Flare activity of stars as a cluster membership criterion

Astrophysics ◽  
1994 ◽  
Vol 36 (3) ◽  
pp. 241-248 ◽  
Author(s):  
L. V. Mirzoyan ◽  
V. V. Hambarian ◽  
A. L. Mirzoyan
Keyword(s):  
Flare Activity ◽  
Cluster Membership ◽  
Membership Criterion

scholarly journals Flare Activity and Cluster Membership Probability of Flare Stars

1995 ◽  
Vol 151 ◽  
pp. 65-66 ◽  
Author(s):  
L.V. Mirzoyan ◽  
V.V. Hambarian ◽  
A.L. Mirzoyan
Keyword(s):  
Flare Activity ◽  
Stellar Clusters ◽  
Proper Motions ◽  
Membership Probability ◽  
Cluster Membership ◽  
Galactic Field ◽  
Stellar Flares ◽  
Membership Criterion ◽  
Flare Stars ◽  
Stellar Clusters And Associations

Mirzoyan (1976) showed that the concentration of flare stars around the center of the Pleiades cluster (Alcyone) was the same, irrespective of their proper motions. At that time, however, proper motions of only a few flare stars were known. The result was confirmed on the basis of more extensive observational material (Chavushian 1979, Mirzoyan 1983). Photographic observations of stellar flares in the general galactic field during 181 hours yielded the detection of only a single flare, i.e. the percentage of flare stars in the general galactic field is about 10% of the total number of flare stars detected in the regions of stellar clusters and associations (Chavushian 1979, Mirzoyan et al. 1988).This result shows that flare activity can be considered as a definitive cluster membership criterion, which appears to be a better one than the cluster membership probability, which is based on proper motions. To prove this, cluster membership probabilities (Stauffer et al. 1991) for 408 Pleiades cluster flare stars from the catalogue by Haro et al. (1982), are used (Table 1).

scholarly journals Sleep and cardiometabolic risk: a cluster analysis of actigraphy-derived sleep profiles in adults and children

SLEEP ◽  
2021 ◽  
Author(s):  
Lisa Matricciani ◽  
Catherine Paquet ◽  
François Fraysse ◽  
Anneke Grobler ◽  
Yichao Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Cardiometabolic Risk ◽  
High Efficiency ◽  
Cardiometabolic Health ◽  
Maturity Stage ◽  
Cluster Membership ◽  
Geographic Clustering ◽  
Adults And Children ◽  
Study Participants ◽  
Generalised Estimating Equations

Abstract Study objectives Sleep plays an important role in cardiometabolic health. While the importance of considering sleep as a multidimensional construct is widely appreciated, studies have largely focused on individual sleep characteristics. The association between actigraphy-derived sleep profiles and cardiometabolic health in healthy adults and children has not been examined. Methods This study used actigraphy-measured sleep data collected between February 2015 and March 2016 in the Child Health CheckPoint study. Participants wore actigraphy monitors (GENEActiv Original, Cambs, UK) on their non-dominant wrist for seven days and sleep characteristics (period, efficiency, timing and variability) were derived from raw actigraphy data. Actigraphy-derived sleep profiles of 1,043 Australian children aged 11-12 years and 1337 adults were determined using K-means cluster analysis. The association between cluster membership and biomarkers of cardiometabolic health (blood pressure, body mass index, apolipoproteins, glycoprotein acetyls, composite metabolic syndrome severity score) were assessed using Generalised Estimating Equations, adjusting for geographic clustering, with sex, socioeconomic status, maturity stage (age for adults, pubertal status for children) and season of data collection as covariates. Results Four actigraphy-derived sleep profiles were identified in both children and adults: Short sleepers, Late to bed, Long sleepers, and Overall good sleepers. The Overall good sleeper pattern (characterised by adequate sleep period time, high efficiency, early bedtime and low day-to-day variability) was associated with better cardiometabolic health in the majority of comparisons (80%). Conclusion Actigraphy-derived sleep profiles are associated with cardiometabolic health in adults and children. The Overall good sleeper pattern is associated with more favourable cardiometabolic health.

scholarly journals On the Short Time Scale Evolutionary History of the Contact Binary VW Cephei

1998 ◽  
Vol 11 (1) ◽  
pp. 396-396
Author(s):  
I. Pustylnik
Keyword(s):  
Evolutionary History ◽  
Light Curves ◽  
Published Data ◽  
Short Time Scale ◽  
Flare Activity ◽  
Considerable Portion ◽  
History Of ◽  
Contact Binary ◽  
Elongation Phase ◽  
Short Time

We study the short-time evolutionary history of the well-known contact binary VW Cep. Our analysis is based partly on the numerous UBV lightcurves obtained at Tartu Observatory, IUE spectra, and samples from the published data. Special attention is given to the effects of asymmetry of the light curves. A higher degree of asymmetry outside the eclipses along with the significant displacements of the brightness maxima in respect to the elongation phase is interpreted as evidence that a considerable portion of the flaring source is concentrated close to the neck connecting the components. We discuss the nature of asymmetry in terms of possible mass exchange and the flare activity and compare the results of our model computations with the record of orbital period variations over the last 60 years.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

scholarly journals Screening for Early Signs of Paternal Perinatal Affective Disorder in Expectant Fathers: A Cluster Analysis Approach

2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Sonia Mangialavori ◽  
Michele Giannotti ◽  
Marco Cacioppo ◽  
Federico Spelzini ◽  
Franco Baldoni
Keyword(s):  
Perceived Stress ◽  
Behavioral Problems ◽  
Marital Adjustment ◽  
Risky Behaviors ◽  
Perinatal Period ◽  
Dyadic Adjustment ◽  
Male Population ◽  
Cluster Membership ◽  
Expectant Fathers ◽  
The Individual

Previous studies documented gender-related differences in the expression of Perinatal Affective Disorders. However, little attention has been paid to screening the male population during the perinatal period. This study was based on three aims: (1) to investigate the mental health of expectant fathers based on their levels of depression, anxiety, addiction, anger attacks/hostility, and somatization, identifying psychological profiles; (2) to analyze the association between these profiles and the individual variable of perceived stress; (3) and to examine the association between these profiles and the couple’s variable of marital adjustment. A total of 350 Italian expectant fathers in the last trimester of pregnancy were asked to fill in questionnaires concerning perceived stress, dyadic adjustment, psychiatric symptomatology, and depression. Three different clusters were found: “psychologically healthy men” (68%) with low levels of symptoms on all the scales; “men at risk of externalized behavioral problems” (17.1%), characterized by one or more addictive or risky behaviors and moderate levels of scales scores; and “men experiencing psychological distress” (14.9%), with the highest scores on all the scales. A significant association emerged among the perceived stress, marital adjustment, and cluster membership. These results highlight the importance of screening fathers in perinatal health services, which are still predominantly mother-centered, and underscore the necessity to create tailored and personalized interventions.

scholarly journals Heart and Mind: Essential Components in Sound Decision-Making

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 364-364
Author(s):  
Michaela Clark ◽  
Julie Hicks Patrick ◽  
Michaela Reardon
Keyword(s):  
Decision Making ◽  
Decision Quality ◽  
Cluster Solution ◽  
Cognitive Resources ◽  
Cluster Membership ◽  
Essential Components ◽  
Decision Making Processes ◽  
Affective Components ◽  
Heart And Mind

Abstract Consumer tasks permit an ecologically-valid context in which to examine the contributions of affective and cognitive resources to decision-making processes and outcomes. Although previous work shows that cognitive factors are important when individuals make decisions (Patrick et al., 2013; Queen et al.), the role of affective components is less clear. We examine these issues in two studies. Study 1 used data from 1000+ adults to inform a cluster analysis examining affective aspects (importance, meaningfulness) of making different types of decisions. A 4-cluster solution resulted. In Study 2, we used affective cluster membership and cognitive performance as predictors of experimental decision-making outcomes among a subset of participants (N = 60). Results of the regression (F(2, 40) = 6.51, p < .01, R2 = .25.) revealed that both the affective clusters (b = .37, p = .01) and cognitive ability (b = -.30, p = .04) uniquely contributed to the variance explained in decision quality. Age did not uniquely contribute. Results are discussed in the context of developing measures that enable us to move the field forward.

scholarly journals Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Molly Mattsson ◽  
Deirdre M. Murray ◽  
Mairead Kiely ◽  
Fergus P. McCarthy ◽  
Elaine McCarthy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Eating Behaviour ◽  
Overweight And Obesity ◽  
Class Analysis ◽  
Class Membership ◽  
Active Play ◽  
Food Avoidance ◽  
Cluster Membership

Abstract Background Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. Methods Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. Results 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. Conclusion Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

scholarly journals Solitons in the Close Binary Systems

1998 ◽  
Vol 11 (1) ◽  
pp. 398-398
Author(s):  
Kenji Tanabe
Keyword(s):  
Gravity Waves ◽  
Binary Systems ◽  
Kdv Equation ◽  
Frame Of Reference ◽  
Close Binary ◽  
Flare Activity ◽  
Lagrangian Point ◽  
Nls Equation ◽  
Close Binary Systems ◽  
The Earth

Propagation of the surface waves of the lobe-filing components of close binary systems is investigated theoretically. Such waves are considered to be analogous to the gravity waves of water on the earth. As a result, the equations of the surface wave in the rotating frame of reference are reduced to the so-called Kortewegde Vries (KdV) equation and non-linear Schroedinger (NLS) equation according to its ”depth”. Each of these equations is known to have the solution of soliton. When this soliton is sent to the other component of the binary system through the Lagrangian point, it can give rise to the flare activity observed in some kinds of close binary systems.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

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