Percolation and epidemics in random clustered networks

Science.gov (United States)

Miller, Joel C.

2009-08-01

The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied percolation or epidemics in clustered networks, but the networks often contain preferential contacts in high degree nodes. We introduce a class of random clustered networks and a class of random unclustered networks with the same preferential mixing. Percolation in the clustered networks reduces the component sizes and increases the epidemic threshold compared to the unclustered networks.

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

Science.gov (United States)

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Personalized PageRank Clustering: A graph clustering algorithm based on random walks

Science.gov (United States)

A. Tabrizi, Shayan; Shakery, Azadeh; Asadpour, Masoud; Abbasi, Maziar; Tavallaie, Mohammad Ali

2013-11-01

Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.

Modeling of correlated data with informative cluster sizes: An evaluation of joint modeling and within-cluster resampling approaches.

Science.gov (United States)

Zhang, Bo; Liu, Wei; Zhang, Zhiwei; Qu, Yanping; Chen, Zhen; Albert, Paul S

2017-08-01

Joint modeling and within-cluster resampling are two approaches that are used for analyzing correlated data with informative cluster sizes. Motivated by a developmental toxicity study, we examined the performances and validity of these two approaches in testing covariate effects in generalized linear mixed-effects models. We show that the joint modeling approach is robust to the misspecification of cluster size models in terms of Type I and Type II errors when the corresponding covariates are not included in the random effects structure; otherwise, statistical tests may be affected. We also evaluate the performance of the within-cluster resampling procedure and thoroughly investigate the validity of it in modeling correlated data with informative cluster sizes. We show that within-cluster resampling is a valid alternative to joint modeling for cluster-specific covariates, but it is invalid for time-dependent covariates. The two methods are applied to a developmental toxicity study that investigated the effect of exposure to diethylene glycol dimethyl ether.

Quenched Large Deviations for Simple Random Walks on Percolation Clusters Including Long-Range Correlations

Science.gov (United States)

Berger, Noam; Mukherjee, Chiranjib; Okamura, Kazuki

2018-03-01

We prove a quenched large deviation principle (LDP) for a simple random walk on a supercritical percolation cluster (SRWPC) on {Z^d} ({d ≥ 2}). The models under interest include classical Bernoulli bond and site percolation as well as models that exhibit long range correlations, like the random cluster model, the random interlacement and the vacant set of random interlacements (for {d ≥ 3}) and the level sets of the Gaussian free field ({d≥ 3}). Inspired by the methods developed by Kosygina et al. (Commun Pure Appl Math 59:1489-1521, 2006) for proving quenched LDP for elliptic diffusions with a random drift, and by Yilmaz (Commun Pure Appl Math 62(8):1033-1075, 2009) and Rosenbluth (Quenched large deviations for multidimensional random walks in a random environment: a variational formula. Ph.D. thesis, NYU, arXiv:0804.1444v1) for similar results regarding elliptic random walks in random environment, we take the point of view of the moving particle and prove a large deviation principle for the quenched distribution of the pair empirical measures of the environment Markov chain in the non-elliptic case of SRWPC. Via a contraction principle, this reduces easily to a quenched LDP for the distribution of the mean velocity of the random walk and both rate functions admit explicit variational formulas. The main difficulty in our set up lies in the inherent non-ellipticity as well as the lack of translation-invariance stemming from conditioning on the fact that the origin belongs to the infinite cluster. We develop a unifying approach for proving quenched large deviations for SRWPC based on exploiting coercivity properties of the relative entropies in the context of convex variational analysis, combined with input from ergodic theory and invoking geometric properties of the supercritical percolation cluster.

Simulation of a directed random-walk model: the effect of pseudo-random-number correlations

OpenAIRE

Shchur, L. N.; Heringa, J. R.; Blöte, H. W. J.

1996-01-01

We investigate the mechanism that leads to systematic deviations in cluster Monte Carlo simulations when correlated pseudo-random numbers are used. We present a simple model, which enables an analysis of the effects due to correlations in several types of pseudo-random-number sequences. This model provides qualitative understanding of the bias mechanism in a class of cluster Monte Carlo algorithms.

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects.

Science.gov (United States)

Dreyhaupt, Jens; Mayer, Benjamin; Keis, Oliver; Öchsner, Wolfgang; Muche, Rainer

2017-01-01

An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called "cluster randomization"). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies. Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

Mathematical modelling of complex contagion on clustered networks

Science.gov (United States)

O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

2015-09-01

The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

Mathematical modelling of complex contagion on clustered networks

Directory of Open Access Journals (Sweden)

David J. P. O'Sullivan

2015-09-01

Full Text Available The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010, adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the complex contagion effects of social reinforcement are important in such diffusion, in contrast to simple contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010, to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects

Directory of Open Access Journals (Sweden)

Dreyhaupt, Jens

2017-05-01

Full Text Available An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called “cluster randomization”. Compared with studies with individual randomization, studies with cluster randomization normally require (significantly larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies.Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

Topics in modelling of clustered data

CERN Document Server

Aerts, Marc; Ryan, Louise M; Geys, Helena

2002-01-01

Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and social science studies. It focuses on providing a comprehensive treatment of marginal, conditional, and random effects models using, among others, likelihood, pseudo-likelihood, and generalized estimating equations methods. The authors motivate and illustrate all aspects of these models in a variety of real applications. They discuss several variations and extensions, including individual-level covariates and combined continuous and discrete outcomes. Flexible modelling with fractional and local polynomials, omnibus lack-of-fit tests, robustification against misspecification, exact, and bootstrap inferential procedures all receive extensive treatment. The application...

Simulating star clusters with the AMUSE software framework. I. Dependence of cluster lifetimes on model assumptions and cluster dissolution modes

International Nuclear Information System (INIS)

Whitehead, Alfred J.; McMillan, Stephen L. W.; Vesperini, Enrico; Portegies Zwart, Simon

2013-01-01

We perform a series of simulations of evolving star clusters using the Astrophysical Multipurpose Software Environment (AMUSE), a new community-based multi-physics simulation package, and compare our results to existing work. These simulations model a star cluster beginning with a King model distribution and a selection of power-law initial mass functions and contain a tidal cutoff. They are evolved using collisional stellar dynamics and include mass loss due to stellar evolution. After studying and understanding that the differences between AMUSE results and results from previous studies are understood, we explored the variation in cluster lifetimes due to the random realization noise introduced by transforming a King model to specific initial conditions. This random realization noise can affect the lifetime of a simulated star cluster by up to 30%. Two modes of star cluster dissolution were identified: a mass evolution curve that contains a runaway cluster dissolution with a sudden loss of mass, and a dissolution mode that does not contain this feature. We refer to these dissolution modes as 'dynamical' and 'relaxation' dominated, respectively. For Salpeter-like initial mass functions, we determined the boundary between these two modes in terms of the dynamical and relaxation timescales.

The Design of Cluster Randomized Trials with Random Cross-Classifications

Science.gov (United States)

Moerbeek, Mirjam; Safarkhani, Maryam

2018-01-01

Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health-care professionals. It is important that the random cross-classification is taken into account…

Cluster Tails for Critical Power-Law Inhomogeneous Random Graphs

Science.gov (United States)

van der Hofstad, Remco; Kliem, Sandra; van Leeuwaarden, Johan S. H.

2018-04-01

Recently, the scaling limit of cluster sizes for critical inhomogeneous random graphs of rank-1 type having finite variance but infinite third moment degrees was obtained in Bhamidi et al. (Ann Probab 40:2299-2361, 2012). It was proved that when the degrees obey a power law with exponent τ \\in (3,4), the sequence of clusters ordered in decreasing size and multiplied through by n^{-(τ -2)/(τ -1)} converges as n→ ∞ to a sequence of decreasing non-degenerate random variables. Here, we study the tails of the limit of the rescaled largest cluster, i.e., the probability that the scaling limit of the largest cluster takes a large value u, as a function of u. This extends a related result of Pittel (J Combin Theory Ser B 82(2):237-269, 2001) for the Erdős-Rényi random graph to the setting of rank-1 inhomogeneous random graphs with infinite third moment degrees. We make use of delicate large deviations and weak convergence arguments.

Cost-Effectiveness of a Chronic Care Model for Frail Older Adults in Primary Care: Economic Evaluation Alongside a Stepped-Wedge Cluster-Randomized Trial

NARCIS (Netherlands)

van Leeuwen, K.M.; Bosmans, J.E.; Jansen, A.P.D.; Hoogendijk, E.O.; Muntinga, M.E.; van Hout, H.P.J.; Nijpels, G.; van der Horst, H.E.; van Tulder, M.W.

2015-01-01

Objectives To evaluate the cost-effectiveness of the Geriatric Care Model (GCM), an integrated care model for frail older adults based on the Chronic Care Model, with that of usual care. Design Economic evaluation alongside a 24-month stepped-wedge cluster-randomized controlled trial. Setting

Cost-Effectiveness of a Chronic Care Model for Frail Older Adults in Primary Care : Economic Evaluation Alongside a Stepped-Wedge Cluster-Randomized Trial

NARCIS (Netherlands)

van Leeuwen, Karen M; Bosmans, Judith E; Jansen, Aaltje P D; Hoogendijk, Emiel O; Muntinga, Maaike E; van Hout, Hein P J; Nijpels, Giel; van der Horst, Henriette E; van Tulder, Maurits W

2015-01-01

OBJECTIVES: To evaluate the cost-effectiveness of the Geriatric Care Model (GCM), an integrated care model for frail older adults based on the Chronic Care Model, with that of usual care. DESIGN: Economic evaluation alongside a 24-month stepped-wedge cluster-randomized controlled trial. SETTING:

Determining the impact of a new physiotherapist-led primary care model for back pain: protocol for a pilot cluster randomized controlled trial.

Science.gov (United States)

Miller, Jordan; Barber, David; Donnelly, Catherine; French, Simon; Green, Michael; Hill, Jonathan; MacDermid, Joy; Marsh, Jacquelyn; Norman, Kathleen; Richardson, Julie; Taljaard, Monica; Wideman, Timothy; Cooper, Lynn; McPhee, Colleen

2017-11-09

Back pain is a leading contributor to disability, healthcare costs, and lost work. Family physicians are the most common first point of contact in the healthcare system for people with back pain, but physiotherapists (PTs) may be able to support the primary care team through evidence-based primary care. A cluster randomized trial is needed to determine the clinical, health system, and societal impact of a primary care model that integrates physiotherapists at the first visit for people with back pain. Prior to conducting a future fully powered cluster randomized trial, we need to demonstrate feasibility of the methods. Therefore, the purpose of this pilot study will be to: 1) Determine feasibility of patient recruitment, assessment procedures, and retention. 2) Determine the feasibility of training and implementation of a new PT-led primary care model for low back pain (LBP) 3) Explore the perspectives of patients and healthcare providers (HCPs) related to their experiences and attitudes towards the new service delivery model, barriers/facilitators to implementation, perceived satisfaction, perceived value, and impact on clinic processes and patient outcomes. This pilot cluster randomized controlled trial will enroll four sites and randomize them to implement a new PT-led primary care model for back pain or a usual physician-led primary care model. All adults booking a primary care visit for back pain will be invited to participate. Feasibility outcomes will include: recruitment and retention rates, completeness of assessment data, PT training participation and confidence after training, and PT treatment fidelity. Secondary outcomes will include the clinical, health system, cost, and process outcomes planned for the future fully powered cluster trial. Results will be analyzed and reported descriptively and qualitatively. To explore perspectives of both HCPs and patients, we will conduct semi-structured qualitative interviews with patients and focus groups with HCPs

A systematic review of the usage of flow diagram in cluster randomized trials

Directory of Open Access Journals (Sweden)

Kostić M.

2014-01-01

Full Text Available Flow diagram represent an integral part of consolidated standards of reporting trials (CONSORT. Its use in reporting cluster randomization trials is highly recommended. The aim of this article is to present frequency of the use of flow diagram in cluster randomized trials in accordance with standards of reporting. The team has researched Medline database and singled-out 474 studies with cluster randomization for analysis. The studies were reviewed to identify the use of graphic representation, compliance with standards of reporting and the date when study was published. Depending from its duration, studies were divided on completed, and those still ongoing. Usage of CONSORT is recorded in 145 (31% literature units. Frequency of flow diagram was statistically much higher in studies which were in compliance with standards (86,2%, in comparison to those which did not use CONSORT guidelines (71,4%, as well as in completed studies (81,2% in comparison to pilot project studies (54,3%. Number of cluster randomized trials gathered through MEDLINE's search of key words 'cluster randomized trial [ti]' and 'cluster randomised trial [ti]', as well as the use of CONSORT in the reports of cluster randomized trials, are showing linear growth over time (p<0,001. Frequency of flow diagram is higher in the reports of cluster randomized trials that were done in accordance with the standards of reporting.

Entropy Characterization of Random Network Models

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Pedro J. Zufiria

2017-06-01

Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.

Bond percolation on a class of correlated and clustered random graphs

International Nuclear Information System (INIS)

Allard, A; Hébert-Dufresne, L; Noël, P-A; Marceau, V; Dubé, L J

2012-01-01

We introduce a formalism for computing bond percolation properties of a class of correlated and clustered random graphs. This class of graphs is a generalization of the configuration model where nodes of different types are connected via different types of hyperedges, edges that can link more than two nodes. We argue that the multitype approach coupled with the use of clustered hyperedges can reproduce a wide spectrum of complex patterns, and thus enhances our capability to model real complex networks. As an illustration of this claim, we use our formalism to highlight unusual behaviours of the size and composition of the components (small and giant) in a synthetic, albeit realistic, social network. (paper)

A Kondo cluster-glass model for spin glass Cerium alloys

International Nuclear Information System (INIS)

Zimmer, F M; Magalhaes, S G; Coqblin, B

2011-01-01

There are clear indications that the presence of disorder in Ce alloys, such as Ce(Ni,Cu) or Ce(Pd,Rh), is responsible for the existence of a cluster spin glass state which changes continuously into inhomogeneous ferromagnetism at low temperatures. We present a study of the competition between magnetism and Kondo effect in a cluster-glass model composed by a random inter-cluster interaction term and an intra-cluster one, which contains an intra-site Kondo interaction J k and an inter-site ferromagnetic one J 0 . The random interaction is given by the van Hemmen type of randomness which allows to solve the problem without the use of the replica method. The inter-cluster term is solved within the cluster mean-field theory and the remaining intra-cluster interactions can be treated by exact diagonalization. Results show the behavior of the cluster glass order parameter and the Kondo correlation function for several sizes of the clusters, J k , J 0 and values of the ferromagnetic inter-cluster average interaction I 0 . Particularly, for small J k , the magnetic solution is strongly dependent on I 0 and J 0 and a Kondo cluster-glass or a mixed phase can be obtained, while, for large J k , the Kondo effect is still dominant, both in good agreement with experiment in Ce(Ni,Cu) or Ce(Pd,Rh) alloys.

3D Building Models Segmentation Based on K-Means++ Cluster Analysis

Science.gov (United States)

Zhang, C.; Mao, B.

2016-10-01

3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model) 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid) and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS

Directory of Open Access Journals (Sweden)

C. Zhang

2016-10-01

Full Text Available 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

Models of epidemics: when contact repetition and clustering should be included

Directory of Open Access Journals (Sweden)

Scholz Roland W

2009-06-01

Full Text Available Abstract Background The spread of infectious disease is determined by biological factors, e.g. the duration of the infectious period, and social factors, e.g. the arrangement of potentially contagious contacts. Repetitiveness and clustering of contacts are known to be relevant factors influencing the transmission of droplet or contact transmitted diseases. However, we do not yet completely know under what conditions repetitiveness and clustering should be included for realistically modelling disease spread. Methods We compare two different types of individual-based models: One assumes random mixing without repetition of contacts, whereas the other assumes that the same contacts repeat day-by-day. The latter exists in two variants, with and without clustering. We systematically test and compare how the total size of an outbreak differs between these model types depending on the key parameters transmission probability, number of contacts per day, duration of the infectious period, different levels of clustering and varying proportions of repetitive contacts. Results The simulation runs under different parameter constellations provide the following results: The difference between both model types is highest for low numbers of contacts per day and low transmission probabilities. The number of contacts and the transmission probability have a higher influence on this difference than the duration of the infectious period. Even when only minor parts of the daily contacts are repetitive and clustered can there be relevant differences compared to a purely random mixing model. Conclusion We show that random mixing models provide acceptable estimates of the total outbreak size if the number of contacts per day is high or if the per-contact transmission probability is high, as seen in typical childhood diseases such as measles. In the case of very short infectious periods, for instance, as in Norovirus, models assuming repeating contacts will also behave

Random clustering ferns for multimodal object recognition

OpenAIRE

Villamizar Vergel, Michael Alejandro; Garrell Zulueta, Anais; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc

2017-01-01

The final publication is available at link.springer.com We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficient...

Evaluation of stability of k-means cluster ensembles with respect to random initialization.

Science.gov (United States)

Kuncheva, Ludmila I; Vetrov, Dmitry P

2006-11-01

Many clustering algorithms, including cluster ensembles, rely on a random component. Stability of the results across different runs is considered to be an asset of the algorithm. The cluster ensembles considered here are based on k-means clusterers. Each clusterer is assigned a random target number of clusters, k and is started from a random initialization. Here, we use 10 artificial and 10 real data sets to study ensemble stability with respect to random k, and random initialization. The data sets were chosen to have a small number of clusters (two to seven) and a moderate number of data points (up to a few hundred). Pairwise stability is defined as the adjusted Rand index between pairs of clusterers in the ensemble, averaged across all pairs. Nonpairwise stability is defined as the entropy of the consensus matrix of the ensemble. An experimental comparison with the stability of the standard k-means algorithm was carried out for k from 2 to 20. The results revealed that ensembles are generally more stable, markedly so for larger k. To establish whether stability can serve as a cluster validity index, we first looked at the relationship between stability and accuracy with respect to the number of clusters, k. We found that such a relationship strongly depends on the data set, varying from almost perfect positive correlation (0.97, for the glass data) to almost perfect negative correlation (-0.93, for the crabs data). We propose a new combined stability index to be the sum of the pairwise individual and ensemble stabilities. This index was found to correlate better with the ensemble accuracy. Following the hypothesis that a point of stability of a clustering algorithm corresponds to a structure found in the data, we used the stability measures to pick the number of clusters. The combined stability index gave best results.

On the Coupling Time of the Heat-Bath Process for the Fortuin-Kasteleyn Random-Cluster Model

Science.gov (United States)

Collevecchio, Andrea; Elçi, Eren Metin; Garoni, Timothy M.; Weigel, Martin

2018-01-01

We consider the coupling from the past implementation of the random-cluster heat-bath process, and study its random running time, or coupling time. We focus on hypercubic lattices embedded on tori, in dimensions one to three, with cluster fugacity at least one. We make a number of conjectures regarding the asymptotic behaviour of the coupling time, motivated by rigorous results in one dimension and Monte Carlo simulations in dimensions two and three. Amongst our findings, we observe that, for generic parameter values, the distribution of the appropriately standardized coupling time converges to a Gumbel distribution, and that the standard deviation of the coupling time is asymptotic to an explicit universal constant multiple of the relaxation time. Perhaps surprisingly, we observe these results to hold both off criticality, where the coupling time closely mimics the coupon collector's problem, and also at the critical point, provided the cluster fugacity is below the value at which the transition becomes discontinuous. Finally, we consider analogous questions for the single-spin Ising heat-bath process.

Spatial cluster modelling

CERN Document Server

Lawson, Andrew B

2002-01-01

Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

Science.gov (United States)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-01-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Clustering, randomness, and regularity in cloud fields. 4. Stratocumulus cloud fields

Science.gov (United States)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-07-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (>900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Production of complex particles in low energy spallation and in fragmentation reactions by in-medium random clusterization

International Nuclear Information System (INIS)

Lacroix, D.; Durand, D.

2005-09-01

Rules for in-medium complex particle production in nuclear reactions are proposed. These rules have been implemented in two models to simulate nucleon-nucleus and nucleus-nucleus reactions around the Fermi energy. Our work emphasizes the effect of randomness in cluster formation, the importance of the nucleonic Fermi motion as well as the role of conservation laws. The concepts of total available phase-space and explored phase-space under constraint imposed by the reaction are clarified. The compatibility of experimental observations with a random clusterization is illustrated in a schematic scenario of a proton-nucleus collision. The role of randomness under constraint is also illustrated in the nucleus-nucleus case. (authors)

Assessment of Random Assignment in Training and Test Sets using Generalized Cluster Analysis Technique

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Sorana D. BOLBOACĂ

2011-06-01

Full Text Available Aim: The properness of random assignment of compounds in training and validation sets was assessed using the generalized cluster technique. Material and Method: A quantitative Structure-Activity Relationship model using Molecular Descriptors Family on Vertices was evaluated in terms of assignment of carboquinone derivatives in training and test sets during the leave-many-out analysis. Assignment of compounds was investigated using five variables: observed anticancer activity and four structure descriptors. Generalized cluster analysis with K-means algorithm was applied in order to investigate if the assignment of compounds was or not proper. The Euclidian distance and maximization of the initial distance using a cross-validation with a v-fold of 10 was applied. Results: All five variables included in analysis proved to have statistically significant contribution in identification of clusters. Three clusters were identified, each of them containing both carboquinone derivatives belonging to training as well as to test sets. The observed activity of carboquinone derivatives proved to be normal distributed on every. The presence of training and test sets in all clusters identified using generalized cluster analysis with K-means algorithm and the distribution of observed activity within clusters sustain a proper assignment of compounds in training and test set. Conclusion: Generalized cluster analysis using the K-means algorithm proved to be a valid method in assessment of random assignment of carboquinone derivatives in training and test sets.

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

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NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Nuclear clustering - a cluster core model study

International Nuclear Information System (INIS)

Paul Selvi, G.; Nandhini, N.; Balasubramaniam, M.

2015-01-01

Nuclear clustering, similar to other clustering phenomenon in nature is a much warranted study, since it would help us in understanding the nature of binding of the nucleons inside the nucleus, closed shell behaviour when the system is highly deformed, dynamics and structure at extremes. Several models account for the clustering phenomenon of nuclei. We present in this work, a cluster core model study of nuclear clustering in light mass nuclei

Person mobility in the design and analysis of cluster-randomized cohort prevention trials.

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Vuchinich, Sam; Flay, Brian R; Aber, Lawrence; Bickman, Leonard

2012-06-01

Person mobility is an inescapable fact of life for most cluster-randomized (e.g., schools, hospitals, clinic, cities, state) cohort prevention trials. Mobility rates are an important substantive consideration in estimating the effects of an intervention. In cluster-randomized trials, mobility rates are often correlated with ethnicity, poverty and other variables associated with disparity. This raises the possibility that estimated intervention effects may generalize to only the least mobile segments of a population and, thus, create a threat to external validity. Such mobility can also create threats to the internal validity of conclusions from randomized trials. Researchers must decide how to deal with persons who leave study clusters during a trial (dropouts), persons and clusters that do not comply with an assigned intervention, and persons who enter clusters during a trial (late entrants), in addition to the persons who remain for the duration of a trial (stayers). Statistical techniques alone cannot solve the key issues of internal and external validity raised by the phenomenon of person mobility. This commentary presents a systematic, Campbellian-type analysis of person mobility in cluster-randomized cohort prevention trials. It describes four approaches for dealing with dropouts, late entrants and stayers with respect to data collection, analysis and generalizability. The questions at issue are: 1) From whom should data be collected at each wave of data collection? 2) Which cases should be included in the analyses of an intervention effect? and 3) To what populations can trial results be generalized? The conclusions lead to recommendations for the design and analysis of future cluster-randomized cohort prevention trials.

Handling missing data in cluster randomized trials: A demonstration of multiple imputation with PAN through SAS

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Jiangxiu Zhou

2014-09-01

Full Text Available The purpose of this study is to demonstrate a way of dealing with missing data in clustered randomized trials by doing multiple imputation (MI with the PAN package in R through SAS. The procedure for doing MI with PAN through SAS is demonstrated in detail in order for researchers to be able to use this procedure with their own data. An illustration of the technique with empirical data was also included. In this illustration thePAN results were compared with pairwise deletion and three types of MI: (1 Normal Model (NM-MI ignoring the cluster structure; (2 NM-MI with dummy-coded cluster variables (fixed cluster structure; and (3 a hybrid NM-MI which imputes half the time ignoring the cluster structure, and the other half including the dummy-coded cluster variables. The empirical analysis showed that using PAN and the other strategies produced comparable parameter estimates. However, the dummy-coded MI overestimated the intraclass correlation, whereas MI ignoring the cluster structure and the hybrid MI underestimated the intraclass correlation. When compared with PAN, the p-value and standard error for the treatment effect were higher with dummy-coded MI, and lower with MI ignoring the clusterstructure, the hybrid MI approach, and pairwise deletion. Previous studies have shown that NM-MI is not appropriate for handling missing data in clustered randomized trials. This approach, in addition to the pairwise deletion approach, leads to a biased intraclass correlation and faultystatistical conclusions. Imputation in clustered randomized trials should be performed with PAN. We have demonstrated an easy way for using PAN through SAS.

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

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Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

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Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

Semiparametric Bayesian analysis of accelerated failure time models with cluster structures.

Science.gov (United States)

Li, Zhaonan; Xu, Xinyi; Shen, Junshan

2017-11-10

In this paper, we develop a Bayesian semiparametric accelerated failure time model for survival data with cluster structures. Our model allows distributional heterogeneity across clusters and accommodates their relationships through a density ratio approach. Moreover, a nonparametric mixture of Dirichlet processes prior is placed on the baseline distribution to yield full distributional flexibility. We illustrate through simulations that our model can greatly improve estimation accuracy by effectively pooling information from multiple clusters, while taking into account the heterogeneity in their random error distributions. We also demonstrate the implementation of our method using analysis of Mayo Clinic Trial in Primary Biliary Cirrhosis. Copyright © 2017 John Wiley & Sons, Ltd.

A pilot cluster randomized controlled trial of structured goal-setting following stroke.

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Taylor, William J; Brown, Melanie; William, Levack; McPherson, Kathryn M; Reed, Kirk; Dean, Sarah G; Weatherall, Mark

2012-04-01

To determine the feasibility, the cluster design effect and the variance and minimal clinical importance difference in the primary outcome in a pilot study of a structured approach to goal-setting. A cluster randomized controlled trial. Inpatient rehabilitation facilities. People who were admitted to inpatient rehabilitation following stroke who had sufficient cognition to engage in structured goal-setting and complete the primary outcome measure. Structured goal elicitation using the Canadian Occupational Performance Measure. Quality of life at 12 weeks using the Schedule for Individualised Quality of Life (SEIQOL-DW), Functional Independence Measure, Short Form 36 and Patient Perception of Rehabilitation (measuring satisfaction with rehabilitation). Assessors were blinded to the intervention. Four rehabilitation services and 41 patients were randomized. We found high values of the intraclass correlation for the outcome measures (ranging from 0.03 to 0.40) and high variance of the SEIQOL-DW (SD 19.6) in relation to the minimally importance difference of 2.1, leading to impractically large sample size requirements for a cluster randomized design. A cluster randomized design is not a practical means of avoiding contamination effects in studies of inpatient rehabilitation goal-setting. Other techniques for coping with contamination effects are necessary.

A cluster randomized theory-guided oral hygiene trial in adolescents-A latent growth model.

Science.gov (United States)

Aleksejūnienė, J; Brukienė, V

2018-05-01

(i) To test whether theory-guided interventions are more effective than conventional dental instruction (CDI) for changing oral hygiene in adolescents and (ii) to examine whether such interventions equally benefit both genders and different socio-economic (SES) groups. A total of 244 adolescents were recruited from three schools, and cluster randomization allocated adolescents to one of the three types of interventions: two were theory-based interventions (Precaution Adoption Process Model or Authoritative Parenting Model) and CDI served as an active control. Oral hygiene levels % (OH) were assessed at baseline, after 3 months and after 12 months. A complete data set was available for 166 adolescents (the total follow-up rate: 69%). There were no significant differences in baseline OH between those who participated throughout the study and those who dropped out. Bivariate and multivariate analyses showed that theory-guided interventions produced significant improvements in oral hygiene and that there were no significant gender or socio-economic differences. Theory-guided interventions produced more positive changes in OH than CDI, and these changes did not differ between gender and SES groups. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

On the limiting characteristics of quantum random number generators at various clusterings of photocounts

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Molotkov, S. N.

2017-03-01

Various methods for the clustering of photocounts constituting a sequence of random numbers are considered. It is shown that the clustering of photocounts resulting in the Fermi-Dirac distribution makes it possible to achieve the theoretical limit of the random number generation rate.

Relative efficiency and sample size for cluster randomized trials with variable cluster sizes.

Science.gov (United States)

You, Zhiying; Williams, O Dale; Aban, Inmaculada; Kabagambe, Edmond Kato; Tiwari, Hemant K; Cutter, Gary

2011-02-01

The statistical power of cluster randomized trials depends on two sample size components, the number of clusters per group and the numbers of individuals within clusters (cluster size). Variable cluster sizes are common and this variation alone may have significant impact on study power. Previous approaches have taken this into account by either adjusting total sample size using a designated design effect or adjusting the number of clusters according to an assessment of the relative efficiency of unequal versus equal cluster sizes. This article defines a relative efficiency of unequal versus equal cluster sizes using noncentrality parameters, investigates properties of this measure, and proposes an approach for adjusting the required sample size accordingly. We focus on comparing two groups with normally distributed outcomes using t-test, and use the noncentrality parameter to define the relative efficiency of unequal versus equal cluster sizes and show that statistical power depends only on this parameter for a given number of clusters. We calculate the sample size required for an unequal cluster sizes trial to have the same power as one with equal cluster sizes. Relative efficiency based on the noncentrality parameter is straightforward to calculate and easy to interpret. It connects the required mean cluster size directly to the required sample size with equal cluster sizes. Consequently, our approach first determines the sample size requirements with equal cluster sizes for a pre-specified study power and then calculates the required mean cluster size while keeping the number of clusters unchanged. Our approach allows adjustment in mean cluster size alone or simultaneous adjustment in mean cluster size and number of clusters, and is a flexible alternative to and a useful complement to existing methods. Comparison indicated that we have defined a relative efficiency that is greater than the relative efficiency in the literature under some conditions. Our measure

Universal Prevention for Anxiety and Depressive Symptoms in Children: A Meta-analysis of Randomized and Cluster-Randomized Trials.

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Ahlen, Johan; Lenhard, Fabian; Ghaderi, Ata

2015-12-01

Although under-diagnosed, anxiety and depression are among the most prevalent psychiatric disorders in children and adolescents, leading to severe impairment, increased risk of future psychiatric problems, and a high economic burden to society. Universal prevention may be a potent way to address these widespread problems. There are several benefits to universal relative to targeted interventions because there is limited knowledge as to how to screen for anxiety and depression in the general population. Earlier meta-analyses of the prevention of depression and anxiety symptoms among children suffer from methodological inadequacies such as combining universal, selective, and indicated interventions in the same analyses, and comparing cluster-randomized trials with randomized trials without any correction for clustering effects. The present meta-analysis attempted to determine the effectiveness of universal interventions to prevent anxiety and depressive symptoms after correcting for clustering effects. A systematic search of randomized studies in PsychINFO, Cochrane Library, and Google Scholar resulted in 30 eligible studies meeting inclusion criteria, namely peer-reviewed, randomized or cluster-randomized trials of universal interventions for anxiety and depressive symptoms in school-aged children. Sixty-three percent of the studies reported outcome data regarding anxiety and 87 % reported outcome data regarding depression. Seventy percent of the studies used randomization at the cluster level. There were small but significant effects regarding anxiety (.13) and depressive (.11) symptoms as measured at immediate posttest. At follow-up, which ranged from 3 to 48 months, effects were significantly larger than zero regarding depressive (.07) but not anxiety (.11) symptoms. There was no significant moderation effect of the following pre-selected variables: the primary aim of the intervention (anxiety or depression), deliverer of the intervention, gender distribution

A semi-supervised method to detect seismic random noise with fuzzy GK clustering

International Nuclear Information System (INIS)

Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert

2008-01-01

We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise

Cluster-cluster correlations and constraints on the correlation hierarchy

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Hamilton, A. J. S.; Gott, J. R., III

1988-01-01

The hypothesis that galaxies cluster around clusters at least as strongly as they cluster around galaxies imposes constraints on the hierarchy of correlation amplitudes in hierachical clustering models. The distributions which saturate these constraints are the Rayleigh-Levy random walk fractals proposed by Mandelbrot; for these fractal distributions cluster-cluster correlations are all identically equal to galaxy-galaxy correlations. If correlation amplitudes exceed the constraints, as is observed, then cluster-cluster correlations must exceed galaxy-galaxy correlations, as is observed.

Running and rotating: modelling the dynamics of migrating cell clusters

Science.gov (United States)

Copenhagen, Katherine; Gov, Nir; Gopinathan, Ajay

Collective motion of cells is a common occurrence in many biological systems, including tissue development and repair, and tumor formation. Recent experiments have shown cells form clusters in a chemical gradient, which display three different phases of motion: translational, rotational, and random. We present a model for cell clusters based loosely on other models seen in the literature that involves a Vicsek-like alignment as well as physical collisions and adhesions between cells. With this model we show that a mechanism for driving rotational motion in this kind of system is an increased motility of rim cells. Further, we examine the details of the relationship between rim and core cells, and find that the phases of the cluster as a whole are correlated with the creation and annihilation of topological defects in the tangential component of the velocity field.

Evaluation of cluster-randomized trials on maternal and child health research in developing countries

DEFF Research Database (Denmark)

Handlos, Line Neerup; Chakraborty, Hrishikesh; Sen, Pranab Kumar

2009-01-01

To summarize and evaluate all publications including cluster-randomized trials used for maternal and child health research in developing countries during the last 10 years. METHODS: All cluster-randomized trials published between 1998 and 2008 were reviewed, and those that met our criteria...... for inclusion were evaluated further. The criteria for inclusion were that the trial should have been conducted in maternal and child health care in a developing country and that the conclusions should have been made on an individual level. Methods of accounting for clustering in design and analysis were......, and the trials generally improved in quality. CONCLUSIONS: Shortcomings exist in the sample-size calculations and in the analysis of cluster-randomized trials conducted during maternal and child health research in developing countries. Even though there has been improvement over time, further progress in the way...

Spectra of random networks in the weak clustering regime

Science.gov (United States)

Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen; Rodrigues, Francisco A.

2018-03-01

The asymptotic behavior of dynamical processes in networks can be expressed as a function of spectral properties of the corresponding adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values of the clustering coefficient. Here we study effects of cycles of order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectral distribution of the network adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Implications to network dynamics are discussed. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.

A Coupled Hidden Conditional Random Field Model for Simultaneous Face Clustering and Naming in Videos

KAUST Repository

Zhang, Yifan; Tang, Zhiqiang; Wu, Baoyuan; Ji, Qiang; Lu, Hanqing

2016-01-01

, we divide the problem into two tasks: face clustering which groups the faces depicting a certain person into a cluster, and name assignment which associates a name to each face. Each task is formulated as a structured prediction problem and modeled

Selection bias and subject refusal in a cluster-randomized controlled trial

Directory of Open Access Journals (Sweden)

Rochelle Yang

2017-07-01

Full Text Available Abstract Background Selection bias and non-participation bias are major methodological concerns which impact external validity. Cluster-randomized controlled trials are especially prone to selection bias as it is impractical to blind clusters to their allocation into intervention or control. This study assessed the impact of selection bias in a large cluster-randomized controlled trial. Methods The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE study examined the impact of a remote pharmacist-led intervention in twelve medical offices. To assess eligibility, a standardized form containing patient demographics and medical information was completed for each screened patient. Eligible patients were approached by the study coordinator for recruitment. Both the study coordinator and the patient were aware of the site’s allocation prior to consent. Patients who consented or declined to participate were compared across control and intervention arms for differing characteristics. Statistical significance was determined using a two-tailed, equal variance t-test and a chi-square test with adjusted Bonferroni p-values. Results were adjusted for random cluster variation. Results There were 2749 completed screening forms returned to research staff with 461 subjects who had either consented or declined participation. Patients with poorly controlled diabetes were found to be significantly more likely to decline participation in intervention sites compared to those in control sites. A higher mean diastolic blood pressure was seen in patients with uncontrolled hypertension who declined in the control sites compared to those who declined in the intervention sites. However, these findings were no longer significant after adjustment for random variation among the sites. After this adjustment, females were now found to be significantly more likely to consent than males (odds ratio = 1.41; 95% confidence interval = 1.03, 1

Cluster model of the nucleus

International Nuclear Information System (INIS)

Horiuchi, H.; Ikeda, K.

1986-01-01

This article reviews the development of the cluster model study. The stress is put on two points; one is how the cluster structure has come to be regarded as a fundamental structure in light nuclei together with the shell-model structure, and the other is how at present the cluster model is extended to and connected with the studies of the various subjects many of which are in the neighbouring fields. The authors the present the main theme with detailed explanations of the fundamentals of the microscopic cluster model which have promoted the development of the cluster mode. Examples of the microscopic cluster model study of light nuclear structure are given

Hospital recruitment for a pragmatic cluster-randomized clinical trial: Lessons learned from the COMPASS study.

Science.gov (United States)

Johnson, Anna M; Jones, Sara B; Duncan, Pamela W; Bushnell, Cheryl D; Coleman, Sylvia W; Mettam, Laurie H; Kucharska-Newton, Anna M; Sissine, Mysha E; Rosamond, Wayne D

2018-01-26

Pragmatic randomized clinical trials are essential to determine the effectiveness of interventions in "real-world" clinical practice. These trials frequently use a cluster-randomized methodology, with randomization at the site level. Despite policymakers' increased interest in supporting pragmatic randomized clinical trials, no studies to date have reported on the unique recruitment challenges faced by cluster-randomized pragmatic trials. We investigated key challenges and successful strategies for hospital recruitment in the Comprehensive Post-Acute Stroke Services (COMPASS) study. The COMPASS study is designed to compare the effectiveness of the COMPASS model versus usual care in improving functional outcomes, reducing the numbers of hospital readmissions, and reducing caregiver strain for patients discharged home after stroke or transient ischemic attack. This model integrates early supported discharge planning with transitional care management, including nurse-led follow-up phone calls after 2, 30, and 60 days and an in-person clinic visit at 7-14 days involving a functional assessment and neurological examination. We present descriptive statistics of the characteristics of successfully recruited hospitals compared with all eligible hospitals, reasons for non-participation, and effective recruitment strategies. We successfully recruited 41 (43%) of 95 eligible North Carolina hospitals. Leading, non-exclusive reasons for non-participation included: insufficient staff or financial resources (n = 33, 61%), lack of health system support (n = 16, 30%), and lack of support of individual decision-makers (n = 11, 20%). Successful recruitment strategies included: building and nurturing relationships, engaging team members and community partners with a diverse skill mix, identifying gatekeepers, finding mutually beneficial solutions, having a central institutional review board, sharing published pilot data, and integrating contracts and review board

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

Science.gov (United States)

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Cluster Based Text Classification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

2011-01-01

We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

Plasmon response in K, Na and Li clusters: systematics using the separable random-phase approximation with pseudo-Hamiltonians

International Nuclear Information System (INIS)

Kleinig, W.; Nesterenko, V.O.; Reinhard, P.-G.; Serra, Ll.

1998-01-01

The systematics of the plasmon response in spherical K, Na and Li clusters in a wide size region (8≤N≤440) is studied. We have considered two simplifying approximations whose validity has been established previously. First, a separable approach to the random-phase approximation is used. This involves an expansion of the residual interaction into a sum of separable terms until convergence is reached. Second, the electron-ion interaction is modelled by using the pseudo-Hamiltonian jellium model (MHJM) which includes nonlocal effects by means of realistic atomic pseudo-Hamiltonians. In cases where nonlocal effects are negligible the Structure Averaged Jellium Model (SAJM) has been used. Good agreement with available experimental data is achieved for K, Na (using the SAJM) and small Li clusters (invoking the PHJM). The trends for peak position and width are generally well reproduced, even up to details of the Landau fragmentation in several clusters. Less good agreement, however, is found for large Li clusters. This remains an open question

Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions

International Nuclear Information System (INIS)

Altarelli, F; Monasson, R; Zamponi, F

2008-01-01

We study the performances of stochastic heuristic search algorithms on Uniquely Extendible Constraint Satisfaction Problems with random inputs. We show that, for any heuristic preserving the Poissonian nature of the underlying instance, the (heuristic-dependent) largest ratio α a of constraints per variables for which a search algorithm is likely to find solutions is smaller than the critical ratio α d above which solutions are clustered and highly correlated. In addition we show that the clustering ratio can be reached when the number k of variables per constraints goes to infinity by the so-called Generalized Unit Clause heuristic

Analyzing indirect effects in cluster randomized trials. The effect of estimation method, number of groups and group sizes on accuracy and power.

Directory of Open Access Journals (Sweden)

Joop eHox

2014-02-01

Full Text Available Cluster randomized trials assess the effect of an intervention that is carried out at the group or cluster level. Ajzen’s theory of planned behaviour is often used to model the effect of the intervention as an indirect effect mediated in turn by attitude, norms and behavioural intention. Structural equation modelling (SEM is the technique of choice to estimate indirect effects and their significance. However, this is a large sample technique, and its application in a cluster randomized trial assumes a relatively large number of clusters. In practice, the number of clusters in these studies tends to be relatively small, e.g. much less than fifty. This study uses simulation methods to find the lowest number of clusters needed when multilevel SEM is used to estimate the indirect effect. Maximum likelihood estimation is compared to Bayesian analysis, with the central quality criteria being accuracy of the point estimate and the confidence interval. We also investigate the power of the test for the indirect effect. We conclude that Bayes estimation works well with much smaller cluster level sample sizes such as 20 cases than maximum likelihood estimation; although the bias is larger the coverage is much better. When only 5 to 10 clusters are available per treatment condition even with Bayesian estimation problems occur.

Cluster tails for critical power-law inhomogeneous random graphs

NARCIS (Netherlands)

van der Hofstad, R.; Kliem, S.; van Leeuwaarden, J.S.H.

2018-01-01

Recently, the scaling limit of cluster sizes for critical inhomogeneous random graphs of rank-1 type having finite variance but infinite third moment degrees was obtained in Bhamidi et al. (Ann Probab 40:2299–2361, 2012). It was proved that when the degrees obey a power law with exponent τ∈ (3 , 4)

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms.

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McGarvey, Richard; Burch, Paul; Matthews, Janet M

2016-01-01

Natural populations of plants and animals spatially cluster because (1) suitable habitat is patchy, and (2) within suitable habitat, individuals aggregate further into clusters of higher density. We compare the precision of random and systematic field sampling survey designs under these two processes of species clustering. Second, we evaluate the performance of 13 estimators for the variance of the sample mean from a systematic survey. Replicated simulated surveys, as counts from 100 transects, allocated either randomly or systematically within the study region, were used to estimate population density in six spatial point populations including habitat patches and Matérn circular clustered aggregations of organisms, together and in combination. The standard one-start aligned systematic survey design, a uniform 10 x 10 grid of transects, was much more precise. Variances of the 10 000 replicated systematic survey mean densities were one-third to one-fifth of those from randomly allocated transects, implying transect sample sizes giving equivalent precision by random survey would need to be three to five times larger. Organisms being restricted to patches of habitat was alone sufficient to yield this precision advantage for the systematic design. But this improved precision for systematic sampling in clustered populations is underestimated by standard variance estimators used to compute confidence intervals. True variance for the survey sample mean was computed from the variance of 10 000 simulated survey mean estimates. Testing 10 published and three newly proposed variance estimators, the two variance estimators (v) that corrected for inter-transect correlation (ν₈ and ν(W)) were the most accurate and also the most precise in clustered populations. These greatly outperformed the two "post-stratification" variance estimators (ν₂ and ν₃) that are now more commonly applied in systematic surveys. Similar variance estimator performance rankings were found with

Cluster Correlation in Mixed Models

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Gardini, A.; Bonometto, S. A.; Murante, G.; Yepes, G.

2000-10-01

We evaluate the dependence of the cluster correlation length, rc, on the mean intercluster separation, Dc, for three models with critical matter density, vanishing vacuum energy (Λ=0), and COBE normalization: a tilted cold dark matter (tCDM) model (n=0.8) and two blue mixed models with two light massive neutrinos, yielding Ωh=0.26 and 0.14 (MDM1 and MDM2, respectively). All models approach the observational value of σ8 (and hence the observed cluster abundance) and are consistent with the observed abundance of damped Lyα systems. Mixed models have a motivation in recent results of neutrino physics; they also agree with the observed value of the ratio σ8/σ25, yielding the spectral slope parameter Γ, and nicely fit Las Campanas Redshift Survey (LCRS) reconstructed spectra. We use parallel AP3M simulations, performed in a wide box (of side 360 h-1 Mpc) and with high mass and distance resolution, enabling us to build artificial samples of clusters, whose total number and mass range allow us to cover the same Dc interval inspected through Automatic Plate Measuring Facility (APM) and Abell cluster clustering data. We find that the tCDM model performs substantially better than n=1 critical density CDM models. Our main finding, however, is that mixed models provide a surprisingly good fit to cluster clustering data.

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

Directory of Open Access Journals (Sweden)

Fei Wang

2017-01-01

Full Text Available An Intensified Charge-Coupled Device (ICCD image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a Different from the independent identically distributed (i.i.d. noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

International Nuclear Information System (INIS)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-01-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

Energy Technology Data Exchange (ETDEWEB)

Nedialkova, Lilia V.; Amat, Miguel A. [Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544 (United States); Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544 (United States); Hummer, Gerhard, E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438 Frankfurt am Main (Germany)

2014-09-21

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

Ethical and policy issues in cluster randomized trials: rationale and design of a mixed methods research study

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Chaudhry Shazia H

2009-07-01

Full Text Available Abstract Background Cluster randomized trials are an increasingly important methodological tool in health research. In cluster randomized trials, intact social units or groups of individuals, such as medical practices, schools, or entire communities – rather than individual themselves – are randomly allocated to intervention or control conditions, while outcomes are then observed on individual cluster members. The substantial methodological differences between cluster randomized trials and conventional randomized trials pose serious challenges to the current conceptual framework for research ethics. The ethical implications of randomizing groups rather than individuals are not addressed in current research ethics guidelines, nor have they even been thoroughly explored. The main objectives of this research are to: (1 identify ethical issues arising in cluster trials and learn how they are currently being addressed; (2 understand how ethics reviews of cluster trials are carried out in different countries (Canada, the USA and the UK; (3 elicit the views and experiences of trial participants and cluster representatives; (4 develop well-grounded guidelines for the ethical conduct and review of cluster trials by conducting an extensive ethical analysis and organizing a consensus process; (5 disseminate the guidelines to researchers, research ethics boards (REBs, journal editors, and research funders. Methods We will use a mixed-methods (qualitative and quantitative approach incorporating both empirical and conceptual work. Empirical work will include a systematic review of a random sample of published trials, a survey and in-depth interviews with trialists, a survey of REBs, and in-depth interviews and focus group discussions with trial participants and gatekeepers. The empirical work will inform the concurrent ethical analysis which will lead to a guidance document laying out principles, policy options, and rationale for proposed guidelines. An

ODE, RDE and SDE models of cell cycle dynamics and clustering in yeast.

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Boczko, Erik M; Gedeon, Tomas; Stowers, Chris C; Young, Todd R

2010-07-01

Biologists have long observed periodic-like oxygen consumption oscillations in yeast populations under certain conditions, and several unsatisfactory explanations for this phenomenon have been proposed. These ‘autonomous oscillations’ have often appeared with periods that are nearly integer divisors of the calculated doubling time of the culture. We hypothesize that these oscillations could be caused by a form of cell cycle synchronization that we call clustering. We develop some novel ordinary differential equation models of the cell cycle. For these models, and for random and stochastic perturbations, we give both rigorous proofs and simulations showing that both positive and negative growth rate feedback within the cell cycle are possible agents that can cause clustering of populations within the cell cycle. It occurs for a variety of models and for a broad selection of parameter values. These results suggest that the clustering phenomenon is robust and is likely to be observed in nature. Since there are necessarily an integer number of clusters, clustering would lead to periodic-like behaviour with periods that are nearly integer divisors of the period of the cell cycle. Related experiments have shown conclusively that cell cycle clustering occurs in some oscillating yeast cultures.

Implementation of client versus care-provider strategies to improve external cephalic version rates: a cluster randomized controlled trial

NARCIS (Netherlands)

Vlemmix, Floortje; Rosman, Ageeth N.; Rijnders, Marlies E.; Beuckens, Antje; Opmeer, Brent C.; Mol, Ben W. J.; Kok, Marjolein; Fleuren, Margot A. H.

2015-01-01

To determine the effectiveness of a client or care-provider strategy to improve the implementation of external cephalic version. Cluster randomized controlled trial. Twenty-five clusters; hospitals and their referring midwifery practices randomly selected in the Netherlands. Singleton breech

Implementation of client versus care-provider strategies to improve external cephalic version rates: a cluster randomized controlled trial

NARCIS (Netherlands)

Vlemmix, F.; Rosman, A.N.; Rijnders, M.E.; Beuckens, A.; Opmeer, B.C.; Mol, B.W.J.; Kok, M.; Fleuren, M.A.H.

2015-01-01

Onjective: To determine the effectiveness of a client or care-provider strategy to improve the implementation of external cephalic version. Design: Cluster randomized controlled trial.Setting: Twenty-five clusters; hospitals and their referring midwifery practices randomly selected in the

Improving Language Comprehension in Preschool Children with Language Difficulties: A Cluster Randomized Trial

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Hagen, Åste M.; Melby-Lervåg, Monica; Lervåg, Arne

2017-01-01

Background: Children with language comprehension difficulties are at risk of educational and social problems, which in turn impede employment prospects in adulthood. However, few randomized trials have examined how such problems can be ameliorated during the preschool years. Methods: We conducted a cluster randomized trial in 148 preschool…

A cluster randomized trial of alcohol prevention in small businesses: a cascade model of help seeking and risk reduction.

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Reynolds, G Shawn; Bennett, Joel B

2015-01-01

The current study adapted two workplace substance abuse prevention programs and tested a conceptual model of workplace training effects on help seeking and alcohol consumption. Questionnaires were collected 1 month before, 1 month after, and 6 months within a cluster randomized field experiment. Texas small businesses in construction, transportation, and service industries. A total of 1510 employees from 45 businesses were randomly assigned to receive no training or one of the interventions. The interventions were 4-hour on-the-job classroom trainings that encouraged healthy lifestyles and seeking professional help (e.g., from the Employee Assistance Program [EAP]). The Team Awareness Program focused on peer referral and team building. The Choices in Health Promotion Program delivered various health topics based on a needs assessment. Questionnaires measured help-seeking attitudes and behavior, frequency of drinking alcohol, and job-related incidents. Mixed-model repeated-measures analyses of covariance were computed. Relative to the control group, training was associated with significantly greater reductions in drinking frequency, willingness to seek help, and seeking help from the EAP. After including help-seeking attitudes as a covariate, the correlation between training and help seeking becomes nonsignificant. Help-seeking behavior was not correlated with drinking frequency. Training improved help-seeking attitudes and behaviors and decreased alcohol risks. The reductions in drinking alcohol were directly correlated with training and independent from help seeking.

Clustered multistate models with observation level random effects, mover-stayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritis.

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Yiu, Sean; Farewell, Vernon T; Tom, Brian D M

2018-02-01

In psoriatic arthritis, it is important to understand the joint activity (represented by swelling and pain) and damage processes because both are related to severe physical disability. The paper aims to provide a comprehensive investigation into both processes occurring over time, in particular their relationship, by specifying a joint multistate model at the individual hand joint level, which also accounts for many of their important features. As there are multiple hand joints, such an analysis will be based on the use of clustered multistate models. Here we consider an observation level random-effects structure with dynamic covariates and allow for the possibility that a subpopulation of patients is at minimal risk of damage. Such an analysis is found to provide further understanding of the activity-damage relationship beyond that provided by previous analyses. Consideration is also given to the modelling of mean sojourn times and jump probabilities. In particular, a novel model parameterization which allows easily interpretable covariate effects to act on these quantities is proposed.

A Random Walk Approach to Query Informative Constraints for Clustering.

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Abin, Ahmad Ali

2017-08-09

This paper presents a random walk approach to the problem of querying informative constraints for clustering. The proposed method is based on the properties of the commute time, that is the expected time taken for a random walk to travel between two nodes and return, on the adjacency graph of data. Commute time has the nice property of that, the more short paths connect two given nodes in a graph, the more similar those nodes are. Since computing the commute time takes the Laplacian eigenspectrum into account, we use this property in a recursive fashion to query informative constraints for clustering. At each recursion, the proposed method constructs the adjacency graph of data and utilizes the spectral properties of the commute time matrix to bipartition the adjacency graph. Thereafter, the proposed method benefits from the commute times distance on graph to query informative constraints between partitions. This process iterates for each partition until the stop condition becomes true. Experiments on real-world data show the efficiency of the proposed method for constraints selection.

Determining characteristic principal clusters in the “cluster-plus-glue-atom” model

International Nuclear Information System (INIS)

Du, Jinglian; Wen, Bin; 2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" data-affiliation=" (M2NeT Lab, Wilfrid Laurier University, Waterloo, 75 University Ave West, Ontario N2L 3C5 (Canada))" >Melnik, Roderick; Kawazoe, Yoshiyuki

2014-01-01

The “cluster-plus-glue-atom” model can easily describe the structure of complex metallic alloy phases. However, the biggest obstacle limiting the application of this model is that it is difficult to determine the characteristic principal cluster. In the case when interatomic force constants (IFCs) inside the cluster lead to stronger interaction than the interaction between the clusters, a new rule for determining the characteristic principal cluster in the “cluster-plus-glue-atom” model has been proposed on the basis of IFCs. To verify this new rule, the alloy phases in Cu–Zr and Al–Ni–Zr systems have been tested, and our results indicate that the present new rule for determining characteristic principal clusters is effective and reliable

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

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Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal

The clustering of local maxima in random noise

International Nuclear Information System (INIS)

Coles, P.

1989-01-01

A mixture of analytic and numerical techniques is used to study the clustering properties of local maxima of random noise. Technical complexities restrict us to the case of 1D noise, but the results obtained should give a reasonably accurate picture of the behaviour of cosmological density peaks in noise defined on a 3D domain. We give estimates of the two-point correlation function of local maxima, for both Gaussian and non-Gaussian noise and show that previous approximations are not accurate. (author)

Infrared Extinction Performance of Randomly Oriented Microbial-Clustered Agglomerate Materials.

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Li, Le; Hu, Yihua; Gu, Youlin; Zhao, Xinying; Xu, Shilong; Yu, Lei; Zheng, Zhi Ming; Wang, Peng

2017-11-01

In this study, the spatial structure of randomly distributed clusters of fungi An0429 spores was simulated using a cluster aggregation (CCA) model, and the single scattering parameters of fungi An0429 spores were calculated using the discrete dipole approximation (DDA) method. The transmittance of 10.6 µm infrared (IR) light in the aggregated fungi An0429 spores swarm is simulated by using the Monte Carlo method. Several parameters that affect the transmittance of 10.6 µm IR light, such as the number and radius of original fungi An0429 spores, porosity of aggregated fungi An0429 spores, and density of aggregated fungi An0429 spores of the formation aerosol area were discussed. Finally, the transmittances of microbial materials with different qualities were measured in the dynamic test platform. The simulation results showed that the parameters analyzed were closely connected with the extinction performance of fungi An0429 spores. By controlling the value of the influencing factors, the transmittance could be lower than a certain threshold to meet the requirement of attenuation in application. In addition, the experimental results showed that the Monte Carlo method could well reflect the attenuation law of IR light in fungi An0429 spore agglomerates swarms.

Co-clustering models, algorithms and applications

CERN Document Server

Govaert, Gérard

2013-01-01

Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixture

Headache cessation by an educational intervention in grammar schools: a cluster randomized trial.

Science.gov (United States)

Albers, L; Heinen, F; Landgraf, M; Straube, A; Blum, B; Filippopulos, F; Lehmann, S; Mansmann, U; Berger, U; Akboga, Y; von Kries, R

2015-02-01

Headache is a common health problem in adolescents. There are a number of risk factors for headache in adolescents that are amenable to intervention. The aim of the study was to assess the effectiveness of a low-level headache prevention programme in the classroom setting to prevent these risk factors. In all, 1674 students in 8th-10th grade at 12 grammar schools in greater Munich, Germany, were cluster randomized into intervention and control groups. A standardized 60-min prevention lesson focusing on preventable risk factors for headache (physical inactivity, coffee consumption, alcohol consumption and smoking) and providing instructions on stress management and neck and shoulder muscle relaxation exercises was given in a classroom setting. Seven months later, students were reassessed. The main outcome parameter was headache cessation. Logistic regression models with random effects for cluster and adjustment for baseline risk factors were calculated. Nine hundred students (intervention group N = 450, control group N = 450) with headache at baseline and complete data for headache and confounders were included in the analysis. Headache cessation was observed in 9.78% of the control group compared with 16.22% in the intervention group (number needed to treat = 16). Accounting for cluster effects and confounders, the probability of headache cessation in the intervention group was 1.77 (95% confidence interval = [1.08; 2.90]) higher than in the control group. The effect was most pronounced in adolescents with tension-type headache: odds ratio = 2.11 (95% confidence interval = [1.15; 3.80]). Our study demonstrates the effectiveness of a one-time, classroom-based headache prevention programme. © 2014 EAN.

Outcomes of a pilot hand hygiene randomized cluster trial to reduce communicable infections among US office-based employees.

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Stedman-Smith, Maggie; DuBois, Cathy L Z; Grey, Scott F; Kingsbury, Diana M; Shakya, Sunita; Scofield, Jennifer; Slenkovich, Ken

2015-04-01

To determine the effectiveness of an office-based multimodal hand hygiene improvement intervention in reducing self-reported communicable infections and work-related absence. A randomized cluster trial including an electronic training video, hand sanitizer, and educational posters (n = 131, intervention; n = 193, control). Primary outcomes include (1) self-reported acute respiratory infections (ARIs)/influenza-like illness (ILI) and/or gastrointestinal (GI) infections during the prior 30 days; and (2) related lost work days. Incidence rate ratios calculated using generalized linear mixed models with a Poisson distribution, adjusted for confounders and random cluster effects. A 31% relative reduction in self-reported combined ARI-ILI/GI infections (incidence rate ratio: 0.69; 95% confidence interval, 0.49 to 0.98). A 21% nonsignificant relative reduction in lost work days. An office-based multimodal hand hygiene improvement intervention demonstrated a substantive reduction in self-reported combined ARI-ILI/GI infections.

Ab initio random structure search for 13-atom clusters of fcc elements

International Nuclear Information System (INIS)

Chou, J P; Hsing, C R; Wei, C M; Cheng, C; Chang, C M

2013-01-01

The 13-atom metal clusters of fcc elements (Al, Rh, Ir, Ni, Pd, Pt, Cu, Ag, Au) were studied by density functional theory calculations. The global minima were searched for by the ab initio random structure searching method. In addition to some new lowest-energy structures for Pd 13 and Au 13 , we found that the effective coordination numbers of the lowest-energy clusters would increase with the ratio of the dimer-to-bulk bond length. This correlation, together with the electronic structures of the lowest-energy clusters, divides the 13-atom clusters of these fcc elements into two groups (except for Au 13 , which prefers a two-dimensional structure due to the relativistic effect). Compact-like clusters that are composed exclusively of triangular motifs are preferred for elements without d-electrons (Al) or with (nearly) filled d-band electrons (Ni, Pd, Cu, Ag). Non-compact clusters composed mainly of square motifs connected by some triangular motifs (Rh, Ir, Pt) are favored for elements with unfilled d-band electrons. (paper)

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

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Ponomarev, A. L.; Brenner, D.; Hlatky, L. R.; Sachs, R. K.

2000-01-01

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

Standardized Effect Size Measures for Mediation Analysis in Cluster-Randomized Trials

Science.gov (United States)

Stapleton, Laura M.; Pituch, Keenan A.; Dion, Eric

2015-01-01

This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the…

Walking Away from Type 2 diabetes: a cluster randomized controlled trial.

Science.gov (United States)

Yates, T; Edwardson, C L; Henson, J; Gray, L J; Ashra, N B; Troughton, J; Khunti, K; Davies, M J

2017-05-01

This study aimed to investigate whether an established behavioural intervention, Walking Away from Type 2 Diabetes, is effective at promoting and sustaining increased walking activity when delivered within primary care. Cluster randomized controlled trial involving 10 general practices recruited from Leicestershire, UK, in 2009-2010. Eight hundred and eight (36% female) individuals with a high risk of Type 2 diabetes mellitus, identified through a validated risk score, were included. Participants in five practices were randomized to Walking Away from Type 2 Diabetes, a pragmatic 3-h group-based structured education programme incorporating pedometer use with annual follow-on refresher sessions. The primary outcome was accelerometer assessed ambulatory activity (steps/day) at 12 months. Longer term maintenance was assessed at 24 and 36 months. Results were analysed using generalized estimating equation models, accounting for clustering. Complete accelerometer data for the primary outcome were available for 571 (71%) participants. Increases in ambulatory activity of 411 steps/day [95% confidence interval (CI): 117, 704] and self-reported vigorous-intensity physical activity of 218 metabolic equivalent min/week (95% CI: 6, 425) at 12 months were observed in the intervention group compared with control; differences between groups were not sustained at 36 months. No differences between groups were observed for markers of cardiometabolic health. Replacing missing data with multiple imputation did not affect the results. A pragmatic low-resource group-based structured education programme with pedometer use resulted in modest increases in ambulatory activity compared with control conditions after 12 months when implemented within a primary care setting to those at high risk of Type 2 diabetes mellitus; however, the results were not maintained over 36 months. © 2016 Diabetes UK.

Hierarchical modeling of cluster size in wildlife surveys

Science.gov (United States)

Royle, J. Andrew

2008-01-01

Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

Statistical properties of random clique networks

Science.gov (United States)

Ding, Yi-Min; Meng, Jun; Fan, Jing-Fang; Ye, Fang-Fu; Chen, Xiao-Song

2017-10-01

In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the Erdös and Rényi (ER) model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.

Random isotropic one-dimensional XY-model

Science.gov (United States)

Gonçalves, L. L.; Vieira, A. P.

1998-01-01

The 1D isotropic s = ½XY-model ( N sites), with random exchange interaction in a transverse random field is considered. The random variables satisfy bimodal quenched distributions. The solution is obtained by using the Jordan-Wigner fermionization and a canonical transformation, reducing the problem to diagonalizing an N × N matrix, corresponding to a system of N noninteracting fermions. The calculations are performed numerically for N = 1000, and the field-induced magnetization at T = 0 is obtained by averaging the results for the different samples. For the dilute case, in the uniform field limit, the magnetization exhibits various discontinuities, which are the consequence of the existence of disconnected finite clusters distributed along the chain. Also in this limit, for finite exchange constants J A and J B, as the probability of J A varies from one to zero, the saturation field is seen to vary from Γ A to Γ B, where Γ A(Γ B) is the value of the saturation field for the pure case with exchange constant equal to J A(J B) .

Search for Directed Networks by Different Random Walk Strategies

Science.gov (United States)

Zhu, Zi-Qi; Jin, Xiao-Ling; Huang, Zhi-Long

2012-03-01

A comparative study is carried out on the efficiency of five different random walk strategies searching on directed networks constructed based on several typical complex networks. Due to the difference in search efficiency of the strategies rooted in network clustering, the clustering coefficient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks. The search processes are performed on the directed networks based on Erdös—Rényi model, Watts—Strogatz model, Barabási—Albert model and clustered scale-free network model. It is found that self-avoiding random walk strategy is the best search strategy for such directed networks. Compared to unrestricted random walk strategy, path-iteration-avoiding random walks can also make the search process much more efficient. However, no-triangle-loop and no-quadrangle-loop random walks do not improve the search efficiency as expected, which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

Can group-based reassuring information alter low back pain behavior? A cluster-randomized controlled trial

DEFF Research Database (Denmark)

Frederiksen, Pernille; Indahl, Aage; Andersen, Lars L

2017-01-01

-randomized controlled trial. METHODS: Publically employed workers (n = 505) from 11 Danish municipality centers were randomized at center-level (cluster) to either intervention (two 1-hour group-based talks at the workplace) or control. The talks provided reassuring information together with a simple non...

On the shell model connection of the cluster model

International Nuclear Information System (INIS)

Cseh, J.; Levai, G.; Kato, K.

2000-01-01

Complete text of publication follows. The interrelation of basic nuclear structure models is a longstanding problem. The connection between the spherical shell model and the quadrupole collective model has been studied extensively, and symmetry considerations proved to be especially useful in this respect. A collective band was interpreted in the shell model language long ago as a set of states (of the valence nucleons) with a specific SU(3) symmetry. Furthermore, the energies of these rotational states are obtained to a good approximation as eigenvalues of an SU(3) dynamically symmetric shell model Hamiltonian. On the other hand the relation of the shell model and cluster model is less well explored. The connection of the harmonic oscillator (i.e. SU(3)) bases of the two approaches is known, but it was established only for the unrealistic harmonic oscillator interactions. Here we investigate the question: Can an SU(3) dynamically symmetric interaction provide a similar connection between the spherical shell model and the cluster model, like the one between the shell and collective models? In other words: whether or not the energy of the states of the cluster bands, defined by a specific SU(3) symmetries, can be obtained from a shell model Hamiltonian (with SU(3) dynamical symmetry). We carried out calculations within the framework of the semimicroscopic algebraic cluster model, in which not only the cluster model space is obtained from the full shell model space by an SU(3) symmetry-dictated truncation, but SU(3) dynamically symmetric interactions are also applied. Actually, Hamiltonians of this kind proved to be successful in describing the gross features of cluster states in a wide energy range. The novel feature of the present work is that we apply exclusively shell model interactions. The energies obtained from such a Hamiltonian for several bands of the ( 12 C, 14 C, 16 O, 20 Ne, 40 Ca) + α systems turn out to be in good agreement with the experimental

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

Science.gov (United States)

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

Cluster Randomized Trail of the uptake of a take-home Infant dose ...

African Journals Online (AJOL)

Objective: To test whether a single take home dose of infant nevirapine increased infant uptake without decreasing institutional deliveries. Design: Cluster randomized post-test only study with control group. Setting: Ten hospitals in urban areas of Coast, Rift Valley, and Western provinces, Kenya. Participants: Pregnant ...

Cluster-cluster correlations in the two-dimensional stationary Ising-model

International Nuclear Information System (INIS)

Klassmann, A.

1997-01-01

In numerical integration of the Cahn-Hillard equation, which describes Oswald rising in a two-phase matrix, N. Masbaum showed that spatial correlations between clusters scale with respect to the mean cluster size (itself a function of time). T. B. Liverpool showed by Monte Carlo simulations for the Ising model that the analogous correlations have a similar form. Both demonstrated that immediately around each cluster there is some depletion area followed by something like a ring of clusters of the same size as the original one. More precisely, it has been shown that the distribution of clusters around a given cluster looks like a sinus-curve decaying exponentially with respect to the distance to a constant value

The transverse spin-1 Ising model with random interactions

Energy Technology Data Exchange (ETDEWEB)

Bouziane, Touria [Department of Physics, Faculty of Sciences, University of Moulay Ismail, B.P. 11201 Meknes (Morocco)], E-mail: touria582004@yahoo.fr; Saber, Mohammed [Department of Physics, Faculty of Sciences, University of Moulay Ismail, B.P. 11201 Meknes (Morocco); Dpto. Fisica Aplicada I, EUPDS (EUPDS), Plaza Europa, 1, San Sebastian 20018 (Spain)

2009-01-15

The phase diagrams of the transverse spin-1 Ising model with random interactions are investigated using a new technique in the effective field theory that employs a probability distribution within the framework of the single-site cluster theory based on the use of exact Ising spin identities. A model is adopted in which the nearest-neighbor exchange couplings are independent random variables distributed according to the law P(J{sub ij})=p{delta}(J{sub ij}-J)+(1-p){delta}(J{sub ij}-{alpha}J). General formulae, applicable to lattices with coordination number N, are given. Numerical results are presented for a simple cubic lattice. The possible reentrant phenomenon displayed by the system due to the competitive effects between exchange interactions occurs for the appropriate range of the parameter {alpha}.

FORMATION OF A INNOVATION REGIONAL CLUSTER MODEL

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G. S. Merzlikina

2015-01-01

Full Text Available Summary. As a result of investigation of science and methodical approaches related problems of building and development of innovation clusters there were some issues in functional assignments of innovation and production clusters. Because of those issues, article’s authors differ conceptions of innovation cluster and production cluster, as they explain notion of innovation-production cluster. The main goal of this article is to reveal existing organizational issues in cluster building and its successful development. Based on regional clusters building analysis carried out there was typical practical structure of cluster members interaction revealed. This structure also have its cons, as following: absence cluster orientation to marketing environment, lack of members’ prolonged relations’ building and development system, along with ineffective management of information, financial and material streams within cluster, narrow competence difference and responsibility zones between cluster members, lack of transparence of cluster’s action, low environment changes adaptivity, hard to use cluster members’ intellectual property, and commercialization of hi-tech products. When all those issues listed above come together, it reduces life activity of existing models of innovative cluster-building along with practical opportunity of cluster realization. Because of that, authors offer an upgraded innovative-productive cluster building model with more efficient business processes management system, which includes advanced innovative cluster structure, competence matrix and subcluster responsibility zone. Suggested model differs from other ones by using unified innovative product development control center, which also controls production and marketing realization.

Modelling baryonic effects on galaxy cluster mass profiles

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Shirasaki, Masato; Lau, Erwin T.; Nagai, Daisuke

2018-06-01

Gravitational lensing is a powerful probe of the mass distribution of galaxy clusters and cosmology. However, accurate measurements of the cluster mass profiles are limited by uncertainties in cluster astrophysics. In this work, we present a physically motivated model of baryonic effects on the cluster mass profiles, which self-consistently takes into account the impact of baryons on the concentration as well as mass accretion histories of galaxy clusters. We calibrate this model using the Omega500 hydrodynamical cosmological simulations of galaxy clusters with varying baryonic physics. Our model will enable us to simultaneously constrain cluster mass, concentration, and cosmological parameters using stacked weak lensing measurements from upcoming optical cluster surveys.

Modelling Baryonic Effects on Galaxy Cluster Mass Profiles

Science.gov (United States)

Shirasaki, Masato; Lau, Erwin T.; Nagai, Daisuke

2018-03-01

Gravitational lensing is a powerful probe of the mass distribution of galaxy clusters and cosmology. However, accurate measurements of the cluster mass profiles are limited by uncertainties in cluster astrophysics. In this work, we present a physically motivated model of baryonic effects on the cluster mass profiles, which self-consistently takes into account the impact of baryons on the concentration as well as mass accretion histories of galaxy clusters. We calibrate this model using the Omega500 hydrodynamical cosmological simulations of galaxy clusters with varying baryonic physics. Our model will enable us to simultaneously constrain cluster mass, concentration, and cosmological parameters using stacked weak lensing measurements from upcoming optical cluster surveys.

Percolation for a model of statistically inhomogeneous random media

International Nuclear Information System (INIS)

Quintanilla, J.; Torquato, S.

1999-01-01

We study clustering and percolation phenomena for a model of statistically inhomogeneous two-phase random media, including functionally graded materials. This model consists of inhomogeneous fully penetrable (Poisson distributed) disks and can be constructed for any specified variation of volume fraction. We quantify the transition zone in the model, defined by the frontier of the cluster of disks which are connected to the disk-covered portion of the model, by defining the coastline function and correlation functions for the coastline. We find that the behavior of these functions becomes largely independent of the specific choice of grade in volume fraction as the separation of length scales becomes large. We also show that the correlation function behaves in a manner similar to that of fractal Brownian motion. Finally, we study fractal characteristics of the frontier itself and compare to similar properties for two-dimensional percolation on a lattice. In particular, we show that the average location of the frontier appears to be related to the percolation threshold for homogeneous fully penetrable disks. copyright 1999 American Institute of Physics

Cluster-size entropy in the Axelrod model of social influence: Small-world networks and mass media

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Gandica, Y.; Charmell, A.; Villegas-Febres, J.; Bonalde, I.

2011-10-01

We study the Axelrod's cultural adaptation model using the concept of cluster-size entropy Sc, which gives information on the variability of the cultural cluster size present in the system. Using networks of different topologies, from regular to random, we find that the critical point of the well-known nonequilibrium monocultural-multicultural (order-disorder) transition of the Axelrod model is given by the maximum of the Sc(q) distributions. The width of the cluster entropy distributions can be used to qualitatively determine whether the transition is first or second order. By scaling the cluster entropy distributions we were able to obtain a relationship between the critical cultural trait qc and the number F of cultural features in two-dimensional regular networks. We also analyze the effect of the mass media (external field) on social systems within the Axelrod model in a square network. We find a partially ordered phase whose largest cultural cluster is not aligned with the external field, in contrast with a recent suggestion that this type of phase cannot be formed in regular networks. We draw a q-B phase diagram for the Axelrod model in regular networks.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

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Can Tolga

2009-09-01

Full Text Available Abstract Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL, and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.

Structuring communication relationships for interprofessional teamwork (SCRIPT: a cluster randomized controlled trial

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Kenaszchuk Chris

2007-09-01

Full Text Available Abstract Background Despite a burgeoning interest in using interprofessional approaches to promote effective collaboration in health care, systematic reviews find scant evidence of benefit. This protocol describes the first cluster randomized controlled trial (RCT to design and evaluate an intervention intended to improve interprofessional collaborative communication and patient-centred care. Objectives The objective is to evaluate the effects of a four-component, hospital-based staff communication protocol designed to promote collaborative communication between healthcare professionals and enhance patient-centred care. Methods The study is a multi-centre mixed-methods cluster randomized controlled trial involving twenty clinical teaching teams (CTTs in general internal medicine (GIM divisions of five Toronto tertiary-care hospitals. CTTs will be randomly assigned either to receive an intervention designed to improve interprofessional collaborative communication, or to continue usual communication practices. Non-participant naturalistic observation, shadowing, and semi-structured, qualitative interviews were conducted to explore existing patterns of interprofessional collaboration in the CTTs, and to support intervention development. Interviews and shadowing will continue during intervention delivery in order to document interactions between the intervention settings and adopters, and changes in interprofessional communication. The primary outcome is the rate of unplanned hospital readmission. Secondary outcomes are length of stay (LOS; adherence to evidence-based prescription drug therapy; patients' satisfaction with care; self-report surveys of CTT staff perceptions of interprofessional collaboration; and frequency of calls to paging devices. Outcomes will be compared on an intention-to-treat basis using adjustment methods appropriate for data from a cluster randomized design. Discussion Pre-intervention qualitative analysis revealed that a

On the shell-model-connection of the cluster model

International Nuclear Information System (INIS)

Cseh, J.

2000-01-01

Complete text of publication follows. The interrelation of basic nuclear structure models is a longstanding problem. The connection between the spherical shell model and the quadrupole collective model has been studied extensively, and symmetry considerations proved to be especially useful in this respect. A collective band was interpreted in the shell model language long ago [1] as a set of states (of the valence nucleons) with a specific SU(3) symmetry. Furthermore, the energies of these rotational states are obtained to a good approximation as eigenvalues of an SU(3) dynamically symmetric shell model Hamiltonian. On the other hand the relation of the shell model and cluster model is less well explored. The connection of the harmonic oscillator (i.e. SU(3)) bases of the two approaches is known [2] but it was established only for the unrealistic harmonic oscillator interactions. Here we investigate the question: Can an SU(3) dynamically symmetric interaction provide a similar connection between the spherical shell model and the cluster model, like the one between the shell and collective models? In other words: whether or not the energy of the states of the cluster bands, defined by a specific SU(3) symmetries, can be obtained from a shell model Hamiltonian (with SU(3) dynamical symmetry). We carried out calculations within the framework of the semimicroscopic algebraic cluster model [3,4] in order to find an answer to this question, which seems to be affirmative. In particular, the energies obtained from such a Hamiltonian for several bands of the ( 12 C, 14 C, 16 O, 20 Ne, 40 Ca) + α systems turn out to be in good agreement with the experimental values. The present results show that the simple and transparent SU(3) connection between the spherical shell model and the cluster model is valid not only for the harmonic oscillator interactions, but for much more general (SU(3) dynamically symmetric) Hamiltonians as well, which result in realistic energy spectra. Via

The Effectiveness of Healthy Start Home Visit Program: Cluster Randomized Controlled Trial

Science.gov (United States)

Leung, Cynthia; Tsang, Sandra; Heung, Kitty

2015-01-01

Purpose: The study reported the effectiveness of a home visit program for disadvantaged Chinese parents with preschool children, using cluster randomized controlled trial design. Method: Participants included 191 parents and their children from 24 preschools, with 84 dyads (12 preschools) in the intervention group and 107 dyads (12 preschools) in…

CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks

Science.gov (United States)

Franke, R.

2016-11-01

In many networks discovered in biology, medicine, neuroscience and other disciplines special properties like a certain degree distribution and hierarchical cluster structure (also called communities) can be observed as general organizing principles. Detecting the cluster structure of an unknown network promises to identify functional subdivisions, hierarchy and interactions on a mesoscale. It is not trivial choosing an appropriate detection algorithm because there are multiple network, cluster and algorithmic properties to be considered. Edges can be weighted and/or directed, clusters overlap or build a hierarchy in several ways. Algorithms differ not only in runtime, memory requirements but also in allowed network and cluster properties. They are based on a specific definition of what a cluster is, too. On the one hand, a comprehensive network creation model is needed to build a large variety of benchmark networks with different reasonable structures to compare algorithms. On the other hand, if a cluster structure is already known, it is desirable to separate effects of this structure from other network properties. This can be done with null model networks that mimic an observed cluster structure to improve statistics on other network features. A third important application is the general study of properties in networks with different cluster structures, possibly evolving over time. Currently there are good benchmark and creation models available. But what is left is a precise sandbox model to build hierarchical, overlapping and directed clusters for undirected or directed, binary or weighted complex random networks on basis of a sophisticated blueprint. This gap shall be closed by the model CHIMERA (Cluster Hierarchy Interconnection Model for Evaluation, Research and Analysis) which will be introduced and described here for the first time.

Complementary feeding: a Global Network cluster randomized controlled trial

Directory of Open Access Journals (Sweden)

Pasha Omrana

2011-01-01

Full Text Available Abstract Background Inadequate and inappropriate complementary feeding are major factors contributing to excess morbidity and mortality in young children in low resource settings. Animal source foods in particular are cited as essential to achieve micronutrient requirements. The efficacy of the recommendation for regular meat consumption, however, has not been systematically evaluated. Methods/Design A cluster randomized efficacy trial was designed to test the hypothesis that 12 months of daily intake of beef added as a complementary food would result in greater linear growth velocity than a micronutrient fortified equi-caloric rice-soy cereal supplement. The study is being conducted in 4 sites of the Global Network for Women's and Children's Health Research located in Guatemala, Pakistan, Democratic Republic of the Congo (DRC and Zambia in communities with toddler stunting rates of at least 20%. Five clusters per country were randomized to each of the food arms, with 30 infants in each cluster. The daily meat or cereal supplement was delivered to the home by community coordinators, starting when the infants were 6 months of age and continuing through 18 months. All participating mothers received nutrition education messages to enhance complementary feeding practices delivered by study coordinators and through posters at the local health center. Outcome measures, obtained at 6, 9, 12, and 18 months by a separate assessment team, included anthropometry; dietary variety and diversity scores; biomarkers of iron, zinc and Vitamin B12 status (18 months; neurocognitive development (12 and 18 months; and incidence of infectious morbidity throughout the trial. The trial was supervised by a trial steering committee, and an independent data monitoring committee provided oversight for the safety and conduct of the trial. Discussion Findings from this trial will test the efficacy of daily intake of meat commencing at age 6 months and, if beneficial, will

Nonlinear random resistor diode networks and fractal dimensions of directed percolation clusters.

Science.gov (United States)

Stenull, O; Janssen, H K

2001-07-01

We study nonlinear random resistor diode networks at the transition from the nonpercolating to the directed percolating phase. The resistor-like bonds and the diode-like bonds under forward bias voltage obey a generalized Ohm's law V approximately I(r). Based on general grounds such as symmetries and relevance we develop a field theoretic model. We focus on the average two-port resistance, which is governed at the transition by the resistance exponent straight phi(r). By employing renormalization group methods we calculate straight phi(r) for arbitrary r to one-loop order. Then we address the fractal dimensions characterizing directed percolation clusters. Via considering distinct values of the nonlinearity r, we determine the dimension of the red bonds, the chemical path, and the backbone to two-loop order.

Cluster-based analysis of multi-model climate ensembles

Science.gov (United States)

Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

2018-06-01

Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ˜ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ˜ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and

Inadequacy of ethical conduct and reporting of stepped wedge cluster randomized trials: Results from a systematic review.

Science.gov (United States)

Taljaard, Monica; Hemming, Karla; Shah, Lena; Giraudeau, Bruno; Grimshaw, Jeremy M; Weijer, Charles

2017-08-01

Background/aims The use of the stepped wedge cluster randomized design is rapidly increasing. This design is commonly used to evaluate health policy and service delivery interventions. Stepped wedge cluster randomized trials have unique characteristics that complicate their ethical interpretation. The 2012 Ottawa Statement provides comprehensive guidance on the ethical design and conduct of cluster randomized trials, and the 2010 CONSORT extension for cluster randomized trials provides guidelines for reporting. Our aims were to assess the adequacy of the ethical conduct and reporting of stepped wedge trials to date, focusing on research ethics review and informed consent. Methods We conducted a systematic review of stepped wedge cluster randomized trials in health research published up to 2014 in English language journals. We extracted details of study intervention and data collection procedures, as well as reporting of research ethics review and informed consent. Two reviewers independently extracted data from each trial; discrepancies were resolved through discussion. We identified the presence of any research participants at the cluster level and the individual level. We assessed ethical conduct by tabulating reporting of research ethics review and informed consent against the presence of research participants. Results Of 32 identified stepped wedge trials, only 24 (75%) reported review by a research ethics committee, and only 16 (50%) reported informed consent from any research participants-yet, all trials included research participants at some level. In the subgroup of 20 trials with research participants at cluster level, only 4 (20%) reported informed consent from such participants; in 26 trials with individual-level research participants, only 15 (58%) reported their informed consent. Interventions (regardless of whether targeting cluster- or individual-level participants) were delivered at the group level in more than two-thirds of trials; nine trials (28

Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression.

Science.gov (United States)

Candel, Math J J M; Van Breukelen, Gerard J P

2010-06-30

Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most. (c) 2010 John Wiley & Sons, Ltd.

Effect of village-wide use of long-lasting insecticidal nets on visceral Leishmaniasis vectors in India and Nepal: a cluster randomized trial.

Science.gov (United States)

Picado, Albert; Das, Murari L; Kumar, Vijay; Kesari, Shreekant; Dinesh, Diwakar S; Roy, Lalita; Rijal, Suman; Das, Pradeep; Rowland, Mark; Sundar, Shyam; Coosemans, Marc; Boelaert, Marleen; Davies, Clive R

2010-01-26

Visceral leishmaniasis (VL) control in the Indian subcontinent is currently based on case detection and treatment, and on vector control using indoor residual spraying (IRS). The use of long-lasting insecticidal nets (LN) has been postulated as an alternative or complement to IRS. Here we tested the impact of comprehensive distribution of LN on the density of Phlebotomus argentipes in VL-endemic villages. A cluster-randomized controlled trial with household P. argentipes density as outcome was designed. Twelve clusters from an ongoing LN clinical trial--three intervention and three control clusters in both India and Nepal--were selected on the basis of accessibility and VL incidence. Ten houses per cluster selected on the basis of high pre-intervention P. argentipes density were monitored monthly for 12 months after distribution of LN using CDC light traps (LT) and mouth aspiration methods. Ten cattle sheds per cluster were also monitored by aspiration. A random effect linear regression model showed that the cluster-wide distribution of LNs significantly reduced the P. argentipes density/house by 24.9% (95% CI 1.80%-42.5%) as measured by means of LTs. The ongoing clinical trial, designed to measure the impact of LNs on VL incidence, will confirm whether LNs should be adopted as a control strategy in the regional VL elimination programs. The entomological evidence described here provides some evidence that LNs could be usefully deployed as part of the VL control program. ClinicalTrials.gov CT-2005-015374.

Fitting Latent Cluster Models for Networks with latentnet

Directory of Open Access Journals (Sweden)

Pavel N. Krivitsky

2007-12-01

Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.

A Cluster-Randomized Trial of Restorative Practices: An Illustration to Spur High-Quality Research and Evaluation

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Acosta, Joie D.; Chinman, Matthew; Ebener, Patricia; Phillips, Andrea; Xenakis, Lea; Malone, Patrick S.

2016-01-01

Restorative practices in schools lack rigorous evaluation studies. As an example of rigorous school-based research, this article describes the first randomized control trial of restorative practices to date, the Study of Restorative Practices. It is a 5-year, cluster-randomized controlled trial (RCT) of the Restorative Practices Intervention (RPI)…

Exergaming and older adult cognition: a cluster randomized clinical trial.

Science.gov (United States)

Anderson-Hanley, Cay; Arciero, Paul J; Brickman, Adam M; Nimon, Joseph P; Okuma, Naoko; Westen, Sarah C; Merz, Molly E; Pence, Brandt D; Woods, Jeffrey A; Kramer, Arthur F; Zimmerman, Earl A

2012-02-01

Dementia cases may reach 100 million by 2050. Interventions are sought to curb or prevent cognitive decline. Exercise yields cognitive benefits, but few older adults exercise. Virtual reality-enhanced exercise or "exergames" may elicit greater participation. To test the following hypotheses: (1) stationary cycling with virtual reality tours ("cybercycle") will enhance executive function and clinical status more than traditional exercise; (2) exercise effort will explain improvement; and (3) brain-derived neurotrophic growth factor (BDNF) will increase. Multi-site cluster randomized clinical trial (RCT) of the impact of 3 months of cybercycling versus traditional exercise, on cognitive function in older adults. Data were collected in 2008-2010; analyses were conducted in 2010-2011. 102 older adults from eight retirement communities enrolled; 79 were randomized and 63 completed. A recumbent stationary ergometer was utilized; virtual reality tours and competitors were enabled on the cybercycle. Executive function (Color Trails Difference, Stroop C, Digits Backward); clinical status (mild cognitive impairment; MCI); exercise effort/fitness; and plasma BDNF. Intent-to-treat analyses, controlling for age, education, and cluster randomization, revealed a significant group X time interaction for composite executive function (p=0.002). Cybercycling yielded a medium effect over traditional exercise (d=0.50). Cybercyclists had a 23% relative risk reduction in clinical progression to MCI. Exercise effort and fitness were comparable, suggesting another underlying mechanism. A significant group X time interaction for BDNF (p=0.05) indicated enhanced neuroplasticity among cybercyclists. Cybercycling older adults achieved better cognitive function than traditional exercisers, for the same effort, suggesting that simultaneous cognitive and physical exercise has greater potential for preventing cognitive decline. This study is registered at Clinicaltrials.gov NCT01167400. Copyright

A simple sample size formula for analysis of covariance in cluster randomized trials.

NARCIS (Netherlands)

Teerenstra, S.; Eldridge, S.; Graff, M.J.; Hoop, E. de; Borm, G.F.

2012-01-01

For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An

Effects of Nurse-Led Multifactorial Care to Prevent Disability in Community-Living Older People : Cluster Randomized Trial

NARCIS (Netherlands)

Suijker, Jacqueline J.; van Rijn, Marjon; Buurman, Bianca M.; ter Riet, Gerben; van Charante, Eric P. Moll; de Rooij, Sophia E.

2016-01-01

Background To evaluate the effects of nurse-led multifactorial care to prevent disability in community-living older people. Methods In a cluster randomized trail, 11 practices (n = 1,209 participants) were randomized to the intervention group, and 13 practices (n = 1,074 participants) were

Effects of integrated chronic care models on hypertension outcomes and spending: a multi-town clustered randomized trial in China.

Science.gov (United States)

Zhang, Yuting; Tang, Wenxi; Zhang, Yan; Liu, Lulu; Zhang, Liang

2017-03-11

Hypertension affects one billion people globally and is one of the leading risk factors for cardiovascular and renal diseases. However, hypertension management remains poor, especially in rural China. A clustered randomized controlled trial was conducted in six towns in China's Qianjiang county between 7/2012 and 6/2014, including 5462 hypertension patients above 35 years old. Six towns were randomly assigned to three groups: Group 1 had the integrated care model including a multidisciplinary team and continuous care coordination, Group 2 had both the integrated care model and provider-level financial incentives, and the control group had the usual care. Primary outcomes were systolic blood pressure and health-related quality of life measured by SF36; secondary outcomes included hypertension-related hospitalization rate and inpatient spending. Blood pressure was measured sixteen times bimonthly between 12/1/2011 and 6/30/2014, and quality of life was measured on 7/1/2012 and 6/30/2014. Inpatient data between 7/1/2010 and 8/31/2014 were used. This trial is registered at the World Health Organization's International Clinical Trials Registry, number ChiCTR-OOR-14005563. We found that the integrated care model effectively lowered blood pressure by 1.93 mmHg (95% CI 0.063-3.8), improved self-assessed health-related quality of life, and reduced the rate of hypertension-related hospitalization by 0.17 percentage points (95% CI 0.094-0.24). We also found that the provider-level financial contract further lowered blood pressure by 1.76 mmHg (95% CI 0.73-2.79) and reduced rates of hospitalization and inpatient spending, but it also reduced patients' self-assessed health-related quality of life. Integrated care and financial incentives are effective in lowering blood pressure and reducing hospitalization rate, but financial contracts may hurt patient quality of life. This trial was registered at the Chinese Clinical Trial Registry (ChiCTR-OOR-14005563) on November 23, 2014

Effects of integrated chronic care models on hypertension outcomes and spending: a multi-town clustered randomized trial in China

Directory of Open Access Journals (Sweden)

Yuting Zhang

2017-03-01

Full Text Available Abstract Background Hypertension affects one billion people globally and is one of the leading risk factors for cardiovascular and renal diseases. However, hypertension management remains poor, especially in rural China. Methods A clustered randomized controlled trial was conducted in six towns in China’s Qianjiang county between 7/2012 and 6/2014, including 5462 hypertension patients above 35 years old. Six towns were randomly assigned to three groups: Group 1 had the integrated care model including a multidisciplinary team and continuous care coordination, Group 2 had both the integrated care model and provider-level financial incentives, and the control group had the usual care. Primary outcomes were systolic blood pressure and health-related quality of life measured by SF36; secondary outcomes included hypertension-related hospitalization rate and inpatient spending. Blood pressure was measured sixteen times bimonthly between 12/1/2011 and 6/30/2014, and quality of life was measured on 7/1/2012 and 6/30/2014. Inpatient data between 7/1/2010 and 8/31/2014 were used. This trial is registered at the World Health Organization’s International Clinical Trials Registry, number ChiCTR-OOR-14005563. Results We found that the integrated care model effectively lowered blood pressure by 1.93 mmHg (95% CI 0.063–3.8, improved self-assessed health-related quality of life, and reduced the rate of hypertension-related hospitalization by 0.17 percentage points (95% CI 0.094–0.24. We also found that the provider-level financial contract further lowered blood pressure by 1.76 mmHg (95% CI 0.73–2.79 and reduced rates of hospitalization and inpatient spending, but it also reduced patients’ self-assessed health-related quality of life. Conclusions Integrated care and financial incentives are effective in lowering blood pressure and reducing hospitalization rate, but financial contracts may hurt patient quality of life. This trial was registered at

Recruitment to Online Therapies for Depression: Pilot Cluster Randomized Controlled Trial

OpenAIRE

Jones, Ray B; Goldsmith, Lesley; Hewson, Paul; Williams, Christopher J

2013-01-01

Background Raising awareness of online cognitive behavioral therapy (CBT) could benefit many people with depression, but we do not know how purchasing online advertising compares to placing free links from relevant local websites in increasing uptake. Objective To pilot a cluster randomized controlled trial (RCT) comparing purchase of Google AdWords with placing free website links in raising awareness of online CBT resources for depression in order to better understand research design issues....

Novel Ordered Stepped-Wedge Cluster Trial Designs for Detecting Ebola Vaccine Efficacy Using a Spatially Structured Mathematical Model.

Directory of Open Access Journals (Sweden)

Ibrahim Diakite

2016-08-01

Full Text Available During the 2014 Ebola virus disease (EVD outbreak, policy-makers were confronted with difficult decisions on how best to test the efficacy of EVD vaccines. On one hand, many were reluctant to withhold a vaccine that might prevent a fatal disease from study participants randomized to a control arm. On the other, regulatory bodies called for rigorous placebo-controlled trials to permit direct measurement of vaccine efficacy prior to approval of the products. A stepped-wedge cluster study (SWCT was proposed as an alternative to a more traditional randomized controlled vaccine trial to address these concerns. Here, we propose novel "ordered stepped-wedge cluster trial" (OSWCT designs to further mitigate tradeoffs between ethical concerns, logistics, and statistical rigor.We constructed a spatially structured mathematical model of the EVD outbreak in Sierra Leone. We used the output of this model to simulate and compare a series of stepped-wedge cluster vaccine studies. Our model reproduced the observed order of first case occurrence within districts of Sierra Leone. Depending on the infection risk within the trial population and the trial start dates, the statistical power to detect a vaccine efficacy of 90% varied from 14% to 32% for standard SWCT, and from 67% to 91% for OSWCTs for an alpha error of 5%. The model's projection of first case occurrence was robust to changes in disease natural history parameters.Ordering clusters in a step-wedge trial based on the cluster's underlying risk of infection as predicted by a spatial model can increase the statistical power of a SWCT. In the event of another hemorrhagic fever outbreak, implementation of our proposed OSWCT designs could improve statistical power when a step-wedge study is desirable based on either ethical concerns or logistical constraints.

Quark cluster model in the three-nucleon system

International Nuclear Information System (INIS)

Osman, A.

1986-11-01

The quark cluster model is used to investigate the structure of the three-nucleon systems. The nucleon-nucleon interaction is proposed considering the colour-nucleon clusters and incorporating the quark degrees of freedom. The quark-quark potential in the quark compound bag model agrees with the central force potentials. The confinement potential reduces the short-range repulsion. The colour van der Waals force is determined. Then, the probability of quark clusters in the three-nucleon bound state systems are numerically calculated using realistic nuclear wave functions. The results of the present calculations show that quarks cluster themselves in three-quark systems building the quark cluster model for the trinucleon system. (author)

Cluster-randomized xylitol toothpaste trial for early childhood caries prevention

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Chi, Donald L.; Tut, Ohnmar K.; Milgrom, Peter

2013-01-01

Purpose We assessed the efficacy of supervised toothbrushing with xylitol toothpaste to prevent early childhood caries (ECC) and to reduce mutans streptococci (MS). Methods In this cluster-randomized efficacy trial, 4 Head Start classrooms in the Marshall Islands were randomly assigned to supervised toothbrushing with 1,400ppm/31% fluoride-xylitol (Epic Dental, Provo, UT) or 1,450ppm fluoride-sorbitol toothpaste (Colgate-Palmolive, New York, NY) (N=196 children, ages 4–5 yrs). We hypothesized no difference in efficacy between the two types of toothpaste. The primary outcome was primary molar d2-3mfs increment after 6 mos. A single examiner was blinded to classroom assignments. Two classrooms were assigned to the fluoride-xylitol group (85 children) and 2 classrooms to the fluoride-sorbitol group (83 children). The child-level analyses accounted for clustering. Results There was no difference between the two groups in baseline or end-of-trial mean d2-3mfs. The mean d2-3mfs increment was greater in the fluoride-xylitol group compared to the fluoride-sorbitol group (2.5 and 1.4 d2-3mfs, respectively), but the difference was not significant (95% CI:−0.17, 2.37;P=0.07). No adverse effects were reported. Conclusion After 6 mos, brushing with a low strength xylitol/fluoride toothpaste is no more efficacious in reducing ECC than a fluoride only toothpaste in a high caries risk child population. PMID:24709430

Cluster-randomized xylitol toothpaste trial for early childhood caries prevention.

Science.gov (United States)

Chi, Donald L; Tut, Ohnmar; Milgrom, Peter

2014-01-01

The purpose of this study was to assess the efficacy of supervised tooth-brushing with xylitol toothpaste to prevent early childhood caries (ECC) and reduce mutans streptococci. In this cluster-randomized efficacy trial, 196 four- to five-year-old children in four Head Start classrooms in the Marshall Islands were randomly assigned to supervised toothbrushing with 1,400 ppm/31 percent fluoride xylitol or 1,450 ppm fluoride sorbitol toothpaste. We hypothesized that there would be no difference in efficacy between the two types of toothpaste. The primary outcome was the surface-level primary molar caries increment (d(2-3)mfs) after six months. A single examiner was blinded to classroom assignments. Two classrooms were assigned to the fluoride-xylitol group (85 children), and two classrooms were assigned to the fluoride-sorbitol group (83 children). The child-level analyses accounted for clustering. There was no difference between the two groups in baseline or end-of-trial mean d(2-3)mfs. The mean d(2-3)mfs increment was greater in the fluoride-xylitol group compared to the fluoride-sorbitol group (2.5 and 1.4 d(2-3)mfs, respectively), but the difference was not significant (95% confidence interval: -0.17, 2.37; P=.07). No adverse effects were reported. After six months, brushing with a low-strength xylitol/fluoride tooth-paste is no more efficacious in reducing ECC than a fluoride-only toothpaste in a high caries-risk child population.

Modeling the formation of globular cluster systems in the Virgo cluster

International Nuclear Information System (INIS)

Li, Hui; Gnedin, Oleg Y.

2014-01-01

The mass distribution and chemical composition of globular cluster (GC) systems preserve fossil record of the early stages of galaxy formation. The observed distribution of GC colors within massive early-type galaxies in the ACS Virgo Cluster Survey (ACSVCS) reveals a multi-modal shape, which likely corresponds to a multi-modal metallicity distribution. We present a simple model for the formation and disruption of GCs that aims to match the ACSVCS data. This model tests the hypothesis that GCs are formed during major mergers of gas-rich galaxies and inherit the metallicity of their hosts. To trace merger events, we use halo merger trees extracted from a large cosmological N-body simulation. We select 20 halos in the mass range of 2 × 10 12 to 7 × 10 13 M ☉ and match them to 19 Virgo galaxies with K-band luminosity between 3 × 10 10 and 3 × 10 11 L ☉ . To set the [Fe/H] abundances, we use an empirical galaxy mass-metallicity relation. We find that a minimal merger ratio of 1:3 best matches the observed cluster metallicity distribution. A characteristic bimodal shape appears because metal-rich GCs are produced by late mergers between massive halos, while metal-poor GCs are produced by collective merger activities of less massive hosts at early times. The model outcome is robust to alternative prescriptions for cluster formation rate throughout cosmic time, but a gradual evolution of the mass-metallicity relation with redshift appears to be necessary to match the observed cluster metallicities. We also affirm the age-metallicity relation, predicted by an earlier model, in which metal-rich clusters are systematically several billion younger than their metal-poor counterparts.

Cluster-cluster clustering

International Nuclear Information System (INIS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

1985-01-01

The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic.

Science.gov (United States)

Leyrat, Clémence; Caille, Agnès; Foucher, Yohann; Giraudeau, Bruno

2016-01-22

Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.

A random walk evolution model of wireless sensor networks and virus spreading

International Nuclear Information System (INIS)

Wang Ya-Qi; Yang Xiao-Yuan

2013-01-01

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations. (general)

Non-random intrachromosomal distribution of radiation-induced chromatid aberrations in Vicia faba. [Aberration clustering

Energy Technology Data Exchange (ETDEWEB)

Schubert, I; Rieger, R [Akademie der Wissenschaften der DDR, Gatersleben. Zentralinst. fuer Genetik und Kulturpflanzenforschung

1976-04-01

A reconstructed karyotype of Vicia faba, with all chromosomes individually distinguishable, was treated with X-rays, fast neutrons, (/sup 3/H) uridine (/sup 3/HU). The distribution within metaphase chromosomes of induced chromatid aberrations was non-random for all agents used. Aberration clustering, in part agent specific, occurred in chromosome segments containing heterochromatin as defined by the presence of G bands. The pattern of aberration clustering found after treatment with /sup 3/HU did not allow the recognition of chromosome regions active in transcription during treatment. Furthermore, it was impossible to obtain unambiguous indications of the presence of AT- and GC-base clusters from the patterns of /sup 3/HT- and /sup 3/HC-induced chromatid aberrations, respectively. Possible reasons underlying these observations are discussed.

A cluster-based randomized controlled trial promoting community participation in arsenic mitigation efforts in Bangladesh

OpenAIRE

George, Christine Marie; van Geen, Alexander; Slavkovich, Vesna; Singha, Ashit; Levy, Diane; Islam, Tariqul; Ahmed, Kazi Matin; Moon-Howard, Joyce; Tarozzi, Alessandro; Liu, Xinhua; Factor-Litvak, Pam; Graziano, Joseph

2012-01-01

Abstract Objective To reduce arsenic (As) exposure, we evaluated the effectiveness of training community members to perform water arsenic (WAs) testing and provide As education compared to sending representatives from outside communities to conduct these tasks. Methods We conducted a cluster based randomized controlled trial of 20 villages in Singair, Bangladesh. Fifty eligible respondents were randomly selected in each village. In 10 villages, a community member provided As education and WAs...

Point-of-care cluster randomized trial in stroke secondary prevention using electronic health records

NARCIS (Netherlands)

Dregan, Alex; van Staa, Tjeerd P; McDermott, Lisa; McCann, Gerard; Ashworth, Mark; Charlton, Judith; Wolfe, Charles D A; Rudd, Anthony; Yardley, Lucy; Gulliford, Martin C

BACKGROUND AND PURPOSE: The aim of this study was to evaluate whether the remote introduction of electronic decision support tools into family practices improves risk factor control after first stroke. This study also aimed to develop methods to implement cluster randomized trials in stroke using

Efficacy of a workplace osteoporosis prevention intervention: a cluster randomized trial.

Science.gov (United States)

Tan, Ai May; LaMontagne, Anthony D; English, Dallas R; Howard, Peter

2016-08-24

Osteoporosis is a debilitating disease. Adequate calcium consumption and physical activity are the two major modifiable risk factors. This paper describes the major outcomes and efficacy of a workplace-based targeted behaviour change intervention to improve the dietary and physical activity behaviours of working women in sedentary occupations in Singapore. A cluster-randomized design was used, comparing the efficacy of a tailored intervention to standard care. Workplaces were the units of randomization and intervention. Sixteen workplaces were recruited from a pool of 97, and randomly assigned to intervention and control arms (eight workplaces in each). Women meeting specified inclusion criteria were then recruited to participate. Workplaces in the intervention arm received three participatory workshops and organization-wide educational activities. Workplaces in the control/standard care arm received print resources. Outcome measures were calcium intake (milligrams/day) and physical activity level (duration: minutes/week), measured at baseline, 4 weeks and 6 months post intervention. Adjusted cluster-level analyses were conducted comparing changes in intervention versus control groups, following intention-to-treat principles and CONSORT guidelines. Workplaces in the intervention group reported a significantly greater increase in calcium intake and duration of load-bearing moderate to vigorous physical activity (MVPA) compared with the standard care control group. Four weeks after intervention, the difference in adjusted mean calcium intake was 343.2 mg/day (95 % CI = 337.4 to 349.0, p workplace-based intervention substantially improved calcium intake and load-bearing moderate to vigorous physical activity 6 months after the intervention began. Australia New Zealand Clinical Trial Registry ACTRN12616000079448 . Registered 25 January 2016 (retrospectively registered).

A Correlated Random Effects Model for Non-homogeneous Markov Processes with Nonignorable Missingness.

Science.gov (United States)

Chen, Baojiang; Zhou, Xiao-Hua

2013-05-01

Life history data arising in clusters with prespecified assessment time points for patients often feature incomplete data since patients may choose to visit the clinic based on their needs. Markov process models provide a useful tool describing disease progression for life history data. The literature mainly focuses on time homogeneous process. In this paper we develop methods to deal with non-homogeneous Markov process with incomplete clustered life history data. A correlated random effects model is developed to deal with the nonignorable missingness, and a time transformation is employed to address the non-homogeneity in the transition model. Maximum likelihood estimate based on the Monte-Carlo EM algorithm is advocated for parameter estimation. Simulation studies demonstrate that the proposed method works well in many situations. We also apply this method to an Alzheimer's disease study.

Structuring communication relationships for interprofessional teamwork (SCRIPT): a cluster randomized controlled trial

OpenAIRE

Zwarenstein, Merrick; Reeves, Scott; Russell, Ann; Kenaszchuk, Chris; Conn, Lesley Gotlib; Miller, Karen-Lee; Lingard, Lorelei; Thorpe, Kevin E

2007-01-01

Abstract Background Despite a burgeoning interest in using interprofessional approaches to promote effective collaboration in health care, systematic reviews find scant evidence of benefit. This protocol describes the first cluster randomized controlled trial (RCT) to design and evaluate an intervention intended to improve interprofessional collaborative communication and patient-centred care. Objectives The objective is to evaluate the effects of a four-component, hospital-based staff commun...

blockcluster: An R Package for Model-Based Co-Clustering

Directory of Open Access Journals (Sweden)

Parmeet Singh Bhatia

2017-02-01

Full Text Available Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach has been recently proposed based on the latent block model (Govaert and Nadif 2003 which takes into account the block clustering problem on both the individual and variable sets. This article presents our R package blockcluster for co-clustering of binary, contingency and continuous data based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.

A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery

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WANG Shaoyu

2016-12-01

Full Text Available Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.

A cluster randomized controlled trial testing the effectiveness of Houvast: A strengths-based intervention for homeless young adults

NARCIS (Netherlands)

Krabbenborg, M.A.M.; Boersma, S.N.; Veld, W.M. van der; Hulst, B. van; Vollebergh, W.A.M.; Wolf, J.R.L.M.

2017-01-01

Objective: To test the effectiveness of Houvast: a strengths-based intervention for homeless young adults. Method: A cluster randomized controlled trial was conducted with 10 Dutch shelter facilities randomly allocated to an intervention and a control group. Homeless young adults were interviewed

Fragmentation of percolation cluster perimeters

Science.gov (United States)

Debierre, Jean-Marc; Bradley, R. Mark

1996-05-01

We introduce a model for the fragmentation of porous random solids under the action of an external agent. In our model, the solid is represented by a bond percolation cluster on the square lattice and bonds are removed only at the external perimeter (or `hull') of the cluster. This model is shown to be related to the self-avoiding walk on the Manhattan lattice and to the disconnection events at a diffusion front. These correspondences are used to predict the leading and the first correction-to-scaling exponents for several quantities defined for hull fragmentation. Our numerical results support these predictions. In addition, the algorithm used to construct the perimeters reveals itself to be a very efficient tool for detecting subtle correlations in the pseudo-random number generator used. We present a quantitative test of two generators which supports recent results reported in more systematic studies.

Clustering of European winter storms: A multi-model perspective

Science.gov (United States)

Renggli, Dominik; Buettner, Annemarie; Scherb, Anke; Straub, Daniel; Zimmerli, Peter

2016-04-01

The storm series over Europe in 1990 (Daria, Vivian, Wiebke, Herta) and 1999 (Anatol, Lothar, Martin) are very well known. Such clusters of severe events strongly affect the seasonally accumulated damage statistics. The (re)insurance industry has quantified clustering by using distribution assumptions deduced from the historical storm activity of the last 30 to 40 years. The use of storm series simulated by climate models has only started recently. Climate model runs can potentially represent 100s to 1000s of years, allowing a more detailed quantification of clustering than the history of the last few decades. However, it is unknown how sensitive the representation of clustering is to systematic biases. Using a multi-model ensemble allows quantifying that uncertainty. This work uses CMIP5 decadal ensemble hindcasts to study clustering of European winter storms from a multi-model perspective. An objective identification algorithm extracts winter storms (September to April) in the gridded 6-hourly wind data. Since the skill of European storm predictions is very limited on the decadal scale, the different hindcast runs are interpreted as independent realizations. As a consequence, the available hindcast ensemble represents several 1000 simulated storm seasons. The seasonal clustering of winter storms is quantified using the dispersion coefficient. The benchmark for the decadal prediction models is the 20th Century Reanalysis. The decadal prediction models are able to reproduce typical features of the clustering characteristics observed in the reanalysis data. Clustering occurs in all analyzed models over the North Atlantic and European region, in particular over Great Britain and Scandinavia as well as over Iberia (i.e. the exit regions of the North Atlantic storm track). Clustering is generally weaker in the models compared to reanalysis, although the differences between different models are substantial. In contrast to existing studies, clustering is driven by weak

Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster

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Xia-an Bi

2018-02-01

Full Text Available Autism spectrum disorder (ASD is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus, we used multiple SVMs to classify ASD patients and typical controls (TC. Resting-state functional magnetic resonance imaging (fMRI data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange (ABIDE database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2 mm or ±2°. Then the random SVM cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (IFG (orbital and opercula part, hippocampus, and precuneus. It is indicated that the method of random SVM cluster may apply to the auxiliary diagnosis of ASD.

Re-estimating sample size in cluster randomized trials with active recruitment within clusters

NARCIS (Netherlands)

van Schie, Sander; Moerbeek, Mirjam

2014-01-01

Often only a limited number of clusters can be obtained in cluster randomised trials, although many potential participants can be recruited within each cluster. Thus, active recruitment is feasible within the clusters. To obtain an efficient sample size in a cluster randomised trial, the cluster

Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods

DEFF Research Database (Denmark)

Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne

studies have used non-valid analysis of skewed data. We propose two different methods to compare mean cost in two groups. Firstly, we use a non-parametric bootstrap method where the re-sampling takes place on two levels in order to take into account the cluster effect. Secondly, we proceed with a log......-transformation of the cost data and apply the normal theory on these data. Again we try to account for the cluster effect. The performance of these two methods is investigated in a simulation study. The advantages and disadvantages of the different approaches are discussed.......  We consider health care data from a cluster-randomized intervention study in primary care to test whether the average health care costs among study patients differ between the two groups. The problems of analysing cost data are that most data are severely skewed. Median instead of mean...

Clustering, randomness, and regularity in cloud fields: 2. Cumulus cloud fields

Science.gov (United States)

Zhu, T.; Lee, J.; Weger, R. C.; Welch, R. M.

1992-12-01

During the last decade a major controversy has been brewing concerning the proper characterization of cumulus convection. The prevailing view has been that cumulus clouds form in clusters, in which cloud spacing is closer than that found for the overall cloud field and which maintains its identity over many cloud lifetimes. This "mutual protection hypothesis" of Randall and Huffman (1980) has been challenged by the "inhibition hypothesis" of Ramirez et al. (1990) which strongly suggests that the spatial distribution of cumuli must tend toward a regular distribution. A dilemma has resulted because observations have been reported to support both hypotheses. The present work reports a detailed analysis of cumulus cloud field spatial distributions based upon Landsat, Advanced Very High Resolution Radiometer, and Skylab data. Both nearest-neighbor and point-to-cloud cumulative distribution function statistics are investigated. The results show unequivocally that when both large and small clouds are included in the cloud field distribution, the cloud field always has a strong clustering signal. The strength of clustering is largest at cloud diameters of about 200-300 m, diminishing with increasing cloud diameter. In many cases, clusters of small clouds are found which are not closely associated with large clouds. As the small clouds are eliminated from consideration, the cloud field typically tends towards regularity. Thus it would appear that the "inhibition hypothesis" of Ramirez and Bras (1990) has been verified for the large clouds. However, these results are based upon the analysis of point processes. A more exact analysis also is made which takes into account the cloud size distributions. Since distinct clouds are by definition nonoverlapping, cloud size effects place a restriction upon the possible locations of clouds in the cloud field. The net effect of this analysis is that the large clouds appear to be randomly distributed, with only weak tendencies towards

Validating clustering of molecular dynamics simulations using polymer models

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Phillips Joshua L

2011-11-01

Full Text Available Abstract Background Molecular dynamics (MD simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our

Dense neuron clustering explains connectivity statistics in cortical microcircuits.

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Vladimir V Klinshov

Full Text Available Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes.

Fit 5 Kids TV reduction program for Latino preschoolers: A cluster randomized controlled trial

Science.gov (United States)

Reducing Latino preschoolers' TV viewing is needed to reduce their risk of obesity and other chronic diseases. This study's objective was to evaluate the Fit 5 Kids (F5K) TV reduction program's impact on Latino preschooler's TV viewing. The study design was a cluster randomized controlled trial (RCT...

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

Science.gov (United States)

Hensman, James; Lawrence, Neil D; Rattray, Magnus

2013-08-20

Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.

Dynamic Load Balanced Clustering using Elitism based Random Immigrant Genetic Approach for Wireless Sensor Networks

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K. Mohaideen Pitchai

2017-07-01

Full Text Available Wireless Sensor Network (WSN consists of a large number of small sensors with restricted energy. Prolonged network lifespan, scalability, node mobility and load balancing are important needs for several WSN applications. Clustering the sensor nodes is an efficient technique to reach these goals. WSN have the characteristics of topology dynamics because of factors like energy conservation and node movement that leads to Dynamic Load Balanced Clustering Problem (DLBCP. In this paper, Elitism based Random Immigrant Genetic Approach (ERIGA is proposed to solve DLBCP which adapts to topology dynamics. ERIGA uses the dynamic Genetic Algorithm (GA components for solving the DLBCP. The performance of load balanced clustering process is enhanced with the help of this dynamic GA. As a result, the ERIGA achieves to elect suitable cluster heads which balances the network load and increases the lifespan of the network.

K­MEANS CLUSTERING FOR HIDDEN MARKOV MODEL

NARCIS (Netherlands)

Perrone, M.P.; Connell, S.D.

2004-01-01

An unsupervised k­means clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of as

Droplet localization in the random XXZ model and its manifestations

Science.gov (United States)

Elgart, A.; Klein, A.; Stolz, G.

2018-01-01

We examine many-body localization properties for the eigenstates that lie in the droplet sector of the random-field spin- \\frac 1 2 XXZ chain. These states satisfy a basic single cluster localization property (SCLP), derived in Elgart et al (2018 J. Funct. Anal. (in press)). This leads to many consequences, including dynamical exponential clustering, non-spreading of information under the time evolution, and a zero velocity Lieb-Robinson bound. Since SCLP is only applicable to the droplet sector, our definitions and proofs do not rely on knowledge of the spectral and dynamical characteristics of the model outside this regime. Rather, to allow for a possible mobility transition, we adapt the notion of restricting the Hamiltonian to an energy window from the single particle setting to the many body context.

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.

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Fokkema, M; Smits, N; Zeileis, A; Hothorn, T; Kelderman, H

2017-10-25

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.

Alpha cluster model and spectrum of 16O

International Nuclear Information System (INIS)

Bauhoff, W.; Schultheis, H.; Schultheis, R.

1983-01-01

The structure of 16 O is studied in the alpha cluster model with parity and angular-momentum projection for several nucleon-nucleon interactions. The method differs from previous studies in that the states of positive and negative parity are determined without the customary restriction of the variational space to cluster positions with certain assumed symmetries. It is demonstrated that the alpha cluster model of 16 O is capable of explaining most of the experimental T = O levels up to about 15 MeV excitation. A shell-model analysis of the excited cluster-model states shows the necessity of including a very large number of shells. The evidence for the recently proposed tetrahedral symmetry of some excited states is also discussed

GraphCrunch 2: Software tool for network modeling, alignment and clustering.

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Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša

2011-01-19

Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an

GraphCrunch 2: Software tool for network modeling, alignment and clustering

Directory of Open Access Journals (Sweden)

Hayes Wayne

2011-01-01

Full Text Available Abstract Background Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. Results We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL" for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other

Engineering practice variation through provider agreement: a cluster-randomized feasibility trial.

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McCarren, Madeline; Twedt, Elaine L; Mansuri, Faizmohamed M; Nelson, Philip R; Peek, Brian T

2014-01-01

Minimal-risk randomized trials that can be embedded in practice could facilitate learning health-care systems. A cluster-randomized design was proposed to compare treatment strategies by assigning clusters (eg, providers) to "favor" a particular drug, with providers retaining autonomy for specific patients. Patient informed consent might be waived, broadening inclusion. However, it is not known if providers will adhere to the assignment or whether institutional review boards will waive consent. We evaluated the feasibility of this trial design. Agreeable providers were randomized to "favor" either hydrochlorothiazide or chlorthalidone when starting patients on thiazide-type therapy for hypertension. The assignment applied when the provider had already decided to start a thiazide, and providers could deviate from the strategy as needed. Prescriptions were aggregated to produce a provider strategy-adherence rate. All four institutional review boards waived documentation of patient consent. Providers (n=18) followed their assigned strategy for most of their new thiazide prescriptions (n=138 patients). In the "favor hydrochlorothiazide" group, there was 99% adherence to that strategy. In the "favor chlorthalidone" group, chlorthalidone comprised 77% of new thiazide starts, up from 1% in the pre-study period. When the assigned strategy was followed, dosing in the recommended range was 48% for hydrochlorothiazide (25-50 mg/day) and 100% for chlorthalidone (12.5-25.0 mg/day). Providers were motivated to participate by a desire to contribute to a comparative effectiveness study. A study promotional mug, provider information letter, and interactions with the site investigator were identified as most helpful in reminding providers of their study drug strategy. Providers prescribed according to an assigned drug-choice strategy most of the time for the purpose of a comparative effectiveness study. This simple design could facilitate research participation and behavior change

Using Cluster Bootstrapping to Analyze Nested Data with a Few Clusters

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Huang, Francis L.

2018-01-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials…

Quark cluster model and confinement

International Nuclear Information System (INIS)

Koike, Yuji; Yazaki, Koichi

2000-01-01

How confinement of quarks is implemented for multi-hadron systems in the quark cluster model is reviewed. In order to learn the nature of the confining interaction for fermions we first study 1+1 dimensional QED and QCD, in which the gauge field can be eliminated exactly and generates linear interaction of fermions. Then, we compare the two-body potential model, the flip-flop model and the Born-Oppenheimer approach in the strong coupling lattice QCD for the meson-meson system. Having shown how the long-range attraction between hadrons, van der Waals interaction, shows up in the two-body potential model, we discuss two distinct attempts beyond the two-body potential model: one is a many-body potential model, the flip-flop model, and the other is the Born-Oppenheimer approach in the strong coupling lattice QCD. We explain how the emergence of the long-range attraction is avoided in these attempts. Finally, we present the results of the application of the flip-flop model to the baryon-baryon scattering in the quark cluster model. (author)

A scale invariant clustering of genes on human chromosome 7

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Kendal Wayne S

2004-01-01

Full Text Available Abstract Background Vertebrate genes often appear to cluster within the background of nontranscribed genomic DNA. Here an analysis of the physical distribution of gene structures on human chromosome 7 was performed to confirm the presence of clustering, and to elucidate possible underlying statistical and biological mechanisms. Results Clustering of genes was confirmed by virtue of a variance of the number of genes per unit physical length that exceeded the respective mean. Further evidence for clustering came from a power function relationship between the variance and mean that possessed an exponent of 1.51. This power function implied that the spatial distribution of genes on chromosome 7 was scale invariant, and that the underlying statistical distribution had a Poisson-gamma (PG form. A PG distribution for the spatial scattering of genes was validated by stringent comparisons of both the predicted variance to mean power function and its cumulative distribution function to data derived from chromosome 7. Conclusion The PG distribution was consistent with at least two different biological models: In the microrearrangement model, the number of genes per unit length of chromosome represented the contribution of a random number of smaller chromosomal segments that had originated by random breakage and reconstruction of more primitive chromosomes. Each of these smaller segments would have necessarily contained (on average a gamma distributed number of genes. In the gene cluster model, genes would be scattered randomly to begin with. Over evolutionary timescales, tandem duplication, mutation, insertion, deletion and rearrangement could act at these gene sites through a stochastic birth death and immigration process to yield a PG distribution. On the basis of the gene position data alone it was not possible to identify the biological model which best explained the observed clustering. However, the underlying PG statistical model implicated neutral

Testing a workplace physical activity intervention: a cluster randomized controlled trial.

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McEachan, Rosemary R C; Lawton, Rebecca J; Jackson, Cath; Conner, Mark; Meads, David M; West, Robert M

2011-04-11

Increased physical activity levels benefit both an individuals' health and productivity at work. The purpose of the current study was to explore the impact and cost-effectiveness of a workplace physical activity intervention designed to increase physical activity levels. A total of 1260 participants from 44 UK worksites (based within 5 organizations) were recruited to a cluster randomized controlled trial with worksites randomly allocated to an intervention or control condition. Measurement of physical activity and other variables occurred at baseline, and at 0 months, 3 months and 9 months post-intervention. Health outcomes were measured during a 30 minute health check conducted in worksites at baseline and 9 months post intervention. The intervention consisted of a 3 month tool-kit of activities targeting components of the Theory of Planned Behavior, delivered in-house by nominated facilitators. Self-reported physical activity (measured using the IPAQ short-form) and health outcomes were assessed. Multilevel modelling found no significant effect of the intervention on MET minutes of activity (from the IPAQ) at any of the follow-up time points controlling for baseline activity. However, the intervention did significantly reduce systolic blood pressure (B=-1.79 mm/Hg) and resting heart rate (B=-2.08 beats) and significantly increased body mass index (B=.18 units) compared to control. The intervention was found not to be cost-effective, however the substantial variability round this estimate suggested that further research is warranted. The current study found mixed support for this worksite physical activity intervention. The paper discusses some of the tensions involved in conducting rigorous evaluations of large-scale randomized controlled trials in real-world settings. © 2011 McEachan et al; licensee BioMed Central Ltd.

Parameters of oscillation generation regions in open star cluster models

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Danilov, V. M.; Putkov, S. I.

2017-07-01

We determine the masses and radii of central regions of open star cluster (OCL) models with small or zero entropy production and estimate the masses of oscillation generation regions in clustermodels based on the data of the phase-space coordinates of stars. The radii of such regions are close to the core radii of the OCL models. We develop a new method for estimating the total OCL masses based on the cluster core mass, the cluster and cluster core radii, and radial distribution of stars. This method yields estimates of dynamical masses of Pleiades, Praesepe, and M67, which agree well with the estimates of the total masses of the corresponding clusters based on proper motions and spectroscopic data for cluster stars.We construct the spectra and dispersion curves of the oscillations of the field of azimuthal velocities v φ in OCL models. Weak, low-amplitude unstable oscillations of v φ develop in cluster models near the cluster core boundary, and weak damped oscillations of v φ often develop at frequencies close to the frequencies of more powerful oscillations, which may reduce the non-stationarity degree in OCL models. We determine the number and parameters of such oscillations near the cores boundaries of cluster models. Such oscillations points to the possible role that gradient instability near the core of cluster models plays in the decrease of the mass of the oscillation generation regions and production of entropy in the cores of OCL models with massive extended cores.

Outcome-Driven Cluster Analysis with Application to Microarray Data.

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Jessie J Hsu

Full Text Available One goal of cluster analysis is to sort characteristics into groups (clusters so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes into groups of highly correlated genes that have the same effect on the outcome (recovery. We propose a random effects model where the genes within each group (cluster equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

MOCK OBSERVATIONS OF BLUE STRAGGLERS IN GLOBULAR CLUSTER MODELS

International Nuclear Information System (INIS)

Sills, Alison; Glebbeek, Evert; Chatterjee, Sourav; Rasio, Frederic A.

2013-01-01

We created artificial color-magnitude diagrams of Monte Carlo dynamical models of globular clusters and then used observational methods to determine the number of blue stragglers in those clusters. We compared these blue stragglers to various cluster properties, mimicking work that has been done for blue stragglers in Milky Way globular clusters to determine the dominant formation mechanism(s) of this unusual stellar population. We find that a mass-based prescription for selecting blue stragglers will select approximately twice as many blue stragglers than a selection criterion that was developed for observations of real clusters. However, the two numbers of blue stragglers are well-correlated, so either selection criterion can be used to characterize the blue straggler population of a cluster. We confirm previous results that the simplified prescription for the evolution of a collision or merger product in the BSE code overestimates their lifetimes. We show that our model blue stragglers follow similar trends with cluster properties (core mass, binary fraction, total mass, collision rate) as the true Milky Way blue stragglers as long as we restrict ourselves to model clusters with an initial binary fraction higher than 5%. We also show that, in contrast to earlier work, the number of blue stragglers in the cluster core does have a weak dependence on the collisional parameter Γ in both our models and in Milky Way globular clusters

Sparsity enabled cluster reduced-order models for control

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Kaiser, Eurika; Morzyński, Marek; Daviller, Guillaume; Kutz, J. Nathan; Brunton, Bingni W.; Brunton, Steven L.

2018-01-01

Characterizing and controlling nonlinear, multi-scale phenomena are central goals in science and engineering. Cluster-based reduced-order modeling (CROM) was introduced to exploit the underlying low-dimensional dynamics of complex systems. CROM builds a data-driven discretization of the Perron-Frobenius operator, resulting in a probabilistic model for ensembles of trajectories. A key advantage of CROM is that it embeds nonlinear dynamics in a linear framework, which enables the application of standard linear techniques to the nonlinear system. CROM is typically computed on high-dimensional data; however, access to and computations on this full-state data limit the online implementation of CROM for prediction and control. Here, we address this key challenge by identifying a small subset of critical measurements to learn an efficient CROM, referred to as sparsity-enabled CROM. In particular, we leverage compressive measurements to faithfully embed the cluster geometry and preserve the probabilistic dynamics. Further, we show how to identify fewer optimized sensor locations tailored to a specific problem that outperform random measurements. Both of these sparsity-enabled sensing strategies significantly reduce the burden of data acquisition and processing for low-latency in-time estimation and control. We illustrate this unsupervised learning approach on three different high-dimensional nonlinear dynamical systems from fluids with increasing complexity, with one application in flow control. Sparsity-enabled CROM is a critical facilitator for real-time implementation on high-dimensional systems where full-state information may be inaccessible.

A Cluster Randomized Controlled Trial Testing the Effectiveness of Houvast: A Strengths-Based Intervention for Homeless Young Adults

Science.gov (United States)

Krabbenborg, Manon A. M.; Boersma, Sandra N.; van der Veld, William M.; van Hulst, Bente; Vollebergh, Wilma A. M.; Wolf, Judith R. L. M.

2017-01-01

Objective: To test the effectiveness of Houvast: a strengths-based intervention for homeless young adults. Method: A cluster randomized controlled trial was conducted with 10 Dutch shelter facilities randomly allocated to an intervention and a control group. Homeless young adults were interviewed when entering the facility and when care ended.…

Self-consistent approximation for muffin-tin models of random substitutional alloys with environmental disorder

International Nuclear Information System (INIS)

Kaplan, T.; Gray, L.J.

1984-01-01

The self-consistent approximation of Kaplan, Leath, Gray, and Diehl is applied to models for substitutional random alloys with muffin-tin potentials. The particular advantage of this approximation is that, in addition to including cluster scattering, the muffin-tin potentials in the alloy can depend on the occupation of the surrounding sites (i.e., environmental disorder is included)

Probability on graphs random processes on graphs and lattices

CERN Document Server

Grimmett, Geoffrey

2018-01-01

This introduction to some of the principal models in the theory of disordered systems leads the reader through the basics, to the very edge of contemporary research, with the minimum of technical fuss. Topics covered include random walk, percolation, self-avoiding walk, interacting particle systems, uniform spanning tree, random graphs, as well as the Ising, Potts, and random-cluster models for ferromagnetism, and the Lorentz model for motion in a random medium. This new edition features accounts of major recent progress, including the exact value of the connective constant of the hexagonal lattice, and the critical point of the random-cluster model on the square lattice. The choice of topics is strongly motivated by modern applications, and focuses on areas that merit further research. Accessible to a wide audience of mathematicians and physicists, this book can be used as a graduate course text. Each chapter ends with a range of exercises.

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

Directory of Open Access Journals (Sweden)

Ayad Hendalianpour

2016-11-01

Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

RELICS: Strong Lens Models for Five Galaxy Clusters from the Reionization Lensing Cluster Survey

Science.gov (United States)

Cerny, Catherine; Sharon, Keren; Andrade-Santos, Felipe; Avila, Roberto J.; Bradač, Maruša; Bradley, Larry D.; Carrasco, Daniela; Coe, Dan; Czakon, Nicole G.; Dawson, William A.; Frye, Brenda L.; Hoag, Austin; Huang, Kuang-Han; Johnson, Traci L.; Jones, Christine; Lam, Daniel; Lovisari, Lorenzo; Mainali, Ramesh; Oesch, Pascal A.; Ogaz, Sara; Past, Matthew; Paterno-Mahler, Rachel; Peterson, Avery; Riess, Adam G.; Rodney, Steven A.; Ryan, Russell E.; Salmon, Brett; Sendra-Server, Irene; Stark, Daniel P.; Strolger, Louis-Gregory; Trenti, Michele; Umetsu, Keiichi; Vulcani, Benedetta; Zitrin, Adi

2018-06-01

Strong gravitational lensing by galaxy clusters magnifies background galaxies, enhancing our ability to discover statistically significant samples of galaxies at {\\boldsymbol{z}}> 6, in order to constrain the high-redshift galaxy luminosity functions. Here, we present the first five lens models out of the Reionization Lensing Cluster Survey (RELICS) Hubble Treasury Program, based on new HST WFC3/IR and ACS imaging of the clusters RXC J0142.9+4438, Abell 2537, Abell 2163, RXC J2211.7–0349, and ACT-CLJ0102–49151. The derived lensing magnification is essential for estimating the intrinsic properties of high-redshift galaxy candidates, and properly accounting for the survey volume. We report on new spectroscopic redshifts of multiply imaged lensed galaxies behind these clusters, which are used as constraints, and detail our strategy to reduce systematic uncertainties due to lack of spectroscopic information. In addition, we quantify the uncertainty on the lensing magnification due to statistical and systematic errors related to the lens modeling process, and find that in all but one cluster, the magnification is constrained to better than 20% in at least 80% of the field of view, including statistical and systematic uncertainties. The five clusters presented in this paper span the range of masses and redshifts of the clusters in the RELICS program. We find that they exhibit similar strong lensing efficiencies to the clusters targeted by the Hubble Frontier Fields within the WFC3/IR field of view. Outputs of the lens models are made available to the community through the Mikulski Archive for Space Telescopes.

Individualization as driving force of clustering phenomena in humans.

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Michael Mäs

Full Text Available One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict "monoculture" in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness has not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution to the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in

Comparative effectiveness of childhood obesity interventions in pediatric primary care: a cluster-randomized clinical trial.

Science.gov (United States)

Taveras, Elsie M; Marshall, Richard; Kleinman, Ken P; Gillman, Matthew W; Hacker, Karen; Horan, Christine M; Smith, Renata L; Price, Sarah; Sharifi, Mona; Rifas-Shiman, Sheryl L; Simon, Steven R

2015-06-01

Evidence of effective treatment of childhood obesity in primary care settings is limited. To examine the extent to which computerized clinical decision support (CDS) delivered to pediatric clinicians at the point of care of obese children, with or without individualized family coaching, improved body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and quality of care. We conducted a cluster-randomized, 3-arm clinical trial. We enrolled 549 children aged 6 to 12 years with a BMI at the 95% percentile or higher from 14 primary care practices in Massachusetts from October 1, 2011, through June 30, 2012. Patients were followed up for 1 year (last follow-up, August 30, 2013). In intent-to-treat analyses, we used linear mixed-effects models to account for clustering by practice and within each person. In 5 practices randomized to CDS, pediatric clinicians received decision support on obesity management, and patients and their families received an intervention for self-guided behavior change. In 5 practices randomized to CDS + coaching, decision support was augmented by individualized family coaching. The remaining 4 practices were randomized to usual care. Smaller age-associated change in BMI and the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures for obesity during the 1-year follow-up. At baseline, mean (SD) patient age and BMI were 9.8 (1.9) years and 25.8 (4.3), respectively. At 1 year, we obtained BMI from 518 children (94.4%) and HEDIS measures from 491 visits (89.4%). The 3 randomization arms had different effects on BMI over time (P = .04). Compared with the usual care arm, BMI increased less in children in the CDS arm during 1 year (-0.51 [95% CI, -0.91 to -0.11]). The CDS + coaching arm had a smaller magnitude of effect (-0.34 [95% CI, -0.75 to 0.07]). We found substantially greater achievement of childhood obesity HEDIS measures in the CDS arm (adjusted odds ratio, 2.28 [95% CI, 1

Exploring the transparency mechanism and evaluating the effect of public reporting on prescription: a protocol for a cluster randomized controlled trial.

Science.gov (United States)

Du, Xin; Wang, Dan; Wang, Xuan; Yang, Shiru; Zhang, Xinping

2015-03-21

The public reporting of health outcomes has become one of the most popular topics and is accepted as a quality improvement method in the healthcare field. However, little research has been conducted on the transparency mechanism, and results are mixed with regard to the evaluation of the effect of public reporting on quality improvement. The objectives of this trial are to investigate the transparency mechanism and to evaluate the effect of public reporting on prescription at the level of individual participants. This study involves a cluster randomized controlled trial conducted in 20 primary-care facilities (clusters). Eligible clusters are those facilities with excellent hospital information systems and that have agreed to participate in the trial. The 20 clusters are matched into 10 pairs according to Technique for Order Preference by Similarity to Ideal Solution score. As the unit of randomization, each pair of facilities is assigned at random to a control or an intervention group through coin flipping. Prescribed ranking information is publicly reported in the intervention group. The public materials include the posters of individuals and of facilities, the ranking lists of general practitioners, and brochures of patients, which are updated monthly. The intervention began on 13th November 2013 and lasted for one year. Specifically, participants are surveyed at five points in time (baseline, quarterly following the intervention) through questionnaires, interviews, and observations. These participants include an average of 600 patients, 300 general practitioners, 15 directors, and 6 health bureau administrators. The primary outcomes are the transparency mechanism model and the changes in medicine-prescribe. Subsequently, the modifications in the transparency mechanism constructs are evaluated. The outcomes are measured at the individual participant level, and the professional who analyzes the data is blind to the randomization status. This study protocol

Clustering disaggregated load profiles using a Dirichlet process mixture model

International Nuclear Information System (INIS)

Granell, Ramon; Axon, Colin J.; Wallom, David C.H.

2015-01-01

Highlights: • We show that the Dirichlet process mixture model is scaleable. • Our model does not require the number of clusters as an input. • Our model creates clusters only by the features of the demand profiles. • We have used both residential and commercial data sets. - Abstract: The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called ‘Chinese restaurant process’ method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships

Color Gradients Within Globular Clusters: Restricted Numerical Simulation

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Young-Jong Sohn

1997-06-01

Full Text Available The results of a restricted numerical simulation for the color gradients within globular clusters have been presented. The standard luminosity function of M3 and Salpeter's initial mass functions were used to generate model clusters as a fundamental population. Color gradients with the sample clusters for both King and power law cusp models of surface brightness distributions are discussed in the case of using the standard luminosity function. The dependence of color gradients on several parameters for the simulations with Salpeter's initial mass functions, such as slope of initial mass functions, cluster ages, metallicities, concentration parameters of King model, and slopes of power law, are also discussed. No significant radial color gradients are shown to the sample clusters which are regenerated by a random number generation technique with various parameters in both of King and power law cusp models of surface brightness distributions. Dynamical mass segregation and stellar evolution of horizontal branch stars and blue stragglers should be included for the general case of model simulations to show the observed radial color gradients within globular clusters.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

Science.gov (United States)

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Automatic Decentralized Clustering for Wireless Sensor Networks

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Wen Chih-Yu

2005-01-01

Full Text Available We propose a decentralized algorithm for organizing an ad hoc sensor network into clusters. Each sensor uses a random waiting timer and local criteria to determine whether to form a new cluster or to join a current cluster. The algorithm operates without a centralized controller, it operates asynchronously, and does not require that the location of the sensors be known a priori. Simplified models are used to estimate the number of clusters formed, and the energy requirements of the algorithm are investigated. The performance of the algorithm is described analytically and via simulation.

An algebraic model for three-cluster giant molecules

International Nuclear Information System (INIS)

Hess, P.O.; Bijker, R.; Misicu, S.

2001-01-01

After an introduction to the algebraic U(7) model for three bodies, we present a relation of a geometrical description of three-cluster molecule to the algebraic U(7) model. Stiffness parameters of oscillations between each of two clusters are calculated and translated to the model parameter values of the algebraic model. The model is applied to the trinuclear system l32 Sn+ α + ll6 Pd which occurs in the ternary cold fission of 252 Cf. (Author)

Implementation of client versus care-provider strategies to improve external cephalic version rates: a cluster randomized controlled trial.

Science.gov (United States)

Vlemmix, Floortje; Rosman, Ageeth N; Rijnders, Marlies E; Beuckens, Antje; Opmeer, Brent C; Mol, Ben W J; Kok, Marjolein; Fleuren, Margot A H

2015-05-01

To determine the effectiveness of a client or care-provider strategy to improve the implementation of external cephalic version. Cluster randomized controlled trial. Twenty-five clusters; hospitals and their referring midwifery practices randomly selected in the Netherlands. Singleton breech presentation from 32 weeks of gestation onwards. We randomized clusters to a client strategy (written information leaflets and decision aid), a care-provider strategy (1-day counseling course focused on knowledge and counseling skills), a combined client and care-provider strategy and care-as-usual strategy. We performed an intention-to-treat analysis. Rate of external cephalic version in various strategies. Secondary outcomes were the percentage of women counseled and opting for a version attempt. The overall implementation rate of external cephalic version was 72% (1169 of 1613 eligible clients) with a range between clusters of 8-95%. Neither the client strategy (OR 0.8, 95% CI 0.4-1.5) nor the care-provider strategy (OR 1.2, 95% CI 0.6-2.3) showed significant improvements. Results were comparable when we limited the analysis to those women who were actually offered intervention (OR 0.6, 95% CI 0.3-1.4 and OR 2.0, 95% CI 0.7-4.5). Neither a client nor a care-provider strategy improved the external cephalic version implementation rate for breech presentation, neither with regard to the number of version attempts offered nor the number of women accepting the procedure. © 2015 Nordic Federation of Societies of Obstetrics and Gynecology.

Exactly soluble models for surface partition of large clusters

International Nuclear Information System (INIS)

Bugaev, K.A.; Bugaev, K.A.; Elliott, J.B.

2007-01-01

The surface partition of large clusters is studied analytically within a framework of the 'Hills and Dales Model'. Three formulations are solved exactly by using the Laplace-Fourier transformation method. In the limit of small amplitude deformations, the 'Hills and Dales Model' gives the upper and lower bounds for the surface entropy coefficient of large clusters. The found surface entropy coefficients are compared with those of large clusters within the 2- and 3-dimensional Ising models

Random-field Potts model for the polar domains of lead magnesium niobate and lead scandium tantalate

Energy Technology Data Exchange (ETDEWEB)

Qian, H.; Bursill, L.A

1997-06-01

A random filed Potts model is used to establish the spatial relationship between the nanoscale distribution of charges chemical defects and nanoscale polar domains for the perovskite-based relaxor materials lead magnesium niobate (PMN) and lead scandium tantalate (PST). The random fields are not set stochastically but are determined initially by the distribution of B-site cations (Mg, Nb) or (Sc, Ta) generated by Monte Carlo NNNI-model simulations for the chemical defects. An appropriate random field Potts model is derived and algorithms developed for a 2D lattice. It is shown that the local fields are strongly correlated with the chemical domain walls and that polar domains as a function of decreasing temperature is simulated for the two cases of PMN and PST. The dynamics of the polar clusters is also discussed. 33 refs., 9 figs.

The effectiveness of xylitol in a school-based cluster-randomized clinical trial.

Science.gov (United States)

Lee, Wonik; Spiekerman, Charles; Heima, Masahiro; Eggertsson, Hafsteinn; Ferretti, Gerald; Milgrom, Peter; Nelson, Suchitra

2015-01-01

The purpose of this double-blind, cluster-randomized clinical trial was to examine the effects of xylitol gummy bear snacks on dental caries progression in primary and permanent teeth of inner-city school children. A total of 562 children aged 5-6 years were recruited from five elementary schools in East Cleveland, Ohio. Children were randomized by classroom to receive xylitol (7.8 g/day) or placebo (inulin fiber 20 g/day) gummy bears. Gummy bears were given three times per day for the 9-month kindergarten year within a supervised school environment. Children in both groups also received oral health education, toothbrush and fluoridated toothpaste, topical fluoride varnish treatment and dental sealants. The numbers of new decayed, missing, and filled surfaces for primary teeth (dmfs) and permanent teeth (DMFS) from baseline to the middle of 2nd grade (exit exam) were compared between the treatment (xylitol/placebo) groups using an optimally-weighted permutation test for cluster-randomized data. The mean new d(3-6)mfs at the exit exam was 5.0 ± 7.6 and 4.0 ± 6.5 for the xylitol and placebo group, respectively. Similarly, the mean new D(3-6)MFS was 0.38 ± 0.88 and 0.48 ± 1.39 for the xylitol and placebo group, respectively. The adjusted mean difference between the two groups was not statistically significant: new d(3-6)mfs: mean 0.4, 95% CI -0.25, 0.8), and new D(3-6)MFS: mean 0.16, 95% CI -0.16, 0.43. Xylitol consumption did not have additional benefit beyond other preventive measures. Caries progression in the permanent teeth of both groups was minimal, suggesting that other simultaneous prevention modalities may have masked the possible beneficial effects of xylitol in this trial. © 2014 S. Karger AG, Basel.

A Variational Level Set Model Combined with FCMS for Image Clustering Segmentation

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Liming Tang

2014-01-01

Full Text Available The fuzzy C means clustering algorithm with spatial constraint (FCMS is effective for image segmentation. However, it lacks essential smoothing constraints to the cluster boundaries and enough robustness to the noise. Samson et al. proposed a variational level set model for image clustering segmentation, which can get the smooth cluster boundaries and closed cluster regions due to the use of level set scheme. However it is very sensitive to the noise since it is actually a hard C means clustering model. In this paper, based on Samson’s work, we propose a new variational level set model combined with FCMS for image clustering segmentation. Compared with FCMS clustering, the proposed model can get smooth cluster boundaries and closed cluster regions due to the use of level set scheme. In addition, a block-based energy is incorporated into the energy functional, which enables the proposed model to be more robust to the noise than FCMS clustering and Samson’s model. Some experiments on the synthetic and real images are performed to assess the performance of the proposed model. Compared with some classical image segmentation models, the proposed model has a better performance for the images contaminated by different noise levels.

Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding

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Yeo‐Chan Yoon

2017-08-01

Full Text Available In this paper, we propose a fine‐grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high‐performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the K‐means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

Science.gov (United States)

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith

A Latent Variable Clustering Method for Wireless Sensor Networks

DEFF Research Database (Denmark)

Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir

2016-01-01

In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and...

Cluster radioactive decay within the preformed cluster model using relativistic mean-field theory densities

International Nuclear Information System (INIS)

Singh, BirBikram; Patra, S. K.; Gupta, Raj K.

2010-01-01

We have studied the (ground-state) cluster radioactive decays within the preformed cluster model (PCM) of Gupta and collaborators [R. K. Gupta, in Proceedings of the 5th International Conference on Nuclear Reaction Mechanisms, Varenna, edited by E. Gadioli (Ricerca Scientifica ed Educazione Permanente, Milano, 1988), p. 416; S. S. Malik and R. K. Gupta, Phys. Rev. C 39, 1992 (1989)]. The relativistic mean-field (RMF) theory is used to obtain the nuclear matter densities for the double folding procedure used to construct the cluster-daughter potential with M3Y nucleon-nucleon interaction including exchange effects. Following the PCM approach, we have deduced empirically the preformation probability P 0 emp from the experimental data on both the α- and exotic cluster-decays, specifically of parents in the trans-lead region having doubly magic 208 Pb or its neighboring nuclei as daughters. Interestingly, the RMF-densities-based nuclear potential supports the concept of preformation for both the α and heavier clusters in radioactive nuclei. P 0 α(emp) for α decays is almost constant (∼10 -2 -10 -3 ) for all the parent nuclei considered here, and P 0 c(emp) for cluster decays of the same parents decrease with the size of clusters emitted from different parents. The results obtained for P 0 c(emp) are reasonable and are within two to three orders of magnitude of the well-accepted phenomenological model of Blendowske-Walliser for light clusters.

A cluster expansion model for predicting activation barrier of atomic processes

International Nuclear Information System (INIS)

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit

2013-01-01

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEB results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog

A Novel Algorithm of Quantum Random Walk in Server Traffic Control and Task Scheduling

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Dong Yumin

2014-01-01

Full Text Available A quantum random walk optimization model and algorithm in network cluster server traffic control and task scheduling is proposed. In order to solve the problem of server load balancing, we research and discuss the distribution theory of energy field in quantum mechanics and apply it to data clustering. We introduce the method of random walk and illuminate what the quantum random walk is. Here, we mainly research the standard model of one-dimensional quantum random walk. For the data clustering problem of high dimensional space, we can decompose one m-dimensional quantum random walk into m one-dimensional quantum random walk. In the end of the paper, we compare the quantum random walk optimization method with GA (genetic algorithm, ACO (ant colony optimization, and SAA (simulated annealing algorithm. In the same time, we prove its validity and rationality by the experiment of analog and simulation.

Experience of BESIII data production with local cluster and distributed computing model

International Nuclear Information System (INIS)

Deng, Z Y; Li, W D; Liu, H M; Sun, Y Z; Zhang, X M; Lin, L; Nicholson, C; Zhemchugov, A

2012-01-01

The BES III detector is a new spectrometer which works on the upgraded high-luminosity collider, BEPCII. The BES III experiment studies physics in the tau-charm energy region from 2 GeV to 4.6 GeV . From 2009 to 2011, BEPCII has produced 106M ψ(2S) events, 225M J/ψ events, 2.8 fb −1 ψ(3770) data, and 500 pb −1 data at 4.01 GeV. All the data samples were processed successfully and many important physics results have been achieved based on these samples. Doing data production correctly and efficiently with limited CPU and storage resources is a big challenge. This paper will describe the implementation of the experiment-specific data production for BESIII in detail, including data calibration with event-level parallel computing model, data reconstruction, inclusive Monte Carlo generation, random trigger background mixing and multi-stream data skimming. Now, with the data sample increasing rapidly, there is a growing demand to move from solely using a local cluster to a more distributed computing model. A distributed computing environment is being set up and expected to go into production use in 2012. The experience of BESIII data production, both with a local cluster and with a distributed computing model, is presented here.

The Parental Environment Cluster Model of Child Neglect: An Integrative Conceptual Model.

Science.gov (United States)

Burke, Judith; Chandy, Joseph; Dannerbeck, Anne; Watt, J. Wilson

1998-01-01

Presents Parental Environment Cluster model of child neglect which identifies three clusters of factors involved in parents' neglectful behavior: (1) parenting skills and functions; (2) development and use of positive social support; and (3) resource availability and management skills. Model offers a focal theory for research, structure for…

Modeling blue stragglers in young clusters

International Nuclear Information System (INIS)

Lu Pin; Deng Licai; Zhang Xiaobin

2011-01-01

A grid of binary evolution models are calculated for the study of a blue straggler (BS) population in intermediate age (log Age = 7.85–8.95) star clusters. The BS formation via mass transfer and merging is studied systematically using our models. Both Case A and B close binary evolutionary tracks are calculated for a large range of parameters. The results show that BSs formed via Case B are generally bluer and even more luminous than those produced by Case A. Furthermore, the larger range in orbital separations of Case B models provides a probability of producing more BSs than in Case A. Based on the grid of models, several Monte-Carlo simulations of BS populations in the clusters in the age range are carried out. The results show that BSs formed via different channels populate different areas in the color magnitude diagram (CMD). The locations of BSs in CMD for a number of clusters are compared to our simulations as well. In order to investigate the influence of mass transfer efficiency in the models and simulations, a set of models is also calculated by implementing a constant mass transfer efficiency, β = 0.5, during Roche lobe overflow (Case A binary evolution excluded). The result shows BSs can be formed via mass transfer at any given age in both cases. However, the distributions of the BS populations on CMD are different.

Random Intercept and Random Slope 2-Level Multilevel Models

Directory of Open Access Journals (Sweden)

Rehan Ahmad Khan

2012-11-01

Full Text Available Random intercept model and random intercept & random slope model carrying two-levels of hierarchy in the population are presented and compared with the traditional regression approach. The impact of students’ satisfaction on their grade point average (GPA was explored with and without controlling teachers influence. The variation at level-1 can be controlled by introducing the higher levels of hierarchy in the model. The fanny movement of the fitted lines proves variation of student grades around teachers.

Ac hopping conduction at extreme disorder takes place on the percolating cluster

DEFF Research Database (Denmark)

Schrøder, Thomas; Dyre, J. C.

2008-01-01

Simulations of the random barrier model show that ac currents at extreme disorder are carried almost entirely by the percolating cluster slightly above threshold; thus contributions from isolated low activation-energy clusters are negligible. The effective medium approximation in conjunction...

The formation of magnetic silicide Fe3Si clusters during ion implantation

Science.gov (United States)

Balakirev, N.; Zhikharev, V.; Gumarov, G.

2014-05-01

A simple two-dimensional model of the formation of magnetic silicide Fe3Si clusters during high-dose Fe ion implantation into silicon has been proposed and the cluster growth process has been computer simulated. The model takes into account the interaction between the cluster magnetization and magnetic moments of Fe atoms random walking in the implanted layer. If the clusters are formed in the presence of the external magnetic field parallel to the implanted layer, the model predicts the elongation of the growing cluster in the field direction. It has been proposed that the cluster elongation results in the uniaxial magnetic anisotropy in the plane of the implanted layer, which is observed in iron silicide films ion-beam synthesized in the external magnetic field.

The formation of magnetic silicide Fe3Si clusters during ion implantation

International Nuclear Information System (INIS)

Balakirev, N.; Zhikharev, V.; Gumarov, G.

2014-01-01

A simple two-dimensional model of the formation of magnetic silicide Fe 3 Si clusters during high-dose Fe ion implantation into silicon has been proposed and the cluster growth process has been computer simulated. The model takes into account the interaction between the cluster magnetization and magnetic moments of Fe atoms random walking in the implanted layer. If the clusters are formed in the presence of the external magnetic field parallel to the implanted layer, the model predicts the elongation of the growing cluster in the field direction. It has been proposed that the cluster elongation results in the uniaxial magnetic anisotropy in the plane of the implanted layer, which is observed in iron silicide films ion-beam synthesized in the external magnetic field

Spatial-Temporal Clustering of Tornadoes

Science.gov (United States)

Malamud, Bruce D.; Turcotte, Donald L.; Brooks, Harold E.

2017-04-01

The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated

Effectiveness of a cognitive behavioural workbook for changing beliefs about antipsychotic polypharmacy: analysis from a cluster randomized controlled trial.

Science.gov (United States)

Thompson, Andrew; Sullivan, Sarah; Barley, Maddi; Moore, Laurence; Rogers, Paul; Sipos, Attila; Harrison, Glynn

2010-06-01

Educational workbooks have been used in psychiatry to influence patient but not clinician behaviour. Targeted education interventions to change prescribing practice in other areas of medicine have only looked at changes in prescribing and not attitudes or beliefs related to the prescribing. We aimed to examine whether clinicians' beliefs about a common prescribing issue in psychiatry (antipsychotic polypharmacy prescription) changed alongside behaviour as a result of a complex intervention. Medical and nursing staff were recruited from 19 general adult psychiatry units in the south-west of the UK as part of a cluster randomized controlled trial. A questionnaire was used to assess beliefs on the prescribing of antipsychotic polypharmacy as a secondary outcome before and after completion of a cognitive behavioural 'self-help' style workbook (one part of a complex intervention). A factor analysis suggested three dimensions of the questionnaire that corresponded to predetermined themes. The data were analysed using a random-effects regression model (adjusting for clustering) controlling for possible confounders. There was a significant change in beliefs on both of the factors: antipsychotic polypharmacy (coefficient = -0.89, P change in antipsychotic polypharmacy prescribing (odds ratio 0.43, 95% confidence intervals 0.21-0.90). The workbook appeared to change staff beliefs about antipsychotic polypharmacy, but achieving substantial changes in clinician behaviour may require further exploration of other factors important in complex prescribing issues.

NUCORE - A system for nuclear structure calculations with cluster-core models

International Nuclear Information System (INIS)

Heras, C.A.; Abecasis, S.M.

1982-01-01

Calculation of nuclear energy levels and their electromagnetic properties, modelling the nucleus as a cluster of a few particles and/or holes interacting with a core which in turn is modelled as a quadrupole vibrator (cluster-phonon model). The members of the cluster interact via quadrupole-quadrupole and pairing forces. (orig.)

Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.

Science.gov (United States)

Shen, Chung-Wei; Chen, Yi-Hau

2018-03-13

We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within-cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly. © 2018, The International Biometric Society.

Binary model for the coma cluster of galaxies

International Nuclear Information System (INIS)

Valtonen, M.J.; Byrd, G.G.

1979-01-01

We study the dynamics of galaxies in the Coma cluster and find that the cluster is probably dominated by a central binary of galaxies NGC 4874--NGC4889. We estimate their total mass to be about 3 x 10 14 M/sub sun/ by two independent methods (assuming in Hubble constant of 100 km s -1 Mpc -1 ). This binary is efficient in dynamically ejecting smaller galaxies, some of of which are seen in projection against the inner 3 0 radius of the cluster and which, if erroneously considered as bound members, cause a serious overestimate of the mass of the entire cluster. Taking account of the ejected galaxies, we estimate the total cluster mass to be 4--9 x 10 14 M/sub sun/, with a corresponding mass-to-light ratio for a typical galaxy in the range of 20--120 solar units. The origin of the secondary maximum observed in the radial surface density profile is studied. We consider it to be a remnant of a shell of galaxies which formed around the central binary. This shell expanded, then collapsed into the binary, and is now reexpanding. This is supported by the coincidence of the minimum in the cluster eccentricity and radical velocity dispersion at the same radial distance as the secondary maximum. Numerical simulations of a cluster model with a massive central binary and a spherical shell of test particles are performed, and they reproduce the observed shape, galaxy density, and radial velocity distributions in the Coma cluster fairly well. Consequences of extending the model to other clusters are discussed

Effectiveness of the 'Healthy School and Drugs' prevention programme on adolescents' substance use: a randomized clustered trial

NARCIS (Netherlands)

Malmberg, M.; Kleinjan, M.; Overbeek, G.; Vermulst, A.; Monshouwer, K.; Lammers, J.; Vollebergh, W.A.M.; Engels, R.C.M.E.

2014-01-01

Aim: To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design: Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting: General population of 11-15-year-old

Effectiveness of the 'Healthy School and Drugs' prevention programme on adolescents' substance use : A randomized clustered trial

NARCIS (Netherlands)

Malmberg, Monique; Kleinjan, Marloes; Overbeek, Geertjan; Vermulst, Ad; Monshouwer, Karin; Lammers, Jeroen; Vollebergh, Wilma A M; Engels, Rutger C M E

2014-01-01

Aim: To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design: Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting: General population of 11-15-year-old

Effectiveness of the 'Healthy School and Drugs' prevention programme on adolescents' substance use: a randomized clustered trial

NARCIS (Netherlands)

Malmberg, M.; Kleinjan, M.; Overbeek, G.J.; Vermulst, A.A.; Monshouwer, K.; Lammers, J.; Vollebergh, W.A.M.; Engels, R.C.M.E.

2014-01-01

Aim To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting General population of 11-15-year-old adolescents

Old star clusters: Bench tests of low mass stellar models

Directory of Open Access Journals (Sweden)

Salaris M.

2013-03-01

Full Text Available Old star clusters in the Milky Way and external galaxies have been (and still are traditionally used to constrain the age of the universe and the timescales of galaxy formation. A parallel avenue of old star cluster research considers these objects as bench tests of low-mass stellar models. This short review will highlight some recent tests of stellar evolution models that make use of photometric and spectroscopic observations of resolved old star clusters. In some cases these tests have pointed to additional physical processes efficient in low-mass stars, that are not routinely included in model computations. Moreover, recent results from the Kepler mission about the old open cluster NGC6791 are adding new tight constraints to the models.

Efficacy of a workplace osteoporosis prevention intervention: a cluster randomized trial

Directory of Open Access Journals (Sweden)

Ai May Tan

2016-08-01

Full Text Available Abstract Background Osteoporosis is a debilitating disease. Adequate calcium consumption and physical activity are the two major modifiable risk factors. This paper describes the major outcomes and efficacy of a workplace-based targeted behaviour change intervention to improve the dietary and physical activity behaviours of working women in sedentary occupations in Singapore. Methods A cluster-randomized design was used, comparing the efficacy of a tailored intervention to standard care. Workplaces were the units of randomization and intervention. Sixteen workplaces were recruited from a pool of 97, and randomly assigned to intervention and control arms (eight workplaces in each. Women meeting specified inclusion criteria were then recruited to participate. Workplaces in the intervention arm received three participatory workshops and organization-wide educational activities. Workplaces in the control/standard care arm received print resources. Outcome measures were calcium intake (milligrams/day and physical activity level (duration: minutes/week, measured at baseline, 4 weeks and 6 months post intervention. Adjusted cluster-level analyses were conducted comparing changes in intervention versus control groups, following intention-to-treat principles and CONSORT guidelines. Results Workplaces in the intervention group reported a significantly greater increase in calcium intake and duration of load-bearing moderate to vigorous physical activity (MVPA compared with the standard care control group. Four weeks after intervention, the difference in adjusted mean calcium intake was 343.2 mg/day (95 % CI = 337.4 to 349.0, p < .0005 and the difference in adjusted mean load-bearing MVPA was 55.6 min/week (95 % CI = 54.5 to 56.6, p < .0005. Six months post intervention, the mean differences attenuated slightly to 290.5 mg/day (95 % CI = 285.3 to 295.7, p < .0005 and 50.9 min/week (95 % CI =49.3 to 52.6, p < .0005

Effectiveness of a self-management program for dual sensory impaired seniors in aged care settings: study protocol for a cluster randomized controlled trial.

Science.gov (United States)

Roets-Merken, Lieve M; Graff, Maud J L; Zuidema, Sytse U; Hermsen, Pieter G J M; Teerenstra, Steven; Kempen, Gertrudis I J M; Vernooij-Dassen, Myrra J F J

2013-10-07

Five to 25 percent of residents in aged care settings have a combined hearing and visual sensory impairment. Usual care is generally restricted to single sensory impairment, neglecting the consequences of dual sensory impairment on social participation and autonomy. The aim of this study is to evaluate the effectiveness of a self-management program for seniors who acquired dual sensory impairment at old age. In a cluster randomized, single-blind controlled trial, with aged care settings as the unit of randomization, the effectiveness of a self-management program will be compared to usual care. A minimum of 14 and maximum of 20 settings will be randomized to either the intervention cluster or the control cluster, aiming to include a total of 132 seniors with dual sensory impairment. Each senior will be linked to a licensed practical nurse working at the setting. During a five to six month intervention period, nurses at the intervention clusters will be trained in a self-management program to support and empower seniors to use self-management strategies. In two separate diaries, nurses keep track of the interviews with the seniors and their reflections on their own learning process. Nurses of the control clusters offer care as usual. At senior level, the primary outcome is the social participation of the seniors measured using the Hearing Handicap Questionnaire and the Activity Card Sort, and secondary outcomes are mood, autonomy and quality of life. At nurse level, the outcome is job satisfaction. Effectiveness will be evaluated using linear mixed model analysis. The results of this study will provide evidence for the effectiveness of the Self-Management Program for seniors with dual sensory impairment living in aged care settings. The findings are expected to contribute to the knowledge on the program's potential to enhance social participation and autonomy of the seniors, as well as increasing the job satisfaction of the licensed practical nurses. Furthermore, an

A grand unified model for liganded gold clusters

Science.gov (United States)

Xu, Wen Wu; Zhu, Beien; Zeng, Xiao Cheng; Gao, Yi

2016-12-01

A grand unified model (GUM) is developed to achieve fundamental understanding of rich structures of all 71 liganded gold clusters reported to date. Inspired by the quark model by which composite particles (for example, protons and neutrons) are formed by combining three quarks (or flavours), here gold atoms are assigned three `flavours' (namely, bottom, middle and top) to represent three possible valence states. The `composite particles' in GUM are categorized into two groups: variants of triangular elementary block Au3(2e) and tetrahedral elementary block Au4(2e), all satisfying the duet rule (2e) of the valence shell, akin to the octet rule in general chemistry. The elementary blocks, when packed together, form the cores of liganded gold clusters. With the GUM, structures of 71 liganded gold clusters and their growth mechanism can be deciphered altogether. Although GUM is a predictive heuristic and may not be necessarily reflective of the actual electronic structure, several highly stable liganded gold clusters are predicted, thereby offering GUM-guided synthesis of liganded gold clusters by design.

Aerosol cluster impact and break-up: model and implementation

International Nuclear Information System (INIS)

Lechman, Jeremy B.

2010-01-01

In this report a model for simulating aerosol cluster impact with rigid walls is presented. The model is based on JKR adhesion theory and is implemented as an enhancement to the granular (DEM) package within the LAMMPS code. The theory behind the model is outlined and preliminary results are shown. Modeling the interactions of small particles is relevant to a number of applications (e.g., soils, powders, colloidal suspensions, etc.). Modeling the behavior of aerosol particles during agglomeration and cluster dynamics upon impact with a wall is of particular interest. In this report we describe preliminary efforts to develop and implement physical models for aerosol particle interactions. Future work will consist of deploying these models to simulate aerosol cluster behavior upon impact with a rigid wall for the purpose of developing relationships for impact speed and probability of stick/bounce/break-up as well as to assess the distribution of cluster sizes if break-up occurs. These relationships will be developed consistent with the need for inputs into system-level codes. Section 2 gives background and details on the physical model as well as implementations issues. Section 3 presents some preliminary results which lead to discussion in Section 4 of future plans.

Identifying Clusters with Mixture Models that Include Radial Velocity Observations

Science.gov (United States)

Czarnatowicz, Alexis; Ybarra, Jason E.

2018-01-01

The study of stellar clusters plays an integral role in the study of star formation. We present a cluster mixture model that considers radial velocity data in addition to spatial data. Maximum likelihood estimation through the Expectation-Maximization (EM) algorithm is used for parameter estimation. Our mixture model analysis can be used to distinguish adjacent or overlapping clusters, and estimate properties for each cluster.Work supported by awards from the Virginia Foundation for Independent Colleges (VFIC) Undergraduate Science Research Fellowship and The Research Experience @Bridgewater (TREB).

Globular cluster metallicity scale: evidence from stellar models

International Nuclear Information System (INIS)

Demarque, P.; King, C.R.; Diaz, A.

1982-01-01

Theoretical giant branches have been constructed to determine their relative positions for metallicities in the range -2.3 0 )/sub 0,g/ based on these models is presented which yields good agreement over the observed range of metallicities for galactic globular clusters and old disk clusters. The metallicity of 47 Tuc and M71 given by this calibration is about -0.8 dex. Subject headings: clusters, globular: stars: abundances: stars: interiors

A Clustered Extragalactic Foreground Model for the EoR

Science.gov (United States)

Murray, S. G.; Trott, C. M.; Jordan, C. H.

2018-05-01

We review an improved statistical model of extra-galactic point-source foregrounds first introduced in Murray et al. (2017), in the context of the Epoch of Reionization. This model extends the instrumentally-convolved foreground covariance used in inverse-covariance foreground mitigation schemes, by considering the cosmological clustering of the sources. In this short work, we show that over scales of k ~ (0.6, 40.)hMpc-1, ignoring source clustering is a valid approximation. This is in contrast to Murray et al. (2017), who found a possibility of false detection if the clustering was ignored. The dominant cause for this change is the introduction of a Galactic synchrotron component which shadows the clustering of sources.

Topic modeling for cluster analysis of large biological and medical datasets.

Science.gov (United States)

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

Effectiveness of a multifaceted implementation strategy on physicians' referral behavior to an evidence-based psychosocial intervention in dementia: a cluster randomized controlled trial

NARCIS (Netherlands)

Dopp, C.M.E.; Graff, M.J.L.; Teerenstra, S.; Nijhuis-Van der Sanden, M.W.; Olde Rikkert, M.G.M.; Vernooij-Dassen, M.J.F.J.

2013-01-01

BACKGROUND: To evaluate the effectiveness of a multifaceted implementation strategy on physicians' referral rate to and knowledge on the community occupational therapy in dementia program (COTiD program). METHODS: A cluster randomized controlled trial with 28 experimental and 17 control clusters was

The formation of magnetic silicide Fe{sub 3}Si clusters during ion implantation

Energy Technology Data Exchange (ETDEWEB)

Balakirev, N. [Kazan National Research Technological University, K.Marx st. 68, Kazan 420015 (Russian Federation); Zhikharev, V., E-mail: valzhik@mail.ru [Kazan National Research Technological University, K.Marx st. 68, Kazan 420015 (Russian Federation); Gumarov, G. [Zavoiskii Physico-Technical Institute of Russian Academy of Sciences, 10/7 Sibirskii trakt st., Kazan 420029 (Russian Federation)

2014-05-01

A simple two-dimensional model of the formation of magnetic silicide Fe{sub 3}Si clusters during high-dose Fe ion implantation into silicon has been proposed and the cluster growth process has been computer simulated. The model takes into account the interaction between the cluster magnetization and magnetic moments of Fe atoms random walking in the implanted layer. If the clusters are formed in the presence of the external magnetic field parallel to the implanted layer, the model predicts the elongation of the growing cluster in the field direction. It has been proposed that the cluster elongation results in the uniaxial magnetic anisotropy in the plane of the implanted layer, which is observed in iron silicide films ion-beam synthesized in the external magnetic field.

Molecular dynamics modelling of EGCG clusters on ceramide bilayers

Energy Technology Data Exchange (ETDEWEB)

Yeo, Jingjie; Cheng, Yuan; Li, Weifeng; Zhang, Yong-Wei [Institute of High Performance Computing, A*STAR, 138632 (Singapore)

2015-12-31

A novel method of atomistic modelling and characterization of both pure ceramide and mixed lipid bilayers is being developed, using only the General Amber ForceField. Lipid bilayers modelled as pure ceramides adopt hexagonal packing after equilibration, and the area per lipid and bilayer thickness are consistent with previously reported theoretical results. Mixed lipid bilayers are modelled as a combination of ceramides, cholesterol, and free fatty acids. This model is shown to be stable after equilibration. Green tea extract, also known as epigallocatechin-3-gallate, is introduced as a spherical cluster on the surface of the mixed lipid bilayer. It is demonstrated that the cluster is able to bind to the bilayers as a cluster without diffusing into the surrounding water.

COCOA code for creating mock observations of star cluster models

Science.gov (United States)

Askar, Abbas; Giersz, Mirek; Pych, Wojciech; Dalessandro, Emanuele

2018-04-01

We introduce and present results from the COCOA (Cluster simulatiOn Comparison with ObservAtions) code that has been developed to create idealized mock photometric observations using results from numerical simulations of star cluster evolution. COCOA is able to present the output of realistic numerical simulations of star clusters carried out using Monte Carlo or N-body codes in a way that is useful for direct comparison with photometric observations. In this paper, we describe the COCOA code and demonstrate its different applications by utilizing globular cluster (GC) models simulated with the MOCCA (MOnte Carlo Cluster simulAtor) code. COCOA is used to synthetically observe these different GC models with optical telescopes, perform point spread function photometry, and subsequently produce observed colour-magnitude diagrams. We also use COCOA to compare the results from synthetic observations of a cluster model that has the same age and metallicity as the Galactic GC NGC 2808 with observations of the same cluster carried out with a 2.2 m optical telescope. We find that COCOA can effectively simulate realistic observations and recover photometric data. COCOA has numerous scientific applications that maybe be helpful for both theoreticians and observers that work on star clusters. Plans for further improving and developing the code are also discussed in this paper.

Cluster models, factors and characteristics for the competitive advantage of Lithuanian Maritime sector

OpenAIRE

Viederytė, Rasa; Didžiokas, Rimantas

2014-01-01

Paper analyses several cluster models on the basis of competitiveness: Nine-factor model, Double diamond model, Funnel model of cluster determinants, Destination Competitiveness and sustainability models, which are related to Porter’s Diamond model and concentrate to the classical one - adopt M. Porter’s Diamond model methodology to the evaluation of Lithuanian Maritime sector’s clustering on the basis of competitiveness. Despite the advances in cluster research, this model remains a complex ...

A Distributed Agent Implementation of Multiple Species Flocking Model for Document Partitioning Clustering

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

2006-01-01

The Flocking model, first proposed by Craig Reynolds, is one of the first bio-inspired computational collective behavior models that has many popular applications, such as animation. Our early research has resulted in a flock clustering algorithm that can achieve better performance than the Kmeans or the Ant clustering algorithms for data clustering. This algorithm generates a clustering of a given set of data through the embedding of the highdimensional data items on a two-dimensional grid for efficient clustering result retrieval and visualization. In this paper, we propose a bio-inspired clustering model, the Multiple Species Flocking clustering model (MSF), and present a distributed multi-agent MSF approach for document clustering.

Alloy design as an inverse problem of cluster expansion models

DEFF Research Database (Denmark)

Larsen, Peter Mahler; Kalidindi, Arvind R.; Schmidt, Søren

2017-01-01

Central to a lattice model of an alloy system is the description of the energy of a given atomic configuration, which can be conveniently developed through a cluster expansion. Given a specific cluster expansion, the ground state of the lattice model at 0 K can be solved by finding the configurat......Central to a lattice model of an alloy system is the description of the energy of a given atomic configuration, which can be conveniently developed through a cluster expansion. Given a specific cluster expansion, the ground state of the lattice model at 0 K can be solved by finding...... the inverse problem in terms of energetically distinct configurations, using a constraint satisfaction model to identify constructible configurations, and show that a convex hull can be used to identify ground states. To demonstrate the approach, we solve for all ground states for a binary alloy in a 2D...

Electromagnetic properties of 6Li in a cluster model with breathing clusters

International Nuclear Information System (INIS)

Kruppa, A.T.; Beck, R.; Dickmann, F.

1987-01-01

Electromagnetic properties of 6 Li are studied using a microscopic (α+δ) cluster model. In addition to the ground state of the clusters, their breathing excited states are included in the wave function in order to take into account the distortion of the clusters. The elastic charge form factor is in good agreement with experiment up to a momentum transfer of 8 fm -2 . The ground state magnetic form factor and the inelastic charge form factor are also well described. The effect of the breathing states of α on the form factors proves to be negligible except at high momentum transfer. The ground-state charge density, rms charge radius, the magnetic dipole moment and a reduced transition strength are also obtained in fair agreement with experiment. (author)

Leveraging microfinance to impact HIV and financial behaviors among adolescents and their mothers in West Bengal: a cluster randomized trial.

Science.gov (United States)

Spielberg, Freya; Crookston, Benjamin T; Chanani, Sheila; Kim, Jaewhan; Kline, Sean; Gray, Bobbi L

2013-01-01

Microfinance can be used to reach women and adolescent girls with HIV prevention education. We report findings from a cluster-randomized control trial among 55 villages in West Bengal to determine the impact of non-formal education on knowledge, attitudes and behaviors for HIV prevention and savings. Multilevel regression models were used to evaluate differences between groups for key outcomes while adjusting for cluster correlation and differences in baseline characteristics. Women and girls who received HIV education showed significant gains in HIV knowledge, awareness that condoms can prevent HIV, self-efficacy for HIV prevention, and confirmed use of clean needles, as compared to the control group. Condom use was rare and did not improve for women. While HIV testing was uncommon, knowledge of HIV-testing resources significantly increased among girls, and trended in the positive direction among women in intervention groups. Conversely, the savings education showed no impact on financial knowledge or behavior change.

Emergence of clustering in an acquaintance model without homophily

International Nuclear Information System (INIS)

Bhat, Uttam; Krapivsky, P L; Redner, S

2014-01-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks. (paper)

Emergence of clustering in an acquaintance model without homophily

Science.gov (United States)

Bhat, Uttam; Krapivsky, P. L.; Redner, S.

2014-11-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes.

Science.gov (United States)

Prague, Melanie; Wang, Rui; Stephens, Alisa; Tchetgen Tchetgen, Eric; DeGruttola, Victor

2016-12-01

Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR); it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method. © 2016, The International Biometric Society.

Quantitative properties of clustering within modern microscopic nuclear models

International Nuclear Information System (INIS)

Volya, A.; Tchuvil’sky, Yu. M.

2016-01-01

A method for studying cluster spectroscopic properties of nuclear fragmentation, such as spectroscopic amplitudes, cluster form factors, and spectroscopic factors, is developed on the basis of modern precision nuclear models that take into account the mixing of large-scale shell-model configurations. Alpha-cluster channels are considered as an example. A mathematical proof of the need for taking into account the channel-wave-function renormalization generated by exchange terms of the antisymmetrization operator (Fliessbach effect) is given. Examples where this effect is confirmed by a high quality of the description of experimental data are presented. By and large, the method in question extends substantially the possibilities for studying clustering phenomena in nuclei and for improving the quality of their description.

Impact of a cancer clinical trials web site on discussions about trial participation: a cluster randomized trial.

Science.gov (United States)

Dear, R F; Barratt, A L; Askie, L M; Butow, P N; McGeechan, K; Crossing, S; Currow, D C; Tattersall, M H N

2012-07-01

Cancer patients want access to reliable information about currently recruiting clinical trials. Oncologists and their patients were randomly assigned to access a consumer-friendly cancer clinical trials web site [Australian Cancer Trials (ACT), www.australiancancertrials.gov.au] or to usual care in a cluster randomized controlled trial. The primary outcome, measured from audio recordings of oncologist-patient consultations, was the proportion of patients with whom participation in any clinical trial was discussed. Analysis was by intention-to-treat accounting for clustering and stratification. Thirty medical oncologists and 493 patients were recruited. Overall, 46% of consultations in the intervention group compared with 34% in the control group contained a discussion about clinical trials (P=0.08). The mean consultation length in both groups was 29 min (P=0.69). The proportion consenting to a trial was 10% in both groups (P=0.65). Patients' knowledge about randomized trials was lower in the intervention than the control group (mean score 3.0 versus 3.3, P=0.03) but decisional conflict scores were similar (mean score 42 versus 43, P=0.83). Good communication between patients and physicians is essential. Within this context, a web site such as Australian Cancer Trials may be an important tool to encourage discussion about clinical trial participation.

Disseminating quality improvement: study protocol for a large cluster-randomized trial

Directory of Open Access Journals (Sweden)

French Michael T

2011-04-01

Full Text Available Abstract Background Dissemination is a critical facet of implementing quality improvement in organizations. As a field, addiction treatment has produced effective interventions but disseminated them slowly and reached only a fraction of people needing treatment. This study investigates four methods of disseminating quality improvement (QI to addiction treatment programs in the U.S. It is, to our knowledge, the largest study of organizational change ever conducted in healthcare. The trial seeks to determine the most cost-effective method of disseminating quality improvement in addiction treatment. Methods The study is evaluating the costs and effectiveness of different QI approaches by randomizing 201 addiction-treatment programs to four interventions. Each intervention used a web-based learning kit plus monthly phone calls, coaching, face-to-face meetings, or the combination of all three. Effectiveness is defined as reducing waiting time (days between first contact and treatment, increasing program admissions, and increasing continuation in treatment. Opportunity costs will be estimated for the resources associated with providing the services. Outcomes The study has three primary outcomes: waiting time, annual program admissions, and continuation in treatment. Secondary outcomes include: voluntary employee turnover, treatment completion, and operating margin. We are also seeking to understand the role of mediators, moderators, and other factors related to an organization's success in making changes. Analysis We are fitting a mixed-effect regression model to each program's average monthly waiting time and continuation rates (based on aggregated client records, including terms to isolate state and intervention effects. Admissions to treatment are aggregated to a yearly level to compensate for seasonality. We will order the interventions by cost to compare them pair-wise to the lowest cost intervention (monthly phone calls. All randomized sites

A Cluster Randomized Evaluation of a Health Department Data to Care Intervention Designed to Increase Engagement in HIV Care and Antiretroviral Use.

Science.gov (United States)

Dombrowski, Julia C; Hughes, James P; Buskin, Susan E; Bennett, Amy; Katz, David; Fleming, Mark; Nunez, Angela; Golden, Matthew R

2018-06-01

Many US health departments have implemented Data to Care interventions, which use HIV surveillance data to identify persons who are inadequately engaged in HIV medical care and assist them with care reengagement, but the effectiveness of this strategy is uncertain. We conducted a stepped-wedge, cluster-randomized evaluation of a Data to Care intervention in King County, Washington, 2011 to 2014. Persons diagnosed as having HIV for at least 6 months were eligible based on 1 of 2 criteria: (1) viral load (VL) greater than 500 copies/mL and CD4 less than 350 cells/μL at the last report in the past 12 months or (2) no CD4 or VL reported to the health department for at least 12 months. The intervention included medical provider contact, patient contact, and a structured individual interview. Health department staff assisted patients with reengagement using health systems navigation, brief counseling, and referral to support services. We clustered all eligible cases in the county by the last known medical provider and randomized the order of clusters for intervention, creating contemporaneous intervention and control periods (cases in later clusters contributed person-time to the control period at the same time that cases in earlier clusters contributed person-time to the intervention period). We compared the time to viral suppression (VL <200 copies/mL) for individuals during intervention and control periods using a Cox proportional hazards model. We identified 997 persons (intention to treat [ITT]), 18% of whom had moved or died. Of the remaining 822 (modified ITT), 161 (20%) had an undetectable VL reported before contact and 164 (20%) completed the individual interview. The hazard ratio (HR) for time to viral suppression did not differ between the intervention and control periods in ITT (HR, 1.21 [95% confidence interval, 0.85-1.71]) or modified ITT (HR, 1.18 [95% confidence interval, 0.83-1.68]) analysis. The Data to Care intervention did not impact time to viral

Generalization of Random Intercept Multilevel Models

Directory of Open Access Journals (Sweden)

Rehan Ahmad Khan

2013-10-01

Full Text Available The concept of random intercept models in a multilevel model developed by Goldstein (1986 has been extended for k-levels. The random variation in intercepts at individual level is marginally split into components by incorporating higher levels of hierarchy in the single level model. So, one can control the random variation in intercepts by incorporating the higher levels in the model.

Effect of Reassuring Information About Musculoskeletal and Mental Health Complaints at the Workplace: A Cluster Randomized Trial of the atWork Intervention.

Science.gov (United States)

Johnsen, Tone Langjordet; Eriksen, Hege Randi; Baste, Valborg; Indahl, Aage; Odeen, Magnus; Tveito, Torill Helene

2018-05-21

Purpose The purpose of this study was to investigate the possible difference between the Modified atWork intervention (MAW) and the Original atWork intervention (OAW) on sick leave and other health related outcomes. atWork is a group intervention using the workplace as an arena for distribution of evidence-based knowledge about musculoskeletal and mental health complaints. Methods A cluster randomized controlled trial with 93 kindergartens, comprising a total of 1011 employees, was conducted. Kindergartens were stratified by county and size and randomly allocated to MAW (45 clusters, 324 respondents) or OAW (48 clusters, 313 respondents). The randomization and intervention allocation processes were concealed. There was no blinding to group allocation. Primary outcome was register data on sick leave at cluster level. Secondary outcomes were health complaints, job satisfaction, social support, coping, and beliefs about musculoskeletal and mental health complaints, measured at the individual level. Results The MAW group reduced sick leave by 5.7% during the intervention year, while the OAW group had a 7.5% increase. Overall, the changes were not statistically significant, and no difference was detected between groups, based on 45 and 47 kindergartens. Compared to the OAW group, the MAW group had a smaller reduction for two of the statements concerning faulty beliefs about back pain, but believed less in the hereditary nature of depression. Conclusions The MAW did not have a different effect on sick leave at cluster level compared to the OAW. Trial registration https://Clinicaltrials.gov/ : NCT02396797. Registered March 23th, 2015.

Effectiveness of a multifaceted implementation strategy on physicians? referral behavior to an evidence-based psychosocial intervention in dementia: a cluster randomized controlled trial

OpenAIRE

D?pp, Carola ME; Graff, Maud JL; Teerenstra, Steven; Nijhuis-van der Sanden, Maria WG; Olde Rikkert, Marcel GM; Vernooij-Dassen, Myrra JFJ

2013-01-01

BACKGROUND: To evaluate the effectiveness of a multifaceted implementation strategy on physicians' referral rate to and knowledge on the community occupational therapy in dementia program (COTiD program). METHODS: A cluster randomized controlled trial with 28 experimental and 17 control clusters was conducted. Cluster included a minimum of one physician, one manager, and two occupational therapists. In the control group physicians and managers received no interventions and occupational therap...

Calculations of light scattering matrices for stochastic ensembles of nanosphere clusters

International Nuclear Information System (INIS)

Bunkin, N.F.; Shkirin, A.V.; Suyazov, N.V.; Starosvetskiy, A.V.

2013-01-01

Results of the calculation of the light scattering matrices for systems of stochastic nanosphere clusters are presented. A mathematical model of spherical particle clustering with allowance for cluster–cluster aggregation is used. The fractal properties of cluster structures are explored at different values of the model parameter that governs cluster–cluster interaction. General properties of the light scattering matrices of nanosphere-cluster ensembles as dependent on their mean fractal dimension have been found. The scattering-matrix calculations were performed for finite samples of 10 3 random clusters, made up of polydisperse spherical nanoparticles, having lognormal size distribution with the effective radius 50 nm and effective variance 0.02; the mean number of monomers in a cluster and its standard deviation were set to 500 and 70, respectively. The implemented computation environment, modeling the scattering matrices for overall sequences of clusters, is based upon T-matrix program code for a given single cluster of spheres, which was developed in [1]. The ensemble-averaged results have been compared with orientation-averaged ones calculated for individual clusters. -- Highlights: ► We suggested a hierarchical model of cluster growth allowing for cluster–cluster aggregation. ► We analyzed the light scattering by whole ensembles of nanosphere clusters. ► We studied the evolution of the light scattering matrix when changing the fractal dimension

Synthetic properties of models of globular clusters

Energy Technology Data Exchange (ETDEWEB)

Angeletti, L; Dolcetta, R; Giannone, P. (Rome Univ. (Italy). Osservatorio Astronomico)

1980-05-01

Synthetic and projected properties of models of globular clusters have been computed on the basis of stellar evolution and time changes of the dynamical cluster structure. Clusters with five and eight stellar groups (each group consisting of stars with the same mass) were studied. Mass loss from evolved stars was taken into account. Observational features were obtained at ages of 10-19 x 10/sup 9/ yr. The basic importance of the horizontal- and asymptotic-branch stars was pointed out. A comparison of the results with observed data of M3 is discussed with the purpose of obtaining general indications rather than a specific fit.

Synthetic properties of models of globular clusters

International Nuclear Information System (INIS)

Angeletti, L.; Dolcetta, R.; Giannone, P.

1980-01-01

Synthetic and projected properties of models of globular clusters have been computed on the basis of stellar evolution and time changes of the dynamical cluster structure. Clusters with five and eight stellar groups (each group consisting of stars with the same mass) were studied. Mass loss from evolved stars was taken into account. Observational features were obtained at ages of 10-19 x 10 9 yr. The basic importance of the horizontal- and asymptotic-branch stars was pointed out. A comparison of the results with observed data of M3 is discussed with the purpose of obtaining general indications rather than a specific fit. (orig.)

Towards Accurate Modelling of Galaxy Clustering on Small Scales: Testing the Standard ΛCDM + Halo Model

Science.gov (United States)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron K.; Scoccimarro, Roman; Piscionere, Jennifer A.; Wibking, Benjamin D.

2018-04-01

Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter halos. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models statistically. It is thus difficult to use the information encoded in small scales to test cosmological models or probe subtle features of the galaxy-halo connection. In this paper, we attempt to push halo modelling into the "accurate" regime with a fully numerical mock-based methodology and careful treatment of statistical and systematic errors. With our forward-modelling approach, we can incorporate clustering statistics beyond the traditional two-point statistics. We use this modelling methodology to test the standard ΛCDM + halo model against the clustering of SDSS DR7 galaxies. Specifically, we use the projected correlation function, group multiplicity function and galaxy number density as constraints. We find that while the model fits each statistic separately, it struggles to fit them simultaneously. Adding group statistics leads to a more stringent test of the model and significantly tighter constraints on model parameters. We explore the impact of varying the adopted halo definition and cosmological model and find that changing the cosmology makes a significant difference. The most successful model we tried (Planck cosmology with Mvir halos) matches the clustering of low luminosity galaxies, but exhibits a 2.3σ tension with the clustering of luminous galaxies, thus providing evidence that the "standard" halo model needs to be extended. This work opens the door to adding interesting freedom to the halo model and including additional clustering statistics as constraints.

Control of Phlebotomus argentipes (Diptera: Psychodidae sand fly in Bangladesh: A cluster randomized controlled trial.

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Rajib Chowdhury

2017-09-01

Full Text Available A number of studies on visceral leishmaniasis (VL vector control have been conducted during the past decade, sometimes came to very different conclusion. The present study on a large sample investigated different options which are partially unexplored including: (1 indoor residual spraying (IRS with alpha cypermethrin 5WP; (2 long lasting insecticide impregnated bed-net (LLIN; (3 impregnation of local bed-nets with slow release insecticide K-O TAB 1-2-3 (KOTAB; (4 insecticide spraying in potential breeding sites outside of house using chlorpyrifos 20EC (OUT and different combinations of the above.The study was a cluster randomized controlled trial where 3089 houses from 11 villages were divided into 10 sections, each section with 6 clusters and each cluster having approximately 50 houses. Based on vector density (males plus females during baseline survey, the 60 clusters were categorized into 3 groups: (1 high, (2 medium and (3 low. Each group had 20 clusters. From these three groups, 6 clusters (about 300 households were randomly selected for each type of intervention and control arms. Vector density was measured before and 2, 4, 5, 7, 11, 14, 15, 18 and 22 months after intervention using CDC light traps. The impact of interventions was measured by using the difference-in-differences regression model.A total of 17,434 sand flies were collected at baseline and during the surveys conducted over 9 months following the baseline measurements. At baseline, the average P. argentipes density per household was 10.6 (SD = 11.5 in the control arm and 7.3 (SD = 8.46 to 11.5 (SD = 20.2 in intervention arms. The intervention results presented as the range of percent reductions of sand flies (males plus females and rate ratios in 9 measurements over 22 months. Among single type interventions, the effect of IRS with 2 rounds of spraying (applied by the research team ranged from 13% to 75% reduction of P. argentipes density compared to the control arm (rate

Theoretical study of electronic and dynamic properties of simple metal clusters in jellium model

International Nuclear Information System (INIS)

El-Amine Madjet, M.

1994-01-01

We have studied the electronic properties of alkali-metal clusters in various theoretical approximations and in the framework of the spherical jellium model. We have investigated the ground state properties of alkali clusters both in the LDA (local density approximation) and in HF (Hartree-Fock) theory. We have compared the LDA predictions of the ground state properties to predictions obtained within the HF theory. Such a comparison permitted us to check the validity of the local density functional theory in describing the ground state of a finite fermion system. For the study of collective dipolar excitations in clusters, we have considered an electromagnetic excitation. We have investigated the collective modes in the following approximations: random phase approximation (RPA), time-dependent local-density approximation (TDLDA) and the sum-rules approach. An assessment of the approximation for the continuum state within the RPA is made by comparing with TDLDA calculations for the static and dynamic electronic properties. The comparative study that we have done on the exchange-correlation effects on the electronic and optical properties have shown that the discrepancies with measured data are due mostly to the jellium approximation for the ionic background. (author). 69 refs., 30 figs., 18 tabs

Interpreting semantic clustering effects in free recall.

Science.gov (United States)

Manning, Jeremy R; Kahana, Michael J

2012-07-01

The order in which participants choose to recall words from a studied list of randomly selected words provides insights into how memories of the words are represented, organised, and retrieved. One pervasive finding is that when a pair of semantically related words (e.g., "cat" and "dog") is embedded in the studied list, the related words are often recalled successively. This tendency to successively recall semantically related words is termed semantic clustering (Bousfield, 1953; Bousfield & Sedgewick, 1944; Cofer, Bruce, & Reicher, 1966). Measuring semantic clustering effects requires making assumptions about which words participants consider to be similar in meaning. However, it is often difficult to gain insights into individual participants' internal semantic models, and for this reason researchers typically rely on standardised semantic similarity metrics. Here we use simulations to gain insights into the expected magnitudes of semantic clustering effects given systematic differences between participants' internal similarity models and the similarity metric used to quantify the degree of semantic clustering. Our results provide a number of useful insights into the interpretation of semantic clustering effects in free recall.

Effectiveness of a selective intervention program targeting personality risk factors for alcohol misuse among young adolescents: results of a cluster randomized controlled trial

NARCIS (Netherlands)

Lammers, J.; Goossens, F.; Conrod, P.; Engels, R.C.M.E.; Wiers, R.W.H.J.; Kleinjan, M.

2015-01-01

Aim The effectiveness of Preventure was tested on drinking behaviour of young adolescents in secondary education in the Netherlands. Design A cluster randomized controlled trial was carried out, with participants assigned randomly to a two-session coping skills intervention or a control

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

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Gulnaz Ahmed

2017-02-01

Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

A new combined strategy to implement a community occupational therapy intervention: designing a cluster randomized controlled trial

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Adang Eddy

2011-03-01

Full Text Available Abstract Background Even effective interventions for people with dementia and their caregivers require specific implementation efforts. A pilot study showed that the highly effective community occupational therapy in dementia (COTiD program was not implemented optimally due to various barriers. To decrease these barriers and make implementation of the program more effective a combined implementation (CI strategy was developed. In our study we will compare the effectiveness of this CI strategy with the usual educational (ED strategy. Methods In this cluster randomized, single-blinded, controlled trial, each cluster consists of at least two occupational therapists, a manager, and a physician working at Dutch healthcare organizations that deliver community occupational therapy. Forty-five clusters, stratified by healthcare setting (nursing home, hospital, mental health service, have been allocated randomly to either the intervention group (CI strategy or the control group (ED strategy. The study population consists of the professionals included in each cluster and community-dwelling people with dementia and their caregivers. The primary outcome measures are the use of community OT, the adherence of OTs to the COTiD program, and the cost effectiveness of implementing the COTiD program in outpatient care. Secondary outcome measures are patient and caregiver outcomes and knowledge of managers, physicians and OTs about the COTiD program. Discussion Implementation research is fairly new in the field of occupational therapy, making this a unique study. This study does not only evaluate the effects of the CI-strategy on professionals, but also the effects of professionals' degree of implementation on client and caregiver outcomes. Clinical trials registration NCT01117285

Testing Numerical Models of Cool Core Galaxy Cluster Formation with X-Ray Observations

Science.gov (United States)

Henning, Jason W.; Gantner, Brennan; Burns, Jack O.; Hallman, Eric J.

2009-12-01

Using archival Chandra and ROSAT data along with numerical simulations, we compare the properties of cool core and non-cool core galaxy clusters, paying particular attention to the region beyond the cluster cores. With the use of single and double β-models, we demonstrate a statistically significant difference in the slopes of observed cluster surface brightness profiles while the cluster cores remain indistinguishable between the two cluster types. Additionally, through the use of hardness ratio profiles, we find evidence suggesting cool core clusters are cooler beyond their cores than non-cool core clusters of comparable mass and temperature, both in observed and simulated clusters. The similarities between real and simulated clusters supports a model presented in earlier work by the authors describing differing merger histories between cool core and non-cool core clusters. Discrepancies between real and simulated clusters will inform upcoming numerical models and simulations as to new ways to incorporate feedback in these systems.

Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model

International Nuclear Information System (INIS)

Ellefsen, Karl J.; Smith, David B.

2016-01-01

Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples. - Highlights: • We evaluate a clustering procedure by applying it to geochemical data. • The procedure generates a hierarchy of clusters. • Different levels of the hierarchy show geochemical processes at different spatial scales. • The clustering method is Bayesian finite mixture modeling. • Model parameters are estimated with Hamiltonian Monte Carlo sampling.

Regularity of the Speed of Biased Random Walk in a One-Dimensional Percolation Model

Science.gov (United States)

Gantert, Nina; Meiners, Matthias; Müller, Sebastian

2018-03-01

We consider biased random walks on the infinite cluster of a conditional bond percolation model on the infinite ladder graph. Axelson-Fisk and Häggström established for this model a phase transition for the asymptotic linear speed \\overline{v} of the walk. Namely, there exists some critical value λ c>0 such that \\overline{v}>0 if λ \\in (0,λ c) and \\overline{v}=0 if λ ≥ λ c. We show that the speed \\overline{v} is continuous in λ on (0,∞) and differentiable on (0,λ c/2). Moreover, we characterize the derivative as a covariance. For the proof of the differentiability of \\overline{v} on (0,λ c/2), we require and prove a central limit theorem for the biased random walk. Additionally, we prove that the central limit theorem fails to hold for λ ≥ λ c/2.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

Xu, Zhiqiang

2017-02-16

Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke

2017-01-01

Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

Beyond Hydrodynamic Modeling of AGN Heating in Galaxy Clusters

Science.gov (United States)

Yang, Hsiang-Yi Karen

Clusters of galaxies hold a unique position in hierarchical structure formation - they are both powerful cosmological probes and excellent astrophysical laboratories. Accurate modeling of the cluster properties is crucial for reducing systematic uncertainties in cluster cosmology. However, theoretical modeling of the intracluster medium (ICM) has long suffered from the "cooling-flow problem" - clusters with short central times or cool cores (CCs) are predicted to host massive inflows of gas that are not observed. Feedback from active galactic nuclei (AGN) is by far the most promising heating mechanism to counteract radiative cooling. Recent hydrodynamic simulations have made remarkable progress reproducing properties of the CCs. However, there remain two major questions that cannot be probed using purely hydrodynamic models: (1) what are the roles of cosmic rays (CRs)? (2) how is the existing picture altered when the ICM is modeled as weakly collisional plasma? We propose to move beyond limitations of pure hydrodynamics and progress toward a complete understanding of how AGN jet-inflated bubbles interact with their surroundings and provide heat to the ICM. Our objectives include: (1) understand how CR-dominated bubbles heat the ICM; (2) understand bubble evolution and sound-wave dissipation in the ICM with different assumptions of plasma properties, e.g., collisionality of the ICM, with or without anisotropic transport processes; (3) Develop a subgrid model of AGN heating that can be adopted in cosmological simulations based on state-of-the-art isolated simulations. We will use a combination of analytical calculations and idealized simulations to advance our understanding of each individual physical process. We will then perform the first three-dimensional (3D) magnetohydrodynamic (MHD) simulations of self-regulated AGN feedback with relevant CR and anisotropic transport processes in order to quantify the amount and distribution of heating from the AGN. Our

Complex scaling in the cluster model

International Nuclear Information System (INIS)

Kruppa, A.T.; Lovas, R.G.; Gyarmati, B.

1987-01-01

To find the positions and widths of resonances, a complex scaling of the intercluster relative coordinate is introduced into the resonating-group model. In the generator-coordinate technique used to solve the resonating-group equation the complex scaling requires minor changes in the formulae and code. The finding of the resonances does not need any preliminary guess or explicit reference to any asymptotic prescription. The procedure is applied to the resonances in the relative motion of two ground-state α clusters in 8 Be, but is appropriate for any systems consisting of two clusters. (author) 23 refs.; 5 figs

Making birthing safe for Pakistan women: a cluster randomized trial

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Khan Muhammad

2012-07-01

Full Text Available Abstract Background Two out of three neonatal deaths occur in just 10 countries and Pakistan stands third among them. Maternal mortality is also high with most deaths occurring during labor, birth, and first few hours after birth. Enhanced access and utilization of skilled delivery and emergency obstetric care is the demonstrated strategy in reducing maternal and neonatal mortality. This trial aims to compare reduction in neonate mortality and utilization of available safe birthing and Emergency Obstetric and Neonatal Care services among pregnant mothers receiving ‘structured birth planning’, and/or ‘transport facilitation’ compared to routine care. Methods A pragmatic cluster randomized trial, with qualitative and economic studies, will be conducted in Jhang, Chiniot and Khanewal districts of Punjab, Pakistan, from February 2011 to May 2013. At least 29,295 pregnancies will be registered in the three arms, seven clusters per arm; 1 structured birth planning and travel facilitation, 2 structured birth planning, and 3 control arm. Trial will be conducted through the Lady Health Worker program. Main outcomes are difference in neonatal mortality and service utilization; maternal mortality being the secondary outcome. Cluster level analysis will be done according to intention-to-treat. Discussion A nationwide network of about 100,000 lady health workers is already involved in antenatal and postnatal care of pregnant women. They also act as “gatekeepers” for the child birthing services. This gate keeping role mainly includes counseling and referral for skill birth attendance and travel arrangements for emergency obstetric care (if required. The review of current arrangements and practices show that the care delivery process needs enhancement to include adequate information provision as well as informed “decision” making and planned “action” by the pregnant women. The proposed three-year research is to develop, through national

Assessing the feasibility of interrupting the transmission of soil-transmitted helminths through mass drug administration: The DeWorm3 cluster randomized trial protocol.

Science.gov (United States)

Ásbjörnsdóttir, Kristjana Hrönn; Ajjampur, Sitara S Rao; Anderson, Roy M; Bailey, Robin; Gardiner, Iain; Halliday, Katherine E; Ibikounle, Moudachirou; Kalua, Khumbo; Kang, Gagandeep; Littlewood, D Timothy J; Luty, Adrian J F; Means, Arianna Rubin; Oswald, William; Pullan, Rachel L; Sarkar, Rajiv; Schär, Fabian; Szpiro, Adam; Truscott, James E; Werkman, Marleen; Yard, Elodie; Walson, Judd L

2018-01-01

Current control strategies for soil-transmitted helminths (STH) emphasize morbidity control through mass drug administration (MDA) targeting preschool- and school-age children, women of childbearing age and adults in certain high-risk occupations such as agricultural laborers or miners. This strategy is effective at reducing morbidity in those treated but, without massive economic development, it is unlikely it will interrupt transmission. MDA will therefore need to continue indefinitely to maintain benefit. Mathematical models suggest that transmission interruption may be achievable through MDA alone, provided that all age groups are targeted with high coverage. The DeWorm3 Project will test the feasibility of interrupting STH transmission using biannual MDA targeting all age groups. Study sites (population ≥80,000) have been identified in Benin, Malawi and India. Each site will be divided into 40 clusters, to be randomized 1:1 to three years of twice-annual community-wide MDA or standard-of-care MDA, typically annual school-based deworming. Community-wide MDA will be delivered door-to-door, while standard-of-care MDA will be delivered according to national guidelines. The primary outcome is transmission interruption of the STH species present at each site, defined as weighted cluster-level prevalence ≤2% by quantitative polymerase chain reaction (qPCR), 24 months after the final round of MDA. Secondary outcomes include the endline prevalence of STH, overall and by species, and the endline prevalence of STH among children under five as an indicator of incident infections. Secondary analyses will identify cluster-level factors associated with transmission interruption. Prevalence will be assessed using qPCR of stool samples collected from a random sample of cluster residents at baseline, six months after the final round of MDA and 24 months post-MDA. A smaller number of individuals in each cluster will be followed with annual sampling to monitor trends in

Implementing international osteoarthritis treatment guidelines in primary health care: study protocol for the SAMBA stepped wedge cluster randomized controlled trial.

Science.gov (United States)

Østerås, Nina; van Bodegom-Vos, Leti; Dziedzic, Krysia; Moseng, Tuva; Aas, Eline; Andreassen, Øyvor; Mdala, Ibrahim; Natvig, Bård; Røtterud, Jan Harald; Schjervheim, Unni-Berit; Vlieland, Thea Vliet; Hagen, Kåre Birger

2015-12-02

Previous research indicates that people with osteoarthritis (OA) are not receiving the recommended and optimal treatment. Based on international treatment recommendations for hip and knee OA and previous research, the SAMBA model for integrated OA care in Norwegian primary health care has been developed. The model includes physiotherapist (PT) led patient OA education sessions and an exercise programme lasting 8-12 weeks. This study aims to assess the effectiveness, feasibility, and costs of a tailored strategy to implement the SAMBA model. A cluster randomized controlled trial with stepped wedge design including an effect, process, and cost evaluation will be conducted in six municipalities (clusters) in Norway. The municipalities will be randomized for time of crossover from current usual care to the implementation of the SAMBA model by a tailored strategy. The tailored strategy includes interactive workshops for general practitioners (GPs) and PTs in primary care covering the SAMBA model for integrated OA care, educational material, educational outreach visits, feedback, and reminder material. Outcomes will be measured at the patient, GP, and PT levels using self-report, semi-structured interviews, and register based data. The primary outcome measure is patient-reported quality of care (OsteoArthritis Quality Indicator questionnaire) at 6-month follow-up. Secondary outcomes include referrals to PT, imaging, and referrals to the orthopaedic surgeon as well as participants' treatment satisfaction, symptoms, physical activity level, body weight, and self-reported and measured lower limb function. The actual exposure to the tailor made implementation strategy and user experiences will be measured in a process evaluation. In the economic evaluation, the difference in costs of usual OA care and the SAMBA model for integrated OA care will be compared with the difference in health outcomes and reported by the incremental cost-effectiveness ratio (ICER). The results

Citywide cluster randomized trial to restore blighted vacant land and its effects on violence, crime, and fear

Science.gov (United States)

Charles C. Branas; Eugenia South; Michelle C. Kondo; Bernadette C. Hohl; Philippe Bourgois; Douglas J. Wiebe; John M. MacDonald

2018-01-01

Vacant and blighted urban land is a widespread and potentially risky environmental condition encountered by millions of people on a daily basis. About 15% of the land in US cities is deemed vacant or abandoned, an area roughly the size of Switzerland. In a citywide cluster randomized controlled trial, we investigated the effects of standardized, reproducible...

Geographic analysis of vaccine uptake in a cluster-randomized controlled trial in Hue, Vietnam.

Science.gov (United States)

Ali, Mohammad; Thiem, Vu Dinh; Park, Jin-Kyung; Ochiai, Rion Leon; Canh, Do Gia; Danovaro-Holliday, M Carolina; Kaljee, Linda M; Clemens, John D; Acosta, Camilo J

2007-09-01

This paper identifies spatial patterns and predictors of vaccine uptake in a cluster-randomized controlled trial in Hue, Vietnam. Data for this study result from the integration of demographic surveillance, vaccine record, and geographic data of the study area. A multi-level cross-classified (non-hierarchical) model was used for analyzing the non-nested nature of individual's ecological data. Vaccine uptake was unevenly distributed in space and there was spatial variability among predictors of vaccine uptake. Vaccine uptake was higher among students with younger, male, or not literate family heads. Students from households with higher per-capita income were less likely to participate in the trial. Residency south of the river or further from a hospital/polyclinic was associated with higher vaccine uptake. Younger students were more likely to be vaccinated than older students in high- or low-risk areas, but not in the entire study area. The findings are important for the management of vaccine campaigns during a trial and for interpretation of disease patterns during vaccine-efficacy evaluation.

Does clinical equipoise apply to cluster randomized trials in health research?

Science.gov (United States)

2011-01-01

This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs) in health research. In the introductory paper in this series, Weijer and colleagues set out six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation. This paper addresses the third of the questions posed, namely, does clinical equipoise apply to CRTs in health research? The ethical principle of beneficence is the moral obligation not to harm needlessly and, when possible, to promote the welfare of research subjects. Two related ethical problems have been discussed in the CRT literature. First, are control groups that receive only usual care unduly disadvantaged? Second, when accumulating data suggests the superiority of one intervention in a trial, is there an ethical obligation to act? In individually randomized trials involving patients, similar questions are addressed by the concept of clinical equipoise, that is, the ethical requirement that, at the start of a trial, there be a state of honest, professional disagreement in the community of expert practitioners as to the preferred treatment. Since CRTs may not involve physician-researchers and patient-subjects, the applicability of clinical equipoise to CRTs is uncertain. Here we argue that clinical equipoise may be usefully grounded in a trust relationship between the state and research subjects, and, as a result, clinical equipoise is applicable to CRTs. Clinical equipoise is used to argue that control groups receiving only usual care are not disadvantaged so long as the evidence supporting the experimental and control interventions is such that experts would disagree as to which is preferred. Further, while data accumulating during the course of a CRT may favor one intervention over another, clinical equipoise supports continuing the trial until the results are likely to be broadly convincing, often coinciding with the planned completion of the trial

Effects of Mindfulness-Based Stress Reduction on the Mental Health of Clinical Clerkship Students: A Cluster-Randomized Controlled Trial

NARCIS (Netherlands)

Dijk, I. van; Lucassen, P.L.B.J.; Akkermans, R.P.; Engelen, B.G.M. van; Weel, C. van; Speckens, A.E.M.

2017-01-01

PURPOSE: To examine the effect of mindfulness-based stress reduction training (MBSR) on the mental health of medical students during clinical clerkships. METHOD: Between February 2011 and May 2014, the authors conducted a cluster-randomized controlled trial of clerkships as usual (CAU) and

Correlation of electron transport and photocatalysis of nanocrystalline clusters studied by Monte-Carlo continuity random walking.

Science.gov (United States)

Liu, Baoshun; Li, Ziqiang; Zhao, Xiujian

2015-02-21

In this research, Monte-Carlo Continuity Random Walking (MC-RW) model was used to study the relation between electron transport and photocatalysis of nano-crystalline (nc) clusters. The effects of defect energy disorder, spatial disorder of material structure, electron density, and interfacial transfer/recombination on the electron transport and the photocatalysis were studied. Photocatalytic activity is defined as 1/τ from a statistical viewpoint with τ being the electron average lifetime. Based on the MC-RW simulation, a clear physical and chemical "picture" was given for the photocatalytic kinetic analysis of nc-clusters. It is shown that the increase of defect energy disorder and material spatial structural disorder, such as the decrease of defect trap number, the increase of crystallinity, the increase of particle size, and the increase of inter-particle connection, can enhance photocatalytic activity through increasing electron transport ability. The increase of electron density increases the electron Fermi level, which decreases the activation energy for electron de-trapping from traps to extending states, and correspondingly increases electron transport ability and photocatalytic activity. Reducing recombination of electrons and holes can increase electron transport through the increase of electron density and then increases the photocatalytic activity. In addition to the electron transport, the increase of probability for electrons to undergo photocatalysis can increase photocatalytic activity through the increase of the electron interfacial transfer speed.

Clustering promotes switching dynamics in networks of noisy neurons

Science.gov (United States)

Franović, Igor; Klinshov, Vladimir

2018-02-01

Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

Solvable single-species aggregation-annihilation model for chain-shaped cluster growth

International Nuclear Information System (INIS)

Ke Jianhong; Lin Zhenquan; Zheng Yizhuang; Chen Xiaoshuang; Lu Wei

2007-01-01

We propose a single-species aggregation-annihilation model, in which an aggregation reaction between two clusters produces an active cluster and an annihilation reaction produces an inert one. By means of the mean-field rate equation, we respectively investigate the kinetic scaling behaviours of three distinct systems. The results exhibit that: (i) for the general aggregation-annihilation system, the size distribution of active clusters consistently approaches the conventional scaling form; (ii) for the system with the self-degeneration of the cluster's activities, it takes the modified scaling form; and (iii) for the system with the self-closing of active clusters, it does not scale. Moreover, the size distribution of inert clusters with small size takes a power-law form, while that of large inert clusters obeys the scaling law. The results also show that all active clusters will eventually transform into inert ones and the inert clusters of any size can be produced by such an aggregation-annihilation process. This model can be used to mimic the chain-shaped cluster growth and can provide some useful predictions for the kinetic behaviour of the system

Irritable bowel syndrome and upper dyspepsia among the elderly: a study of symptom clusters in a random 70 year old population

DEFF Research Database (Denmark)

Kay, L; Jørgensen, Torben; Schultz-Larsen, K

1996-01-01

. Heartburn/acid regurgitation did not show a consistent association to any other symptoms and may be considered as a cluster of it own. Pain characteristics traditionally related to upper dyspepsia did not specifically relate to any cluster. It is concluded that, in this 70-year-old population abdominal......With the aim to assess the clustering of abdominal symptoms in a random population, data from a cohort study of a 70 year old Danish population were analysed. The cohort comprised 1,119 subjects of which 72% participated in a primary study and 91% of the survivors in a similar study five years...

Cost-Effectiveness of a Long-Term Internet-Delivered Worksite Health Promotion Programme on Physical Activity and Nutrition: A Cluster Randomized Controlled Trial

Science.gov (United States)

Robroek, Suzan J. W.; Polinder, Suzanne; Bredt, Folef J.; Burdorf, Alex

2012-01-01

This study aims to evaluate the cost-effectiveness of a long-term workplace health promotion programme on physical activity (PA) and nutrition. In total, 924 participants enrolled in a 2-year cluster randomized controlled trial, with departments (n = 74) within companies (n = 6) as the unit of randomization. The intervention was compared with a…

Involving patients in setting priorities for healthcare improvement: a cluster randomized trial.

Science.gov (United States)

Boivin, Antoine; Lehoux, Pascale; Lacombe, Réal; Burgers, Jako; Grol, Richard

2014-02-20

Patients are increasingly seen as active partners in healthcare. While patient involvement in individual clinical decisions has been extensively studied, no trial has assessed how patients can effectively be involved in collective healthcare decisions affecting the population. The goal of this study was to test the impact of involving patients in setting healthcare improvement priorities for chronic care at the community level. Cluster randomized controlled trial. Local communities were randomized in intervention (priority setting with patient involvement) and control sites (no patient involvement). Communities in a canadian region were required to set priorities for improving chronic disease management in primary care, from a list of 37 validated quality indicators. Patients were consulted in writing, before participating in face-to-face deliberation with professionals. Professionals established priorities among themselves, without patient involvement. A total of 172 individuals from six communities participated in the study, including 83 chronic disease patients, and 89 health professionals. The primary outcome was the level of agreement between patients' and professionals' priorities. Secondary outcomes included professionals' intention to use the selected quality indicators, and the costs of patient involvement. Priorities established with patients were more aligned with core generic components of the Medical Home and Chronic Care Model, including: access to primary care, self-care support, patient participation in clinical decisions, and partnership with community organizations (p Priorities established by professionals alone placed more emphasis on the technical quality of single disease management. The involvement intervention fostered mutual influence between patients and professionals, which resulted in a 41% increase in agreement on common priorities (95%CI: +12% to +58%, p priorities. Patient involvement can change priorities driving healthcare

Latent Clustering Models for Outlier Identification in Telecom Data

Directory of Open Access Journals (Sweden)

Ye Ouyang

2016-01-01

Full Text Available Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar high-speed machines. The ability to quickly identify unexpected traffic data in this stream is critical for mobile carriers, as it can be caused by either fraudulent intrusion or technical problems. Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers. In this article, we develop and compare clustering models for telecom data, focusing on those that include time-stamp information management. Two main models are introduced, solved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA and time-dependent Gaussian Mixture Models (time-GMM. These models are then compared with other different clustering models, such as Gaussian model and GMM (which do not contain time-stamp information. We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low tuning parameters or expertise requirement.

Mathematical model for research and analyze relations and functions between enterprises, members of cluster

Science.gov (United States)

Angelov, Kiril; Kaynakchieva, Vesela

2017-12-01

The aim of the current study is to research and analyze Mathematical model for research and analyze of relations and functions between enterprises, members of cluster, and its approbation in given cluster. Subject of the study are theoretical mechanisms for the definition of mathematical models for research and analyze of relations and functions between enterprises, members of cluster. Object of the study are production enterprises, members of cluster. Results of this study show that described theoretical mathematical model is applicable for research and analyze of functions and relations between enterprises, members of cluster from different industrial sectors. This circumstance creates alternatives for election of cluster, where is experimented this model for interaction improvement between enterprises, members of cluster.

Impact on Prehospital Delay of a Stroke Preparedness Campaign: A SW-RCT (Stepped-Wedge Cluster Randomized Controlled Trial).

Science.gov (United States)

Denti, Licia; Caminiti, Caterina; Scoditti, Umberto; Zini, Andrea; Malferrari, Giovanni; Zedde, Maria Luisa; Guidetti, Donata; Baratti, Mario; Vaghi, Luca; Montanari, Enrico; Marcomini, Barbara; Riva, Silvia; Iezzi, Elisa; Castellini, Paola; Olivato, Silvia; Barbi, Filippo; Perticaroli, Eva; Monaco, Daniela; Iafelice, Ilaria; Bigliardi, Guido; Vandelli, Laura; Guareschi, Angelica; Artoni, Andrea; Zanferrari, Carla; Schulz, Peter J

2017-12-01

Public campaigns to increase stroke preparedness have been tested in different contexts, showing contradictory results. We evaluated the effectiveness of a stroke campaign, designed specifically for the Italian population in reducing prehospital delay. According to an SW-RCT (Stepped-Wedge Cluster Randomized Controlled Trial) design, the campaign was launched in 4 provinces in the northern part of the region Emilia Romagna at 3-month intervals in randomized sequence. The units of analysis were the patients admitted to hospital, with stroke and transient ischemic attack, over a time period of 15 months, beginning 3 months before the intervention was launched in the first province to allow for baseline data collection. The proportion of early arrivals (within 2 hours of symptom onset) was the primary outcome. Thrombolysis rate and some behavioral end points were the secondary outcomes. Data were analyzed using a fixed-effect model, adjusting for cluster and time trends. We enrolled 1622 patients, 912 exposed and 710 nonexposed to the campaign. The proportion of early access was nonsignificantly lower in exposed patients (354 [38.8%] versus 315 [44.4%]; adjusted odds ratio, 0.81; 95% confidence interval, 0.60-1.08; P =0.15). As for secondary end points, an increase was found for stroke recognition, which approximated but did not reach statistical significance ( P =0.07). Our campaign was not effective in reducing prehospital delay. Even if some limitations of the intervention, mainly in terms of duration, are taken into account, our study demonstrates that new communication strategies should be tested before large-scale implementation. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01881152. © 2017 American Heart Association, Inc.

A cluster randomized trial of strategies to increase uptake amongst young women invited for their first cervical screen: The STRATEGIC trial.

Science.gov (United States)

Kitchener, H; Gittins, M; Cruickshank, M; Moseley, C; Fletcher, S; Albrow, R; Gray, A; Brabin, L; Torgerson, D; Crosbie, E J; Sargent, A; Roberts, C

2018-06-01

Objectives To measure the feasibility and effectiveness of interventions to increase cervical screening uptake amongst young women. Methods A two-phase cluster randomized trial conducted in general practices in the NHS Cervical Screening Programme. In Phase 1, women in practices randomized to intervention due for their first invitation to cervical screening received a pre-invitation leaflet and, separately, access to online booking. In Phase 2, non-attenders at six months were randomized to one of: vaginal self-sample kits sent unrequested or offered; timed appointments; nurse navigator; or the choice between nurse navigator or self-sample kits. Primary outcome was uplift in intervention vs. control practices, at 3 and 12 months post invitation. Results Phase 1 randomized 20,879 women. Neither pre-invitation leaflet nor online booking increased screening uptake by three months (18.8% pre-invitation leaflet vs. 19.2% control and 17.8% online booking vs. 17.2% control). Uptake was higher amongst human papillomavirus vaccinees at three months (OR 2.07, 95% CI 1.69-2.53, p < 0.001). Phase 2 randomized 10,126 non-attenders, with 32-34 clusters for each intervention and 100 clusters as controls. Sending self-sample kits increased uptake at 12 months (OR 1.51, 95% CI 1.20-1.91, p = 0.001), as did timed appointments (OR 1.41, 95% CI 1.14-1.74, p = 0.001). The offer of a nurse navigator, a self-sample kits on request, and choice between timed appointments and nurse navigator were ineffective. Conclusions Amongst non-attenders, self-sample kits sent and timed appointments achieved an uplift in screening over the short term; longer term impact is less certain. Prior human papillomavirus vaccination was associated with increased screening uptake.

Oxide-supported metal clusters: models for heterogeneous catalysts

International Nuclear Information System (INIS)

Santra, A K; Goodman, D W

2003-01-01

Understanding the size-dependent electronic, structural and chemical properties of metal clusters on oxide supports is an important aspect of heterogeneous catalysis. Recently model oxide-supported metal catalysts have been prepared by vapour deposition of catalytically relevant metals onto ultra-thin oxide films grown on a refractory metal substrate. Reactivity and spectroscopic/microscopic studies have shown that these ultra-thin oxide films are excellent models for the corresponding bulk oxides, yet are sufficiently electrically conductive for use with various modern surface probes including scanning tunnelling microscopy (STM). Measurements on metal clusters have revealed a metal to nonmetal transition as well as changes in the crystal and electronic structures (including lattice parameters, band width, band splitting and core-level binding energy shifts) as a function of cluster size. Size-dependent catalytic reactivity studies have been carried out for several important reactions, and time-dependent catalytic deactivation has been shown to arise from sintering of metal particles under elevated gas pressures and/or reactor temperatures. In situ STM methodologies have been developed to follow the growth and sintering kinetics on a cluster-by-cluster basis. Although several critical issues have been addressed by several groups worldwide, much more remains to be done. This article highlights some of these accomplishments and summarizes the challenges that lie ahead. (topical review)

CLUMP-3D: Testing ΛCDM with Galaxy Cluster Shapes

Science.gov (United States)

Sereno, Mauro; Umetsu, Keiichi; Ettori, Stefano; Sayers, Jack; Chiu, I.-Non; Meneghetti, Massimo; Vega-Ferrero, Jesús; Zitrin, Adi

2018-06-01

The ΛCDM model of structure formation makes strong predictions on the concentration and shape of dark matter (DM) halos, which are determined by mass accretion processes. Comparison between predicted shapes and observations provides a geometric test of the ΛCDM model. Accurate and precise measurements needs a full three-dimensional (3D) analysis of the cluster mass distribution. We accomplish this with a multi-probe 3D analysis of the X-ray regular Cluster Lensing and Supernova survey with Hubble (CLASH) clusters combining strong and weak lensing, X-ray photometry and spectroscopy, and the Sunyaev–Zel’dovich effect (SZe). The cluster shapes and concentrations are consistent with ΛCDM predictions. The CLASH clusters are randomly oriented, as expected given the sample selection criteria. Shapes agree with numerical results for DM-only halos, which hints at baryonic physics being less effective in making halos rounder.

GENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLING

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Christopher Ouma Onyango

2010-09-01

Full Text Available Chambers and Dorfman (2002 constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008 have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis.

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Zhang, Chao; Zhang, Pengcheng; Zhang, Weizhan

2017-09-27

A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences.

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Zhang Zhang

2009-06-01

Full Text Available A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification of cluster size or cluster count. Here we derive and demonstrate a maximum likelihood method of hierarchical clustering. Our method incorporates a tripartite divide-and-conquer strategy that models sequence heterogeneity, delineates clusters, and yields a profile of the level of clustering associated with each site. The clustering model may be evaluated via model selection using the Akaike Information Criterion, the corrected Akaike Information Criterion, and the Bayesian Information Criterion. Furthermore, model averaging using weighted model likelihoods may be applied to incorporate model uncertainty into the profile of heterogeneity across sites. We evaluated our method by examining its performance on a number of simulated datasets as well as on empirical polymorphism data from diverse natural alleles of the Drosophila alcohol dehydrogenase gene. Our method yielded greater power for the detection of clustered sites across a breadth of parameter ranges, and achieved better accuracy and precision of estimation of clusters, than did the existing empirical cumulative distribution function statistics.

A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks

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A. Tajari Siahmarzkooh

2016-12-01

Full Text Available In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.

Three-Dimensional Modeling of Fracture Clusters in Geothermal Reservoirs

Energy Technology Data Exchange (ETDEWEB)

Ghassemi, Ahmad [Univ. of Oklahoma, Norman, OK (United States)

2017-08-11

The objective of this is to develop a 3-D numerical model for simulating mode I, II, and III (tensile, shear, and out-of-plane) propagation of multiple fractures and fracture clusters to accurately predict geothermal reservoir stimulation using the virtual multi-dimensional internal bond (VMIB). Effective development of enhanced geothermal systems can significantly benefit from improved modeling of hydraulic fracturing. In geothermal reservoirs, where the temperature can reach or exceed 350oC, thermal and poro-mechanical processes play an important role in fracture initiation and propagation. In this project hydraulic fracturing of hot subsurface rock mass will be numerically modeled by extending the virtual multiple internal bond theory and implementing it in a finite element code, WARP3D, a three-dimensional finite element code for solid mechanics. The new constitutive model along with the poro-thermoelastic computational algorithms will allow modeling the initiation and propagation of clusters of fractures, and extension of pre-existing fractures. The work will enable the industry to realistically model stimulation of geothermal reservoirs. The project addresses the Geothermal Technologies Office objective of accurately predicting geothermal reservoir stimulation (GTO technology priority item). The project goal will be attained by: (i) development of the VMIB method for application to 3D analysis of fracture clusters; (ii) development of poro- and thermoelastic material sub-routines for use in 3D finite element code WARP3D; (iii) implementation of VMIB and the new material routines in WARP3D to enable simulation of clusters of fractures while accounting for the effects of the pore pressure, thermal stress and inelastic deformation; (iv) simulation of 3D fracture propagation and coalescence and formation of clusters, and comparison with laboratory compression tests; and (v) application of the model to interpretation of injection experiments (planned by our

Recent developments of the quantum chemical cluster approach for modeling enzyme reactions.

Science.gov (United States)

Siegbahn, Per E M; Himo, Fahmi

2009-06-01

The quantum chemical cluster approach for modeling enzyme reactions is reviewed. Recent applications have used cluster models much larger than before which have given new modeling insights. One important and rather surprising feature is the fast convergence with cluster size of the energetics of the reactions. Even for reactions with significant charge separation it has in some cases been possible to obtain full convergence in the sense that dielectric cavity effects from outside the cluster do not contribute to any significant extent. Direct comparisons between quantum mechanics (QM)-only and QM/molecular mechanics (MM) calculations for quite large clusters in a case where the results differ significantly have shown that care has to be taken when using the QM/MM approach where there is strong charge polarization. Insights from the methods used, generally hybrid density functional methods, have also led to possibilities to give reasonable error limits for the results. Examples are finally given from the most extensive study using the cluster model, the one of oxygen formation at the oxygen-evolving complex in photosystem II.

On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio

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Tatjana Miljkovic

2018-05-01

Full Text Available We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM and mixture-based clustering for an ordered stereotype model (OSM. The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.

Cluster infall in the concordance LCDM model

OpenAIRE

Pivato, Maximiliano C.; Padilla, Nelson D.; Lambas, Diego G.

2005-01-01

We perform statistical analyses of the infall of dark-matter onto clusters in numerical simulations within the concordance LCDM model. By studying the infall profile around clusters of different mass, we find a linear relation between the maximum infall velocity and mass which reach 900km/s for the most massive groups. The maximum infall velocity and the group mass follow a suitable power law fit of the form, V_{inf}^{max} = (M/m_0)^{gamma}. By comparing the measured infall velocity to the li...

Android Malware Classification Using K-Means Clustering Algorithm

Science.gov (United States)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Testing feedback message framing and comparators to address prescribing of high-risk medications in nursing homes: protocol for a pragmatic, factorial, cluster-randomized trial.

Science.gov (United States)

Ivers, Noah M; Desveaux, Laura; Presseau, Justin; Reis, Catherine; Witteman, Holly O; Taljaard, Monica K; McCleary, Nicola; Thavorn, Kednapa; Grimshaw, Jeremy M

2017-07-14

Audit and feedback (AF) interventions that leverage routine administrative data offer a scalable and relatively low-cost method to improve processes of care. AF interventions are usually designed to highlight discrepancies between desired and actual performance and to encourage recipients to act to address such discrepancies. Comparing to a regional average is a common approach, but more recipients would have a discrepancy if compared to a higher-than-average level of performance. In addition, how recipients perceive and respond to discrepancies may depend on how the feedback itself is framed. We aim to evaluate the effectiveness of different comparators and framing in feedback on high-risk prescribing in nursing homes. This is a pragmatic, 2 × 2 factorial, cluster-randomized controlled trial testing variations in the comparator and framing on the effectiveness of quarterly AF in changing high-risk prescribing in nursing homes in Ontario, Canada. We grouped homes that share physicians into clusters and randomized these clusters into the four experimental conditions. Outcomes will be assessed after 6 months; all primary analyses will be by intention-to-treat. The primary outcome (monthly number of high-risk medications received by each patient) will be analysed using a general linear mixed effects regression model. We will present both four-arm and factorial analyses. With 160 clusters and an average of 350 beds per cluster, assuming no interaction and similar effects for each intervention, we anticipate 90% power to detect an absolute mean difference of 0.3 high-risk medications prescribed. A mixed-methods process evaluation will explore potential mechanisms underlying the observed effects, exploring targeted constructs including intention, self-efficacy, outcome expectations, descriptive norms, and goal prioritization. An economic analysis will examine cost-effectiveness analysis from the perspective of the publicly funded health care system. This protocol

Effectiveness of a Geriatric Care Model for frail older adults in primary care: Results from a stepped wedge cluster randomized trial.

Science.gov (United States)

Hoogendijk, Emiel O; van der Horst, Henriëtte E; van de Ven, Peter M; Twisk, Jos W R; Deeg, Dorly J H; Frijters, Dinnus H M; van Leeuwen, Karen M; van Campen, Jos P C M; Nijpels, Giel; Jansen, Aaltje P D; van Hout, Hein P J

2016-03-01

Primary care-based comprehensive care programs have the potential to improve outcomes in frail older adults. We evaluated the impact of the Geriatric Care Model (GCM) on the quality of life of community-dwelling frail older adults. A 24-month stepped wedge cluster randomized controlled trial was conducted between May 2010 and March 2013 in 35 primary care practices in the Netherlands, and included 1147 frail older adults. The intervention consisted of a geriatric in-home assessment by a practice nurse, followed by a tailored care plan. Reassessment occurred every six months. Nurses worked together with primary care physicians and were supervised and trained by geriatric expert teams. Complex patients were reviewed in multidisciplinary consultations. The primary outcome was quality of life (SF-12). Secondary outcomes were health-related quality of life, functional limitations, self-rated health, psychological wellbeing, social functioning and hospitalizations. Intention-to-treat analyses based on multilevel modeling showed no significant differences between the intervention group and usual care regarding SF-12 and most secondary outcomes. Only for IADL limitations we found a small intervention effect in patients who received the intervention for 18months (B=-0.25, 95%CI=-0.43 to -0.06, p=0.007), but this effect was not statistically significant after correction for multiple comparisons. The GCM did not show beneficial effects on quality of life in frail older adults in primary care, compared to usual care. This study strengthens the idea that comprehensive care programs add very little to usual primary care for this population. The Netherlands National Trial Register NTR2160. Copyright © 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Design and protocol of the weight loss lottery- a cluster randomized trial.

Science.gov (United States)

van der Swaluw, Koen; Lambooij, Mattijs S; Mathijssen, Jolanda J P; Schipper, Maarten; Zeelenberg, Marcel; Polder, Johan J; Prast, Henriëtte M

2016-07-01

People often intend to exercise but find it difficult to attend their gyms on a regular basis. At times, people seek and accept deadlines with consequences to realize their own goals (i.e. commitment devices). The aim of our cluster randomized controlled trial is to test whether a lottery-based commitment device can promote regular gym attendance. The winners of the lottery always get feedback on the outcome but can only claim their prize if they attended their gyms on a regular basis. In this paper we present the design and baseline characteristics of a three-arm trial which is performed with 163 overweight participants in six in-company fitness centers in the Netherlands. Copyright © 2016 Elsevier Inc. All rights reserved.

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis

Directory of Open Access Journals (Sweden)

Chao Zhang

2017-09-01

Full Text Available A wireless-powered sensor network (WPSN consisting of one hybrid access point (HAP, a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

Cluster-based upper body marker models for three-dimensional kinematic analysis: Comparison with an anatomical model and reliability analysis.

Science.gov (United States)

Boser, Quinn A; Valevicius, Aïda M; Lavoie, Ewen B; Chapman, Craig S; Pilarski, Patrick M; Hebert, Jacqueline S; Vette, Albert H

2018-04-27

Quantifying angular joint kinematics of the upper body is a useful method for assessing upper limb function. Joint angles are commonly obtained via motion capture, tracking markers placed on anatomical landmarks. This method is associated with limitations including administrative burden, soft tissue artifacts, and intra- and inter-tester variability. An alternative method involves the tracking of rigid marker clusters affixed to body segments, calibrated relative to anatomical landmarks or known joint angles. The accuracy and reliability of applying this cluster method to the upper body has, however, not been comprehensively explored. Our objective was to compare three different upper body cluster models with an anatomical model, with respect to joint angles and reliability. Non-disabled participants performed two standardized functional upper limb tasks with anatomical and cluster markers applied concurrently. Joint angle curves obtained via the marker clusters with three different calibration methods were compared to those from an anatomical model, and between-session reliability was assessed for all models. The cluster models produced joint angle curves which were comparable to and highly correlated with those from the anatomical model, but exhibited notable offsets and differences in sensitivity for some degrees of freedom. Between-session reliability was comparable between all models, and good for most degrees of freedom. Overall, the cluster models produced reliable joint angles that, however, cannot be used interchangeably with anatomical model outputs to calculate kinematic metrics. Cluster models appear to be an adequate, and possibly advantageous alternative to anatomical models when the objective is to assess trends in movement behavior. Copyright © 2018 Elsevier Ltd. All rights reserved.

Unmatter Entities inside Nuclei, Predicted by the Brightsen Nucleon Cluster Model

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Smarandache F.

2006-01-01

Full Text Available Applying the R. A. Brightsen Nucleon Cluster Model of the atomic nucleus we discuss how unmatter entities (the conjugations of matter and antimatter may be formed as clusters inside a nucleus. The model supports a hypothesis that antimatter nucleon clusters are present as a parton (sensu Feynman superposition within the spatial confinement of the proton (1H1, the neutron, and the deuteron (1H2. If model predictions can be confirmed both mathematically and experimentally, a new physics is suggested. A proposed experiment is connected to othopositronium annihilation anomalies, which, being related to one of known unmatter entity, orthopositronium (built on electron and positron, opens a way to expand the Standard Model.

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

OpenAIRE

S. Parkash  Kumar; K. S. Ramaswami

2011-01-01

Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using...

Impacts of clustering on interacting epidemics.

Science.gov (United States)

Wang, Bing; Cao, Lang; Suzuki, Hideyuki; Aihara, Kazuyuki

2012-07-07

Since community structures in real networks play a major role for the epidemic spread, we therefore explore two interacting diseases spreading in networks with community structures. As a network model with community structures, we propose a random clique network model composed of different orders of cliques. We further assume that each disease spreads only through one type of cliques; this assumption corresponds to the issue that two diseases spread inside communities and outside them. Considering the relationship between the susceptible-infected-recovered (SIR) model and the bond percolation theory, we apply this theory to clique random networks under the assumption that the occupation probability is clique-type dependent, which is consistent with the observation that infection rates inside a community and outside it are different, and obtain a number of statistical properties for this model. Two interacting diseases that compete the same hosts are also investigated, which leads to a natural generalization of analyzing an arbitrary number of infectious diseases. For two-disease dynamics, the clustering effect is hypersensitive to the cohesiveness and concentration of cliques; this illustrates the impacts of clustering and the composition of subgraphs in networks on epidemic behavior. The analysis of coexistence/bistability regions provides significant insight into the relationship between the network structure and the potential epidemic prevalence. Copyright © 2012 Elsevier Ltd. All rights reserved.

Variational cluster perturbation theory for Bose-Hubbard models

International Nuclear Information System (INIS)

Koller, W; Dupuis, N

2006-01-01

We discuss the application of the variational cluster perturbation theory (VCPT) to the Mott-insulator-to-superfluid transition in the Bose-Hubbard model. We show how the VCPT can be formulated in such a way that it gives a translation invariant excitation spectrum-free of spurious gaps-despite the fact that it formally breaks translation invariance. The phase diagram and the single-particle Green function in the insulating phase are obtained for one-dimensional systems. When the chemical potential of the cluster is taken as a variational parameter, the VCPT reproduces the dimensional dependence of the phase diagram even for one-site clusters. We find a good quantitative agreement with the results of the density-matrix renormalization group when the number of sites in the cluster becomes of order 10. The extension of the method to the superfluid phase is discussed

E-learning or educational leaflet: does it make a difference in oral health promotion? A clustered randomized trial.

Science.gov (United States)

Al Bardaweel, Susan; Dashash, Mayssoon

2018-05-10

The early recognition of technology together with great ability to use computers and smart systems have promoted researchers to investigate the possibilities of utilizing technology for improving health care in children. The aim of this study was to compare between the traditional educational leaflets and E-applications in improving oral health knowledge, oral hygiene and gingival health in schoolchildren of Damascus city, Syria. A clustered randomized controlled trial at two public primary schools was performed. About 220 schoolchildren aged 10-11 years were included in this study and grouped into two clusters. Children in Leaflet cluster received oral health education through leaflets, while children in E-learning cluster received oral health education through an E-learning program. A questionnaire was designed to register information related to oral health knowledge and to record Plaque and Gingival indices. Questionnaire administration and clinical assessment were undertaken at baseline, 6 and at 12 weeks of oral health education. Data was analysed using one way repeated measures ANOVA, post hoc Bonferroni test and independent samples t-test. Leaflet cluster (107 participants) had statistically significant better oral health knowledge than E-learning cluster (104 participants) at 6 weeks (P E-learning cluster:100 participants). The mean knowledge gain compared to baseline was higher in Leaflet cluster than in E-learning cluster. A significant reduction in the PI means at 6 weeks and 12 weeks was observed in both clusters (P E-learning cluster at 6 weeks (P E-learning cluster at 6 weeks (P < 0.05) and 12 weeks (P < 0.05). Traditional educational leaflets are an effective tool in the improvement of both oral health knowledge as well as clinical indices of oral hygiene and care among Syrian children. Leaflets can be used in school-based oral health education for a positive outcome. Australian New Zealand Clinical Trials Registry ( ACTRN

Generating Realistic Labelled, Weighted Random Graphs

Directory of Open Access Journals (Sweden)

Michael Charles Davis

2015-12-01

Full Text Available Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs. Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

Non-Higgsable clusters for 4D F-theory models

International Nuclear Information System (INIS)

Morrison, David R.; Taylor, Washington

2015-01-01

We analyze non-Higgsable clusters of gauge groups and matter that can arise at the level of geometry in 4D F-theory models. Non-Higgsable clusters seem to be generic features of F-theory compactifications, and give rise naturally to structures that include the nonabelian part of the standard model gauge group and certain specific types of potential dark matter candidates. In particular, there are nine distinct single nonabelian gauge group factors, and only five distinct products of two nonabelian gauge group factors with matter, including SU(3)×SU(2), that can be realized through 4D non-Higgsable clusters. There are also more complicated configurations involving more than two gauge factors; in particular, the collection of gauge group factors with jointly charged matter can exhibit branchings, loops, and long linear chains.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Science.gov (United States)

Tokuda, Tomoki; Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Directory of Open Access Journals (Sweden)

Tomoki Tokuda

Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

Science.gov (United States)

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

Modeling familial clustered breast cancer using published data

NARCIS (Netherlands)

Jonker, MA; Jacobi, CE; Hoogendoorn, WE; Nagelkerke, NJD; de Bock, GH; van Houwelingen, JC

2003-01-01

The purpose of this research was to model the familial clustering of breast cancer and to provide an accurate risk estimate for individuals from the general population, based on their family history of breast and ovarian cancer. We constructed a genetic model as an extension of a model by Claus et

Comparison of media literacy and usual education to prevent tobacco use: a cluster-randomized trial.

Science.gov (United States)

Primack, Brian A; Douglas, Erika L; Land, Stephanie R; Miller, Elizabeth; Fine, Michael J

2014-02-01

Media literacy programs have shown potential for reduction of adolescent tobacco use. We aimed to determine if an anti-smoking media literacy curriculum improves students' media literacy and affects factors related to adolescent smoking. We recruited 1170 9th-grade students from 64 classrooms in 3 public urban high schools. Students were randomized by classroom to a media literacy curriculum versus a standard educational program. In an intent-to-treat analysis, we used multilevel modeling to determine if changes in study outcomes were associated with the curricular intervention, controlling for baseline student covariates and the clustering of students within classrooms. Among participants, mean age was 14.5 years and 51% were male, with no significant differences in baseline characteristics between groups. Smoking media literacy changed more among intervention participants compared with control participants (0.24 vs. 0.08, p media literacy curriculum is more effective than a standard educational program in teaching media literacy and improving perceptions of the true prevalence of smoking among adolescents. © 2014, American School Health Association.

A Clustered Randomized Controlled Trial of the Positive Prevention PLUS Adolescent Pregnancy Prevention Program.

Science.gov (United States)

LaChausse, Robert G

2016-09-01

To determine the impact of Positive Prevention PLUS, a school-based adolescent pregnancy prevention program on delaying sexual intercourse, birth control use, and pregnancy. I randomly assigned a diverse sample of ninth grade students in 21 suburban public high schools in California into treatment (n = 2483) and control (n = 1784) groups that participated in a clustered randomized controlled trial. Between October 2013 and May 2014, participants completed baseline and 6-month follow-up surveys regarding sexual behavior and pregnancy. Participants in the treatment group were offered Positive Prevention PLUS, an 11-lesson adolescent pregnancy prevention program. The program had statistically significant impacts on delaying sexual intercourse and increasing the use of birth control. However, I detected no program effect on pregnancy rates at 6-month follow-up. The Positive Prevention PLUS program demonstrated positive impacts on adolescent sexual behavior. This suggests that programs that focus on having students practice risk reduction skills may delay sexual activity and increase birth control use.

Mobile phone intervention reduces perinatal mortality in zanzibar: secondary outcomes of a cluster randomized controlled trial.

Science.gov (United States)

Lund, Stine; Rasch, Vibeke; Hemed, Maryam; Boas, Ida Marie; Said, Azzah; Said, Khadija; Makundu, Mkoko Hassan; Nielsen, Birgitte Bruun

2014-03-26

Mobile phones are increasingly used in health systems in developing countries and innovative technical solutions have great potential to overcome barriers of access to reproductive and child health care. However, despite widespread support for the use of mobile health technologies, evidence for its role in health care is sparse. We aimed to evaluate the association between a mobile phone intervention and perinatal mortality in a resource-limited setting. This study was a pragmatic, cluster-randomized, controlled trial with primary health care facilities in Zanzibar as the unit of randomization. At their first antenatal care visit, 2550 pregnant women (1311 interventions and 1239 controls) who attended antenatal care at selected primary health care facilities were included in this study and followed until 42 days after delivery. Twenty-four primary health care facilities in six districts were randomized to either mobile phone intervention or standard care. The intervention consisted of a mobile phone text message and voucher component. Secondary outcome measures included stillbirth, perinatal mortality, and death of a child within 42 days after birth as a proxy of neonatal mortality. Within the first 42 days of life, 2482 children were born alive, 54 were stillborn, and 36 died. The overall perinatal mortality rate in the study was 27 per 1000 total births. The rate was lower in the intervention clusters, 19 per 1000 births, than in the control clusters, 36 per 1000 births. The intervention was associated with a significant reduction in perinatal mortality with an odds ratio (OR) of 0.50 (95% CI 0.27-0.93). Other secondary outcomes showed an insignificant reduction in stillbirth (OR 0.65, 95% CI 0.34-1.24) and an insignificant reduction in death within the first 42 days of life (OR 0.79, 95% CI 0.36-1.74). Mobile phone applications may contribute to improved health of the newborn and should be considered by policy makers in resource-limited settings. Clinical

Directional clustering in highest energy cosmic rays

International Nuclear Information System (INIS)

Goldberg, Haim; Weiler, Thomas J.

2001-01-01

An unexpected degree of small-scale clustering is observed in highest-energy cosmic ray events. Some directional clustering can be expected due to purely statistical fluctuations for sources distributed randomly in the sky. This creates a background for events originating in clustered sources. We derive analytic formulas to estimate the probability of random cluster configurations, and use these formulas to study the strong potential of the HiRes, Auger, Telescope Array and EUSO-OWL-AirWatch facilities for deciding whether any observed clustering is most likely due to nonrandom sources. For a detailed comparison to data, our analytical approach cannot compete with Monte Carlo simulations, including experimental systematics. However, our derived formulas do offer two advantages: (i) easy assessment of the significance of any observed clustering, and most importantly, (ii) an explicit dependence of cluster probabilities on the chosen angular bin size

Square-lattice random Potts model: criticality and pitchfork bifurcation

International Nuclear Information System (INIS)

Costa, U.M.S.; Tsallis, C.

1983-01-01

Within a real space renormalization group framework based on self-dual clusters, the criticality of the quenched bond-mixed q-state Potts ferromagnet on square lattice is discussed. On qualitative grounds it is exhibited that the crossover from the pure fixed point to the random one occurs, while q increases, through a pitchfork bifurcation; the relationship with Harris criterion is analyzed. On quantitative grounds high precision numerical values are presented for the critical temperatures corresponding to various concentrations of the coupling constants J 1 and J 2 , and various ratios J 1 /J 2 . The pure, random and crossover critical exponents are discussed as well. (Author) [pt

Effect of Behavior Modification on Outcome in Early- to Moderate-Stage Chronic Kidney Disease: A Cluster-Randomized Trial.

Science.gov (United States)

Yamagata, Kunihiro; Makino, Hirofumi; Iseki, Kunitoshi; Ito, Sadayoshi; Kimura, Kenjiro; Kusano, Eiji; Shibata, Takanori; Tomita, Kimio; Narita, Ichiei; Nishino, Tomoya; Fujigaki, Yoshihide; Mitarai, Tetsuya; Watanabe, Tsuyoshi; Wada, Takashi; Nakamura, Teiji; Matsuo, Seiichi

2016-01-01

Owing to recent changes in our understanding of the underlying cause of chronic kidney disease (CKD), the importance of lifestyle modification for preventing the progression of kidney dysfunction and complications has become obvious. In addition, effective cooperation between general physicians (GPs) and nephrologists is essential to ensure a better care system for CKD treatment. In this cluster-randomized study, we studied the effect of behavior modification on the outcome of early- to moderate-stage CKD. Stratified open cluster-randomized trial. A total of 489 GPs belonging to 49 local medical associations (clusters) in Japan. A total of 2,379 patients (1,195 in group A (standard intervention) and 1,184 in group B (advanced intervention)) aged between 40 and 74 years, who had CKD and were under consultation with GPs. All patients were managed in accordance with the current CKD guidelines. The group B clusters received three additional interventions: patients received both educational intervention for lifestyle modification and a CKD status letter, attempting to prevent their withdrawal from treatment, and the group B GPs received data sheets to facilitate reducing the gap between target and practice. The primary outcome measures were 1) the non-adherence rate of accepting continuous medical follow-up of the patients, 2) the collaboration rate between GPs and nephrologists, and 3) the progression of CKD. The rate of discontinuous clinical visits was significantly lower in group B (16.2% in group A vs. 11.5% in group B, p = 0.01). Significantly higher referral and co-treatment rates were observed in group B (pbehavior modification of CKD patients, namely, significantly lower discontinuous clinical visits, and behavior modification of both GPs and nephrologists, namely significantly higher referral and co-treatment rates, resulting in the retardation of CKD progression, especially in patients with proteinuric Stage 3 CKD. The University Hospital Medical Information

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

Science.gov (United States)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

Cluster Randomized Controlled Trial Evaluation of a Gender Equity and Family Planning Intervention for Married Men and Couples in Rural India.

Directory of Open Access Journals (Sweden)

Anita Raj

Full Text Available Despite ongoing recommendations to increase male engagement and gender-equity (GE counseling in family planning (FP services, few such programs have been implemented and rigorously evaluated. This study evaluates the impact of CHARM, a three-session GE+FP counseling intervention delivered by male health care providers to married men, alone (sessions 1&2 and with their wives (session 3 in India.A two-armed cluster randomized controlled trial was conducted with young married couples (N = 1081 couples recruited from 50 geographic clusters (25 clusters randomized to CHARM and a control condition, respectively in rural Maharashtra, India. Couples were surveyed on demographics, contraceptive behaviors, and intimate partner violence (IPV attitudes and behaviors at baseline and 9 &18-month follow-ups, with pregnancy testing at baseline and 18-month follow-up. Outcome effects on contraceptive use and incident pregnancy, and secondarily, on contraceptive communication and men's IPV attitudes and behaviors, were assessed using logistic generalized linear mixed models. Most men recruited from CHARM communities (91.3% received at least one CHARM intervention session; 52.5% received the couple's session with their wife. Findings document that women from the CHARM condition, relative to controls, were more likely to report contraceptive communication at 9-month follow-up (AOR = 1.77, p = 0.04 and modern contraceptive use at 9 and 18-month follow-ups (AORs = 1.57-1.58, p = 0.05, and they were less likely to report sexual IPV at 18-month follow-up (AOR = 0.48, p = 0.01. Men in the CHARM condition were less likely than those in the control clusters to report attitudes accepting of sexual IPV at 9-month (AOR = 0.64, p = 0.03 and 18-month (AOR = 0.51, p = 0.004 follow-up, and attitudes accepting of physical IPV at 18-month follow-up (AOR = 0.64, p = 0.02. No significant effect on pregnancy was seen.Findings demonstrate that men can be engaged in FP programming in

Clustering gene expression time series data using an infinite Gaussian process mixture model.

Science.gov (United States)

McDowell, Ian C; Manandhar, Dinesh; Vockley, Christopher M; Schmid, Amy K; Reddy, Timothy E; Engelhardt, Barbara E

2018-01-01

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

Clustering gene expression time series data using an infinite Gaussian process mixture model.

Directory of Open Access Journals (Sweden)

Ian C McDowell

2018-01-01

Full Text Available Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP, which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN KOMBINASI RFM MODEL DAN TEKNIK CLUSTERING

Directory of Open Access Journals (Sweden)

Beta Estri Adiana

2018-04-01

Full Text Available Intense competition in the business field motivates a small and medium enterprises (SMEs to manage customer services to the maximal. Improve of customer royalty by grouping cunstomers into some of groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be performed by data mining approach with clustering method. The main purpose of this paper is customer segmentation and measure their loyalty to a SME’s product. Using CRISP-DM method which consist of six phases, namely business understanding, data understanding, data preparatuin, modeling, evaluation and deployment. The K-Means algorithm is used for cluster formation and RapidMiner as a tool used to evaluate the result of clusters. Cluster formation is based on RFM (recency, frequency, monetary analysis. Davies Bouldin Index (DBI is used to find the optimal number of clusters (k. The customers are divided into 3 clusters, total of customer in first cluster is 30 customers who entered in typical customer category, the second cluster there are 8 customer whho entered in superstar customer and 89 customers in third cluster is dormant cluster category.

A Cluster-Randomized Controlled Intervention Study to Assess the Effect of a Contact Intervention in Reducing Leprosy-Related Stigma in Indonesia

NARCIS (Netherlands)

Peters, R.M.H.; Zweekhorst, M.B.M.; Bunders-Aelen, J.G.F.; van Brakel, W.H.

2015-01-01

Background: Can deliberate interaction between the public and persons affected by leprosy reduce stigmatization? The study described in this paper hypothesises that it can and assesses the effectiveness of a ‘contact intervention’. Methods/Principal Findings: This cluster-randomized controlled

Channel Parameter Estimation for Scatter Cluster Model Using Modified MUSIC Algorithm

Directory of Open Access Journals (Sweden)

Jinsheng Yang

2012-01-01

Full Text Available Recently, the scatter cluster models which precisely evaluate the performance of the wireless communication system have been proposed in the literature. However, the conventional SAGE algorithm does not work for these scatter cluster-based models because it performs poorly when the transmit signals are highly correlated. In this paper, we estimate the time of arrival (TOA, the direction of arrival (DOA, and Doppler frequency for scatter cluster model by the modified multiple signal classification (MUSIC algorithm. Using the space-time characteristics of the multiray channel, the proposed algorithm combines the temporal filtering techniques and the spatial smoothing techniques to isolate and estimate the incoming rays. The simulation results indicated that the proposed algorithm has lower complexity and is less time-consuming in the dense multipath environment than SAGE algorithm. Furthermore, the estimations’ performance increases with elements of receive array and samples length. Thus, the problem of the channel parameter estimation of the scatter cluster model can be effectively addressed with the proposed modified MUSIC algorithm.

Analysis of the dynamical cluster approximation for the Hubbard model

OpenAIRE

Aryanpour, K.; Hettler, M. H.; Jarrell, M.

2002-01-01

We examine a central approximation of the recently introduced Dynamical Cluster Approximation (DCA) by example of the Hubbard model. By both analytical and numerical means we study non-compact and compact contributions to the thermodynamic potential. We show that approximating non-compact diagrams by their cluster analogs results in a larger systematic error as compared to the compact diagrams. Consequently, only the compact contributions should be taken from the cluster, whereas non-compact ...

A Cluster-Randomized Trial of Restorative Practices: An Illustration to Spur High-Quality Research and Evaluation.

Science.gov (United States)

Acosta, Joie D; Chinman, Matthew; Ebener, Patricia; Phillips, Andrea; Xenakis, Lea; Malone, Patrick S

2016-01-01

Restorative Practices in schools lack rigorous evaluation studies. As an example of rigorous school-based research, this paper describes the first randomized control trial of restorative practices to date, the Study of Restorative Practices. It is a 5-year, cluster-randomized controlled trial (RCT) of the Restorative Practices Intervention (RPI) in 14 middle schools in Maine to assess whether RPI impacts both positive developmental outcomes and problem behaviors and whether the effects persist during the transition from middle to high school. The two-year RPI intervention began in the 2014-2015 school year. The study's rationale and theoretical concerns are discussed along with methodological concerns including teacher professional development. The theoretical rationale and description of the methods from this study may be useful to others conducting rigorous research and evaluation in this area.

On the applicability of the jellium model to the description of alkali clusters

International Nuclear Information System (INIS)

Matveentsev, A.; Lyalin, A.; Solovyov, I.A.; Solovyov, A.V.; Greiner, W.

2003-01-01

This work is devoted to the elucidation of the applicability of the jellium model to the description of alkali cluster properties. We compare the jellium model results with those derived within ab initio theoretical approaches and with experiments. On the basis of Hartree–Fock and local-density approximations we have calculated the binding energies per atom, ionization potentials, deformation parameters and optimized values of the Wigner–Seitz radii for neutral and singly charged sodium clusters with the number of atoms N ≤ 20. The characteristics calculated within the framework of the deformed jellium model are compared with the results derived from ab initio simulations of cluster electronic and ionic structure based on density functional theory and systematic post Hartree–Fock many-body perturbation theory accounting for all electrons in the system. The comparison performed demonstrates the great role of the cluster shape deformations in the formation cluster properties and quite reasonable level of applicability of the deformed jellium model. This elucidates the similarities of atomic cluster physics with the physics of atomic nuclei. (author)

Critical behavior in a random field classical Heisenberg model for amorphous systems

International Nuclear Information System (INIS)

Albuquerque, Douglas F. de; Alves, Sandro Roberto L.; Arruda, Alberto S. de

2005-01-01

By using the differential operator technique and the effective field theory scheme, the critical behavior of amorphous classical Heisenberg ferromagnet of spin-1/2 in a random field is studied. The phase diagram in the T-H and T-α planes on a simple cubic lattice for a cluster with two spins is obtained. Tricritical points, reentrant phenomena and influence of the random field and amorphization on the transition temperature are discussed

Network clustering analysis using mixture exponential-family random graph models and its application in genetic interaction data.

Science.gov (United States)

Wang, Yishu; Zhao, Hongyu; Deng, Minghua; Fang, Huaying; Yang, Dejie

2017-08-24

Epistatic miniarrary profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. It provides an incredible set of molecular tools and advanced technologies that should be efficiently understanding the relationship between the genotypes and phenotypes of individuals. However, the network information gained from EMAP cannot be fully exploited using the traditional statistical network models. Because the genetic network is always heterogeneous, for example, the network structure features for one subset of nodes are different from those of the left nodes. Exponentialfamily random graph models (ERGMs) are a family of statistical models, which provide a principled and flexible way to describe the structural features (e.g. the density, centrality and assortativity) of an observed network. However, the single ERGM is not enough to capture this heterogeneity of networks. In this paper, we consider a mixture ERGM (MixtureEGRM) networks, which model a network with several communities, where each community is described by a single EGRM.

Informing resource-poor populations and the delivery of entitled health and social services in rural India: a cluster randomized controlled trial.

Science.gov (United States)

Pandey, Priyanka; Sehgal, Ashwini R; Riboud, Michelle; Levine, David; Goyal, Madhav

2007-10-24

A lack of awareness about entitled health and social services may contribute to poor delivery of such services in developing countries, especially among individuals of low socioeconomic status. To determine the impact of informing resource-poor rural populations about entitled services. Community-based, cluster randomized controlled trial conducted from May 2004 to May 2005 in 105 randomly selected village clusters in Uttar Pradesh state in India. Households (548 intervention and 497 control) were selected by a systematic sampling design, including both low-caste and mid- to high-caste households. Four to 6 public meetings were held in each intervention village cluster to disseminate information on entitled health services, entitled education services, and village governance requirements. No intervention took place in control village clusters. Visits by nurse midwife; prenatal examinations, tetanus vaccinations, and prenatal supplements received by pregnant women; vaccinations received by infants; excess school fees charged; occurrence of village council meetings; and development work in villages. At baseline, there were no significant differences in self-reported delivery of health and social services. After 1 year, intervention villagers reported better delivery of several services compared with control villagers: in a multivariate analysis, 30% more prenatal examinations (95% confidence interval [CI], 17%-43%; P India about entitled services enhanced the delivery of health and social services among both low- and mid- to high-caste households. Interventions that emphasize educating resource-poor populations about entitled services may improve the delivery of such services. clinicaltrials.gov Identifier: NCT00421291.

Cluster expansion for vacuum confining fields

International Nuclear Information System (INIS)

Simonov, Yu.A.

1987-01-01

Colored particle Green functions in vacuum background random fields are written as path integrals. Averaging over random fields is done using the cluster (cumulant) expansion. The existence of a finite correlation length for vacuum background fields is shown to produce the linear confinement, in agreement with the results, obtained with the help of averaged Hamiltonians. A modified form of cluster expansion for nonabelian fields is introduced using the path-ordered cumulants

Cluster dynamics models of irradiation damage accumulation in ferritic iron. I. Trap mediated interstitial cluster diffusion

Energy Technology Data Exchange (ETDEWEB)

Kohnert, Aaron A.; Wirth, Brian D. [University of Tennessee, Knoxville, Tennessee 37996-2300 (United States)

2015-04-21

The microstructure that develops under low temperature irradiation in ferritic alloys is dominated by a high density of small (2–5 nm) defects. These defects have been widely observed to move via occasional discrete hops during in situ thin film irradiation experiments. Cluster dynamics models are used to describe the formation of these defects as an aggregation process of smaller clusters created as primary damage. Multiple assumptions regarding the mobility of these damage features are tested in the models, both with and without explicit consideration of such irradiation induced hops. Comparison with experimental data regarding the density of these defects demonstrates the importance of including such motions in a valid model. In particular, discrete hops inform the limited dependence of defect density on irradiation temperature observed in experiments, which the model was otherwise incapable of producing.

A collision avoidance model for two-pedestrian groups: Considering random avoidance patterns

Science.gov (United States)

Zhou, Zhuping; Cai, Yifei; Ke, Ruimin; Yang, Jiwei

2017-06-01

Grouping is a common phenomenon in pedestrian crowds and group modeling is still an open challenging problem. When grouping pedestrians avoid each other, different patterns can be observed. Pedestrians can keep close with group members and avoid other groups in cluster. Also, they can avoid other groups separately. Considering this randomness in avoidance patterns, we propose a collision avoidance model for two-pedestrian groups. In our model, the avoidance model is proposed based on velocity obstacle method at first. Then grouping model is established using Distance constrained line (DCL), by transforming DCL into the framework of velocity obstacle, the avoidance model and grouping model are successfully put into one unified calculation structure. Within this structure, an algorithm is developed to solve the problem when solutions of the two models conflict with each other. Two groups of bidirectional pedestrian experiments are designed to verify the model. The accuracy of avoidance behavior and grouping behavior is validated in the microscopic level, while the lane formation phenomenon and fundamental diagrams is validated in the macroscopic level. The experiments results show our model is convincing and has a good expansibility to describe three or more pedestrian groups.

Modeling and clustering users with evolving profiles in usage streams

KAUST Repository

Zhang, Chongsheng; Masseglia, Florent; Zhang, Xiangliang

2012-01-01

Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.

Modeling and clustering users with evolving profiles in usage streams

KAUST Repository

Zhang, Chongsheng

2012-09-01

Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users\\' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users\\' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.

CLUSTERS AS A MODEL OF ECONOMIC DEVELOPMENT OF SERBIA

Directory of Open Access Journals (Sweden)

Marko Laketa

2013-12-01

Full Text Available Insufficient competitiveness of small and medium enterprises in Serbia can be significantly improved by a system of business associations through clusters, business incubators and technology parks. This connection contributes to the growth and development of not only the cluster members, but has a regional and national dimension as well because without it there is no significant breakthrough on the international market. The process of association of small and medium enterprises in clusters and other forms of interconnection in Serbia is far from the required and potential level.The awareness on the importance of clusters in a local economic development through contributions to the advancement of small and medium sized enterprises is not yet sufficiently mature. Support to associating into clusters and usage of their benefits after the model of highly developed countries is the basis for leading a successful economic policy and in Serbia there are all necessary prerequisites for it.

A Collaboration Service Model for a Global Port Cluster

Directory of Open Access Journals (Sweden)

Keith K.T. Toh

2010-03-01

Full Text Available The importance of port clusters to a global city may be viewed from a number of perspectives. The development of port clusters and economies of agglomeration and their contribution to a regional economy is underpinned by information and physical infrastructure that facilitates collaboration between business entities within the cluster. The maturity of technologies providing portals, web and middleware services provides an opportunity to push the boundaries of contemporary service reference models and service catalogues to what the authors propose to be "collaboration services". Servicing port clusters, portal engineers of the future must consider collaboration services to benefit a region. Particularly, service orchestration through a "public user portal" must gain better utilisation of publically owned infrastructure, to share knowledge and collaborate among organisations through information systems.

Conditional Monte Carlo randomization tests for regression models.

Science.gov (United States)

Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

2014-08-15

We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

Infinite Random Graphs as Statistical Mechanical Models

DEFF Research Database (Denmark)

Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria

2011-01-01

We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe a ...

Cluster management.

Science.gov (United States)

Katz, R

1992-11-01

Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

RMBNToolbox: random models for biochemical networks

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Niemi Jari

2007-05-01

Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

A Structural Modeling Approach to a Multilevel Random Coefficients Model.

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Rovine, Michael J.; Molenaar, Peter C. M.

2000-01-01

Presents a method for estimating the random coefficients model using covariance structure modeling and allowing one to estimate both fixed and random effects. The method is applied to real and simulated data, including marriage data from J. Belsky and M. Rovine (1990). (SLD)

Spirometry and regular follow-up do not improve quality of life in children or adolescents with asthma: Cluster randomized controlled trials.

Science.gov (United States)

Abramson, Michael J; Schattner, Rosa L; Holton, Christine; Simpson, Pam; Briggs, Nancy; Beilby, Justin; Nelson, Mark R; Wood-Baker, Richard; Thien, Francis; Sulaiman, Nabil D; Colle, Eleonora Del; Wolfe, Rory; Crockett, Alan J; Massie, R John

2015-10-01

To determine whether spirometry and regular medical review improved quality of life or other outcomes in children and adolescents with asthma. We conducted two cluster randomized controlled trials. We recruited 238 asthma patients aged between 7 and 17 years from 56 general practices in South Eastern Australia. Participants were randomized to receive an intervention that included spirometry or usual care. The main outcome measure was asthma related quality of life. Baseline characteristics were well matched between the intervention and control groups. Neither trial found any difference in asthma related quality of life between groups. However because of measurement properties, a formal meta-analysis could not be performed. Nor were there any significant effects of the intervention upon asthma attacks, limitation to usual activities, nocturnal cough, bother during physical activity, worry about asthma, or written asthma action plans. The findings do not support more widespread use of spirometry for the management of childhood asthma in general practice, unless it is integrated into a complete management model. © 2014 Wiley Periodicals, Inc.

[Work-Related Medical Rehabilitation in Cancer Rehabilitation - Short-Term Results from a Cluster-Randomized Multicenter-Trial].

Science.gov (United States)

Wienert, Julian; Bethge, Matthias

2018-05-25

Rehabilitation programs that support return to work become increasingly relevant for cancer survivors. In Germany, such programs were established as work-related medical rehabilitation (WMR). The study investigated whether WMR leads to better results compared to medical rehabilitation (MR). We report effects on secondary outcomes when the rehabilitation program was completed. Clusters of participants were randomly assigned to WMR or MR. Patients of working age and an elevated risk of not returning to work were included. The grade of implementation was assessed by dose delivered and dose received. Study outcomes were assessed using scales measuring functioning and symptoms, coping with illness as well as self-reported work ability. Treatment effects were estimated using mixed linear models. From 232 planned randomized intervention groups, 165 (71%) were realized. In total, 476 patients were included. Mean age of participants was 50.7 years (SD=7.3). Most frequent primary diagnoses were malignant neoplasms of the breast. Participants in the WMR program reported significantly better outcomes regarding quality of life (SMD=0.17-0.25), fatigue (SMD=0.18-0.27), coping with illness (SMD=0.17-0.22), and self-reported work-ability (SMD=0.16) compared to participants in MR program (all p<0.05). The results indicate a positive effect in favor of WMR for cancer patients with an elevated risk of not returning to work at the end of their treatment. © Georg Thieme Verlag KG Stuttgart · New York.

The effect of three-monthly albendazole treatment on malarial parasitemia and allergy: a household-based cluster-randomized, double-blind, placebo-controlled trial

NARCIS (Netherlands)

Wiria, Aprilianto E.; Hamid, Firdaus; Wammes, Linda J.; Kaisar, Maria M. M.; May, Linda; Prasetyani, Margaretta A.; Wahyuni, Sitti; Djuardi, Yenny; Ariawan, Iwan; Wibowo, Heri; Lell, Bertrand; Sauerwein, Robert; Brice, Gary T.; Sutanto, Inge; van Lieshout, Lisette; de Craen, Anton J. M.; van Ree, Ronald; Verweij, Jaco J.; Tsonaka, Roula; Houwing-Duistermaat, Jeanine J.; Luty, Adrian J. F.; Sartono, Erliyani; Supali, Taniawati; Yazdanbakhsh, Maria

2013-01-01

Helminth infections are proposed to have immunomodulatory activities affecting health outcomes either detrimentally or beneficially. We evaluated the effects of albendazole treatment, every three months for 21 months, on STH, malarial parasitemia and allergy. A household-based cluster-randomized,

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

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Moumita Saha

2016-01-01

Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

Model study in chemisorption: atomic hydrogen on beryllium clusters

International Nuclear Information System (INIS)

Bauschlicher, C.W. Jr.

1976-08-01

The interaction between atomic hydrogen and the (0001) surface of Be metal has been studied by ab initio electronic structure theory. Self-consistent-field (SCF) calculations have been performed using minimum, optimized minimum, double zeta and mixed basis sets for clusters as large as 22 Be atoms. The binding energy and equilibrium geometry (the distance to the surface) were determined for 4 sites. Both spatially restricted (the wavefunction was constrained to transform as one of the irreducible representations of the molecular point group) and unrestricted SCF calculations were performed. Using only the optimized minimum basis set, clusters containing as many as 22 beryllium atoms have been investigated. From a variety of considerations, this cluster is seen to be nearly converged within the model used, providing the most reliable results for chemisorption. The site dependence of the frequency is shown to be a geometrical effect depending on the number and angle of the bonds. The diffusion of atomic hydrogen through a perfect beryllium crystal is predicted to be energetically unfavorable. The cohesive energy, the ionization energy and the singlet-triplet separation were computed for the clusters without hydrogen. These quantities can be seen as a measure of the total amount of edge effects. The chemisorptive properties are not related to the total amount of edge effects, but rather the edge effects felt by the adsorbate bonding berylliums. This lack of correlation with the total edge effects illustrates the local nature of the bonding, further strengthening the cluster model for chemisorption. A detailed discussion of the bonding and electronic structure is included. The remaining edge effects for the Be 22 cluster are discussed

Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

Directory of Open Access Journals (Sweden)

Mi-Yuan Shan

2013-01-01

Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.

Effectiveness of a group diabetes education programme in underserved communities in South Africa: pragmatic cluster randomized control trial.

Science.gov (United States)

Mash, Bob; Levitt, Naomi; Steyn, Krisela; Zwarenstein, Merrick; Rollnick, Stephen

2012-12-24

Diabetes is an important contributor to the burden of disease in South Africa and prevalence rates as high as 33% have been recorded in Cape Town. Previous studies show that quality of care and health outcomes are poor. The development of an effective education programme should impact on self-care, lifestyle change and adherence to medication; and lead to better control of diabetes, fewer complications and better quality of life. Pragmatic cluster randomized controlled trialParticipants: Type 2 diabetic patients attending 45 public sector community health centres in Cape TownInterventions: The intervention group will receive 4 sessions of group diabetes education delivered by a health promotion officer in a guiding style. The control group will receive usual care which consists of ad hoc advice during consultations and occasional educational talks in the waiting room. To evaluate the effectiveness of the group diabetes education programmeOutcomes: diabetes self-care activities, 5% weight loss, 1% reduction in HbA1c. self-efficacy, locus of control, mean blood pressure, mean weight loss, mean waist circumference, mean HbA1c, mean total cholesterol, quality of lifeRandomisation: Computer generated random numbersBlinding: Patients, health promoters and research assistants could not be blinded to the health centre's allocationNumbers randomized: Seventeen health centres (34 in total) will be randomly assigned to either control or intervention groups. A sample size of 1360 patients in 34 clusters of 40 patients will give a power of 80% to detect the primary outcomes with 5% precision. Altogether 720 patients were recruited in the intervention arm and 850 in the control arm giving a total of 1570. The study will inform policy makers and managers of the district health system, particularly in low to middle income countries, if this programme can be implemented more widely. Pan African Clinical Trial Registry PACTR201205000380384.

Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models.

Science.gov (United States)

Najaf, Pooya; Duddu, Venkata R; Pulugurtha, Srinivas S

2018-03-01

Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine both ML and statistical methods to develop hybrid link-level crash frequency models with high predictability and interpretability. For this purpose, M5' model trees method (M5') is introduced and applied to classify the crash data and then calibrate a model for each homogenous class. The data for 1134 and 345 randomly selected links on urban arterials in the city of Charlotte, North Carolina was used to develop and validate models, respectively. The outputs from the hybrid approach are compared with the outputs from cluster-based negative binomial regression (NBR) and general NBR models. Findings indicate that M5' has high predictability and is very reliable to interpret the role of different attributes on crash frequency compared to other developed models.

Alpha-cluster preformation factor within cluster-formation model for odd-A and odd-odd heavy nuclei

Science.gov (United States)

Saleh Ahmed, Saad M.

2017-06-01

The alpha-cluster probability that represents the preformation of alpha particle in alpha-decay nuclei was determined for high-intensity alpha-decay mode odd-A and odd-odd heavy nuclei, 82 CSR) and the hypothesised cluster-formation model (CFM) as in our previous work. Our previous successful determination of phenomenological values of alpha-cluster preformation factors for even-even nuclei motivated us to expand the work to cover other types of nuclei. The formation energy of interior alpha cluster needed to be derived for the different nuclear systems with considering the unpaired-nucleon effect. The results showed the phenomenological value of alpha preformation probability and reflected the unpaired nucleon effect and the magic and sub-magic effects in nuclei. These results and their analyses presented are very useful for future work concerning the calculation of the alpha decay constants and the progress of its theory.

Preventing knee injuries in adolescent female football players - design of a cluster randomized controlled trial [NCT00894595].

Science.gov (United States)

Hägglund, Martin; Waldén, Markus; Atroshi, Isam

2009-06-23

Knee injuries in football are common regardless of age, gender or playing level, but adolescent females seem to have the highest risk. The consequences after severe knee injury, for example anterior cruciate ligament (ACL) injury, are well-known, but less is known about knee injury prevention. We have designed a cluster randomized controlled trial (RCT) to evaluate the effect of a warm-up program aimed at preventing acute knee injury in adolescent female football. In this cluster randomized trial 516 teams (309 clusters) in eight regional football districts in Sweden with female players aged 13-17 years were randomized into an intervention group (260 teams) or a control group (256 teams). The teams in the intervention group were instructed to do a structured warm-up program at two training sessions per week throughout the 2009 competitive season (April to October) and those in the control group were informed to train and play as usual. Sixty-eight sports physical therapists are assigned to the clubs to assist both groups in data collection and to examine the players' acute knee injuries during the study period. Three different forms are used in the trial: (1) baseline player data form collected at the start of the trial, (2) computer-based registration form collected every month, on which one of the coaches/team leaders documents individual player exposure, and (3) injury report form on which the study therapists report acute knee injuries resulting in time loss from training or match play. The primary outcome is the incidence of ACL injury and the secondary outcomes are the incidence of any acute knee injury (except contusion) and incidence of severe knee injury (defined as injury resulting in absence of more than 4 weeks). Outcome measures are assessed after the end of the 2009 season. Prevention of knee injury is beneficial for players, clubs, insurance companies, and society. If the warm-up program is proven to be effective in reducing the incidence of knee

Reducing Tobacco Use among Low Socio-Economic Status Youth in Delhi, India: Outcomes from Project ACTIVITY, a Cluster Randomized Trial

Science.gov (United States)

Harrell, Melissa B.; Arora, Monika; Bassi, Shalini; Gupta, Vinay K.; Perry, Cheryl L.; Reddy, K. Srinath

2016-01-01

To test the efficacy of an intervention to reduce tobacco use among youth (10-19 years old) in slum communities in Delhi, India. This community-based cluster-randomized trial included 14 slums composed of purposely built resettlement colonies and adjacent inhabitant-built Jhuggi Jhopris. Youth in the intervention received a 2 year…

Cluster-Randomized Controlled Trial Evaluating the Effectiveness of Computer-Assisted Intervention Delivered by Educators for Children with Speech Sound Disorders

Science.gov (United States)

McLeod, Sharynne; Baker, Elise; McCormack, Jane; Wren, Yvonne; Roulstone, Sue; Crowe, Kathryn; Masso, Sarah; White, Paul; Howland, Charlotte

2017-01-01

Purpose: The aim was to evaluate the effectiveness of computer-assisted input-based intervention for children with speech sound disorders (SSD). Method: The Sound Start Study was a cluster-randomized controlled trial. Seventy-nine early childhood centers were invited to participate, 45 were recruited, and 1,205 parents and educators of 4- and…

Multilevel covariance regression with correlated random effects in the mean and variance structure.

Science.gov (United States)

Quintero, Adrian; Lesaffre, Emmanuel

2017-09-01

Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling and Testing Dark Energy and Gravity with Galaxy Cluster Data

Science.gov (United States)

Rapetti, David; Cataneo, Matteo; Heneka, Caroline; Mantz, Adam; Allen, Steven W.; Von Der Linden, Anja; Schmidt, Fabian; Lombriser, Lucas; Li, Baojiu; Applegate, Douglas; Kelly, Patrick; Morris, Glenn

2018-06-01

The abundance of galaxy clusters is a powerful probe to constrain the properties of dark energy and gravity at large scales. We employed a self-consistent analysis that includes survey, observable-mass scaling relations and weak gravitational lensing data to obtain constraints on f(R) gravity, which are an order of magnitude tighter than the best previously achieved, as well as on cold dark energy of negligible sound speed. The latter implies clustering of the dark energy fluid at all scales, allowing us to measure the effects of dark energy perturbations at cluster scales. For this study, we recalibrated the halo mass function using the following non-linear characteristic quantities: the spherical collapse threshold, the virial overdensity and an additional mass contribution for cold dark energy. We also presented a new modeling of the f(R) gravity halo mass function that incorporates novel corrections to capture key non-linear effects of the Chameleon screening mechanism, as found in high resolution N-body simulations. All these results permit us to predict, as I will also exemplify, and eventually obtain the next generation of cluster constraints on such models, and provide us with frameworks that can also be applied to other proposed dark energy and modified gravity models using cluster abundance observations.

Strategies to enhance venous thromboprophylaxis in hospitalized medical patients (SENTRY: a pilot cluster randomized trial

Directory of Open Access Journals (Sweden)

Pai Menaka

2013-01-01

Full Text Available Abstract Background Venous thromboembolism (VTE is a common preventable cause of mortality in hospitalized medical patients. Despite rigorous randomized trials generating strong recommendations for anticoagulant use to prevent VTE, nearly 40% of medical patients receive inappropriate thromboprophylaxis. Knowledge-translation strategies are needed to bridge this gap. Methods We conducted a 16-week pilot cluster randomized controlled trial (RCT to determine the proportion of medical patients that were appropriately managed for thromboprophylaxis (according to the American College of Chest Physician guidelines within 24 hours of admission, through the use of a multicomponent knowledge-translation intervention. Our primary goal was to determine the feasibility of conducting this study on a larger scale. The intervention comprised clinician education, a paper-based VTE risk assessment algorithm, printed physicians’ orders, and audit and feedback sessions. Medical wards at six hospitals (representing clusters in Ontario, Canada were included; three were randomized to the multicomponent intervention and three to usual care (i.e., no active strategies for thromboprophylaxis in place. Blinding was not used. Results A total of 2,611 patients (1,154 in the intervention and 1,457 in the control group were eligible and included in the analysis. This multicomponent intervention did not lead to a significant difference in appropriate VTE prophylaxis rates between intervention and control hospitals (appropriate management rate odds ratio = 0.80; 95% confidence interval: 0.50, 1.28; p = 0.36; intra-class correlation coefficient: 0.022, and thus was not considered feasible. Major barriers to effective knowledge translation were poor attendance by clinical staff at education and feedback sessions, difficulty locating preprinted orders, and lack of involvement by clinical and administrative leaders. We identified several factors that may increase uptake of a VTE

Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model

KAUST Repository

Liu, Bo

2016-02-03

An ensemble-based Gaussian mixture (GM) filtering framework is studied in this paper in term of its dependence on the choice of the clustering method to construct the GM. In this approach, a number of particles sampled from the posterior distribution are first integrated forward with the dynamical model for forecasting. A GM representation of the forecast distribution is then constructed from the forecast particles. Once an observation becomes available, the forecast GM is updated according to Bayes’ rule. This leads to (i) a Kalman filter-like update of the particles, and (ii) a Particle filter-like update of their weights, generalizing the ensemble Kalman filter update to non-Gaussian distributions. We focus on investigating the impact of the clustering strategy on the behavior of the filter. Three different clustering methods for constructing the prior GM are considered: (i) a standard kernel density estimation, (ii) clustering with a specified mixture component size, and (iii) adaptive clustering (with a variable GM size). Numerical experiments are performed using a two-dimensional reactive contaminant transport model in which the contaminant concentration and the heterogenous hydraulic conductivity fields are estimated within a confined aquifer using solute concentration data. The experimental results suggest that the performance of the GM filter is sensitive to the choice of the GM model. In particular, increasing the size of the GM does not necessarily result in improved performances. In this respect, the best results are obtained with the proposed adaptive clustering scheme.

Clustering of noise-induced oscillations

DEFF Research Database (Denmark)

Sosnovtseva, Olga; Fomin, A I; Postnov, D E

2001-01-01

The subject of our study is clustering in a population of excitable systems driven by Gaussian white noise and with randomly distributed coupling strength. The cluster state is frequency-locked state in which all functional units run at the same noise-induced frequency. Cooperative dynamics...

A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos

KAUST Repository

Wu, Baoyuan; Hu, Bao-Gang; Ji, Qiang

2016-01-01

Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face

A Motor-Gradient and Clustering Model of the Centripetal Motility of MTOCs in Meiosis I of Mouse Oocytes

Science.gov (United States)

2016-01-01

Asters nucleated by Microtubule (MT) organizing centers (MTOCs) converge on chromosomes during spindle assembly in mouse oocytes undergoing meiosis I. Time-lapse imaging suggests that this centripetal motion is driven by a biased ‘search-and-capture’ mechanism. Here, we develop a model of a random walk in a drift field to test the nature of the bias and the spatio-temporal dynamics of the search process. The model is used to optimize the spatial field of drift in simulations, by comparison to experimental motility statistics. In a second step, this optimized gradient is used to determine the location of immobilized dynein motors and MT polymerization parameters, since these are hypothesized to generate the gradient of forces needed to move MTOCs. We compare these scenarios to self-organized mechanisms by which asters have been hypothesized to find the cell-center- MT pushing at the cell-boundary and clustering motor complexes. By minimizing the error between simulation outputs and experiments, we find a model of “pulling” by a gradient of dynein motors alone can drive the centripetal motility. Interestingly, models of passive MT based “pushing” at the cortex, clustering by cross-linking motors and MT-dynamic instability gradients alone, by themselves do not result in the observed motility. The model predicts the sensitivity of the results to motor density and stall force, but not MTs per aster. A hybrid model combining a chromatin-centered immobilized dynein gradient, diffusible minus-end directed clustering motors and pushing at the cell cortex, is required to comprehensively explain the available data. The model makes experimentally testable predictions of a spatial bias and self-organized mechanisms by which MT asters can find the center of a large cell. PMID:27706163

Hopping models and ac universality

DEFF Research Database (Denmark)

Dyre, Jeppe; Schrøder, Thomas

2002-01-01

Some general relations for hopping models are established. We proceed to discuss the universality of the ac conductivity which arises in the extreme disorder limit of the random barrier model. It is shown that the relevant dimension entering into the diffusion cluster approximation (DCA) is the h......Some general relations for hopping models are established. We proceed to discuss the universality of the ac conductivity which arises in the extreme disorder limit of the random barrier model. It is shown that the relevant dimension entering into the diffusion cluster approximation (DCA......) is the harmonic (fracton) dimension of the diffusion cluster. The temperature scaling of the dimensionless frequency entering into the DCA is discussed. Finally, some open problems regarding ac universality are listed....

Knowledge Translation Interventions to Improve the Timing of Dialysis Initiation: Protocol for a Cluster Randomized Trial.

Science.gov (United States)

Chau, Elaine M T; Manns, Braden J; Garg, Amit X; Sood, Manish M; Kim, S Joseph; Naimark, David; Nesrallah, Gihad E; Soroka, Steven D; Beaulieu, Monica; Dixon, Stephanie; Alam, Ahsan; Tangri, Navdeep

2016-01-01

Early initiation of chronic dialysis (starting dialysis with higher vs lower kidney function) has risen rapidly in the past 2 decades in Canada and internationally, despite absence of established health benefits and higher costs. In 2014, a Canadian guideline on the timing of dialysis initiation, recommending an intent-to-defer approach, was published. The objective of this study is to evaluate the efficacy and safety of a knowledge translation intervention to promote the intent-to-defer approach in clinical practice. This study is a multicenter, 2-arm parallel, cluster randomized trial. The study involves 55 advanced chronic kidney disease clinics across Canada. Patients older than 18 years who are managed by nephrologists for more than 3 months, and initiate dialysis in the follow-up period are included in the study. Outcomes will be measured at the patient-level and enumerated within a cluster. Data on characteristics of each dialysis start will be determined by linkages with the Canadian Organ Replacement Register. Primary outcomes include the proportion of patients who start dialysis early with an estimated glomerular filtration rate greater than 10.5 mL/min/1.73 m 2 and start dialysis in hospital as inpatients or in an emergency room setting. Secondary outcomes include the rate of change in early dialysis starts; rates of hospitalizations, deaths, and cost of predialysis care (wherever available); quarterly proportion of new starts; and acceptability of the knowledge translation materials. We randomized 55 multidisciplinary chronic disease clinics (clusters) in Canada to receive either an active knowledge translation intervention or no intervention for the uptake of the guideline on the timing of dialysis initiation. The active knowledge translation intervention consists of audit and feedback as well as patient- and provider-directed educational tools delivered at a comprehensive in-person medical detailing visit. Control clinics are only exposed to guideline

Riemannian multi-manifold modeling and clustering in brain networks

Science.gov (United States)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Testing lowered isothermal models with direct N-body simulations of globular clusters - II. Multimass models

Science.gov (United States)

Peuten, M.; Zocchi, A.; Gieles, M.; Hénault-Brunet, V.

2017-09-01

Lowered isothermal models, such as the multimass Michie-King models, have been successful in describing observational data of globular clusters. In this study, we assess whether such models are able to describe the phase space properties of evolutionary N-body models. We compare the multimass models as implemented in limepy (Gieles & Zocchi) to N-body models of star clusters with different retention fractions for the black holes and neutron stars evolving in a tidal field. We find that multimass models successfully reproduce the density and velocity dispersion profiles of the different mass components in all evolutionary phases and for different remnants retention. We further use these results to study the evolution of global model parameters. We find that over the lifetime of clusters, radial anisotropy gradually evolves from the low- to the high-mass components and we identify features in the properties of observable stars that are indicative of the presence of stellar-mass black holes. We find that the model velocity scale depends on mass as m-δ, with δ ≃ 0.5 for almost all models, but the dependence of central velocity dispersion on m can be shallower, depending on the dark remnant content, and agrees well with that of the N-body models. The reported model parameters, and correlations amongst them, can be used as theoretical priors when fitting these types of mass models to observational data.

A combined community- and facility-based approach to improve pregnancy outcomes in low-resource settings: a Global Network cluster randomized trial.

Science.gov (United States)

Pasha, Omrana; McClure, Elizabeth M; Wright, Linda L; Saleem, Sarah; Goudar, Shivaprasad S; Chomba, Elwyn; Patel, Archana; Esamai, Fabian; Garces, Ana; Althabe, Fernando; Kodkany, Bhala; Mabeya, Hillary; Manasyan, Albert; Carlo, Waldemar A; Derman, Richard J; Hibberd, Patricia L; Liechty, Edward K; Krebs, Nancy; Hambidge, K Michael; Buekens, Pierre; Moore, Janet; Jobe, Alan H; Koso-Thomas, Marion; Wallace, Dennis D; Stalls, Suzanne; Goldenberg, Robert L

2013-10-03

Fetal and neonatal mortality rates in low-income countries are at least 10-fold greater than in high-income countries. These differences have been related to poor access to and poor quality of obstetric and neonatal care. This trial tested the hypothesis that teams of health care providers, administrators and local residents can address the problem of limited access to quality obstetric and neonatal care and lead to a reduction in perinatal mortality in intervention compared to control locations. In seven geographic areas in five low-income and one middle-income country, most with high perinatal mortality rates and substantial numbers of home deliveries, we performed a cluster randomized non-masked trial of a package of interventions that included community mobilization focusing on birth planning and hospital transport, community birth attendant training in problem recognition, and facility staff training in the management of obstetric and neonatal emergencies. The primary outcome was perinatal mortality at ≥28 weeks gestation or birth weight ≥1000 g. Despite extensive effort in all sites in each of the three intervention areas, no differences emerged in the primary or any secondary outcome between the intervention and control clusters. In both groups, the mean perinatal mortality was 40.1/1,000 births (P = 0.9996). Neither were there differences between the two groups in outcomes in the last six months of the project, in the year following intervention cessation, nor in the clusters that best implemented the intervention. This cluster randomized comprehensive, large-scale, multi-sector intervention did not result in detectable impact on the proposed outcomes. While this does not negate the importance of these interventions, we expect that achieving improvement in pregnancy outcomes in these settings will require substantially more obstetric and neonatal care infrastructure than was available at the sites during this trial, and without them provider training

Supportive supervision for volunteers to deliver reproductive health education: a cluster randomized trial.

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Singh, Debra; Negin, Joel; Orach, Christopher Garimoi; Cumming, Robert

2016-10-03

Community Health Volunteers (CHVs) can be effective in improving pregnancy and newborn outcomes through community education. Inadequate supervision of CHVs, whether due to poor planning, irregular visits, or ineffective supervisory methods, is, however, recognized as a weakness in many programs. There has been little research on best practice supervisory or accompaniment models. From March 2014 to February 2015 a proof of concept study was conducted to compare training alone versus training and supportive supervision by paid CHWs (n = 4) on the effectiveness of CHVs (n = 82) to deliver education about pregnancy, newborn care, family planning and hygiene. The pair-matched cluster randomized trial was conducted in eight villages (four intervention and four control) in Budondo sub-county in Jinja, Uganda. Increases in desired behaviors were seen in both the intervention and control arms over the study period. Both arms showed high retention rates of CHVs (95 %). At 1 year follow-up there was a significantly higher prevalence of installed and functioning tippy taps for hand washing (p services. Supportive supervision involves creating a non-threatening, empowering environment in which both the CHV and the supervising CHW learn together and overcome obstacles that might otherwise demotivate the CHV. While the results seem promising for added value with supportive supervision for CHVs undertaking reproductive health activities, further research on a larger scale will be needed to substantiate the effect.

Cluster structure in the correlation coefficient matrix can be characterized by abnormal eigenvalues

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Nie, Chun-Xiao

2018-02-01

In a large number of previous studies, the researchers found that some of the eigenvalues of the financial correlation matrix were greater than the predicted values of the random matrix theory (RMT). Here, we call these eigenvalues as abnormal eigenvalues. In order to reveal the hidden meaning of these abnormal eigenvalues, we study the toy model with cluster structure and find that these eigenvalues are related to the cluster structure of the correlation coefficient matrix. In this paper, model-based experiments show that in most cases, the number of abnormal eigenvalues of the correlation matrix is equal to the number of clusters. In addition, empirical studies show that the sum of the abnormal eigenvalues is related to the clarity of the cluster structure and is negatively correlated with the correlation dimension.

A participatory parent-focused intervention promoting physical activity in preschools: design of a cluster-randomized trial

Directory of Open Access Journals (Sweden)

Hoffmann Kristina

2010-01-01

Full Text Available Abstract Background With rates of childhood obesity increasing, physical activity (PA promotion especially in young children has assumed greater importance. Given the limited effectiveness of most interventions to date, new approaches are needed. The General Systems theory suggests that involving parents as intervention targets may be effective in fostering healthier life styles in children. We describe the development of a parent-focused participatory intervention and the procedures used to evaluate its effectiveness in increasing daily PA in preschoolers. Methods/Design Thirty-seven South German preschools were identified for this study and agreed to participate. Using a two-armed, controlled cluster-randomized trial design we test a participatory intervention with parents as the primary target group and potential agents of behavioural change. Specifically, the intervention is designed to engage parents in the development, refinement and selection of project ideas to promote PA and in incorporating these ideas into daily routines within the preschool community, consisting of children, teachers and parents. Our study is embedded within an existing state-sponsored programme providing structured gym lessons to preschool children. Thus, child-based PA outcomes from the study arm with the parent-focused intervention and the state-sponsored programme are compared with those from the study arm with the state-sponsored programme alone. The evaluation entails baseline measurements of study outcomes as well as follow-up measurements at 6 and 12 months. Accelerometry measures PA intensity over a period of six days, with the mean over six days used as the primary outcome measure. Secondary outcomes include childrens' BMI, a sum of averaged skin fold thickness measurements across multiple sites, and PA behaviour. Longitudinal multilevel models are used to assess within-subject change and between-group differences in study outcomes, adjusted for covariates

Transportability of an Evidence-Based Early Childhood Intervention in a Low-Income African Country: Results of a Cluster Randomized Controlled Study.

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Huang, Keng-Yen; Nakigudde, Janet; Rhule, Dana; Gumikiriza-Onoria, Joy Louise; Abura, Gloria; Kolawole, Bukky; Ndyanabangi, Sheila; Kim, Sharon; Seidman, Edward; Ogedegbe, Gbenga; Brotman, Laurie Miller

2017-11-01

Children in Sub-Saharan Africa (SSA) are burdened by significant unmet mental health needs. Despite the successes of numerous school-based interventions for promoting child mental health, most evidence-based interventions (EBIs) are not available in SSA. This study investigated the implementation quality and effectiveness of one component of an EBI from a developed country (USA) in a SSA country (Uganda). The EBI component, Professional Development, was provided by trained Ugandan mental health professionals to Ugandan primary school teachers. It included large-group experiential training and small-group coaching to introduce and support a range of evidence-based practices (EBPs) to create nurturing and predictable classroom experiences. The study was guided by the Consolidated Framework for Implementation Research, the Teacher Training Implementation Model, and the RE-AIM evaluation framework. Effectiveness outcomes were studied using a cluster randomized design, in which 10 schools were randomized to intervention and wait-list control conditions. A total of 79 early childhood teachers participated. Teacher knowledge and the use of EBPs were assessed at baseline and immediately post-intervention (4-5 months later). A sample of 154 parents was randomly selected to report on child behavior at baseline and post-intervention. Linear mixed effect modeling was applied to examine effectiveness outcomes. Findings support the feasibility of training Ugandan mental health professionals to provide Professional Development for Ugandan teachers. Professional Development was delivered with high levels of fidelity and resulted in improved teacher EBP knowledge and the use of EBPs in the classroom, and child social competence.

Development of an interdisciplinary model cluster for tidal water environments

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Dietrich, Stephan; Winterscheid, Axel; Jens, Wyrwa; Hartmut, Hein; Birte, Hein; Stefan, Vollmer; Andreas, Schöl

2013-04-01

Global climate change has a high potential to influence both the persistence and the transport pathways of water masses and its constituents in tidal waters and estuaries. These processes are linked through dispersion processes, thus directly influencing the sediment and solid suspend matter budgets, and thus the river morphology. Furthermore, the hydrologic regime has an impact on the transport of nutrients, phytoplankton, suspended matter, and temperature that determine the oxygen content within water masses, which is a major parameter describing the water quality. This project aims at the implementation of a so-called (numerical) model cluster in tidal waters, which includes the model compartments hydrodynamics, morphology and ecology. For the implementation of this cluster it is required to continue with the integration of different models that work in a wide range of spatial and temporal scales. The model cluster is thus suggested to lead to a more precise knowledge of the feedback processes between the single interdisciplinary model compartments. In addition to field measurements this model cluster will provide a complementary scientific basis required to address a spectrum of research questions concerning the integral management of estuaries within the Federal Institute of Hydrology (BfG, Germany). This will in particular include aspects like sediment and water quality management as well as adaptation strategies to climate change. The core of the model cluster will consist of the 3D-hydrodynamic model Delft3D (Roelvink and van Banning, 1994), long-term hydrodynamics in the estuaries are simulated with the Hamburg Shelf Ocean Model HAMSOM (Backhaus, 1983; Hein et al., 2012). The simulation results will be compared with the unstructured grid based SELFE model (Zhang and Bapista, 2008). The additional coupling of the BfG-developed 1D-water quality model QSim (Kirchesch and Schöl, 1999; Hein et al., 2011) with the morphological/hydrodynamic models is an

The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments.

Science.gov (United States)

Hedt-Gauthier, Bethany L; Mitsunaga, Tisha; Hund, Lauren; Olives, Casey; Pagano, Marcello

2013-10-26

Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda. To determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations.The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications. We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.

Model-independent X-ray Mass Determinations for Clusters of Galaxies

Science.gov (United States)

Nulsen, Paul

2005-09-01

We propose to use high quality X-ray data from the Chandra archive to determine the mass distributions of about 60 clusters of galaxies over the largest possible range of radii. By avoiding unwarranted assumptions, model-independent methods make best use of high quality data. We will employ two model-independent methods. That used by Nulsen & Boehringer (1995) to determine the mass of the Virgo Cluster and a new method, that will be developed as part of the project. The new method will fit a general mass model directly to the X-ray spectra, making best possible use of the fitting errors to constrain mass profiles.

Investigation of the cluster formation in lithium niobate crystals by computer modeling method

Energy Technology Data Exchange (ETDEWEB)

Voskresenskii, V. M.; Starodub, O. R., E-mail: ol-star@mail.ru; Sidorov, N. V.; Palatnikov, M. N. [Russian Academy of Sciences, Tananaev Institute of Chemistry and Technology of Rare Earth Elements and Mineral Raw Materials, Kola Science Centre (Russian Federation)

2017-03-15

The processes occurring upon the formation of energetically equilibrium oxygen-octahedral clusters in the ferroelectric phase of a stoichiometric lithium niobate (LiNbO{sub 3}) crystal have been investigated by the computer modeling method within the semiclassical atomistic model. An energetically favorable cluster size (at which a structure similar to that of a congruent crystal is organized) is shown to exist. A stoichiometric cluster cannot exist because of the electroneutrality loss. The most energetically favorable cluster is that with a Li/Nb ratio of about 0.945, a value close to the lithium-to-niobium ratio for a congruent crystal.

Adaptive Noise Model for Transform Domain Wyner-Ziv Video using Clustering of DCT Blocks

DEFF Research Database (Denmark)

Luong, Huynh Van; Huang, Xin; Forchhammer, Søren

2011-01-01

The noise model is one of the most important aspects influencing the coding performance of Distributed Video Coding. This paper proposes a novel noise model for Transform Domain Wyner-Ziv (TDWZ) video coding by using clustering of DCT blocks. The clustering algorithm takes advantage of the residual...... modelling. Furthermore, the proposed cluster level noise model is adaptively combined with a coefficient level noise model in this paper to robustly improve coding performance of TDWZ video codec up to 1.24 dB (by Bjøntegaard metric) compared to the DISCOVER TDWZ video codec....... information of all frequency bands, iteratively classifies blocks into different categories and estimates the noise parameter in each category. The experimental results show that the coding performance of the proposed cluster level noise model is competitive with state-ofthe- art coefficient level noise...

Electronic properties of large metal clusters in Jellium and pseudo-jellium models

International Nuclear Information System (INIS)

Catara, F.; Van Giai, N.; Chomaz, P.

1994-08-01

The energy-density functional approach and jellium-like models are used to examine two important electronic properties of metal (Li, Na, K) clusters: their shell and supershell structures, and the behaviour of plasmon energies with increasing cluster sizes. A comparative study is made between predictions of the usual jellium model and those of the pseudo-jellium model where pseudo-Hamiltonians are used. (authors) 10 figs., 5 tabs., 16 refs

Design, rationale, and baseline demographics of SEARCH I: a prospective cluster-randomized study

Directory of Open Access Journals (Sweden)

Albers F

2012-07-01

Full Text Available Frank Albers,1 Asif Shaikh,2 Ahmar Iqbal,31Medical Affairs Respiratory, 2Clinical Development and Medical Affairs, Field Based Medicine-Respiratory, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA; 3Respiratory Medical Affairs, Pfizer Inc, New York, NY, USAAbstract: Questionnaires are available to identify patients at risk for several chronic diseases, including COPD, but are infrequently utilized in primary care. COPD is often underdiagnosed, while at the same time the US Preventive Services Task Force recommends against spirometric screening for COPD in asymptomatic adults. Use of a symptom-based questionnaire and subsequent handheld spirometric device depending on the answers to the questionnaire is a promising approach to identify patients at risk for COPD. Screening, Evaluating and Assessing Rate CHanges of diagnosing respiratory conditions in primary care 1 (SEARCH I was a prospective cluster-randomized study in 168 US primary care practices evaluating the effect of the COPD-Population Screener (COPD-PS™ questionnaire. The effect of this questionnaire alone or sequentially with the handheld copd-6TM device was evaluated on new diagnoses of COPD and on respiratory diagnostic practice patterns (including referrals for pulmonary function testing, referrals to pulmonologists, new diagnoses of COPD, and new respiratory medication prescriptions. Participating practices entered a total of 9704 consecutive consenting subjects aged ≥ 40 years attending primary care clinics. Study arm results were compared for new COPD diagnosis rates between usual care and (1 COPD-PS plus copd-6 and (2 COPD-PS alone. A cluster-randomization design allowed comparison of the intervention effects at the practice level instead of individuals being the subjects of the intervention. Regional principal investigators controlled the flow of study information to sub-investigators at participating practices to reduce observation bias (Hawthorne effect. The

Cost-Effectiveness of a Chronic Care Model for Frail Older Adults in Primary Care: Economic Evaluation Alongside a Stepped-Wedge Cluster-Randomized Trial.

Science.gov (United States)

van Leeuwen, Karen M; Bosmans, Judith E; Jansen, Aaltje P D; Hoogendijk, Emiel O; Muntinga, Maaike E; van Hout, Hein P J; Nijpels, Giel; van der Horst, Henriette E; van Tulder, Maurits W

2015-12-01

To evaluate the cost-effectiveness of the Geriatric Care Model (GCM), an integrated care model for frail older adults based on the Chronic Care Model, with that of usual care. Economic evaluation alongside a 24-month stepped-wedge cluster-randomized controlled trial. Primary care (35 practices) in two regions in the Netherlands. Community-dwelling older adults who were frail according to their primary care physicians and the Program on Research for Integrating Services for the Maintenance of Autonomy case-finding tool questionnaire (N = 1,147). The GCM consisted of the following components: a regularly scheduled in-home comprehensive geriatric assessment by a practice nurse followed by a customized care plan, management and training of practice nurses by a geriatric expert team, and coordination of care through community network meetings and multidisciplinary team consultations of individuals with complex care needs. Outcomes were measured every 6 months and included costs from a societal perspective, health-related quality of life (Medical Outcomes Study 12-item Short-Form Survey (SF-12) physical (PCS) and mental component summary (MCS) scales), functional limitations (Katz activities of daily living and instrumental activities of daily living), and quality-adjusted life years based on the EQ-5D. Multilevel regression models adjusted for time and baseline confounders showed no significant differences in costs ($356, 95% confidence interval = -$488-1,134) and outcomes between intervention and usual care phases. Cost-effectiveness acceptability curves showed that, for the SF-12 PCS and MCS, the probability of the intervention being cost-effective was 0.76 if decision-makers are willing to pay $30,000 per point improvement on the SF-12 scales (range 0-100). For all other outcomes the probability of the intervention being cost-effective was low. Because the GCM was not cost-effective compared to usual care after 24 months of follow-up, widespread implementation

On the applicability of deformed jellium model to the description of metal clusters

DEFF Research Database (Denmark)

Lyalin, Andrey G.; Matveentsev, Anton; Solov'yov, Ilia

2003-01-01

-density approximation deformed jellium model we have calculated the binding energies per atom, ionization potentials, deformation parameters and the optimized values of the Wigner-Seitz radii for neutral and singly charged sodium clusters with the number of atoms $N0$. These characteristics are compared...... shape deformations in the formation cluster properties and the quite reasonable level of applicability of the deformed jellium model.......This work is devoted to the elucidation the applicability of jellium model to the description of alkali cluster properties on the basis of comparison the jellium model results with those derived from experiment and within ab initio theoretical framework. On the basis of the Hartree-Fock and local...

Protocol for Work place adjusted Intelligent physical exercise reducing Musculoskeletal pain in Shoulder and neck (VIMS: a cluster randomized controlled trial

Directory of Open Access Journals (Sweden)

Feveile Helene

2010-08-01

Full Text Available Abstract Background Neck and shoulder complaints are common among employees in sedentary occupations characterized by intensive computer use. Specific strength training is a promising type of physical exercise for relieving neck and shoulder pain in office workers. However, the optimal combination of frequency and exercise duration, as well as the importance of exercise supervision, is unknown. The VIMS study investigates in a cluster randomized controlled design the effectiveness of different time wise combinations of specific strength training with identical accumulated volume, and the relevance of training supervision for safe and effective training. Methods/design A cluster randomized controlled trial of 20 weeks duration where employed office workers are randomized to 1 × 60 min, 3 × 20 min, 9 × 7 min per week of specific strength training with training supervision, to 3 × 20 min per week of specific strength training with a minimal amount of training supervision, or to a reference group without training. A questionnaire will be sent to 2000 employees in jobs characterized by intensive computer work. Employees with cardiovascular disease, trauma, hypertension, or serious chronic disease will be excluded. The main outcome measure is pain in the neck and shoulders at week 20. Trial Registration The trial is registered at ClinicalTrials.gov, number NCT01027390.

The Impact of Combined Music and Tai Chi on Depressive Symptoms Among Community-Dwelling Older Persons: A Cluster Randomized Controlled Trial.

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Liao, S J; Tan, M P; Chong, M C; Chua, Y P

2018-05-01

The effectiveness of pharmacological treatment may be limited in older persons. Several studies using Tai Chi or music therapy separately confirmed positive effects in the reduction of depressive symptoms. We conducted a cluster randomized controlled trial to evaluate the possible synergistic effect of combined music and Tai Chi on depressive symptoms. One hundred and seven older adults with mild to moderate depressive symptoms were recruited from Ya'an city. Fifty-five participants were cluster randomized to combined music and Tai Chi group for three months, while the other fifty-two individuals were randomized to the control group that entailed routine health education delivered monthly by community nurses. The primary outcome of depressive symptoms was measured with the Geriatric Depression Scale (GDS) at baseline and monthly for three months. At three-month follow-up, a statistically significant improvement in depressive symptoms was found in the intervention group compared with control group (F(3,315) = 69.661, P < 0.001). Following adjustments for socio-demographic data, the true effect of intervention on depressive symptoms was significant (F = 41.725, P < 0.01, η p 2 = 0.574). Combined music and Tai Chi reduced depressive symptoms among community-dwelling older persons. This represents an economically viable solution to the management of depression in highly populous developing nations.

Possible world based consistency learning model for clustering and classifying uncertain data.

Science.gov (United States)

Liu, Han; Zhang, Xianchao; Zhang, Xiaotong

2018-06-01

Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good. In this paper, we propose a novel possible world based consistency learning model for uncertain data, which can be extended both for clustering and classifying uncertain data. This model utilizes the consistency principle to learn a consensus affinity matrix for uncertain data, which can make full use of the information across different possible worlds and then improve the clustering and classification performance. Meanwhile, this model imposes a new rank constraint on the Laplacian matrix of the consensus affinity matrix, thereby ensuring that the number of connected components in the consensus affinity matrix is exactly equal to the number of classes. This also means that the clustering and classification results can be directly obtained without any post-processing procedure. Furthermore, for the clustering and classification tasks, we respectively derive the efficient optimization methods to solve the proposed model. Experimental results on real benchmark datasets and real world uncertain datasets show that the proposed model outperforms the state-of-the-art uncertain data clustering and classification algorithms in effectiveness and performs competitively in efficiency. Copyright © 2018 Elsevier Ltd. All rights reserved.

Space-time clusters for early detection of grizzly bear predation.

Science.gov (United States)

Kermish-Wells, Joseph; Massolo, Alessandro; Stenhouse, Gordon B; Larsen, Terrence A; Musiani, Marco

2018-01-01

Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small-prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space-time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears ( Ursus arctos ) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013-2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006-2007, 187 of which were within space-time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013-2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based

Formation mechanism of solute clusters under neutron irradiation in ferritic model alloys and in a reactor pressure vessel steel: clusters of defects

International Nuclear Information System (INIS)

Meslin-Chiffon, E.

2007-11-01

The embrittlement of reactor pressure vessel (RPV) under irradiation is partly due to the formation of point defects (PD) and solute clusters. The aim of this work was to gain more insight into the formation mechanisms of solute clusters in low copper ([Cu] = 0.1 wt%) FeCu and FeCuMnNi model alloys, in a copper free FeMnNi model alloy and in a low copper French RPV steel (16MND5). These materials were neutron-irradiated around 300 C in a test reactor. Solute clusters were characterized by tomographic atom probe whereas PD clusters were simulated with a rate theory numerical code calibrated under cascade damage conditions using transmission electron microscopy analysis. The confrontation between experiments and simulation reveals that a heterogeneous irradiation-induced solute precipitation/segregation probably occurs on PD clusters. (author)

Generating clustered scale-free networks using Poisson based localization of edges

Science.gov (United States)

Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.