The Patterns of High-Level Magnetic Activity Occurring on the Surface of V1285 Aql: The OPEA Model of Flares and DFT Models of Stellar Spots

Statistically analyzing Johnson UBVR observations of V1285 Aql during the three observing seasons, both activity level and behavior of the star are discussed in respect to obtained results. We also discuss the out-of-flare variation due to rotational modulation. Eighty-three flares were detected in the U-band observations of season 2006 . First, depending on statistical analyses using the independent samples t-test, the flares were divided into two classes as the fast and the slow flares. According to the results of the test, there is a difference of about 73 s between the flare-equivalent durations of slow and fast flares. The difference should be the difference mentioned in the theoretical models. Second, using the one-phase exponential association function, the distribution of the flare-equivalent durations versus the flare total durations was modeled. Analyzing the model, some parameters such as plateau, half-life values, mean average of the flare-equivalent durations, maximum flare rise, and total duration times are derived. The plateau value, which is an indicator of the saturation level of white-light flares, was derived as 2.421{\pm}0.058 s in this model, while half-life is computed as 201 s. Analyses showed that observed maximum value of flare total duration is 4641 s, while observed maximum flare rise time is 1817 s. According to these results, although computed energies of the flares occurring on the surface of V1285 Aql are generally lower than those of other stars, the length of its flaring loop can be higher than those of more active stars.Comment: 44 pages, 10 figures, 5 tables, 2011PASP..123..659

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence

A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context. Here, we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate generalized linear mixed model as a special case and can also operate on the original scale of sensitivity, specificity, and disease prevalence. Our general methodology is illustrated by re-analyzing the data of two published meta-analyses. Our study suggests that there can be an improvement on trivariate generalized linear mixed model in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness, including reflection asymmetric tail dependence, and computational feasibility despite their three dimensionality

A review of methods for addressing components of interventions in meta-analysis

Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the metaanalytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder. Copyright

A New Method for Classifying Flares of UV Ceti Type Stars: Differences Between Slow and Fast Flares

In this study, a new method is presented to classify flares derived from the photoelectric photometry of UV Ceti type stars. This method is based on statistical analyses using an independent samples t-test. The data used in analyses were obtained from four flare stars observed between 2004 and 2007. The total number of flares obtained in the observations of AD Leo, EV Lac, EQ Peg, and V1054 Oph is 321 in the standard Johnson U band. As a result flares can be separated into two types, slow and fast, depending on the ratio of flare decay time to flare rise time. The ratio is below 3.5 for all slow flares, while it is above 3.5 for all fast flares. Also, according to the independent samples t-test, there is a difference of about 157 s between equivalent durations of slow and fast flares. In addition, there are significant differences between amplitudes and rise times of slow and fast flares.Comment: 46 pages, 7 figures, 4 tabels, 2010AJ....140..483

On composite likelihood in bivariate meta-analysis of diagnostic test accuracy studies

The composite likelihood (CL) is amongst the computational methods used for estimation of the generalized linear mixed model (GLMM) in the context of bivariate meta-analysis of diagnostic test accuracy studies. Its advantage is that the likelihood can be derived conveniently under the assumption of independence between the random effects, but there has not been a clear analysis of the merit or necessity of this method. For synthesis of diagnostic test accuracy studies, a copula mixed model has been proposed in the biostatistics literature. This general model includes the GLMM as a special case and can also allow for flexible dependence modelling, different from assuming simple linear correlation structures, normality and tail independence in the joint tails. A maximum likelihood (ML) method, which is based on evaluating the bi-dimensional integrals of the likelihood with quadrature methods has been proposed, and in fact it eases any computational difficulty that might be caused by the double integral in the likelihood function. Both methods are thoroughly examined with extensive simulations and illustrated with data of a published meta-analysis. It is shown that the ML method has non-convergence issues or computational difficulties and at the same time allows estimation of the dependence between study-specific sensitivity and specificity and thus prediction via summary receiver operating curves

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

A qualitative study of stakeholders' perspectives on the social network service environment

Over two billion people are using the Internet at present, assisted by the mediating activities of software agents which deal with the diversity and complexity of information. There are, however, ethical issues due to the monitoring-and-surveillance, data mining and autonomous nature of software agents. Considering the context, this study aims to comprehend stakeholders' perspectives on the social network service environment in order to identify the main considerations for the design of software agents in social network services in the near future. Twenty-one stakeholders, belonging to three key stakeholder groups, were recruited using a purposive sampling strategy for unstandardised semi-structured e-mail interviews. The interview data were analysed using a qualitative content analysis method. It was possible to identify three main considerations for the design of software agents in social network services, which were classified into the following categories: comprehensive understanding of users' perception of privacy, user type recognition algorithms for software agent development and existing software agents enhancement