior procurement of a given contracting authority, or work for the public sector in general, has a statistically significant effect on the conclusion of contracts. The use of less-transparent forms of input has a strong impact. The non-transparent selection of suppliers rather than repetition of contracts generally results in the over-pricing of contracts. The IT sector is an exception. Social implications This research is also essential for real public policy. Given the amount of GDP allocated to the public procurement market, it makes sense to continually seek room for improvement. Here is an attempt to find this by examining the contracting authorities’ behavior when awarding repeated contracts. Originality/value This research is original because it looks at the problem of the contracting authority in the wider context and optics of the path dependency theory, which has not yet been applied to the public procurement environment. The focus is also on IT procurement, which according to this study has not been empirically investigated in this way, is also innovative.

scholarly journals A CME Automatic Detection Method Based on Adaptive Background Learning Technology

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Zhenping Qiang ◽  
Xianyong Bai ◽  
Qinghui Zhang ◽  
Hong Lin
Keyword(s):  
Detection Method ◽  
Maximum Velocity ◽  
Position Angle ◽  
Image Sequences ◽  
Angular Width ◽  
Learning Technology ◽  
Dynamic Features ◽  
Automatic Methods ◽  
Difference Time ◽  
Minimum Velocity

In this paper, we describe a technique, which uses an adaptive background learning method to detect the CME (coronal mass ejections) automatically from SOHO/LASCO C2 image sequences. The method consists of several modules: adaptive background module, candidate CME area detection module, and CME detection module. The core of the method is based on adaptive background learning, where CMEs are assumed to be a foreground moving object outward as observed in running-difference time series. Using the static and dynamic features to model the corona observation scene can more accurately describe the complex background. Moreover, the method can detect the subtle changes in the corona sequences while filtering their noise effectively. We applied this method to a month of continuous corona images, compared the result with CDAW, CACTus, SEEDS, and CORIMP catalogs and found a good detection rate in the automatic methods. It detected about 73% of the CMEs listed in the CDAW CME catalog, which is identified by human visual inspection. Currently, the derived parameters are position angle, angular width, linear velocity, minimum velocity, and maximum velocity of CMES. Other parameters could also easily be added if needed.

scholarly journals How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques

Molecules ◽  
2020 ◽  
Vol 25 (6) ◽  
pp. 1452
Author(s):  
Igor Sieradzki ◽  
Damian Leśniak ◽  
Sabina Podlewska
Keyword(s):  
Machine Learning ◽  
Training Set ◽  
Activity Prediction ◽  
Prediction Uncertainty ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Model Training ◽  
Short Time ◽  
Selection Of

A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able to evaluate in relatively short time enormous amounts of data. However, the quality of machine-learning-based prediction depends on the data supplied for model training. In this study, we used deep neural networks for the task of compound activity prediction and developed dropout-based approaches for estimating prediction uncertainty. Several types of analyses were performed: the relationships between the prediction error, similarity to the training set, prediction uncertainty, number and standard deviation of activity values were examined. It was tested whether incorporation of information about prediction uncertainty influences compounds ranking based on predicted activity and prediction uncertainty was used to search for the potential errors in the ChEMBL database. The obtained outcome indicates that incorporation of information about uncertainty of compound activity prediction can be of great help during virtual screening experiments.

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