Solar cycle variations of the EUV network characteristics from SDO/AIA

2022 ◽  
Vol 367 (1) ◽  
Author(s):  
Bilin Susan Varghese ◽  
K. P. Raju ◽  
P. J. Kurian ◽  
Issac Paul
Keyword(s):  
Solar Cycle ◽  
Network Characteristics

Calculated Coronal Magnetic Fields and Their Comparison with the Coronal Structures as Observed during Five Solar Eclipses

1994 ◽  
Vol 144 ◽  
pp. 559-564
Author(s):  
P. Ambrož ◽  
J. Sýkora
Keyword(s):  
Magnetic Field ◽  
Solar Cycle ◽  
Magnetic Fields ◽  
Solar Corona ◽  
Coronal Magnetic Field ◽  
Coronal Magnetic Fields ◽  
Solar Eclipses ◽  
Coronal Structures

AbstractWe were successful in observing the solar corona during five solar eclipses (1973-1991). For the eclipse days the coronal magnetic field was calculated by extrapolation from the photosphere. Comparison of the observed and calculated coronal structures is carried out and some peculiarities of this comparison, related to the different phases of the solar cycle, are presented.

The Hemispheric Sign Rule of Current Helicity during the Rising Phase of Cycle 23

2000 ◽  
Vol 179 ◽  
pp. 303-306
Author(s):  
S. D. Bao ◽  
G. X. Ai ◽  
H. Q. Zhang
Keyword(s):  
Solar Cycle ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Active Regions ◽  
Hemispheric Preference ◽  
Current Helicity

AbstractWe compute the signs of two different current helicity parameters (i.e., αbestandHc) for 87 active regions during the rise of cycle 23. The results indicate that 59% of the active regions in the northern hemisphere have negative αbestand 65% in the southern hemisphere have positive. This is consistent with that of the cycle 22. However, the helicity parameterHcshows a weaker opposite hemispheric preference in the new solar cycle. Possible reasons are discussed.

Prominences and Solar Activity

1979 ◽  
Vol 44 ◽  
pp. 357-372
Author(s):  
Z. Švestka
Keyword(s):  
Solar Activity ◽  
Solar Cycle ◽  
List Type ◽  
Type Number ◽  
Flare Loops ◽  
Filament Activation

The following subjects were discussed:(1)Filament activation(2)Post-flare loops.(3)Surges and sprays.(4)Coronal transients.(5)Disk vs. limb observations.(6)Solar cycle variations of prominence occurrence.(7)Active prominences patrol service.Of all these items, (1) and (2) were discussed in most detail and we also pay most attention to them in this report. Items (3) and (4) did not bring anything new when compared with the earlier invited presentations given by RUST and ZIRIN and therefore, we omit them.

Social network characteristics of injection drug users in Montreal, Canada

2005 ◽  
Author(s):  
Prithwish De ◽  
Joseph Cox ◽  
Carole Morissette ◽  
Ann Jolly ◽  
Jean-Francois Boivin
Keyword(s):  
Social Network ◽  
Injection Drug Users ◽  
Drug Users ◽  
Injection Drug ◽  
Network Characteristics

Regression Analysis of Sunspot Numbers for the Solar Cycle 24 in Comparison to Previous Three Cycles

2014 ◽  
Vol 4 (2) ◽  
pp. 477-483
Author(s):  
Debojyoti Halder
Keyword(s):  
Regression Analysis ◽  
Solar Cycle ◽  
Sunspot Number ◽  
The Sun ◽  
Sunspot Numbers ◽  
Solar Cycle 24 ◽  
Current Cycle ◽  
Cycle 24

Sunspots are temporary phenomena on the photosphere of the Sun which appear visibly as dark spots compared to surrounding regions. Sunspot populations usually rise fast but fall more slowly when observed for any particular solar cycle. The sunspot numbers for the current cycle 24 and the previous three cycles have been plotted for duration of first four years for each of them. It appears that the value of peak sunspot number for solar cycle 24 is smaller than the three preceding cycles. When regression analysis is made it exhibits a trend of slow rising phase of the cycle 24 compared to previous three cycles. Our analysis further shows that cycle 24 is approaching to a longer-period but with smaller occurrences of sunspot number.

HEXACO personality factors and their associations with Facebook use and Facebook network characteristics

2018 ◽  
Author(s):  
Riana Brown ◽  
Sam G. B. Roberts ◽  
Thomas V. Pollet
Keyword(s):  
Social Networks ◽  
Network Size ◽  
Openness To Experience ◽  
Personality Factors ◽  
Number Of Clusters ◽  
Network Characteristics ◽  
Online Networks ◽  
Facebook Use ◽  
Objectively Measured ◽  
Size And Structure

Personality factors affect the properties of ‘offline’ social networks, but how they are associated with the structural properties of online networks is still unclear. We investigated how the six HEXACO personality factors (Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to Experience) relate to Facebook use and three objectively measured Facebook network characteristics - network size, density, and number of clusters. Participants (n = 107, mean age = 20.6, 66% female) extracted their Facebook networks using the GetNet app, completed the 60-item HEXACO questionnaire and the Facebook Usage Questionnaire. Users high in Openness to Experience spent less time on Facebook. Extraversion was positively associated with network size and the number of network clusters (but not after controlling for size). These findings suggest that personality factors are associated with Facebook use and the size and structure of Facebook networks, and that personality is an important influence on both online and offline sociality.

Whether, How, and Why Networks Influence Men’s and Women’s Career Success: Review and Research Agenda

2020 ◽  
Vol 47 (1) ◽  
pp. 207-236
Author(s):  
Meredith L. Woehler ◽  
Kristin L. Cullen-Lester ◽  
Caitlin M. Porter ◽  
Katherine A. Frear
Keyword(s):  
Research Agenda ◽  
Career Success ◽  
Future Research ◽  
Men And Women ◽  
Network Characteristics ◽  
Integrative Framework ◽  
Relative Status ◽  
Same Gender ◽  
Professional Contexts ◽  
Women's Networks

Substantial research has documented challenges women experience building and benefiting from networks to achieve career success. Yet fundamental questions remain regarding which aspects of men’s and women’s networks differ and how differences impact their careers. To spur future research to address these questions, we present an integrative framework to clarify how and why gender and networks—in concert—may explain career inequality. We delineate two distinct, complementary explanations: (1) unequal network characteristics (UNC) asserts that men and women have different network characteristics, which account for differences in career success; (2) unequal network returns (UNR) asserts that even when men and women have the same network characteristics, they yield different degrees of career success. Further, we explain why UNC and UNR emerge by identifying mechanisms related to professional contexts, actors, and contacts. Using this framework, we review evidence of UNC and UNR for specific network characteristics. We found that men’s and women’s networks are similar in structure (i.e., size, openness, closeness, contacts’ average and structural status) but differ in composition (i.e., proportion of men, same-gender, and kin contacts). Many differences mattered for career success. We identified evidence of UNC only (same-gender contacts), UNR only (actors’ and contacts’ network openness, contacts’ relative status), neither UNC nor UNR (size), and both UNC and UNR (proportion of men contacts). Based on these initial findings, we offer guidance to organizations aiming to address inequality resulting from gender differences in network creation and utilization, and we present a research agenda for scholars to advance these efforts.

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