Membership Model Research Articles

IMPACT OF COVID 19 PANDEMIC ON THE PERFORMANCE OF MEDICAL AID SOCIETIES IN ZIMBABWE

This research study was inspirational to know and understand the impact of COCOVID-19andemic on the performance of Medical Aid Societies in Zimbabwe. The objectives of the study were to determine the impact of COVID-19 on membership uptake, examine the impact of COVID-19 on the profitability of medical aid and assess the impact of COVID-19 on claims management. A literature review provided the foundation and knowledge, understanding and scholarly debates of the impact of COVID-19 medical aid societies. The study adopted the quantitative research methodology and constructed a questionnaire and conducted interviews distributing them to 80 correspondents. Data collected was analysed using IBM SPSS statistics version 20 and the findings of the data were represented on tables. The hypothesis of the study H0 was accepted as COVID19 had an impact on the membership uptake, profitability, and claims of medical AID Schemes in Zimbabwe. The sample size used was 80. Recommendations on how to manage medical aid societies’ performance in a crisis is according to the findings of the study are proposed. The recommendations include engaged membership, blockchain technology, and hybrid billing model. The study proposed that future researchers can compare the use of telehealth services in other countries during pandemics of this nature and its role in Zimbabwean Medical Aid societies for the sustenance of their performance.

A Mixed-Membership Model for Social Network Clustering

We propose a simple mixed membership model for social network clustering in this paper. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies based on synthetic networks for further assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly.

Mixed membership Gaussians

In recent years, there has been significant progress in unsupervised representation learning. Classical statistical and machine learning techniques can be used on these representations for downstream tasks and applications. Here, we consider an unsupervised downstream task, namely, topic modeling with mixed membership models. Prototypical mixed membership models like the latent Dirichlet allocation (LDA) topic model use only simple discrete observed features. However, state-of-the-art representations of images, text, and other modalities are given by continuous feature vectors. Therefore, we study mixed membership Gaussians that operate on continuous feature vectors. We prove that the parameters of this model can be learned efficiently and effectively by Pearson’s method of moments and corroborate our theoretical findings on synthetic data. Experiments on standard labeled image data sets show that mixed membership Gaussians on state-of-the-art image representations yield topics that are semantically meaningful, coherent, and cover the labels that have not been used during training well.

A simple linear model to aid in analyses of the β Pictoris moving group

ABSTRACT We build a 4D linear model of object membership in the $\beta$ Pictoris moving group (BPMG), using two nested applications of Principal Component Analysis (PCA) for ∼1.5 million objects with high-quality data. These data consist of 3D-Galactic space velocities and Gaia G magnitudes. Through PCA, they ultimately result in a 4D straight line, referred to as PC 1′, about which both the bona fide members used to obtain the straight line and the candidate members used to test the model congregate at generally small distances. As a proof of concept, we select bona fide members from a recent, Gaia DR2-based compilation and candidate members mostly from a (pre-Gaia DR2) compilation from 2017. Using a standard procedure to flag groups of outliers in data sets, we find possible outliers flagged on account of their large distances to PC 1′, and find evidence that discrepant radial velocity measurements may indeed bring into question their validity as BPMG members. We propose that PC 1′ be added to the tool set for BPMG analyses and potentially extended to other young stellar moving groups.

Estimating mixed-memberships using the symmetric laplacian inverse matrix

Mixed membership community detection is a challenging problem. In this paper, to detect mixed memberships, we propose a new method Mixed-SLIM which is a spectral clustering method on the symmetrized Laplacian inverse matrix under the degree-corrected mixed membership model. We provide theoretical bounds for the estimation error on the proposed algorithm and its regularized version under mild conditions. Meanwhile, we provide some extensions of the proposed method to deal with large networks in practice. These Mixed-SLIM methods outperform state-of-art methods in simulations and substantial empirical datasets for both community detection and mixed membership community detection problems.

Perceived Choices of Graduation among Master Students: Expectancy-Task Value Theoretical Perspective

This study investigated whether motivational beliefs (expectancy of success and value in research and practice related tasks) among master students predict the achievement related choices for graduation (thesis or non-thesis) and examined the likelihood of those motivational beliefs in thesis and non-thesis preferring group. Participants studied in various master programs of two universities in Cambodia completed the questionnaires about their expectancy of success and values toward research and practice related tasks and preferences of graduation. Results of logistic regression analysis revealed that, among the motivational beliefs, only cost in research indicated a significant predictor of graduation preferences (p = .003). Furthermore, the associated likelihood of thesis preferring group was anchored at students who have higher positive beliefs on cost in research (ß = 2.386, p = .003, Exp (ß) = 10.867, Odd = 986.9), interest in research (ß = .933, p = .431, Exp (ß) = 2.542, Odd = 154.2), utility in practice (ß = .835, p = .226, Exp (ß) = 2.306, Odd = 130.6), and attainment in research (ß = .218, p = .699, Exp (ß) = 1.243, Odd = 24.3) although the rest of expectancy-values components was not completely discriminated in its membership model. Implications of the findings to graduate programs and future direction are discussed.

Real-Time Charging Risk Assessment for Electric Vehicles Based on Improved Broad BP-AHP

In the research of the electric vehicle charging safety evaluation model, the key problem is to determine the influencing factors of charging safety scientifically and ensure the accuracy and real-time performance of the model evaluation. This article takes the real-time charging message data of electric vehicles as the research object, analyzes and establishes the membership model of charging safety influencing factors, and proposes a real-time charging risk assessment method for electric vehicles based on the improved broad back-propagation and analytic hierarchy process (BBP-AHP) evaluation method. First, the characteristic membership degree of electric vehicle charging message data is calculated. Then, the improved broad BP neural network based on the compression factor is established to obtain the evaluation results of several experts, and the AHP model is optimized. Finally, the charging safety influencing factor system is constructed by the AHP to evaluate the charging safety of the real-time message. According to the accuracy comparison of the AHP model and the improved broad BP-AHP model, the experimental results show that the improved broad BP-AHP model can more accurately evaluate the safety risk of the electric vehicle real-time charging.

ESCP President's report.

ESCP President's report.

Business Models of Local and Hyperlocal Media During Local Mass Media Reforms in Ukraine

The research relies on the world trends in the development of local and hyperlocal media and their business models to analyse the experience of Ukrainian local mass media in their search for sustainable funding. The financial sustainability of local and hyperlocal media projects is the key to expanding citizens’ access to high-quality news and information. The choice of a business model determines the media’s financial sustainability and efficiency, its philosophy, content and ways of content delivery. Crowdfunding and its types (membership model, club model, gift-crowdfunding) is considered one of the most effective business models for hyperlocal media. These models presuppose engaging audiences (communities) in media financing. The research into the key services, resources, information processes and flows, expenditure structure and revenue sources reveal that an effective business model for Ukrainian media aims to combine business values with consumers’ values and needs. The combination of these factors ensures sustainable development and independence of the editorial policy.

A Useful Criterion on Studying Consistent Estimation in Community Detection.

In network analysis, developing a unified theoretical framework that can compare methods under different models is an interesting problem. This paper proposes a partial solution to this problem. We summarize the idea of using a separation condition for a standard network and sharp threshold of the Erdös–Rényi random graph to study consistent estimation, and compare theoretical error rates and requirements on the network sparsity of spectral methods under models that can degenerate to a stochastic block model as a four-step criterion SCSTC. Using SCSTC, we find some inconsistent phenomena on separation condition and sharp threshold in community detection. In particular, we find that the original theoretical results of the SPACL algorithm introduced to estimate network memberships under the mixed membership stochastic blockmodel are sub-optimal. To find the formation mechanism of inconsistencies, we re-establish the theoretical convergence rate of this algorithm by applying recent techniques on row-wise eigenvector deviation. The results are further extended to the degree-corrected mixed membership model. By comparison, our results enjoy smaller error rates, lesser dependence on the number of communities, weaker requirements on network sparsity, and so forth. The separation condition and sharp threshold obtained from our theoretical results match the classical results, so the usefulness of this criterion on studying consistent estimation is guaranteed. Numerical results for computer-generated networks support our finding that spectral methods considered in this paper achieve the threshold of separation condition.

Estimating cumulative spatial risk over time with low-rankkriging multiple membership models.

Many health outcomes result from accumulated exposures to one or more environmental factors. Accordingly, spatial risk studies have begun to consider multiple residential locations of participants, acknowledging that participants move and thus are exposed to environmental factors in several places. However, novel methods are needed to estimate cumulative spatial risk for disease while accounting for other risk factors. To this end, we propose a Bayesian model (LRK-MMM) that embeds a multiple membership model (MMM) into a low-rank kriging (LRK) model in order to estimate cumulative spatial risk at the point level while allowing for multiple residential locations per subject. The LRK approach offers a more computationally efficient means to analyze spatial risk in case-control study data at the point level compared with a Bayesian generalized additive model, and as increased precision in spatial risk estimates by analyzing point locations instead of administrative areas. Through a simulation study, we demonstrate the efficacy of the model and its improvement upon an existing multiple membership model that uses area-level spatial random effects to estimate risk. The results show that our proposed method provides greater spatial sensitivity (improvements ranging from 0.12 to 0.54) and power (improvements ranging from 0.02 to 0.94) to detect regions of elevated risk for disease across a range of exposure scenarios. Finally, we apply our model to case-control data from the New England bladder cancer study to estimate cumulative spatial risk while adjusting for many covariates.

Renewing post-national citizenship

ABSTRACT This essay explores how the critique of national citizenship has evolved over the past 25 years. Specifically, I focus on the literature which grew out of Yasemin Soysal’s seminal work Limits of Citizenship. Soysal argued for a post-national model of membership based on observations that rights traditionally associated with citizenship were becoming increasingly separated from the nation. I explore how, while some have taken up the study of post-national citizenship as a cosmopolitan research agenda, many others have subjected this idea to sustained critique on the basis that the empirical observations on which it rests have not materialised. Indeed, the continued hegemony of the national citizenship model suggests that this is not soon to change. However, I look to the literature on acts of citizenship, itself a major thread of research over the past quarter of a century, to argue that there is scope to reclaim post-nationalism as a fruitful lens for the study of citizenship-as-practice.

Measurement, modeling, reduction of decision-theoretic multigranulation fuzzy rough sets based on three-way decisions

Variable precision multigranulation fuzzy rough sets (VP-MFRSs) use two direct integrations: the multigranulation maximum and minimum. Their optimistic and pessimistic models facilitate uncertainty informatization but also imply a potential limitation of extremes. This study improves VP-MFRSs for extension and balance, and thus, decision-theoretic multigranulation fuzzy rough sets (DT-MFRSs) are proposed by systematically fusing the multigranulation maximum and minimum. For DT-MFRSs, their tri-level analysis of measurement, modeling, and reduction is deeply acquired via three-way decisions. First, maximum and minimum membership degrees are linearly combined, and the weight parameter guides a generalized multigranulation membership degree. This adjustable measure motivates DT-MFRSs with positive, negative, and boundary regions, while attitude-preference values of 1, 0, and 0.5 respectively produce optimistic, pessimistic, and compromised models. Then, nonmonotonicity and uncertainty of membership degrees and model regions are determined, and these fundamental characteristics induce new reduction criteria of region preservations. Also, three-way attribute reducts are proposed by preserving positive, negative, and positive–negative regions, and their systematic relationships are obtained. Finally, tri-level results of measures, models, and reducts are validated by table examples and data experiments. In this study, DT-MFRSs extend and improve VP-MFRSs via systematic fusion of membership measurement, and contain optimistic, pessimistic, compromised models, etc., thereby exhibiting extended diversity and applied robustness. Their three-way attribute reduction also perfects uncertainty optimization.

Welcome to the future of membership

I hope you have been following along over the last 4 years as the American Chemical Society Committee on Membership Affairs (MAC), governance colleagues, and staff have modernized the ACS membership model. As many of you will have experienced firsthand by now, and others will shortly as they come up for the 2022 membership renewal, this extensive transformation of the membership experience was fully realized on January 1, 2022. This transformation positions ACS to bring together chemists and allied professionals to cultivate a stronger and more engaged community that will be exemplary among scientific organizations in its vibrancy, scale, and diversity. A big thank you is due to you, ACS members, for your passion over these last few years. Your refreshing comments, guidance, and gratitude kept us focused on why we were transforming ACS membership. I have received a few questions around the exact details of the transformation, and I

Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning.

The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors. To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics. In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2). We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design. By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.

Incorporating student mobility in studying academic growth in math: comparing several alternative multilevel formulations

ABSTRACT Increasing pupil mobility has led to widespread concern among parents, educators, and policymakers regarding its negative effects on academic performance. An important issue in examining mobility effects in longitudinal school achievement comparisons is providing accurate estimates. The presence of pupil mobility suggests that we should model pupils as belonging to the series of schools attended and not just their first or final school. We discuss several challenges in accounting for student mobility and, because of the presence of mobile pupils, how to represent the contribution of multiple schools attended on estimating academic growth properly. We then contrast several previous longitudinal multilevel models utilized with two cross-classified multiple membership (CCMM) models, which have been proposed to cumulate annual school effects on pupils’ academic growth better given the complexity of the multilevel data structure. We discuss our results in terms of their theoretical and practical implications for research on school academic improvement.

Simple: Statistical Inference on Membership Profiles in Large Networks

AbstractNetwork data are prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference on membership profiles in large networks (SIMPLE) in the setting of degree-corrected mixed membership model, where the null hypothesis assumes that the pair of nodes share the same profile of community memberships. In the simpler case of no degree heterogeneity, the model reduces to the mixed membership model for which an alternative more robust test is also proposed. Both tests are of the Hotelling-type statistics based on the rows of empirical eigenvectors or their ratios, whose asymptotic covariance matrices are very challenging to derive and estimate. Nevertheless, their analytical expressions are unveiled and the unknown covariance matrices are consistently estimated. Under some mild regularity conditions, we establish the exact limiting distributions of the two forms of SIMPLE test statistics under the null hypothesis and contiguous alternative hypothesis. They are the chi-square distributions and the noncentral chi-square distributions, respectively, with degrees of freedom depending on whether the degrees are corrected or not. We also address the important issue of estimating the unknown number of communities and establish the asymptotic properties of the associated test statistics. The advantages and practical utility of our new procedures in terms of both size and power are demonstrated through several simulation examples and real network applications.

MULTIVARIATE MIXED MEMBERSHIP MODELING: INFERRING DOMAIN-SPECIFIC RISK PROFILES.

Characterizing the shared memberships of individuals in a classification scheme poses severe interpretability issues, even when using a moderate number of classes (say 4). Mixed membership models quantify this phenomenon, but they typically focus on goodness-of-fit more than on interpretable inference. To achieve a good numerical fit, these models may in fact require many extreme profiles, making the results difficult to interpret. We introduce a new class of multivariate mixed membership models that, when variables can be partitioned into subject-matter based domains, can provide a good fit to the data using fewer profiles than standard formulations. The proposed model explicitly accounts for the blocks of variables corresponding to the distinct domains along with a cross-domain correlation structure, which provides new information about shared membership of individuals in a complex classification scheme. We specify a multivariate logistic normal distribution for the membership vectors, which allows easy introduction of auxiliary information leveraging a latent multivariate logistic regression. A Bayesian approach to inference, relying on Pólya gamma data augmentation, facilitates efficient posterior computation via Markov Chain Monte Carlo. We apply this methodology to a spatially explicit study of malaria risk over time on the Brazilian Amazon frontier.

The exploitation of “Grade of Membership (GoM) model” and “Combined Analysis method” in analysing of urban dynamics and urban hierarchy in Khorasan region (1956-2016)

The exploitation of “Grade of Membership (GoM) model” and “Combined Analysis method” in analysing of urban dynamics and urban hierarchy in Khorasan region (1956-2016)

A simulation-based study of ZIP regression with various zero-inflated submodels

In this paper, we are interested in the robustness of the estimation in the Zero-Inflated Poisson regression model, when varying the class membership model of the underlying mixture. We propose an estimation procedure based on the maximum likelihood estimator. Simulations are used to examine the performance of the MLE. The results suggest that maximum likelihood allows for accurate inference. Using simulated datasets, we show that the proposed alternative link functions are quite flexible and outperform the standard link function. Also, an application to a real dataset is provided.