scholarly journals On the SuperDARN cross polar cap potential saturation effect

2009 ◽  
Vol 27 (10) ◽  
pp. 3755-3764 ◽  
Author(s):  
A. V. Koustov ◽  
G. Ya. Khachikjan ◽  
R. A. Makarevich ◽  
C. Bryant
Keyword(s):  
Electric Field ◽  
Linear Trend ◽  
Limited Data ◽  
Polar Cap ◽  
Data Set ◽  
Northern Summer ◽  
Radar Network ◽  
Cross Polar Cap Potential ◽  
Hemisphere Winter ◽  
Magnetic Index

Abstract. Variation of the cross polar cap potential (CPCP) with the interplanetary electric field (IEF), the merging electric field EKL, the Polar Cap North (PCN) magnetic index, and the solar wind-magnetosphere coupling function EC of Newell et al. (2007) is investigated by considering convection data collected by the Super Dual Auroral Radar Network (SuperDARN) in the Northern Hemisphere. Winter and summer observations are considered separately. All variations considered show close to linear trend at small values of the parameters and tendency for the saturation at large values. The threshold values starting from which the non-linearity was evident were estimated to be IEF*~EKL*~3 mV/m, PCN*~3–4, and EC*~1.5×104. The data indicate that saturation starts at larger values of the above parameters and reaches larger (up to 10 kV) saturation levels during summer. Conclusions are supported by a limited data set of simultaneous SuperDARN observations in the Northern (summer) and Southern (winter) Hemispheres. It is argued that the SuperDARN CPCP saturation levels and the thresholds for the non-linearity to be seen are affected by the method of the CPCP estimates.

scholarly journals Effects of the solar wind electric field and ionospheric conductance on the cross polar cap potential: Results of global MHD modeling

2003 ◽  
Vol 30 (23) ◽  
pp. n/a-n/a ◽  
Author(s):  
V. G. Merkine ◽  
K. Papadopoulos ◽  
G. Milikh ◽  
A. S. Sharma ◽  
X. Shao ◽  
...  
Keyword(s):  
Solar Wind ◽  
Electric Field ◽  
Polar Cap ◽  
Mhd Modeling ◽  
The Cross ◽  
Cross Polar Cap Potential

scholarly journals Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions

2021 ◽  
Vol 8 ◽  
Author(s):  
Agnit Mukhopadhyay ◽  
Xianzhe Jia ◽  
Daniel T. Welling ◽  
Michael W. Liemohn
Keyword(s):  
Polar Cap ◽  
Weather Patterns ◽  
Performance Metric ◽  
Radar Network ◽  
Metric Analysis ◽  
Ionospheric Electrodynamics ◽  
Model Predictions ◽  
Cross Polar Cap Potential ◽  
Magnetohydrodynamic Simulations ◽  
Almost All

The performance of three global magnetohydrodynamic (MHD) models in estimating the Earth's magnetopause location and ionospheric cross polar cap potential (CPCP) have been presented. Using the Community Coordinated Modeling Center's Run-on-Request system and extensive database on results of various magnetospheric scenarios simulated for a variety of solar weather patterns, the aforementioned model predictions have been compared with magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, the models' reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations were quantified, and salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities were identified. Results indicate mixed outcomes for different models during different events, with almost all models underperforming during the extreme-most events. The quantification also indicates a tendency to underpredict magnetopause distances in the absence of an inner magnetospheric model, and an inclination toward over predicting CPCP values under general conditions.

scholarly journals Deep Learning Models for Estimation of the SuperDARN Cross Polar Cap Potential

2020 ◽  
Vol 7 (8) ◽  
Author(s):  
Erxiao Liu ◽  
Hongqiao Hu ◽  
Jianjun Liu ◽  
Lei Qiao
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Polar Cap ◽  
Cross Polar Cap Potential

scholarly journals Extreme geomagnetic activities: a statistical study

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Ryuho Kataoka
Keyword(s):  
Magnetic Storms ◽  
Magnetic Observatory ◽  
Statistical Distributions ◽  
Limited Data ◽  
Data Set ◽  
Sudden Commencements ◽  
The One ◽  
Log Normal ◽  
Geomagnetic Activities ◽  
Log Normal Distribution

Abstract Statistical distributions are investigated for magnetic storms, sudden commencements (SCs), and substorms to identify the possible amplitude of the one in 100-year and 1000-year events from a limited data set of less than 100 years. The lists of magnetic storms and SCs are provided from Kakioka Magnetic Observatory, while the lists of substorms are obtained from SuperMAG. It is found that majorities of events essentially follow the log-normal distribution, as expected from the random output from a complex system. However, it is uncertain that large-amplitude events follow the same log-normal distributions, and rather follow the power-law distributions. Based on the statistical distributions, the probable amplitudes of the 100-year (1000-year) events can be estimated for magnetic storms, SCs, and substorms as approximately 750 nT (1100 nT), 230 nT (450 nT), and 5000 nT (6200 nT), respectively. The possible origin to cause the statistical distributions is also discussed, consulting the other space weather phenomena such as solar flares, coronal mass ejections, and solar energetic particles.

In vitro effectiveness of biapenem against IMP-producing Enterobacteriaceae

2021 ◽  
Vol 70 (10) ◽  
Author(s):  
Kazuyoshi Gotoh ◽  
Makoto Miyoshi ◽  
I Putu Bayu Mayura ◽  
Koji Iio ◽  
Osamu Matsushita ◽  
...  
Keyword(s):  
Therapeutic Option ◽  
Small Data ◽  
Limited Data ◽  
Data Set ◽  
Content Type ◽  
Link Type ◽  
Almost All ◽  
New Treatments ◽  
Time Kill

The options available for treating infections with carbapenemase-producing Enterobacteriaceae (CPE) are limited; with the increasing threat of these infections, new treatments are urgently needed. Biapenem (BIPM) is a carbapenem, and limited data confirming its in vitro killing effect against CPE are available. In this study, we examined the minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of BIPM for 14 IMP-1-producing Enterobacteriaceae strains isolated from the Okayama region in Japan. The MICs against almost all the isolates were lower than 0.5 µg ml−1, indicating susceptibility to BIPM, while approximately half of the isolates were confirmed to be bacteriostatic to BIPM. However, initial killing to a 99.9 % reduction was observed in seven out of eight strains in a time–kill assay. Despite the small data set, we concluded that the in vitro efficacy of BIPM suggests that the drug could be a new therapeutic option against infection with IMP-producing CPE.

Identifying and intercepting health misinformation on Reddit dermatology forums with artificially intelligent bots using natural language processing (Preprint)

2021 ◽  
Author(s):  
Monique B. Sager ◽  
Aditya M. Kashyap ◽  
Mila Tamminga ◽  
Sadhana Ravoori ◽  
Christopher Callison-Burch ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The United States ◽  
Test Accuracy ◽  
Limited Data ◽  
Test Environment ◽  
Data Set ◽  
Inappropriate Care ◽  
Processing Techniques

BACKGROUND Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to inappropriate care. Initial testing has shown that artificially intelligent bots can detect misinformation on Reddit forums and may be able to produce responses to posts containing misinformation. OBJECTIVE To analyze the ability of bots to find and respond to health misinformation on Reddit’s dermatology forums in a controlled test environment. METHODS Using natural language processing techniques, we trained bots to target misinformation using relevant keywords and to post pre-fabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. RESULTS Our models yielded data test accuracies ranging from 95%-100%, with a BERT fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective pre-fabricated responses to misinformation. CONCLUSIONS Using a limited data set, bots had near-perfect ability to detect these examples of health misinformation within Reddit dermatology forums. Given that these bots can then post pre-fabricated responses, this technique may allow for interception of misinformation. Providing correct information, even instantly, however, does not mean users will be receptive or find such interventions persuasive. Further work should investigate this strategy’s effectiveness to inform future deployment of bots as a technique in combating health misinformation. CLINICALTRIAL N/A

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