Model selection for clustering of pharmacokinetic responses.

PubMed

Guerra, Rui P; Carvalho, Alexandra M; Mateus, Paulo

2018-08-01

Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject's high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection. We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood. Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses. In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community. Copyright © 2018 Elsevier B.V. All rights reserved.

Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes.

PubMed

Johnson, Jacqueline L; Kreidler, Sarah M; Catellier, Diane J; Murray, David M; Muller, Keith E; Glueck, Deborah H

2015-11-30

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes and accounts for within-cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist, and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least six clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Because small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach. Copyright © 2015 John Wiley & Sons, Ltd.

A theoretical study of water equilibria: The cluster distribution versus temperature and pressure for (H2O)n, n=1-60, and ice

NASA Astrophysics Data System (ADS)

Lenz, Annika; Ojamäe, Lars

2009-10-01

The size distribution of water clusters at equilibrium is studied using quantum-chemical calculations in combination with statistical thermodynamics. The necessary energetic data is obtained by quantum-chemical B3LYP computations and through extrapolations from the B3LYP results for the larger clusters. Clusters with up to 60 molecules are included in the equilibrium computations. Populations of different cluster sizes are calculated using both an ideal gas model with noninteracting clusters and a model where a correction for the interaction energy is included analogous to the van der Waals law. In standard vapor the majority of the water molecules are monomers. For the ideal gas model at 1 atm large clusters [56-mer (0-120 K) and 28-mer (100-260 K)] dominate at low temperatures and separate to smaller clusters [21-22-mer (170-280 K) and 4-6-mer (270-320 K) and to monomers (300-350 K)] when the temperature is increased. At lower pressure the transition from clusters to monomers lies at lower temperatures and fewer cluster sizes are formed. The computed size distribution exhibits enhanced peaks for the clusters consisting of 21 and 28 water molecules; these sizes are for protonated water clusters often referred to as magic numbers. If cluster-cluster interactions are included in the model the transition from clusters to monomers is sharper (i.e., occurs over a smaller temperature interval) than when the ideal-gas model is used. Clusters with 20-22 molecules dominate in the liquid region. When a large icelike cluster is included it will dominate for temperatures up to 325 K for the noninteracting clusters model. Thermodynamic properties (Cp, ΔH) were calculated with in general good agreement with experimental values for the solid and gas phase. A formula for the number of H-bond topologies in a given cluster structure is derived. For the 20-mer it is shown that the number of topologies contributes to making the population of dodecahedron-shaped cluster larger than that of a lower-energy fused prism cluster at high temperatures.

A theoretical study of water equilibria: the cluster distribution versus temperature and pressure for (H2O)n, n = 1-60, and ice.

PubMed

Lenz, Annika; Ojamäe, Lars

2009-10-07

The size distribution of water clusters at equilibrium is studied using quantum-chemical calculations in combination with statistical thermodynamics. The necessary energetic data is obtained by quantum-chemical B3LYP computations and through extrapolations from the B3LYP results for the larger clusters. Clusters with up to 60 molecules are included in the equilibrium computations. Populations of different cluster sizes are calculated using both an ideal gas model with noninteracting clusters and a model where a correction for the interaction energy is included analogous to the van der Waals law. In standard vapor the majority of the water molecules are monomers. For the ideal gas model at 1 atm large clusters [56-mer (0-120 K) and 28-mer (100-260 K)] dominate at low temperatures and separate to smaller clusters [21-22-mer (170-280 K) and 4-6-mer (270-320 K) and to monomers (300-350 K)] when the temperature is increased. At lower pressure the transition from clusters to monomers lies at lower temperatures and fewer cluster sizes are formed. The computed size distribution exhibits enhanced peaks for the clusters consisting of 21 and 28 water molecules; these sizes are for protonated water clusters often referred to as magic numbers. If cluster-cluster interactions are included in the model the transition from clusters to monomers is sharper (i.e., occurs over a smaller temperature interval) than when the ideal-gas model is used. Clusters with 20-22 molecules dominate in the liquid region. When a large icelike cluster is included it will dominate for temperatures up to 325 K for the noninteracting clusters model. Thermodynamic properties (C(p), DeltaH) were calculated with in general good agreement with experimental values for the solid and gas phase. A formula for the number of H-bond topologies in a given cluster structure is derived. For the 20-mer it is shown that the number of topologies contributes to making the population of dodecahedron-shaped cluster larger than that of a lower-energy fused prism cluster at high temperatures.

"K"-Means May Perform as well as Mixture Model Clustering but May Also Be Much Worse: Comment on Steinley and Brusco (2011)

ERIC Educational Resources Information Center

Vermunt, Jeroen K.

2011-01-01

Steinley and Brusco (2011) presented the results of a huge simulation study aimed at evaluating cluster recovery of mixture model clustering (MMC) both for the situation where the number of clusters is known and is unknown. They derived rather strong conclusions on the basis of this study, especially with regard to the good performance of…

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

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 are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Users matter : multi-agent systems model of high performance computing cluster users.

DOE Office of Scientific and Technical Information (OSTI.GOV)

North, M. J.; Hood, C. S.; Decision and Information Sciences

2005-01-01

High performance computing clusters have been a critical resource for computational science for over a decade and have more recently become integral to large-scale industrial analysis. Despite their well-specified components, the aggregate behavior of clusters is poorly understood. The difficulties arise from complicated interactions between cluster components during operation. These interactions have been studied by many researchers, some of whom have identified the need for holistic multi-scale modeling that simultaneously includes network level, operating system level, process level, and user level behaviors. Each of these levels presents its own modeling challenges, but the user level is the most complex duemore » to the adaptability of human beings. In this vein, there are several major user modeling goals, namely descriptive modeling, predictive modeling and automated weakness discovery. This study shows how multi-agent techniques were used to simulate a large-scale computing cluster at each of these levels.« less

Focusing cosmic telescopes: systematics of strong lens modeling

NASA Astrophysics Data System (ADS)

Johnson, Traci Lin; Sharon, Keren q.

2018-01-01

The use of strong gravitational lensing by galaxy clusters has become a popular method for studying the high redshift universe. While diverse in computational methods, lens modeling techniques have grasped the means for determining statistical errors on cluster masses and magnifications. However, the systematic errors have yet to be quantified, arising from the number of constraints, availablity of spectroscopic redshifts, and various types of image configurations. I will be presenting my dissertation work on quantifying systematic errors in parametric strong lensing techniques. I have participated in the Hubble Frontier Fields lens model comparison project, using simulated clusters to compare the accuracy of various modeling techniques. I have extended this project to understanding how changing the quantity of constraints affects the mass and magnification. I will also present my recent work extending these studies to clusters in the Outer Rim Simulation. These clusters are typical of the clusters found in wide-field surveys, in mass and lensing cross-section. These clusters have fewer constraints than the HFF clusters and thus, are more susceptible to systematic errors. With the wealth of strong lensing clusters discovered in surveys such as SDSS, SPT, DES, and in the future, LSST, this work will be influential in guiding the lens modeling efforts and follow-up spectroscopic campaigns.

Effects of cluster-shell competition and BCS-like pairing in 12C

NASA Astrophysics Data System (ADS)

Matsuno, H.; Itagaki, N.

2017-12-01

The antisymmetrized quasi-cluster model (AQCM) was proposed to describe α-cluster and jj-coupling shell models on the same footing. In this model, the cluster-shell transition is characterized by two parameters, R representing the distance between α clusters and Λ describing the breaking of α clusters, and the contribution of the spin-orbit interaction, very important in the jj-coupling shell model, can be taken into account starting with the α-cluster model wave function. Not only the closure configurations of the major shells but also the subclosure configurations of the jj-coupling shell model can be described starting with the α-cluster model wave functions; however, the particle-hole excitations of single particles have not been fully established yet. In this study we show that the framework of AQCM can be extended even to the states with the character of single-particle excitations. For ^{12}C, two-particle-two-hole (2p2h) excitations from the subclosure configuration of 0p_{3/2} corresponding to a BCS-like pairing are described, and these shell model states are coupled with the three α-cluster model wave functions. The correlation energy from the optimal configuration can be estimated not only in the cluster part but also in the shell model part. We try to pave the way to establish a generalized description of the nuclear structure.

[Bibliometrics and visualization analysis of land use regression models in ambient air pollution research].

PubMed

Zhang, Y J; Zhou, D H; Bai, Z P; Xue, F X

2018-02-10

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives . Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Cluster adsorption on amorphous and crystalline surfaces - A molecular dynamics study of model Pt on Cu and model Pd on Pt

NASA Technical Reports Server (NTRS)

Garofalini, S. H.; Halicioglu, T.; Pound, G. M.

1981-01-01

Molecular dynamics was used to study the structure, dispersion and short-time behavior of ten-atom clusters adsorbed onto amorphous and crystalline substrates, in which the cluster atoms differed from the substrate atoms. Two adatom-substrate model systems were chosen; one, in which the interaction energy between adatom pairs was greater than that between substrate pairs, and the other, in which the reverse was true. At relatively low temperature ranges, increased dispersion of cluster atoms occurred: (a) on the amorphous substrate as compared to the FCC(100) surface, (b) with increasing reduced temperature, and (c) with adatom-substrate interaction energy stronger than adatom-adatom interaction. Two-dimensional clusters (rafts) on the FCC(100) surface displayed migration of edge atoms only, indicating a mechanism for the cluster rotation and shape changes found in experimental studies.

Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes

PubMed Central

Shimo, Hanae; Arjunan, Satya Nanda Vel; Machiyama, Hiroaki; Nishino, Taiko; Suematsu, Makoto; Fujita, Hideaki; Tomita, Masaru; Takahashi, Koichi

2015-01-01

Oxidative stress mediated clustering of membrane protein band 3 plays an essential role in the clearance of damaged and aged red blood cells (RBCs) from the circulation. While a number of previous experimental studies have observed changes in band 3 distribution after oxidative treatment, the details of how these clusters are formed and how their properties change under different conditions have remained poorly understood. To address these issues, a framework that enables the simultaneous monitoring of the temporal and spatial changes following oxidation is needed. In this study, we established a novel simulation strategy that incorporates deterministic and stochastic reactions with particle reaction-diffusion processes, to model band 3 cluster formation at single molecule resolution. By integrating a kinetic model of RBC antioxidant metabolism with a model of band 3 diffusion, we developed a model that reproduces the time-dependent changes of glutathione and clustered band 3 levels, as well as band 3 distribution during oxidative treatment, observed in prior studies. We predicted that cluster formation is largely dependent on fast reverse reaction rates, strong affinity between clustering molecules, and irreversible hemichrome binding. We further predicted that under repeated oxidative perturbations, clusters tended to progressively grow and shift towards an irreversible state. Application of our model to simulate oxidation in RBCs with cytoskeletal deficiency also suggested that oxidation leads to more enhanced clustering compared to healthy RBCs. Taken together, our model enables the prediction of band 3 spatio-temporal profiles under various situations, thus providing valuable insights to potentially aid understanding mechanisms for removing senescent and premature RBCs. PMID:26046580

Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes.

PubMed

Shimo, Hanae; Arjunan, Satya Nanda Vel; Machiyama, Hiroaki; Nishino, Taiko; Suematsu, Makoto; Fujita, Hideaki; Tomita, Masaru; Takahashi, Koichi

2015-06-01

Oxidative stress mediated clustering of membrane protein band 3 plays an essential role in the clearance of damaged and aged red blood cells (RBCs) from the circulation. While a number of previous experimental studies have observed changes in band 3 distribution after oxidative treatment, the details of how these clusters are formed and how their properties change under different conditions have remained poorly understood. To address these issues, a framework that enables the simultaneous monitoring of the temporal and spatial changes following oxidation is needed. In this study, we established a novel simulation strategy that incorporates deterministic and stochastic reactions with particle reaction-diffusion processes, to model band 3 cluster formation at single molecule resolution. By integrating a kinetic model of RBC antioxidant metabolism with a model of band 3 diffusion, we developed a model that reproduces the time-dependent changes of glutathione and clustered band 3 levels, as well as band 3 distribution during oxidative treatment, observed in prior studies. We predicted that cluster formation is largely dependent on fast reverse reaction rates, strong affinity between clustering molecules, and irreversible hemichrome binding. We further predicted that under repeated oxidative perturbations, clusters tended to progressively grow and shift towards an irreversible state. Application of our model to simulate oxidation in RBCs with cytoskeletal deficiency also suggested that oxidation leads to more enhanced clustering compared to healthy RBCs. Taken together, our model enables the prediction of band 3 spatio-temporal profiles under various situations, thus providing valuable insights to potentially aid understanding mechanisms for removing senescent and premature RBCs.

GraphTeams: a method for discovering spatial gene clusters in Hi-C sequencing data.

PubMed

Schulz, Tizian; Stoye, Jens; Doerr, Daniel

2018-05-08

Hi-C sequencing offers novel, cost-effective means to study the spatial conformation of chromosomes. We use data obtained from Hi-C experiments to provide new evidence for the existence of spatial gene clusters. These are sets of genes with associated functionality that exhibit close proximity to each other in the spatial conformation of chromosomes across several related species. We present the first gene cluster model capable of handling spatial data. Our model generalizes a popular computational model for gene cluster prediction, called δ-teams, from sequences to graphs. Following previous lines of research, we subsequently extend our model to allow for several vertices being associated with the same label. The model, called δ-teams with families, is particular suitable for our application as it enables handling of gene duplicates. We develop algorithmic solutions for both models. We implemented the algorithm for discovering δ-teams with families and integrated it into a fully automated workflow for discovering gene clusters in Hi-C data, called GraphTeams. We applied it to human and mouse data to find intra- and interchromosomal gene cluster candidates. The results include intrachromosomal clusters that seem to exhibit a closer proximity in space than on their chromosomal DNA sequence. We further discovered interchromosomal gene clusters that contain genes from different chromosomes within the human genome, but are located on a single chromosome in mouse. By identifying δ-teams with families, we provide a flexible model to discover gene cluster candidates in Hi-C data. Our analysis of Hi-C data from human and mouse reveals several known gene clusters (thus validating our approach), but also few sparsely studied or possibly unknown gene cluster candidates that could be the source of further experimental investigations.

Testing prediction methods: Earthquake clustering versus the Poisson model

USGS Publications Warehouse

Michael, A.J.

1997-01-01

Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

Regression analysis of clustered failure time data with informative cluster size under the additive transformation models.

PubMed

Chen, Ling; Feng, Yanqin; Sun, Jianguo

2017-10-01

This paper discusses regression analysis of clustered failure time data, which occur when the failure times of interest are collected from clusters. In particular, we consider the situation where the correlated failure times of interest may be related to cluster sizes. For inference, we present two estimation procedures, the weighted estimating equation-based method and the within-cluster resampling-based method, when the correlated failure times of interest arise from a class of additive transformation models. The former makes use of the inverse of cluster sizes as weights in the estimating equations, while the latter can be easily implemented by using the existing software packages for right-censored failure time data. An extensive simulation study is conducted and indicates that the proposed approaches work well in both the situations with and without informative cluster size. They are applied to a dental study that motivated this study.

Cluster analysis of multiple planetary flow regimes

NASA Technical Reports Server (NTRS)

Mo, Kingtse; Ghil, Michael

1987-01-01

A modified cluster analysis method was developed to identify spatial patterns of planetary flow regimes, and to study transitions between them. This method was applied first to a simple deterministic model and second to Northern Hemisphere (NH) 500 mb data. The dynamical model is governed by the fully-nonlinear, equivalent-barotropic vorticity equation on the sphere. Clusters of point in the model's phase space are associated with either a few persistent or with many transient events. Two stationary clusters have patterns similar to unstable stationary model solutions, zonal, or blocked. Transient clusters of wave trains serve as way stations between the stationary ones. For the NH data, cluster analysis was performed in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters are found in the low-frequency band of more than 10 days, and transient clusters in the bandpass frequency window between 2.5 and 6 days. In the low-frequency band three pairs of clusters determine, respectively, EOFs 1, 2, and 3. They exhibit well-known regional features, such as blocking, the Pacific/North American (PNA) pattern and wave trains. Both model and low-pass data show strong bimodality. Clusters in the bandpass window show wave-train patterns in the two jet exit regions. They are related, as in the model, to transitions between stationary clusters.

Optimal Partitioning of a Data Set Based on the "p"-Median Model

ERIC Educational Resources Information Center

Brusco, Michael J.; Kohn, Hans-Friedrich

2008-01-01

Although the "K"-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The "p"-median model is an especially well-studied clustering problem that requires the selection of "p" objects to serve as…

Students' Changing Attitudes and Aspirations Towards Physics During Secondary School

NASA Astrophysics Data System (ADS)

Sheldrake, Richard; Mujtaba, Tamjid; Reiss, Michael J.

2017-11-01

Many countries desire more students to study science subjects, although relatively few students decide to study non-compulsory physics at upper-secondary school and at university. To gain insight into students' intentions to study non-compulsory physics, a longitudinal sample (covering 2258 students across 88 secondary schools in England) was surveyed in year 8 (age 12/13) and again in year 10 (age 14/15). Predictive modelling highlighted that perceived advice, perceived utility of physics, interest in physics, self-concept beliefs (students' subjective beliefs of their current abilities and performance) and home support specifically orientated to physics were key predictors of students' intentions. Latent-transition analysis via Markov models revealed clusters of students, given these factors at years 8 and 10. Students' intentions varied across the clusters, and at year 10 even varied when accounting for the students' underlying attitudes and beliefs, highlighting that considering clusters offered additional explanatory power and insight. Regardless of whether three-cluster, four-cluster, or five-cluster models were considered, the majority of students remained in the same cluster over time; for those who transitioned clusters, more students changed clusters reflecting an increase in attitudes than changed clusters reflecting a decrease. Students in the cluster with the most positive attitudes were most likely to remain within that cluster, while students in clusters with less positive attitudes were more likely to change clusters. Overall, the cluster profiles highlighted that students' attitudes and beliefs may be more closely related than previously assumed, but that changes in their attitudes and beliefs were indeed possible.

Study of cluster behavior in the riser of CFB by the DSMC method

NASA Astrophysics Data System (ADS)

Liu, H. P.; Liu, D. Y.; Liu, H.

2010-03-01

The flow behaviors of clusters in the riser of a two-dimensional (2D) circulating fluidized bed was numerically studied based on the Euler-Lagrangian approach. Gas turbulence was modeled by means of Large Eddy Simulation (LES). Particle collision was modeled by means of the direct simulation Monte Carlo (DSMC) method. Clusters' hydrodynamic characteristics are obtained using a cluster identification method proposed by sharrma et al. (2000). The descending clusters near the wall region and the up- and down-flowing clusters in the core were studied separately due to their different flow behaviors. The effects of superficial gas velocity on the cluster behavior were analyzed. Simulated results showed that near wall clusters flow downward and the descent velocity is about -45 cm/s. The occurrence frequency of the up-flowing cluster is higher than that of down-flowing cluster in the core of riser. With the increase of superficial gas velocity, the solid concentration and occurrence frequency of clusters decrease, while the cluster axial velocity increase. Simulated results were in agreement with experimental data. The stochastic method used in present paper is feasible for predicting the cluster flow behavior in CFBs.

Study of Clusters and Hypernuclei production within PHSD+FRIGA model

NASA Astrophysics Data System (ADS)

Kireyeu, V.; Le Fèvre, A.; Bratkovskaya, E.

2017-01-01

We report on the results on the dynamical modelling of cluster formation with the new combined PHSD+FRIGA model at Nuclotron and NICA energies. The FRIGA clusterisation algorithm, which can be applied to the transport models, is based on the simulated annealing technique to obtain the most bound configuration of fragments and nucleons. The PHSD+FRIGA model is able to predict isotope yields as well as hyper-nucleus production. Based on present predictions of the combined model we study the possibility to detect such clusters and hypernuclei in the BM@N and MPD/NICA detectors.

A class of spherical, truncated, anisotropic models for application to globular clusters

NASA Astrophysics Data System (ADS)

de Vita, Ruggero; Bertin, Giuseppe; Zocchi, Alice

2016-05-01

Recently, a class of non-truncated, radially anisotropic models (the so-called f(ν)-models), originally constructed in the context of violent relaxation and modelling of elliptical galaxies, has been found to possess interesting qualities in relation to observed and simulated globular clusters. In view of new applications to globular clusters, we improve this class of models along two directions. To make them more suitable for the description of small stellar systems hosted by galaxies, we introduce a "tidal" truncation by means of a procedure that guarantees full continuity of the distribution function. The new fT(ν)-models are shown to provide a better fit to the observed photometric and spectroscopic profiles for a sample of 13 globular clusters studied earlier by means of non-truncated models; interestingly, the best-fit models also perform better with respect to the radial-orbit instability. Then, we design a flexible but simple two-component family of truncated models to study the separate issues of mass segregation and multiple populations. We do not aim at a fully realistic description of globular clusters to compete with the description currently obtained by means of dedicated simulations. The goal here is to try to identify the simplest models, that is, those with the smallest number of free parameters, but still have the capacity to provide a reasonable description for clusters that are evidently beyond the reach of one-component models. With this tool, we aim at identifying the key factors that characterize mass segregation or the presence of multiple populations. To reduce the relevant parameter space, we formulate a few physical arguments based on recent observations and simulations. A first application to two well-studied globular clusters is briefly described and discussed.

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

PubMed

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.

Quantitative properties of clustering within modern microscopic nuclear models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volya, A.; Tchuvil’sky, Yu. M., E-mail: tchuvl@nucl-th.sinp.msu.ru

2016-09-15

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 themore » possibilities for studying clustering phenomena in nuclei and for improving the quality of their description.« less

Resolving the problem of galaxy clustering on small scales: any new physics needed?

NASA Astrophysics Data System (ADS)

Kang, X.

2014-02-01

Galaxy clustering sets strong constraints on the physics governing galaxy formation and evolution. However, most current models fail to reproduce the clustering of low-mass galaxies on small scales (r < 1 Mpc h-1). In this paper, we study the galaxy clusterings predicted from a few semi-analytical models. We first compare two Munich versions, Guo et al. and De Lucia & Blaizot. The Guo11 model well reproduces the galaxy stellar mass function, but overpredicts the clustering of low-mass galaxies on small scales. The DLB07 model provides a better fit to the clustering on small scales, but overpredicts the stellar mass function. These seem to be puzzling. The clustering on small scales is dominated by galaxies in the same dark matter halo, and there is slightly more fraction of satellite galaxies residing in massive haloes in the Guo11 model, which is the dominant contribution to the clustering discrepancy between the two models. However, both models still overpredict the clustering at 0.1 < r < 10 Mpc h-1 for low-mass galaxies. This is because both models overpredict the number of satellites by 30 per cent in massive haloes than the data. We show that the Guo11 model could be slightly modified to simultaneously fit the stellar mass function and clusterings, but that cannot be easily achieved in the DLB07 model. The better agreement of DLB07 model with the data actually comes as a coincidence as it predicts too many low-mass central galaxies which are less clustered and thus brings down the total clustering. Finally, we show the predictions from the semi-analytical models of Kang et al. We find that this model can simultaneously fit the stellar mass function and galaxy clustering if the supernova feedback in satellite galaxies is stronger. We conclude that semi-analytical models are now able to solve the small-scales clustering problem, without invoking of any other new physics or changing the dark matter properties, such as the recent favoured warm dark matter.

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a time period because biologically related gene groups can share the same change patterns. Many clustering algorithms have been proposed to group observation data. However, because of the complexity of the underlying functions there have not been many studies on grouping data based on change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. The sample Fourier coefficients not only provide information about the underlying functions, but also reduce the dimension. In addition, as their limiting distribution is a multivariate normal, a model-based clustering method incorporating statistical properties would be appropriate. This work is aimed at discovering gene groups with similar change patterns that share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. The model-based method is advantageous over other methods in our proposed model because the sample Fourier coefficients asymptotically follow the multivariate normal distribution. Change patterns are automatically estimated with the Fourier representation in our model. Our model was tested in simulations and on real gene data sets. The simulation results showed that the model-based clustering method with the sample Fourier coefficients has a lower clustering error rate than K-means clustering. Even when the number of repeated time points was small, the same results were obtained. We also applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns. The R program is available upon the request.

Model-based clustering for RNA-seq data.

PubMed

Si, Yaqing; Liu, Peng; Li, Pinghua; Brutnell, Thomas P

2014-01-15

RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

2013-12-01

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 noisemore » 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.« less

Strong Lens Models for Massive Galaxy Clusters in the Reionization Lensing Cluster Survey

NASA Astrophysics Data System (ADS)

Cerny, Catherine; Sharon, Keren; Coe, Dan A.; Paterno-Mahler, Rachel; Jones, Christine; Czakon, Nicole G.; Umetsu, Keiichi; Stark, Daniel; Bradley, Larry D.; Trenti, Michele; Johnson, Traci; Bradac, Marusa; Dawson, William; Rodney, Steven A.; Strolger, Louis-Gregory; RELICS Team

2017-01-01

We present strong lensing models for five galaxy clusters from the Planck SZ cluster catalog as a part of the Reionization Lensing Cluster Survey (RELICS), a program that seeks to constrain the galaxy luminosity function past z~9 by conducting a wide field survey of massive galaxy clusters with HST (GO-14096, PI: Coe). The strong gravitational lensing effects of these clusters significantly magnify background galaxies, which enhances our ability to discover the large numbers of high redshift galaxies at z~9-12 needed to create a representative sample. We use strong lensing models for these clusters to study their mass distribution and magnification, which allows us to quantify the lensing effect on the background galaxies. These models can then be utilized in the RELICS survey in order to identify high redshift galaxy candidates that may be lensed by the clusters. The intrinsic properties of these galaxy candidates can be derived by removing the lensing effect as predicted by our models, which will meet the science goals of the RELICS survey. We use HST WFC3 and ACS imaging to create lensing models for the clusters RXC J0142.9+4438, ACO-2537, ACO-2163, RXCJ2211.7-0349, and ACT-CLJ0102-49151.

Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials.

PubMed

Grieve, Richard; Nixon, Richard; Thompson, Simon G

2010-01-01

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

PubMed

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Modeling tensional homeostasis in multicellular clusters.

PubMed

Tam, Sze Nok; Smith, Michael L; Stamenović, Dimitrije

2017-03-01

Homeostasis of mechanical stress in cells, or tensional homeostasis, is essential for normal physiological function of tissues and organs and is protective against disease progression, including atherosclerosis and cancer. Recent experimental studies have shown that isolated cells are not capable of maintaining tensional homeostasis, whereas multicellular clusters are, with stability increasing with the size of the clusters. Here, we proposed simple mathematical models to interpret experimental results and to obtain insight into factors that determine homeostasis. Multicellular clusters were modeled as one-dimensional arrays of linearly elastic blocks that were either jointed or disjointed. Fluctuating forces that mimicked experimentally measured cell-substrate tractions were obtained from Monte Carlo simulations. These forces were applied to the cluster models, and the corresponding stress field in the cluster was calculated by solving the equilibrium equation. It was found that temporal fluctuations of the cluster stress field became attenuated with increasing cluster size, indicating that the cluster approached tensional homeostasis. These results were consistent with previously reported experimental data. Furthermore, the models revealed that key determinants of tensional homeostasis in multicellular clusters included the cluster size, the distribution of traction forces, and mechanical coupling between adjacent cells. Based on these findings, we concluded that tensional homeostasis was a multicellular phenomenon. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model

NASA Astrophysics Data System (ADS)

Di, Nur Faraidah Muhammad; Satari, Siti Zanariah

2017-05-01

Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. Clustering technique basically utilizes distance measure to define distance between various data points. Here, we introduce the similarity distance based on Euclidean distance for circular model and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. We classify the cluster group that exceeds the stopping rule as potential outlier. Our aim is to demonstrate the effectiveness of proposed algorithms with the similarity distances in detecting the outliers. It is found that the proposed methods are performed well and applicable for circular regression model.

Intracluster age gradients in numerous young stellar clusters

NASA Astrophysics Data System (ADS)

Getman, K. V.; Feigelson, E. D.; Kuhn, M. A.; Bate, M. R.; Broos, P. S.; Garmire, G. P.

2018-05-01

The pace and pattern of star formation leading to rich young stellar clusters is quite uncertain. In this context, we analyse the spatial distribution of ages within 19 young (median t ≲ 3 Myr on the Siess et al. time-scale), morphologically simple, isolated, and relatively rich stellar clusters. Our analysis is based on young stellar object (YSO) samples from the Massive Young Star-Forming Complex Study in Infrared and X-ray and Star Formation in Nearby Clouds surveys, and a new estimator of pre-main sequence (PMS) stellar ages, AgeJX, derived from X-ray and near-infrared photometric data. Median cluster ages are computed within four annular subregions of the clusters. We confirm and extend the earlier result of Getman et al. (2014): 80 per cent of the clusters show age trends where stars in cluster cores are younger than in outer regions. Our cluster stacking analyses establish the existence of an age gradient to high statistical significance in several ways. Time-scales vary with the choice of PMS evolutionary model; the inferred median age gradient across the studied clusters ranges from 0.75 to 1.5 Myr pc-1. The empirical finding reported in the present study - late or continuing formation of stars in the cores of star clusters with older stars dispersed in the outer regions - has a strong foundation with other observational studies and with the astrophysical models like the global hierarchical collapse model of Vázquez-Semadeni et al.

Nuclear quantum effects in water clusters: the role of the molecular flexibility.

PubMed

González, Briesta S; Noya, Eva G; Vega, Carlos; Sesé, Luis M

2010-02-25

With the objective of establishing the importance of water flexibility in empirical models which explicitly include nuclear quantum effects, we have carried out path integral Monte Carlo simulations in water clusters with up to seven molecules. Two recently developed models have been used for comparison: the rigid TIP4PQ/2005 and the flexible q-TIP4P/F models, both inspired by the rigid TIP4P/2005 model. To obtain a starting configuration for our simulations, we have located the global minima for the rigid TIP4P/2005 and TIP4PQ/2005 models and for the flexible q-TIP4P/F model. All the structures are similar to those predicted by the rigid TIP4P potential showing that the charge distribution mainly determines the global minimum structure. For the flexible q-TIP4P/F model, we have studied the geometrical distortion upon isotopic substitution by studying tritiated water clusters. Our results show that tritiated water clusters exhibit an r(OT) distance shorter than the r(OH) distance in water clusters, not significant changes in the Phi(HOH) angle, and a lower average dipole moment than water clusters. We have also carried out classical simulations with the rigid TIP4PQ/2005 model showing that the rotational kinetic energy is greatly affected by quantum effects, but the translational kinetic energy is only slightly modified. The potential energy is also noticeably higher than in classical simulations. Finally, as a concluding remark, we have calculated the formation energies of water clusters using both models, finding that the formation energies predicted by the rigid TIP4PQ/2005 model are lower by roughly 0.6 kcal/mol than those of the flexible q-TIP4P/F model for clusters of moderate size, the origin of this difference coming mainly from the geometrical distortion of the water molecule in the clusters that causes an increase in the intramolecular potential energy.

Degradation of Perfluorinated Ether Lubricants on Pure Aluminum Surfaces: Semiempirical Quantum Chemical Modeling

NASA Technical Reports Server (NTRS)

Slaby, Scott M.; Ewing, David W.; Zehe, Michael J.

1997-01-01

The AM1 semiempirical quantum chemical method was used to model the interaction of perfluoroethers with aluminum surfaces. Perfluorodimethoxymethane and perfluorodimethyl ether were studied interacting with aluminum surfaces, which were modeled by a five-atom cluster and a nine-atom cluster. Interactions were studied for edge (high index) sites and top (low index) sites of the clusters. Both dissociative binding and nondissociative binding were found, with dissociative binding being stronger. The two different ethers bound and dissociated on the clusters in different ways: perfluorodimethoxymethane through its oxygen atoms, but perfluorodimethyl ether through its fluorine atoms. The acetal linkage of perfluorodimeth-oxymethane was the key structural feature of this molecule in its binding and dissociation on the aluminum surface models. The high-index sites of the clusters caused the dissociation of both ethers. These results are consistent with the experimental observation that perfluorinated ethers decompose in contact with sputtered aluminum surfaces.

Cluster-specific small airway modeling for imaging-based CFD analysis of pulmonary air flow and particle deposition in COPD smokers

NASA Astrophysics Data System (ADS)

Haghighi, Babak; Choi, Jiwoong; Choi, Sanghun; Hoffman, Eric A.; Lin, Ching-Long

2017-11-01

Accurate modeling of small airway diameters in patients with chronic obstructive pulmonary disease (COPD) is a crucial step toward patient-specific CFD simulations of regional airflow and particle transport. We proposed to use computed tomography (CT) imaging-based cluster membership to identify structural characteristics of airways in each cluster and use them to develop cluster-specific airway diameter models. We analyzed 284 COPD smokers with airflow limitation, and 69 healthy controls. We used multiscale imaging-based cluster analysis (MICA) to classify smokers into 4 clusters. With representative cluster patients and healthy controls, we performed multiple regressions to quantify variation of airway diameters by generation as well as by cluster. The cluster 2 and 4 showed more diameter decrease as generation increases than other clusters. The cluster 4 had more rapid decreases of airway diameters in the upper lobes, while cluster 2 in the lower lobes. We then used these regression models to estimate airway diameters in CT unresolved regions to obtain pressure-volume hysteresis curves using a 1D resistance model. These 1D flow solutions can be used to provide the patient-specific boundary conditions for 3D CFD simulations in COPD patients. Support for this study was provided, in part, by NIH Grants U01-HL114494, R01-HL112986 and S10-RR022421.

Cosmological study with galaxy clusters detected by the Sunyaev-Zel'dovich effect

NASA Astrophysics Data System (ADS)

Mak, Suet-Ying

In this work, we present various studies to forecast the power of the galaxy clusters detected by the Sunyaev-Zel'dovich (SZ) effect in constraining cosmological models. The SZ effect is regarded as one of the new and promising technique to identify and study cluster physics. With the latest data being released in recent years from the SZ telescopes, it is essential to explore their potentials in providing cosmological information and investigate their relative strengths with respect to galaxy cluster data from X-ray and optical, as well as other cosmological probes such as Cosmic Microwave Background (CMB). One of the topics regard resolving the debate on the existence of an anomalous large scale bulk flow as measured from the kinetic SZ signal of galaxy clusters in the WMAP CMB data. We predict that if such measurement is done with the latest CMB data from the Planck satellite, the sensitivity will be improved by a factor of >5 and thus be able to provide an independent view of its existence. As it turns out, the Planck data, when using the technique developed in this work, find that the observed bulk flow amplitude is consistent with those expected from the LambdaCDM, which is in clear contradiction to the previous claim of a significant bulk flow detection in the WMAP data. We also forecast on the capability of the ongoing and future cluster surveys identified through thermal SZ (tSZ) in constraining three extended models to the LambdaCDM model: modified gravity f( R) model, primordial non-Gaussianity of density perturbation, and the presence of massive neutrinos. We do so by employing their effects on the cluster number count and power spectrum and using Fisher Matrix analysis to estimate the errors on the model parameters. We find that SZ cluster surveys can provide vital complementary information to those expected from non-cluster probes. Our results therefore give the confidence for pursuing these extended cosmological models with SZ clusters.

A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures

PubMed Central

Chen, Yun; Yang, Hui

2016-01-01

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering. PMID:27966581

A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures.

PubMed

Chen, Yun; Yang, Hui

2016-12-14

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering.

Prospects for Determining the Mass Distributions of Galaxy Clusters on Large Scales Using Weak Gravitational Lensing

NASA Astrophysics Data System (ADS)

Fong, M.; Bowyer, R.; Whitehead, A.; Lee, B.; King, L.; Applegate, D.; McCarthy, I.

2018-05-01

For more than two decades, the Navarro, Frenk, and White (NFW) model has stood the test of time; it has been used to describe the distribution of mass in galaxy clusters out to their outskirts. Stacked weak lensing measurements of clusters are now revealing the distribution of mass out to and beyond their virial radii, where the NFW model is no longer applicable. In this study we assess how well the parameterised Diemer & Kravstov (DK) density profile describes the characteristic mass distribution of galaxy clusters extracted from cosmological simulations. This is determined from stacked synthetic lensing measurements of the 50 most massive clusters extracted from the Cosmo-OWLS simulations, using the Dark Matter Only run and also the run that most closely matches observations. The characteristics of the data reflect the Weighing the Giants survey and data from the future Large Synoptic Survey Telescope (LSST). In comparison with the NFW model, the DK model favored by the stacked data, in particular for the future LSST data, where the number density of background galaxies is higher. The DK profile depends on the accretion history of clusters which is specified in the current study. Eventually however subsamples of galaxy clusters with qualities indicative of disparate accretion histories could be studied.

A Note on Cluster Effects in Latent Class Analysis

ERIC Educational Resources Information Center

Kaplan, David; Keller, Bryan

2011-01-01

This article examines the effects of clustering in latent class analysis. A comprehensive simulation study is conducted, which begins by specifying a true multilevel latent class model with varying within- and between-cluster sample sizes, varying latent class proportions, and varying intraclass correlations. These models are then estimated under…

From the Superatom Model to a Diverse Array of Super-Elements: A Systematic Study of Dopant Influence on the Electronic Structure of Thiolate-Protected Gold Clusters.

PubMed

Schacht, Julia; Gaston, Nicola

2016-10-18

The electronic properties of doped thiolate-protected gold clusters are often referred to as tunable, but their study to date, conducted at different levels of theory, does not allow a systematic evaluation of this claim. Here, using density functional theory, the applicability of the superatomic model to these clusters is critically evaluated, and related to the degree of structural distortion and electronic inhomogeneity in the differently doped clusters, with dopant atoms Pd, Pt, Cu, and Ag. The effect of electron number is systematically evaluated by varying the charge on the overall cluster, and the nominal number of delocalized electrons, employed in the superatomic model, is compared to the numbers obtained from Bader analysis of individual atomic charges. We find that the superatomic model is highly applicable to all of these clusters, and is able to predict and explain the changing electronic structure as a function of charge. However, significant perturbations of the model arise due to doping, due to distortions of the core structure of the Au 13 [RS(AuSR) 2 ] 6 - cluster. In addition, analysis of the electronic structure indicates that the superatomic character is distributed further across the ligand shell in the case of the doped clusters, which may have implications for the self-assembly of these clusters into materials. The prediction of appropriate clusters for such superatomic solids relies critically on such quantitative analysis of the tunability of the electronic structure. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

PubMed

Sun, Zhe; Wang, Ting; Deng, Ke; Wang, Xiao-Feng; Lafyatis, Robert; Ding, Ying; Hu, Ming; Chen, Wei

2018-01-01

Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. wei.chen@chp.edu or hum@ccf.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Formation patterns of water clusters in CMK-3 and CMK-5 mesoporous carbons: a computational recognition study.

PubMed

Peng, Xuan; Jain, Surendra Kumar; Singh, Jayant Kumar; Liu, Anqi; Jin, Qibing

2018-06-13

Grand canonical Monte Carlo simulations are performed to study the adsorption of water in realistic CMK-3 and CMK-5 models at 300 K. The adsorption isotherms are characterized by negligible uptake at lower chemical potentials and complete pore filling once the threshold chemical potential is increased. Results for the isosteric heat of adsorption, radial distribution function (O-O and O-H), hydrogen bond statistics and the cluster size distribution of water molecules are presented. The snapshots of GCMC simulations in CMK-3 and CMK-5 models show that the adsorption happens via the formation of water clusters. For the CMK-3 model, it was found that the pore filling occurred via the formation of a single water cluster and a few very small clusters. The water cluster size increased with an increase in pore size of the CMK-3 model. For the CMK-5 model, it was found that the adsorption first occurred in the inner porosity (via cluster formation). There was no adsorption of water in the outer porosity during the filling of the inner porosity. After the inner porosity was completely filled, the water begins to fill the outer porosity. Snapshots from GCMC simulations of the CMK-5 model clearly show that the water adsorption in the outer porosity occurs via the formation and growth of clusters and there was no formation of layers of water in the porosity as seen for nonpolar fluids like nitrogen.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

NASA Astrophysics Data System (ADS)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Formation and Assembly of Massive Star Clusters

NASA Astrophysics Data System (ADS)

McMillan, Stephen

The formation of stars and star clusters is a major unresolved problem in astrophysics. It is central to modeling stellar populations and understanding galaxy luminosity distributions in cosmological models. Young massive clusters are major components of starburst galaxies, while globular clusters are cornerstones of the cosmic distance scale and represent vital laboratories for studies of stellar dynamics and stellar evolution. Yet how these clusters form and how rapidly and efficiently they expel their natal gas remain unclear, as do the consequences of this gas expulsion for cluster structure and survival. Also unclear is how the properties of low-mass clusters, which form from small-scale instabilities in galactic disks and inform much of our understanding of cluster formation and star-formation efficiency, differ from those of more massive clusters, which probably formed in starburst events driven by fast accretion at high redshift, or colliding gas flows in merging galaxies. Modeling cluster formation requires simulating many simultaneous physical processes, placing stringent demands on both software and hardware. Simulations of galaxies evolving in cosmological contexts usually lack the numerical resolution to simulate star formation in detail. They do not include detailed treatments of important physical effects such as magnetic fields, radiation pressure, ionization, and supernova feedback. Simulations of smaller clusters include these effects, but fall far short of the mass of even single young globular clusters. With major advances in computing power and software, we can now directly address this problem. We propose to model the formation of massive star clusters by integrating the FLASH adaptive mesh refinement magnetohydrodynamics (MHD) code into the Astrophysical Multi-purpose Software Environment (AMUSE) framework, to work with existing stellar-dynamical and stellar evolution modules in AMUSE. All software will be freely distributed on-line, allowing open access to state-of- the-art simulation techniques within a modern, modular software environment. We will follow the gravitational collapse of 0.1-10 million-solar mass gas clouds through star formation and coalescence into a star cluster, modeling in detail the coupling of the gas and the newborn stars. We will study the effects of star formation by detecting accreting regions of gas in self-gravitating, turbulent, MHD, FLASH models that we will translate into collisional dynamical systems of stars modeled with an N-body code, coupled together in the AMUSE framework. Our FLASH models will include treatments of radiative transfer from the newly formed stars, including heating and radiative acceleration of the surrounding gas. Specific questions to be addressed are: (1) How efficiently does the gas in a star forming region form stars, how does this depend on mass, metallicity, and other parameters, and what terminates star formation? What observational predictions can be made to constrain our models? (2) How important are different mechanisms for driving turbulence and removing gas from a cluster: accretion, radiative feedback, and mechanical feedback? (3) How does the infant mortality rate of young clusters depend on the initial properties of the parent cloud? (4) What are the characteristic formation timescales of massive star clusters, and what observable imprints does the assembly process leave on their structure at an age of 10-20 Myr, when formation is essentially complete and many clusters can be observed? These studies are directly relevant to NASA missions at many electromagnetic wavelengths, including Chandra, GALEX, Hubble, and Spitzer. Each traces different aspects of cluster formation and evolution: X-rays trace supernovae, ultraviolet traces young stars, visible colors can distinguish between young blue stars and older red stars, and the infrared directly shows young embedded star clusters.

Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.

PubMed

Schramm, Catherine; Vial, Céline; Bachoud-Lévi, Anne-Catherine; Katsahian, Sandrine

2018-01-01

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

PubMed

Kim, Sehwi; Jung, Inkyung

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data

PubMed Central

Kim, Sehwi

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns. PMID:28753674

Integrating data from randomized controlled trials and observational studies to predict the response to pregabalin in patients with painful diabetic peripheral neuropathy.

PubMed

Alexander, Joe; Edwards, Roger A; Savoldelli, Alberto; Manca, Luigi; Grugni, Roberto; Emir, Birol; Whalen, Ed; Watt, Stephen; Brodsky, Marina; Parsons, Bruce

2017-07-20

More patient-specific medical care is expected as more is learned about variations in patient responses to medical treatments. Analytical tools enable insights by linking treatment responses from different types of studies, such as randomized controlled trials (RCTs) and observational studies. Given the importance of evidence from both types of studies, our goal was to integrate these types of data into a single predictive platform to help predict response to pregabalin in individual patients with painful diabetic peripheral neuropathy (pDPN). We utilized three pivotal RCTs of pregabalin (398 North American patients) and the largest observational study of pregabalin (3159 German patients). We implemented a hierarchical cluster analysis to identify patient clusters in the Observational Study to which RCT patients could be matched using the coarsened exact matching (CEM) technique, thereby creating a matched dataset. We then developed autoregressive moving average models (ARMAXs) to estimate weekly pain scores for pregabalin-treated patients in each cluster in the matched dataset using the maximum likelihood method. Finally, we validated ARMAX models using Observational Study patients who had not matched with RCT patients, using t tests between observed and predicted pain scores. Cluster analysis yielded six clusters (287-777 patients each) with the following clustering variables: gender, age, pDPN duration, body mass index, depression history, pregabalin monotherapy, prior gabapentin use, baseline pain score, and baseline sleep interference. CEM yielded 1528 unique patients in the matched dataset. The reduction in global imbalance scores for the clusters after adding the RCT patients (ranging from 6 to 63% depending on the cluster) demonstrated that the process reduced the bias of covariates in five of the six clusters. ARMAX models of pain score performed well (R 2 : 0.85-0.91; root mean square errors: 0.53-0.57). t tests did not show differences between observed and predicted pain scores in the 1955 patients who had not matched with RCT patients. The combination of cluster analyses, CEM, and ARMAX modeling enabled strong predictive capabilities with respect to pain scores. Integrating RCT and Observational Study data using CEM enabled effective use of Observational Study data to predict patient responses.

Identifying optimal threshold statistics for elimination of hookworm using a stochastic simulation model.

PubMed

Truscott, James E; Werkman, Marleen; Wright, James E; Farrell, Sam H; Sarkar, Rajiv; Ásbjörnsdóttir, Kristjana; Anderson, Roy M

2017-06-30

There is an increased focus on whether mass drug administration (MDA) programmes alone can interrupt the transmission of soil-transmitted helminths (STH). Mathematical models can be used to model these interventions and are increasingly being implemented to inform investigators about expected trial outcome and the choice of optimum study design. One key factor is the choice of threshold for detecting elimination. However, there are currently no thresholds defined for STH regarding breaking transmission. We develop a simulation of an elimination study, based on the DeWorm3 project, using an individual-based stochastic disease transmission model in conjunction with models of MDA, sampling, diagnostics and the construction of study clusters. The simulation is then used to analyse the relationship between the study end-point elimination threshold and whether elimination is achieved in the long term within the model. We analyse the quality of a range of statistics in terms of the positive predictive values (PPV) and how they depend on a range of covariates, including threshold values, baseline prevalence, measurement time point and how clusters are constructed. End-point infection prevalence performs well in discriminating between villages that achieve interruption of transmission and those that do not, although the quality of the threshold is sensitive to baseline prevalence and threshold value. Optimal post-treatment prevalence threshold value for determining elimination is in the range 2% or less when the baseline prevalence range is broad. For multiple clusters of communities, both the probability of elimination and the ability of thresholds to detect it are strongly dependent on the size of the cluster and the size distribution of the constituent communities. Number of communities in a cluster is a key indicator of probability of elimination and PPV. Extending the time, post-study endpoint, at which the threshold statistic is measured improves PPV value in discriminating between eliminating clusters and those that bounce back. The probability of elimination and PPV are very sensitive to baseline prevalence for individual communities. However, most studies and programmes are constructed on the basis of clusters. Since elimination occurs within smaller population sub-units, the construction of clusters introduces new sensitivities for elimination threshold values to cluster size and the underlying population structure. Study simulation offers an opportunity to investigate key sources of sensitivity for elimination studies and programme designs in advance and to tailor interventions to prevailing local or national conditions.

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

NASA Astrophysics Data System (ADS)

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.

Baseline adjustments for binary data in repeated cross-sectional cluster randomized trials.

PubMed

Nixon, R M; Thompson, S G

2003-09-15

Analysis of covariance models, which adjust for a baseline covariate, are often used to compare treatment groups in a controlled trial in which individuals are randomized. Such analysis adjusts for any baseline imbalance and usually increases the precision of the treatment effect estimate. We assess the value of such adjustments in the context of a cluster randomized trial with repeated cross-sectional design and a binary outcome. In such a design, a new sample of individuals is taken from the clusters at each measurement occasion, so that baseline adjustment has to be at the cluster level. Logistic regression models are used to analyse the data, with cluster level random effects to allow for different outcome probabilities in each cluster. We compare the estimated treatment effect and its precision in models that incorporate a covariate measuring the cluster level probabilities at baseline and those that do not. In two data sets, taken from a cluster randomized trial in the treatment of menorrhagia, the value of baseline adjustment is only evident when the number of subjects per cluster is large. We assess the generalizability of these findings by undertaking a simulation study, and find that increased precision of the treatment effect requires both large cluster sizes and substantial heterogeneity between clusters at baseline, but baseline imbalance arising by chance in a randomized study can always be effectively adjusted for. Copyright 2003 John Wiley & Sons, Ltd.

Testing modified gravity with globular clusters: the case of NGC 2419

NASA Astrophysics Data System (ADS)

Llinares, Claudio

2018-05-01

The dynamics of globular clusters has been studied in great detail in the context of general relativity as well as with modifications of gravity that strongly depart from the standard paradigm such as Modified Newtonian Dynamics. However, at present there are no studies that aim to test the impact that less extreme modifications of gravity (e.g. models constructed as alternatives to dark energy) have on the behaviour of globular clusters. This Letter presents fits to the velocity dispersion profile of the cluster NGC 2419 under the symmetron-modified gravity model. The data show an increase in the velocity dispersion towards the centre of the cluster which could be difficult to explain within general relativity. By finding the best-fitting solution associated with the symmetron model, we show that this tension does not exist in modified gravity. However, the best-fitting parameters give a model that is inconsistent with the dynamics of the Solar system. Exploration of different screening mechanisms should give us the chance to understand if it is possible to maintain the appealing properties of the symmetron model when it comes to globular clusters and at the same time recover the Solar system dynamics properly.

Measurement Error Correction Formula for Cluster-Level Group Differences in Cluster Randomized and Observational Studies

ERIC Educational Resources Information Center

Cho, Sun-Joo; Preacher, Kristopher J.

2016-01-01

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…

A Gaia study of the Hyades open cluster

NASA Astrophysics Data System (ADS)

Reino, Stella; de Bruijne, Jos; Zari, Eleonora; d'Antona, Francesca; Ventura, Paolo

2018-03-01

We present a study of the membership of the Hyades open cluster, derive kinematically-modelled parallaxes of its members, and study the colour-absolute magnitude diagram of the cluster. We use Gaia DR1 Tycho-Gaia Astrometric Solution (TGAS) data complemented by Hipparcos-2 data for bright stars not contained in TGAS. We supplement the astrometric data with radial velocities collected from a dozen literature sources. By assuming that all cluster members move with the mean cluster velocity to within the velocity dispersion, we use the observed and the expected motions of the stars to determine individual cluster membership probabilities. We subsequently derive improved parallaxes through maximum-likelihood kinematic modelling of the cluster. This method has an iterative component to deal with 'outliers', caused for instance by double stars or escaping members. Our method extends an existing method and supports the mixed presence of stars with and without radial velocities. We find 251 candidate members, 200 of which have a literature radial velocity, and 70 of which are new candidate members with TGAS astrometry. The cluster is roughly spherical in its centre but significantly flattened at larger radii. The observed colour-absolute magnitude diagram shows a clear binary sequence. The kinematically-modelled parallaxes that we derive are a factor ˜1.7 / 2.9 more precise than the TGAS / Hipparcos-2 values and allow to derive an extremely sharp main sequence. This sequence shows evidence for fine-detailed structure which is elegantly explained by the full spectrum turbulence model of convection.

Chaos theory perspective for industry clusters development

NASA Astrophysics Data System (ADS)

Yu, Haiying; Jiang, Minghui; Li, Chengzhang

2016-03-01

Industry clusters have outperformed in economic development in most developing countries. The contributions of industrial clusters have been recognized as promotion of regional business and the alleviation of economic and social costs. It is no doubt globalization is rendering clusters in accelerating the competitiveness of economic activities. In accordance, many ideas and concepts involve in illustrating evolution tendency, stimulating the clusters development, meanwhile, avoiding industrial clusters recession. The term chaos theory is introduced to explain inherent relationship of features within industry clusters. A preferred life cycle approach is proposed for industrial cluster recessive theory analysis. Lyapunov exponents and Wolf model are presented for chaotic identification and examination. A case study of Tianjin, China has verified the model effectiveness. The investigations indicate that the approaches outperform in explaining chaos properties in industrial clusters, which demonstrates industrial clusters evolution, solves empirical issues and generates corresponding strategies.

Diffusion Of Mass In Evaporating Multicomponent Drops

NASA Technical Reports Server (NTRS)

Bellan, Josette; Harstad, Kenneth G.

1992-01-01

Report summarizes study of diffusion of mass and related phenomena occurring in evaporation of dense and dilute clusters of drops of multicomponent liquids intended to represent fuels as oil, kerosene, and gasoline. Cluster represented by simplified mathematical model, including global conservation equations for entire cluster and conditions on boundary between cluster and ambient gas. Differential equations of model integrated numerically. One of series of reports by same authors discussing evaporation and combustion of sprayed liquid fuels.

Validation of gait analysis with dynamic radiostereometric analysis (RSA) in patients operated with total hip arthroplasty.

PubMed

Zügner, Roland; Tranberg, Roy; Lisovskaja, Vera; Shareghi, Bita; Kärrholm, Johan

2017-07-01

We simultaneously examined 14 patients with OTS and dynamic radiostereometric analysis (RSA) to evaluate the accuracy of both skin- and a cluster-marker models. The mean differences between the OTS and RSA system in hip flexion, abduction, and rotation varied up to 9.5° for the skin-marker and up to 11.3° for the cluster-marker models, respectively. Both models tended to underestimate the amount of flexion and abduction, but a significant systematic difference between the marker and RSA evaluations could only be established for recordings of hip abduction using cluster markers (p = 0.04). The intra-class correlation coefficient (ICC) was 0.7 or higher during flexion for both models and during abduction using skin markers, but decreased to 0.5-0.6 when abduction motion was studied with cluster markers. During active hip rotation, the two marker models tended to deviate from the RSA recordings in different ways with poor correlations at the end of the motion (ICC ≤0.4). During active hip motions soft tissue displacements occasionally induced considerable differences when compared to skeletal motions. The best correlation between RSA recordings and the skin- and cluster-marker model was found for studies of hip flexion and abduction with the skin-marker model. Studies of hip abduction with use of cluster markers were associated with a constant underestimation of the motion. Recordings of skeletal motions with use of skin or cluster markers during hip rotation were associated with high mean errors amounting up to about 10° at certain positions. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1515-1522, 2017. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

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

PubMed

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.

Formation and decay of resonance states in 9Be and 9B nuclei: Microscopic three-cluster model investigations

NASA Astrophysics Data System (ADS)

Vasilevsky, V. S.; Katō, K.; Takibayev, N. Zh.

2017-09-01

We study the nature of the low-lying resonance states in mirror nuclei 9Be and 9B. Investigations are performed within a three-cluster model. The model makes use of the hyperspherical harmonics, which provides a convenient description of the three-cluster continuum. The dominant three-cluster configurations α +α +n and α +α +p in 9Be and 9B, respectively, are taken into account. Dominant decay channels for all resonance states in 9Be and 9B are explored. Much attention is paid to the controversial 1 /2+ resonance states in both nuclei. We study effects of the Coulomb interaction on the energy and width of three-cluster resonances in the mirror nuclei 9Be and 9B. We also search for the Hoyle-analog state, which is a key step for alternative ways to synthesize 9Be and 9B in triple collisions of clusters in a stellar environment.

A clustering approach applied to time-lapse ERT interpretation - Case study of Lascaux cave

NASA Astrophysics Data System (ADS)

Xu, Shan; Sirieix, Colette; Riss, Joëlle; Malaurent, Philippe

2017-09-01

The Lascaux cave, located in southwest France, is one of the most important prehistoric cave in the world that shows Paleolithic paintings. This study aims to characterize the structure of the weathered epikarst setting located above the cave using Time-Lapse Electrical Resistivity Tomography (ERT) combined with local hydrogeological and climatic environmental data. Twenty ERT profiles were carried out for two years and helped us to record the seasonal and spatial variations of the electrical resistivity of the hydraulic upstream area of the Lascaux cave. The 20 interpreted resistivity models were merged into a single synthetic model using a multidimensional statistical method (Hierarchical Agglomerative Clustering). The individual blocks from the synthetic model associated with a similar resistivity variability were gathered into 7 clusters. We combined the resistivity temporal variations with climatic and hydrogeological data to propose a geo-electrical model that relates to a conceptual geological model. We provide a geological interpretation for each cluster regarding epikarst features. The superficial clusters (no 1 & 2) are linked to effective rainfall and trees, probably a fractured limestone. Another two clusters (no 6 & 7) are linked to detrital formations (sand and clay respectively). The cluster 3 may correspond to a marly limestone that forms a non-permeable horizon. Finally, the electrical behavior of the last two clusters (no 4 & 5) is correlated with the variation of flow rate; they may be a privileged feed zone of the flow in the cave.

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

MACS J0416.1-2403: Impact of line-of-sight structures on strong gravitational lensing modelling of galaxy clusters

NASA Astrophysics Data System (ADS)

Chirivì, G.; Suyu, S. H.; Grillo, C.; Halkola, A.; Balestra, I.; Caminha, G. B.; Mercurio, A.; Rosati, P.

2018-06-01

Exploiting the powerful tool of strong gravitational lensing by galaxy clusters to study the highest-redshift Universe and cluster mass distributions relies on precise lens mass modelling. In this work, we aim to present the first attempt at modelling line-of-sight (LOS) mass distribution in addition to that of the cluster, extending previous modelling techniques that assume mass distributions to be on a single lens plane. We have focussed on the Hubble Frontier Field cluster MACS J0416.1-2403, and our multi-plane model reproduces the observed image positions with a rms offset of 0.''53. Starting from this best-fitting model, we simulated a mock cluster that resembles MACS J0416.1-2403 in order to explore the effects of LOS structures on cluster mass modelling. By systematically analysing the mock cluster under different model assumptions, we find that neglecting the lensing environment has a significant impact on the reconstruction of image positions (rms 0.''3); accounting for LOS galaxies as if they were at the cluster redshift can partially reduce this offset. Moreover, foreground galaxies are more important to include into the model than the background ones. While the magnification factor of the lensed multiple images are recovered within 10% for 95% of them, those 5% that lie near critical curves can be significantly affected by the exclusion of the lensing environment in the models. In addition, LOS galaxies cannot explain the apparent discrepancy in the properties of massive sub-halos between MACS J0416.1-2403 and N-body simulated clusters. Since our model of MACS J0416.1-2403 with LOS galaxies only reduced modestly the rms offset in the image positions, we conclude that additional complexities would be needed in future models of MACS J0416.1-2403.

Galaxy cluster lensing masses in modified lensing potentials

DOE PAGES

Barreira, Alexandre; Li, Baojiu; Jennings, Elise; ...

2015-10-28

In this study, we determine the concentration–mass relation of 19 X-ray selected galaxy clusters from the Cluster Lensing and Supernova Survey with Hubble survey in theories of gravity that directly modify the lensing potential. We model the clusters as Navarro–Frenk–White haloes and fit their lensing signal, in the Cubic Galileon and Nonlocal gravity models, to the lensing convergence profiles of the clusters. We discuss a number of important issues that need to be taken into account, associated with the use of non-parametric and parametric lensing methods, as well as assumptions about the background cosmology. Our results show that the concentrationmore » and mass estimates in the modified gravity models are, within the error bars, the same as in Λ cold dark matter. This result demonstrates that, for the Nonlocal model, the modifications to gravity are too weak at the cluster redshifts, and for the Galileon model, the screening mechanism is very efficient inside the cluster radius. However, at distances ~ [2–20] Mpc/h from the cluster centre, we find that the surrounding force profiles are enhanced by ~ 20–40% in the Cubic Galileon model. This has an impact on dynamical mass estimates, which means that tests of gravity based on comparisons between lensing and dynamical masses can also be applied to the Cubic Galileon model.« less

Biclustering Models for Two-Mode Ordinal Data.

PubMed

Matechou, Eleni; Liu, Ivy; Fernández, Daniel; Farias, Miguel; Gjelsvik, Bergljot

2016-09-01

The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets.

Transient Properties of Probability Distribution for a Markov Process with Size-dependent Additive Noise

NASA Astrophysics Data System (ADS)

Yamada, Yuhei; Yamazaki, Yoshihiro

2018-04-01

This study considered a stochastic model for cluster growth in a Markov process with a cluster size dependent additive noise. According to this model, the probability distribution of the cluster size transiently becomes an exponential or a log-normal distribution depending on the initial condition of the growth. In this letter, a master equation is obtained for this model, and derivation of the distributions is discussed.

Critical thinking in higher education: The influence of teaching styles and peer collaboration on science and math learning

NASA Astrophysics Data System (ADS)

Quitadamo, Ian Joseph

Many higher education faculty perceive a deficiency in students' ability to reason, evaluate, and make informed judgments, skills that are deemed necessary for academic and job success in science and math. These skills, often collected within a domain called critical thinking (CT), have been studied and are thought to be influenced by teaching styles (the combination of beliefs, behavior, and attitudes used when teaching) and small group collaborative learning (SGCL). However, no existing studies show teaching styles and SGCL cause changes in student CT performance. This study determined how combinations of teaching styles called clusters and peer-facilitated SGCL (a specific form of SGCL) affect changes in undergraduate student CT performance using a quasi-experimental pre-test/post-test research design and valid and reliable CT performance indicators. Quantitative analyses of three teaching style cluster models (Grasha's cluster model, a weighted cluster model, and a student-centered/teacher-centered cluster model) and peer-facilitated SGCL were performed to evaluate their ability to cause measurable changes in student CT skills. Based on results that indicated weighted teaching style clusters and peer-facilitated SGCL are associated with significant changes in student CT, we conclude that teaching styles and peer-facilitated SGCL influence the development of undergraduate CT in higher education science and math.

Effects of Cluster Location on Human Performance on the Traveling Salesperson Problem

ERIC Educational Resources Information Center

MacGregor, James N.

2013-01-01

Most models of human performance on the traveling salesperson problem involve clustering of nodes, but few empirical studies have examined effects of clustering in the stimulus array. A recent exception varied degree of clustering and concluded that the more clustered a stimulus array, the easier a TSP is to solve (Dry, Preiss, & Wagemans,…

Network-based spatial clustering technique for exploring features in regional industry

NASA Astrophysics Data System (ADS)

Chou, Tien-Yin; Huang, Pi-Hui; Yang, Lung-Shih; Lin, Wen-Tzu

2008-10-01

In the past researches, industrial cluster mainly focused on single or particular industry and less on spatial industrial structure and mutual relations. Industrial cluster could generate three kinds of spillover effects, including knowledge, labor market pooling, and input sharing. In addition, industrial cluster indeed benefits industry development. To fully control the status and characteristics of district industrial cluster can facilitate to improve the competitive ascendancy of district industry. The related researches on industrial spatial cluster were of great significance for setting up industrial policies and promoting district economic development. In this study, an improved model, GeoSOM, that combines DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and SOM (Self-Organizing Map) was developed for analyzing industrial cluster. Different from former distance-based algorithm for industrial cluster, the proposed GeoSOM model can calculate spatial characteristics between firms based on DBSCAN algorithm and evaluate the similarity between firms based on SOM clustering analysis. The demonstrative data sets, the manufacturers around Taichung County in Taiwan, were analyzed for verifying the practicability of the proposed model. The analyzed results indicate that GeoSOM is suitable for evaluating spatial industrial cluster.

Using hierarchical cluster models to systematically identify groups of jobs with similar occupational questionnaire response patterns to assist rule-based expert exposure assessment in population-based studies.

PubMed

Friesen, Melissa C; Shortreed, Susan M; Wheeler, David C; Burstyn, Igor; Vermeulen, Roel; Pronk, Anjoeka; Colt, Joanne S; Baris, Dalsu; Karagas, Margaret R; Schwenn, Molly; Johnson, Alison; Armenti, Karla R; Silverman, Debra T; Yu, Kai

2015-05-01

Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m(-3) respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters' homogeneity (defined as >75% with the same estimate) was examined compared to a dichotomized probability estimate (<5 versus ≥5%; <50 versus ≥50%). Second, for the ordinal probability metric and continuous intensity and frequency metrics, we calculated the intraclass correlation coefficients (ICCs) between each job's estimate and the mean estimate for all jobs within the cluster. Within-cluster homogeneity increased when more clusters were used. For example, ≥80% of the clusters were homogeneous when 500 clusters were used. Similarly, ICCs were generally above 0.7 when ≥200 clusters were used, indicating minimal within-cluster variability. The most within-cluster variability was observed for the frequency metric (ICCs from 0.4 to 0.8). We estimated that using an expert to assign exposure at the cluster-level assignment and then to review each job in non-homogeneous clusters would require ~2000 decisions per expert, in contrast to evaluating 4255 unique questionnaire patterns or 14983 individual jobs. This proof-of-concept shows that using cluster models as a data reduction step to identify jobs with similar response patterns prior to obtaining expert ratings has the potential to aid rule-based assessment by systematically reducing the number of exposure decisions needed. While promising, additional research is needed to quantify the actual reduction in exposure decisions and the resulting homogeneity of exposure estimates within clusters for an exposure assessment effort that obtains cluster-level expert assessments as part of the assessment process. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2014.

Cooperative effects in the structuring of fluoride water clusters: Ab initio hybrid quantum mechanical/molecular mechanical model incorporating polarizable fluctuating charge solvent

NASA Astrophysics Data System (ADS)

Bryce, Richard A.; Vincent, Mark A.; Malcolm, Nathaniel O. J.; Hillier, Ian H.; Burton, Neil A.

1998-08-01

A new hybrid quantum mechanical/molecular mechanical model of solvation is developed and used to describe the structure and dynamics of small fluoride/water clusters, using an ab initio wave function to model the ion and a fluctuating charge potential to model the waters. Appropriate parameters for the water-water and fluoride-water interactions are derived, with the fluoride anion being described by density functional theory and a large Gaussian basis. The role of solvent polarization in determining the structure and energetics of F(H2O)4- clusters is investigated, predicting a slightly greater stability of the interior compared to the surface structure, in agreement with ab initio studies. An extended Lagrangian treatment of the polarizable water, in which the water atomic charges fluctuate dynamically, is used to study the dynamics of F(H2O)4- cluster. A simulation using a fixed solvent charge distribution indicates principally interior, solvated states for the cluster. However, a preponderance of trisolvated configurations is observed using the polarizable model at 300 K, which involves only three direct fluoride-water hydrogen bonds. Ab initio calculations confirm this trisolvated species as a thermally accessible state at room temperature, in addition to the tetrasolvated interior and surface structures. Extension of this polarizable water model to fluoride clusters with five and six waters gave less satisfactory agreement with experimental energies and with ab initio geometries. However, our results do suggest that a quantitative model of solvent polarization is fundamental for an accurate understanding of the properties of anionic water clusters.

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

PubMed

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

Young star clusters in nearby molecular clouds

NASA Astrophysics Data System (ADS)

Getman, K. V.; Kuhn, M. A.; Feigelson, E. D.; Broos, P. S.; Bate, M. R.; Garmire, G. P.

2018-06-01

The SFiNCs (Star Formation in Nearby Clouds) project is an X-ray/infrared study of the young stellar populations in 22 star-forming regions with distances ≲ 1 kpc designed to extend our earlier MYStIX (Massive Young Star-Forming Complex Study in Infrared and X-ray) survey of more distant clusters. Our central goal is to give empirical constraints on cluster formation mechanisms. Using parametric mixture models applied homogeneously to the catalogue of SFiNCs young stars, we identify 52 SFiNCs clusters and 19 unclustered stellar structures. The procedure gives cluster properties including location, population, morphology, association with molecular clouds, absorption, age (AgeJX), and infrared spectral energy distribution (SED) slope. Absorption, SED slope, and AgeJX are age indicators. SFiNCs clusters are examined individually, and collectively with MYStIX clusters, to give the following results. (1) SFiNCs is dominated by smaller, younger, and more heavily obscured clusters than MYStIX. (2) SFiNCs cloud-associated clusters have the high ellipticities aligned with their host molecular filaments indicating morphology inherited from their parental clouds. (3) The effect of cluster expansion is evident from the radius-age, radius-absorption, and radius-SED correlations. Core radii increase dramatically from ˜0.08 to ˜0.9 pc over the age range 1-3.5 Myr. Inferred gas removal time-scales are longer than 1 Myr. (4) Rich, spatially distributed stellar populations are present in SFiNCs clouds representing early generations of star formation. An appendix compares the performance of the mixture models and non-parametric minimum spanning tree to identify clusters. This work is a foundation for future SFiNCs/MYStIX studies including disc longevity, age gradients, and dynamical modelling.

Properties of highly clustered networks

NASA Astrophysics Data System (ADS)

Newman, M. E.

2003-08-01

We propose and solve exactly a model of a network that has both a tunable degree distribution and a tunable clustering coefficient. Among other things, our results indicate that increased clustering leads to a decrease in the size of the giant component of the network. We also study susceptible/infective/recovered type epidemic processes within the model and find that clustering decreases the size of epidemics, but also decreases the epidemic threshold, making it easier for diseases to spread. In addition, clustering causes epidemics to saturate sooner, meaning that they infect a near-maximal fraction of the network for quite low transmission rates.

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

NASA Astrophysics Data System (ADS)

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.

Using Hierarchical Cluster Models to Systematically Identify Groups of Jobs With Similar Occupational Questionnaire Response Patterns to Assist Rule-Based Expert Exposure Assessment in Population-Based Studies

PubMed Central

Friesen, Melissa C.; Shortreed, Susan M.; Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Pronk, Anjoeka; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Armenti, Karla R.; Silverman, Debra T.; Yu, Kai

2015-01-01

Objectives: Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Methods: Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m−3 respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters’ homogeneity (defined as >75% with the same estimate) was examined compared to a dichotomized probability estimate (<5 versus ≥5%; <50 versus ≥50%). Second, for the ordinal probability metric and continuous intensity and frequency metrics, we calculated the intraclass correlation coefficients (ICCs) between each job’s estimate and the mean estimate for all jobs within the cluster. Results: Within-cluster homogeneity increased when more clusters were used. For example, ≥80% of the clusters were homogeneous when 500 clusters were used. Similarly, ICCs were generally above 0.7 when ≥200 clusters were used, indicating minimal within-cluster variability. The most within-cluster variability was observed for the frequency metric (ICCs from 0.4 to 0.8). We estimated that using an expert to assign exposure at the cluster-level assignment and then to review each job in non-homogeneous clusters would require ~2000 decisions per expert, in contrast to evaluating 4255 unique questionnaire patterns or 14983 individual jobs. Conclusions: This proof-of-concept shows that using cluster models as a data reduction step to identify jobs with similar response patterns prior to obtaining expert ratings has the potential to aid rule-based assessment by systematically reducing the number of exposure decisions needed. While promising, additional research is needed to quantify the actual reduction in exposure decisions and the resulting homogeneity of exposure estimates within clusters for an exposure assessment effort that obtains cluster-level expert assessments as part of the assessment process. PMID:25477475

A Bimodal Hybrid Model for Time-Dependent Probabilistic Seismic Hazard Analysis

NASA Astrophysics Data System (ADS)

Yaghmaei-Sabegh, Saman; Shoaeifar, Nasser; Shoaeifar, Parva

2018-03-01

The evaluation of evidence provided by geological studies and historical catalogs indicates that in some seismic regions and faults, multiple large earthquakes occur in cluster. Then, the occurrences of large earthquakes confront with quiescence and only the small-to-moderate earthquakes take place. Clustering of large earthquakes is the most distinguishable departure from the assumption of constant hazard of random occurrence of earthquakes in conventional seismic hazard analysis. In the present study, a time-dependent recurrence model is proposed to consider a series of large earthquakes that occurs in clusters. The model is flexible enough to better reflect the quasi-periodic behavior of large earthquakes with long-term clustering, which can be used in time-dependent probabilistic seismic hazard analysis with engineering purposes. In this model, the time-dependent hazard results are estimated by a hazard function which comprises three parts. A decreasing hazard of last large earthquake cluster and an increasing hazard of the next large earthquake cluster, along with a constant hazard of random occurrence of small-to-moderate earthquakes. In the final part of the paper, the time-dependent seismic hazard of the New Madrid Seismic Zone at different time intervals has been calculated for illustrative purpose.

Inference from clustering with application to gene-expression microarrays.

PubMed

Dougherty, Edward R; Barrera, Junior; Brun, Marcel; Kim, Seungchan; Cesar, Roberto M; Chen, Yidong; Bittner, Michael; Trent, Jeffrey M

2002-01-01

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. The model means can be selected by hand to test the separability of expected types of biological expression patterns. Alternatively, the model can be seeded by real data to test the expected precision of that output or the extent of improvement in precision that replication could provide. In the latter case, a clustering algorithm is used to form clusters, and the model is seeded with the means and variances of these clusters. Other algorithms are then tested relative to the seeding algorithm. Results are averaged over various seeds. Output includes error tables and graphs, confusion matrices, principal-component plots, and validation measures. Five algorithms are studied in detail: K-means, fuzzy C-means, self-organizing maps, hierarchical Euclidean-distance-based and correlation-based clustering. The toolbox is applied to gene-expression clustering based on cDNA microarrays using real data. Expression profile graphics are generated and error analysis is displayed within the context of these profile graphics. A large amount of generated output is available over the web.

Beyond the Young-Laplace model for cluster growth during dewetting of thin films: effective coarsening exponents and the role of long range dewetting interactions.

PubMed

Constantinescu, Adi; Golubović, Leonardo; Levandovsky, Artem

2013-09-01

Long range dewetting forces acting across thin films, such as the fundamental van der Waals interactions, may drive the formation of large clusters (tall multilayer islands) and pits, observed in thin films of diverse materials such as polymers, liquid crystals, and metals. In this study we further develop the methodology of the nonequilibrium statistical mechanics of thin films coarsening within continuum interface dynamics model incorporating long range dewetting interactions. The theoretical test bench model considered here is a generalization of the classical Mullins model for the dynamics of solid film surfaces. By analytic arguments and simulations of the model, we study the coarsening growth laws of clusters formed in thin films due to the dewetting interactions. The ultimate cluster growth scaling laws at long times are strongly universal: Short and long range dewetting interactions yield the same coarsening exponents. However, long range dewetting interactions, such as the van der Waals forces, introduce a distinct long lasting early time scaling behavior characterized by a slow growth of the cluster height/lateral size aspect ratio (i.e., a time-dependent Young angle) and by effective coarsening exponents that depend on cluster size. In this study, we develop a theory capable of analytically calculating these effective size-dependent coarsening exponents characterizing the cluster growth in the early time regime. Such a pronounced early time scaling behavior has been indeed seen in experiments; however, its physical origin has remained elusive to this date. Our theory attributes these observed phenomena to ubiquitous long range dewetting interactions acting across thin solid and liquid films. Our results are also applicable to cluster growth in initially very thin fluid films, formed by depositing a few monolayers or by a submonolayer deposition. Under this condition, the dominant coarsening mechanism is diffusive intercluster mass transport while the cluster coalescence plays a minor role, both in solid and in fluid films.

Comparison between volatility return intervals of the S&P 500 index and two common models

NASA Astrophysics Data System (ADS)

Vodenska-Chitkushev, I.; Wang, F. Z.; Weber, P.; Yamasaki, K.; Havlin, S.; Stanley, H. E.

2008-01-01

We analyze the S&P 500 index data for the 13-year period, from January 1, 1984 to December 31, 1996, with one data point every 10 min. For this database, we study the distribution and clustering of volatility return intervals, which are defined as the time intervals between successive volatilities above a certain threshold q. We find that the long memory in the volatility leads to a clustering of above-median as well as below-median return intervals. In addition, it turns out that the short return intervals form larger clusters compared to the long return intervals. When comparing the empirical results to the ARMA-FIGARCH and fBm models for volatility, we find that the fBm model predicts scaling better than the ARMA-FIGARCH model, which is consistent with the argument that both ARMA-FIGARCH and fBm capture the long-term dependence in return intervals to a certain extent, but only fBm accounts for the scaling. We perform the Student's t-test to compare the empirical data with the shuffled records, ARMA-FIGARCH and fBm. We analyze separately the clusters of above-median return intervals and the clusters of below-median return intervals for different thresholds q. We find that the empirical data are statistically different from the shuffled data for all thresholds q. Our results also suggest that the ARMA-FIGARCH model is statistically different from the S&P 500 for intermediate q for both above-median and below-median clusters, while fBm is statistically different from S&P 500 for small and large q for above-median clusters and for small q for below-median clusters. Neither model can fully explain the entire regime of q studied.

A dynamical study of Galactic globular clusters under different relaxation conditions

NASA Astrophysics Data System (ADS)

Zocchi, A.; Bertin, G.; Varri, A. L.

2012-03-01

Aims: We perform a systematic combined photometric and kinematic analysis of a sample of globular clusters under different relaxation conditions, based on their core relaxation time (as listed in available catalogs), by means of two well-known families of spherical stellar dynamical models. Systems characterized by shorter relaxation time scales are expected to be better described by isotropic King models, while less relaxed systems might be interpreted by means of non-truncated, radially-biased anisotropic f(ν) models, originally designed to represent stellar systems produced by a violent relaxation formation process and applied here for the first time to the study of globular clusters. Methods: The comparison between dynamical models and observations is performed by fitting simultaneously surface brightness and velocity dispersion profiles. For each globular cluster, the best-fit model in each family is identified, along with a full error analysis on the relevant parameters. Detailed structural properties and mass-to-light ratios are also explicitly derived. Results: We find that King models usually offer a good representation of the observed photometric profiles, but often lead to less satisfactory fits to the kinematic profiles, independently of the relaxation condition of the systems. For some less relaxed clusters, f(ν) models provide a good description of both observed profiles. Some derived structural characteristics, such as the total mass or the half-mass radius, turn out to be significantly model-dependent. The analysis confirms that, to answer some important dynamical questions that bear on the formation and evolution of globular clusters, it would be highly desirable to acquire larger numbers of accurate kinematic data-points, well distributed over the cluster field. Appendices are available in electronic form at http://www.aanda.org

A Model for Protostellar Cluster Luminosities and the Impact on the CO–H2 Conversion Factor

NASA Astrophysics Data System (ADS)

Gaches, Brandt A. L.; Offner, Stella S. R.

2018-02-01

We construct a semianalytic model to study the effect of far-ultraviolet (FUV) radiation on gas chemistry from embedded protostars. We use the protostellar luminosity function (PLF) formalism of Offner & McKee to calculate the total, FUV, and ionizing cluster luminosity for various protostellar accretion histories and cluster sizes. We2 compare the model predictions with surveys of Gould Belt star-forming regions and find that the tapered turbulent core model matches best the mean luminosities and the spread in the data. We combine the cluster model with the photodissociation region astrochemistry code, 3D-PDR, to compute the impact of the FUV luminosity from embedded protostars on the CO-to-H2 conversion factor, X CO, as a function of cluster size, gas mass, and star formation efficiency. We find that X CO has a weak dependence on the FUV radiation from embedded sources for large clusters owing to high cloud optical depths. In smaller and more efficient clusters the embedded FUV increases X CO to levels consistent with the average Milky Way values. The internal physical and chemical structures of the cloud are significantly altered, and X CO depends strongly on the protostellar cluster mass for small efficient clouds.

Classical plasma dynamics of Mie-oscillations in atomic clusters

NASA Astrophysics Data System (ADS)

Kull, H.-J.; El-Khawaldeh, A.

2018-04-01

Mie plasmons are of basic importance for the absorption of laser light by atomic clusters. In this work we first review the classical Rayleigh-theory of a dielectric sphere in an external electric field and Thomson’s plum-pudding model applied to atomic clusters. Both approaches allow for elementary discussions of Mie oscillations, however, they also indicate deficiencies in describing the damping mechanisms by electrons crossing the cluster surface. Nonlinear oscillator models have been widely studied to gain an understanding of damping and absorption by outer ionization of the cluster. In the present work, we attempt to address the issue of plasmon relaxation in atomic clusters in more detail based on classical particle simulations. In particular, we wish to study the role of thermal motion on plasmon relaxation, thereby extending nonlinear models of collective single-electron motion. Our simulations are particularly adopted to the regime of classical kinetics in weakly coupled plasmas and to cluster sizes extending the Debye-screening length. It will be illustrated how surface scattering leads to the relaxation of Mie oscillations in the presence of thermal motion and of electron spill-out at the cluster surface. This work is intended to give, from a classical perspective, further insight into recent work on plasmon relaxation in quantum plasmas [1].

Large-scale motions in the universe: Using clusters of galaxies as tracers

NASA Technical Reports Server (NTRS)

Gramann, Mirt; Bahcall, Neta A.; Cen, Renyue; Gott, J. Richard

1995-01-01

Can clusters of galaxies be used to trace the large-scale peculiar velocity field of the universe? We answer this question by using large-scale cosmological simulations to compare the motions of rich clusters of galaxies with the motion of the underlying matter distribution. Three models are investigated: Omega = 1 and Omega = 0.3 cold dark matter (CDM), and Omega = 0.3 primeval baryonic isocurvature (PBI) models, all normalized to the Cosmic Background Explorer (COBE) background fluctuations. We compare the cluster and mass distribution of peculiar velocities, bulk motions, velocity dispersions, and Mach numbers as a function of scale for R greater than or = 50/h Mpc. We also present the large-scale velocity and potential maps of clusters and of the matter. We find that clusters of galaxies trace well the large-scale velocity field and can serve as an efficient tool to constrain cosmological models. The recently reported bulk motion of clusters 689 +/- 178 km/s on approximately 150/h Mpc scale (Lauer & Postman 1994) is larger than expected in any of the models studied (less than or = 190 +/- 78 km/s).

Strong Lensing Mass Reconstruction: from Frontier Fields to the Typical Lensing Clusters of Future Surveys

NASA Astrophysics Data System (ADS)

Sharon, Keren; Gladders, Michael D.; Rigby, Jane R.; Bayliss, Matthew B.; Wuyts, Eva; Dahle, Håkon; Johnson, Traci L.; Florian, Michael K.; Dunham, Samuel; Murray, Katherine; Whitaker, Kate; Li, Nan

Driven by the unprecedented wealth of high quality data that is accumulating for the Frontier Fields, they are becoming some of the best-studied strong lensing clusters to date, and probably the next few years. As will be discussed intensively in this focus meeting, the FF prove transformative for many fields: from studies of the high redshift Universe, to the assembly and structure of the clusters themselves. The FF data and the extensive collaborative effort around this program will also allow us to examine and improve upon current lens modeling techniques. Strong lensing is a powerful tool for mass reconstruction of the cores of galaxy clusters of all scales, providing an estimate of the total (dark and seen) projected mass density distribution out to 0.5 Mpc. Though SL mass may be biased by contribution from structures along the line of sight, its strength is that it is relatively insensitive to assumptions on cluster baryon astrophysics and dynamical state. Like the Frontier Fields clusters, the most ``famous'' strong lensing clusters are at the high mass end; they lens dozens of background sources into multiple images, providing ample lensing constraints. In this talk, I will focus on how we can leverage what we learn from modeling the FF clusters in strong lensing studies of the hundreds of clusters that will be discovered in upcoming surveys. In typical clusters, unlike the Frontier Fields, the Bullet Cluster and A1689, we observe only one to a handful of background sources, and have limited lensing constraints. I will describe the limitations that such a configuration imposes on strong lens modeling, highlight measurements that are robust to the richness of lensing evidence, and address the sources of uncertainty and what sort of information can help reduce those uncertainties. This category of lensing clusters is most relevant to the wide cluster surveys of the future.

Strong Lensing Mass Reconstruction: from Frontier Fields to the Typical Lensing Clusters of Future Surveys

NASA Astrophysics Data System (ADS)

Sharon, Keren

2015-08-01

Driven by the unprecedented wealth of high quality data that is accumulating for the Frontier Fields, they are becoming some of the best-studied strong lensing clusters to date, and probably the next few years. As will be discussed intensively in this focus meeting, the FF prove transformative for many fields: from studies of the high redshift Universe, to the assembly and structure of the clusters themselves. The FF data and the extensive collaborative effort around this program will also allow us to examine and improve upon current lens modeling techniques. Strong lensing is a powerful tool for mass reconstruction of the cores of galaxy clusters of all scales, providing an estimate of the total (dark and seen) projected mass density distribution out to ~0.5 Mpc. Though SL mass may be biased by contribution from structures along the line of sight, its strength is that it is relatively insensitive to assumptions on cluster baryon astrophysics and dynamical state. Like the Frontier Fields clusters, the most "famous" strong lensing clusters are at the high mass end; they lens dozens of background sources into multiple images, providing ample lensing constraints. In this talk, I will focus on how we can leverage what we learn from modeling the FF clusters in strong lensing studies of the hundreds of clusters that will be discovered in upcoming surveys. In typical clusters, unlike the Frontier Fields, the Bullet Cluster and A1689, we observe only one to a handful of background sources, and have limited lensing constraints. I will describe the limitations that such a configuration imposes on strong lens modeling, highlight measurements that are robust to the richness of lensing evidence, and address the sources of uncertainty and what sort of information can help reduce those uncertainties. This category of lensing clusters is most relevant to the wide cluster surveys of the future.

Master-equation approach to the study of phase-change processes in data storage media

NASA Astrophysics Data System (ADS)

Blyuss, K. B.; Ashwin, P.; Bassom, A. P.; Wright, C. D.

2005-07-01

We study the dynamics of crystallization in phase-change materials using a master-equation approach in which the state of the crystallizing material is described by a cluster size distribution function. A model is developed using the thermodynamics of the processes involved and representing the clusters of size two and greater as a continuum but clusters of size one (monomers) as a separate equation. We present some partial analytical results for the isothermal case and for large cluster sizes, but principally we use numerical simulations to investigate the model. We obtain results that are in good agreement with experimental data and the model appears to be useful for the fast simulation of reading and writing processes in phase-change optical and electrical memories.

A study of cooling flows in poor clusters of galaxies

NASA Technical Reports Server (NTRS)

Kriss, Gerard A.; Dillingham, Stephen

1995-01-01

We observed three poor clusters with central dominant galaxies (AWM 4, MKW 4, and MKW 3's) using the Position Sensitive Proportional Counter on the ROSAT X-ray satellite. The images reveal smooth, symmetrical X-ray emission filling the cluster with a sharp peak on each central galaxy. The cluster surface brightness profiles can be decomposed using superposed King models for the central galaxy and the intracluster medium. The King model parameters for the cluster portions are consistent with previous observations of these clusters. The newly measured King model parameters for the central galaxies are typical of the X-ray surface brightness distributions of isolated elliptical galaxies. Spatially resolved temperature measurements in annular rings throughout the clusters show a nearly isothermal profile. Temperatures are consistent with previously measured values, but are much better determined. There is no significant drop in temperature noted in the innermost bins where cooling flows are likely to be present, nor is any excess absorption by cold gas required. All cold gas columns are consistent with galactic foreground absorption. We derive mass profiles for the clusters assuming both isothermal temperature profiles and cooling flow models with constant mass flow rates. Our results are consistent with previous Einstein IPC observations by Kriss, Cioffi, & Canizares, but extend the mass profiles out to 1 Mpc in these poor clusters.

Topic modeling for cluster analysis of large biological and medical datasets

PubMed Central

2014-01-01

Background 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. Results 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. Conclusion 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 that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets. PMID:25350106

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

PubMed

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 that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

Special and general superatoms.

PubMed

Luo, Zhixun; Castleman, A Welford

2014-10-21

Bridging the gap between atoms and macroscopic matter, clusters continue to be a subject of increasing research interest. Among the realm of cluster investigations, an exciting development is the realization that chosen stable clusters can mimic the chemical behavior of an atom or a group of the periodic table of elements. This major finding known as a superatom concept was originated experimentally from the study of aluminum cluster reactivity conducted in 1989 by noting a dramatic size dependence of the reactivity where cluster anions containing a certain number of Al atoms were unreactive toward oxygen while the other species were etched away. This observation was well interpreted by shell closings on the basis of the jellium model, and the related concept (originally termed "unified atom") spawned a wide range of pioneering studies in the 1990s pertaining to the understanding of factors governing the properties of clusters. Under the inspiration of a superatom concept, advances in cluster science in finding stable species not only shed light on magic clusters (i.e., superatomic noble gas) but also enlightened the exploration of stable clusters to mimic the chemical behavior of atoms leading to the discovery of superhalogens, alkaline-earth metals, superalkalis, etc. Among them, certain clusters could enable isovalent isomorphism of precious metals, indicating application potential for inexpensive superatoms for industrial catalysis, while a few superalkalis were found to validate the interesting "harpoon mechanism" involved in the superatomic cluster reactivity; recently also found were the magnetic superatoms of which the cluster-assembled materials could be used in spin electronics. Up to now, extensive studies in cluster science have allowed the stability of superatomic clusters to be understood within a few models, including the jellium model, also aromaticity and Wade-Mingos rules depending on the geometry and metallicity of the cluster. However, the scope of application of the jellium model and modification of the theory to account for nonspherical symmetry and nonmetal-doped metal clusters are still illusive to be further developed. It is still worth mentioning that a superatom concept has also been introduced in ligand-stabilized metal clusters which could also follow the major shell-closing electron count for a spherical, square-well potential. By proposing a new concept named as special and general superatoms, herein we try to summarize all these investigations in series, expecting to provide an overview of this field with a primary focus on the joint undertakings which have given rise to the superatom concept. To be specific, for special superatoms, we limit to clusters under a strict jellium model and simply classify them into groups based on their valence electron counts. While for general superatoms we emphasize on nonmetal-doped metal clusters and ligand-stabilized metal clusters, as well as a few isovalent cluster systems. Hopefully this summary of special and general superatoms benefits the further development of cluster-related theory, and lights up the prospect of using them as building blocks of new materials with tailored properties, such as inexpensive isovalent systems for industrial catalysis, semiconductive superatoms for transistors, and magnetic superatoms for spin electronics.

Ceria nanoclusters on graphene/Ru(0001): A new model catalyst system

DOE PAGES

Novotny, Z.; Netzer, F. P.; Dohnalek, Z.

2016-03-22

In this study, the growth of ceria nanoclusters on single-layer graphene on Ru(0001) has been examined, with a view towards fabricating a stable system for model catalysis studies. The surface morphology and cluster distribution as a function of oxide coverage and substrate temperature has been monitored by scanning tunneling microscopy (STM), whereas the chemical composition of the cluster deposits has been determined by Auger electron spectroscopy (AES). The ceria nanoparticles are of the CeO 2(111)-type and are anchored at the intrinsic defects of the graphene surface, resulting in a variation of the cluster densities across the macroscopic sample surface. Themore » ceria clusters on graphene display a remarkable stability against reduction in ultrahigh vacuum up to 900 K, but some sintering of clusters is observed for temperatures > 450 K. The evolution of the cluster size distribution suggests that the sintering proceeds via a Smoluchowski ripening mechanism, i.e. diffusion and aggregation of entire clusters.« less

Multi-exemplar affinity propagation.

PubMed

Wang, Chang-Dong; Lai, Jian-Huang; Suen, Ching Y; Zhu, Jun-Yong

2013-09-01

The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP-hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

Globular clusters and environmental effects in galaxy clusters

NASA Astrophysics Data System (ADS)

Sales, Laura

2016-10-01

Globular clusters are old compact stellar systems orbiting around galaxies of all types. Tens of thousands of them can also be found populating the intra-cluster regions of nearby galaxy clusters like Virgo and Coma. Thanks to the HST Frontier Fields program, GCs are starting now to be detected also in intermediate redshift clusters. Yet, despite their ubiquity, a theoretical model for the formation and evolution of GCs is still missing, especially within the cosmological context.Here we propose to use cosmological hydrodynamical simulations of 18 galaxy clusters coupled to a post-processing GC formation model to explore the assembly of galaxies in clusters together with their expected GC population. The method, which has already been implemented and tested, will allow us to characterize for the first time the number, radial distribution and kinematics of GCs in clusters, with products directly comparable to observational maps. We will explore cluster-to-cluster variations and also characterize the build up of the intra-cluster component of GCs with time.As the method relies on a detailed study of the star-formation history of galaxies, we will jointly constrain the predicted quenching time-scales for satellites and the occurrence of starburst events associated to infall and orbital pericenters of galaxies in massive clusters. This will inform further studies on the distribution, velocity and properties of post-starburst galaxies in past, ongoing and future HST programs.

A comprehensive comparative test of seven widely used spectral synthesis models against multi-band photometry of young massive-star clusters

NASA Astrophysics Data System (ADS)

Wofford, A.; Charlot, S.; Bruzual, G.; Eldridge, J. J.; Calzetti, D.; Adamo, A.; Cignoni, M.; de Mink, S. E.; Gouliermis, D. A.; Grasha, K.; Grebel, E. K.; Lee, J. C.; Östlin, G.; Smith, L. J.; Ubeda, L.; Zackrisson, E.

2016-04-01

We test the predictions of spectral synthesis models based on seven different massive-star prescriptions against Legacy ExtraGalactic UV Survey (LEGUS) observations of eight young massive clusters in two local galaxies, NGC 1566 and NGC 5253, chosen because predictions of all seven models are available at the published galactic metallicities. The high angular resolution, extensive cluster inventory, and full near-ultraviolet to near-infrared photometric coverage make the LEGUS data set excellent for this study. We account for both stellar and nebular emission in the models and try two different prescriptions for attenuation by dust. From Bayesian fits of model libraries to the observations, we find remarkably low dispersion in the median E(B - V) (˜0.03 mag), stellar masses (˜104 M⊙), and ages (˜1 Myr) derived for individual clusters using different models, although maximum discrepancies in these quantities can reach 0.09 mag and factors of 2.8 and 2.5, respectively. This is for ranges in median properties of 0.05-0.54 mag, 1.8-10 × 104 M⊙, and 1.6-40 Myr spanned by the clusters in our sample. In terms of best fit, the observations are slightly better reproduced by models with interacting binaries and least well reproduced by models with single rotating stars. Our study provides a first quantitative estimate of the accuracies and uncertainties of the most recent spectral synthesis models of young stellar populations, demonstrates the good progress of models in fitting high-quality observations, and highlights the needs for a larger cluster sample and more extensive tests of the model parameter space.

A spatial scan statistic for nonisotropic two-level risk cluster.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2012-01-30

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

Who Visits a National Park and What do They Get Out of It?: A Joint Visitor Cluster Analysis and Travel Cost Model for Yellowstone National Park

NASA Astrophysics Data System (ADS)

Benson, Charles; Watson, Philip; Taylor, Garth; Cook, Philip; Hollenhorst, Steve

2013-10-01

Yellowstone National Park visitor data were obtained from a survey collected for the National Park Service by the Park Studies Unit at the University of Idaho. Travel cost models have been conducted for national parks in the United States; however, this study builds on these studies and investigates how benefits vary by types of visitors who participate in different activities while at the park. Visitor clusters were developed based on activities in which a visitor participated while at the park. The clusters were analyzed and then incorporated into a travel cost model to determine the economic value (consumer surplus) that the different visitor groups received from visiting the park. The model was estimated using a zero-truncated negative binomial regression corrected for endogenous stratification. The travel cost price variable was estimated using both 1/3 and 1/4 the wage rate to test for sensitivity to opportunity cost specification. The average benefit across all visitor cluster groups was estimated at between 235 and 276 per person per trip. However, per trip benefits varied substantially across clusters; from 90 to 103 for the "value picnickers," to 185-263 for the "backcountry enthusiasts," 189-278 for the "do it all adventurists," 204-303 for the "windshield tourists," and 323-714 for the "creature comfort" cluster group.

Comparison of Cluster, Slab, and Analytic Potential Models for the Dimethyl Methylphosphonate (DMMP)/TiO2 (110) Intermolecular Interaction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Li; Tunega, Daniel; Xu, Lai

2013-08-29

In a previous study (J. Phys. Chem. C 2011, 115, 12403) cluster models for the TiO2 rutile (110) surface and MP2 calculations were used to develop an analytic potential energy function for dimethyl methylphosphonate (DMMP) interacting with this surface. In the work presented here, this analytic potential and MP2 cluster models are compared with DFT "slab" calculations for DMMP interacting with the TiO2 (110) surface and with DFT cluster models for the TiO2 (110) surface. The DFT slab calculations were performed with the PW91 and PBE functionals. The analytic potential gives DMMP/ TiO2 (110) potential energy curves in excellent agreementmore » with those obtained from the slab calculations. The cluster models for the TiO2 (110) surface, used for the MP2 calculations, were extended to DFT calculations with the B3LYP, PW91, and PBE functional. These DFT calculations do not give DMMP/TiO2 (110) interaction energies which agree with those from the DFT slab calculations. Analyses of the wave functions for these cluster models show that they do not accurately represent the HOMO and LUMO for the surface, which should be 2p and 3d orbitals, respectively, and the models also do not give an accurate band gap. The MP2 cluster models do not accurately represent the LUMO and that they give accurate DMMP/TiO2 (110) interaction energies is apparently fortuitous, arising from their highly inaccurate band gaps. Accurate cluster models, consisting of 7, 10, and 15 Ti-atoms and which have the correct HOMO and LUMO properties, are proposed. The work presented here illustrates the care that must be taken in "constructing" cluster models which accurately model surfaces.« less

Cluster Physics with Merging Galaxy Clusters

NASA Astrophysics Data System (ADS)

Molnar, Sandor

Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard ΛCDM model, where the total density is dominated by the cosmological constant (Λ) and the matter density by cold dark matter (CDM), structure formation is hierarchical, and clusters grow mostly by merging. Mergers of two massive clusters are the most energetic events in the universe after the Big Bang, hence they provide a unique laboratory to study cluster physics. The two main mass components in clusters behave differently during collisions: the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulence are developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thus our review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clusters is to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses. New high spatial and spectral resolution ground and space based telescopes will come online in the near future. Motivated by these new opportunities, we briefly discuss methods which will be feasible in the near future in studying merging clusters.

Exploring the Internal Dynamics of Globular Clusters

NASA Astrophysics Data System (ADS)

Watkins, Laura L.; van der Marel, Roeland; Bellini, Andrea; Luetzgendorf, Nora; HSTPROMO Collaboration

2018-01-01

Exploring the Internal Dynamics of Globular ClustersThe formation histories and structural properties of globular clusters are imprinted on their internal dynamics. Energy equipartition results in velocity differences for stars of different mass, and leads to mass segregation, which results in different spatial distributions for stars of different mass. Intermediate-mass black holes significantly increase the velocity dispersions at the centres of clusters. By combining accurate measurements of their internal kinematics with state-of-the-art dynamical models, we can characterise both the velocity dispersion and mass profiles of clusters, tease apart the different effects, and understand how clusters may have formed and evolved.Using proper motions from the Hubble Space Telescope Proper Motion (HSTPROMO) Collaboration for a set of 22 Milky Way globular clusters, and our discrete dynamical modelling techniques designed to work with large, high-quality datasets, we are studying a variety of internal cluster properties. We will present the results of theoretical work on simulated clusters that demonstrates the efficacy of our approach, and preliminary results from application to real clusters.

Multi-mode clustering model for hierarchical wireless sensor networks

NASA Astrophysics Data System (ADS)

Hu, Xiangdong; Li, Yongfu; Xu, Huifen

2017-03-01

The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

Improving Performance for Gifted Students in a Cluster Grouping Model

ERIC Educational Resources Information Center

Brulles, Dina; Saunders, Rachel; Cohn, Sanford J.

2010-01-01

Although experts in gifted education widely promote cluster grouping gifted students, little empirical evidence is available to attest to its effectiveness. This study is an example of comparative action research in the form of a quantitative case study that focused on the mandated cluster grouping practices for gifted students in an urban…

A hierarchical linear model for tree height prediction.

Treesearch

Vicente J. Monleon

2003-01-01

Measuring tree height is a time-consuming process. Often, tree diameter is measured and height is estimated from a published regression model. Trees used to develop these models are clustered into stands, but this structure is ignored and independence is assumed. In this study, hierarchical linear models that account explicitly for the clustered structure of the data...

Using experimental data to test an n -body dynamical model coupled with an energy-based clusterization algorithm at low incident energies

NASA Astrophysics Data System (ADS)

Kumar, Rohit; Puri, Rajeev K.

2018-03-01

Employing the quantum molecular dynamics (QMD) approach for nucleus-nucleus collisions, we test the predictive power of the energy-based clusterization algorithm, i.e., the simulating annealing clusterization algorithm (SACA), to describe the experimental data of charge distribution and various event-by-event correlations among fragments. The calculations are constrained into the Fermi-energy domain and/or mildly excited nuclear matter. Our detailed study spans over different system masses, and system-mass asymmetries of colliding partners show the importance of the energy-based clusterization algorithm for understanding multifragmentation. The present calculations are also compared with the other available calculations, which use one-body models, statistical models, and/or hybrid models.

Schoolwide Mathematics Achievement within the Gifted Cluster Grouping Model

ERIC Educational Resources Information Center

Brulles, Dina; Peters, Scott J.; Saunders, Rachel

2012-01-01

An increasing number of schools are implementing gifted cluster grouping models as a cost-effective way to provide gifted services. This study is an example of comparative action research in the form of a quantitative case study that focused on mathematic achievement for nongifted students in a district that incorporated a schoolwide cluster…

Dissociation of doubly charged clusters of lithium acetate: Asymmetric fission and breakdown of the liquid drop model: Dissociation of doubly charged clusters of lithium acetate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shukla, Anil

2016-06-08

Unimolecular and collision-induced dissociation of doubly charged lithium acetate clusters, (CH3COOLi)nLi22+, demonstrated that Coulomb fission via charge separation is the dominant dissociation process with no contribution from the neutral evaporation processes for all such ions from the critical limit to larger cluster ions, although latter process have normally been observed in all earlier studies. These results are clearly in disagreement with the Rayleigh’s liquid drop model that has been used successfully to predict the critical size and explain the fragmentation behavior of multiply charged clusters.

A phase field model for segregation and precipitation induced by irradiation in alloys

NASA Astrophysics Data System (ADS)

Badillo, A.; Bellon, P.; Averback, R. S.

2015-04-01

A phase field model is introduced to model the evolution of multicomponent alloys under irradiation, including radiation-induced segregation and precipitation. The thermodynamic and kinetic components of this model are derived using a mean-field model. The mobility coefficient and the contribution of chemical heterogeneity to free energy are rescaled by the cell size used in the phase field model, yielding microstructural evolutions that are independent of the cell size. A new treatment is proposed for point defect clusters, using a mixed discrete-continuous approach to capture the stochastic character of defect cluster production in displacement cascades, while retaining the efficient modeling of the fate of these clusters using diffusion equations. The model is tested on unary and binary alloy systems using two-dimensional simulations. In a unary system, the evolution of point defects under irradiation is studied in the presence of defect clusters, either pre-existing ones or those created by irradiation, and compared with rate theory calculations. Binary alloys with zero and positive heats of mixing are then studied to investigate the effect of point defect clustering on radiation-induced segregation and precipitation in undersaturated solid solutions. Lastly, irradiation conditions and alloy parameters leading to irradiation-induced homogeneous precipitation are investigated. The results are discussed in the context of experimental results reported for Ni-Si and Al-Zn undersaturated solid solutions subjected to irradiation.

Hierarchical modeling of cluster size in wildlife surveys

USGS Publications Warehouse

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).

Warming rays in cluster cool cores

NASA Astrophysics Data System (ADS)

Colafrancesco, S.; Marchegiani, P.

2008-06-01

Context: Cosmic rays are confined in the atmospheres of galaxy clusters and, therefore, they can play a crucial role in the heating of their cool cores. Aims: We discuss here the thermal and non-thermal features of a model of cosmic ray heating of cluster cores that can provide a solution to the cooling-flow problems. To this aim, we generalize a model originally proposed by Colafrancesco, Dar & DeRujula (2004) and we show that our model predicts specific correlations between the thermal and non-thermal properties of galaxy clusters and enables various observational tests. Methods: The model reproduces the observed temperature distribution in clusters by using an energy balance condition in which the X-ray energy emitted by clusters is supplied, in a quasi-steady state, by the hadronic cosmic rays, which act as “warming rays” (WRs). The temperature profile of the intracluster (IC) gas is strictly correlated with the pressure distribution of the WRs and, consequently, with the non-thermal emission (radio, hard X-ray and gamma-ray) induced by the interaction of the WRs with the IC gas and the IC magnetic field. Results: The temperature distribution of the IC gas in both cool-core and non cool-core clusters is successfully predicted from the measured IC plasma density distribution. Under this contraint, the WR model is also able to reproduce the thermal and non-thermal pressure distribution in clusters, as well as their radial entropy distribution, as shown by the analysis of three clusters studied in detail: Perseus, A2199 and Hydra. The WR model provides other observable features of galaxy clusters: a correlation of the pressure ratio (WRs to thermal IC gas) with the inner cluster temperature (P_WR/P_th) ˜ (kT_inner)-2/3, a correlation of the gamma-ray luminosity with the inner cluster temperature Lγ ˜ (kT_inner)4/3, a substantial number of cool-core clusters observable with the GLAST-LAT experiment, a surface brightness of radio halos in cool-core clusters that recovers the observed one, a hard X-ray ICS emission from cool-core clusters that is systematically lower than the observed limits and yet observable with the next generation high-sensitivity and spatial resolution HXR experiments like Simbol-X. Conclusions: The specific theoretical properties and the multi-frequency distribution of the e.m. signals predicted in the WR model render it quite different from the other models so far proposed for the heating of clusters' cool-cores. Such differences make it possible to prove or disprove our model as an explanation for the cooling-flow problems on the basis of multi-frequency observations of galaxy clusters.

A Simple Model for the Earthquake Cycle Combining Self-Organized Criticality with Critical Point Behavior

NASA Astrophysics Data System (ADS)

Newman, W. I.; Turcotte, D. L.

2002-12-01

We have studied a hybrid model combining the forest-fire model with the site-percolation model in order to better understand the earthquake cycle. We consider a square array of sites. At each time step, a "tree" is dropped on a randomly chosen site and is planted if the site is unoccupied. When a cluster of "trees" spans the site (a percolating cluster), all the trees in the cluster are removed ("burned") in a "fire." The removal of the cluster is analogous to a characteristic earthquake and planting "trees" is analogous to increasing the regional stress. The clusters are analogous to the metastable regions of a fault over which an earthquake rupture can propagate once triggered. We find that the frequency-area statistics of the metastable regions are power-law with a negative exponent of two (as in the forest-fire model). This is analogous to the Gutenberg-Richter distribution of seismicity. This "self-organized critical behavior" can be explained in terms of an inverse cascade of clusters. Individual trees move from small to larger clusters until they are destroyed. This inverse cascade of clusters is self-similar and the power-law distribution of cluster sizes has been shown to have an exponent of two. We have quantified the forecasting of the spanning fires using error diagrams. The assumption that "fires" (earthquakes) are quasi-periodic has moderate predictability. The density of trees gives an improved degree of predictability, while the size of the largest cluster of trees provides a substantial improvement in forecasting a "fire."

Effects of cluster location and cluster distribution on performance on the traveling salesman problem.

PubMed

MacGregor, James N

2015-10-01

Research on human performance in solving traveling salesman problems typically uses point sets as stimuli, and most models have proposed a processing stage at which stimulus dots are clustered. However, few empirical studies have investigated the effects of clustering on performance. In one recent study, researchers compared the effects of clustered, random, and regular stimuli, and concluded that clustering facilitates performance (Dry, Preiss, & Wagemans, 2012). Another study suggested that these results may have been influenced by the location rather than the degree of clustering (MacGregor, 2013). Two experiments are reported that mark an attempt to disentangle these factors. The first experiment tested several combinations of degree of clustering and cluster location, and revealed mixed evidence that clustering influences performance. In a second experiment, both factors were varied independently, showing that they interact. The results are discussed in terms of the importance of clustering effects, in particular, and perceptual factors, in general, during performance of the traveling salesman problem.

Periodic and Aperiodic Close Packing: A Spontaneous Hard-Sphere Model.

ERIC Educational Resources Information Center

van de Waal, B. W.

1985-01-01

Shows how to make close-packed models from balloons and table tennis balls to illustrate structural features of clusters and organometallic cluster-compounds (which are of great interest in the study of chemical reactions). These models provide a very inexpensive and tactile illustration of the organization of matter for concrete operational…

Lifetime of Major Histocompatibility Complex Class-I Membrane Clusters Is Controlled by the Actin Cytoskeleton

PubMed Central

Lavi, Yael; Gov, Nir; Edidin, Michael; Gheber, Levi A.

2012-01-01

Lateral heterogeneity of cell membranes has been demonstrated in numerous studies showing anomalous diffusion of membrane proteins; it has been explained by models and experiments suggesting dynamic barriers to free diffusion, that temporarily confine membrane proteins into microscopic patches. This picture, however, comes short of explaining a steady-state patchy distribution of proteins, in face of the transient opening of the barriers. In our previous work we directly imaged persistent clusters of MHC-I, a type I transmembrane protein, and proposed a model of a dynamic equilibrium between proteins newly delivered to the cell surface by vesicle traffic, temporary confinement by dynamic barriers to lateral diffusion, and dispersion of the clusters by diffusion over the dynamic barriers. Our model predicted that the clusters are dynamic, appearing when an exocytic vesicle fuses with the plasma membrane and dispersing with a typical lifetime that depends on lateral diffusion and the dynamics of barriers. In a subsequent work, we showed this to be the case. Here we test another prediction of the model, and show that changing the stability of actin barriers to lateral diffusion changes cluster lifetimes. We also develop a model for the distribution of cluster lifetimes, consistent with the function of barriers to lateral diffusion in maintaining MHC-I clusters. PMID:22500754

Mechanism for Collective Cell Alignment in Myxococcus xanthus Bacteria

PubMed Central

Balagam, Rajesh; Igoshin, Oleg A.

2015-01-01

Myxococcus xanthus cells self-organize into aligned groups, clusters, at various stages of their lifecycle. Formation of these clusters is crucial for the complex dynamic multi-cellular behavior of these bacteria. However, the mechanism underlying the cell alignment and clustering is not fully understood. Motivated by studies of clustering in self-propelled rods, we hypothesized that M. xanthus cells can align and form clusters through pure mechanical interactions among cells and between cells and substrate. We test this hypothesis using an agent-based simulation framework in which each agent is based on the biophysical model of an individual M. xanthus cell. We show that model agents, under realistic cell flexibility values, can align and form cell clusters but only when periodic reversals of cell directions are suppressed. However, by extending our model to introduce the observed ability of cells to deposit and follow slime trails, we show that effective trail-following leads to clusters in reversing cells. Furthermore, we conclude that mechanical cell alignment combined with slime-trail-following is sufficient to explain the distinct clustering behaviors observed for wild-type and non-reversing M. xanthus mutants in recent experiments. Our results are robust to variation in model parameters, match the experimentally observed trends and can be applied to understand surface motility patterns of other bacterial species. PMID:26308508

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

NASA Astrophysics Data System (ADS)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Coupling of Acoustic Cavitation with Dem-Based Particle Solvers for Modeling De-agglomeration of Particle Clusters in Liquid Metals

NASA Astrophysics Data System (ADS)

Manoylov, Anton; Lebon, Bruno; Djambazov, Georgi; Pericleous, Koulis

2017-11-01

The aerospace and automotive industries are seeking advanced materials with low weight yet high strength and durability. Aluminum and magnesium-based metal matrix composites with ceramic micro- and nano-reinforcements promise the desirable properties. However, larger surface-area-to-volume ratio in micro- and especially nanoparticles gives rise to van der Waals and adhesion forces that cause the particles to agglomerate in clusters. Such clusters lead to adverse effects on final properties, no longer acting as dislocation anchors but instead becoming defects. Also, agglomeration causes the particle distribution to become uneven, leading to inconsistent properties. To break up clusters, ultrasonic processing may be used via an immersed sonotrode, or alternatively via electromagnetic vibration. This paper combines a fundamental study of acoustic cavitation in liquid aluminum with a study of the interaction forces causing particles to agglomerate, as well as mechanisms of cluster breakup. A non-linear acoustic cavitation model utilizing pressure waves produced by an immersed horn is presented, and then applied to cavitation in liquid aluminum. Physical quantities related to fluid flow and quantities specific to the cavitation solver are passed to a discrete element method particles model. The coupled system is then used for a detailed study of clusters' breakup by cavitation.

Individual participant data meta-analyses should not ignore clustering

PubMed Central

Abo-Zaid, Ghada; Guo, Boliang; Deeks, Jonathan J.; Debray, Thomas P.A.; Steyerberg, Ewout W.; Moons, Karel G.M.; Riley, Richard David

2013-01-01

Objectives Individual participant data (IPD) meta-analyses often analyze their IPD as if coming from a single study. We compare this approach with analyses that rather account for clustering of patients within studies. Study Design and Setting Comparison of effect estimates from logistic regression models in real and simulated examples. Results The estimated prognostic effect of age in patients with traumatic brain injury is similar, regardless of whether clustering is accounted for. However, a family history of thrombophilia is found to be a diagnostic marker of deep vein thrombosis [odds ratio, 1.30; 95% confidence interval (CI): 1.00, 1.70; P = 0.05] when clustering is accounted for but not when it is ignored (odds ratio, 1.06; 95% CI: 0.83, 1.37; P = 0.64). Similarly, the treatment effect of nicotine gum on smoking cessation is severely attenuated when clustering is ignored (odds ratio, 1.40; 95% CI: 1.02, 1.92) rather than accounted for (odds ratio, 1.80; 95% CI: 1.29, 2.52). Simulations show models accounting for clustering perform consistently well, but downwardly biased effect estimates and low coverage can occur when ignoring clustering. Conclusion Researchers must routinely account for clustering in IPD meta-analyses; otherwise, misleading effect estimates and conclusions may arise. PMID:23651765

DIRECT N-BODY MODELING OF THE OLD OPEN CLUSTER NGC 188: A DETAILED COMPARISON OF THEORETICAL AND OBSERVED BINARY STAR AND BLUE STRAGGLER POPULATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geller, Aaron M.; Hurley, Jarrod R.; Mathieu, Robert D., E-mail: a-geller@northwestern.edu, E-mail: mathieu@astro.wisc.edu, E-mail: jhurley@astro.swin.edu.au

2013-01-01

Following on from a recently completed radial-velocity survey of the old (7 Gyr) open cluster NGC 188 in which we studied in detail the solar-type hard binaries and blue stragglers of the cluster, here we investigate the dynamical evolution of NGC 188 through a sophisticated N-body model. Importantly, we employ the observed binary properties of the young (180 Myr) open cluster M35, where possible, to guide our choices for parameters of the initial binary population. We apply pre-main-sequence tidal circularization and a substantial increase to the main-sequence tidal circularization rate, both of which are necessary to match the observed tidalmore » circularization periods in the literature, including that of NGC 188. At 7 Gyr the main-sequence solar-type hard-binary population in the model matches that of NGC 188 in both binary frequency and distributions of orbital parameters. This agreement between the model and observations is in a large part due to the similarities between the NGC 188 and M35 solar-type binaries. Indeed, among the 7 Gyr main-sequence binaries in the model, only those with P {approx}> 1000 days begin to show potentially observable evidence for modifications by dynamical encounters, even after 7 Gyr of evolution within the star cluster. This emphasizes the importance of defining accurate initial conditions for star cluster models, which we propose is best accomplished through comparisons with observations of young open clusters like M35. Furthermore, this finding suggests that observations of the present-day binaries in even old open clusters can provide valuable information on their primordial binary populations. However, despite the model's success at matching the observed solar-type main-sequence population, the model underproduces blue stragglers and produces an overabundance of long-period circular main-sequence-white-dwarf binaries as compared with the true cluster. We explore several potential solutions to the paucity of blue stragglers and conclude that the model dramatically underproduces blue stragglers through mass-transfer processes. We suggest that common-envelope evolution may have been incorrectly imposed on the progenitors of the spurious long-period circular main-sequence-white-dwarf binaries, which perhaps instead should have gone through stable mass transfer to create blue stragglers, thereby bringing both the number and binary frequency of the blue straggler population in the model into agreement with the true blue stragglers in NGC 188. Thus, improvements in the physics of mass transfer and common-envelope evolution employed in the model may in fact solve both discrepancies with the observations. This project highlights the unique accessibility of open clusters to both comprehensive observational surveys and full-scale N-body simulations, both of which have only recently matured sufficiently to enable such a project, and underscores the importance of open clusters to the study of star cluster dynamics.« less

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

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,…

Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies.

PubMed

Huang, Yangxin; Lu, Xiaosun; Chen, Jiaqing; Liang, Juan; Zangmeister, Miriam

2017-10-27

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Small traveling clusters in attractive and repulsive Hamiltonian mean-field models.

PubMed

Barré, Julien; Yamaguchi, Yoshiyuki Y

2009-03-01

Long-lasting small traveling clusters are studied in the Hamiltonian mean-field model by comparing between attractive and repulsive interactions. Nonlinear Landau damping theory predicts that a Gaussian momentum distribution on a spatially homogeneous background permits the existence of traveling clusters in the repulsive case, as in plasma systems, but not in the attractive case. Nevertheless, extending the analysis to a two-parameter family of momentum distributions of Fermi-Dirac type, we theoretically predict the existence of traveling clusters in the attractive case; these findings are confirmed by direct N -body numerical simulations. The parameter region with the traveling clusters is much reduced in the attractive case with respect to the repulsive case.

Cluster kinetics model for mixtures of glassformers

NASA Astrophysics Data System (ADS)

Brenskelle, Lisa A.; McCoy, Benjamin J.

2007-10-01

For glassformers we propose a binary mixture relation for parameters in a cluster kinetics model previously shown to represent pure compound data for viscosity and dielectric relaxation as functions of either temperature or pressure. The model parameters are based on activation energies and activation volumes for cluster association-dissociation processes. With the mixture parameters, we calculated dielectric relaxation times and compared the results to experimental values for binary mixtures. Mixtures of sorbitol and glycerol (seven compositions), sorbitol and xylitol (three compositions), and polychloroepihydrin and polyvinylmethylether (three compositions) were studied.

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

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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 S(c), 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 S(c)(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 q(c) 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.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chandar, Rupali; Fall, S. Michael; Whitmore, Bradley C., E-mail: Rupali.Chandar@utoledo.ed, E-mail: fall@stsci.ed, E-mail: whitmore@stsci.ed

We compare the observed bivariate distribution of masses (M) and ages (tau) of star clusters in the Large Magellanic Cloud (LMC) with the predicted distributions g(M, tau) from three idealized models for the disruption of star clusters: (1) sudden mass-dependent disruption, (2) gradual mass-dependent disruption, and (3) gradual mass-independent disruption. The model with mass-independent disruption provides a good, first-order description of these cluster populations, with g(M, tau) {proportional_to} M {sup beta}tau{sup g}amma, beta = -1.8 +- 0.2 and gamma = -0.8 +- 0.2, at least for clusters with ages tau {approx}< 10{sup 9} yr and masses M {approx}> 10{sup 3}more » M{sub sun} (more specifically, tau {approx}< 10{sup 7}(M/10{sup 2} M{sub sun}){sup 1.3} yr). This model predicts that the clusters should have a power-law luminosity function, dN/dL {proportional_to} L {sup -1.8}, in agreement with observations. The first two models, on the other hand, fare poorly when describing the observations, refuting previous claims that mass-dependent disruption of star clusters is observed in the LMC over the studied M-tau domain. Clusters in the SMC can be described by the same g(M, tau) distribution as for the LMC, but with smaller samples and hence larger uncertainties. The successful g(M, tau) model for clusters in the Magellanic Clouds is virtually the same as the one for clusters in the merging Antennae galaxies, but extends the domain of validity to lower masses and to older ages. This indicates that the dominant disruption processes are similar in these very different galaxies over at least tau {approx}< 10{sup 8} yr and possibly tau {approx}< 10{sup 9} yr. The mass functions for young clusters in the LMC are power laws, while that for ancient globular clusters is peaked. We show that the observed shapes of these mass functions are consistent with expectations from the simple evaporation model presented by McLaughlin and Fall.« less

Automated modal parameter estimation using correlation analysis and bootstrap sampling

NASA Astrophysics Data System (ADS)

Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.

2018-02-01

The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical.

Clustering and Phase Transitions on a Neutral Landscape

NASA Astrophysics Data System (ADS)

Scott, Adam; King, Dawn; Maric, Nevena; Bahar, Sonya

2012-02-01

The problem of speciation and species aggregation on a neutral landscape, subject to random mutational fluctuations rather than selective drive, has been a focus of research since the seminal work of Kimura on genetic drift. These ideas have received increased attention due to the more recent development of a neutral ecological theory by Hubbell. De Aguiar et al. recently demonstrated, in a computational model, that speciation can occur under neutral conditions; this study bears some comparison with more mathematical studies of clustering on neutral landscapes in the context of branching and annihilating random walks. Here, we show that clustering can occur on a neutral landscape where the dimensions specify the simulated organisms' phenotypes. Unlike the De Aguiar et al. model, we simulate sympatric speciation: the organisms cluster phenotypically, but are not spatially separated. Moreover, we find that clustering occurs not only in the case of assortative mating, but also in the case of asexual fission. Clustering is not observed in a control case where organisms can mate randomly. We find that the population size and the number of clusters undergo phase-transition-like behavior as the maximum mutation size is varied.

Volume shift and charge instability of simple-metal clusters

NASA Astrophysics Data System (ADS)

Brajczewska, M.; Vieira, A.; Fiolhais, C.; Perdew, J. P.

1996-12-01

Experiment indicates that small clusters show changes (mostly contractions) of the bond lengths with respect to bulk values. We use the stabilized jellium model to study the self-expansion and self-compression of spherical clusters (neutral or ionized) of simple metals. Results from Kohn - Sham density functional theory are presented for small clusters of Al and Na, including negatively-charged ones. We also examine the stability of clusters with respect to charging.

Catalysis applications of size-selected cluster deposition

DOE PAGES

Vajda, Stefan; White, Michael G.

2015-10-23

In this Perspective, we review recent studies of size-selected cluster deposition for catalysis applications performed at the U.S. DOE National Laboratories, with emphasis on work at Argonne National Laboratory (ANL) and Brookhaven National Laboratory (BNL). The focus is on the preparation of model supported catalysts in which the number of atoms in the deposited clusters is precisely controlled using a combination of gas-phase cluster ion sources, mass spectrometry, and soft-landing techniques. This approach is particularly effective for investigations of small nanoclusters, 0.5-2 nm (<200 atoms), where the rapid evolution of the atomic and electronic structure makes it essential to havemore » precise control over cluster size. Cluster deposition allows for independent control of cluster size, coverage, and stoichiometry (e.g., the metal-to-oxygen ratio in an oxide cluster) and can be used to deposit on any substrate without constraints of nucleation and growth. Examples are presented for metal, metal oxide, and metal sulfide cluster deposition on a variety of supports (metals, oxides, carbon/diamond) where the reactivity, cluster-support electronic interactions, and cluster stability and morphology are investigated. Both UHV and in situ/operando studies are presented that also make use of surface-sensitive X-ray characterization tools from synchrotron radiation facilities. Novel applications of cluster deposition to electrochemistry and batteries are also presented. This review also highlights the application of modern ab initio electronic structure calculations (density functional theory), which can essentially model the exact experimental system used in the laboratory (i.e., cluster and support) to provide insight on atomic and electronic structure, reaction energetics, and mechanisms. As amply demonstrated in this review, the powerful combination of atomically precise cluster deposition and theory is able to address fundamental aspects of size-effects, cluster-support interactions, and reaction mechanisms of cluster materials that are central to how catalysts function. Lastly, the insight gained from such studies can be used to further the development of novel nanostructured catalysts with high activity and selectivity.« less

Coagulation-fragmentation for a finite number of particles and application to telomere clustering in the yeast nucleus

NASA Astrophysics Data System (ADS)

Hozé, Nathanaël; Holcman, David

2012-01-01

We develop a coagulation-fragmentation model to study a system composed of a small number of stochastic objects moving in a confined domain, that can aggregate upon binding to form local clusters of arbitrary sizes. A cluster can also dissociate into two subclusters with a uniform probability. To study the statistics of clusters, we combine a Markov chain analysis with a partition number approach. Interestingly, we obtain explicit formulas for the size and the number of clusters in terms of hypergeometric functions. Finally, we apply our analysis to study the statistical physics of telomeres (ends of chromosomes) clustering in the yeast nucleus and show that the diffusion-coagulation-fragmentation process can predict the organization of telomeres.

Neck formation and deformation effects in a preformed cluster model of exotic cluster decays

NASA Astrophysics Data System (ADS)

Kumar, Satish; Gupta, Raj K.

1997-01-01

Using the nuclear proximity approach and the two center nuclear shape parametrization, the interaction potential between two deformed and pole-to-pole oriented nuclei forming a necked configuration in the overlap region is calculated and its role is studied for the cluster decay half-lives. The barrier is found to move to a larger relative separation, with its proximity minimum lying in the neighborhood of the Q value of decay and its height and width reduced considerably. For cluster decay calculations in the preformed cluster model of Malik and Gupta, due to deformations and orientations of nuclei, the (empirical) preformation factor is found to get reduced considerably and agrees nicely with other model calculations known to be successful for their predictions of cluster decay half-lives. Comparison with the earlier case of nuclei treated as spheres suggests that the effects of both deformations and neck formation get compensated by choosing the position of cluster preformation and the inner classical turning point for penetrability calculations at the touching configuration of spherical nuclei.

Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study.

PubMed

Barker, Daniel; D'Este, Catherine; Campbell, Michael J; McElduff, Patrick

2017-03-09

Stepped wedge cluster randomised trials frequently involve a relatively small number of clusters. The most common frameworks used to analyse data from these types of trials are generalised estimating equations and generalised linear mixed models. A topic of much research into these methods has been their application to cluster randomised trial data and, in particular, the number of clusters required to make reasonable inferences about the intervention effect. However, for stepped wedge trials, which have been claimed by many researchers to have a statistical power advantage over the parallel cluster randomised trial, the minimum number of clusters required has not been investigated. We conducted a simulation study where we considered the most commonly used methods suggested in the literature to analyse cross-sectional stepped wedge cluster randomised trial data. We compared the per cent bias, the type I error rate and power of these methods in a stepped wedge trial setting with a binary outcome, where there are few clusters available and when the appropriate adjustment for a time trend is made, which by design may be confounding the intervention effect. We found that the generalised linear mixed modelling approach is the most consistent when few clusters are available. We also found that none of the common analysis methods for stepped wedge trials were both unbiased and maintained a 5% type I error rate when there were only three clusters. Of the commonly used analysis approaches, we recommend the generalised linear mixed model for small stepped wedge trials with binary outcomes. We also suggest that in a stepped wedge design with three steps, at least two clusters be randomised at each step, to ensure that the intervention effect estimator maintains the nominal 5% significance level and is also reasonably unbiased.

A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

PubMed

Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

2007-01-01

Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

PubMed Central

Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric

2016-01-01

Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. PMID:27212939

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

B. subtilis as a Model for Studying the Assembly of Fe-S Clusters in Gram-Positive Bacteria.

PubMed

Dos Santos, Patricia C

2017-01-01

Complexes of iron and sulfur (Fe-S clusters) are widely distributed in nature and participate in essential biochemical reactions. The biological formation of Fe-S clusters involves dedicated pathways responsible for the mobilization of sulfur, the assembly of Fe-S clusters, and the transfer of these clusters to target proteins. Genomic analysis of Bacillus subtilis and other Gram-positive bacteria indicated the presence of only one Fe-S cluster biosynthesis pathway, which is distinct in number of components and organization from previously studied systems. B. subtilis has been used as a model system for the characterization of cysteine desulfurases responsible for sulfur mobilization reactions in the biogenesis of Fe-S clusters and other sulfur-containing cofactors. Cysteine desulfurases catalyze the cleavage of the C-S bond from the amino acid cysteine and subsequent transfer of sulfur to acceptor molecules. These reactions can be monitored by the rate of alanine formation, the first product in the reaction, and sulfide formation, a byproduct of reactions performed under reducing conditions. The assembly of Fe-S clusters on protein scaffolds and the transfer of these clusters to target acceptors are determined through a combination of spectroscopic methods probing the rate of cluster assembly and transfer. This chapter provides a description of reactions promoting the assembly of Fe-S clusters in bacteria as well as methods used to study functions of each biosynthetic component and identify mechanistic differences employed by these enzymes across different pathways. © 2017 Elsevier Inc. All rights reserved.

Infrared spectroscopy of protonated trimethylamine-(benzene)(n) (n = 1-4) as model clusters of the quaternary ammonium-aromatic ring interaction.

PubMed

Shishido, Ryunosuke; Kawai, Yuki; Fujii, Asuka

2014-09-04

The essence of the molecular recognition of the neurotransmitter acetylcholine has been attributed to the attractive interaction between a quaternary ammonium and aromatic rings. We employed protonated trimethylamine-(benzene)n clusters (n = 1-4) in the gas phase as a model to study the recognition mechanism of acetylcholine at the microscopic level. We applied size-selective infrared spectroscopy to the clusters and observed the NH and CH stretching vibrational regions. We also performed density functional theory calculations of stable structures, charge distributions, and infrared spectra of the clusters. It was shown that the methyl groups of protonated trimethylamine are solvated by benzene one at a time in the n > 1 clusters, and the validity of these clusters as a model system of the acetylcholine recognition was demonstrated. The nature of the interactions between a quaternary ammonium and aromatic rings is discussed on the basis of the observed infrared spectra and the theoretical calculations.

Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials

PubMed Central

Andridge, Rebecca. R.

2011-01-01

In cluster randomized trials (CRTs), identifiable clusters rather than individuals are randomized to study groups. Resulting data often consist of a small number of clusters with correlated observations within a treatment group. Missing data often present a problem in the analysis of such trials, and multiple imputation (MI) has been used to create complete data sets, enabling subsequent analysis with well-established analysis methods for CRTs. We discuss strategies for accounting for clustering when multiply imputing a missing continuous outcome, focusing on estimation of the variance of group means as used in an adjusted t-test or ANOVA. These analysis procedures are congenial to (can be derived from) a mixed effects imputation model; however, this imputation procedure is not yet available in commercial statistical software. An alternative approach that is readily available and has been used in recent studies is to include fixed effects for cluster, but the impact of using this convenient method has not been studied. We show that under this imputation model the MI variance estimator is positively biased and that smaller ICCs lead to larger overestimation of the MI variance. Analytical expressions for the bias of the variance estimator are derived in the case of data missing completely at random (MCAR), and cases in which data are missing at random (MAR) are illustrated through simulation. Finally, various imputation methods are applied to data from the Detroit Middle School Asthma Project, a recent school-based CRT, and differences in inference are compared. PMID:21259309

I. Excluded volume effects in Ising cluster distributions and nuclear multifragmentation. II. Multiple-chance effects in alpha-particle evaporation

NASA Astrophysics Data System (ADS)

Breus, Dimitry Eugene

In Part I, geometric clusters of the Ising model are studied as possible model clusters for nuclear multifragmentation. These clusters may not be considered as non-interacting (ideal gas) due to excluded volume effect which predominantly is the artifact of the cluster's finite size. Interaction significantly complicates the use of clusters in the analysis of thermodynamic systems. Stillinger's theory is used as a basis for the analysis, which within the RFL (Reiss, Frisch, Lebowitz) fluid-of-spheres approximation produces a prediction for cluster concentrations well obeyed by geometric clusters of the Ising model. If thermodynamic condition of phase coexistence is met, these concentrations can be incorporated into a differential equation procedure of moderate complexity to elucidate the liquid-vapor phase diagram of the system with cluster interaction included. The drawback of increased complexity is outweighted by the reward of greater accuracy of the phase diagram, as it is demonstrated by the Ising model. A novel nuclear-cluster analysis procedure is developed by modifying Fisher's model to contain cluster interaction and employing the differential equation procedure to obtain thermodynamic variables. With this procedure applied to geometric clusters, the guidelines are developed to look for excluded volume effect in nuclear multifragmentation. In Part II, an explanation is offered for the recently observed oscillations in the energy spectra of alpha-particles emitted from hot compound nuclei. Contrary to what was previously expected, the oscillations are assumed to be caused by the multiple-chance nature of alpha-evaporation. In a semi-empirical fashion this assumption is successfully confirmed by a technique of two-spectra decomposition which treats experimental alpha-spectra as having contributions from at least two independent emitters. Building upon the success of the multiple-chance explanation of the oscillations, Moretto's single-chance evaporation theory is augmented to include multiple-chance emission and tested on experimental data to yield positive results.

Formation of Very Young Massive Clusters and Implications for Globular Clusters

NASA Astrophysics Data System (ADS)

Banerjee, Sambaran; Kroupa, Pavel

How Very Young Massive star Clusters (VYMCs; also known as "starburst" clusters), which typically are of ≳ 104 M ⊙ and are a few Myr old, form out of Giant Molecular Clouds is still largely an open question. Increasingly detailed observations of young star clusters and star-forming molecular clouds and computational studies provide clues about their formation scenarios and the underlying physical processes involved. This chapter is focused on reviewing the decade-long studies that attempt to computationally reproduce the well-observed nearby VYMCs, such as the Orion Nebula Cluster, R136 and NGC 3603 young cluster, thereby shedding light on birth conditions of massive star clusters, in general. On this regard, focus is given on direct N-body modelling of real-sized massive star clusters, with a monolithic structure and undergoing residual gas expulsion, which have consistently reproduced the observed characteristics of several VYMCs and also of young star clusters, in general. The connection of these relatively simplified model calculations with the structural richness of dense molecular clouds and the complexity of hydrodynamic calculations of star cluster formation is presented in detail. Furthermore, the connections of such VYMCs with globular clusters, which are nearly as old as our Universe, is discussed. The chapter is concluded by addressing long-term deeply gas-embedded (at least apparently) and substructured systems like W3 Main. While most of the results are quoted from existing and up-to-date literature, in an integrated fashion, several new insights and discussions are provided.

Electronic and geometric properties of ETS-10: QM/MM studies of cluster models.

PubMed

Zimmerman, Anne Marie; Doren, Douglas J; Lobo, Raul F

2006-05-11

Hybrid DFT/MM methods have been used to investigate the electronic and geometric properties of the microporous titanosilicate ETS-10. A comparison of finite length and periodic models demonstrates that band gap energies for ETS-10 can be well represented with relatively small cluster models. Optimization of finite clusters leads to different local geometries for bulk and end sites, where the local bulk TiO6 geometry is in good agreement with recent experimental results. Geometry optimizations reveal that any asymmetry within the axial O-Ti-O chain is negligible. The band gap in the optimized model corresponds to a O(2p) --> Tibulk(3d) transition. The results suggest that the three Ti atom, single chain, symmetric, finite cluster is an effective model for the geometric and electronic properties of bulk and end TiO6 groups in ETS-10.

Attempt to probe nuclear charge radii by cluster and proton emissions

NASA Astrophysics Data System (ADS)

Qian, Yibin; Ren, Zhongzhou; Ni, Dongdong

2013-05-01

We deduce the rms nuclear charge radii for ground states of light and medium-mass nuclei from experimental data of cluster radioactivity and proton emission in a unified framework. On the basis of the density-dependent cluster model, the calculated decay half-lives are obtained within the modified two-potential approach. The charge distribution of emitted clusters in the cluster decay and that of daughter nuclei in the proton emission are determined to correspondingly reproduce the experimental half-lives within the folding model. The obtained charge distribution is then employed to give the rms charge radius of the studied nuclei. Satisfactory agreement between theory and experiment is achieved for available experimental data, and the present results are found to be consistent with theoretical estimations. This study is expected to be helpful in the future detection of nuclear sizes, especially for these exotic nuclei near the proton dripline.

Marginal regression approach for additive hazards models with clustered current status data.

PubMed

Su, Pei-Fang; Chi, Yunchan

2014-01-15

Current status data arise naturally from tumorigenicity experiments, epidemiology studies, biomedicine, econometrics and demographic and sociology studies. Moreover, clustered current status data may occur with animals from the same litter in tumorigenicity experiments or with subjects from the same family in epidemiology studies. Because the only information extracted from current status data is whether the survival times are before or after the monitoring or censoring times, the nonparametric maximum likelihood estimator of survival function converges at a rate of n(1/3) to a complicated limiting distribution. Hence, semiparametric regression models such as the additive hazards model have been extended for independent current status data to derive the test statistics, whose distributions converge at a rate of n(1/2) , for testing the regression parameters. However, a straightforward application of these statistical methods to clustered current status data is not appropriate because intracluster correlation needs to be taken into account. Therefore, this paper proposes two estimating functions for estimating the parameters in the additive hazards model for clustered current status data. The comparative results from simulation studies are presented, and the application of the proposed estimating functions to one real data set is illustrated. Copyright © 2013 John Wiley & Sons, Ltd.

Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases.

PubMed

Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie; Brown, Alison E; Gill, O Noel; Tostevin, Anna; Fraser, Christophe; Volz, Erik M

2018-06-01

Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Managing Clustered Data Using Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.

2012-01-01

Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…

Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

EPA Science Inventory

In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER bindi...

Energy spectra of vibron and cluster models in molecular and nuclear systems

NASA Astrophysics Data System (ADS)

Jalili Majarshin, A.; Sabri, H.; Jafarizadeh, M. A.

2018-03-01

The relation of the algebraic cluster model, i.e., of the vibron model and its extension, to the collective structure, is discussed. In the first section of the paper, we study the energy spectra of vibron model, for diatomic molecule then we derive the rotation-vibration spectrum of 2α, 3α and 4α configuration in the low-lying spectrum of 8Be, 12C and 16O nuclei. All vibrational and rotational states with ground and excited A, E and F states appear to have been observed, moreover the transitional descriptions of the vibron model and α-cluster model were considered by using an infinite-dimensional algebraic method based on the affine \\widehat{SU(1,1)} Lie algebra. The calculated energy spectra are compared with experimental data. Applications to the rotation-vibration spectrum for the diatomic molecule and many-body nuclear clusters indicate that there are solvable models and they can be approximated very well using the transitional theory.

Impact of Personality Disorder Cluster on Depression Outcomes Within Collaborative Care Management Model of Care.

PubMed

George, Merit P; Garrison, Gregory M; Merten, Zachary; Heredia, Dagoberto; Gonzales, Cesar; Angstman, Kurt B

2018-01-01

Previous studies have suggested that having a comorbid personality disorder (PD) along with major depression is associated with poorer depression outcomes relative to those without comorbid PD. However, few studies have examined the influence of specific PD cluster types. The purpose of the current study is to compare depression outcomes between cluster A, cluster B, and cluster C PD patients treated within a collaborative care management (CCM), relative to CCM patients without a PD diagnosis. The overarching goal was to identify cluster types that might confer a worse clinical prognosis. This retrospective chart review study examined 2826 adult patients with depression enrolled in CCM. The cohort was divided into 4 groups based on the presence of a comorbid PD diagnosis (cluster A/nonspecified, cluster B, cluster C, or no PD). Baseline clinical and demographic variables, along with 6-month follow-up Patient Health Questionnaire-9 (PHQ-9) scores were obtained for all groups. Depression remission was defined as a PHQ-9 score <5 at 6 months, and persistent depressive symptoms (PDS) was defined as a PHQ-9 score ≥10 at 6 months. Adjusted odds ratios (AORs) were determined for both remission and PDS using logistic regression modeling for the 6-month PHQ-9 outcome, while retaining all study variables. A total of 59 patients (2.1%) had a cluster A or nonspecified PD diagnosis, 122 patients (4.3%) had a cluster B diagnosis, 35 patients (1.2%) had a cluster C diagnosis, and 2610 patients (92.4%) did not have any PD diagnosis. The presence of a cluster A/nonspecified PD diagnosis was associated with a 62% lower likelihood of remission at 6 months (AOR = 0.38; 95% CI 0.20-0.70). The presence of a cluster B PD diagnosis was associated with a 71% lower likelihood of remission at 6 months (AOR = 0.29; 95% CI 0.18-0.47). Conversely, having a cluster C diagnosis was not associated with a significantly lower likelihood of remission at 6 months (AOR = 0.83; 95% CI 0.42-1.65). Increased odds of having PDS at 6-month follow-up were seen with cluster A/nonspecified PD patients (AOR = 3.35; 95% CI 1.92-5.84) as well as cluster B patients (AOR = 3.66; 95% CI 2.45-5.47). However, cluster C patents did not have significantly increased odds of experiencing persistent depressive symptoms at 6-month follow-up (AOR = 0.95; 95% CI 0.45-2.00). Out of the 3 clusters, the presence of a cluster B PD diagnosis was most significantly associated with poorer depression outcomes at 6-month follow-up, including reduced remission rates and increased risk for PDS. The cluster A/nonspecified PD group also showed poor outcomes; however, the heterogeneity of this subgroup with regard to PD features must be noted. The development of novel targeted interventions for at-risk clusters may be warranted in order to improve outcomes of these patients within the CCM model of care.

Modelling baryonic effects on galaxy cluster mass profiles

NASA Astrophysics Data System (ADS)

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.

Strong Lens Models for 10 Galaxy Clusters from the Sloan Giant Arcs Survey

NASA Astrophysics Data System (ADS)

Dunham, Samuel; Sharon, Keren; Bayliss, Matthew; Dahle, Hakon; Florian, Michael; Gladders, Michael; Johnson, Traci; Murray, Katherine; Rigby, Jane R.; Whitaker, Katherine E.; Wuyts, Eva

2016-01-01

We present the results from modeling several strong gravitational lenses as part of the Sloan Giant Arcs Survey (SGAS). HST cannot resolve star-formation in galaxies at redshifts >~1 because they are too far away, but by using the magnification by galaxy clusters at these redshifts (1

Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

NASA Astrophysics Data System (ADS)

Li, Xiwang

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of this thesis can be used for building cluster energy forecasting model development and model based control and operation optimization. The thesis concludes with a summary of the key outcomes of this research, as well as a list of recommendations for future work.

MOCCA-SURVEY Database I: Is NGC 6535 a dark star cluster harbouring an IMBH?

NASA Astrophysics Data System (ADS)

Askar, Abbas; Bianchini, Paolo; de Vita, Ruggero; Giersz, Mirek; Hypki, Arkadiusz; Kamann, Sebastian

2017-01-01

We describe the dynamical evolution of a unique type of dark star cluster model in which the majority of the cluster mass at Hubble time is dominated by an intermediate-mass black hole (IMBH). We analysed results from about 2000 star cluster models (Survey Database I) simulated using the Monte Carlo code MOnte Carlo Cluster simulAtor and identified these dark star cluster models. Taking one of these models, we apply the method of simulating realistic `mock observations' by utilizing the Cluster simulatiOn Comparison with ObservAtions (COCOA) and Simulating Stellar Cluster Observation (SISCO) codes to obtain the photometric and kinematic observational properties of the dark star cluster model at 12 Gyr. We find that the perplexing Galactic globular cluster NGC 6535 closely matches the observational photometric and kinematic properties of the dark star cluster model presented in this paper. Based on our analysis and currently observed properties of NGC 6535, we suggest that this globular cluster could potentially harbour an IMBH. If it exists, the presence of this IMBH can be detected robustly with proposed kinematic observations of NGC 6535.

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

PubMed

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.

Utility of K-Means clustering algorithm in differentiating apparent diffusion coefficient values between benign and malignant neck pathologies

PubMed Central

Srinivasan, A.; Galbán, C.J.; Johnson, T.D.; Chenevert, T.L.; Ross, B.D.; Mukherji, S.K.

2014-01-01

Purpose The objective of our study was to analyze the differences between apparent diffusion coefficient (ADC) partitions (created using the K-Means algorithm) between benign and malignant neck lesions and evaluate its benefit in distinguishing these entities. Material and methods MRI studies of 10 benign and 10 malignant proven neck pathologies were post-processed on a PC using in-house software developed in MATLAB (The MathWorks, Inc., Natick, MA). Lesions were manually contoured by two neuroradiologists with the ADC values within each lesion clustered into two (low ADC-ADCL, high ADC-ADCH) and three partitions (ADCL, intermediate ADC-ADCI, ADCH) using the K-Means clustering algorithm. An unpaired two-tailed Student’s t-test was performed for all metrics to determine statistical differences in the means between the benign and malignant pathologies. Results Statistically significant difference between the mean ADCL clusters in benign and malignant pathologies was seen in the 3 cluster models of both readers (p=0.03, 0.022 respectively) and the 2 cluster model of reader 2 (p=0.04) with the other metrics (ADCH, ADCI, whole lesion mean ADC) not revealing any significant differences. Receiver operating characteristics curves demonstrated the quantitative difference in mean ADCH and ADCL in both the 2 and 3 cluster models to be predictive of malignancy (2 clusters: p=0.008, area under curve=0.850, 3 clusters: p=0.01, area under curve=0.825). Conclusion The K-Means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared to whole lesion mean ADC alone. PMID:20007723

Utility of the k-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies.

PubMed

Srinivasan, A; Galbán, C J; Johnson, T D; Chenevert, T L; Ross, B D; Mukherji, S K

2010-04-01

Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies. MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADC(L); high, ADC-ADC(H)) and 3 partitions (ADC(L); intermediate, ADC-ADC(I); ADC(H)) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies. A statistically significant difference between the mean ADC(L) clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers (P = .03 and .022, respectively) and the 2-cluster model of reader 2 (P = .04), with the other metrics (ADC(H), ADC(I); whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADC(H) and ADC(L) in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825). The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.

Activity of a social dynamics model

NASA Astrophysics Data System (ADS)

Reia, Sandro M.; Neves, Ubiraci P. C.

2015-10-01

Axelrod's model was proposed to study interactions between agents and the formation of cultural domains. It presents a transition from a monocultural to a multicultural steady state which has been studied in the literature by evaluation of the relative size of the largest cluster. In this article, we propose new measurements based on the concept of activity per agent to study the Axelrod's model on the square lattice. We show that the variance of system activity can be used to indicate the critical points of the transition. Furthermore the frequency distribution of the system activity is able to show a coexistence of phases typical of a first order phase transition. Finally, we verify a power law dependence between cluster activity and cluster size for multicultural steady state configurations at the critical point.

A Model-Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior.

PubMed

Shaffer, Anne; Whitehead, Monica; Davis, Molly; Morelen, Diana; Suveg, Cynthia

2017-10-15

In a diverse community sample of mothers (N = 108) and their preschool-aged children (M age  = 3.50 years), this study conducted person-oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self-report indicators. A model-based cluster analysis was applied to five indicators of maternal ER: maternal self-report, observed negative affect in a parent-child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model-based cluster analyses revealed four maternal ER profiles, including a group of mothers with average ER functioning, characterized by socioeconomic advantage and more positive parenting behavior. A dysregulated cluster demonstrated the greatest challenges with parenting and dyadic interactions. Two clusters of intermediate dysregulation were also identified. Implications for assessment and applications to parenting interventions are discussed. © 2017 Family Process Institute.

Dimensional structure of DSM-5 posttraumatic stress disorder symptoms: results from the National Health and Resilience in Veterans Study.

PubMed

Tsai, Jack; Harpaz-Rotem, Ilan; Armour, Cherie; Southwick, Steven M; Krystal, John H; Pietrzak, Robert H

2015-05-01

To evaluate the prevalence of DSM-5 posttraumatic stress disorder (PTSD) and factor structure of PTSD symptomatology in a nationally representative sample of US veterans and examine how PTSD symptom clusters are related to depression, anxiety, suicidal ideation, hostility, physical and mental health-related functioning, and quality of life. Data were analyzed from the National Health and Resilience in Veterans Study, a nationally representative survey of 1,484 US veterans conducted from September through October 2013. Confirmatory factor analyses were conducted to evaluate the factor structure of PTSD symptoms, and structural equation models were constructed to examine the association between PTSD symptom clusters and external correlates. 12.0% of veterans screened positive for lifetime PTSD and 5.2% for past-month PTSD. A 5-factor dysphoric arousal model and a newly proposed 6-factor model both fit the data significantly better than the 4-factor model of DSM-5. The 6-factor model fit the data best in the full sample, as well as in subsamples of female veterans and veterans with lifetime PTSD. The emotional numbing symptom cluster was more strongly related to depression (P < .001) and worse mental health-related functioning (P < .001) than other symptom clusters, while the externalizing behavior symptom cluster was more strongly related to hostility (P < .001). A total of 5.2% of US veterans screened positive for past-month DSM-5 PTSD. A 6-factor model of DSM-5 PTSD symptoms, which builds on extant models and includes a sixth externalizing behavior factor, provides the best dimensional representation of DSM-5 PTSD symptom clusters and demonstrates validity in assessing health outcomes of interest in this population. © Copyright 2015 Physicians Postgraduate Press, Inc.

Impact of a star formation efficiency profile on the evolution of open clusters

NASA Astrophysics Data System (ADS)

Shukirgaliyev, B.; Parmentier, G.; Berczik, P.; Just, A.

2017-09-01

Aims: We study the effect of the instantaneous expulsion of residual star-forming gas on star clusters in which the residual gas has a density profile that is shallower than that of the embedded cluster. This configuration is expected if star formation proceeds with a given star-formation efficiency per free-fall time in a centrally concentrated molecular gas clump. Methods: We performed direct N-body simulations whose initial conditions were generated by the program "mkhalo" from the package "falcON", adapted for our models. Our model clusters initially had a Plummer profile and are in virial equilibrium with the gravitational potential of the cluster-forming clump. The residual gas contribution was computed based on a local-density driven clustered star formation model. Our simulations included mass loss by stellar evolution and the tidal field of a host galaxy. Results: We find that a star cluster with a minimum global star formation efficiency (SFE) of 15 percent is able to survive instantaneous gas expulsion and to produce a bound cluster. Its violent relaxation lasts no longer than 20 Myr, independently of its global SFE and initial stellar mass. At the end of violent relaxation, the bound fractions of the surviving clusters with the same global SFEs are similar, regardless of their initial stellar mass. Their subsequent lifetime in the gravitational field of the Galaxy depends on their bound stellar masses. Conclusions: We therefore conclude that the critical SFE needed to produce a bound cluster is 15 percent, which is roughly half the earlier estimates of 33 percent. Thus we have improved the survival likelihood of young clusters after instantaneous gas expulsion. Young clusters can now survive instantaneous gas expulsion with a global SFEs as low as the SFEs observed for embedded clusters in the solar neighborhood (15-30 percent). The reason is that the star cluster density profile is steeper than that of the residual gas. However, in terms of the effective SFE, measured by the virial ratio of the cluster at gas expulsion, our results are in agreement with previous studies.

Modeling solute clustering in the diffusion layer around a growing crystal.

PubMed

Shiau, Lie-Ding; Lu, Yung-Fang

2009-03-07

The mechanism of crystal growth from solution is often thought to consist of a mass transfer diffusion step followed by a surface reaction step. Solute molecules might form clusters in the diffusion step before incorporating into the crystal lattice. A model is proposed in this work to simulate the evolution of the cluster size distribution due to the simultaneous aggregation and breakage of solute molecules in the diffusion layer around a growing crystal in the stirred solution. The crystallization of KAl(SO(4))(2)12H(2)O from aqueous solution is studied to illustrate the effect of supersaturation and diffusion layer thickness on the number-average degree of clustering and the size distribution of solute clusters in the diffusion layer.

Discharge-nitrate data clustering for characterizing surface-subsurface flow interaction and calibration of a hydrologic model

NASA Astrophysics Data System (ADS)

Shrestha, R. R.; Rode, M.

2008-12-01

Concentration of reactive chemicals has different chemical signatures in baseflow and surface runoff. Previous studies on nitrate export from a catchment indicate that the transport processes are driven by subsurface flow. Therefore nitrate signature can be used for understanding the event and pre-event contributions to streamflow and surface-subsurface flow interactions. The study uses flow and nitrate concentration time series data for understanding the relationship between these two variables. Unsupervised artificial neural network based learning method called self organizing map is used for the identification of clusters in the datasets. Based on the cluster results, five different pattern in the datasets are identified which correspond to (i) baseflow, (ii) subsurface flow increase, (iii) surface runoff increase, (iv) surface runoff recession, and (v) subsurface flow decrease regions. The cluster results in combination with a hydrologic model are used for discharge separation. For this purpose, a multi-objective optimization tool NSGA-II is used, where violation of cluster results is used as one of the objective functions. The results show that the use of cluster results as supplementary information for the calibration of a hydrologic model gives a plausible simulation of subsurface flow as well total runoff at the catchment outlet. The study is undertaken using data from the Weida catchment in the North-Eastern Germany, which is a sub-catchment of the Weisse Elster river in the Elbe river basin.

Systematic study of cluster radioactivity of superheavy nuclei

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Wang, Y. Z.

2018-01-01

The probable cluster radioactivity (CR) of 294118, 296120, and 298122 is studied by using the unified description (UD) formula, universal (UNIV) curve, Horoi formula, and universal decay law (UDL). The predictions by the former three models suggest that the probable emitted clusters are lighter nuclei, and the calculations within the UDL formula give a different prediction: that both the lighter clusters and heavier ones can be emitted from the parent nuclei. A further study on the competition between α decay and CR of Z =104 -124 isotopes is performed. The former three models predict that α decay is the dominant decay mode, but the UDL formula suggests that CR dominates over α decay for Z ≥118 nuclei and the isotopes of 118 292 -296 ,308 -318 , 120 , 284 -304 ,308 -324 and 122-322316 are the most likely candidates as the cluster emitters. Because the former three formulas are just preformation models, the lighter cluster emissions can be described. However, the UDL formula can predict the lighter and heavier CR owing to the inclusion of the preformation and fissionlike mechanisms. Finally, it is found that the shortest CR half-lives are always obtained when the daughter nuclei are around the double magic 208Pb within the UDL formula, which indicates that shell effect has an important influence on CR.

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture.

PubMed

Chen, Yingyi; Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.

Clustering of diet- and activity-related parenting practices: cross-sectional findings of the INPACT study

PubMed Central

2013-01-01

Background Various diet- and activity-related parenting practices are positive determinants of child dietary and activity behaviour, including home availability, parental modelling and parental policies. There is evidence that parenting practices cluster within the dietary domain and within the activity domain. This study explores whether diet- and activity-related parenting practices cluster across the dietary and activity domain. Also examined is whether the clusters are related to child and parental background characteristics. Finally, to indicate the relevance of the clusters in influencing child dietary and activity behaviour, we examined whether clusters of parenting practices are related to these behaviours. Methods Data were used from 1480 parent–child dyads participating in the Dutch IVO Nutrition and Physical Activity Child cohorT (INPACT). Parents of children aged 8–11 years completed questionnaires at home assessing their diet- and activity-related parenting practices, child and parental background characteristics, and child dietary and activity behaviours. Principal component analysis (PCA) was used to identify clusters of parenting practices. Backward regression analysis was used to examine the relationship between child and parental background characteristics with cluster scores, and partial correlations to examine associations between cluster scores and child dietary and activity behaviours. Results PCA revealed five clusters of parenting practices: 1) high visibility and accessibility of screens and unhealthy food, 2) diet- and activity-related rules, 3) low availability of unhealthy food, 4) diet- and activity-related positive modelling, and 5) positive modelling on sports and fruit. Low parental education was associated with unhealthy cluster 1, while high(er) education was associated with healthy clusters 2, 3 and 5. Separate clusters were related to both child dietary and activity behaviour in the hypothesized directions: healthy clusters were positively related to obesity-reducing behaviours and negatively to obesity-inducing behaviours. Conclusion Parenting practices cluster across the dietary and activity domain. Parental education can be seen as an indicator of a broader parental context in which clusters of parenting practices operate. Separate clusters are related to both child dietary and activity behaviour. Interventions that focus on clusters of parenting practices to assist parents (especially low-educated parents) in changing their child’s dietary and activity behaviour seems justified. PMID:23531232

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

NASA Astrophysics Data System (ADS)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron; Scoccimarro, Roman

2015-01-01

The large-scale distribution of galaxies can be explained fairly simply by assuming (i) a cosmological model, which determines the dark matter halo distribution, and (ii) a simple connection between galaxies and the halos they inhabit. This conceptually simple framework, called the halo model, has been remarkably successful at reproducing the clustering of galaxies on all scales, as observed in various galaxy redshift surveys. However, none of these previous studies have carefully modeled the systematics and thus truly tested the halo model in a statistically rigorous sense. We present a new accurate and fully numerical halo model framework and test it against clustering measurements from two luminosity samples of galaxies drawn from the SDSS DR7. We show that the simple ΛCDM cosmology + halo model is not able to simultaneously reproduce the galaxy projected correlation function and the group multiplicity function. In particular, the more luminous sample shows significant tension with theory. We discuss the implications of our findings and how this work paves the way for constraining galaxy formation by accurate simultaneous modeling of multiple galaxy clustering statistics.

A combined experimental and theoretical spectroscopic protocol for determination of the structure of heterogeneous catalysts: developing the information content of the resonance Raman spectra of M1 MoVO x .

PubMed

Kubas, Adam; Noak, Johannes; Trunschke, Annette; Schlögl, Robert; Neese, Frank; Maganas, Dimitrios

2017-09-01

Absorption and multiwavelength resonance Raman spectroscopy are widely used to investigate the electronic structure of transition metal centers in coordination compounds and extended solid systems. In combination with computational methodologies that have predictive accuracy, they define powerful protocols to study the spectroscopic response of catalytic materials. In this work, we study the absorption and resonance Raman spectra of the M1 MoVO x catalyst. The spectra were calculated by time-dependent density functional theory (TD-DFT) in conjunction with the independent mode displaced harmonic oscillator model (IMDHO), which allows for detailed bandshape predictions. For this purpose cluster models with up to 9 Mo and V metallic centers are considered to represent the bulk structure of MoVO x . Capping hydrogens were used to achieve valence saturation at the edges of the cluster models. The construction of model structures was based on a thorough bonding analysis which involved conventional DFT and local coupled cluster (DLPNO-CCSD(T)) methods. Furthermore the relationship of cluster topology to the computed spectral features is discussed in detail. It is shown that due to the local nature of the involved electronic transitions, band assignment protocols developed for molecular systems can be applied to describe the calculated spectral features of the cluster models as well. The present study serves as a reference for future applications of combined experimental and computational protocols in the field of solid-state heterogeneous catalysis.

Hydrodynamic clustering of droplets in turbulence

NASA Astrophysics Data System (ADS)

Kunnen, Rudie; Yavuz, Altug; van Heijst, Gertjan; Clercx, Herman

2017-11-01

Small, inertial particles are known to cluster in turbulent flows: particles are centrifuged out of eddies and gather in the strain-dominated regions. This so-called preferential concentration is reflected in the radial distribution function (RDF; a quantitative measure of clustering). We study clustering of water droplets in a loudspeaker-driven turbulence chamber. We track the motion of droplets in 3D and calculate the RDF. At moderate scales (a few Kolmogorov lengths) we find the typical power-law scaling of preferential concentration in the RDF. However, at even smaller scales (a few droplet diameters), we encounter a hitherto unobserved additional clustering. We postulate that the additional clustering is due to hydrodynamic interactions, an effect which is typically disregarded in modeling. Using a perturbative expansion of inertial effects in a Stokes-flow description of two interacting spheres, we obtain an expression for the RDF which indeed includes the additional clustering. The additional clustering enhances the collision probability of droplets, which enhances their growth rate due to coalescence. The additional clustering is thus an essential effect in precipitation modeling.

Starburst clusters in the Galactic center

NASA Astrophysics Data System (ADS)

Habibi, Maryam

2014-09-01

The central region of the Galaxy is the most active site of star formation in the Milky Way, where massive stars have formed very recently and are still forming today. The rich population of massive stars in the Galactic center provide a unique opportunity to study massive stars in their birth environment and probe their initial mass function, which is the spectrum of stellar masses at their birth. The Arches cluster is the youngest among the three massive clusters in the Galactic center, providing a collection of high-mass stars and a very dense core which makes this cluster an excellent site to address questions about massive star formation, the stellar mass function and the dynamical evolution of massive clusters in the Galactic center. In this thesis, I perform an observational study of the Arches cluster using K_{s}-band imaging obtained with NAOS/CONICA at the VLT combined with Subaru/Cisco J-band data to gain a full understanding of the cluster mass distribution out to its tidal radius for the first time. Since the light from the Galactic center reaches us through the Galactic disc, the extinction correction is crucial when studying this region. I use a Bayesian method to construct a realistic extinction map of the cluster. It is shown in this study that the determination of the mass of the most massive star in the Arches cluster, which had been used in previous studies to establish an upper mass limit for the star formation process in the Milky Way, strongly depends on the assumed slope of the extinction law. Assuming the two regimes of widely used infrared extinction laws, I show that the difference can reach up to 30% for individually derived stellar masses and Δ A_{Ks}˜ 1 magnitude in acquired K_{s}-band extinction, while the present-day mass function slope changes by ˜ 0.17 dex. The present-day mass function slope derived assuming the more recent extinction law, which suggests a steeper wavelength dependence for the infrared extinction law, reveals an overpopulation of massive stars in the core (r<0.2 pc) with a flat slope of α_{Nishi}=-1.50 ±0.35 in comparison to the Salpeter slope of α=-2.3. The slope of the mass function increases to α_{Nishi}=-2.21 ±0.27 in the intermediate annulus (0.2

Subgroups of advanced cancer patients clustered by their symptom profiles: quality-of-life outcomes.

PubMed

Husain, Amna; Myers, Jeff; Selby, Debbie; Thomson, Barbara; Chow, Edward

2011-11-01

Symptom cluster analysis is a new frontier of research in symptom management. This study clustered patients by their symptom profiles to identify subgroups that may be at higher risk for poor quality of life (QOL) and that may, therefore, benefit most from targeted interventions. Longitudinal study of metastatic cancer patients using the Edmonton Symptom Assessment Scale (ESAS). We generated two-, three-, and four-cluster subgroups and examined the relationship of cluster membership with patient outcomes. To address the problem of missing longitudinal data, we developed a novel outcome variable (QualTime) that measures both QOL and time in study. Two hundred and twenty-one patients with a mean Palliative Performance Scale (PPS) of 59.1 were enrolled. The three-cluster model was chosen for further analysis. The low-burden subgroup had all low severity symptom scores. The intermediate subgroup separates from the low-burden group on the "debility" profile of fatigue, drowsiness, appetite, and well-being. The high-burden group separates from the intermediate-burden group on pain, depression, and anxiety. At baseline, PPS (p=0.0003) and cluster membership (p<0.0001) contributed significantly to global QOL. In univariate analysis, cluster membership was related to the longitudinal outcome, QualTime. In a multivariate model, the relationship of PPS to QualTime was still significant (p=0.0002), but subgroup membership was no longer significant (p=0.1009). PPS is a stronger predictor of the longitudinal variable than cluster subgroups; however, cluster subgroups provide a target for clinical interventions that may improve QOL.

Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials.

PubMed

Scott, JoAnna M; deCamp, Allan; Juraska, Michal; Fay, Michael P; Gilbert, Peter B

2017-04-01

Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators.

Lung and stomach cancer associations with groundwater radon in North Carolina, USA

PubMed Central

Messier, Kyle P; Serre, Marc L

2017-01-01

Abstract Background: The risk of indoor air radon for lung cancer is well studied, but the risks of groundwater radon for both lung and stomach cancer are much less studied, and with mixed results. Methods: Geomasked and geocoded stomach and lung cancer cases in North Carolina from 1999 to 2009 were obtained from the North Carolina Central Cancer Registry. Models for the association with groundwater radon and multiple confounders were implemented at two scales: (i) an ecological model estimating cancer incidence rates at the census tract level; and (ii) a case-only logistic model estimating the odds that individual cancer cases are members of local cancer clusters. Results: For the lung cancer incidence rate model, groundwater radon is associated with an incidence rate ratio of 1.03 [95% confidence interval (CI) = 1.01, 1.06] for every 100 Bq/l increase in census tract averaged concentration. For the cluster membership models, groundwater radon exposure results in an odds ratio for lung cancer of 1.13 (95% CI = 1.04, 1.23) and for stomach cancer of 1.24 (95% CI = 1.03, 1.49), which means groundwater radon, after controlling for multiple confounders and spatial auto-correlation, increases the odds that lung and stomach cancer cases are members of their respective cancer clusters. Conclusion: Our study provides epidemiological evidence of a positive association between groundwater radon exposure and lung cancer incidence rates. The cluster membership model results find groundwater radon increases the odds that both lung and stomach cancer cases occur within their respective cancer clusters. The results corroborate previous biokinetic and mortality studies that groundwater radon is associated with increased risk for lung and stomach cancer. PMID:27639278

Lung and stomach cancer associations with groundwater radon in North Carolina, USA.

PubMed

Messier, Kyle P; Serre, Marc L

2017-04-01

The risk of indoor air radon for lung cancer is well studied, but the risks of groundwater radon for both lung and stomach cancer are much less studied, and with mixed results. Geomasked and geocoded stomach and lung cancer cases in North Carolina from 1999 to 2009 were obtained from the North Carolina Central Cancer Registry. Models for the association with groundwater radon and multiple confounders were implemented at two scales: (i) an ecological model estimating cancer incidence rates at the census tract level; and (ii) a case-only logistic model estimating the odds that individual cancer cases are members of local cancer clusters. For the lung cancer incidence rate model, groundwater radon is associated with an incidence rate ratio of 1.03 [95% confidence interval (CI) = 1.01, 1.06] for every 100 Bq/l increase in census tract averaged concentration. For the cluster membership models, groundwater radon exposure results in an odds ratio for lung cancer of 1.13 (95% CI = 1.04, 1.23) and for stomach cancer of 1.24 (95% CI = 1.03, 1.49), which means groundwater radon, after controlling for multiple confounders and spatial auto-correlation, increases the odds that lung and stomach cancer cases are members of their respective cancer clusters. Our study provides epidemiological evidence of a positive association between groundwater radon exposure and lung cancer incidence rates. The cluster membership model results find groundwater radon increases the odds that both lung and stomach cancer cases occur within their respective cancer clusters. The results corroborate previous biokinetic and mortality studies that groundwater radon is associated with increased risk for lung and stomach cancer. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

SU-G-TeP3-14: Three-Dimensional Cluster Model in Inhomogeneous Dose Distribution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, J; Penagaricano, J; Narayanasamy, G

2016-06-15

Purpose: We aim to investigate 3D cluster formation in inhomogeneous dose distribution to search for new models predicting radiation tissue damage and further leading to new optimization paradigm for radiotherapy planning. Methods: The aggregation of higher dose in the organ at risk (OAR) than a preset threshold was chosen as the cluster whose connectivity dictates the cluster structure. Upon the selection of the dose threshold, the fractional density defined as the fraction of voxels in the organ eligible to be part of the cluster was determined according to the dose volume histogram (DVH). A Monte Carlo method was implemented tomore » establish a case pertinent to the corresponding DVH. Ones and zeros were randomly assigned to each OAR voxel with the sampling probability equal to the fractional density. Ten thousand samples were randomly generated to ensure a sufficient number of cluster sets. A recursive cluster searching algorithm was developed to analyze the cluster with various connectivity choices like 1-, 2-, and 3-connectivity. The mean size of the largest cluster (MSLC) from the Monte Carlo samples was taken to be a function of the fractional density. Various OARs from clinical plans were included in the study. Results: Intensive Monte Carlo study demonstrates the inverse relationship between the MSLC and the cluster connectivity as anticipated and the cluster size does not change with fractional density linearly regardless of the connectivity types. An initially-slow-increase to exponential growth transition of the MSLC from low to high density was observed. The cluster sizes were found to vary within a large range and are relatively independent of the OARs. Conclusion: The Monte Carlo study revealed that the cluster size could serve as a suitable index of the tissue damage (percolation cluster) and the clinical outcome of the same DVH might be potentially different.« less

Validation of hierarchical cluster analysis for identification of bacterial species using 42 bacterial isolates

NASA Astrophysics Data System (ADS)

Ghebremedhin, Meron; Yesupriya, Shubha; Luka, Janos; Crane, Nicole J.

2015-03-01

Recent studies have demonstrated the potential advantages of the use of Raman spectroscopy in the biomedical field due to its rapidity and noninvasive nature. In this study, Raman spectroscopy is applied as a method for differentiating between bacteria isolates for Gram status and Genus species. We created models for identifying 28 bacterial isolates using spectra collected with a 785 nm laser excitation Raman spectroscopic system. In order to investigate the groupings of these samples, partial least squares discriminant analysis (PLSDA) and hierarchical cluster analysis (HCA) was implemented. In addition, cluster analyses of the isolates were performed using various data types consisting of, biochemical tests, gene sequence alignment, high resolution melt (HRM) analysis and antimicrobial susceptibility tests of minimum inhibitory concentration (MIC) and degree of antimicrobial resistance (SIR). In order to evaluate the ability of these models to correctly classify bacterial isolates using solely Raman spectroscopic data, a set of 14 validation samples were tested using the PLSDA models and consequently the HCA models. External cluster evaluation criteria of purity and Rand index were calculated at different taxonomic levels to compare the performance of clustering using Raman spectra as well as the other datasets. Results showed that Raman spectra performed comparably, and in some cases better than, the other data types with Rand index and purity values up to 0.933 and 0.947, respectively. This study clearly demonstrates that the discrimination of bacterial species using Raman spectroscopic data and hierarchical cluster analysis is possible and has the potential to be a powerful point-of-care tool in clinical settings.

Finding Groups Using Model-based Cluster Analysis: Heterogeneous Emotional Self-regulatory Processes and Heavy Alcohol Use Risk

PubMed Central

Mun, Eun-Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2010-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of non-nested models using the Bayesian Information Criterion (BIC) to compare multiple models and identify the optimum number of clusters. The current study clustered 36 young men and women based on their baseline heart rate (HR) and HR variability (HRV), chronic alcohol use, and reasons for drinking. Two cluster groups were identified and labeled High Alcohol Risk and Normative groups. Compared to the Normative group, individuals in the High Alcohol Risk group had higher levels of alcohol use and more strongly endorsed disinhibition and suppression reasons for use. The High Alcohol Risk group showed significant HRV changes in response to positive and negative emotional and appetitive picture cues, compared to neutral cues. In contrast, the Normative group showed a significant HRV change only to negative cues. Findings suggest that the individuals with autonomic self-regulatory difficulties may be more susceptible to heavy alcohol use and use alcohol for emotional regulation. PMID:18331138

Interaction of intense ultrashort pulse lasers with clusters.

NASA Astrophysics Data System (ADS)

Petrov, George

2007-11-01

The last ten years have witnessed an explosion of activity involving the interaction of clusters with intense ultrashort pulse lasers. Atomic or molecular clusters are targets with unique properties, as they are halfway between solid and gases. The intense laser radiation creates hot dense plasma, which can provide a compact source of x-rays and energetic particles. The focus of this investigation is to understand the salient features of energy absorption and Coulomb explosion by clusters. The evolution of clusters is modeled with a relativistic time-dependent 3D Molecular Dynamics (MD) model [1]. The Coulomb interaction between particles is handled by a fast tree algorithm, which allows large number of particles to be used in simulations [2]. The time histories of all particles in a cluster are followed in time and space. The model accounts for ionization-ignition effects (enhancement of the laser field in the vicinity of ions) and a variety of elementary processes for free electrons and charged ions, such as optical field and collisional ionization, outer ionization and electron recapture. The MD model was applied to study small clusters (1-20 nm) irradiated by a high-intensity (10^16-10^20 W/cm^2) sub-picosecond laser pulse. We studied fundamental cluster features such as energy absorption, x-ray emission, particle distribution, average charge per atom, and cluster explosion as a function of initial cluster radius, laser peak intensity and wavelength. Simulations of novel applications, such as table-top nuclear fusion from exploding deuterium clusters [3] and high power synchrotron radiation for biological applications and imaging [4] have been performed. The application for nuclear fusion was motivated by the efficient absorption of laser energy (˜100%) and its high conversion efficiency into ion kinetic energy (˜50%), resulting in neutron yield of 10^6 neutrons/Joule laser energy. Contributors: J. Davis and A. L. Velikovich. [1] G. M. Petrov, et al Phys. Plasmas 12 063103 (2005); 13 033106 (2006) [2] G. M. Petrov, J. Davis, European Phys. J. D 41 629 (2007) [3] G. M. Petrov, J. Davis, A. L. Velikovich, Plasma Phys. Contr. Fusion 48 1721 (2006) [4] G. M. Petrov, J. Davis, A. L. Velikovich, J. Phys. B 39 4617 (2006)

Self-Organizing Hidden Markov Model Map (SOHMMM): Biological Sequence Clustering and Cluster Visualization.

PubMed

Ferles, Christos; Beaufort, William-Scott; Ferle, Vanessa

2017-01-01

The present study devises mapping methodologies and projection techniques that visualize and demonstrate biological sequence data clustering results. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are depicted graphically. Both operate in combination/synergy with the Self-Organizing Hidden Markov Model Map (SOHMMM). The resulting unified framework is in position to analyze automatically and directly raw sequence data. This analysis is carried out with little, or even complete absence of, prior information/domain knowledge.

The [(AI 2O 3) 2] - Anion Cluster: Electron Localization-Delocalization Isomerism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sierka, Marek; Dobler, Jens; Sauer, Joachim

2009-10-05

Three-dimensional bulk alumina and its two-dimensional thin films show great structural diversity, posing considerable challenges to their experimental structural characterization and computational modeling. Recently, structural diversity has also been demonstrated for zerodimensional gas phase aluminum oxide clusters. Mass-selected clusters not only make systematic studies of the structural and electronic properties as a function of size possible, but lately have also emerged as powerful molecular models of complex surfaces and solid catalysts. In particular, the [(Al 2O 3) 3-5] + clusters were the first example of polynuclear maingroup metal oxide cluster that are able to thermally activate CH 4. Over themore » past decades gas phase aluminum oxide clusters have been extensively studied both experimentally and computationally, but definitive structural assignments were made for only a handful of them: the planar [Al 3O 3] - and [Al 5O 4] - cluster anions, and the [(Al 2O 3) 1-4(AlO)] + cluster cations. For stoichiometric clusters only the atomic structures of [(Al 2O 3) 4] +/0 have been nambiguously resolved. Here we report on the structures of the [(Al 2O 3) 2] -/0 clusters combining photoelectron spectroscopy (PES) and quantum chemical calculations employing a genetic algorithm as a global optimization technique. The [(Al 2O 3) 2] - cluster anion show energetically close lying but structurally distinct cage and sheet-like isomers which differ by delocalization/localization of the extra electron. The experimental results are crucial for benchmarking the different computational methods applied with respect to a proper description of electron localization and the relative energies for the isomers which will be of considerable value for future computational studies of aluminum oxide and related systems.« less

Posttraumatic Stress Disorder Symptom Clusters and the Interpersonal Theory of Suicide in a Large Military Sample.

PubMed

Pennings, Stephanie M; Finn, Joseph; Houtsma, Claire; Green, Bradley A; Anestis, Michael D

2017-10-01

Prior studies examining posttraumatic stress disorder (PTSD) symptom clusters and the components of the interpersonal theory of suicide (ITS) have yielded mixed results, likely stemming in part from the use of divergent samples and measurement techniques. This study aimed to expand on these findings by utilizing a large military sample, gold standard ITS measures, and multiple PTSD factor structures. Utilizing a sample of 935 military personnel, hierarchical multiple regression analyses were used to test the association between PTSD symptom clusters and the ITS variables. Additionally, we tested for indirect effects of PTSD symptom clusters on suicidal ideation through thwarted belongingness, conditional on levels of perceived burdensomeness. Results indicated that numbing symptoms are positively associated with both perceived burdensomeness and thwarted belongingness and hyperarousal symptoms (dysphoric arousal in the 5-factor model) are positively associated with thwarted belongingness. Results also indicated that hyperarousal symptoms (anxious arousal in the 5-factor model) were positively associated with fearlessness about death. The positive association between PTSD symptom clusters and suicidal ideation was inconsistent and modest, with mixed support for the ITS model. Overall, these results provide further clarity regarding the association between specific PTSD symptom clusters and suicide risk factors. © 2016 The American Association of Suicidology.

Study of baryon production mechanism in e+e- annihilation into hadrons

NASA Astrophysics Data System (ADS)

Topaz Collaboration; Aoki, M.; Itoh, R.; Watanabe, Y.; Kaneyuki, K.; Ohshima, Y.; Ochi, A.; Tanimori, T.; Abe, K.; Abe, T.; Adachi, I.; Adachi, K.; Aoki, M.; Emi, K.; Enomoto, R.; Fujii, H.; Fujii, T.; Fujii, K.; Fujimoto, J.; Fujiwara, N.; Hayashii, H.; Hirano, H.; Howell, B.; Ikeda, H.; Inoue, Y.; Itami, S.; Iwasaki, H.; Iwasaki, M.; Kajikawa, R.; Kato, S.; Kawabata, S.; Kichimi, H.; Kobayashi, M.; Koltick, D.; Levine, I.; Mamada, H.; Miyabayashi, K.; Miyamoto, A.; Nagai, K.; Nakabayashi, K.; Nakamura, M.; Nakano, E.; Nitoh, O.; Noguchi, S.; Ochiai, F.; Ohishi, N.; Ohnishi, Y.; Okuno, H.; Okusawa, T.; Shibata, E.; Sugiyama, A.; Suzuki, S.; Takahashi, K.; Takahashi, T.; Teramoto, Y.; Tauchi, T.; Tomoto, M.; Tsukamoto, T.; Tsumura, T.; Uno, S.; Yamamoto, A.; Yamauchi, M.

1998-11-01

The mechanism of baryon-anti-baryon pair production in e+e- annihilation into hadrons has been studied using the TOPAZ detector at the TRISTAN e+e- collider at an average center-of-mass energy of 58 GeV. The distributions of various p¯p correlations were compared with two prominent models: the cluster-fragmentation model and the string-fragmentation model. We rejected the cluster-fragmentation model at the 90% C.L. Furthermore, in the context of the string-fragmentation model, we favor the ``popcorn'' model, rejecting the ``diquark'' model, where a diquark is considered to be a fundamental entity, at the 95% C.L.

An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening

PubMed Central

2012-01-01

Background A discrete choice experiment (DCE) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks. Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar. In this study, we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer (CRC) screening tests conducted in Hamilton, Ontario, Canada in 2002. Methods A two-stage DCE design was used to investigate the impact of six attributes on participants' preferences for CRC screening test and willingness to undertake the test. We compared six models for clustered binary outcomes (logistic and probit regressions using cluster-robust standard error (SE), random-effects and generalized estimating equation approaches) and three models for clustered nominal outcomes (multinomial logistic and probit regressions with cluster-robust SE and random-effects multinomial logistic model). We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes. The rank of relative importance between attributes and the estimates of β coefficient within attributes were used to assess the model robustness. Results In total 468 participants with each completing 10 choices were analyzed. Similar results were reported for the rank of relative importance and β coefficients across models for stage-one data on evaluating participants' preferences for the test. The six attributes ranked from high to low as follows: cost, specificity, process, sensitivity, preparation and pain. However, the results differed across models for stage-two data on evaluating participants' willingness to undertake the tests. Little within-patient correlation (ICC ≈ 0) was found in stage-one data, but substantial within-patient correlation existed (ICC = 0.659) in stage-two data. Conclusions When small clustering effect presented in DCE data, results remained robust across statistical models. However, results varied when larger clustering effect presented. Therefore, it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies. PMID:22348526

Sponge Transgenic Mouse Model Reveals Important Roles for the MicroRNA-183 (miR-183)/96/182 Cluster in Postmitotic Photoreceptors of the Retina*

PubMed Central

Zhu, Qubo; Sun, Wenyu; Okano, Kiichiro; Chen, Yu; Zhang, Ning; Maeda, Tadao; Palczewski, Krzysztof

2011-01-01

MicroRNA-183 (miR-183), miR-96, and miR-182 comprising the miR-183/96/182 cluster are highly expressed in photoreceptor cells. Although in vitro data have indicated an important role for this cluster in the retina, details of its in vivo biological activity are still unknown. To observe the impact of the miR-183/96/182 cluster on retinal maintenance and light adaptation, we generated a sponge transgenic mouse model that disrupted the activities of the three-component microRNAs simultaneously and selectively in the retina. Although our morphological and functional studies showed no differences between transgenic and wild type mice under normal laboratory lighting conditions, sponge transgenic mice displayed severe retinal degeneration after 30 min of exposure to 10,000 lux light. Histological studies showed that the outer nuclear layer thickness was dramatically reduced in the superior retina of transgenic mice. Real time PCR experiments in both the sponge transgenic mouse model and different microRNA stable cell lines identified Arrdc3, Neurod4, and caspase-2 (Casp2) as probable downstream targets of this cluster, a result also supported by luciferase assay and immunoblotting analyses. Further studies indicated that expression of both the cluster and Casp2 increased in response to light exposure. Importantly, Casp2 expression was enhanced in transgenic mice, and inhibition of Casp2 partially rescued their light-induced retinal degeneration. By connecting the microRNA and apoptotic pathways, these findings imply an important role for the miR-183/96/182 cluster in acute light-induced retinal degeneration of mice. This study demonstrates a clear involvement of miRs in the physiology of postmitotic cells in vivo. PMID:21768104

Charging and hybridization in the finite cluster model

NASA Technical Reports Server (NTRS)

Bauschlicher, C. W., Jr.; Bagus, P. S.; Nelin, C. J.

1984-01-01

Cluster wavefunctions which have appropriate hybridization and polarization lead to reasonable properties for the interaction of an adsorbate with a solid surface. However, for Al clusters, it was found that the atomic change distribution is not uniform. The finite cluster size leads to changes not representative for an extended system. This effect appears to be dependent on the particular materials being studied; it does not occur in all cases.

Role of demographic stochasticity in a speciation model with sexual reproduction

NASA Astrophysics Data System (ADS)

Lafuerza, Luis F.; McKane, Alan J.

2016-03-01

Recent theoretical studies have shown that demographic stochasticity can greatly increase the tendency of asexually reproducing phenotypically diverse organisms to spontaneously evolve into localized clusters, suggesting a simple mechanism for sympatric speciation. Here we study the role of demographic stochasticity in a model of competing organisms subject to assortative mating. We find that in models with sexual reproduction, noise can also lead to the formation of phenotypic clusters in parameter ranges where deterministic models would lead to a homogeneous distribution. In some cases, noise can have a sizable effect, rendering the deterministic modeling insufficient to understand the phenotypic distribution.

Cancer detection based on Raman spectra super-paramagnetic clustering

NASA Astrophysics Data System (ADS)

González-Solís, José Luis; Guizar-Ruiz, Juan Ignacio; Martínez-Espinosa, Juan Carlos; Martínez-Zerega, Brenda Esmeralda; Juárez-López, Héctor Alfonso; Vargas-Rodríguez, Héctor; Gallegos-Infante, Luis Armando; González-Silva, Ricardo Armando; Espinoza-Padilla, Pedro Basilio; Palomares-Anda, Pascual

2016-08-01

The clustering of Raman spectra of serum sample is analyzed using the super-paramagnetic clustering technique based in the Potts spin model. We investigated the clustering of biochemical networks by using Raman data that define edge lengths in the network, and where the interactions are functions of the Raman spectra's individual band intensities. For this study, we used two groups of 58 and 102 control Raman spectra and the intensities of 160, 150 and 42 Raman spectra of serum samples from breast and cervical cancer and leukemia patients, respectively. The spectra were collected from patients from different hospitals from Mexico. By using super-paramagnetic clustering technique, we identified the most natural and compact clusters allowing us to discriminate the control and cancer patients. A special interest was the leukemia case where its nearly hierarchical observed structure allowed the identification of the patients's leukemia type. The goal of this study is to apply a model of statistical physics, as the super-paramagnetic, to find these natural clusters that allow us to design a cancer detection method. To the best of our knowledge, this is the first report of preliminary results evaluating the usefulness of super-paramagnetic clustering in the discipline of spectroscopy where it is used for classification of spectra.

CLUSTERING SOUTH AFRICAN HOUSEHOLDS BASED ON THEIR ASSET STATUS USING LATENT VARIABLE MODELS

PubMed Central

McParland, Damien; Gormley, Isobel Claire; McCormick, Tyler H.; Clark, Samuel J.; Kabudula, Chodziwadziwa Whiteson; Collinson, Mark A.

2014-01-01

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure—this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region. PMID:25485026

Growth of Ni nanoclusters on irradiated graphene: a molecular dynamics study.

PubMed

Valencia, F J; Hernandez-Vazquez, E E; Bringa, E M; Moran-Lopez, J L; Rogan, J; Gonzalez, R I; Munoz, F

2018-04-23

We studied the soft landing of Ni atoms on a previously damaged graphene sheet by means of molecular dynamics simulations. We found a monotonic decrease of the cluster frequency as a function of its size, but few big clusters comprise an appreciable fraction of the total number of Ni atoms. The aggregation of Ni atoms is also modeled by means of a simple phenomenological model. The results are in clear contrast with the case of hard or energetic landing of metal atoms, where there is a tendency to form mono-disperse metal clusters. This behavior is attributed to the high diffusion of unattached Ni atoms, together with vacancies acting as capture centers. The findings of this work show that a simple study of the energetics of the system is not enough in the soft landing regime, where it is unavoidable to also consider the growth process of metal clusters.

Simulating the Birth of Massive Star Clusters: Is Destruction Inevitable?

NASA Astrophysics Data System (ADS)

Rosen, Anna

2013-10-01

Very early in its operation, the Hubble Space Telescope {HST} opened an entirely new frontier: study of the demographics and properties of star clusters far beyond the Milky Way. However, interpretation of HST's observations has proven difficult, and has led to the development of two conflicting models. One view is that most massive star clusters are disrupted during their infancy by feedback from newly formed stars {i.e., "infant mortality"}, independent of cluster mass or environment. The other model is that most star clusters survive their infancy and are disrupted later by mass-dependent dynamical processes. Since observations at present have failed to discriminate between these views, we propose a theoretical investigation to provide new insight. We will perform radiation-hydrodynamic simulations of the formation of massive star clusters, including for the first time a realistic treatment of the most important stellar feedback processes. These simulations will elucidate the physics of stellar feedback, and allow us to determine whether cluster disruption is mass-dependent or -independent. We will also use our simulations to search for observational diagnostics that can distinguish bound from unbound clusters, and to predict how cluster disruption affects the cluster luminosity function in a variety of galactic environments.

The shape of galaxy dark matter halos in massive galaxy clusters: Insights from strong gravitational lensing

NASA Astrophysics Data System (ADS)

Jauzac, Mathilde; Harvey, David; Massey, Richard

2018-04-01

We assess how much unused strong lensing information is available in the deep Hubble Space Telescope imaging and VLT/MUSE spectroscopy of the Frontier Field clusters. As a pilot study, we analyse galaxy cluster MACS J0416.1-2403 (z=0.397, M(R < 200 kpc)=1.6×1014M⊙), which has 141 multiple images with spectroscopic redshifts. We find that many additional parameters in a cluster mass model can be constrained, and that adding even small amounts of extra freedom to a model can dramatically improve its figures of merit. We use this information to constrain the distribution of dark matter around cluster member galaxies, simultaneously with the cluster's large-scale mass distribution. We find tentative evidence that some galaxies' dark matter has surprisingly similar ellipticity to their stars (unlike in the field, where it is more spherical), but that its orientation is often misaligned. When non-coincident dark matter and stellar halos are allowed, the model improves by 35%. This technique may provide a new way to investigate the processes and timescales on which dark matter is stripped from galaxies as they fall into a massive cluster. Our preliminary conclusions will be made more robust by analysing the remaining five Frontier Field clusters.

The shape of galaxy dark matter haloes in massive galaxy clusters: insights from strong gravitational lensing

NASA Astrophysics Data System (ADS)

Jauzac, Mathilde; Harvey, David; Massey, Richard

2018-07-01

We assess how much unused strong lensing information is available in the deep Hubble Space Telescope imaging and Very Large Telescope/Multi Unit Spectroscopic Explorer spectroscopy of the Frontier Field clusters. As a pilot study, we analyse galaxy cluster MACS J0416.1-2403 (z = 0.397, M(R < 200 kpc) = 1.6 × 1014 M⊙), which has 141 multiple images with spectroscopic redshifts. We find that many additional parameters in a cluster mass model can be constrained, and that adding even small amounts of extra freedom to a model can dramatically improve its figures of merit. We use this information to constrain the distribution of dark matter around cluster member galaxies, simultaneously with the cluster's large-scale mass distribution. We find tentative evidence that some galaxies' dark matter has surprisingly similar ellipticity to their stars (unlike in the field, where it is more spherical), but that its orientation is often misaligned. When non-coincident dark matter and stellar haloes are allowed, the model improves by 35 per cent. This technique may provide a new way to investigate the processes and time-scales on which dark matter is stripped from galaxies as they fall into a massive cluster. Our preliminary conclusions will be made more robust by analysing the remaining five Frontier Field clusters.

Mid-infrared Integrated-light Photometry Of LMC Star Clusters

NASA Astrophysics Data System (ADS)

Pessev, Peter; Goudfrooij, P.; Puzia, T.; Chandar, R.

2008-03-01

Massive star clusters (Galactic Globular Clusters and Populous Clusters in the Magellanic Clouds) are the best available approximation of Simple Stellar Populations (SSPs). Since the stellar populations in these nearby objects are studied in details, they provide fundamental age/metallicity templates for interpretation of the galaxy properties, testing and calibration of the SSP Models. Magellanic Cloud clusters are particularly important since they populate a region of the age/metallicity parameter space that is not easily accessible in our Galaxy. We present the first Mid-IR integrated-light measurements for six LMC clusters based on our Spitzer IRAC imaging program. Since we are targeting a specific group of intermediate-age clusters, our imaging goes deeper compared to SAGE-LMC survey data. We present a literature compilation of clusters' properties along with multi-wavelength integrated light photometry database spanning from the optical (Johnson U band) to the Mid-IR (IRAC Channel 4). This data provides an important empirical baseline for the interpretation of galaxy colors in the Mid-IR (especially high-z objects whose integrated-light is dominated by TP-AGB stars emission). It is also a valuable tool to check the SSP model predictions in the intermediate-age regime and provides calibration data for the next generation of SSP models.

To center or not to center? Investigating inertia with a multilevel autoregressive model.

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P

2014-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.

To center or not to center? Investigating inertia with a multilevel autoregressive model

PubMed Central

Hamaker, Ellen L.; Grasman, Raoul P. P. P.

2015-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model. PMID:25688215

Bias and inference from misspecified mixed-effect models in stepped wedge trial analysis.

PubMed

Thompson, Jennifer A; Fielding, Katherine L; Davey, Calum; Aiken, Alexander M; Hargreaves, James R; Hayes, Richard J

2017-10-15

Many stepped wedge trials (SWTs) are analysed by using a mixed-effect model with a random intercept and fixed effects for the intervention and time periods (referred to here as the standard model). However, it is not known whether this model is robust to misspecification. We simulated SWTs with three groups of clusters and two time periods; one group received the intervention during the first period and two groups in the second period. We simulated period and intervention effects that were either common-to-all or varied-between clusters. Data were analysed with the standard model or with additional random effects for period effect or intervention effect. In a second simulation study, we explored the weight given to within-cluster comparisons by simulating a larger intervention effect in the group of the trial that experienced both the control and intervention conditions and applying the three analysis models described previously. Across 500 simulations, we computed bias and confidence interval coverage of the estimated intervention effect. We found up to 50% bias in intervention effect estimates when period or intervention effects varied between clusters and were treated as fixed effects in the analysis. All misspecified models showed undercoverage of 95% confidence intervals, particularly the standard model. A large weight was given to within-cluster comparisons in the standard model. In the SWTs simulated here, mixed-effect models were highly sensitive to departures from the model assumptions, which can be explained by the high dependence on within-cluster comparisons. Trialists should consider including a random effect for time period in their SWT analysis model. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Bias and inference from misspecified mixed‐effect models in stepped wedge trial analysis

PubMed Central

Fielding, Katherine L.; Davey, Calum; Aiken, Alexander M.; Hargreaves, James R.; Hayes, Richard J.

2017-01-01

Many stepped wedge trials (SWTs) are analysed by using a mixed‐effect model with a random intercept and fixed effects for the intervention and time periods (referred to here as the standard model). However, it is not known whether this model is robust to misspecification. We simulated SWTs with three groups of clusters and two time periods; one group received the intervention during the first period and two groups in the second period. We simulated period and intervention effects that were either common‐to‐all or varied‐between clusters. Data were analysed with the standard model or with additional random effects for period effect or intervention effect. In a second simulation study, we explored the weight given to within‐cluster comparisons by simulating a larger intervention effect in the group of the trial that experienced both the control and intervention conditions and applying the three analysis models described previously. Across 500 simulations, we computed bias and confidence interval coverage of the estimated intervention effect. We found up to 50% bias in intervention effect estimates when period or intervention effects varied between clusters and were treated as fixed effects in the analysis. All misspecified models showed undercoverage of 95% confidence intervals, particularly the standard model. A large weight was given to within‐cluster comparisons in the standard model. In the SWTs simulated here, mixed‐effect models were highly sensitive to departures from the model assumptions, which can be explained by the high dependence on within‐cluster comparisons. Trialists should consider including a random effect for time period in their SWT analysis model. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28556355

Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome.

PubMed

Morgan, Katy E; Forbes, Andrew B; Keogh, Ruth H; Jairath, Vipul; Kahan, Brennan C

2017-01-30

In cluster randomised cross-over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two-period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period-within-cluster, which do not account for any extra within-period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within-period correlation was present, a hierarchical model with random effects for cluster and period-within-cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster-level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within-period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within-period correlation should be accounted for. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The Development of a Model for Estimating the Costs Associated with the Delivery of a Metals Cluster Program.

ERIC Educational Resources Information Center

Hunt, Charles R.

A study developed a model to assist school administrators to estimate costs associated with the delivery of a metals cluster program at Norfolk State College, Virginia. It sought to construct the model so that costs could be explained as a function of enrollment levels. Data were collected through a literature review, computer searches of the…

Interpreting semantic clustering effects in free recall.

PubMed

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.

Metal Cluster Models for Heterogeneous Catalysis: A Matrix-Isolation Perspective.

PubMed

Hübner, Olaf; Himmel, Hans-Jörg

2018-02-19

Metal cluster models are of high relevance for establishing new mechanistic concepts for heterogeneous catalysis. The high reactivity and particular selectivity of metal clusters is caused by the wealth of low-lying electronically excited states that are often thermally populated. Thereby the metal clusters are flexible with regard to their electronic structure and can adjust their states to be appropriate for the reaction with a particular substrate. The matrix isolation technique is ideally suited for studying excited state reactivity. The low matrix temperatures (generally 4-40 K) of the noble gas matrix host guarantee that all clusters are in their electronic ground-state (with only a very few exceptions). Electronically excited states can then be selectively populated and their reactivity probed. Unfortunately, a systematic research in this direction has not been made up to date. The purpose of this review is to provide the grounds for a directed approach to understand cluster reactivity through matrix-isolation studies combined with quantum chemical calculations. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Random phase approximation and cluster mean field studies of hard core Bose Hubbard model

NASA Astrophysics Data System (ADS)

Alavani, Bhargav K.; Gaude, Pallavi P.; Pai, Ramesh V.

2018-04-01

We investigate zero temperature and finite temperature properties of the Bose Hubbard Model in the hard core limit using Random Phase Approximation (RPA) and Cluster Mean Field Theory (CMFT). We show that our RPA calculations are able to capture quantum and thermal fluctuations significantly better than CMFT.

Mindfulness-Based Stress Reduction in Post-treatment Breast Cancer Patients: Immediate and Sustained Effects Across Multiple Symptom Clusters.

PubMed

Reich, Richard R; Lengacher, Cecile A; Alinat, Carissa B; Kip, Kevin E; Paterson, Carly; Ramesar, Sophia; Han, Heather S; Ismail-Khan, Roohi; Johnson-Mallard, Versie; Moscoso, Manolete; Budhrani-Shani, Pinky; Shivers, Steve; Cox, Charles E; Goodman, Matthew; Park, Jong

2017-01-01

Breast cancer survivors (BCS) face adverse physical and psychological symptoms, often co-occurring. Biologic and psychological factors may link symptoms within clusters, distinguishable by prevalence and/or severity. Few studies have examined the effects of behavioral interventions or treatment of symptom clusters. The aim of this study was to identify symptom clusters among post-treatment BCS and determine symptom cluster improvement following the Mindfulness-Based Stress Reduction for Breast Cancer (MBSR(BC)) program. Three hundred twenty-two Stage 0-III post-treatment BCS were randomly assigned to either a six-week MBSR(BC) program or usual care. Psychological (depression, anxiety, stress, and fear of recurrence), physical (fatigue, pain, sleep, and drowsiness), and cognitive symptoms and quality of life were assessed at baseline, six, and 12 weeks, along with demographic and clinical history data at baseline. A three-step analytic process included the error-accounting models of factor analysis and structural equation modeling. Four symptom clusters emerged at baseline: pain, psychological, fatigue, and cognitive. From baseline to six weeks, the model demonstrated evidence of MBSR(BC) effectiveness in both the psychological (anxiety, depression, perceived stress and QOL, emotional well-being) (P = 0.007) and fatigue (fatigue, sleep, and drowsiness) (P < 0.001) clusters. Results between six and 12 weeks showed sustained effects, but further improvement was not observed. Our results provide clinical effectiveness evidence that MBSR(BC) works to improve symptom clusters, particularly for psychological and fatigue symptom clusters, with the greatest improvement occurring during the six-week program with sustained effects for several weeks after MBSR(BC) training. Name and URL of Registry: ClinicalTrials.gov. Registration number: NCT01177124. Copyright © 2016. Published by Elsevier Inc.

Ages of intermediate-age Magellanic Cloud star clusters

NASA Technical Reports Server (NTRS)

Flower, P. J.

1984-01-01

Ages of intermediate-age Large Magellanic Cloud star clusters have been estimated without locating the faint, unevolved portion of cluster main sequences. Six clusters with established color-magnitude diagrams were selected for study: SL 868, NGC 1783, NGC 1868, NGC 2121, NGC 2209, and NGC 2231. Since red giant photometry is more accurate than the necessarily fainter main-sequence photometry, the distributions of red giants on the cluster color-magnitude diagrams were compared to a grid of 33 stellar evolutionary tracks, evolved from the main sequence through core-helium exhaustion, spanning the expected mass and metallicity range for Magellanic Cloud cluster red giants. The time-dependent behavior of the luminosity of the model red giants was used to estimate cluster ages from the observed cluster red giant luminosities. Except for the possibility of SL 868 being an old globular cluster, all clusters studied were found to have ages less than 10 to the 9th yr. It is concluded that there is currently no substantial evidence for a major cluster population of large, populous clusters greater than 10 to the 9th yr old in the Large Magellanic Cloud.

Dynamical Formation of Low-mass Merging Black Hole Binaries like GW151226

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatterjee, Sourav; Rodriguez, Carl L.; Kalogera, Vicky

2017-02-20

Using numerical models for star clusters spanning a wide range in ages and metallicities (Z) we study the masses of binary black holes (BBHs) produced dynamically and merging in the local universe ( z ≲ 0.2). After taking into account cosmological constraints on star formation rate and metallicity evolution, which realistically relate merger delay times obtained from models with merger redshifts, we show here for the first time that while old, metal-poor globular clusters can naturally produce merging BBHs with heavier components, as observed in GW150914, lower-mass BBHs like GW151226 are easily formed dynamically in younger, higher-metallicity clusters. More specifically,more » we show that the mass of GW151226 is well within 1 σ of the mass distribution obtained from our models for clusters with Z/Z{sub ⊙} ≳ 0.5. Indeed, dynamical formation of a system like GW151226 likely requires a cluster that is younger and has a higher metallicity than typical Galactic globular clusters. The LVT151012 system, if real, could have been created in any cluster with Z/Z{sub ⊙} ≲ 0.25. On the other hand, GW150914 is more massive (beyond 1 σ ) than typical BBHs from even the lowest-metallicity (Z/Z{sub ⊙} = 0.005) clusters we consider, but is within 2 σ of the intrinsic mass distribution from our cluster models with Z/Z{sub ⊙} ≲ 0.05; of course, detection biases also push the observed distributions toward higher masses.« less

[Prognostic differences of phenotypes in pT1-2N0 invasive breast cancer: a large cohort study with cluster analysis].

PubMed

Wang, Z; Wang, W H; Wang, S L; Jin, J; Song, Y W; Liu, Y P; Ren, H; Fang, H; Tang, Y; Chen, B; Qi, S N; Lu, N N; Li, N; Tang, Y; Liu, X F; Yu, Z H; Li, Y X

2016-06-23

To find phenotypic subgroups of patients with pT1-2N0 invasive breast cancer by means of cluster analysis and estimate the prognosis and clinicopathological features of these subgroups. From 1999 to 2013, 4979 patients with pT1-2N0 invasive breast cancer were recruited for hierarchical clustering analysis. Age (≤40, 41-70, 70+ years), size of primary tumor, pathological type, grade of differentiation, microvascular invasion, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER-2) were chosen as distance metric between patients. Hierarchical cluster analysis was performed using Ward's method. Cophenetic correlation coefficient (CPCC) and Spearman correlation coefficient were used to validate clustering structures. The CPCC was 0.603. The Spearman correlation coefficient was 0.617 (P<0.001), which indicated a good fit of hierarchy to the data. A twelve-cluster model seemed to best illustrate our patient cohort. Patients in cluster 5, 9 and 12 had best prognosis and were characterized by age >40 years, smaller primary tumor, lower histologic grade, positive ER and PR status, and mainly negative HER-2. Patients in the cluster 1 and 11 had the worst prognosis, The cluster 1 was characterized by a larger tumor, higher grade and negative ER and PR status, while the cluster 11 was characterized by positive microvascular invasion. Patients in other 7 clusters had a moderate prognosis, and patients in each cluster had distinctive clinicopathological features and recurrent patterns. This study identified distinctive clinicopathologic phenotypes in a large cohort of patients with pT1-2N0 breast cancer through hierarchical clustering and revealed different prognosis. This integrative model may help physicians to make more personalized decisions regarding adjuvant therapy.

Running climate model on a commercial cloud computing environment: A case study using Community Earth System Model (CESM) on Amazon AWS

NASA Astrophysics Data System (ADS)

Chen, Xiuhong; Huang, Xianglei; Jiao, Chaoyi; Flanner, Mark G.; Raeker, Todd; Palen, Brock

2017-01-01

The suites of numerical models used for simulating climate of our planet are usually run on dedicated high-performance computing (HPC) resources. This study investigates an alternative to the usual approach, i.e. carrying out climate model simulations on commercially available cloud computing environment. We test the performance and reliability of running the CESM (Community Earth System Model), a flagship climate model in the United States developed by the National Center for Atmospheric Research (NCAR), on Amazon Web Service (AWS) EC2, the cloud computing environment by Amazon.com, Inc. StarCluster is used to create virtual computing cluster on the AWS EC2 for the CESM simulations. The wall-clock time for one year of CESM simulation on the AWS EC2 virtual cluster is comparable to the time spent for the same simulation on a local dedicated high-performance computing cluster with InfiniBand connections. The CESM simulation can be efficiently scaled with the number of CPU cores on the AWS EC2 virtual cluster environment up to 64 cores. For the standard configuration of the CESM at a spatial resolution of 1.9° latitude by 2.5° longitude, increasing the number of cores from 16 to 64 reduces the wall-clock running time by more than 50% and the scaling is nearly linear. Beyond 64 cores, the communication latency starts to outweigh the benefit of distributed computing and the parallel speedup becomes nearly unchanged.

Implementation of Self Organizing Map (SOM) as decision support: Indonesian telematics services MSMEs empowerment

NASA Astrophysics Data System (ADS)

Tosida, E. T.; Maryana, S.; Thaheer, H.; Hardiani

2017-01-01

Information technology and communication (telematics) is one of the most rapidly developing business sectors in Indonesia. It has strategic position in its contribution towards planning and implementation of developmental, economics, social, politics and defence strategies in business, communication and education. Aid absorption for the national telecommunication SMEs is relatively low; therefore, improvement is needed using analysis on business support cluster of which basis is types of business. In the study, the business support cluster analysis is specifically implemented for Indonesian telecommunication service. The data for the business are obtained from the National Census of Economic (Susenas 2006). The method used to develop cluster model is an Artificial Neural Network (ANN) system called Self-Organizing Maps (SOM) algorithm. Based on Index of Davies Bouldin (IDB), the accuracy level of the cluster model is 0.37 or can be categorized as good. The cluster model is developed to find out telecommunication business clusters that has influence towards the national economy so that it is easier for the government to supervise telecommunication business.

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

PubMed Central

Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic. PMID:28245222

An Approach to Cluster EU Member States into Groups According to Pathways of Salmonella in the Farm-to-Consumption Chain for Pork Products.

PubMed

Vigre, Håkan; Domingues, Ana Rita Coutinho Calado; Pedersen, Ulrik Bo; Hald, Tine

2016-03-01

The aim of the project as the cluster analysis was to in part to develop a generic structured quantitative microbiological risk assessment (QMRA) model of human salmonellosis due to pork consumption in EU member states (MSs), and the objective of the cluster analysis was to group the EU MSs according to the relative contribution of different pathways of Salmonella in the farm-to-consumption chain of pork products. In the development of the model, by selecting a case study MS from each cluster the model was developed to represent different aspects of pig production, pork production, and consumption of pork products across EU states. The objective of the cluster analysis was to aggregate MSs into groups of countries with similar importance of different pathways of Salmonella in the farm-to-consumption chain using available, and where possible, universal register data related to the pork production and consumption in each country. Based on MS-specific information about distribution of (i) small and large farms, (ii) small and large slaughterhouses, (iii) amount of pork meat consumed, and (iv) amount of sausages consumed we used nonhierarchical and hierarchical cluster analysis to group the MSs. The cluster solutions were validated internally using statistic measures and externally by comparing the clustered MSs with an estimated human incidence of salmonellosis due to pork products in the MSs. Finally, each cluster was characterized qualitatively using the centroids of the clusters. © 2016 Society for Risk Analysis.

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks.

PubMed

Wu, Jibing; Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.

A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

PubMed Central

Peng, Yi; Zhang, Yong; Kou, Gang; Shi, Yong

2012-01-01

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study. PMID:22870181

Outcome-Driven Cluster Analysis with Application to Microarray Data.

PubMed

Hsu, Jessie J; Finkelstein, Dianne M; Schoenfeld, David A

2015-01-01

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.

Deriving physical parameters of unresolved star clusters. V. M 31 PHAT star clusters

NASA Astrophysics Data System (ADS)

de Meulenaer, P.; Stonkutė, R.; Vansevičius, V.

2017-06-01

Context. This study is the fifth of a series that investigates the degeneracy and stochasticity problems present in the determination of physical parameters such as age, mass, extinction, and metallicity of partially resolved or unresolved star cluster populations in external galaxies when using HST broad-band photometry. Aims: In this work we aim to derive parameters of star clusters using models with fixed and free metallicity based on the HST WFC3+ACS photometric system. The method is applied to derive parameters of a subsample of 1363 star clusters in the Andromeda galaxy observed with the HST. Methods: Following Paper III, we derive the star cluster parameters using a large grid of stochastic models that are compared to the six observed integrated broad-band WFC3+ACS magnitudes of star clusters. Results: We show that the age, mass, and extinction of the M 31 star clusters, derived assuming fixed solar metallicity, are in agreement with previous studies. We also demonstrate the ability of the WFC3+ACS photometric system to derive metallicity of star clusters older than 1 Gyr. We show that the metallicity derived using broad-band photometry of 36 massive M 31 star clusters is in good agreement with the metallicity derived using spectroscopy. Table 1 is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/602/A112

A First Estimate of the X-Ray Binary Frequency as a Function of Star Cluster Mass in a Single Galactic System

NASA Astrophysics Data System (ADS)

Clark, D. M.; Eikenberry, S. S.; Brandl, B. R.; Wilson, J. C.; Carson, J. C.; Henderson, C. P.; Hayward, T. L.; Barry, D. J.; Ptak, A. F.; Colbert, E. J. M.

2008-05-01

We use the previously identified 15 infrared star cluster counterparts to X-ray point sources in the interacting galaxies NGC 4038/4039 (the Antennae) to study the relationship between total cluster mass and X-ray binary number. This significant population of X-Ray/IR associations allows us to perform, for the first time, a statistical study of X-ray point sources and their environments. We define a quantity, η, relating the fraction of X-ray sources per unit mass as a function of cluster mass in the Antennae. We compute cluster mass by fitting spectral evolutionary models to Ks luminosity. Considering that this method depends on cluster age, we use four different age distributions to explore the effects of cluster age on the value of η and find it varies by less than a factor of 4. We find a mean value of η for these different distributions of η = 1.7 × 10-8 M-1⊙ with ση = 1.2 × 10-8 M-1⊙. Performing a χ2 test, we demonstrate η could exhibit a positive slope, but that it depends on the assumed distribution in cluster ages. While the estimated uncertainties in η are factors of a few, we believe this is the first estimate made of this quantity to "order of magnitude" accuracy. We also compare our findings to theoretical models of open and globular cluster evolution, incorporating the X-ray binary fraction per cluster.

Next Generation Virgo Cluster Survey. XXI. The Weak Lensing Masses of the CFHTLS and NGVS RedGOLD Galaxy Clusters and Calibration of the Optical Richness

NASA Astrophysics Data System (ADS)

Parroni, Carolina; Mei, Simona; Erben, Thomas; Van Waerbeke, Ludovic; Raichoor, Anand; Ford, Jes; Licitra, Rossella; Meneghetti, Massimo; Hildebrandt, Hendrik; Miller, Lance; Côté, Patrick; Covone, Giovanni; Cuillandre, Jean-Charles; Duc, Pierre-Alain; Ferrarese, Laura; Gwyn, Stephen D. J.; Puzia, Thomas H.

2017-10-01

We measured stacked weak lensing cluster masses for a sample of 1323 galaxy clusters detected by the RedGOLD algorithm in the Canada-France-Hawaii Telescope Legacy Survey W1 and the Next Generation Virgo Cluster Survey at 0.2< z< 0.5, in the optical richness range 10< λ < 70. This is the most comprehensive lensing study of a ˜ 100 % complete and ˜ 80 % pure optical cluster catalog in this redshift range. We test different mass models, and our final model includes a basic halo model with a Navarro Frenk and White profile, as well as correction terms that take into account cluster miscentering, non-weak shear, the two-halo term, the contribution of the Brightest Cluster Galaxy, and an a posteriori correction for the intrinsic scatter in the mass-richness relation. With this model, we obtain a mass-richness relation of {log}{M}200/{M}⊙ =(14.46+/- 0.02)+(1.04+/- 0.09){log}(λ /40) (statistical uncertainties). This result is consistent with other published lensing mass-richness relations. We give the coefficients of the scaling relations between the lensing mass and X-ray mass proxies, L X and T X, and compare them with previous results. When compared to X-ray masses and mass proxies, our results are in agreement with most previous results and simulations, and consistent with the expected deviations from self-similarity.

A quasi-static approach to structure formation in black hole universes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Durk, Jessie; Clifton, Timothy, E-mail: j.durk@qmul.ac.uk, E-mail: t.clifton@qmul.ac.uk

Motivated by the existence of hierarchies of structure in the Universe, we present four new families of exact initial data for inhomogeneous cosmological models at their maximum of expansion. These data generalise existing black hole lattice models to situations that contain clusters of masses, and hence allow the consequences of cosmological structures to be considered in a well-defined and non-perturbative fashion. The degree of clustering is controlled by a parameter λ, in such a way that for λ ∼ 0 or 1 we have very tightly clustered masses, whilst for λ ∼ 0.5 all masses are separated by cosmological distancemore » scales. We study the consequences of structure formation on the total net mass in each of our clusters, as well as calculating the cosmological consequences of the interaction energies both within and between clusters. The locations of the shared horizons that appear around groups of black holes, when they are brought sufficiently close together, are also identified and studied. We find that clustering can have surprisingly large effects on the scale of the cosmology, with models that contain thousands of black holes sometimes being as little as 30% of the size of comparable Friedmann models with the same total proper mass. This deficit is comparable to what might be expected to occur from neglecting gravitational interaction energies in Friedmann cosmology, and suggests that these quantities may have a significant influence on the properties of the large-scale cosmology.« less

Cold Fronts in Clusters of Galaxies: Observations and Modeling

NASA Technical Reports Server (NTRS)

Markevitch, Maxim

2012-01-01

Mergers of galaxy clusters -- some of the most energetic events in the Universe -- produce disturbances in hot intracluster medium, such as shocks and cold fronts, that can be used as tools to study the physics of galaxy clusters. Cold fronts may constrain viscosity and the structure and strength of the cluster magnetic fields. Combined with radio data, these observations also shed light on the production of ultrarelativistic particles that are known to coexist with the cluster thermal plasma. This talk will summarize the current X-ray observations of cluster mergers, as well as some recent radio data and high resolution hydrodynamic simulations.

Brownian model of transcriptome evolution and phylogenetic network visualization between tissues.

PubMed

Gu, Xun; Ruan, Hang; Su, Zhixi; Zou, Yangyun

2017-09-01

While phylogenetic analysis of transcriptomes of the same tissue is usually congruent with the species tree, the controversy emerges when multiple tissues are included, that is, whether species from the same tissue are clustered together, or different tissues from the same species are clustered together. Recent studies have suggested that phylogenetic network approach may shed some lights on our understanding of multi-tissue transcriptome evolution; yet the underlying evolutionary mechanism remains unclear. In this paper we develop a Brownian-based model of transcriptome evolution under the phylogenetic network that can statistically distinguish between the patterns of species-clustering and tissue-clustering. Our model can be used as a null hypothesis (neutral transcriptome evolution) for testing any correlation in tissue evolution, can be applied to cancer transcriptome evolution to study whether two tumors of an individual appeared independently or via metastasis, and can be useful to detect convergent evolution at the transcriptional level. Copyright © 2017. Published by Elsevier Inc.

Percolation of the site random-cluster model by Monte Carlo method

NASA Astrophysics Data System (ADS)

Wang, Songsong; Zhang, Wanzhou; Ding, Chengxiang

2015-08-01

We propose a site random-cluster model by introducing an additional cluster weight in the partition function of the traditional site percolation. To simulate the model on a square lattice, we combine the color-assignation and the Swendsen-Wang methods to design a highly efficient cluster algorithm with a small critical slowing-down phenomenon. To verify whether or not it is consistent with the bond random-cluster model, we measure several quantities, such as the wrapping probability Re, the percolating cluster density P∞, and the magnetic susceptibility per site χp, as well as two exponents, such as the thermal exponent yt and the fractal dimension yh of the percolating cluster. We find that for different exponents of cluster weight q =1.5 , 2, 2.5 , 3, 3.5 , and 4, the numerical estimation of the exponents yt and yh are consistent with the theoretical values. The universalities of the site random-cluster model and the bond random-cluster model are completely identical. For larger values of q , we find obvious signatures of the first-order percolation transition by the histograms and the hysteresis loops of percolating cluster density and the energy per site. Our results are helpful for the understanding of the percolation of traditional statistical models.

Cluster randomised crossover trials with binary data and unbalanced cluster sizes: application to studies of near-universal interventions in intensive care.

PubMed

Forbes, Andrew B; Akram, Muhammad; Pilcher, David; Cooper, Jamie; Bellomo, Rinaldo

2015-02-01

Cluster randomised crossover trials have been utilised in recent years in the health and social sciences. Methods for analysis have been proposed; however, for binary outcomes, these have received little assessment of their appropriateness. In addition, methods for determination of sample size are currently limited to balanced cluster sizes both between clusters and between periods within clusters. This article aims to extend this work to unbalanced situations and to evaluate the properties of a variety of methods for analysis of binary data, with a particular focus on the setting of potential trials of near-universal interventions in intensive care to reduce in-hospital mortality. We derive a formula for sample size estimation for unbalanced cluster sizes, and apply it to the intensive care setting to demonstrate the utility of the cluster crossover design. We conduct a numerical simulation of the design in the intensive care setting and for more general configurations, and we assess the performance of three cluster summary estimators and an individual-data estimator based on binomial-identity-link regression. For settings similar to the intensive care scenario involving large cluster sizes and small intra-cluster correlations, the sample size formulae developed and analysis methods investigated are found to be appropriate, with the unweighted cluster summary method performing well relative to the more optimal but more complex inverse-variance weighted method. More generally, we find that the unweighted and cluster-size-weighted summary methods perform well, with the relative efficiency of each largely determined systematically from the study design parameters. Performance of individual-data regression is adequate with small cluster sizes but becomes inefficient for large, unbalanced cluster sizes. When outcome prevalences are 6% or less and the within-cluster-within-period correlation is 0.05 or larger, all methods display sub-nominal confidence interval coverage, with the less prevalent the outcome the worse the coverage. As with all simulation studies, conclusions are limited to the configurations studied. We confined attention to detecting intervention effects on an absolute risk scale using marginal models and did not explore properties of binary random effects models. Cluster crossover designs with binary outcomes can be analysed using simple cluster summary methods, and sample size in unbalanced cluster size settings can be determined using relatively straightforward formulae. However, caution needs to be applied in situations with low prevalence outcomes and moderate to high intra-cluster correlations. © The Author(s) 2014.

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

NASA Astrophysics Data System (ADS)

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.

A clustering algorithm for sample data based on environmental pollution characteristics

NASA Astrophysics Data System (ADS)

Chen, Mei; Wang, Pengfei; Chen, Qiang; Wu, Jiadong; Chen, Xiaoyun

2015-04-01

Environmental pollution has become an issue of serious international concern in recent years. Among the receptor-oriented pollution models, CMB, PMF, UNMIX, and PCA are widely used as source apportionment models. To improve the accuracy of source apportionment and classify the sample data for these models, this study proposes an easy-to-use, high-dimensional EPC algorithm that not only organizes all of the sample data into different groups according to the similarities in pollution characteristics such as pollution sources and concentrations but also simultaneously detects outliers. The main clustering process consists of selecting the first unlabelled point as the cluster centre, then assigning each data point in the sample dataset to its most similar cluster centre according to both the user-defined threshold and the value of similarity function in each iteration, and finally modifying the clusters using a method similar to k-Means. The validity and accuracy of the algorithm are tested using both real and synthetic datasets, which makes the EPC algorithm practical and effective for appropriately classifying sample data for source apportionment models and helpful for better understanding and interpreting the sources of pollution.

Breast cancer and symptom clusters during radiotherapy.

PubMed

Matthews, Ellyn E; Schmiege, Sarah J; Cook, Paul F; Sousa, Karen H

2012-01-01

Symptom clusters assessment shifts the clinical focus from a specific symptom to the patient's experience as a whole. Few studies have examined breast cancer symptom clusters during treatment, and fewer studies have addressed symptom clusters during radiation therapy (RT). The theoretical underpinning of this study is the Symptoms Experience Model. Research is needed to identify antecedents and consequences of cancer-related symptom clusters. The present study was intended to determine the clustering of symptoms during RT in women with breast cancer and significant correlations among the symptoms, individual characteristics, and mood. A secondary data analysis from a descriptive correlational study of 93 women at weeks 3 to 7 of RT from centers in the mid-Atlantic region of the United States, Symptom Distress Scale, the subscales of the Positive and Negative Affect Scale, Life Orientation Test, and Self-transcendence Scale were completed. Confirmatory factor analysis revealed symptoms grouped into 3 distinct clusters: pain-insomnia-fatigue, cognitive disturbance-outlook, and gastrointestinal. The pain-insomnia-fatigue and cognitive disturbance-outlook clusters were associated with individual characteristics, optimism, self-transcendence, and positive and negative mood. The gastrointestinal cluster correlated significantly only with positive mood. This study provides insight into symptoms that group together and the relationship of symptom clusters to antecedents and mood. These findings underscore the need to define and standardize the measurement of symptom clusters and understand variability in concurrent symptoms. Attention to symptom clusters shifts the clinical focus from a specific symptom to the patient's experience as a whole and helps identify the most effective interventions.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

PubMed Central

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565

Analyzing Patients' Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

PubMed Central

Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs. PMID:25045741

Analyzing patients' values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan.

PubMed

Wu, Hsin-Hung; Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs.

Control of clustered action potential firing in a mathematical model of entorhinal cortex stellate cells.

PubMed

Tait, Luke; Wedgwood, Kyle; Tsaneva-Atanasova, Krasimira; Brown, Jon T; Goodfellow, Marc

2018-07-14

The entorhinal cortex is a crucial component of our memory and spatial navigation systems and is one of the first areas to be affected in dementias featuring tau pathology, such as Alzheimer's disease and frontotemporal dementia. Electrophysiological recordings from principle cells of medial entorhinal cortex (layer II stellate cells, mEC-SCs) demonstrate a number of key identifying properties including subthreshold oscillations in the theta (4-12 Hz) range and clustered action potential firing. These single cell properties are correlated with network activity such as grid firing and coupling between theta and gamma rhythms, suggesting they are important for spatial memory. As such, experimental models of dementia have revealed disruption of organised dorsoventral gradients in clustered action potential firing. To better understand the mechanisms underpinning these different dynamics, we study a conductance based model of mEC-SCs. We demonstrate that the model, driven by extrinsic noise, can capture quantitative differences in clustered action potential firing patterns recorded from experimental models of tau pathology and healthy animals. The differential equation formulation of our model allows us to perform numerical bifurcation analyses in order to uncover the dynamic mechanisms underlying these patterns. We show that clustered dynamics can be understood as subcritical Hopf/homoclinic bursting in a fast-slow system where the slow sub-system is governed by activation of the persistent sodium current and inactivation of the slow A-type potassium current. In the full system, we demonstrate that clustered firing arises via flip bifurcations as conductance parameters are varied. Our model analyses confirm the experimentally suggested hypothesis that the breakdown of clustered dynamics in disease occurs via increases in AHP conductance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Parameters of oscillation generation regions in open star cluster models

NASA Astrophysics Data System (ADS)

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.

Assessing variation in life-history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists.

PubMed

Hamel, Sandra; Yoccoz, Nigel G; Gaillard, Jean-Michel

2017-05-01

Mixed models are now well-established methods in ecology and evolution because they allow accounting for and quantifying within- and between-individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi-modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life-history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life-history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long-term studies of large mammals to illustrate the potential of using mixture models for assessing within-population variation in life-history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life-history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users. © 2016 Cambridge Philosophical Society.

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients.

PubMed

Torheim, Turid; Groendahl, Aurora R; Andersen, Erlend K F; Lyng, Heidi; Malinen, Eirik; Kvaal, Knut; Futsaether, Cecilia M

2016-11-01

Solid tumors are known to be spatially heterogeneous. Detection of treatment-resistant tumor regions can improve clinical outcome, by enabling implementation of strategies targeting such regions. In this study, K-means clustering was used to group voxels in dynamic contrast enhanced magnetic resonance images (DCE-MRI) of cervical cancers. The aim was to identify clusters reflecting treatment resistance that could be used for targeted radiotherapy with a dose-painting approach. Eighty-one patients with locally advanced cervical cancer underwent DCE-MRI prior to chemoradiotherapy. The resulting image time series were fitted to two pharmacokinetic models, the Tofts model (yielding parameters K trans and ν e ) and the Brix model (A Brix , k ep and k el ). K-means clustering was used to group similar voxels based on either the pharmacokinetic parameter maps or the relative signal increase (RSI) time series. The associations between voxel clusters and treatment outcome (measured as locoregional control) were evaluated using the volume fraction or the spatial distribution of each cluster. One voxel cluster based on the RSI time series was significantly related to locoregional control (adjusted p-value 0.048). This cluster consisted of low-enhancing voxels. We found that tumors with poor prognosis had this RSI-based cluster gathered into few patches, making this cluster a potential candidate for targeted radiotherapy. None of the voxels clusters based on Tofts or Brix parameter maps were significantly related to treatment outcome. We identified one group of tumor voxels significantly associated with locoregional relapse that could potentially be used for dose painting. This tumor voxel cluster was identified using the raw MRI time series rather than the pharmacokinetic maps.

The Cluster Environment of Two High-mass Protostars

NASA Astrophysics Data System (ADS)

Montes, Virginie; Hofner, Peter

2017-06-01

Characterizing the environment and stellar population in which high-mass stars form is an important step to decide between the main massive star formation theories. In the monolithic collapse model, the mass of the core will determine the final stellar mass (e.g., McKee & Tan 2003). In contrast, in the competitive accretion model (e.g., Bonnell & Bate 2006), the mass of the high-mass star is related to the properties of the cluster. As dynamical processes substantially affect the appearance of a cluster, we study early stages of high-mass star formation. These regions often show extended emission from hot dust at infrared wavelengths, which can cause difficulties to define the cluster. We use a multi-wavelength technique to study nearby high-mass star clusters, based on X-ray observations with the Chandra X-Ray Telescope, in conjunction with infrared data and VLA data. The technique relies on the fact that YSOs are particularly bright in X-ray and that contamination is relatively small. X-ray observations allow us to determine the cluster size. The cluster membership and YSOs classification is established using infrared identification of the X-ray sources, and color-color and color-magnitude diagrams.In this talk, I will present our findings on the cluster study of two high-mass star forming regions: IRAS 20126+4104 and IRAS 16562-3959. While most massive stars appear to be formed in rich a cluster environment, those two sources are candidates for the formation of massive stars in a relatively poor cluster. In contrast to what was found in previous studies (Qiu et al. 2008), the dominant B0-type protostar in IRAS 20126+4104 is associated with a small cluster of low-mass stars. I will also show our current work on IRAS 16562-3959, which contains one of the most luminous O-type protostars in the Galaxy. In the vicinity of this particularly interesting region there is a multitude of small clusters, for which I will present how their stellar population differ from the high-mass star-forming cluster IRAS 16562-3959.

Compressive Sensing Cluster Expansion Studies of Lithium Intercalation and Phase Transformation in MoS2 for Energy Storage

NASA Astrophysics Data System (ADS)

Liu, Chi-Ping; Zhou, Fei; Ozolins, Vidvuds; University of California, Los Angeles Collaboration; Lawrence livermore national laboratory Collaboration

2015-03-01

Bulk molybdenum disulfide (MoS2) is a good electrode material candidate for energy storage applications, such as lithium ion batteries and supercapacitors due to its high theoretical energy and power density. First-principles density-functional theory (DFT) calculations combined with cluster expansion are an effective method to study thermodynamic and kinetic properties of electrode materials. In order to construct accurate models for cluster expansion, it is important to effectively choose clusters with significant contributions. In this work, we employ a compressive sensing based technique to select relevant clusters in order to build an accurate Hamiltonian for cluster expansion, enabling the study of Li intercalation in MoS2. We find that the 2H MoS2 structure is only stable at low Li content while 1T MoS2 is the preferred phase at high Li content. The results show that the 2H MoS2 phase transforms into the disordered 1T phase and the disordered 1T structure remains after the first Li insertion/deinsertion cycle suggesting that disordered 1T MoS2 is stable even at dilute Li content. This work also highlights that cluster expansion treated with compressive sensing is an effective and powerful tool for model construction and can be applied to advanced battery and supercapacitor electrode materials.

The spatial clustering of obesity: does the built environment matter?

PubMed

Huang, R; Moudon, A V; Cook, A J; Drewnowski, A

2015-12-01

Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes. © 2014 The British Dietetic Association Ltd.

Low Temperature Kinetics of the First Steps of Water Cluster Formation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bourgalais, J.; Roussel, V.; Capron, M.

2016-03-01

We present a combined experimental and theoretical low temperature kinetic study of water cluster formation. Water cluster growth takes place in low temperature (23-69 K) supersonic flows. The observed kinetics of formation of water clusters are reproduced with a kinetic model based on theoretical predictions for the first steps of clusterization. The temperature-and pressure-dependent association and dissociation rate coefficients are predicted with an ab initio transition state theory based master equation approach over a wide range of temperatures (20-100 K) and pressures (10(-6) - 10 bar).

A Geometric and Electrostatic Study of the [4Fe-4S] Cluster of Adenosine-5´-Phosphosulfate Reductase from Broken Symmetry Density Functional Calculations and Extended X-ray Absorption Fine Structure Spectroscopy

PubMed Central

Bhave, Devayani P.; Han, Wen-Ge; Pazicni, Samuel; Penner-Hahn, James E.; Carroll, Kate S.; Noodleman, Louis

2011-01-01

Adenosine-5’-phosphosulfate reductase (APSR) is an iron-sulfur protein that catalyses the reduction of adenosine-5’-phosphosulfate (APS) to sulfite. APSR coordinates to a [4Fe-4S] cluster via a conserved CC-X~80-CXXC motif and the cluster is essential for catalysis. Despite extensive functional, structural and spectroscopic studies, the exact role of the iron-sulfur cluster in APS reduction remains unknown. To gain an understanding into the role of the cluster, density functional theory (DFT) analysis and extended X-ray fine structure spectroscopy (EXAFS) have been performed to reveal insights into the coordination, geometry and electrostatics of the [4Fe-4S] cluster. XANES data confirms that the cluster is in the [4Fe-4S]2+ state in both native and substrate-bound APSR while EXAFS data recorded at ~0.1 Å resolution indicates that there is no significant change in the structure of the [4Fe-4S] cluster between the native and substrate-bound forms of the protein. On the other hand, DFT calculations provide an insight into the subtle differences between the geometry of the cluster in the native and APS-bound forms of APSR. A comparison between models with and without the tandem cysteine pair coordination of the cluster suggests a role for the unique coordination in facilitating a compact geometric structure and ‘fine-tuning’ the electronic structure to prevent reduction of the cluster. Further, calculations using models in which residue Lys144 is mutated to Ala confirm the finding that Lys144 serves as a crucial link in the interactions involving the [4Fe-4S] cluster and APS. PMID:21678934

Studies of Copper, Silver, and Gold Cluster Anions: Evidence of Electronic Shell Structure.

NASA Astrophysics Data System (ADS)

Pettiette, Claire Lynn

A new Ultraviolet Magnetic Time-of-Flight Photoelectron Spectrometer (MTOFPES) has been developed for the study of the electronic structure of clusters produced in a pulsed supersonic molecular beam. This is the first technique which has been successful in probing the valence electronic states of metal clusters. The ultraviolet photoelectron spectra of negative cluster ions of the noble metals have been taken at several different photon energies. These are presented along with the electron affinity and HOMO-LUMO gap measurements for Cu_6^- to Cu_ {41}^-, using 4.66 eV and 6.42 eV detachment energies; Ag_3^- to Ag_{21}^-, using 6.42 eV detachment energy; and Au_3^ - to Au_{21}^-, using 6.42 eV and 7.89 eV detachment energies. The spectra provide the first detailed probes of the s valence electrons of the noble metal clusters. In addition, the 6.42 eV and 7.89 eV spectra probe the first one to two electron volts of the molecular orbitals of the d valence electrons of copper and gold clusters. The electron affinity and HOMO-LUMO gap measurements of the noble metal clusters agree with the predictions of the ellipsoidal shell model for mono-valent metal clusters. In particular, cluster numbers 8, 20, and 40--which correspond to the spherical shell closings of this model--have low electron affinities and large HOMO-LUMO gaps. The spectra of the gold cluster ions indicate that the molecular orbital energies of the cluster valence electrons are more widely spaced for gold than for copper or silver. This is to be expected for the heavy atom clusters when relativistic effects are taken into account.

Significance of flow clustering and sequencing on sediment transport: 1D sediment transport modelling

NASA Astrophysics Data System (ADS)

Hassan, Kazi; Allen, Deonie; Haynes, Heather

2016-04-01

This paper considers 1D hydraulic model data on the effect of high flow clusters and sequencing on sediment transport. Using observed flow gauge data from the River Caldew, England, a novel stochastic modelling approach was developed in order to create alternative 50 year flow sequences. Whilst the observed probability density of gauge data was preserved in all sequences, the order in which those flows occurred was varied using the output from a Hidden Markov Model (HMM) with generalised Pareto distribution (GP). In total, one hundred 50 year synthetic flow series were generated and used as the inflow boundary conditions for individual flow series model runs using the 1D sediment transport model HEC-RAS. The model routed graded sediment through the case study river reach to define the long-term morphological changes. Comparison of individual simulations provided a detailed understanding of the sensitivity of channel capacity to flow sequence. Specifically, each 50 year synthetic flow sequence was analysed using a 3-month, 6-month or 12-month rolling window approach and classified for clusters in peak discharge. As a cluster is described as a temporal grouping of flow events above a specified threshold, the threshold condition used herein is considered as a morphologically active channel forming discharge event. Thus, clusters were identified for peak discharges in excess of 10%, 20%, 50%, 100% and 150% of the 1 year Return Period (RP) event. The window of above-peak flows also required cluster definition and was tested for timeframes 1, 2, 10 and 30 days. Subsequently, clusters could be described in terms of the number of events, maximum peak flow discharge, cumulative flow discharge and skewness (i.e. a description of the flow sequence). The model output for each cluster was analysed for the cumulative flow volume and cumulative sediment transport (mass). This was then compared to the total sediment transport of a single flow event of equivalent flow volume. Results illustrate that clustered flood events generated sediment loads up to an order of magnitude greater than that of individual events of the same flood volume. Correlations were significant for sediment volume compared to both maximum flow discharge (R2<0.8) and number of events (R2 -0.5 to -0.7) within the cluster. The strongest correlations occurred for clusters with a greater number of flow events only slightly above-threshold. This illustrates that the numerical model can capture a degree of the non-linear morphological response to flow magnitude. Analysis of the relationship between morphological change and the skewness of flow events within each cluster was also determined, illustrating only minor sensitivity to cluster peak distribution skewness. This is surprising and discussion is presented on model limitations, including the capability of sediment transport formulae to effectively account for temporal processes of antecedent flow, hysteresis, local supply etc.

Competing risks regression for clustered data

PubMed Central

Zhou, Bingqing; Fine, Jason; Latouche, Aurelien; Labopin, Myriam

2012-01-01

A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine–Gray proportional hazards model for the subdistribution to situations, where individuals within a cluster may be correlated due to unobserved shared factors. Estimators of the regression parameters in the marginal model are developed under an independence working assumption where the correlation across individuals within a cluster is completely unspecified. The estimators are consistent and asymptotically normal, and variance estimation may be achieved without specifying the form of the dependence across individuals. A simulation study evidences that the inferential procedures perform well with realistic sample sizes. The practical utility of the methods is illustrated with data from the European Bone Marrow Transplant Registry. PMID:22045910

Experimental and theoretical investigation of the magnetization dynamics of an artificial square spin ice cluster

NASA Astrophysics Data System (ADS)

Pohlit, Merlin; Stockem, Irina; Porrati, Fabrizio; Huth, Michael; Schröder, Christian; Müller, Jens

2016-10-01

We study the magnetization dynamics of a spin ice cluster which is a building block of an artificial square spin ice fabricated by focused electron-beam-induced deposition both experimentally and theoretically. The spin ice cluster is composed of twelve interacting Co nanoislands grown directly on top of a high-resolution micro-Hall sensor. By employing micromagnetic simulations and a macrospin model, we calculate the magnetization and the experimentally investigated stray field emanating from a single nanoisland. The parameters determined from a comparison with the experimental hysteresis loop are used to derive an effective single-dipole macrospin model that allows us to investigate the dynamics of the spin ice cluster. Our model reproduces the experimentally observed non-deterministic sequences in the magnetization curves as well as the distinct temperature dependence of the hysteresis loop.

Efficient Organometallic Spin Filter between Single-Wall Carbon Nanotube or Graphene Electrodes

NASA Astrophysics Data System (ADS)

Koleini, Mohammad; Paulsson, Magnus; Brandbyge, Mads

2007-05-01

We present a theoretical study of spin transport in a class of molecular systems consisting of an organometallic benzene-vanadium cluster placed in between graphene or single-wall carbon-nanotube-model contacts. Ab initio modeling is performed by combining spin density functional theory and nonequilibrium Green’s function techniques. We consider weak and strong cluster-contact bonds. Depending on the bonding we find from 73% (strong bonds) up to 99% (weak bonds) spin polarization of the electron transmission, and enhanced polarization with increased cluster length.

RELICS: Reionization Lensing Cluster Survey - Discovering Brightly Lensed Distant Galaxies for JWST

NASA Astrophysics Data System (ADS)

Coe, Dan; Bradley, Larry; Salmon, Brett; Avila, Roberto J.; Ogaz, Sara; Bradac, Marusa; Huang, Kuang-Han; Strait, Victoria; Hoag, Austin; Sharon, Keren q.; Cerny, Catherine; Paterno-Mahler, Rachel; Johnson, Traci Lin; Mahler, Guillaume; Zitrin, Adi; Sendra Server, Irene; Acebron, Ana; Cibirka, Nathália; Rodney, Steven; Strolger, Louis; Riess, Adam; Dawson, William; Jones, Christine; Andrade-Santos, Felipe; Lovisari, Lorenzo; Czakon, Nicole; Umetsu, Keiichi; Trenti, Michele; Vulcani, Benedetta; Carrasco, Daniela; Livermore, Rachael; Stark, Daniel P.; Mainali, Ramesh; Frye, Brenda; Oesch, Pascal; Lam, Daniel; Toft, Sune; Ryan, Russell; Peterson, Avery; Past, Matthew; Kikuchihara, Shotaro; Ouchi, Masami; Oguri, Masamune

2018-01-01

The Reionization Lensing Cluster Survey (RELICS) Hubble Treasury Program has completed observations of 41 massive galaxy clusters with 188 orbits of HST ACS and WFC3/IR imaging and 390 hours of Spitzer IRAC imaging. This poster presents an overview of the program and data releases. Reduced images, catalogs, and lens models for all clusters are now available on MAST. RELICS is studying the clusters, supernovae, and lensed high-redshift galaxies. A companion poster presents our high-redshift results: over 300 lensed z ~ 6 - 10 candidates, including some of the brightest known at these redshifts (Salmon et al. 2018). These will be excellent targets for detailed follow-up study in JWST Cycle 1 GO proposals.

X-Ray Morphological Analysis of the Planck ESZ Clusters

NASA Astrophysics Data System (ADS)

Lovisari, Lorenzo; Forman, William R.; Jones, Christine; Ettori, Stefano; Andrade-Santos, Felipe; Arnaud, Monique; Démoclès, Jessica; Pratt, Gabriel W.; Randall, Scott; Kraft, Ralph

2017-09-01

X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev-Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper we determine eight morphological parameters for the Planck Early Sunyaev-Zeldovich (ESZ) objects observed with XMM-Newton. We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.

X-Ray Morphological Analysis of the Planck ESZ Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lovisari, Lorenzo; Forman, William R.; Jones, Christine

2017-09-01

X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev–Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper wemore » determine eight morphological parameters for the Planck Early Sunyaev–Zeldovich (ESZ) objects observed with XMM-Newton . We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.« less

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

PubMed Central

Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies. PMID:29466394

Modeling and Testing Dark Energy and Gravity with Galaxy Cluster Data

NASA Astrophysics Data System (ADS)

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.

Mechanism of cell alignment in groups of Myxococcus xanthus bacteria

NASA Astrophysics Data System (ADS)

Balgam, Rajesh; Igoshin, Oleg

2015-03-01

Myxococcus xanthus is a model for studying self-organization in bacteria. These flexible cylindrical bacteria move along. In groups, M. xanthus cells align themselves into dynamic cell clusters but the mechanism underlying their formation is unknown. It has been shown that steric interactions can cause alignment in self-propelled hard rods but it is not clear how flexibility and reversals affect the alignment and cluster formation. We have investigated cell alignment process using our biophysical model of M. xanthus cell in an agent-based simulation framework under realistic cell flexibility values. We observed that flexible model cells can form aligned cell clusters when reversals are suppressed but these clusters disappeared when reversals frequency becomes similar to the observed value. However, M. xanthus cells follow slime (polysaccharide gel like material) trails left by other cells and we show that implementing this into our model rescues cell clustering for reversing cells. Our results show that slime following along with periodic cell reversals act as positive feedback to reinforce existing slime trails and recruit more cells. Furthermore, we have observed that mechanical cell alignment combined with slime following is sufficient to explain the distinct clustering patterns of reversing and non-reversing cells as observed in recent experiments. This work is supported by NSF MCB 0845919 and 1411780.

Testing the Reliability of Cluster Mass Indicators with a Systematics Limited Dataset

NASA Technical Reports Server (NTRS)

Juett, Adrienne M.; Davis, David S.; Mushotzky, Richard

2009-01-01

We present the mass X-ray observable scaling relationships for clusters of galaxies using the XMM-Newton cluster catalog of Snowden et al. Our results are roughly consistent with previous observational and theoretical work, with one major exception. We find 2-3 times the scatter around the best fit mass scaling relationships as expected from cluster simulations or seen in other observational studies. We suggest that this is a consequence of using hydrostatic mass, as opposed to virial mass, and is due to the explicit dependence of the hydrostatic mass on the gradients of the temperature and gas density profiles. We find a larger range of slope in the cluster temperature profiles at radii 500 than previous observational studies. Additionally, we find only a weak dependence of the gas mass fraction on cluster mass, consistent with a constant. Our average gas mass fraction results also argue for a closer study of the systematic errors due to instrumental calibration and modeling method variations between analyses. We suggest that a more careful study of the differences between various observational results and with cluster simulations is needed to understand sources of bias and scatter in cosmological studies of galaxy clusters.

UV laser photoactivation of hexachloroplatinate bound to individual nucleobases in vacuo as molecular level probes of a model photopharmaceutical.

PubMed

Matthews, Edward; Sen, Ananya; Yoshikawa, Naruo; Bergström, Ed; Dessent, Caroline E H

2016-06-01

Isolated molecular clusters of adenine, cytosine, thymine and uracil bound to hexachloroplatinate, PtCl6(2-), have been studied using laser electronic photodissociation spectroscopy to investigate photoactivation of a platinum complex in the vicinity of a nucleobase. These metal complex-nucleobase clusters represent model systems for identifying the fundamental photochemical processes occurring in photodynamic platinum drug therapies that target DNA. This is the first study to explore the specific role of a strongly photoactive platinum compound in the aggregate complex. Each of the clusters studied displays a broadly similar absorption spectra, with a strong λmax ∼ 4.6 eV absorption band and a subsequent increase in the absorption intensity towards higher spectral-energy. The absorption bands are traced to ligand-to-metal-charge-transfer excitations on the PtCl6(2-) moiety within the cluster, and result in Cl(-)·nucleobase and PtCl5(-) as primary photofragments. These results demonstrate how selective photoexcitation can drive distinctive photodecay channels for a model photo-pharmaceutical. In addition, cluster absorption due to excitation of nucleobase-centred chromophores is observed in the region around 5 eV. For the uracil cluster, photofragments consistent with ultrafast decay of the excited state and vibrational predissociation on the ground-state surface are observed. However, this decay channel becomes successively weaker on going from thymine to cytosine to adenine, due to differential coupling of the excited states to the electron detachment continuum. These effects demonstrate the distinctive photophysical characteristics of the different nucleobases, and are discussed in the context of the recently recorded photoelectron spectra of theses clusters.

Shell Corrections Stabilizing Superheavy Nuclei and Semi-spheroidal Atomic Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poenaru, Dorin N.

2008-01-24

The macroscopic-microscopic method is used to illustrate the shell effect stabilizing superheavy nuclei and to study the stability of semi-spheroidal clusters deposited on planar surfaces. The alpha decay of superheavy nuclei is calculated using three models: the analytical superasymmetric fission model; the universal curve, and the semiempirical formula taking into account the shell effects. Analytical relationships are obtained for the energy levels of the new semi-spheroidal harmonic oscillator (SSHO) single-particle model and for the surface and curvature energies of the semi-spheroidal clusters. The maximum degeneracy of the SSHO is reached at a super-deformed prolate shape for which the minimum ofmore » the liquid drop model energy is also attained.« less

The role of Dark Matter sub-halos in the non-thermal emission of galaxy clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marchegiani, Paolo; Colafrancesco, Sergio, E-mail: Paolo.Marchegiani@wits.ac.za, E-mail: Sergio.Colafrancesco@wits.ac.za

2016-11-01

Annihilation of Dark Matter (DM) particles has been recognized as one of the possible mechanisms for the production of non-thermal particles and radiation in galaxy clusters. Previous studies have shown that, while DM models can reproduce the spectral properties of the radio halo in the Coma cluster, they fail in reproducing the shape of the radio halo surface brightness because they produce a shape that is too concentrated towards the center of the cluster with respect to the observed one. However, in previous studies the DM distribution was modeled as a single spherically symmetric halo, while the DM distribution inmore » Coma is found to have a complex and elongated shape. In this work we calculate a range of non-thermal emissions in the Coma cluster by using the observed distribution of DM sub-halos. We find that, by including the observed sub-halos in the DM model, we obtain a radio surface brightness with a shape similar to the observed one, and that the sub-halos boost the radio emission by a factor between 5 and 20%, thus allowing to reduce the gap between the annihilation cross section required to reproduce the radio halo flux and the upper limits derived from other observations, and that this gap can be explained by realistic values of the boosting factor due to smaller substructures. Models with neutralino mass of 9 GeV and composition τ{sup +} τ{sup −}, and mass of 43 GeV and composition b b-bar can fit the radio halo spectrum using the observed properties of the magnetic field in Coma, and do not predict a gamma-ray emission in excess compared to the recent Fermi-LAT upper limits. These findings make these DM models viable candidate to explain the origin of radio halos in galaxy clusters, avoiding the problems connected to the excessive gamma-ray emission expected from proton acceleration in most of the currently proposed models, where the acceleration of particles is directly or indirectly connected to events related to clusters merging. Therefore, DM models deserve to be better studied both from the theoretical and observational sides; the best spectral bands where it is possible to obtain better information are the radio and the gamma-ray bands, while we do not expect a strong emission in the X-ray band.« less

Binary Black Hole Mergers from Globular Clusters: Implications for Advanced LIGO.

PubMed

Rodriguez, Carl L; Morscher, Meagan; Pattabiraman, Bharath; Chatterjee, Sourav; Haster, Carl-Johan; Rasio, Frederic A

2015-07-31

The predicted rate of binary black hole mergers from galactic fields can vary over several orders of magnitude and is extremely sensitive to the assumptions of stellar evolution. But in dense stellar environments such as globular clusters, binary black holes form by well-understood gravitational interactions. In this Letter, we study the formation of black hole binaries in an extensive collection of realistic globular cluster models. By comparing these models to observed Milky Way and extragalactic globular clusters, we find that the mergers of dynamically formed binaries could be detected at a rate of ∼100 per year, potentially dominating the binary black hole merger rate. We also find that a majority of cluster-formed binaries are more massive than their field-formed counterparts, suggesting that Advanced LIGO could identify certain binaries as originating from dense stellar environments.

Galaxy clusters and cold dark matter - A low-density unbiased universe?

NASA Technical Reports Server (NTRS)

Bahcall, Neta A.; Cen, Renyue

1992-01-01

Large-scale simulations of a universe dominated by cold dark matter (CDM) are tested against two fundamental properties of clusters of galaxies: the cluster mass function and the cluster correlation function. We find that standard biased CDM models are inconsistent with these observations for any bias parameter b. A low-density, low-bias CDM-type model, with or without a cosmological constant, appears to be consistent with both the cluster mass function and the cluster correlations. The low-density model agrees well with the observed correlation function of the Abell, Automatic Plate Measuring Facility (APM), and Edinburgh-Durham cluster catalogs. The model is in excellent agreement with the observed dependence of the correlation strength on cluster mean separation, reproducing the measured universal dimensionless cluster correlation. The low-density model is also consistent with other large-scale structure observations, including the APM angular galaxy-correlations, and for lambda = 1-Omega with the COBE results of the microwave background radiation fluctuations.

Multi-scale study of condensation in water jets using ellipsoidal-statistical Bhatnagar-Gross-Krook and molecular dynamics modeling

NASA Astrophysics Data System (ADS)

Li, Zheng; Borner, Arnaud; Levin, Deborah A.

2014-06-01

Homogeneous water condensation and ice formation in supersonic expansions to vacuum for stagnation pressures from 12 to 1000 mbar are studied using the particle-based Ellipsoidal-Statistical Bhatnagar-Gross-Krook (ES-BGK) method. We find that when condensation starts to occur, at a stagnation pressure of 96 mbar, the increase in the degree of condensation causes an increase in the rotational temperature due to the latent heat of vaporization. The simulated rotational temperature profiles along the plume expansion agree well with measurements confirming the kinetic homogeneous condensation models and the method of simulation. Comparisons of the simulated gas and cluster number densities, cluster size for different stagnation pressures along the plume centerline were made and it is found that the cluster size increase linearly with respect to stagnation pressure, consistent with classical nucleation theory. The sensitivity of our results to cluster nucleation model and latent heat values based on bulk water, specific cluster size, or bulk ice are examined. In particular, the ES-BGK simulations are found to be too coarse-grained to provide information on the phase or structure of the clusters formed. For this reason, molecular dynamics simulations of water condensation in a one-dimensional free expansion to simulate the conditions in the core of a plume are performed. We find that the internal structure of the clusters formed depends on the stagnation temperature. A larger cluster of average size 21 was tracked down the expansion, and a calculation of its average internal temperature as well as a comparison of its radial distribution functions (RDFs) with values measured for solid amorphous ice clusters lead us to conclude that this cluster is in a solid-like rather than liquid form. In another molecular-dynamics simulation at a much lower stagnation temperature, a larger cluster of size 324 and internal temperature 200 K was extracted from an expansion plume and equilibrated to determine its RDF and self-diffusion coefficient. The value of the latter shows that this cluster is formed in a supercooled liquid state rather than in an amorphous solid state.

Cluster Quantum Chemical Study of the Grignard Reagent Formation

NASA Astrophysics Data System (ADS)

Tulub, A. V.; Porsev, V. V.

The main stages of the Grignard reagent formation are described in a framework of quantum chemical cluster model. We have established two kinds of the adsorption of CH3Hal on Mgn clusters, one of which leads to radical formation and the second is responsible for radical free dissociate adsorption. The charge redistribution in cluster CH3MgnHal result to the strong electrostatic interaction with ether and Grignard reagent formation without any activation barrier.

Bayesian investigation of isochrone consistency using the old open cluster NGC 188

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hills, Shane; Courteau, Stéphane; Von Hippel, Ted

2015-03-01

This paper provides a detailed comparison of the differences in parameters derived for a star cluster from its color–magnitude diagrams (CMDs) depending on the filters and models used. We examine the consistency and reliability of fitting three widely used stellar evolution models to 15 combinations of optical and near-IR photometry for the old open cluster NGC 188. The optical filter response curves match those of theoretical systems and are thus not the source of fit inconsistencies. NGC 188 is ideally suited to this study thanks to a wide variety of high-quality photometry and available proper motions and radial velocities thatmore » enable us to remove non-cluster members and many binaries. Our Bayesian fitting technique yields inferred values of age, metallicity, distance modulus, and absorption as a function of the photometric band combinations and stellar models. We show that the historically favored three-band combinations of UBV and VRI can be meaningfully inconsistent with each other and with longer baseline data sets such as UBVRIJHK{sub S}. Differences among model sets can also be substantial. For instance, fitting Yi et al. (2001) and Dotter et al. (2008) models to UBVRIJHK{sub S} photometry for NGC 188 yields the following cluster parameters: age = (5.78 ± 0.03, 6.45 ± 0.04) Gyr, [Fe/H] = (+0.125 ± 0.003, −0.077 ± 0.003) dex, (m−M){sub V} = (11.441 ± 0.007, 11.525 ± 0.005) mag, and A{sub V} = (0.162 ± 0.003, 0.236 ± 0.003) mag, respectively. Within the formal fitting errors, these two fits are substantially and statistically different. Such differences among fits using different filters and models are a cautionary tale regarding our current ability to fit star cluster CMDs. Additional modeling of this kind, with more models and star clusters, and future Gaia parallaxes are critical for isolating and quantifying the most relevant uncertainties in stellar evolutionary models.« less

Helium segregation on surfaces of plasma-exposed tungsten

DOE PAGES

Maroudas, Dimitrios; Blondel, Sophie; Hu, Lin; ...

2016-01-21

Here we report a hierarchical multi-scale modeling study of implanted helium segregation on surfaces of tungsten, considered as a plasma facing component in nuclear fusion reactors. We employ a hierarchy of atomic-scale simulations based on a reliable interatomic interaction potential, including molecular-statics simulations to understand the origin of helium surface segregation, targeted molecular-dynamics (MD) simulations of near-surface cluster reactions, and large-scale MD simulations of implanted helium evolution in plasma-exposed tungsten. We find that small, mobile He-n (1 <= n <= 7) clusters in the near-surface region are attracted to the surface due to an elastic interaction force that provides themore » thermodynamic driving force for surface segregation. Elastic interaction force induces drift fluxes of these mobile Hen clusters, which increase substantially as the migrating clusters approach the surface, facilitating helium segregation on the surface. Moreover, the clusters' drift toward the surface enables cluster reactions, most importantly trap mutation, in the near-surface region at rates much higher than in the bulk material. Moreover, these near-surface cluster dynamics have significant effects on the surface morphology, near-surface defect structures, and the amount of helium retained in the material upon plasma exposure. We integrate the findings of such atomic-scale simulations into a properly parameterized and validated spatially dependent, continuum-scale reaction-diffusion cluster dynamics model, capable of predicting implanted helium evolution, surface segregation, and its near-surface effects in tungsten. This cluster-dynamics model sets the stage for development of fully atomistically informed coarse-grained models for computationally efficient simulation predictions of helium surface segregation, as well as helium retention and surface morphological evolution, toward optimal design of plasma facing components.« less

Clustering mechanism of oxocarboxylic acids involving hydration reaction: Implications for the atmospheric models

NASA Astrophysics Data System (ADS)

Liu, Ling; Kupiainen-Määttä, Oona; Zhang, Haijie; Li, Hao; Zhong, Jie; Kurtén, Theo; Vehkamäki, Hanna; Zhang, Shaowen; Zhang, Yunhong; Ge, Maofa; Zhang, Xiuhui; Li, Zesheng

2018-06-01

The formation of atmospheric aerosol particles from condensable gases is a dominant source of particulate matter in the boundary layer, but the mechanism is still ambiguous. During the clustering process, precursors with different reactivities can induce various chemical reactions in addition to the formation of hydrogen bonds. However, the clustering mechanism involving chemical reactions is rarely considered in most of the nucleation process models. Oxocarboxylic acids are common compositions of secondary organic aerosol, but the role of oxocarboxylic acids in secondary organic aerosol formation is still not fully understood. In this paper, glyoxylic acid, the simplest and the most abundant atmospheric oxocarboxylic acid, has been selected as a representative example of oxocarboxylic acids in order to study the clustering mechanism involving hydration reactions using density functional theory combined with the Atmospheric Clusters Dynamic Code. The hydration reaction of glyoxylic acid can occur either in the gas phase or during the clustering process. Under atmospheric conditions, the total conversion ratio of glyoxylic acid to its hydration reaction product (2,2-dihydroxyacetic acid) in both gas phase and clusters can be up to 85%, and the product can further participate in the clustering process. The differences in cluster structures and properties induced by the hydration reaction lead to significant differences in cluster formation rates and pathways at relatively low temperatures.

Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models.

PubMed

Liu, Jingxia; Colditz, Graham A

2018-05-01

There is growing interest in conducting cluster randomized trials (CRTs). For simplicity in sample size calculation, the cluster sizes are assumed to be identical across all clusters. However, equal cluster sizes are not guaranteed in practice. Therefore, the relative efficiency (RE) of unequal versus equal cluster sizes has been investigated when testing the treatment effect. One of the most important approaches to analyze a set of correlated data is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which the "working correlation structure" is introduced and the association pattern depends on a vector of association parameters denoted by ρ. In this paper, we utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in CRTs. The variances of the estimator of the treatment effect are derived for the different types of outcome. RE is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal cluster sizes. We discuss a commonly used structure in CRTs-exchangeable, and derive the simpler formula of RE with continuous, binary, and count outcomes. Finally, REs are investigated for several scenarios of cluster size distributions through simulation studies. We propose an adjusted sample size due to efficiency loss. Additionally, we also propose an optimal sample size estimation based on the GEE models under a fixed budget for known and unknown association parameter (ρ) in the working correlation structure within the cluster. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Subspace K-means clustering.

PubMed

Timmerman, Marieke E; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

2013-12-01

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

Star cluster formation in a turbulent molecular cloud self-regulated by photoionization feedback

NASA Astrophysics Data System (ADS)

Gavagnin, Elena; Bleuler, Andreas; Rosdahl, Joakim; Teyssier, Romain

2017-12-01

Most stars in the Galaxy are believed to be formed within star clusters from collapsing molecular clouds. However, the complete process of star formation, from the parent cloud to a gas-free star cluster, is still poorly understood. We perform radiation-hydrodynamical simulations of the collapse of a turbulent molecular cloud using the RAMSES-RT code. Stars are modelled using sink particles, from which we self-consistently follow the propagation of the ionizing radiation. We study how different feedback models affect the gas expulsion from the cloud and how they shape the final properties of the emerging star cluster. We find that the star formation efficiency is lower for stronger feedback models. Feedback also changes the high-mass end of the stellar mass function. Stronger feedback also allows the establishment of a lower density star cluster, which can maintain a virial or sub-virial state. In the absence of feedback, the star formation efficiency is very high, as well as the final stellar density. As a result, high-energy close encounters make the cluster evaporate quickly. Other indicators, such as mass segregation, statistics of multiple systems and escaping stars confirm this picture. Observations of young star clusters are in best agreement with our strong feedback simulation.

Dynamical Competition of IC-Industry Clustering from Taiwan to China

NASA Astrophysics Data System (ADS)

Tsai, Bi-Huei; Tsai, Kuo-Hui

2009-08-01

Most studies employ qualitative approach to explore the industrial clusters; however, few research has objectively quantified the evolutions of industry clustering. The purpose of this paper is to quantitatively analyze clustering among IC design, IC manufacturing as well as IC packaging and testing industries by using the foreign direct investment (FDI) data. The Lotka-Volterra system equations are first adopted here to capture the competition or cooperation among such three industries, thus explaining their clustering inclinations. The results indicate that the evolution of FDI into China for IC design industry significantly inspire the subsequent FDI of IC manufacturing as well as IC packaging and testing industries. Since IC design industry lie in the upstream stage of IC production, the middle-stream IC manufacturing and downstream IC packing and testing enterprises tend to cluster together with IC design firms, in order to sustain a steady business. Finally, Taiwan IC industry's FDI amount into China is predicted to cumulatively increase, which supports the industrial clustering tendency for Taiwan IC industry. Particularly, the FDI prediction of Lotka-Volterra model performs superior to that of the conventional Bass model after the forecast accuracy of these two models are compared. The prediction ability is dramatically improved as the industrial mutualism among each IC production stage is taken into account.

Mass Modeling of Frontier Fields Cluster MACS J1149.5+2223 Using Strong and Weak Lensing

NASA Astrophysics Data System (ADS)

Finney, Emily Quinn; Bradač, Maruša; Huang, Kuang-Han; Hoag, Austin; Morishita, Takahiro; Schrabback, Tim; Treu, Tommaso; Borello Schmidt, Kasper; Lemaux, Brian C.; Wang, Xin; Mason, Charlotte

2018-05-01

We present a gravitational-lensing model of MACS J1149.5+2223 using ultra-deep Hubble Frontier Fields imaging data and spectroscopic redshifts from HST grism and Very Large Telescope (VLT)/MUSE spectroscopic data. We create total mass maps using 38 multiple images (13 sources) and 608 weak-lensing galaxies, as well as 100 multiple images of 31 star-forming regions in the galaxy that hosts supernova Refsdal. We find good agreement with a range of recent models within the HST field of view. We present a map of the ratio of projected stellar mass to total mass (f ⋆) and find that the stellar mass fraction for this cluster peaks on the primary BCG. Averaging within a radius of 0.3 Mpc, we obtain a value of < {f}\\star > ={0.012}-0.003+0.004, consistent with other recent results for this ratio in cluster environments, though with a large global error (up to δf ⋆ = 0.005) primarily due to the choice of IMF. We compare values of f ⋆ and measures of star formation efficiency for this cluster to other Hubble Frontier Fields clusters studied in the literature, finding that MACS1149 has a higher stellar mass fraction than these other clusters but a star formation efficiency typical of massive clusters.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan.

PubMed

Park, Rachel; O'Brien, Thomas F; Huang, Susan S; Baker, Meghan A; Yokoe, Deborah S; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-11-01

While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.

Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles.

PubMed

Sani-Kast, Nicole; Scheringer, Martin; Slomberg, Danielle; Labille, Jérôme; Praetorius, Antonia; Ollivier, Patrick; Hungerbühler, Konrad

2015-12-01

Engineered nanoparticle (ENP) fate models developed to date - aimed at predicting ENP concentration in the aqueous environment - have limited applicability because they employ constant environmental conditions along the modeled system or a highly specific environmental representation; both approaches do not show the effects of spatial and/or temporal variability. To address this conceptual gap, we developed a novel modeling strategy that: 1) incorporates spatial variability in environmental conditions in an existing ENP fate model; and 2) analyzes the effect of a wide range of randomly sampled environmental conditions (representing variations in water chemistry). This approach was employed to investigate the transport of nano-TiO2 in the Lower Rhône River (France) under numerous sets of environmental conditions. The predicted spatial concentration profiles of nano-TiO2 were then grouped according to their similarity by using cluster analysis. The analysis resulted in a small number of clusters representing groups of spatial concentration profiles. All clusters show nano-TiO2 accumulation in the sediment layer, supporting results from previous studies. Analysis of the characteristic features of each cluster demonstrated a strong association between the water conditions in regions close to the ENP emission source and the cluster membership of the corresponding spatial concentration profiles. In particular, water compositions favoring heteroaggregation between the ENPs and suspended particulate matter resulted in clusters of low variability. These conditions are, therefore, reliable predictors of the eventual fate of the modeled ENPs. The conclusions from this study are also valid for ENP fate in other large river systems. Our results, therefore, shift the focus of future modeling and experimental research of ENP environmental fate to the water characteristic in regions near the expected ENP emission sources. Under conditions favoring heteroaggregation in these regions, the fate of the ENPs can be readily predicted. Copyright © 2014 Elsevier B.V. All rights reserved.

Studies of the evolution of the x ray emission of clusters of galaxies

NASA Technical Reports Server (NTRS)

Henry, J. Patrick

1990-01-01

The x ray luminosity function of clusters of galaxies was determined at different cosmic epoches using data from the Einstein Observatory Extended Medium Survey. The sample consisted of 67 x ray selected clusters that were grouped into three redshift shells. Evolution was detected in the x ray properties of clusters. The present volume density of high luminosity clusters was found to be greater than it was in the past. This result is the first convincing evidence for evolution in the x ray properties of clusters. Investigations into the constraints provided by these data on various Cold Dark Matter models are underway.

Studies of the Virgo Cluster. I - Photometry of 109 galaxies near the cluster center to serve as standards

NASA Technical Reports Server (NTRS)

Sandage, A.; Tarenghi, M.; Binggeli, B.

1984-01-01

Attention is given to the technical aspects of photometric measurements of 109 galaxies near the center of the Virgo Cluster, noting various types of radii and surface brightness for about 50 E and dE galaxies in the sample that range in absolute magnitude from -20 to -12. These data are combined with data from the literature for giant E and dwarf E galaxies in the Local Group to study the systematic properties of E galaxies over a range of one million luminosities. The radial intensity profiles derived are fitted to the manifold of King (1978) models to derive model-dependent central surface brightness, core radii, and cutoff radii.

The weak lensing analysis of the CFHTLS and NGVS RedGOLD galaxy clusters

NASA Astrophysics Data System (ADS)

Parroni, C.; Mei, S.; Erben, T.; Van Waerbeke, L.; Raichoor, A.; Ford, J.; Licitra, R.; Meneghetti, M.; Hildebrandt, H.; Miller, L.; Côté, P.; Covone, G.; Cuillandre, J.-C.; Duc, P.-A.; Ferrarese, L.; Gwyn, S. D. J.; Puzia, T. H.

2017-12-01

An accurate estimation of galaxy cluster masses is essential for their use in cosmological and astrophysical studies. We studied the accuracy of the optical richness obtained by our RedGOLD cluster detection algorithm tep{licitra2016a, licitra2016b} as a mass proxy, using weak lensing and X-ray mass measurements. We measured stacked weak lensing cluster masses for a sample of 1323 galaxy clusters in the Canada-France-Hawaii Telescope Legacy Survey W1 and the Next Generation Virgo Cluster Survey at 0.2

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

PubMed

Goldbaum, Michael H; Jang, Gil-Jin; Bowd, Chris; Hao, Jiucang; Zangwill, Linda M; Liebmann, Jeffrey; Girkin, Christopher; Jung, Tzyy-Ping; Weinreb, Robert N; Sample, Pamela A

2009-12-01

To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss. SITA fields were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Inc, Dublin, California) on 1,146 normal eyes and 939 glaucoma eyes from subjects followed by the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. VIM modifies independent component analysis (ICA) to develop separate sets of ICA axes in the cluster of normal fields and the 2 clusters of abnormal fields. Of 360 models, the model with the best separation of normal and glaucomatous fields was chosen for creating the maximally independent axes. Grayscale displays of fields generated by VIM on each axis were compared. SITA fields most closely associated with each axis and displayed in grayscale were evaluated for consistency of pattern at all severities. The best VIM model had 3 clusters. Cluster 1 (1,193) was mostly normal (1,089, 95% specificity) and had 2 axes. Cluster 2 (596) contained mildly abnormal fields (513) and 2 axes; cluster 3 (323) held mostly moderately to severely abnormal fields (322) and 5 axes. Sensitivity for clusters 2 and 3 combined was 88.9%. The VIM-generated field patterns differed from each other and resembled glaucomatous defects (eg, nasal step, arcuate, temporal wedge). SITA fields assigned to an axis resembled each other and the VIM-generated patterns for that axis. Pattern severity increased in the positive direction of each axis by expansion or deepening of the axis pattern. VIM worked well on SITA fields, separating them into distinctly different yet recognizable patterns of glaucomatous field defects. The axis and pattern properties make VIM a good candidate as a preliminary process for detecting progression.

Hubble Frontier Fields: systematic errors in strong lensing models of galaxy clusters - implications for cosmography

NASA Astrophysics Data System (ADS)

Acebron, Ana; Jullo, Eric; Limousin, Marceau; Tilquin, André; Giocoli, Carlo; Jauzac, Mathilde; Mahler, Guillaume; Richard, Johan

2017-09-01

Strong gravitational lensing by galaxy clusters is a fundamental tool to study dark matter and constrain the geometry of the Universe. Recently, the Hubble Space Telescope Frontier Fields programme has allowed a significant improvement of mass and magnification measurements but lensing models still have a residual root mean square between 0.2 arcsec and few arcseconds, not yet completely understood. Systematic errors have to be better understood and treated in order to use strong lensing clusters as reliable cosmological probes. We have analysed two simulated Hubble-Frontier-Fields-like clusters from the Hubble Frontier Fields Comparison Challenge, Ares and Hera. We use several estimators (relative bias on magnification, density profiles, ellipticity and orientation) to quantify the goodness of our reconstructions by comparing our multiple models, optimized with the parametric software lenstool, with the input models. We have quantified the impact of systematic errors arising, first, from the choice of different density profiles and configurations and, secondly, from the availability of constraints (spectroscopic or photometric redshifts, redshift ranges of the background sources) in the parametric modelling of strong lensing galaxy clusters and therefore on the retrieval of cosmological parameters. We find that substructures in the outskirts have a significant impact on the position of the multiple images, yielding tighter cosmological contours. The need for wide-field imaging around massive clusters is thus reinforced. We show that competitive cosmological constraints can be obtained also with complex multimodal clusters and that photometric redshifts improve the constraints on cosmological parameters when considering a narrow range of (spectroscopic) redshifts for the sources.

Evolutionary models of rotating dense stellar systems: challenges in software and hardware

NASA Astrophysics Data System (ADS)

Fiestas, Jose

2016-02-01

We present evolutionary models of rotating self-gravitating systems (e.g. globular clusters, galaxy cores). These models are characterized by the presence of initial axisymmetry due to rotation. Central black hole seeds are alternatively included in our models, and black hole growth due to consumption of stellar matter is simulated until the central potential dominates the kinematics in the core. Goal is to study the long-term evolution (~ Gyr) of relaxed dense stellar systems, which deviate from spherical symmetry, their morphology and final kinematics. With this purpose, we developed a 2D Fokker-Planck analytical code, which results we confirm by detailed N-Body techniques, applying a high performance code, developed for GPU machines. We compare our models to available observations of galactic rotating globular clusters, and conclude that initial rotation modifies significantly the shape and lifetime of these systems, and can not be neglected in studying the evolution of globular clusters, and the galaxy itself.

INTER- AND INTRA-CLUSTER AGE GRADIENTS IN MASSIVE STAR FORMING REGIONS AND INDIVIDUAL NEARBY STELLAR CLUSTERS REVEALED BY MYStIX

NASA Astrophysics Data System (ADS)

Getman, Konstantin V.; Feigelson, Eric; Kuhn, Michael A.; Broos, Patrick S; Townsley, Leisa K.; Naylor, Tim; Povich, Matthew S.; Luhman, Kevin; Garmire, Gordon

2014-08-01

The MYStIX (Massive Young Star-Forming Complex Study in Infrared and X-ray) project seeks to characterize 20 OB-dominated young star forming regions (SFRs) at distances <4 kpc using photometric catalogs from the Chandra X-ray Observatory, Spitzer Space Telescope, UKIRT and 2MASS surveys. As part of the MYStIX project, we developed a new stellar chronometer that employs near-infrared and X-ray photometry data, AgeJX. Computing AgeJX averaged over MYStIX (sub)clusters reveals previously unknown age gradients across most of the MYStIX regions as well as within some individual rich clusters. Within the SFRs, the inferred AgeJX ages are youngest in obscured locations in molecular clouds, intermediate in revealed stellar clusters, and oldest in distributed stellar populations. Noticeable intra-cluster gradients are seen in the NGC 2024 (Flame Nebula) star cluster and the Orion Nebula Cluster (ONC): stars in cluster cores appear younger and thus were formed later than stars in cluster halos. The latter result has two important implications for the formation of young stellar clusters. Clusters likely form slowly: they do not arise from a single nearly-instantaneous burst of star formation. The simple models where clusters form inside-out are likely incorrect, and more complex models are needed. We provide several star formation scenarios that alone or in combination may lead to the observed core-halo age gradients.

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jianbao; Ma, Zhongjun, E-mail: mzj1234402@163.com; Chen, Guanrong

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding ormore » deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.« less

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

NASA Astrophysics Data System (ADS)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-06-01

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

A model of jam formation in congested traffic

NASA Astrophysics Data System (ADS)

Bunzarova, N. Zh; Pesheva, N. C.; Priezzhev, V. B.; Brankov, J. G.

2017-12-01

We study a model of irreversible jam formation in congested vehicular traffic on an open segment of a single-lane road. The vehicles obey a stochastic discrete-time dynamics which is a limiting case of the generalized Totally Asymmetric Simple Exclusion Process. Its characteristic features are: (a) the existing clusters of jammed cars cannot break into parts; (b) when the leading vehicle of a cluster hops to the right, the whole cluster follows it deterministically, and (c) any two clusters of vehicles, occupying consecutive positions on the chain, may become nearest-neighbors and merge irreversibly into a single cluster. The above dynamics was used in a one-dimensional model of irreversible aggregation by Bunzarova and Pesheva [Phys. Rev. E 95, 052105 (2017)]. The model has three stationary non-equilibrium phases, depending on the probabilities of injection (α), ejection (β), and hopping (p) of particles: a many-particle one, MP, a completely jammed phase CF, and a mixed MP+CF phase. An exact expression for the stationary probability P(1) of a completely jammed configuration in the mixed MP+CF phase is obtained. The gap distribution between neighboring clusters of jammed cars at large lengths L of the road is studied. Three regimes of evolution of the width of a single gap are found: (i) growing gaps with length of the order O(L) when β > p; (ii) shrinking gaps with length of the order O(1) when β < p; and (iii) critical gaps at β = p, of the order O(L 1/2). These results are supported by extensive Monte Carlo calculations.

Study of clustering structures through breakup reactions

NASA Astrophysics Data System (ADS)

Capel, Pierre

2014-12-01

Models for the description of breakup reactions used to study the structure of exotic cluster structures like halos are reviewed. The sensitivity of these models to the projectile description is presented. Calculations are sensitive to the projectile ground state mostly through its asymptotic normalisation coefficient (ANC). They also probe the continuum of the projectile. This enables studying not only resonant states of the projectile but also its non-resonant continuum both resonant and non-resonant. This opens the possibility to study correlations between both halo neutrons in two-neutron halo nuclei.

Adsorption and diffusion of fructose in zeolite HZSM-5: selection of models and methods for computational studies.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheng, L.; Curtiss, L. A.; Assary, R. S.

The adsorption and protonation of fructose in HZSM-5 have been studied for the assessment of models for accurate reaction energy calculations and the evaluation of molecular diffusivity. The adsorption and protonation were calculated using 2T, 5T, and 46T clusters as well as a periodic model. The results indicate that the reaction thermodynamics cannot be predicted correctly using small cluster models, such as 2T or 5T, because these small cluster models fail to represent the electrostatic effect of a zeolite cage, which provides additional stabilization to the ion pair formed upon the protonation of fructose. Structural parameters optimized using the 46Tmore » cluster model agree well with those of the full periodic model; however, the calculated reaction energies are in significant error due to the poor account of dispersion effects by density functional theory. The dispersion effects contribute -30.5 kcal/mol to the binding energy of fructose in the zeolite pore based on periodic model calculations that include dispersion interactions. The protonation of the fructose ternary carbon hydroxyl group was calculated to be exothermic by 5.5 kcal/mol with a reaction barrier of 2.9 kcal/mol using the periodic model with dispersion effects. Our results suggest that the internal diffusion of fructose in HZSM-5 is very likely to be energetically limited and only occurs at high temperature due to the large size of the molecule.« less

Comparing selected morphological models of hydrated Nafion using large scale molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Knox, Craig K.

Experimental elucidation of the nanoscale structure of hydrated Nafion, the most popular polymer electrolyte or proton exchange membrane (PEM) to date, and its influence on macroscopic proton conductance is particularly challenging. While it is generally agreed that hydrated Nafion is organized into distinct hydrophilic domains or clusters within a hydrophobic matrix, the geometry and length scale of these domains continues to be debated. For example, at least half a dozen different domain shapes, ranging from spheres to cylinders, have been proposed based on experimental SAXS and SANS studies. Since the characteristic length scale of these domains is believed to be ˜2 to 5 nm, very large molecular dynamics (MD) simulations are needed to accurately probe the structure and morphology of these domains, especially their connectivity and percolation phenomena at varying water content. Using classical, all-atom MD with explicit hydronium ions, simulations have been performed to study the first-ever hydrated Nafion systems that are large enough (~2 million atoms in a ˜30 nm cell) to directly observe several hydrophilic domains at the molecular level. These systems consisted of six of the most significant and relevant morphological models of Nafion to-date: (1) the cluster-channel model of Gierke, (2) the parallel cylinder model of Schmidt-Rohr, (3) the local-order model of Dreyfus, (4) the lamellar model of Litt, (5) the rod network model of Kreuer, and (6) a 'random' model, commonly used in previous simulations, that does not directly assume any particular geometry, distribution, or morphology. These simulations revealed fast intercluster bridge formation and network percolation in all of the models. Sulfonates were found inside these bridges and played a significant role in percolation. Sulfonates also strongly aggregated around and inside clusters. Cluster surfaces were analyzed to study the hydrophilic-hydrophobic interface. Interfacial area and cluster volume significantly increased during the simulations, suggesting the need for morphological model refinement and improvement. Radial distribution functions and structure factors were calculated. All nonrandom models exhibited the characteristic experimental scattering peak, underscoring the insensitivity of this measurement to hydrophilic domain structure and highlighting the need for future work to clearly distinguish morphological models of Nafion.

Recent advances in the Suf Fe-S cluster biogenesis pathway: Beyond the Proteobacteria

PubMed Central

Outten, F. Wayne

2014-01-01

Fe-S clusters play critical roles in cellular function throughout all three kingdoms of life. Consequently, Fe-S cluster biogenesis systems are present in most organisms. The Suf (sulfur formation) system is the most ancient of the three characterized Fe-S cluster biogenesis pathways, which also include the Isc and Nif systems. Much of the first work on the Suf system took place in Gram-negative Proteobacteria used as model organisms. These early studies led to a wealth of biochemical, genetic, and physiological information on Suf function. From those studies we have learned that SufB functions as an Fe-S scaffold in conjunction with SufC (and in some cases SufD). SufS and SufE together mobilize sulfur for cluster assembly and SufA traffics the complete Fe-S cluster from SufB to target apo-proteins. However, recent progress on the Suf system in other organisms has opened up new avenues of research and new hypotheses about Suf function. This review focuses primarily on the most recent discoveries about the Suf pathway and where those new models may lead the field. PMID:25447545

Galaxy Kinematics and Mass Calibration in Massive SZE Selected Galaxy Clusters to z=1.3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Capasso, R.; et al.

The galaxy phase-space distribution in galaxy clusters provides insights into the formation and evolution of cluster galaxies, and it can also be used to measure cluster mass profiles. We present a dynamical study based onmore » $$\\sim$$3000 passive, non-emission line cluster galaxies drawn from 110 galaxy clusters. The galaxy clusters were selected using the Sunyaev-Zel'dovich effect (SZE) in the 2500 deg$^2$ SPT-SZ survey and cover the redshift range $0.2 < z < 1.3$. We model the clusters using the Jeans equation, while adopting NFW mass profiles and a broad range of velocity dispersion anisotropy profiles. The data prefer velocity dispersion anisotropy profiles that are approximately isotropic near the center and increasingly radial toward the cluster virial radius, and this is true for all redshifts and masses we study. The pseudo-phase-space density profile of the passive galaxies is consistent with expectations for dark matter particles and subhalos from cosmological $N$-body simulations. The dynamical mass constraints are in good agreement with external mass estimates of the SPT cluster sample from either weak lensing, velocity dispersions, or X-ray $$Y_X$$ measurements. However, the dynamical masses are lower (at the 2.2$$\\sigma$$ level) when compared to the mass calibration favored when fitting the SPT cluster data to a LCDM model with external cosmological priors, including CMB anisotropy data from Planck. The tension grows with redshift, where in the highest redshift bin the ratio of dynamical to SPT+Planck masses is $$\\eta=0.63^{+0.13}_{-0.08}\\pm0.05$$ (statistical and systematic), corresponding to 2.6$$\\sigma$$ tension.« less

Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?

NASA Astrophysics Data System (ADS)

Bentes, Sónia R.; Menezes, Rui; Mendes, Diana A.

2008-06-01

Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomenon is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered.

From the Cluster Temperature Function to the Mass Function at Low Z

NASA Technical Reports Server (NTRS)

Mushotzky, Richard (Technical Monitor); Markevitch, Maxim

2004-01-01

This XMM project consisted of three observations of the nearby, hot galaxy cluster Triangulum Australis, one of the cluster center and two offsets. The goal was to measure the radial gas temperature profile out to large radii and derive the total gravitating mass within the radius of average mass overdensity 500. The central pointing also provides data for a detailed two-dimensional gas temperature map of this interesting cluster. We have analyzed all three observations. The derivation of the temperature map using the central pointing is complete, and the paper is soon to be submitted. During the course of this study and of the analysis of archival XMM cluster observations, it became apparent that the commonly used XMM background flare screening techniques are often not accurate enough for studies of the cluster outer regions. The information on the cluster's total masses is contained at large off-center distances, and it is precisely the temperatures for those low-brightness regions that are most affected by the detector background anomalies. In particular, our two offset observations of the Triangulum have been contaminated by the background flares ("bad cosmic weather") to a degree where they could not be used for accurate spectral analysis. This forced us to expand the scope of our project. We needed to devise a more accurate method of screening and modeling the background flares, and to evaluate the uncertainty of the XMM background modeling. To do this, we have analyzed a large number of archival EPIC blank-field and closed-cover observations. As a result, we have derived stricter background screening criteria. It also turned out that mild flares affecting EPIC-pn can be modeled with an adequate accuracy. Such modeling has been used to derive our Triangulum temperature map. The results of our XMM background analysis, including the modeling recipes, are presented in a paper which is in final preparation and will be submitted soon. It will be useful not only for our future analysis but for other XMM cluster observations as well.

Phase-locked cluster oscillations in periodically forced integrate-and-fire-or-burst neuronal populations.

PubMed

Langdon, Angela J; Breakspear, Michael; Coombes, Stephen

2012-12-01

The minimal integrate-and-fire-or-burst neuron model succinctly describes both tonic firing and postinhibitory rebound bursting of thalamocortical cells in the sensory relay. Networks of integrate-and-fire-or-burst (IFB) neurons with slow inhibitory synaptic interactions have been shown to support stable rhythmic states, including globally synchronous and cluster oscillations, in which network-mediated inhibition cyclically generates bursting in coherent subgroups of neurons. In this paper, we introduce a reduced IFB neuronal population model to study synchronization of inhibition-mediated oscillatory bursting states to periodic excitatory input. Using numeric methods, we demonstrate the existence and stability of 1:1 phase-locked bursting oscillations in the sinusoidally forced IFB neuronal population model. Phase locking is shown to arise when periodic excitation is sufficient to pace the onset of bursting in an IFB cluster without counteracting the inhibitory interactions necessary for burst generation. Phase-locked bursting states are thus found to destabilize when periodic excitation increases in strength or frequency. Further study of the IFB neuronal population model with pulse-like periodic excitatory input illustrates that this synchronization mechanism generalizes to a broad range of n:m phase-locked bursting states across both globally synchronous and clustered oscillatory regimes.

Protein Ligation of the Photosynthetic Oxygen-Evolving Center

PubMed Central

Debus, Richard J.

2008-01-01

Photosynthetic water oxidation is catalyzed by a unique Mn4Ca cluster in Photosystem II. The ligation environment of the Mn4Ca cluster optimizes the cluster’s reactivity at each step in the catalytic cycle and minimizes the release of toxic, partly oxidized intermediates. However, our understanding of the cluster’s ligation environment remains incomplete. Although the recent X-ray crystallographic structural models have provided great insight and are consistent with most conclusions of earlier site-directed mutagenesis studies, the ligation environments of the Mn4Ca cluster in the two available structural models differ in important respects. Furthermore, while these structural models and the earlier mutagenesis studies agree on the identity of most of the Mn4Ca cluster’s amino acid ligands, they disagree on the identity of others. This review describes mutant characterizations that have been undertaken to probe the ligation environment of the Mn4Ca cluster, some of which have been inspired by the recent X-ray crystallographic structural models. Many of these characterizations have involved Fourier Transform Infrared (FTIR) difference spectroscopy because of the extreme sensitivity of this form of spectroscopy to the dynamic structural changes that occur during an enzyme’s catalytic cycle. PMID:18496594

Activity-induced clustering in model dumbbell swimmers: the role of hydrodynamic interactions.

PubMed

Furukawa, Akira; Marenduzzo, Davide; Cates, Michael E

2014-08-01

Using a fluid-particle dynamics approach, we numerically study the effects of hydrodynamic interactions on the collective dynamics of active suspensions within a simple model for bacterial motility: each microorganism is modeled as a stroke-averaged dumbbell swimmer with prescribed dipolar force pairs. Using both simulations and qualitative arguments, we show that, when the separation between swimmers is comparable to their size, the swimmers' motions are strongly affected by activity-induced hydrodynamic forces. To further understand these effects, we investigate semidilute suspensions of swimmers in the presence of thermal fluctuations. A direct comparison between simulations with and without hydrodynamic interactions shows these to enhance the dynamic clustering at a relatively small volume fraction; with our chosen model the key ingredient for this clustering behavior is hydrodynamic trapping of one swimmer by another, induced by the active forces. Furthermore, the density dependence of the motility (of both the translational and rotational motions) exhibits distinctly different behaviors with and without hydrodynamic interactions; we argue that this is linked to the clustering tendency. Our study illustrates the fact that hydrodynamic interactions not only affect kinetic pathways in active suspensions, but also cause major changes in their steady state properties.

Activity-induced clustering in model dumbbell swimmers: The role of hydrodynamic interactions

NASA Astrophysics Data System (ADS)

Furukawa, Akira; Marenduzzo, Davide; Cates, Michael E.

2014-08-01

Using a fluid-particle dynamics approach, we numerically study the effects of hydrodynamic interactions on the collective dynamics of active suspensions within a simple model for bacterial motility: each microorganism is modeled as a stroke-averaged dumbbell swimmer with prescribed dipolar force pairs. Using both simulations and qualitative arguments, we show that, when the separation between swimmers is comparable to their size, the swimmers' motions are strongly affected by activity-induced hydrodynamic forces. To further understand these effects, we investigate semidilute suspensions of swimmers in the presence of thermal fluctuations. A direct comparison between simulations with and without hydrodynamic interactions shows these to enhance the dynamic clustering at a relatively small volume fraction; with our chosen model the key ingredient for this clustering behavior is hydrodynamic trapping of one swimmer by another, induced by the active forces. Furthermore, the density dependence of the motility (of both the translational and rotational motions) exhibits distinctly different behaviors with and without hydrodynamic interactions; we argue that this is linked to the clustering tendency. Our study illustrates the fact that hydrodynamic interactions not only affect kinetic pathways in active suspensions, but also cause major changes in their steady state properties.

Influence of Aromatic Molecules on the Structure and Spectroscopy of Water Clusters

NASA Astrophysics Data System (ADS)

Tabor, Daniel P.; Sibert, Edwin; Walsh, Patrick S.; Zwier, Timothy S.

2016-06-01

Isomer-specific resonant ion-dip infrared spectra are presented for benzene-(water)_n, 1-2-diphenoxyethane-(water)_n, and tricyclophane-(water)_n clusters. The IR spectra are modeled with a local mode Hamiltonian that was originally formulated for the analysis of benzene-(water)_n clusters with up to seven waters. The model accounts for stretch-bend Fermi coupling, which can complicate the IR spectra in the 3150-3300 cm-1 region. When the water clusters interact with each of the solutes, the hydrogen bond lengths between the water molecules change in a characteristic way, reflecting the strength of the solute-water interaction. These structural effects are also reflected spectroscopically in the shifts of the local mode OH stretch frequencies. When diphenoxyethane is the solute, the water clusters distort more significantly than when bound to benzene. Tricyclophane's structure provides an aromatic-rich binding pocket for the water clusters. The local mode model is used to extract Hamiltonians for individual water molecules. These monomer Hamiltonians divide into groups based on their local H-bonding architecture, allowing for further classification of the wide variety of water environments encountered in this study.

Is gender policy related to the gender gap in external cause and circulatory disease mortality? A mixed effects model of 22 OECD countries 1973-2008.

PubMed

Backhans, Mona; Burström, Bo; de Leon, Antonio Ponce; Marklund, Staffan

2012-11-12

Gender differences in mortality vary widely between countries and over time, but few studies have examined predictors of these variations, apart from smoking. The aim of this study is to investigate the link between gender policy and the gender gap in cause-specific mortality, adjusted for economic factors and health behaviours. 22 OECD countries were followed 1973-2008 and the outcomes were gender gaps in external cause and circulatory disease mortality. A previously found country cluster solution was used, which includes indicators on taxes, parental leave, pensions, social insurances and social services in kind. Male breadwinner countries were made reference group and compared to earner-carer, compensatory breadwinner, and universal citizen countries. Specific policies were also analysed. Mixed effect models were used, where years were the level 1-units, and countries were the level 2-units. Both the earner-carer cluster (ns after adjustment for GDP) and policies characteristic of that cluster are associated with smaller gender differences in external causes, particularly due to an association with increased female mortality. Cluster differences in the gender gap in circulatory disease mortality are the result of a larger relative decrease of male mortality in the compensatory breadwinner cluster and the earner-carer cluster. Policies characteristic of those clusters were however generally related to increased mortality. Results for external cause mortality are in concordance with the hypothesis that women become more exposed to risks of accident and violence when they are economically more active. For circulatory disease mortality, results differ depending on approach--cluster or indicator. Whether cluster differences not explained by specific policies reflect other welfare policies or unrelated societal trends is an open question. Recommendations for further studies are made.

Clustering P-Wave Receiver Functions To Constrain Subsurface Seismic Structure

NASA Astrophysics Data System (ADS)

Chai, C.; Larmat, C. S.; Maceira, M.; Ammon, C. J.; He, R.; Zhang, H.

2017-12-01

The acquisition of high-quality data from permanent and temporary dense seismic networks provides the opportunity to apply statistical and machine learning techniques to a broad range of geophysical observations. Lekic and Romanowicz (2011) used clustering analysis on tomographic velocity models of the western United States to perform tectonic regionalization and the velocity-profile clusters agree well with known geomorphic provinces. A complementary and somewhat less restrictive approach is to apply cluster analysis directly to geophysical observations. In this presentation, we apply clustering analysis to teleseismic P-wave receiver functions (RFs) continuing efforts of Larmat et al. (2015) and Maceira et al. (2015). These earlier studies validated the approach with surface waves and stacked EARS RFs from the USArray stations. In this study, we experiment with both the K-means and hierarchical clustering algorithms. We also test different distance metrics defined in the vector space of RFs following Lekic and Romanowicz (2011). We cluster data from two distinct data sets. The first, corresponding to the western US, was by smoothing/interpolation of receiver-function wavefield (Chai et al. 2015). Spatial coherence and agreement with geologic region increase with this simpler, spatially smoothed set of observations. The second data set is composed of RFs for more than 800 stations of the China Digital Seismic Network (CSN). Preliminary results show a first order agreement between clusters and tectonic region and each region cluster includes a distinct Ps arrival, which probably reflects differences in crustal thickness. Regionalization remains an important step to characterize a model prior to application of full waveform and/or stochastic imaging techniques because of the computational expense of these types of studies. Machine learning techniques can provide valuable information that can be used to design and characterize formal geophysical inversion, providing information on spatial variability in the subsurface geology.

Isotropic model for cluster growth on a regular lattice

NASA Astrophysics Data System (ADS)

Yates, Christian A.; Baker, Ruth E.

2013-08-01

There exists a plethora of mathematical models for cluster growth and/or aggregation on regular lattices. Almost all suffer from inherent anisotropy caused by the regular lattice upon which they are grown. We analyze the little-known model for stochastic cluster growth on a regular lattice first introduced by Ferreira Jr. and Alves [J. Stat. Mech. Theo. & Exp.1742-546810.1088/1742-5468/2006/11/P11007 (2006) P11007], which produces circular clusters with no discernible anisotropy. We demonstrate that even in the noise-reduced limit the clusters remain circular. We adapt the model by introducing a specific rearrangement algorithm so that, rather than adding elements to the cluster from the outside (corresponding to apical growth), our model uses mitosis-like cell splitting events to increase the cluster size. We analyze the surface scaling properties of our model and compare it to the behavior of more traditional models. In “1+1” dimensions we discover and explore a new, nonmonotonic surface thickness scaling relationship which differs significantly from the Family-Vicsek scaling relationship. This suggests that, for models whose clusters do not grow through particle additions which are solely dependent on surface considerations, the traditional classification into “universality classes” may not be appropriate.

Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials.

PubMed

Gomes, Manuel; Ng, Edmond S-W; Grieve, Richard; Nixon, Richard; Carpenter, James; Thompson, Simon G

2012-01-01

Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering--seemingly unrelated regression (SUR) without a robust standard error (SE)--and 4 methods that recognized clustering--SUR and generalized estimating equations (GEEs), both with robust SE, a "2-stage" nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92-0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less

Search for Directed Networks by Different Random Walk Strategies

NASA Astrophysics Data System (ADS)

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.

Surface properties for α-cluster nuclear matter

NASA Astrophysics Data System (ADS)

Castro, J. J.; Soto, J. R.; Yépez, E.

2013-03-01

We introduce a new microscopic model for α-cluster matter, which simulates the properties of ordinary nuclear matter and α-clustering in a curved surface of a large but finite nucleus. The model is based on a nested icosahedral fullerene-like multiple-shell structure, where each vertex is occupied by a microscopic α-particle. The novel aspect of this model is that it allows a consistent description of nuclear surface properties from microscopic parameters to be made without using the leptodermous expansion. In particular, we show that the calculated surface energy is in excellent agreement with the corresponding coefficient of the Bethe-Weizäcker semi-empirical mass formula. We discuss the properties of the surface α-cluster state, which resembles an ultra cold bosonic quantum gas trapped in an optical lattice. By comparing the surface and interior states we are able to estimate the α preformation probability. Possible extensions of this model to study nuclear dynamics through surface vibrations and departures from approximate sphericity are mentioned.

Dispersed or clustered housing for adults with intellectual disability: a systematic review.

PubMed

Mansell, Jim; Beadle-Brown, Julie

2009-12-01

The purpose of this review was to evaluate the available research on the quality and costs of dispersed community-based housing when compared with clustered housing. Searches against specified criteria yielded 19 papers based on 10 studies presenting data comparing dispersed housing with some kind of clustered housing (village communities, residential campuses, or clusters of houses). The studies reported the experience of nearly 2,500 people from four different countries. In five of eight quality of life domains there were no studies reporting benefits of clustered settings. In respect of interpersonal relations, emotional, and physical well-being, clustered settings had some advantages. However, in many of these cases the better results refer only to village communities and not to campus housing or clustered housing. In terms of costs, clustered housing was usually less expensive because of lower staffing levels. In two of the three studies that examined costs controlling for user characteristics, there was no statistically significant difference. Dispersed housing appears to be superior to clustered housing on the majority of quality indicators studied. The only exception to this is that village communities for people with less severe disabilities have some benefits; this is not, however, a model which can be feasibly provided for everyone. Clustered housing is usually less expensive than dispersed housing but this is because it provides fewer staff hours per person. There is no evidence that clustered housing can deliver the same quality of life as dispersed housing at a lower cost.

Strategic Development for Middle School Students Struggling With Fractions: Assessment and Intervention.

PubMed

Zhang, Dake; Stecker, Pamela; Huckabee, Sloan; Miller, Rhonda

2016-09-01

Research has suggested that different strategies used when solving fraction problems are highly correlated with students' problem-solving accuracy. This study (a) utilized latent profile modeling to classify students into three different strategic developmental levels in solving fraction comparison problems and (b) accordingly provided differentiated strategic training for students starting from two different strategic developmental levels. In Study 1 we assessed 49 middle school students' performance on fraction comparison problems and categorized students into three clusters of strategic developmental clusters: a cross-multiplication cluster with the highest accuracy, a representation strategy cluster with medium accuracy, and a whole-number strategy cluster with the lowest accuracy. Based on the strategic developmental levels identified in Study 1, in Study 2 we selected three students from the whole-number strategy cluster and another three students from the representation strategy cluster and implemented a differentiated strategic training intervention within a multiple-baseline design. Results showed that both groups of students transitioned from less advanced to more advanced strategies and improved their problem-solving accuracy during the posttest, the maintenance test, and the generalization test. © Hammill Institute on Disabilities 2014.

Population Structure With Localized Haplotype Clusters

PubMed Central

Browning, Sharon R.; Weir, Bruce S.

2010-01-01

We propose a multilocus version of FST and a measure of haplotype diversity using localized haplotype clusters. Specifically, we use haplotype clusters identified with BEAGLE, which is a program implementing a hidden Markov model for localized haplotype clustering and performing several functions including inference of haplotype phase. We apply this methodology to HapMap phase 3 data. With this haplotype-cluster approach, African populations have highest diversity and lowest divergence from the ancestral population, East Asian populations have lowest diversity and highest divergence, and other populations (European, Indian, and Mexican) have intermediate levels of diversity and divergence. These relationships accord with expectation based on other studies and accepted models of human history. In contrast, the population-specific FST estimates obtained directly from single-nucleotide polymorphisms (SNPs) do not reflect such expected relationships. We show that ascertainment bias of SNPs has less impact on the proposed haplotype-cluster-based FST than on the SNP-based version, which provides a potential explanation for these results. Thus, these new measures of FST and haplotype-cluster diversity provide an important new tool for population genetic analysis of high-density SNP data. PMID:20457877

Cluster growth mechanisms in Lennard-Jones fluids: A comparison between molecular dynamics and Brownian dynamics simulations

NASA Astrophysics Data System (ADS)

Jung, Jiyun; Lee, Jumin; Kim, Jun Soo

2015-03-01

We present a simulation study on the mechanisms of a phase separation in dilute fluids of Lennard-Jones (LJ) particles as a model of self-interacting molecules. Molecular dynamics (MD) and Brownian dynamics (BD) simulations of the LJ fluids are employed to model the condensation of a liquid droplet in the vapor phase and the mesoscopic aggregation in the solution phase, respectively. With emphasis on the cluster growth at late times well beyond the nucleation stage, we find that the growth mechanisms can be qualitatively different: cluster diffusion and coalescence in the MD simulations and Ostwald ripening in the BD simulations. We also show that the rates of the cluster growth have distinct scaling behaviors during cluster growth. This work suggests that in the solution phase the random Brownian nature of the solute dynamics may lead to the Ostwald ripening that is qualitatively different from the cluster coalescence in the vapor phase.

Identifying Two Groups of Entitled Individuals: Cluster Analysis Reveals Emotional Stability and Self-Esteem Distinction.

PubMed

Crowe, Michael L; LoPilato, Alexander C; Campbell, W Keith; Miller, Joshua D

2016-12-01

The present study hypothesized that there exist two distinct groups of entitled individuals: grandiose-entitled, and vulnerable-entitled. Self-report scores of entitlement were collected for 916 individuals using an online platform. Model-based cluster analyses were conducted on the individuals with scores one standard deviation above mean (n = 159) using the five-factor model dimensions as clustering variables. The results support the existence of two groups of entitled individuals categorized as emotionally stable and emotionally vulnerable. The emotionally stable cluster reported emotional stability, high self-esteem, more positive affect, and antisocial behavior. The emotionally vulnerable cluster reported low self-esteem and high levels of neuroticism, disinhibition, conventionality, psychopathy, negative affect, childhood abuse, intrusive parenting, and attachment difficulties. Compared to the control group, both clusters reported being more antagonistic, extraverted, Machiavellian, and narcissistic. These results suggest important differences are missed when simply examining the linear relationships between entitlement and various aspects of its nomological network.

Sliding states of a soft-colloid cluster crystal: Cluster versus single-particle hopping

NASA Astrophysics Data System (ADS)

Rossini, Mirko; Consonni, Lorenzo; Stenco, Andrea; Reatto, Luciano; Manini, Nicola

2018-05-01

We study a two-dimensional model for interacting colloidal particles which displays spontaneous clustering. Within this model we investigate the competition between the pinning to a periodic corrugation potential and a sideways constant pulling force which would promote a sliding state. For a few sample particle densities and amplitudes of the periodic corrugation potential we investigate the depinning from the statically pinned to the dynamically sliding regime. This sliding state exhibits the competition between a dynamics where entire clusters are pulled from a minimum to the next and a dynamics where single colloids or smaller groups leave a cluster and move across the corrugation energy barrier to join the next cluster downstream in the force direction. Both kinds of sliding states can occur either coherently across the entire sample or asynchronously: the two regimes result in different average mobilities. Finite temperature tends to destroy separate sliding regimes, generating a smoother dependence of the mobility on the driving force.

Methods in Computational Cosmology

NASA Astrophysics Data System (ADS)

Vakili, Mohammadjavad

State of the inhomogeneous universe and its geometry throughout cosmic history can be studied by measuring the clustering of galaxies and the gravitational lensing of distant faint galaxies. Lensing and clustering measurements from large datasets provided by modern galaxy surveys will forever shape our understanding of the how the universe expands and how the structures grow. Interpretation of these rich datasets requires careful characterization of uncertainties at different stages of data analysis: estimation of the signal, estimation of the signal uncertainties, model predictions, and connecting the model to the signal through probabilistic means. In this thesis, we attempt to address some aspects of these challenges. The first step in cosmological weak lensing analyses is accurate estimation of the distortion of the light profiles of galaxies by large scale structure. These small distortions, known as the cosmic shear signal, are dominated by extra distortions due to telescope optics and atmosphere (in the case of ground-based imaging). This effect is captured by a kernel known as the Point Spread Function (PSF) that needs to be fully estimated and corrected for. We address two challenges a head of accurate PSF modeling for weak lensing studies. The first challenge is finding the centers of point sources that are used for empirical estimation of the PSF. We show that the approximate methods for centroiding stars in wide surveys are able to optimally saturate the information content that is retrievable from astronomical images in the presence of noise. The fist step in weak lensing studies is estimating the shear signal by accurately measuring the shapes of galaxies. Galaxy shape measurement involves modeling the light profile of galaxies convolved with the light profile of the PSF. Detectors of many space-based telescopes such as the Hubble Space Telescope (HST) sample the PSF with low resolution. Reliable weak lensing analysis of galaxies observed by the HST camera requires knowledge of the PSF at a resolution higher than the pixel resolution of HST. This PSF is called the super-resolution PSF. In particular, we present a forward model of the point sources imaged through filters of the HST WFC3 IR channel. We show that this forward model can accurately estimate the super-resolution PSF. We also introduce a noise model that permits us to robustly analyze the HST WFC3 IR observations of the crowded fields. Then we try to address one of the theoretical uncertainties in modeling of galaxy clustering on small scales. Study of small scale clustering requires assuming a halo model. Clustering of halos has been shown to depend on halo properties beyond mass such as halo concentration, a phenomenon referred to as assembly bias. Standard large-scale structure studies with halo occupation distribution (HOD) assume that halo mass alone is sufficient to characterize the connection between galaxies and halos. However, assembly bias could cause the modeling of galaxy clustering to face systematic effects if the expected number of galaxies in halos is correlated with other halo properties. Using high resolution N-body simulations and the clustering measurements of Sloan Digital Sky Survey (SDSS) DR7 main galaxy sample, we show that modeling of galaxy clustering can slightly improve if we allow the HOD model to depend on halo properties beyond mass. One of the key ingredients in precise parameter inference using galaxy clustering is accurate estimation of the error covariance matrix of clustering measurements. This requires generation of many independent galaxy mock catalogs that accurately describe the statistical distribution of galaxies in a wide range of physical scales. We present a fast and accurate method based on low-resolution N-body simulations and an empirical bias model for generating mock catalogs. We use fast particle mesh gravity solvers for generation of dark matter density field and we use Markov Chain Monti Carlo (MCMC) to estimate the bias model that connects dark matter to galaxies. We show that this approach enables the fast generation of mock catalogs that recover clustering at a percent-level accuracy down to quasi-nonlinear scales. Cosmological datasets are interpreted by specifying likelihood functions that are often assumed to be multivariate Gaussian. Likelihood free approaches such as Approximate Bayesian Computation (ABC) can bypass this assumption by introducing a generative forward model of the data and a distance metric for quantifying the closeness of the data and the model. We present the first application of ABC in large scale structure for constraining the connections between galaxies and dark matter halos. We present an implementation of ABC equipped with Population Monte Carlo and a generative forward model of the data that incorporates sample variance and systematic uncertainties. (Abstract shortened by ProQuest.).

Inductive Approaches to Improving Diagnosis and Design for Diagnosability

NASA Technical Reports Server (NTRS)

Fisher, Douglas H. (Principal Investigator)

1995-01-01

The first research area under this grant addresses the problem of classifying time series according to their morphological features in the time domain. A supervised learning system called CALCHAS, which induces a classification procedure for signatures from preclassified examples, was developed. For each of several signature classes, the system infers a model that captures the class's morphological features using Bayesian model induction and the minimum message length approach to assign priors. After induction, a time series (signature) is classified in one of the classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. A second area of research assumes two sources of information about a system: a model or domain theory that encodes aspects of the system under study and data from actual system operations over time. A model, when it exists, represents strong prior expectations about how a system will perform. Our work with a diagnostic model of the RCS (Reaction Control System) of the Space Shuttle motivated the development of SIG, a system which combines information from a model (or domain theory) and data. As it tracks RCS behavior, the model computes quantitative and qualitative values. Induction is then performed over the data represented by both the 'raw' features and the model-computed high-level features. Finally, work on clustering for operating mode discovery motivated some important extensions to the clustering strategy we had used. One modification appends an iterative optimization technique onto the clustering system; this optimization strategy appears to be novel in the clustering literature. A second modification improves the noise tolerance of the clustering system. In particular, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus making post-clustering analysis easier.

Generalized vibrating potential model for collective excitations in spherical, deformed and superdeformed systems: (1) Atomic nuclei, (2) Metal clusters

NASA Astrophysics Data System (ADS)

Nesterenko, V. O.; Kleinig, W.

1995-01-01

The self-consistent vibrating potential model (VPM) is extended for description of Eλ collective excitations in atomic nuclei and metal clusters with practically any kind of static deformation. The model is convenient for a qualitative analysis and provides the RPA accuracy of numerical calculations. The VPM is applied to study Eλ giant resonances in spherical metal clusters and deformed and superdeformed nuclei. It is shown that the deformation splitting of superdeformed nuclei results in a very complicated ("jungle-like") structure of the resonances, which makes the experimental observation of E2 and E3 giant resonances in superdeformed nuclei quite problematic. Calculations of E1 giant resonances in spherical sodium clusters Na8, Na20 and Na40 are presented, as a test of the VPM in this field. The results are in qualitative agreement with the experimental data.

Ontology-based topic clustering for online discussion data

NASA Astrophysics Data System (ADS)

Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

2013-03-01

With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

Experimental and theoretical investigation of the magnetization dynamics of an artificial square spin ice cluster

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pohlit, Merlin, E-mail: pohlit@physik.uni-frankfurt.de; Porrati, Fabrizio; Huth, Michael

We study the magnetization dynamics of a spin ice cluster which is a building block of an artificial square spin ice fabricated by focused electron-beam-induced deposition both experimentally and theoretically. The spin ice cluster is composed of twelve interacting Co nanoislands grown directly on top of a high-resolution micro-Hall sensor. By employing micromagnetic simulations and a macrospin model, we calculate the magnetization and the experimentally investigated stray field emanating from a single nanoisland. The parameters determined from a comparison with the experimental hysteresis loop are used to derive an effective single-dipole macrospin model that allows us to investigate the dynamicsmore » of the spin ice cluster. Our model reproduces the experimentally observed non-deterministic sequences in the magnetization curves as well as the distinct temperature dependence of the hysteresis loop.« less

Effect of clustering on the emission of light charged particles

NASA Astrophysics Data System (ADS)

Kundu, Samir; Bhattacharya, C.; Rana, T. K.; Bhattacharya, S.; Pandey, R.; Banerjee, K.; Roy, Pratap; Meena, J. K.; Mukherjee, G.; Ghosh, T. K.; Mukhopadhyay, S.; Saha, A. K.; Sahoo, J. K.; Mandal Saha, R.; Srivastava, V.; Sinha, M.; Asgar, Md. A.

2018-04-01

Energy spectra of light charged particles emitted in the reaction p + {}^{27}Al → {}^{28}Si^{\\ast} have been studied and compared with statistical model calculation. Unlike 16O + 12C where a large deformation was observed in 28Si*, the energy spectra of α-particles were well explained by the statistical model calculation with standard "deformability parameters" obtained using the rotating liquid drop model. It seems that the α-clustering in the entrance channel causes extra deformation in 28Si* in the case of 16O + 12C, but the reanalysis of other published data shows that there are several cases where extra deformation was observed for composites produced via non-α-cluster entrance channels also. An empirical relation was found between mass-asymmetry in the entrance channel and deformation, which indicates that along with α-clustering, mass-asymmetry may also affect the emission of a light charged particle.

Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Melchior, P.; Suchyta, E.; Huff, E.

2015-03-31

We measure the weak-lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey. This pathfinder study is meant to 1) validate the DECam imager for the task of measuring weak-lensing shapes, and 2) utilize DECam's large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, PSF modeling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Sciencemore » Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well-behaved, but the modeling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting NFW profiles to the clusters in this study, we determine weak-lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak-lensing mass, and richness. In addition, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1 degree (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.« less

Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

DOE PAGES

Melchior, P.; Suchyta, E.; Huff, E.; ...

2015-03-31

We measure the weak-lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey. This pathfinder study is meant to 1) validate the DECam imager for the task of measuring weak-lensing shapes, and 2) utilize DECam's large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, PSF modelling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Sciencemore » Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well-behaved, but the modelling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting NFW profiles to the clusters in this study, we determine weak-lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak-lensing mass, and richness. Additionally, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1degree (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.« less

A coupled-cluster study of photodetachment cross sections of closed-shell anions

NASA Astrophysics Data System (ADS)

Cukras, Janusz; Decleva, Piero; Coriani, Sonia

2014-11-01

We investigate the performance of Stieltjes Imaging applied to Lanczos pseudo-spectra generated at the coupled cluster singles and doubles, coupled cluster singles and approximate iterative doubles and coupled cluster singles levels of theory in modeling the photodetachment cross sections of the closed shell anions H-, Li-, Na-, F-, Cl-, and OH-. The accurate description of double excitations is found to play a much more important role than in the case of photoionization of neutral species.

A coupled-cluster study of photodetachment cross sections of closed-shell anions.

PubMed

Cukras, Janusz; Decleva, Piero; Coriani, Sonia

2014-11-07

We investigate the performance of Stieltjes Imaging applied to Lanczos pseudo-spectra generated at the coupled cluster singles and doubles, coupled cluster singles and approximate iterative doubles and coupled cluster singles levels of theory in modeling the photodetachment cross sections of the closed shell anions H(-), Li(-), Na(-), F(-), Cl(-), and OH(-). The accurate description of double excitations is found to play a much more important role than in the case of photoionization of neutral species.

Earthquake Clustering in Noisy Viscoelastic Systems

NASA Astrophysics Data System (ADS)

Dicaprio, C. J.; Simons, M.; Williams, C. A.; Kenner, S. J.

2006-12-01

Geologic studies show evidence for temporal clustering of earthquakes on certain fault systems. Since post- seismic deformation may result in a variable loading rate on a fault throughout the inter-seismic period, it is reasonable to expect that the rheology of the non-seismogenic lower crust and mantle lithosphere may play a role in controlling earthquake recurrence times. Previously, the role of rheology of the lithosphere on the seismic cycle had been studied with a one-dimensional spring-dashpot-slider model (Kenner and Simons [2005]). In this study we use the finite element code PyLith to construct a two-dimensional continuum model a strike-slip fault in an elastic medium overlying one or more linear Maxwell viscoelastic layers loaded in the far field by a constant velocity boundary condition. Taking advantage of the linear properties of the model, we use the finite element solution to one earthquake as a spatio-temporal Green's function. Multiple Green's function solutions, scaled by the size of each earthquake, are then summed to form an earthquake sequence. When the shear stress on the fault reaches a predefined yield stress it is allowed to slip, relieving all accumulated shear stress. Random variation in the fault yield stress from one earthquake to the next results in a temporally clustered earthquake sequence. The amount of clustering depends on a non-dimensional number, W, called the Wallace number. For models with one viscoelastic layer, W is equal to the standard deviation of the earthquake stress drop divided by the viscosity times the tectonic loading rate. This definition of W is modified from the original one used in Kenner and Simons [2005] by using the standard deviation of the stress drop instead of the mean stress drop. We also use a new, more appropriate, metric to measure the amount of temporal clustering of the system. W is the ratio of the viscoelastic relaxation rate of the system to the tectonic loading rate of the system. For values of W greater than the critical value of about 10, the clustered earthquake behavior is due to the rapid reloading of the fault due to viscoelastic recycling of stress. A model with multiple viscoelastic layers has more complex clustering behavior than a system with only one viscosity. In this case, multiple clustering modes exist; the size and mean period of which are influenced by the viscosities and relative thicknesses of the viscoelastic layers. Kenner, S.J. and Simons, M., (2005), Temporal cluster of major earthquakes along individual faults due to post-seismic reloading, Geophysical Journal International, 160, 179-194.

Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

NASA Astrophysics Data System (ADS)

Toh, Ying Ying; Turner, Andrew G.; Johnson, Stephanie J.; Holloway, Christopher E.

2018-02-01

The fidelity of 28 Coupled Model Intercomparison Project phase 5 (CMIP5) models in simulating mean climate over the Maritime Continent in the Atmospheric Model Intercomparison Project (AMIP) experiment is evaluated in this study. The performance of AMIP models varies greatly in reproducing seasonal mean climate and the seasonal cycle. The multi-model mean has better skill at reproducing the observed mean climate than the individual models. The spatial pattern of 850 hPa wind is better simulated than the precipitation in all four seasons. We found that model horizontal resolution is not a good indicator of model performance. Instead, a model's local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon. The comparison with coupled models in CMIP5 shows that AMIP models generally performed better than coupled models in the simulation of the global monsoon and local Hadley circulation but less well at simulating the Maritime Continent annual cycle of precipitation. To characterize model systematic biases in the AMIP runs, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I models are able to capture both the winter monsoon and summer monsoon shift, but they overestimate the precipitation; especially during the JJA and SON seasons. Cluster II models simulate weaker seasonal migration than observed, and the maximum rainfall position stays closer to the equator throughout the year. The tropics-wide properties of these clusters suggest a connection between the skill of simulating global properties of the monsoon circulation and the skill of simulating the regional scale of Maritime Continent precipitation.

Perspective: Size selected clusters for catalysis and electrochemistry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro

We report that size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this Perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition,more » cluster-support interactions and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modelling based on density functional theory sampling of local minima and energy barriers or ab initio Molecular Dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Lastly, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.« less

Perspective: Size selected clusters for catalysis and electrochemistry

DOE PAGES

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; ...

2018-03-15

We report that size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this Perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition,more » cluster-support interactions and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modelling based on density functional theory sampling of local minima and energy barriers or ab initio Molecular Dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Lastly, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.« less

Perspective: Size selected clusters for catalysis and electrochemistry

NASA Astrophysics Data System (ADS)

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; Vajda, Stefan

2018-03-01

Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.

X-Ray Gas Temperatures in the Arc Clusters MS0440+204 and MS0302+1658

NASA Technical Reports Server (NTRS)

Gioia, Isabella M.; White, Nicholas

1997-01-01

The cluster of galaxies MS0440+02, originally discovered through its X-ray emission, was part of an optical observational program to search for arcs and arclets in a complete sample of X-ray luminous, medium-distant clusters of galaxies. Mauna Kea CCD images of MS0440+02 showed a remarkable optical morphology. The core of the cluster contains 6 bright galaxies and numerous fainter ones embedded in a low surface brightness halo. Besides, MS0440+02 is the most spectacular example that we have found of an arc system in a compact condensed cluster, with arcs symmetrically distributed to draw almost perfect circles around the cluster center. Giant arcs are magnified images of distant galaxies, gravitationally distorted by massive foreground clusters. It is of great importance to compare the results of the lensing studies with those derived from X-ray observations, as the two are independent methods of studying the mass distribution. Thus MS0440+02 was the ideal target to obtain temperature measurement with ASCA and good spatial resolution X-ray observations with ROSAT. The X-ray data have been used in conjunction with Hubble Space Telescope observations to put more stringent constrains on the mass estimates. Most of the different wavelength datasets have been reduced and analyzed. Mass determinations have been separately obtained from galaxy virial motions and X-ray profile fitting using the cluster gas temperature as measured by the ASCA satellite. Assuming that the hot gas is in hydrostatic equilibrium and in a spherical potential, we find from the X-ray data a mass distribution profile that is well described by a Beta model. From the multiple images formed by gravitational lensing (HST data) using the modelling of the gravitational lensed arcs, we have derived Beta model. To reconcile the mass estimates we have explored the possibility of having a supercluster surrounding the MOS0440 cluster, that is a model with two isothermal spheres, one embedded inside the other. These results have been published or are in press.

MIXED MODEL AND ESTIMATING EQUATION APPROACHES FOR ZERO INFLATION IN CLUSTERED BINARY RESPONSE DATA WITH APPLICATION TO A DATING VIOLENCE STUDY1

PubMed Central

Fulton, Kara A.; Liu, Danping; Haynie, Denise L.; Albert, Paul S.

2016-01-01

The NEXT Generation Health study investigates the dating violence of adolescents using a survey questionnaire. Each student is asked to affirm or deny multiple instances of violence in his/her dating relationship. There is, however, evidence suggesting that students not in a relationship responded to the survey, resulting in excessive zeros in the responses. This paper proposes likelihood-based and estimating equation approaches to analyze the zero-inflated clustered binary response data. We adopt a mixed model method to account for the cluster effect, and the model parameters are estimated using a maximum-likelihood (ML) approach that requires a Gaussian–Hermite quadrature (GHQ) approximation for implementation. Since an incorrect assumption on the random effects distribution may bias the results, we construct generalized estimating equations (GEE) that do not require the correct specification of within-cluster correlation. In a series of simulation studies, we examine the performance of ML and GEE methods in terms of their bias, efficiency and robustness. We illustrate the importance of properly accounting for this zero inflation by reanalyzing the NEXT data where this issue has previously been ignored. PMID:26937263

A combined experimental and theoretical spectroscopic protocol for determination of the structure of heterogeneous catalysts: developing the information content of the resonance Raman spectra of M1 MoVOx † †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01771e Click here for additional data file.

PubMed Central

Kubas, Adam; Noak, Johannes

2017-01-01

Absorption and multiwavelength resonance Raman spectroscopy are widely used to investigate the electronic structure of transition metal centers in coordination compounds and extended solid systems. In combination with computational methodologies that have predictive accuracy, they define powerful protocols to study the spectroscopic response of catalytic materials. In this work, we study the absorption and resonance Raman spectra of the M1 MoVOx catalyst. The spectra were calculated by time-dependent density functional theory (TD-DFT) in conjunction with the independent mode displaced harmonic oscillator model (IMDHO), which allows for detailed bandshape predictions. For this purpose cluster models with up to 9 Mo and V metallic centers are considered to represent the bulk structure of MoVOx. Capping hydrogens were used to achieve valence saturation at the edges of the cluster models. The construction of model structures was based on a thorough bonding analysis which involved conventional DFT and local coupled cluster (DLPNO-CCSD(T)) methods. Furthermore the relationship of cluster topology to the computed spectral features is discussed in detail. It is shown that due to the local nature of the involved electronic transitions, band assignment protocols developed for molecular systems can be applied to describe the calculated spectral features of the cluster models as well. The present study serves as a reference for future applications of combined experimental and computational protocols in the field of solid-state heterogeneous catalysis. PMID:28989667

A user credit assessment model based on clustering ensemble for broadband network new media service supervision

NASA Astrophysics Data System (ADS)

Liu, Fang; Cao, San-xing; Lu, Rui

2012-04-01

This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the network. The user credit assessment model based on clustering ensemble proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The model's effective verification experiments are accomplished which are based on standard credit application dataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single model of swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishing ability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which will facilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with the experimental results of Logistic regression based model under the same conditions, this clustering ensemble model is robustness and has better prediction accuracy.

A multi-point perspective on the formation of polar cap arcs: kinetic modeling and observations by Cluster and TIMED

NASA Astrophysics Data System (ADS)

de Keyser, J. M.; Maggiolo, R.; Echim, M.; Simon, C.; Zhang, Y.; Trotignon, J.

2010-12-01

On April 1st, 2004 the GUVI imager onboard the TIMED spacecraft spots an isolated and elongated polar cap arc. Simultaneously, the Cluster spacecraft detects an isolated upflowing ion beam above the polar cap. Cluster observations show that the ions are accelerated upward by a quasi-stationary electric field. The field-aligned potential drop is estimated to about 600 V and the upflowing ions are accompanied by a tenuous population of isotropic protons with a temperature of about 300eV. The footprint of the magnetic field line on which the Cluster spacecraft are situated, is located just outside the GUVI field of view in the prolongation of the polar cap arc. This suggests that the upflowing ion beam and the polar cap arc may be different signatures of the same phenomenon, as suggested by a recent statistical study of polar cap ion beams using Cluster data. We use Cluster observations at high altitude as input to a quasi-stationary magnetosphere-ionosphere (MI) coupling model. Using a Knight-type current-voltage relationship and the current continuity at the topside ionosphere, the model computes the energy spectrum of precipitating electrons at ionospheric altitudes corresponding to the generator electric field observed by Cluster. The MI coupling model provides a field-aligned potential drop in agreement with Cluster observations of upflowing ions and a spatial scale of the polar cap arc consistent with the optical observations by TIMED. The energy spectrum of the precipitating electrons provided by the model is introduced as input to the Trans4 ionospheric transport code. This 1-D model, based on Boltzmann's kinetic formalism, takes into account ionospheric processes like photoionisation and electron/proton precipitation, and computes the optical and UV emissions due to precipitating electrons. The emission rates provided by the Trans4 code are then compared to the optical observations by TIMED. Data and modeling results are consistent with quasi-static acceleration of precipitating magnetospheric electrons. We also discuss possible implications of our modeling results for optical observations of polar cap arcs.

Analysis of LAC Observations of Clusters of Galaxies and Supernova Remnants

NASA Technical Reports Server (NTRS)

Hughes, J.

1996-01-01

The following publications are included and serve as the final report: The X-ray Spectrum of Abell 665; Clusters of Galaxies; Ginga Observation of an Oxygen-rich Supernova Remnant; Ginga Observations of the Coma Cluster and Studies of the Spatial Distribution of Iron; A Measurement of the Hubble Constant from the X-ray Properties and the Sunyaev-Zel'dovich Effect of Abell 2218; Non-polytropic Model for the Coma Cluster; and Abundance Gradients in Cooling Flow Clusters: Ginga LAC (Large Area Counter) and Einstein SSS (Solid State Spectrometer) Spectra of A496, A1795, A2142, and A2199.

Mediator and RNA polymerase II clusters associate in transcription-dependent condensates.

PubMed

Cho, Won-Ki; Spille, Jan-Hendrik; Hecht, Micca; Lee, Choongman; Li, Charles; Grube, Valentin; Cisse, Ibrahim I

2018-06-21

Models of gene control have emerged from genetic and biochemical studies, with limited consideration of the spatial organization and dynamics of key components in living cells. Here we used live cell super-resolution and light sheet imaging to study the organization and dynamics of the Mediator coactivator and RNA polymerase II (Pol II) directly. Mediator and Pol II each form small transient and large stable clusters in living embryonic stem cells. Mediator and Pol II are colocalized in the stable clusters, which associate with chromatin, have properties of phase-separated condensates, and are sensitive to transcriptional inhibitors. We suggest that large clusters of Mediator, recruited by transcription factors at large or clustered enhancer elements, interact with large Pol II clusters in transcriptional condensates in vivo. Copyright © 2018, American Association for the Advancement of Science.

Description of alternating-parity bands within the dinuclear-system model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shneidman, T. M.; Adamian, G. G., E-mail: adamian@theor.jinr.ru; Antonenko, N. V.

2016-11-15

A cluster approach is used to describe ground-state-based alternating-parity bands in even–even nuclei and to study the band-termination mechanism. A method is proposed for testing the cluster nature of alternating-parity bands.

Phase diagram and quench dynamics of the cluster-XY spin chain

NASA Astrophysics Data System (ADS)

Montes, Sebastián; Hamma, Alioscia

2012-08-01

We study the complete phase space and the quench dynamics of an exactly solvable spin chain, the cluster-XY model. In this chain, the cluster term and the XY couplings compete to give a rich phase diagram. The phase diagram is studied by means of the quantum geometric tensor. We study the time evolution of the system after a critical quantum quench using the Loschmidt echo. The structure of the revivals after critical quantum quenches presents a nontrivial behavior depending on the phase of the initial state and the critical point.

Phase diagram and quench dynamics of the cluster-XY spin chain.

PubMed

Montes, Sebastián; Hamma, Alioscia

2012-08-01

We study the complete phase space and the quench dynamics of an exactly solvable spin chain, the cluster-XY model. In this chain, the cluster term and the XY couplings compete to give a rich phase diagram. The phase diagram is studied by means of the quantum geometric tensor. We study the time evolution of the system after a critical quantum quench using the Loschmidt echo. The structure of the revivals after critical quantum quenches presents a nontrivial behavior depending on the phase of the initial state and the critical point.

The development rainfall forecasting using kalman filter

NASA Astrophysics Data System (ADS)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

PubMed

Bianne-Bernard, Anne-Laure; Menasri, Farès; Al-Hajj Mohamad, Rami; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

2011-10-01

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

Environmental Effects on Evolution of Cluster Galaxies in a Λ-dominated Cold Dark Matter Universe

NASA Astrophysics Data System (ADS)

Okamoto, Takashi; Nagashima, Masahiro

2003-04-01

We investigate environmental effects on evolution of bright cluster galaxies (L>L*) in a Λ-dominated cold dark matter universe using a combination of dissipationless N-body simulations and a semianalytic galaxy formation model. The N-body simulations enable us to calculate orbits of galaxies in simulated clusters. Therefore, we can incorporate stripping of cold gas from galactic disks by ram pressure (RP) from the intracluster medium into our model. In this paper we study how ram pressure stripping (RPS) and small starburst induced by a minor merger affect colors, star formation rates (SFRs), and morphologies of cluster galaxies. These processes are new ingredients in our model and have not been studied sufficiently. We find that the RPS is not important for colors and SFRs of galaxies in the cluster core if the star formation timescale is properly chosen, because the star formation is sufficiently suppressed by consumption of the cold gas in the disks. Then observed color and SFR gradients can be reproduced without the RPS. The small starburst triggered by a minor merger hardly affects the SFRs and colors of the galaxies as well. We also examine whether these two processes can resolve the known problem that the hierarchical clustering models based on the major merger-driven bulge formation scenario predict too few galaxies of intermediate bulge-to-total luminosity ratio (B/T) in clusters. When the minor burst is taken into account, the intermediate B/T population is increased, and the observed morphology gradients in clusters are successfully reproduced. Without the minor burst, the RPS cannot increase the intermediate B/T population. On the other hand, when the minor burst is considered, the RPS also plays an important role in formation of the intermediate B/T galaxies. We present redshift evolution of morphological fractions predicted by our models. The predicted number ratios of the intermediate B/T galaxies to the bulge-dominated galaxies show nearly flat or slightly increasing trends with increasing redshift. We conclude that these trends are inevitable when bulges are formed through mergers. We discuss whether our results conflict with observationally suggested NS0/NE evolution in clusters, which is a decreasing function of redshift.

Cluster-based analysis of multi-model climate ensembles

NASA Astrophysics Data System (ADS)

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 useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

[Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

PubMed

Hegymegi-Barakonyi, Bálint; Orfi, László; Kéri, György; Kövesdi, István

2013-01-01

QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data.

PubMed

Hung, Ling-Hong; Samudrala, Ram

2014-06-15

fast_protein_cluster is a fast, parallel and memory efficient package used to cluster 60 000 sets of protein models (with up to 550 000 models per set) generated by the Nutritious Rice for the World project. fast_protein_cluster is an optimized and extensible toolkit that supports Root Mean Square Deviation after optimal superposition (RMSD) and Template Modeling score (TM-score) as metrics. RMSD calculations using a laptop CPU are 60× faster than qcprot and 3× faster than current graphics processing unit (GPU) implementations. New GPU code further increases the speed of RMSD and TM-score calculations. fast_protein_cluster provides novel k-means and hierarchical clustering methods that are up to 250× and 2000× faster, respectively, than Clusco, and identify significantly more accurate models than Spicker and Clusco. fast_protein_cluster is written in C++ using OpenMP for multi-threading support. Custom streaming Single Instruction Multiple Data (SIMD) extensions and advanced vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvidia GPUs. fast_protein_cluster is available under the M.I.T. license. (http://software.compbio.washington.edu/fast_protein_cluster) © The Author 2014. Published by Oxford University Press.

Composition formulas of binary eutectics

PubMed Central

Ma, Y. P.; Dong, D. D.; Dong, C.; Luo, L. J.; Wang, Q.; Qiang, J. B.; Wang, Y. M.

2015-01-01

The present paper addresses the long-standing composition puzzle of eutectic points by introducing a new structural tool for the description of short-range-order structural unit, the cluster-plus-glue-atom model. In this model, any structure is dissociated into a 1st-neighbor cluster and a few glue atoms between the clusters, expressed by a cluster formula [cluster]gluex. This model is applied here to establish the structural model for eutectic liquids, assuming that a eutectic liquid consist of two subunits issued from the relevant eutectic phases, each being expressed by the cluster formula for ideal metallic glasses, i.e., [cluster](glue atom)1 or 3. A structural unit is then composed of two clusters from the relevant eutectic phases plus 2, 4, or 6 glue atoms. Such a dual cluster formulism is well validated in all boron-containing (except those located by the extreme phase diagram ends) and in some commonly-encountered binary eutectics, within accuracies below 1 at.%. The dual cluster formulas vary extensively and are rarely identical even for eutectics of close compositions. They are generally formed with two distinctly different cluster types, with special cluster matching rules such as cuboctahedron plus capped trigonal prism and rhombidodecahedron plus octahedral antiprism. PMID:26658618

LoCuSS: connecting the dominance and shape of brightest cluster galaxies with the assembly history of massive clusters

NASA Astrophysics Data System (ADS)

Smith, Graham P.; Khosroshahi, Habib G.; Dariush, A.; Sanderson, A. J. R.; Ponman, T. J.; Stott, J. P.; Haines, C. P.; Egami, E.; Stark, D. P.

2010-11-01

We study the luminosity gap, Δm12, between the first- and second-ranked galaxies in a sample of 59 massive (~1015Msolar) galaxy clusters, using data from the Hale Telescope, the Hubble Space Telescope, Chandra and Spitzer. We find that the Δm12 distribution, p(Δm12), is a declining function of Δm12 to which we fitted a straight line: p(Δm12) ~ -(0.13 +/- 0.02)Δm12. The fraction of clusters with `large' luminosity gaps is p(Δm12 >= 1) = 0.37 +/- 0.08, which represents a 3σ excess over that obtained from Monte Carlo simulations of a Schechter function that matches the mean cluster galaxy luminosity function. We also identify four clusters with `extreme' luminosity gaps, Δm12 >= 2, giving a fraction of . More generally, large luminosity gap clusters are relatively homogeneous, with elliptical/discy brightest cluster galaxies (BCGs), cuspy gas density profiles (i.e. strong cool cores), high concentrations and low substructure fractions. In contrast, small luminosity gap clusters are heterogeneous, spanning the full range of boxy/elliptical/discy BCG morphologies, the full range of cool core strengths and dark matter concentrations, and have large substructure fractions. Taken together, these results imply that the amplitude of the luminosity gap is a function of both the formation epoch and the recent infall history of the cluster. `BCG dominance' is therefore a phase that a cluster may evolve through and is not an evolutionary `cul-de-sac'. We also compare our results with semi-analytic model predictions based on the Millennium Simulation. None of the models is able to reproduce all of the observational results on Δm12, underlining the inability of the current generation of models to match the empirical properties of BCGs. We identify the strength of active galactic nucleus feedback and the efficiency with which cluster galaxies are replenished after they merge with the BCG in each model as possible causes of these discrepancies.

Gas stripping and mixing in galaxy clusters: a numerical comparison study

NASA Astrophysics Data System (ADS)

Heß, Steffen; Springel, Volker

2012-11-01

The ambient hot intrahalo gas in clusters of galaxies is constantly fed and stirred by infalling galaxies, a process that can be studied in detail with cosmological hydrodynamical simulations. However, different numerical methods yield discrepant predictions for crucial hydrodynamical processes, leading for example to different entropy profiles in clusters of galaxies. In particular, the widely used Lagrangian smoothed particle hydrodynamics (SPH) scheme is suspected to strongly damp fluid instabilities and turbulence, which are both crucial to establish the thermodynamic structure of clusters. In this study, we test to which extent our recently developed Voronoi particle hydrodynamics (VPH) scheme yields different results for the stripping of gas out of infalling galaxies and for the bulk gas properties of cluster. We consider both the evolution of isolated galaxy models that are exposed to a stream of intracluster medium or are dropped into cluster models, as well as non-radiative cosmological simulations of cluster formation. We also compare our particle-based method with results obtained with a fundamentally different discretization approach as implemented in the moving-mesh code AREPO. We find that VPH leads to noticeably faster stripping of gas out of galaxies than SPH, in better agreement with the mesh-code than with SPH. We show that despite the fact that VPH in its present form is not as accurate as the moving mesh code in our investigated cases, its improved accuracy of gradient estimates makes VPH an attractive alternative to SPH.

The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials

ERIC Educational Resources Information Center

Aydin, Burak; Leite, Walter L.; Algina, James

2016-01-01

We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…

Cluster management.

PubMed

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.

Galaxy formation through hierarchical clustering

NASA Astrophysics Data System (ADS)

White, Simon D. M.; Frenk, Carlos S.

1991-09-01

Analytic methods for studying the formation of galaxies by gas condensation within massive dark halos are presented. The present scheme applies to cosmogonies where structure grows through hierarchical clustering of a mixture of gas and dissipationless dark matter. The simplest models consistent with the current understanding of N-body work on dissipationless clustering, and that of numerical and analytic work on gas evolution and cooling are adopted. Standard models for the evolution of the stellar population are also employed, and new models for the way star formation heats and enriches the surrounding gas are constructed. Detailed results are presented for a cold dark matter universe with Omega = 1 and H(0) = 50 km/s/Mpc, but the present methods are applicable to other models. The present luminosity functions contain significantly more faint galaxies than are observed.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra.

PubMed

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-03-13

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Mathematical modelling of complex contagion on clustered networks

NASA Astrophysics Data System (ADS)

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.

Beyond Hydrodynamic Modeling of AGN Heating in Galaxy Clusters

NASA Astrophysics Data System (ADS)

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 proposed work will elucidate the poorly understood CR and anisotropic transport processes in the weakly collisional ICM and shed light on the long-standing mystery of AGN heating in CC clusters. Our investigation, which incorporates plasma effects into fluid models and provides physical foundation for cosmological simulations, will serve as an important bridge between physics on both micro and macro scales. This study will enable robust modeling of the radio-mode feedback of AGN in cosmological simulations of cluster and galaxy formation. It will also directly impact observational studies of clusters including NASA missions such as Chandra, XMM-Newton, Astro-H/Hitomi, Fermi, HST, and Planck.

Intergalactic stellar populations in intermediate redshift clusters

NASA Astrophysics Data System (ADS)

Melnick, J.; Giraud, E.; Toledo, I.; Selman, F.; Quintana, H.

2012-11-01

A substantial fraction of the total stellar mass in rich clusters of galaxies resides in a diffuse intergalactic component usually referred to as the intracluster light (ICL). Theoretical models indicate that these intergalactic stars originate mostly from the tidal interaction of the cluster galaxies during the assembly history of the cluster, and that a significant fraction of these stars could have formed in situ from the late infall of cold metal-poor gas clouds on to the cluster. However, these models also overpredict the fraction of stellar mass in the ICL by a substantial margin, something that is still not well understood. The models also make predictions about the age distribution of the ICL stars, which may provide additional observational constraints. Here we present population synthesis models for the ICL of an intermediate redshift (z = 0.29) X-ray cluster that we have extensively studied in previous papers. The advantage of observing intermediate redshift clusters rather than nearby ones is that the former fit the field of view of multi-object spectrographs in 8-m telescopes and therefore permit us to encompass most of the ICL with only a few well-placed slits. In this paper we show that by stacking spectra at different locations within the ICL it is possible to reach sufficiently high signal-to-noise ratios to fit population synthesis models and derive meaningful results. The models provide ages and metallicities for the dominant populations at several different locations within the ICL and the brightest cluster galaxies (BCG) halo, as well as measures of the kinematics of the stars as a function of distance from the BCG. We thus find that the ICL in our cluster is dominated by old metal-rich stars, at odds with what has been found in nearby clusters where the stars that dominate the ICL are old and metal poor. While we see weak evidence of a young, metal-poor component, if real, these young stars would amount to less than 1 per cent of the total ICL mass, much less than the up to 30 per cent predicted by the models. We propose that the very metal-rich (i.e. 2.5× solar) stars in the ICL of our cluster, which comprise ˜40 per cent of the total mass, originate mostly from the central dumb-bell galaxy, while the remaining solar and metal-poor stars come from spiral, post-starburst (E+A) and metal-poor dwarf galaxies. About 16 per cent of the ICL stars are old and metal poor.

Scalable properties of metal clusters: A comparative study of modern exchange-correlation functionals

NASA Astrophysics Data System (ADS)

Koitz, Ralph; Soini, Thomas M.; Genest, Alexander; Trickey, S. B.; Rösch, Notker

2012-07-01

The performance of eight generalized gradient approximation exchange-correlation (xc) functionals is assessed by a series of scalar relativistic all-electron calculations on octahedral palladium model clusters Pdn with n = 13, 19, 38, 55, 79, 147 and the analogous clusters Aun (for n up through 79). For these model systems, we determined the cohesive energies and average bond lengths of the optimized octahedral structures. We extrapolate these values to the bulk limits and compare with the corresponding experimental values. While the well-established functionals BP, PBE, and PW91 are the most accurate at predicting energies, the more recent forms PBEsol, VMTsol, and VT{84}sol significantly improve the accuracy of geometries. The observed trends are largely similar for both Pd and Au. In the same spirit, we also studied the scalability of the ionization potentials and electron affinities of the Pd clusters, and extrapolated those quantities to estimates of the work function. Overall, the xc functionals can be classified into four distinct groups according to the accuracy of the computed parameters. These results allow a judicious selection of xc approximations for treating transition metal clusters.

Reactive Pendant Mn═O in a Synthetic Structural Model of a Proposed S4 State in the Photosynthetic Oxygen Evolving Complex.

PubMed

Vaddypally, Shivaiah; Kondaveeti, Sandeep K; Karki, Santosh; Van Vliet, Megan M; Levis, Robert J; Zdilla, Michael J

2017-04-05

The molecular mechanism of the Oxygen Evolving Center of photosystem II has been under debate for decades. One frequently cited proposal is the nucleophilic attack by water hydroxide on a pendant Mn═O moiety, though no chemical example of this reactivity at a manganese cubane cluster has been reported. We describe here the preparation, characterization, and a reactivity study of a synthetic manganese cubane cluster with a pendant manganese-oxo moiety. Reaction of this cluster with alkenes results in oxygen and hydrogen atom transfer reactions to form alcohol- and ketone-based oxygen-containing products. Nitrene transfer from core imides is negligible. The inorganic product is a cluster identical to the precursor, but with the pendant Mn═O moiety replaced by a hydrogen abstracted from the organic substrate, and is isolated in quantitative yield. 18 O and 2 H isotopic labeling studies confirm the transfer of atoms between the cluster and the organic substrate. The results suggest that the core cubane structure of this model compound remains intact, and that the pendant Mn═O moiety is preferentially reactive.

Multiwavelength Studies of Young OB Associations

NASA Astrophysics Data System (ADS)

Feigelson, Eric D.

We discuss how contemporary multiwavelength observations of young OB-dominated clusters address long-standing astrophysical questions: Do clusters form rapidly or slowly with an age spread? When do clusters expand and disperse to constitute the field star population? Do rich clusters form by amalgamation of smaller subclusters? What is the pattern and duration of cluster formation in massive star forming regions (MSFRs)? Past observational difficulties in obtaining good stellar censuses of MSFRs have been alleviated in recent studies that combine X-ray and infrared surveys to obtain rich, though still incomplete, censuses of young stars in MSFRs. We describe here one of these efforts, the MYStIX project, that produced a catalog of 31,784 probable members of 20 MSFRs. We find that age spread within clusters is real in the sense that the stars in the core formed after the cluster halo. This is consistent with some recent astrophysical models involving merging star-forming filaments. Cluster expansion is seen in the ensemble of (sub)clusters, and older dispersing populations are found across MSFRs. Long-lived, asynchronous star formation is pervasive across MSFRs.

The clustering of baryonic matter. I: a halo-model approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fedeli, C., E-mail: cosimo.fedeli@oabo.inaf.it

2014-04-01

In this paper I generalize the halo model for the clustering of dark matter in order to produce the power spectra of the two main baryonic matter components in the Universe: stars and hot gas. As a natural extension, this can be also used to describe the clustering of all mass. According to the design of the halo model, the large-scale power spectra of the various matter components are physically connected with the distribution of each component within bound structures and thus, ultimately, with the complete set of physical processes that drive the formation of galaxies and galaxy clusters. Besidesmore » being practical for cosmological and parametric studies, the semi-analytic model presented here has also other advantages. Most importantly, it allows one to understand on physical ground what is the relative contribution of each matter component to the total clustering of mass as a function of scale, and thus it opens an interesting new window to infer the distribution of baryons through high precision cosmic shear measurements. This is particularly relevant for future wide-field photometric surveys such as Euclid. In this work the concept of the model and its uncertainties are illustrated in detail, while in a companion paper we use a set of numerical hydrodynamic simulations to show a practical application and to investigate where the model itself needs to be improved.« less

Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants.

PubMed

Sauzet, Odile; Peacock, Janet L

2017-07-20

The analysis of perinatal outcomes often involves datasets with some multiple births. These are datasets mostly formed of independent observations and a limited number of clusters of size two (twins) and maybe of size three or more. This non-independence needs to be accounted for in the statistical analysis. Using simulated data based on a dataset of preterm infants we have previously investigated the performance of several approaches to the analysis of continuous outcomes in the presence of some clusters of size two. Mixed models have been developed for binomial outcomes but very little is known about their reliability when only a limited number of small clusters are present. Using simulated data based on a dataset of preterm infants we investigated the performance of several approaches to the analysis of binomial outcomes in the presence of some clusters of size two. Logistic models, several methods of estimation for the logistic random intercept models and generalised estimating equations were compared. The presence of even a small percentage of twins means that a logistic regression model will underestimate all parameters but a logistic random intercept model fails to estimate the correlation between siblings if the percentage of twins is too small and will provide similar estimates to logistic regression. The method which seems to provide the best balance between estimation of the standard error and the parameter for any percentage of twins is the generalised estimating equations. This study has shown that the number of covariates or the level two variance do not necessarily affect the performance of the various methods used to analyse datasets containing twins but when the percentage of small clusters is too small, mixed models cannot capture the dependence between siblings.

Development of deformation band clusters in porous quartz sandstones - Contribution from microstructural analysis and numerical modeling

NASA Astrophysics Data System (ADS)

Philit, S.; Soliva, R.; Chemenda, A. I.

2017-12-01

Because sandstones form good reservoirs for hydrocarbon, water or C02 storage, the understanding of the deformation processes in sandstones is major. The deformation band clusters result from the localization of the deformation in porous sandstones under the form of gathered low-permeability cataclastic deformation bands. It has recently been shown that this localization is favored in extensional tectonics. The clusters measure tens to hundreds of meters in extent and propagate vertically as long as the sandstone is clean. Because the clusters can form several kilometers long networks, they are likely to hamper fluid flow during reservoir exploitation. Yet, the processes of band accumulation linked to the evolution of the clusters to a potential faulting are poorly understood. An integrated study coupling a microscopic analysis of the deformed granular material in clusters from 7 sites in the world and distinct element numerical modeling permits to propose a model for cluster growth. Our microscopic analysis reveals that the clusters display varying degree of cataclasis, with the most important degrees in the bands. This cataclasis is accompanied by porosity reduction (more reduced in thrust Andersonian regime), and increased Particle Size Distribution. This testifies of an important packing and implies an increased number of particle coordination. During deformation, the grain shape is both smoothened and roughened; the averaged values of the roundness and circularity indicate a rapid roughening of the clasts at the first stages of deformation followed by a slight smoothening. The roughening of the clasts in densely packed material induces high friction and strengthens the material. High residual porosity at some band edges suggests a local dilatant behavior of sheared material. Our distinct element numerical models and other particle models in the literature confirm this observation. The development of force chains with low particle coordination at these locations would weaken the stress resistance at the contact points. Hence, the cluster growth would be promoted by the successive localization of bands the edges of preexisting bands. Faulting could occur at any stage of the cluster development, probably favored along interfaces of minimized strength with smooth geometry.

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

PubMed

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.

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

PubMed Central

Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.

2015-01-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test–post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences. PMID:29881032

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses.

PubMed

Cho, Sun-Joo; Preacher, Kristopher J; Bottge, Brian A

2015-11-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test-post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test scores that are most often used in MLM are summed item responses (or total scores). In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. To correct for measurement error in the covariate and outcome, a theoretical justification for the use of multilevel structural equation modeling (MSEM) has been established. However, MSEM for binary responses has not been widely applied to detect intervention effects (group differences) in intervention studies. In this article, the use of MSEM for intervention studies is demonstrated and the performance of MSEM is evaluated via a simulation study. Furthermore, the consequences of using MLM instead of MSEM are shown in detecting group differences. Results of the simulation study showed that MSEM performed adequately as the number of clusters, cluster size, and intraclass correlation increased and outperformed MLM for the detection of group differences.

Generating clustered scale-free networks using Poisson based localization of edges

NASA Astrophysics Data System (ADS)

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.

Confronting models of star formation quenching in galaxy clusters with archival Spitzer data

NASA Astrophysics Data System (ADS)

Rudnick, Gregory

Large scale structures in the universe form hierarchically: small structures merge to form larger ones. Over the same epoch where these structures experience significant growth, the fraction of star forming galaxies within them decreases, and at a faster rate than for field galaxies. It is now widely accepted that there must be physical processes at work in these dense environments to actively quench star formation. However, despite no shortage of candidate mechanisms, sophisticated cosmological simulations still cannot reproduce the star formation rate distributions within dense environments, such as galaxy clusters. Insufficient observational constraints are a primary obstacle to further progress. In particular, the interpretation of observations of nearby clusters relies on untested assumptions about the properties of galaxies before they entered the dense cluster environment at higher redshifts. Clearly, direct constraints on these properties are required. Our group has assembled two data sets designed to address these concerns. The first focuses on an intermediate wide-field cluster sample and the second focuses on a well-matched low-redshift cluster sample. We will use these samples, along with sophisticated models of hierarchical galaxy formation, to meet the following objectives: 1. Directly measure the SFR distribution of the progenitors of present-day cluster galaxies. We will use ground-based spectroscopy to identify cluster members within four virial radii of eight intermediate-redshift clusters. We will couple this with archival Spitzer/MIPS data to measure the SFRs of galaxies out to the cluster outskirts. 2. Measure the SFR distribution of the present-day cluster galaxies using Spitzer and WISE. Robust N-body simulations tell us statistically which galaxies at intermediate redshifts will have entered the cluster virial radius by the current epoch. By combining our wide-field coverage at high redshift with our local cluster sample, we will determine the evolution in cluster galaxy SFRs over 6 billion years making minimal assumptions about the infalling galaxy population. 3. Provide a rigorous test of the quenching processes embedded in the theoretical models. We will create observed realizations of the theoretical models by subjecting them to our observational selection. This will enable a fair comparison between the models and the data, which will provide a valuable test of current theoretical implementations of quenching processes. We will also modify the quenching prescriptions in the models to determine the parameters required to reproduce the observations. The proposed research is novel for several reasons. 1) We have wide-field Spitzer/MIPS data that allows us to robustly measure SFRs in our distant cluster galaxies. WISE data on local clusters will provide us with analogous measurements in the nearby Universe. 2) Our significant investment in ancillary spectroscopy allows us to identify infalling galaxies that will eventually join the central regions of the cluster z=0. 3) Our intermediate redshift cluster sample was chosen to have characteristics expected for the progenitors of a large fraction of the known clusters at z=0. 4) We will take advantage of our own cosmological simulations of structure growth to interpret our data. 5) We have optical photometry over the full infall region, allowing us to control for stellar masses and to distinguish passive from dusty star-forming galaxies. We will learn which, if any, of the quenching prescriptions currently employed in semi-analytic models correctly reproduces the observed characteristics of the galaxies that will become cluster galaxies at z=0. We will pinpoint the cluster-centric radii over which quenching takes place between. We will determine the timescale (as a function of stellar mass) over which it must take place. This program will cement the legacy of Spitzer and WISE as tools for studying galaxy formation in clusters.

Understanding Teacher Users of a Digital Library Service: A Clustering Approach

ERIC Educational Resources Information Center

Xu, Beijie

2011-01-01

This research examined teachers' online behaviors while using a digital library service--the Instructional Architect (IA)--through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third…

Internal velocity and mass distributions in simulated clusters of galaxies for a variety of cosmogonic models

NASA Technical Reports Server (NTRS)

Cen, Renyue

1994-01-01

The mass and velocity distributions in the outskirts (0.5-3.0/h Mpc) of simulated clusters of galaxies are examined for a suite of cosmogonic models (two Omega(sub 0) = 1 and two Omega(sub 0) = 0.2 models) utilizing large-scale particle-mesh (PM) simulations. Through a series of model computations, designed to isolate the different effects, we find that both Omega(sub 0) and P(sub k) (lambda less than or = 16/h Mpc) are important to the mass distributions in clusters of galaxies. There is a correlation between power, P(sub k), and density profiles of massive clusters; more power tends to point to the direction of a stronger correlation between alpha and M(r less than 1.5/h Mpc); i.e., massive clusters being relatively extended and small mass clusters being relatively concentrated. A lower Omega(sub 0) universe tends to produce relatively concentrated massive clusters and relatively extended small mass clusters compared to their counterparts in a higher Omega(sub 0) model with the same power. Models with little (initial) small-scale power, such as the hot dark matter (HDM) model, produce more extended mass distributions than the isothermal distribution for most of the mass clusters. But the cold dark matter (CDM) models show mass distributions of most of the clusters more concentrated than the isothermal distribution. X-ray and gravitational lensing observations are beginning providing useful information on the mass distribution in and around clusters; some interesting constraints on Omega(sub 0) and/or the (initial) power of the density fluctuations on scales lambda less than or = 16/h Mpc (where linear extrapolation is invalid) can be obtained when larger observational data sets, such as the Sloan Digital Sky Survey, become available.

Cost comparison of centralized and decentralized wastewater management systems using optimization model.

PubMed

Jung, Youngmee Tiffany; Narayanan, N C; Cheng, Yu-Ling

2018-05-01

There is a growing interest in decentralized wastewater management (DWWM) as a potential alternative to centralized wastewater management (CWWM) in developing countries. However, the comparative cost of CWWM and DWWM is not well understood. In this study, the cost of cluster-type DWWM is simulated and compared to the cost of CWWM in Alibag, India. A three-step model is built to simulate a broad range of potential DWWM configurations with varying number and layout of cluster subsystems. The considered DWWM scheme consists of cluster subsystems, that each uses simplified sewer and DEWATS (Decentralized Wastewater Treatment Systems). We consider CWWM that uses conventional sewer and an activated sludge plant. The results show that the cost of DWWM can vary significantly with the number and layout of the comprising cluster subsystems. The cost of DWWM increased nonlinearly with increasing number of comprising clusters, mainly due to the loss in the economies of scale for DEWATS. For configurations with the same number of comprising cluster subsystems, the cost of DWWM varied by ±5% around the mean, depending on the layout of the cluster subsystems. In comparison to CWWM, DWWM was of lower cost than CWWM when configured with fewer than 16 clusters in Alibag, with significantly less operation and maintenance requirement, but with higher capital and land requirement for construction. The study demonstrates that cluster-type DWWM using simplified sewer and DEWATS may be a cost-competitive alternative to CWWM, when carefully configured to lower the cost. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Hierarchical Framework for State-Space Matrix Inference and Clustering.

PubMed

Zuo, Chandler; Chen, Kailei; Hewitt, Kyle J; Bresnick, Emery H; Keleş, Sündüz

2016-09-01

In recent years, a large number of genomic and epigenomic studies have been focusing on the integrative analysis of multiple experimental datasets measured over a large number of observational units. The objectives of such studies include not only inferring a hidden state of activity for each unit over individual experiments, but also detecting highly associated clusters of units based on their inferred states. Although there are a number of methods tailored for specific datasets, there is currently no state-of-the-art modeling framework for this general class of problems. In this paper, we develop the MBASIC ( M atrix B ased A nalysis for S tate-space I nference and C lustering) framework. MBASIC consists of two parts: state-space mapping and state-space clustering. In state-space mapping, it maps observations onto a finite state-space, representing the activation states of units across conditions. In state-space clustering, MBASIC incorporates a finite mixture model to cluster the units based on their inferred state-space profiles across all conditions. Both the state-space mapping and clustering can be simultaneously estimated through an Expectation-Maximization algorithm. MBASIC flexibly adapts to a large number of parametric distributions for the observed data, as well as the heterogeneity in replicate experiments. It allows for imposing structural assumptions on each cluster, and enables model selection using information criterion. In our data-driven simulation studies, MBASIC showed significant accuracy in recovering both the underlying state-space variables and clustering structures. We applied MBASIC to two genome research problems using large numbers of datasets from the ENCODE project. The first application grouped genes based on transcription factor occupancy profiles of their promoter regions in two different cell types. The second application focused on identifying groups of loci that are similar to a GATA2 binding site that is functional at its endogenous locus by utilizing transcription factor occupancy data and illustrated applicability of MBASIC in a wide variety of problems. In both studies, MBASIC showed higher levels of raw data fidelity than analyzing these data with a two-step approach using ENCODE results on transcription factor occupancy data.

Helium segregation on surfaces of plasma-exposed tungsten

NASA Astrophysics Data System (ADS)

Maroudas, Dimitrios; Blondel, Sophie; Hu, Lin; Hammond, Karl D.; Wirth, Brian D.

2016-02-01

We report a hierarchical multi-scale modeling study of implanted helium segregation on surfaces of tungsten, considered as a plasma facing component in nuclear fusion reactors. We employ a hierarchy of atomic-scale simulations based on a reliable interatomic interaction potential, including molecular-statics simulations to understand the origin of helium surface segregation, targeted molecular-dynamics (MD) simulations of near-surface cluster reactions, and large-scale MD simulations of implanted helium evolution in plasma-exposed tungsten. We find that small, mobile He n (1  ⩽  n  ⩽  7) clusters in the near-surface region are attracted to the surface due to an elastic interaction force that provides the thermodynamic driving force for surface segregation. This elastic interaction force induces drift fluxes of these mobile He n clusters, which increase substantially as the migrating clusters approach the surface, facilitating helium segregation on the surface. Moreover, the clusters’ drift toward the surface enables cluster reactions, most importantly trap mutation, in the near-surface region at rates much higher than in the bulk material. These near-surface cluster dynamics have significant effects on the surface morphology, near-surface defect structures, and the amount of helium retained in the material upon plasma exposure. We integrate the findings of such atomic-scale simulations into a properly parameterized and validated spatially dependent, continuum-scale reaction-diffusion cluster dynamics model, capable of predicting implanted helium evolution, surface segregation, and its near-surface effects in tungsten. This cluster-dynamics model sets the stage for development of fully atomistically informed coarse-grained models for computationally efficient simulation predictions of helium surface segregation, as well as helium retention and surface morphological evolution, toward optimal design of plasma facing components.

A simulation of the intracluster medium with feedback from cluster galaxies

NASA Technical Reports Server (NTRS)

Metzler, Christopher A.; Evrard, August E.

1994-01-01

We detail method and report first results from a three-dimensional hydrodynamical and N-body simulation of the formation and evolution of a Coma-sized cluster of galaxies, with the intent of studying the history of the hot, X-ray emitting intracluster medium. Cluster gas, galaxies, and dark matter are included in the model. The galaxies and dark matter fell gravitational forces; the cluster gas also undergoes hydrodynamical effects such as shock heating and PdV work. For the first time in three dimensions, we include modeling of ejection of processed gas from the simulated galaxies by winds, including heating and heavy element enrichment. For comparison, we employ a `pure infall' simulation using the same initial conditions but with no galaxies or winds. We employ an extreme ejection history for galactic feedback in order to define the boundary of likely models. As expected, feedback raises the entropy of the intracluster gas, preventing it from collapsing to densities as high as those attained in the infall model. The effect is more pronounced in subclusters formed at high redshift. The cluster with feedback is always less X-ray luminous, but experiences more rapid luminosity evolution, than the pure infall cluster. Even employing an extreme ejection model, the final gas temperature is only approximately 15% larger than in the infall model. The radial temperature profile is very nearly isothermal within 1.5 Mpc. The cluster galaxies in the feedback model have a velocity dispersion approximately 15% lower than the dark matter. This results in the true ratio of specific energies in galaxies to gas being less than one, beta(sub spec) approximately 0.7. The infall model predicts beta(sub spec) approximately 1.2. Large excursions in these values occur over time, following the complex dynamical history of the cluster. The morphology of the X-ray emission is little affected by feedback. The emission profiles of both clusters are well described by the standard beta-model with beta(sub fit) approximately equal to 0.7 - 0.9. X-ray mass estimates based on the assumptions of hydrostatic equilibrium and the applicability of the beta-model are quite accurate in both cases. A strong, radial iron abundance gradient is present, which develops as a consequence of the steepening of the galaxy density profile over time. Spectroscopic observations using nonimaging detectors with wide (approximately 45 min) fields of view dramatically smear the gradient. Observations with arcminute resolution, made available with the ASCA satellite, would readily resolve the gradient.

Atomistic modeling of dropwise condensation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sikarwar, B. S., E-mail: bssikarwar@amity.edu; Singh, P. L.; Muralidhar, K.

The basic aim of the atomistic modeling of condensation of water is to determine the size of the stable cluster and connect phenomena occurring at atomic scale to the macroscale. In this paper, a population balance model is described in terms of the rate equations to obtain the number density distribution of the resulting clusters. The residence time is taken to be large enough so that sufficient time is available for all the adatoms existing in vapor-phase to loose their latent heat and get condensed. The simulation assumes clusters of a given size to be formed from clusters of smallermore » sizes, but not by the disintegration of the larger clusters. The largest stable cluster size in the number density distribution is taken to be representative of the minimum drop radius formed in a dropwise condensation process. A numerical confirmation of this result against predictions based on a thermodynamic model has been obtained. Results show that the number density distribution is sensitive to the surface diffusion coefficient and the rate of vapor flux impinging on the substrate. The minimum drop radius increases with the diffusion coefficient and the impinging vapor flux; however, the dependence is weak. The minimum drop radius predicted from thermodynamic considerations matches the prediction of the cluster model, though the former does not take into account the effect of the surface properties on the nucleation phenomena. For a chemically passive surface, the diffusion coefficient and the residence time are dependent on the surface texture via the coefficient of friction. Thus, physical texturing provides a means of changing, within limits, the minimum drop radius. The study reveals that surface texturing at the scale of the minimum drop radius does not provide controllability of the macro-scale dropwise condensation at large timescales when a dynamic steady-state is reached.« less

THE FORMATION OF SECONDARY STELLAR GENERATIONS IN MASSIVE YOUNG STAR CLUSTERS FROM RAPIDLY COOLING SHOCKED STELLAR WINDS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wünsch, R.; Palouš, J.; Ehlerová, S.

We study a model of rapidly cooling shocked stellar winds in young massive clusters and estimate the circumstances under which secondary star formation, out of the reinserted winds from a first stellar generation (1G), is possible. We have used two implementations of the model: a highly idealized, computationally inexpensive, spherically symmetric semi-analytic model, and a complex, three-dimensional radiation-hydrodynamic, simulation; they are in a good mutual agreement. The results confirm our previous findings that, in a cluster with 1G mass 10{sup 7} M {sub ⊙} and half-mass–radius 2.38 pc, the shocked stellar winds become thermally unstable, collapse into dense gaseous structuresmore » that partially accumulate inside the cluster, self-shield against ionizing stellar radiation, and form the second generation (2G) of stars. We have used the semi-analytic model to explore a subset of the parameter space covering a wide range of the observationally poorly constrained parameters: the heating efficiency, η {sub he}, and the mass loading, η {sub ml}. The results show that the fraction of the 1G stellar winds accumulating inside the cluster can be larger than 50% if η {sub he} ≲ 10%, which is suggested by the observations. Furthermore, for low η {sub he}, the model provides a self-consistent mechanism predicting 2G stars forming only in the central zones of the cluster. Finally, we have calculated the accumulated warm gas emission in the H30 α recombination line, analyzed its velocity profile, and estimated its intensity for super star clusters in interacting galaxies NGC4038/9 (Antennae) showing that the warm gas should be detectable with ALMA.« less

CORM: An R Package Implementing the Clustering of Regression Models Method for Gene Clustering

PubMed Central

Shi, Jiejun; Qin, Li-Xuan

2014-01-01

We report a new R package implementing the clustering of regression models (CORM) method for clustering genes using gene expression data and provide data examples illustrating each clustering function in the package. The CORM package is freely available at CRAN from http://cran.r-project.org. PMID:25452684

Comparison of organs' shapes with geometric and Zernike 3D moments.

PubMed

Broggio, D; Moignier, A; Ben Brahim, K; Gardumi, A; Grandgirard, N; Pierrat, N; Chea, M; Derreumaux, S; Desbrée, A; Boisserie, G; Aubert, B; Mazeron, J-J; Franck, D

2013-09-01

The morphological similarity of organs is studied with feature vectors based on geometric and Zernike 3D moments. It is particularly investigated if outliers and average models can be identified. For this purpose, the relative proximity to the mean feature vector is defined, principal coordinate and clustering analyses are also performed. To study the consistency and usefulness of this approach, 17 livers and 76 hearts voxel models from several sources are considered. In the liver case, models with similar morphological feature are identified. For the limited amount of studied cases, the liver of the ICRP male voxel model is identified as a better surrogate than the female one. For hearts, the clustering analysis shows that three heart shapes represent about 80% of the morphological variations. The relative proximity and clustering analysis rather consistently identify outliers and average models. For the two cases, identification of outliers and surrogate of average models is rather robust. However, deeper classification of morphological feature is subject to caution and can only be performed after cross analysis of at least two kinds of feature vectors. Finally, the Zernike moments contain all the information needed to re-construct the studied objects and thus appear as a promising tool to derive statistical organ shapes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Computational Design of Clusters for Catalysis

NASA Astrophysics Data System (ADS)

Jimenez-Izal, Elisa; Alexandrova, Anastassia N.

2018-04-01

When small clusters are studied in chemical physics or physical chemistry, one perhaps thinks of the fundamental aspects of cluster electronic structure, or precision spectroscopy in ultracold molecular beams. However, small clusters are also of interest in catalysis, where the cold ground state or an isolated cluster may not even be the right starting point. Instead, the big question is: What happens to cluster-based catalysts under real conditions of catalysis, such as high temperature and coverage with reagents? Myriads of metastable cluster states become accessible, the entire system is dynamic, and catalysis may be driven by rare sites present only under those conditions. Activity, selectivity, and stability are highly dependent on size, composition, shape, support, and environment. To probe and master cluster catalysis, sophisticated tools are being developed for precision synthesis, operando measurements, and multiscale modeling. This review intends to tell the messy story of clusters in catalysis.

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

PubMed Central

Ahmed, Gulnaz; Zou, Jianhua; Zhao, Xi; Sadiq Fareed, Mian Muhammad

2017-01-01

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. PMID:28241492

Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions: the OPPERA study

PubMed Central

Bair, Eric; Gaynor, Sheila; Slade, Gary D.; Ohrbach, Richard; Fillingim, Roger B.; Greenspan, Joel D.; Dubner, Ronald; Smith, Shad B.; Diatchenko, Luda; Maixner, William

2016-01-01

The classification of most chronic pain disorders gives emphasis to anatomical location of the pain to distinguish one disorder from the other (eg, back pain vs temporomandibular disorder [TMD]) or to define subtypes (eg, TMD myalgia vs arthralgia). However, anatomical criteria overlook etiology, potentially hampering treatment decisions. This study identified clusters of individuals using a comprehensive array of biopsychosocial measures. Data were collected from a case–control study of 1031 chronic TMD cases and 3247 TMD-free controls. Three subgroups were identified using supervised cluster analysis (referred to as the adaptive, pain-sensitive, and global symptoms clusters). Compared with the adaptive cluster, participants in the pain-sensitive cluster showed heightened sensitivity to experimental pain, and participants in the global symptoms cluster showed both greater pain sensitivity and greater psychological distress. Cluster membership was strongly associated with chronic TMD: 91.5% of TMD cases belonged to the pain-sensitive and global symptoms clusters, whereas 41.2% of controls belonged to the adaptive cluster. Temporomandibular disorder cases in the pain-sensitive and global symptoms clusters also showed greater pain intensity, jaw functional limitation, and more comorbid pain conditions. Similar results were obtained when the same methodology was applied to a smaller case–control study consisting of 199 chronic TMD cases and 201 TMD-free controls. During a median 3-year follow-up period of TMD-free individuals, participants in the global symptoms cluster had greater risk of developing first-onset TMD (hazard ratio = 2.8) compared with participants in the other 2 clusters. Cross-cohort predictive modeling was used to demonstrate the reliability of the clusters. PMID:26928952

Age determination of 15 old to intermediate-age small Magellanic cloud star clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parisi, M. C.; Clariá, J. J.; Piatti, A. E.

2014-04-01

We present color-magnitude diagrams in the V and I bands for 15 star clusters in the Small Magellanic Cloud (SMC) based on data taken with the Very Large Telescope (VLT, Chile). We selected these clusters from our previous work, wherein we derived cluster radial velocities and metallicities from calcium II infrared triplet (CaT) spectra also taken with the VLT. We discovered that the ages of six of our clusters have been appreciably underestimated by previous studies, which used comparatively small telescopes, graphically illustrating the need for large apertures to obtain reliable ages of old and intermediate-age SMC star clusters. Inmore » particular, three of these clusters, L4, L6, and L110, turn out to be among the oldest SMC clusters known, with ages of 7.9 ± 1.1, 8.7 ± 1.2, and 7.6 ± 1.0 Gyr, respectively, helping to fill a possible 'SMC cluster age gap'. Using the current ages and metallicities from Parisi et al., we analyze the age distribution, age gradient, and age-metallicity relation (AMR) of a sample of SMC clusters measured homogeneously. There is a suggestion of bimodality in the age distribution but it does not show a constant slope for the first 4 Gyr, and we find no evidence for an age gradient. Due to the improved ages of our cluster sample, we find that our AMR is now better represented in the intermediate/old period than we had derived in Parisi et al., where we simply took ages available in the literature. Additionally, clusters younger than ∼4 Gyr now show better agreement with the bursting model of Pagel and Tautvaišienė, but we confirm that this model is not a good representation of the AMR during the intermediate/old period. A more complicated model is needed to explain the SMC chemical evolution in that period.« less

Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data.

PubMed

Mwangi, Benson; Soares, Jair C; Hasan, Khader M

2014-10-30

Neuroimaging machine learning studies have largely utilized supervised algorithms - meaning they require both neuroimaging scan data and corresponding target variables (e.g. healthy vs. diseased) to be successfully 'trained' for a prediction task. Noticeably, this approach may not be optimal or possible when the global structure of the data is not well known and the researcher does not have an a priori model to fit the data. We set out to investigate the utility of an unsupervised machine learning technique; t-distributed stochastic neighbour embedding (t-SNE) in identifying 'unseen' sample population patterns that may exist in high-dimensional neuroimaging data. Multimodal neuroimaging scans from 92 healthy subjects were pre-processed using atlas-based methods, integrated and input into the t-SNE algorithm. Patterns and clusters discovered by the algorithm were visualized using a 2D scatter plot and further analyzed using the K-means clustering algorithm. t-SNE was evaluated against classical principal component analysis. Remarkably, based on unlabelled multimodal scan data, t-SNE separated study subjects into two very distinct clusters which corresponded to subjects' gender labels (cluster silhouette index value=0.79). The resulting clusters were used to develop an unsupervised minimum distance clustering model which identified 93.5% of subjects' gender. Notably, from a neuropsychiatric perspective this method may allow discovery of data-driven disease phenotypes or sub-types of treatment responders. Copyright © 2014 Elsevier B.V. All rights reserved.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan

PubMed Central

Park, Rachel; O'Brien, Thomas F.; Huang, Susan S.; Baker, Meghan A.; Yokoe, Deborah S.; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-01-01

Objectives While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Methods Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Results Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Conclusion Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures. PMID:27530311

Information sharing and sorting in a community

NASA Astrophysics Data System (ADS)

Bhattacherjee, Biplab; Manna, S. S.; Mukherjee, Animesh

2013-06-01

We present the results of a detailed numerical study of a model for the sharing and sorting of information in a community consisting of a large number of agents. The information gathering takes place in a sequence of mutual bipartite interactions where randomly selected pairs of agents communicate with each other to enhance their knowledge and sort out the common information. Although our model is less restricted compared to the well-established naming game, the numerical results strongly indicate that the whole set of exponents characterizing this model are different from those of the naming game and they assume nontrivial values. Finally, it appears that in analogy to the emergence of clusters in the phenomenon of percolation, one can define clusters of agents here having the same information. We have studied in detail the growth of the largest cluster in this article and performed its finite-size scaling analysis.

Improvements on GPS Location Cluster Analysis for the Prediction of Large Carnivore Feeding Activities: Ground-Truth Detection Probability and Inclusion of Activity Sensor Measures

PubMed Central

Blecha, Kevin A.; Alldredge, Mat W.

2015-01-01

Animal space use studies using GPS collar technology are increasingly incorporating behavior based analysis of spatio-temporal data in order to expand inferences of resource use. GPS location cluster analysis is one such technique applied to large carnivores to identify the timing and location of feeding events. For logistical and financial reasons, researchers often implement predictive models for identifying these events. We present two separate improvements for predictive models that future practitioners can implement. Thus far, feeding prediction models have incorporated a small range of covariates, usually limited to spatio-temporal characteristics of the GPS data. Using GPS collared cougar (Puma concolor) we include activity sensor data as an additional covariate to increase prediction performance of feeding presence/absence. Integral to the predictive modeling of feeding events is a ground-truthing component, in which GPS location clusters are visited by human observers to confirm the presence or absence of feeding remains. Failing to account for sources of ground-truthing false-absences can bias the number of predicted feeding events to be low. Thus we account for some ground-truthing error sources directly in the model with covariates and when applying model predictions. Accounting for these errors resulted in a 10% increase in the number of clusters predicted to be feeding events. Using a double-observer design, we show that the ground-truthing false-absence rate is relatively low (4%) using a search delay of 2–60 days. Overall, we provide two separate improvements to the GPS cluster analysis techniques that can be expanded upon and implemented in future studies interested in identifying feeding behaviors of large carnivores. PMID:26398546

Parity among interpretation methods of MLEE patterns and disparity among clustering methods in epidemiological typing of Candida albicans.

PubMed

Boriollo, Marcelo Fabiano Gomes; Rosa, Edvaldo Antonio Ribeiro; Gonçalves, Reginaldo Bruno; Höfling, José Francisco

2006-03-01

The typing of C. albicans by MLEE (multilocus enzyme electrophoresis) is dependent on the interpretation of enzyme electrophoretic patterns, and the study of the epidemiological relationships of these yeasts can be conducted by cluster analysis. Therefore, the aims of the present study were to first determine the discriminatory power of genetic interpretation (deduction of the allelic composition of diploid organisms) and numerical interpretation (mere determination of the presence and absence of bands) of MLEE patterns, and then to determine the concordance (Pearson product-moment correlation coefficient) and similarity (Jaccard similarity coefficient) of the groups of strains generated by three cluster analysis models, and the discriminatory power of such models as well [model A: genetic interpretation, genetic distance matrix of Nei (d(ij)) and UPGMA dendrogram; model B: genetic interpretation, Dice similarity matrix (S(D1)) and UPGMA dendrogram; model C: numerical interpretation, Dice similarity matrix (S(D2)) and UPGMA dendrogram]. MLEE was found to be a powerful and reliable tool for the typing of C. albicans due to its high discriminatory power (>0.9). Discriminatory power indicated that numerical interpretation is a method capable of discriminating a greater number of strains (47 versus 43 subtypes), but also pointed to model B as a method capable of providing a greater number of groups, suggesting its use for the typing of C. albicans by MLEE and cluster analysis. Very good agreement was only observed between the elements of the matrices S(D1) and S(D2), but a large majority of the groups generated in the three UPGMA dendrograms showed similarity S(J) between 4.8% and 75%, suggesting disparities in the conclusions obtained by the cluster assays.

Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy. PMID:25608211

Cluster analysis applied to localized dispersion curves in East Asia: the limits of surface wave resolution

NASA Astrophysics Data System (ADS)

Witek, M.; van der Lee, S.; Kang, T. S.; Chang, S. J.; Ning, J.; Ning, S.

2017-12-01

We have measured Rayleigh wave group velocity dispersion curves from one year of station-pair cross-correlations of continuous vertical-component broadband data from 1082 seismic stations in regional networks across China, Korea, Taiwan, and Japan for the year 2011. We use the measurements to map local dispersion anomalies for periods in the range 6-40 s. We combined our ambient noise data set with the earthquake group velocity data set of Ma et al. (2014), and then applied agglomerative hierarchical clustering to the localized dispersion curves. We find that the dispersion curves naturally organize themselves into distinct tectonic regions. For our distribution of interstation distances, only 8 distinct regions need to be defined. Additional clusters reduce the overall data misfit by increasingly smaller amounts. The size and number of clusters needed to suitably predict the data may give an indication of the resolving power of the data set. The regions that emerge from the cluster analysis include Tibet, the Sea of Japan, the South China Block and the Korean peninsula, the Ordos and Yangtze cratons, and Mesozoic rift basins such as the Songliao, Bohai Bay and Ulleung basins. We also performed a traditional inversion for 3D S-velocity structure, and the resulting model fits the data as well as the 8-cluster model, while both models fit the earthquake data and ambient noise data better than the LITHO1.0 model of Pasyanos et al. (2014). Our 3D model of the crust and upper mantle confirms many of the features seen in previous studies of the region, most notably the lithospheric thinning going from west to east and low velocity zones in the crust on the Tibetan periphery. We conclude that cluster analysis is able to greatly reduce the dimensionality of surface wave dispersion data, in the sense that a data set of over half a million dispersion curves is sufficiently predicted by appropriately averaging over a relatively small set of distinct tectonic regions. The resulting clustered model objectively quantifies the more intuitive ways in which we usually tend to interpret tomographic models.

The formation and evolution of M33 as revealed by its star clusters

NASA Astrophysics Data System (ADS)

San Roman, Izaskun

2012-03-01

Numerical simulations based on the Lambda-Cold Dark Matter (Λ-CDM) model predict a scenario consistent with observational evidence in terms of the build-up of Milky Way-like halos. Under this scenario, large disk galaxies derive from the merger and accretion of many smaller subsystems. However, it is less clear how low-mass spiral galaxies fit into this picture. The best way to answer this question is to study the nearest example of a dwarf spiral galaxy, M33. We will use star clusters to understand the structure, kinematics and stellar populations of this galaxy. Star clusters provide a unique and powerful tool for studying the star formation histories of galaxies. In particular, the ages and metallicities of star clusters bear the imprint of the galaxy formation process. We have made use of the star clusters to uncover the formation and evolution of M33. In this dissertation, we have carried out a comprehensive study of the M33 star cluster system, including deep photometry as well as high signal-to-noise spectroscopy. In order to mitigate the significant incompleteness presents in previous catalogs, we have conducted ground-based and space-based photometric surveys of M33 star clusters. Using archival images, we have analyzed 12 fields using the Advanced Camera for Surveys Wide Field Channel onboard the Hubble Space Telescope (ACS/HST) along the major axis of the galaxy. We present integrated photometry and color-magnitude diagrams for 161 star clusters in M33, of which 115 were previously uncataloged. This survey extends the depth of the existing M33 cluster catalogs by ˜ 1 mag. We have expanded our search through a photometric survey in a 1° x 1° area centered on M33 using the MegaCam camera on the 3.6m Canada-France-Hawaii Telescope (CFHT). In this work we discuss the photometric properties of the sample, including color-color diagrams of 599 new candidate stellar clusters, and 204 confirmed clusters. Comparisons with models of simple stellar populations suggest a large range of ages some as old as ˜ 10 Gyr. In addition, we find in the color-color diagrams a significant population of very young clusters (< 10 Myr) possessing nebular emission. Analysis of the radial density distribution suggests that the cluster system of M33 has suffered from significant depletion, possibly due to interactions with M31. To further understand the properties of M33 star clusters, we have carried out a morphological study 161 star clusters in M33 using ACS/HST images. We have obtained, for the first time, ellipticities, position angles, and surface brightness profiles of a statistically significant number of clusters. Ellipticities show that, on average, M33 clusters are more flattened than those of the Milky Way and M31, and more similar to clusters in the Small Magellanic Cloud. The ellipticities do not show any correlation with age or mass, suggesting that rotation is not the main cause of elongation in the M33 clusters. The position angles of the clusters show a bimodality with a strong peak perpendicular to the position angle of the galaxy. These results support the notion that tidal forces are the reason for the cluster flattening. We have fit analytical models to the surface brightness profiles, and derived structural parameters. The overall analysis shows several differences between the structural properties of the M33 cluster system and cluster systems in nearby galaxies. Finally, we have performed a spectroscopic study of star clusters in the above mentioned catalog. We present high-precision velocity measures of 45 star clusters, based on observations from the 10.4m Gran Telescopio Canarias (GTC) using OSIRIS and 4.2m William Herschel Telescope (WHT) using WYFFOS. All the clusters have been previously confirmed using HST imaging, and ages and integrated photometry are known. The velocity of the clusters with respect to local disk motion increases with age for young and intermediate clusters. The mean dispersion velocity for the intermediate age clusters in our sample is significantly larger than in previous studies. Analysis of these velocities along the major axis of the galaxy show no net rotation of the intermediate age subsample. The small number of old clusters in our sample does not allow for any conclusive evidence in that age division.

Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: A case study of the South Pars Gas Field, Persian Gulf, Iran

NASA Astrophysics Data System (ADS)

Alizadeh, Bahram; Najjari, Saeid; Kadkhodaie-Ilkhchi, Ali

2012-08-01

Intelligent and statistical techniques were used to extract the hidden organic facies from well log responses in the Giant South Pars Gas Field, Persian Gulf, Iran. Kazhdomi Formation of Mid-Cretaceous and Kangan-Dalan Formations of Permo-Triassic Data were used for this purpose. Initially GR, SGR, CGR, THOR, POTA, NPHI and DT logs were applied to model the relationship between wireline logs and Total Organic Carbon (TOC) content using Artificial Neural Networks (ANN). The correlation coefficient (R2) between the measured and ANN predicted TOC equals to 89%. The performance of the model is measured by the Mean Squared Error function, which does not exceed 0.0073. Using Cluster Analysis technique and creating a binary hierarchical cluster tree the constructed TOC column of each formation was clustered into 5 organic facies according to their geochemical similarity. Later a second model with the accuracy of 84% was created by ANN to determine the specified clusters (facies) directly from well logs for quick cluster recognition in other wells of the studied field. Each created facies was correlated to its appropriate burial history curve. Hence each and every facies of a formation could be scrutinized separately and directly from its well logs, demonstrating the time and depth of oil or gas generation. Therefore potential production zone of Kazhdomi probable source rock and Kangan- Dalan reservoir formation could be identified while well logging operations (especially in LWD cases) were in progress. This could reduce uncertainty and save plenty of time and cost for oil industries and aid in the successful implementation of exploration and exploitation plans.

The Quantitative Analysis of Chennai Automotive Industry Cluster

NASA Astrophysics Data System (ADS)

Bhaskaran, Ethirajan

2016-07-01

Chennai, also called as Detroit of India due to presence of Automotive Industry producing over 40 % of the India's vehicle and components. During 2001-2002, the Automotive Component Industries (ACI) in Ambattur, Thirumalizai and Thirumudivakkam Industrial Estate, Chennai has faced problems on infrastructure, technology, procurement, production and marketing. The objective is to study the Quantitative Performance of Chennai Automotive Industry Cluster before (2001-2002) and after the CDA (2008-2009). The methodology adopted is collection of primary data from 100 ACI using quantitative questionnaire and analyzing using Correlation Analysis (CA), Regression Analysis (RA), Friedman Test (FMT), and Kruskall Wallis Test (KWT).The CA computed for the different set of variables reveals that there is high degree of relationship between the variables studied. The RA models constructed establish the strong relationship between the dependent variable and a host of independent variables. The models proposed here reveal the approximate relationship in a closer form. KWT proves, there is no significant difference between three locations clusters with respect to: Net Profit, Production Cost, Marketing Costs, Procurement Costs and Gross Output. This supports that each location has contributed for development of automobile component cluster uniformly. The FMT proves, there is no significant difference between industrial units in respect of cost like Production, Infrastructure, Technology, Marketing and Net Profit. To conclude, the Automotive Industries have fully utilized the Physical Infrastructure and Centralised Facilities by adopting CDA and now exporting their products to North America, South America, Europe, Australia, Africa and Asia. The value chain analysis models have been implemented in all the cluster units. This Cluster Development Approach (CDA) model can be implemented in industries of under developed and developing countries for cost reduction and productivity increase.

Simple, efficient allocation of modelling runs on heterogeneous clusters with MPI

USGS Publications Warehouse

Donato, David I.

2017-01-01

In scientific modelling and computation, the choice of an appropriate method for allocating tasks for parallel processing depends on the computational setting and on the nature of the computation. The allocation of independent but similar computational tasks, such as modelling runs or Monte Carlo trials, among the nodes of a heterogeneous computational cluster is a special case that has not been specifically evaluated previously. A simulation study shows that a method of on-demand (that is, worker-initiated) pulling from a bag of tasks in this case leads to reliably short makespans for computational jobs despite heterogeneity both within and between cluster nodes. A simple reference implementation in the C programming language with the Message Passing Interface (MPI) is provided.

Interaction of intense laser pulses with hydrogen atomic clusters

NASA Astrophysics Data System (ADS)

Du, Hong-Chuan; Wang, Hui-Qiao; Liu, Zuo-Ye; Sun, Shao-Hua; Li, Lu; Ma, Ling-Ling; Hu, Bi-Tao

2010-03-01

The interaction between intense femtosecond laser pulses and hydrogen atomic clusters is studied by a simplified Coulomb explosion model. The dependences of average proton kinetic energy on cluster size, pulse duration, laser intensity and wavelength are studied respectively. The calculated results indicate that the irradiation of a femtosecond laser of longer wavelength on hydrogen atomic clusters may be a simple, economical way to produce highly kinetic hydrogen ions. The phenomenon suggests that the irradiation of femtosecond laser of longer wavelength on deuterium atomic clusters may be easier than that of shorter wavelength to drive nuclear fusion reactions. The product of the laser intensity and the squared laser wavelength needed to make proton energy saturated as a function of the squared cluster radius is also investigated. The proton energy distribution calculated is also shown and compared with the experimental data. Our results are in agreement with the experimental results fairly well.

Clustering, randomness and regularity in cloud fields. I - Theoretical considerations. II - Cumulus cloud fields

NASA Technical Reports Server (NTRS)

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

1992-01-01

The current controversy existing in reference to the regularity vs. clustering in cloud fields is examined by means of analysis and simulation studies based upon nearest-neighbor cumulative distribution statistics. It is shown that the Poisson representation of random point processes is superior to pseudorandom-number-generated models and that pseudorandom-number-generated models bias the observed nearest-neighbor statistics towards regularity. Interpretation of this nearest-neighbor statistics is discussed for many cases of superpositions of clustering, randomness, and regularity. A detailed analysis is carried out of cumulus cloud field spatial distributions based upon Landsat, AVHRR, and Skylab data, showing that, when both large and small clouds are included in the cloud field distributions, the cloud field always has a strong clustering signal.

Functionalizing graphene by embedded boron clusters

NASA Astrophysics Data System (ADS)

Quandt, Alexander; Kunstmann, Jens; Ozdogan, Cem; Fehske, Holger

2010-03-01

We present results from an ab initio study of B7 clusters implanted into graphene [1,2]. Our model system consists of an alternating chain of quasiplanar B7 clusters. We show that graphene easily accepts these alternating B7-C6 chains and that the implanted boron components may dramatically modify the electronic properties. This suggests that our model system might serve as a blueprint for the controlled layout of graphene based nanodevices, where the semiconducting properties are supplemented by parts of the graphene matrix itself, and the basic metallic wiring is provided by alternating chains of implanted boron clusters. [1] A. Quandt, C. "Ozdogan, J. Kunstmann, and H. Fehske, Nanotechnology 19, 335707 (2008). [2] A. Quandt, C. "Ozdogan, J. Kunstmann, and H. Fehske, phys. stat. solidi (b) 245, 2077 (2008).

FAST TRACK COMMUNICATION Critical exponents of domain walls in the two-dimensional Potts model

NASA Astrophysics Data System (ADS)

Dubail, Jérôme; Lykke Jacobsen, Jesper; Saleur, Hubert

2010-12-01

We address the geometrical critical behavior of the two-dimensional Q-state Potts model in terms of the spin clusters (i.e. connected domains where the spin takes a constant value). These clusters are different from the usual Fortuin-Kasteleyn clusters, and are separated by domain walls that can cross and branch. We develop a transfer matrix technique enabling the formulation and numerical study of spin clusters even when Q is not an integer. We further identify geometrically the crossing events which give rise to conformal correlation functions. This leads to an infinite series of fundamental critical exponents h_{\\ell _1-\\ell _2,2\\ell _1}, valid for 0 <= Q <= 4, that describe the insertion of ell1 thin and ell2 thick domain walls.

Nano-confinement inside molecular metal oxide clusters: Dynamics and modified encapsulation behavior

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhe; Daemen, Luke L.; Cheng, Yongqiang

Encapsulation behavior, as well as the presence of internal catalytically-active sites, has been spurring the applications of a 3 nm hollow spherical metal oxide cluster {Mo 132} as an encapsulation host and a nano-reactor. Due to its well-defined and tunable cluster structures, and nano-scaled internal void space comparable to the volumes of small molecules, this cluster provides a good model to study the dynamics of materials under ultra-confinement. Neutron scattering studies suggest that bulky internal ligands inside the cluster show slower and limited dynamics compared to their counterparts in the bulk state, revealing the rigid nature of the skeleton ofmore » the internal ligands. Furthermore, NMR studies indicate that the rigid internal ligands that partially cover the interfacial pore on the molybdenum oxide shells are able to block some large guest molecules from going inside the capsule cluster, which provides a convincing protocol for size-selective encapsulation and separation.« less

Nano-confinement inside molecular metal oxide clusters: Dynamics and modified encapsulation behavior

DOE PAGES

Wang, Zhe; Daemen, Luke L.; Cheng, Yongqiang; ...

2016-08-19

Encapsulation behavior, as well as the presence of internal catalytically-active sites, has been spurring the applications of a 3 nm hollow spherical metal oxide cluster {Mo 132} as an encapsulation host and a nano-reactor. Due to its well-defined and tunable cluster structures, and nano-scaled internal void space comparable to the volumes of small molecules, this cluster provides a good model to study the dynamics of materials under ultra-confinement. Neutron scattering studies suggest that bulky internal ligands inside the cluster show slower and limited dynamics compared to their counterparts in the bulk state, revealing the rigid nature of the skeleton ofmore » the internal ligands. Furthermore, NMR studies indicate that the rigid internal ligands that partially cover the interfacial pore on the molybdenum oxide shells are able to block some large guest molecules from going inside the capsule cluster, which provides a convincing protocol for size-selective encapsulation and separation.« less

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

Coordinating the Provision of Health Services in Humanitarian Crises: a Systematic Review of Suggested Models.

PubMed

Lotfi, Tamara; Bou-Karroum, Lama; Darzi, Andrea; Hajjar, Rayan; El Rahyel, Ahmed; El Eid, Jamale; Itani, Mira; Brax, Hneine; Akik, Chaza; Osman, Mona; Hassan, Ghayda; El-Jardali, Fadi; Akl, Elie

2016-08-03

Our objective was to identify published models of coordination between entities funding or delivering health services in humanitarian crises, whether the coordination took place during or after the crises. We included reports describing models of coordination in sufficient detail to allow reproducibility. We also included reports describing implementation of identified models, as case studies. We searched Medline, PubMed, EMBASE, Cochrane Central Register of Controlled Trials, CINAHL, PsycINFO, and the WHO Global Health Library. We also searched websites of relevant organizations. We followed standard systematic review methodology. Our search captured 14,309 citations. The screening process identified 34 eligible papers describing five models of coordination of delivering health services: the "Cluster Approach" (with 16 case studies), the 4Ws "Who is Where, When, doing What" mapping tool (with four case studies), the "Sphere Project" (with two case studies), the "5x5" model (with one case study), and the "model of information coordination" (with one case study). The 4Ws and the 5x5 focus on coordination of services for mental health, the remaining models do not focus on a specific health topic. The Cluster approach appears to be the most widely used. One case study was a mixed implementation of the Cluster approach and the Sphere model. We identified no model of coordination for funding of health service. This systematic review identified five proposed coordination models that have been implemented by entities funding or delivering health service in humanitarian crises. There is a need to compare the effect of these different models on outcomes such as availability of and access to health services.

Estimation of Carcinogenicity using Hierarchical Clustering and Nearest Neighbor Methodologies

EPA Science Inventory

Previously a hierarchical clustering (HC) approach and a nearest neighbor (NN) approach were developed to model acute aquatic toxicity end points. These approaches were developed to correlate the toxicity for large, noncongeneric data sets. In this study these approaches applie...

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

USGS Publications Warehouse

Ellefsen, Karl J.; Smith, David

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.

Trap configuration and spacing influences parameter estimates in spatial capture-recapture models

USGS Publications Warehouse

Sun, Catherine C.; Fuller, Angela K.; Royle, J. Andrew

2014-01-01

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

Microbial community pattern detection in human body habitats via ensemble clustering framework.

PubMed

Yang, Peng; Su, Xiaoquan; Ou-Yang, Le; Chua, Hon-Nian; Li, Xiao-Li; Ning, Kang

2014-01-01

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.

Microbial community pattern detection in human body habitats via ensemble clustering framework

PubMed Central

2014-01-01

Background The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. Results To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. Conclusions In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome. PMID:25521415

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.

PubMed

Wu, Dingming; Wang, Dongfang; Zhang, Michael Q; Gu, Jin

2015-12-01

One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data. In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types. LRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/ .

Excitons in Potassium Bromide: A Study using Embedded Time-dependent Density Functional Theory and Equation-of-Motion Coupled Cluster Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Govind, Niranjan; Sushko, Petr V.; Hess, Wayne P.

2009-03-05

We present a study of the electronic excitations in insulating materials using an embedded- cluster method. The excited states of the embedded cluster are studied systematically using time-dependent density functional theory (TDDFT) and high-level equation-of-motion coupled cluster (EOMCC) methods. In particular, we have used EOMCC models with singles and doubles (EOMCCSD) and two approaches which account for the e®ect of triply excited con¯gurations in non-iterative and iterative fashions. We present calculations of the lowest surface excitations of the well-studied potassium bromide (KBr) system and compare our results with experiment. The bulk-surface exciton shift is also calculated at the TDDFT levelmore » and compared with experiment.« less

Synchronization as Aggregation: Cluster Kinetics of Pulse-Coupled Oscillators.

PubMed

O'Keeffe, Kevin P; Krapivsky, P L; Strogatz, Steven H

2015-08-07

We consider models of identical pulse-coupled oscillators with global interactions. Previous work showed that under certain conditions such systems always end up in sync, but did not quantify how small clusters of synchronized oscillators progressively coalesce into larger ones. Using tools from the study of aggregation phenomena, we obtain exact results for the time-dependent distribution of cluster sizes as the system evolves from disorder to synchrony.

Recent advances in the Suf Fe-S cluster biogenesis pathway: Beyond the Proteobacteria.

PubMed

Outten, F Wayne

2015-06-01

Fe-S clusters play critical roles in cellular function throughout all three kingdoms of life. Consequently, Fe-S cluster biogenesis systems are present in most organisms. The Suf (sulfur formation) system is the most ancient of the three characterized Fe-S cluster biogenesis pathways, which also include the Isc and Nif systems. Much of the first work on the Suf system took place in Gram-negative Proteobacteria used as model organisms. These early studies led to a wealth of biochemical, genetic, and physiological information on Suf function. From those studies we have learned that SufB functions as an Fe-S scaffold in conjunction with SufC (and in some cases SufD). SufS and SufE together mobilize sulfur for cluster assembly and SufA traffics the complete Fe-S cluster from SufB to target apo-proteins. However, recent progress on the Suf system in other organisms has opened up new avenues of research and new hypotheses about Suf function. This review focuses primarily on the most recent discoveries about the Suf pathway and where those new models may lead the field. This article is part of a Special Issue entitled: Fe/S proteins: Analysis, structure, function, biogenesis and diseases. Copyright © 2014 Elsevier B.V. All rights reserved.

The peculiar velocities of rich clusters in the hot and cold dark matter scenarios

NASA Technical Reports Server (NTRS)

Rhee, George F.; West, Michael J.; Villumsen, Jens V.

1993-01-01

We present the results of a study of the peculiar velocities of rich clusters of galaxies. The peculiar motion of rich clusters in various cosmological scenarios is of interest for a number of reasons. Observationally, one can measure the peculiar motion of clusters to greater distances than galaxies because cluster peculiar motions can be determined to greater accuracy. One can also test the slope of distance indicator relations using clusters to see if galaxy properties vary with environment. We have used N-body simulations to measure the amplitude and rms cluster peculiar velocity as a function of bias parameter in the hot and cold dark matter scenarios. In addition to measuring the mean and rms peculiar velocity of clusters in the two models, we determined whether the peculiar velocity vector of a given cluster is well aligned with the gravity vector due to all the particles in the simulation and the gravity vector due to the particles present only in the clusters. We have investigated the peculiar velocities of rich clusters of galaxies in the cold dark matter and hot dark matter galaxy formation scenarios. We have derived peculiar velocities and associated errors for the scenarios using four values of the bias parameter ranging from b = 1 to b = 2.5. The growth of the mean peculiar velocity with scale factor has been determined and compared to that predicted by linear theory. In addition, we have compared the orientation of force and velocity in these simulations to see if a program such as that proposed by Bertschinger and Dekel (1989) for elliptical galaxy peculiar motions can be applied to clusters. The method they describe enables one to recover the density field from large scale redshift distance samples. The method makes it possible to do this when only radial velocities are known by assuming that the velocity field is curl free. Our analysis suggests that this program if applied to clusters is only realizable for models with a low value of the bias parameter, i.e., models in which the peculiar velocities of clusters are large enough that the errors do not render the analysis impracticable.

Tumorigenicity of the miR-17-92 cluster distilled.

PubMed

van Haaften, Gijs; Agami, Reuven

2010-01-01

The miR-17-92 gene cluster, with its six different mature microRNAs (miRNAs), has an established oncogenic function. However, the oncogenic contribution of each individual miRNA in the cluster has not been assigned. Two studies published in the December 15, 2009, issue of Genes & Development by Mu and colleagues (pp. 2806-2811) and Olive and colleagues (pp. 2839-2849) dissected the miR-17-92 cluster to its individual miRNA components and identified their relative contributions to oncogenic transformation in mouse model systems.

Shaping Globular Clusters with Black Holes

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2018-03-01

How many black holes lurk within the dense environments of globular clusters, and how do these powerful objects shape the properties of the cluster around them? One such cluster, NGC 3201, is now helping us to answer these questions.Hunting Stellar-Mass Black HolesSince the detection of merging black-hole binaries by the Laser Interferometer Gravitational-Wave Observatory (LIGO), the dense environments of globular clusters have received increasing attention as potential birthplaces of these compact binary systems.The central region of the globular star cluster NGC 3201, as viewed by Hubble. The black hole is in orbit with the star marked by the blue circle. [NASA/ESA]In addition, more and more stellar-mass black-hole candidates have been observed within globular clusters, lurking in binary pairs with luminous, non-compact companions. The most recent of these detections, found in the globular cluster NGC 3201, stands alone as the first stellar-mass black hole candidate discovered via radial velocity observations: the black holes main-sequence companion gave away its presence via a telltale wobble.Now a team of scientists led by Kyle Kremer (CIERA and Northwestern University) is using models of this system to better understand the impact that black holes might have on their host clusters.A Model ClusterThe relationship between black holes and their host clusters is complicated. Though the cluster environment can determine the dynamical evolution of the black holes, the retention rate of black holes in a globular cluster (i.e., how many remain in the cluster when they are born as supernovae, rather than being kicked out during the explosion) influences how the host cluster evolves.Kremer and collaborators track this complex relationship by modeling the evolution of a cluster similar to NGC 3201 with a Monte Carlo code. The code incorporates physics relevant to the evolution of black holes and black-hole binaries in globular clusters, such as two-body relaxation, single and binary star evolution, galactic tides, and multi-body encounters. From their grid of models with varying input parameters, the authors then determine which fit best to NGC 3201s final observational properties.Surface brightness profiles for all globular-cluster models at late times compared to observations of NGC 3201 (yellow circles). Blue lines represent models with few retained black holes; black lines represent models with many retained black holes. [Kremer et al. 2018]Retention MattersKremer and collaborators find that the models that best represent NGC 3201 all retain more than 200 black holes at the end of the simulation; models that lost too many black holes due to natal kicks did not match observations of NGC 3201 as well. The models with large numbers of retained black holes also harbored binaries just like the one recently detected in NGC 3201.Models that retain few black holes, on the other hand, may instead be good descriptions of so-called core-collapsed globular clusters observed in the Milky Way. The authors demonstrate that these clusters could contain black holes in binaries with stars known as blue stragglers, which may also be detectable with radial velocity techniques.Kremer and collaborators results suggest that globular clusters similar to NGC 3201 contain hundreds of invisible black holes waiting to be discovered, and they indicate some of the differences in cluster properties caused by hosting such a large population of black holes. We can hope that future observations and modeling will continue to illuminate the complicated relationship between globular clusters and the black holes that live in them.CitationKyle Kremer et al 2018 ApJL 855 L15. doi:10.3847/2041-8213/aab26c

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

NASA Astrophysics Data System (ADS)

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-04-01

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

De novo assembly of the transcriptome of the non-model plant Streptocarpus rexii employing a novel heuristic to recover locus-specific transcript clusters.

PubMed

Chiara, Matteo; Horner, David S; Spada, Alberto

2013-01-01

De novo transcriptome characterization from Next Generation Sequencing data has become an important approach in the study of non-model plants. Despite notable advances in the assembly of short reads, the clustering of transcripts into unigene-like (locus-specific) clusters remains a somewhat neglected subject. Indeed, closely related paralogous transcripts are often merged into single clusters by current approaches. Here, a novel heuristic method for locus-specific clustering is compared to that implemented in the de novo assembler Oases, using the same initial transcript collections, derived from Arabidopsis thaliana and the developmental model Streptocarpus rexii. We show that the proposed approach improves cluster specificity in the A. thaliana dataset for which the reference genome is available. Furthermore, for the S. rexii data our filtered transcript collection matches a larger number of distinct annotated loci in reference genomes than the Oases set, while containing a reduced overall number of loci. A detailed discussion of advantages and limitations of our approach in processing de novo transcriptome reconstructions is presented. The proposed method should be widely applicable to other organisms, irrespective of the transcript assembly method employed. The S. rexii transcriptome is available as a sophisticated and augmented publicly available online database.

Temporal clustering of tropical cyclones and its ecosystem impacts

PubMed Central

Mumby, Peter J.; Vitolo, Renato; Stephenson, David B.

2011-01-01

Tropical cyclones have massive economic, social, and ecological impacts, and models of their occurrence influence many planning activities from setting insurance premiums to conservation planning. Most impact models allow for geographically varying cyclone rates but assume that individual storm events occur randomly with constant rate in time. This study analyzes the statistical properties of Atlantic tropical cyclones and shows that local cyclone counts vary in time, with periods of elevated activity followed by relative quiescence. Such temporal clustering is particularly strong in the Caribbean Sea, along the coasts of Belize, Honduras, Costa Rica, Jamaica, the southwest of Haiti, and in the main hurricane development region in the North Atlantic between Africa and the Caribbean. Failing to recognize this natural nonstationarity in cyclone rates can give inaccurate impact predictions. We demonstrate this by exploring cyclone impacts on coral reefs. For a given cyclone rate, we find that clustered events have a less detrimental impact than independent random events. Predictions using a standard random hurricane model were overly pessimistic, predicting reef degradation more than a decade earlier than that expected under clustered disturbance. The presence of clustering allows coral reefs more time to recover to healthier states, but the impacts of clustering will vary from one ecosystem to another. PMID:22006300

Genetic k-Means Clustering Approach for Mapping Human Vulnerability to Chemical Hazards in the Industrialized City: A Case Study of Shanghai, China

PubMed Central

Shi, Weifang; Zeng, Weihua

2013-01-01

Reducing human vulnerability to chemical hazards in the industrialized city is a matter of great urgency. Vulnerability mapping is an alternative approach for providing vulnerability-reducing interventions in a region. This study presents a method for mapping human vulnerability to chemical hazards by using clustering analysis for effective vulnerability reduction. Taking the city of Shanghai as the study area, we measure human exposure to chemical hazards by using the proximity model with additionally considering the toxicity of hazardous substances, and capture the sensitivity and coping capacity with corresponding indicators. We perform an improved k-means clustering approach on the basis of genetic algorithm by using a 500 m × 500 m geographical grid as basic spatial unit. The sum of squared errors and silhouette coefficient are combined to measure the quality of clustering and to determine the optimal clustering number. Clustering result reveals a set of six typical human vulnerability patterns that show distinct vulnerability dimension combinations. The vulnerability mapping of the study area reflects cluster-specific vulnerability characteristics and their spatial distribution. Finally, we suggest specific points that can provide new insights in rationally allocating the limited funds for the vulnerability reduction of each cluster. PMID:23787337

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

PubMed Central

Gomes, Manuel; Ng, Edmond S.-W.; Nixon, Richard; Carpenter, James; Thompson, Simon G.

2012-01-01

Aim. Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Methods. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering—seemingly unrelated regression (SUR) without a robust standard error (SE)—and 4 methods that recognized clustering—SUR and generalized estimating equations (GEEs), both with robust SE, a “2-stage” nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Results. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92–0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. Conclusions. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters. PMID:22016450

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

PubMed

Yelland, Lisa N; Salter, Amy B; Ryan, Philip

2011-10-15

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

GASP. IX. Jellyfish galaxies in phase-space: an orbital study of intense ram-pressure stripping in clusters

NASA Astrophysics Data System (ADS)

Jaffé, Yara L.; Poggianti, Bianca M.; Moretti, Alessia; Gullieuszik, Marco; Smith, Rory; Vulcani, Benedetta; Fasano, Giovanni; Fritz, Jacopo; Tonnesen, Stephanie; Bettoni, Daniela; Hau, George; Biviano, Andrea; Bellhouse, Callum; McGee, Sean

2018-06-01

It is well known that galaxies falling into clusters can experience gas stripping due to ram pressure by the intra-cluster medium. The most spectacular examples are galaxies with extended tails of optically bright stripped material known as `jellyfish'. We use the first large homogeneous compilation of jellyfish galaxies in clusters from the WINGS and OmegaWINGS surveys, and follow-up MUSE observations from the GASP MUSE programme to investigate the orbital histories of jellyfish galaxies in clusters and reconstruct their stripping history through position versus velocity phase-space diagrams. We construct analytic models to define the regions in phase-space where ram-pressure stripping is at play. We then study the distribution of cluster galaxies in phase-space and find that jellyfish galaxies have on average higher peculiar velocities (and higher cluster velocity dispersion) than the overall population of cluster galaxies at all cluster-centric radii, which is indicative of recent infall into the cluster and radial orbits. In particular, the jellyfish galaxies with the longest gas tails reside very near the cluster cores (in projection) and are moving at very high speeds, which coincides with the conditions of the most intense ram pressure. We conclude that many of the jellyfish galaxies seen in clusters likely formed via fast (˜1-2 Gyr), incremental, outside-in ram-pressure stripping during first infall into the cluster in highly radial orbits.

Long-term changes in the heat-mortality relationship according to heterogeneous regional climate: a time-series study in South Korea.

PubMed

Heo, Seulkee; Lee, Eunil; Kwon, Bo Yeon; Lee, Suji; Jo, Kyung Hee; Kim, Jinsun

2016-08-03

Several studies identified a heterogeneous impact of heat on mortality in hot and cool regions during a fixed period, whereas less evidence is available for changes in risk over time due to climate change in these regions. We compared changes in risk during periods without (1996-2000) and with (2008-2012) heatwave warning forecasts in regions of South Korea with different climates. Study areas were categorised into 3 clusters based on the spatial clustering of cooling degree days in the period 1993-2012: hottest cluster (cluster H), moderate cluster (cluster M) and cool cluster (cluster C). The risk was estimated according to increases in the daily all-cause, cardiovascular and respiratory mortality per 1°C change in daily temperature above the threshold, using a generalised additive model. The risk of all types of mortality increased in cluster H in 2008-2012, compared with 1996-2000, whereas the risks in all-combined regions and cooler clusters decreased. Temporal increases in mortality risk were larger for some vulnerable subgroups, including younger adults (<75 years), those with a lower education and blue-collar workers, in cluster H as well as all-combined regions. Different patterns of risk change among clusters might be attributable to large increases in heatwave frequency or duration during study periods and the degree of urbanisation in cluster H. People living in hotter regions or with a lower socioeconomic status are at higher risk following an increasing trend of heat-related mortality risks. Continuous efforts are needed to understand factors which affect changes in heat-related mortality risks. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Construct validation of the hybrid model of posttraumatic stress disorder: Distinctiveness of the new symptom clusters.

PubMed

Silverstein, Madison W; Dieujuste, Nathalie; Kramer, Lindsay B; Lee, Daniel J; Weathers, Frank W

2018-03-01

Despite the factor analytic support for the seven-factor hybrid model (Armour et al., 2015) of posttraumatic stress disorder (PTSD), little research has examined the degree to which newly established symptom clusters (i.e., negative affect, anhedonia, dysphoric arousal, anxious arousal, externalizing behavior) functionally and meaningfully differ in their associations with other clinical phenomena. The aim of the current study was to examine the degree to which newly established PTSD symptom clusters differentially relate to co-occurring psychopathology and related clinical phenomena through Wald testing using latent variable modeling. Participants were 535 trauma-exposed undergraduates who completed the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Weathers et al., 2013) and Personality Assessment Inventory (PAI; Morey, 1991). As expected and in line with results from previous studies, significant heterogeneity emerged for dysphoric arousal, anxious arousal, and externalizing behavior. However, there was less evidence for the distinctiveness of negative affect and anhedonia. Results indicate that only some of the newly established symptom clusters significantly differ in their associations with related clinical phenomena and that the hybrid model might not provide a meaningful framework for understanding which PTSD symptoms relate to associated features. Limitations include a non-clinical sample and reliance on retrospective self-report assessment measures. Copyright © 2017 Elsevier Ltd. All rights reserved.

Long-term memory and volatility clustering in high-frequency price changes

NASA Astrophysics Data System (ADS)

oh, Gabjin; Kim, Seunghwan; Eom, Cheoljun

2008-02-01

We studied the long-term memory in diverse stock market indices and foreign exchange rates using Detrended Fluctuation Analysis (DFA). For all high-frequency market data studied, no significant long-term memory property was detected in the return series, while a strong long-term memory property was found in the volatility time series. The possible causes of the long-term memory property were investigated using the return data filtered by the AR(1) model, reflecting the short-term memory property, the GARCH(1,1) model, reflecting the volatility clustering property, and the FIGARCH model, reflecting the long-term memory property of the volatility time series. The memory effect in the AR(1) filtered return and volatility time series remained unchanged, while the long-term memory property diminished significantly in the volatility series of the GARCH(1,1) filtered data. Notably, there is no long-term memory property, when we eliminate the long-term memory property of volatility by the FIGARCH model. For all data used, although the Hurst exponents of the volatility time series changed considerably over time, those of the time series with the volatility clustering effect removed diminish significantly. Our results imply that the long-term memory property of the volatility time series can be attributed to the volatility clustering observed in the financial time series.

Statistical sampling of the distribution of uranium deposits using geologic/geographic clusters

USGS Publications Warehouse

Finch, W.I.; Grundy, W.D.; Pierson, C.T.

1992-01-01

The concept of geologic/geographic clusters was developed particularly to study grade and tonnage models for sandstone-type uranium deposits. A cluster is a grouping of mined as well as unmined uranium occurrences within an arbitrary area about 8 km across. A cluster is a statistical sample that will reflect accurately the distribution of uranium in large regions relative to various geologic and geographic features. The example of the Colorado Plateau Uranium Province reveals that only 3 percent of the total number of clusters is in the largest tonnage-size category, greater than 10,000 short tons U3O8, and that 80 percent of the clusters are hosted by Triassic and Jurassic rocks. The distributions of grade and tonnage for clusters in the Powder River Basin show a wide variation; the grade distribution is highly variable, reflecting a difference between roll-front deposits and concretionary deposits, and the Basin contains about half the number in the greater-than-10,000 tonnage-size class as does the Colorado Plateau, even though it is much smaller. The grade and tonnage models should prove useful in finding the richest and largest uranium deposits. ?? 1992 Oxford University Press.

Analysis and Modeling of Structure Formation in Granular and Fluid-Solid Flows

NASA Astrophysics Data System (ADS)

Murphy, Eric

Granular and multiphase flows are encountered in a number of industrial processes with particular emphasis in this manuscript given to the particular applications in cement pumping, pneumatic conveying, fluid catalytic cracking, CO2 capture, and fast pyrolysis of bio-materials. These processes are often modeled using averaged equations that may be simulated using computational fluid dynamics. Closure models are then required that describe the average forces that arise from both interparticle interactions, e.g. shear stress, and interphase interactions, such as mean drag. One of the biggest hurdles to this approach is the emergence of non-trivial spatio-temporal structures in the particulate phase, which can significantly modify the qualitative behavior of these forces and the resultant flow phenomenology. For example, the formation of large clusters in cohesive granular flows is responsible for a transition from solid-like to fluid-like rheology. Another example is found in gas-solid systems, where clustering at small scales is observed to significantly lower in the observed drag. Moreover, there remains the possibility that structure formation may occur at all scales, leading to a lack of scale separation required for traditional averaging approaches. In this context, several modeling problems are treated 1) first-principles based modeling of the rheology of cement slurries, 2) modeling the mean solid-solid drag experienced by polydisperse particles undergoing segregation, and 3) modeling clustering in homogeneous gas-solid flows. The first and third components are described in greater detail. In the study on the rheology of cements, several sub-problems are introduced, which systematically increase in the number and complexity of interparticle interactions. These interparticle interactions include inelasticity, friction, cohesion, and fluid interactions. In the first study, the interactions between cohesive inelastic particles was fully characterized for the first time. Next, kinetic theory was used to predict the cooling of a gas of such particles. DEM was then used to validate this approach. A study on the rheology of dry cohesive granules with and without friction was then carried out, where the physics of different flow phenomenology was exhaustively explored. Lastly, homogeneous cement slurry simulations were carried out, and compared with vane-rheometer experiments. Qualitative agreement between simulation and experiment were observed. Lastly, the physics of clustering in homogeneous gas-solid flows is explored in the hopes of gaining a mechanistic explanation of how particle-fluid interactions lead to clustering. Exact equations are derived, detailing the evolution of the two particle density, which may be closed using high-fidelity particle-resolved direct numerical simulation. Two canonical gas-solid flows are then addressed, the homogeneously cooling gas-solid flow (HCGSF) and sedimenting gas-solid flow (SGSF). A mechanism responsible for clustering in the HCGSF is identified. Clustering of plane-wave like structures is observed in the SGSF, and the exact terms are quantified. A method for modeling the dynamics of clustering in these systems is proposed, which may aid in the prediction of clustering and other correlation length-scales useful for less expensive computations.

[Investigation of the distribution of water clusters in vegetables, fruits, and natural waters by flicker noise spectroscopy].

PubMed

Zubov, A V; Zubov, K V; Zubov, V A

2007-01-01

The distribution of water clusters in fresh rain water and in rain water that was aged for 30 days (North Germany, 53 degrees 33' N, 12 degrees 47' E, 293 K, rain on 25.06.06) as well as in fresh vegetables and fruits was studied by flicker noise spectroscopy. In addition, the development of water clusters in apples and potatoes during ripening in 2006 was investigated. A different distribution of water clusters in irrigation water (river and rain) and in the biomatrix of vegetables (potatoes, onions, tomatoes, red beets) and fruits (apples, bananas) was observed. It was concluded that the cluster structure of irrigation water differs from that of water of the biomatrix of vegetables and fruits and depends on drought and the biomatrix nature. Water clusters in plants are more stable and reproducible than water clusters in natural water. The main characteristics of cluster formation in materials studied were given. The oscillation frequencies of water clusters in plants (biofield) are given at which they interact with water clusters of the Earth hydrosphere. A model of series of clusters 16(H2O)100 <--> 4(H2O)402 <--> 2(H2O)903 <--> (H2O)1889 in the biomatrix of vegetables and fruits was discussed.

Renormalized coupled cluster approaches in the cluster-in-molecule framework: predicting vertical electron binding energies of the anionic water clusters (H2O)(n)(-).

PubMed

Xu, Peng; Gordon, Mark S

2014-09-04

Anionic water clusters are generally considered to be extremely challenging to model using fragmentation approaches due to the diffuse nature of the excess electron distribution. The local correlation coupled cluster (CC) framework cluster-in-molecule (CIM) approach combined with the completely renormalized CR-CC(2,3) method [abbreviated CIM/CR-CC(2,3)] is shown to be a viable alternative for computing the vertical electron binding energies (VEBE). CIM/CR-CC(2,3) with the threshold parameter ζ set to 0.001, as a trade-off between accuracy and computational cost, demonstrates the reliability of predicting the VEBE, with an average percentage error of ∼15% compared to the full ab initio calculation at the same level of theory. The errors are predominantly from the electron correlation energy. The CIM/CR-CC(2,3) approach provides the ease of a black-box type calculation with few threshold parameters to manipulate. The cluster sizes that can be studied by high-level ab initio methods are significantly increased in comparison with full CC calculations. Therefore, the VEBE computed by the CIM/CR-CC(2,3) method can be used as benchmarks for testing model potential approaches in small-to-intermediate-sized water clusters.

Ab Initio Study of KCl and AgCl Clusters.

NASA Astrophysics Data System (ADS)

McKeough, James; Hira, Ajit; Cathey, Tommy; Valdez, Alexandra

This paper presents a theoretical study of molecular clusters that examines the chemical and physical properties of small KnCln and AgnCln clusters (n = 2 - 24). Due to combinations of attractive and repulsive long-range forces, such clusters exhibit structural and dynamical behavior different from that of homogeneous clusters. The potentially important role of these molecular species in biochemical and medicinal processes is widely known. This work applies the hybrid ab initio methods to derive the different alkali-halide (MnHn) geometries. Of particular interest is the competition between hexagonal ring geometries and rock salt structures. Electronic energies, rotational constants, dipole moments, and vibrational frequencies for these geometries are calculated. Magic numbers for cluster stability are identified and are related to the property of cluster compactness. Mapping of the singlet, triplet, and quintet, potential energy surfaces is performed. Calculations were performed to examine the interactions of these clusters with some atoms and molecules of biological interest, including O, O2, and Fe. Potential design of new medicinal drugs is explored. We will also investigate model and material dependence of the results. AMP program of the National Science Foundation.

First principles calculations for interaction of tyrosine with (ZnO)3 cluster

NASA Astrophysics Data System (ADS)

Singh, Satvinder; Singh, Gurinder; Kaura, Aman; Tripathi, S. K.

2018-04-01

First Principles Calculations have been performed to study interactions of Phenol ring of Tyrosine (C6H5OH) with (ZnO)3 atomic cluster. All the calculations have been performed under the Density Functional Theory (DFT) framework. Structural and electronic properties of (ZnO)3/C6H5OH have been studied. Gaussian basis set approach has been adopted for the calculations. A ring type most stable (ZnO)3 atomic cluster has been modeled, analyzed and used for the calculations. The compatibility of the results with previous studies has been presented here.

Portfolio Decisions and Brain Reactions via the CEAD method.

PubMed

Majer, Piotr; Mohr, Peter N C; Heekeren, Hauke R; Härdle, Wolfgang K

2016-09-01

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Dividing traffic cluster into parts by signal control

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

2018-02-01

When a cluster of vehicles with various speeds moves through the series of signals, the cluster breaks down by stopping at signals and results in smaller groups of vehicles. We present the nonlinear-map model of the motion of vehicles controlled by the signals. We study the breakup of a cluster of vehicles through the series of signals. The cluster of vehicles is divided into various groups by controlling the cycle time of signals. The vehicles within each group move with the same mean velocity. The breakup of the traffic cluster depends highly on the signal control. The dependence of dividing on both cycle time and vehicular speed is clarified. Also, we investigate the effect of the irregular interval between signals on dividing.

Hydration of a Large Anionic Charge Distribution - Naphthalene-Water Cluster Anions

NASA Astrophysics Data System (ADS)

Weber, J. Mathias; Adams, Christopher L.

2010-06-01

We report the infrared spectra of anionic clusters of naphthalene with up to three water molecules. Comparison of the experimental infrared spectra with theoretically predicted spectra from quantum chemistry calculations allow conclusions regarding the structures of the clusters under study. The first water molecule forms two hydrogen bonds with the π electron system of the naphthalene moiety. Subsequent water ligands interact with both the naphthalene and the other water ligands to form hydrogen bonded networks, similar to other hydrated anion clusters. Naphthalene-water anion clusters illustrate how water interacts with negative charge delocalized over a large π electron system. The clusters are interesting model systems that are discussed in the context of wetting of graphene surfaces and polyaromatic hydrocarbons.

Model-based recursive partitioning to identify risk clusters for metabolic syndrome and its components: findings from the International Mobility in Aging Study

PubMed Central

Pirkle, Catherine M; Wu, Yan Yan; Zunzunegui, Maria-Victoria; Gómez, José Fernando

2018-01-01

Objective Conceptual models underpinning much epidemiological research on ageing acknowledge that environmental, social and biological systems interact to influence health outcomes. Recursive partitioning is a data-driven approach that allows for concurrent exploration of distinct mixtures, or clusters, of individuals that have a particular outcome. Our aim is to use recursive partitioning to examine risk clusters for metabolic syndrome (MetS) and its components, in order to identify vulnerable populations. Study design Cross-sectional analysis of baseline data from a prospective longitudinal cohort called the International Mobility in Aging Study (IMIAS). Setting IMIAS includes sites from three middle-income countries—Tirana (Albania), Natal (Brazil) and Manizales (Colombia)—and two from Canada—Kingston (Ontario) and Saint-Hyacinthe (Quebec). Participants Community-dwelling male and female adults, aged 64–75 years (n=2002). Primary and secondary outcome measures We apply recursive partitioning to investigate social and behavioural risk factors for MetS and its components. Model-based recursive partitioning (MOB) was used to cluster participants into age-adjusted risk groups based on variabilities in: study site, sex, education, living arrangements, childhood adversities, adult occupation, current employment status, income, perceived income sufficiency, smoking status and weekly minutes of physical activity. Results 43% of participants had MetS. Using MOB, the primary partitioning variable was participant sex. Among women from middle-incomes sites, the predicted proportion with MetS ranged from 58% to 68%. Canadian women with limited physical activity had elevated predicted proportions of MetS (49%, 95% CI 39% to 58%). Among men, MetS ranged from 26% to 41% depending on childhood social adversity and education. Clustering for MetS components differed from the syndrome and across components. Study site was a primary partitioning variable for all components except HDL cholesterol. Sex was important for most components. Conclusion MOB is a promising technique for identifying disease risk clusters (eg, vulnerable populations) in modestly sized samples. PMID:29500203

Detection of major climatic and environmental predictors of liver fluke exposure risk in Ireland using spatial cluster analysis.

PubMed

Selemetas, Nikolaos; de Waal, Theo

2015-04-30

Fasciolosis caused by Fasciola hepatica (liver fluke) can cause significant economic and production losses in dairy cow farms. The aim of the current study was to identify important weather and environmental predictors of the exposure risk to liver fluke by detecting clusters of fasciolosis in Ireland. During autumn 2012, bulk-tank milk samples from 4365 dairy farms were collected throughout Ireland. Using an in-house antibody-detection ELISA, the analysis of BTM samples showed that 83% (n=3602) of dairy farms had been exposed to liver fluke. The Getis-Ord Gi* statistic identified 74 high-risk and 130 low-risk significant (P<0.01) clusters of fasciolosis. The low-risk clusters were mostly located in the southern regions of Ireland, whereas the high-risk clusters were mainly situated in the western part. Several climatic variables (monthly and seasonal mean rainfall and temperatures, total wet days and rain days) and environmental datasets (soil types, enhanced vegetation index and normalised difference vegetation index) were used to investigate dissimilarities in the exposure to liver fluke between clusters. Rainfall, total wet days and rain days, and soil type were the significant classes of climatic and environmental variables explaining the differences between significant clusters. A discriminant function analysis was used to predict the exposure risk to liver fluke using 80% of data for modelling and the remaining subset of 20% for post hoc model validation. The most significant predictors of the model risk function were total rainfall in August and September and total wet days. The risk model presented 100% sensitivity and 91% specificity and an accuracy of 95% correctly classified cases. A risk map of exposure to liver fluke was constructed with higher probability of exposure in western and north-western regions. The results of this study identified differences between clusters of fasciolosis in Ireland regarding climatic and environmental variables and detected significant predictors of the exposure risk to liver fluke. Copyright © 2015 Elsevier B.V. All rights reserved.

Kinetic model for binary homogeneous nucleation in the H2O-H2SO4 system: comparison with experiments and classical theory of nucleation.

PubMed

Sorokin, A; Vancassel, X; Mirabel, P

2005-12-22

A kinetic model to predict nucleation rates in the sulfuric acid-water system is presented. It allows calculating steady-state nucleation rates and the corresponding time lag, using a direct solution of a system of kinetic equations that describe the populations of sub- and near-critical clusters. This kinetic model takes into account cluster-cluster collisions and decay of clusters into smaller clusters. The model results are compared with some predictions obtained with the classical nucleation theory (CNT) and also with available measurement data obtained in smog chambers or flow tubes. It is shown that in the case of slow nucleation processes, the kinetic model and the CNT as used by Shugard et al. [J. Chem. Phys. 75, 5298 (1974)] give the same results. However, in the case of intensive nucleation, a large part of the nucleation flux is due to cluster-cluster collisions and the CNT underestimates the nucleation rates.

Aerosol Models for the CALIPSO Lidar Inversion Algorithms

NASA Technical Reports Server (NTRS)

Omar, Ali H.; Winker, David M.; Won, Jae-Gwang

2003-01-01

We use measurements and models to develop aerosol models for use in the inversion algorithms for the Cloud Aerosol Lidar and Imager Pathfinder Spaceborne Observations (CALIPSO). Radiance measurements and inversions of the AErosol RObotic NETwork (AERONET1, 2) are used to group global atmospheric aerosols using optical and microphysical parameters. This study uses more than 105 records of radiance measurements, aerosol size distributions, and complex refractive indices to generate the optical properties of the aerosol at more 200 sites worldwide. These properties together with the radiance measurements are then classified using classical clustering methods to group the sites according to the type of aerosol with the greatest frequency of occurrence at each site. Six significant clusters are identified: desert dust, biomass burning, urban industrial pollution, rural background, marine, and dirty pollution. Three of these are used in the CALIPSO aerosol models to characterize desert dust, biomass burning, and polluted continental aerosols. The CALIPSO aerosol model also uses the coarse mode of desert dust and the fine mode of biomass burning to build a polluted dust model. For marine aerosol, the CALIPSO aerosol model uses measurements from the SEAS experiment 3. In addition to categorizing the aerosol types, the cluster analysis provides all the column optical and microphysical properties for each cluster.

Study of lithium cation in water clusters: based on atom-bond electronegativity equalization method fused into molecular mechanics.

PubMed

Li, Xin; Yang, Zhong-Zhi

2005-05-12

We present a potential model for Li(+)-water clusters based on a combination of the atom-bond electronegativity equalization and molecular mechanics (ABEEM/MM) that is to take ABEEM charges of the cation and all atoms, bonds, and lone pairs of water molecules into the intermolecular electrostatic interaction term in molecular mechanics. The model allows point charges on cationic site and seven sites of an ABEEM-7P water molecule to fluctuate responding to the cluster geometry. The water molecules in the first sphere of Li(+) are strongly structured and there is obvious charge transfer between the cation and the water molecules; therefore, the charge constraint on the ionic cluster includes the charged constraint on the Li(+) and the first-shell water molecules and the charge neutrality constraint on each water molecule in the external hydration shells. The newly constructed potential model based on ABEEM/MM is first applied to ionic clusters and reproduces gas-phase state properties of Li(+)(H(2)O)(n) (n = 1-6 and 8) including optimized geometries, ABEEM charges, binding energies, frequencies, and so on, which are in fair agreement with those measured by available experiments and calculated by ab initio methods. Prospects and benefits introduced by this potential model are pointed out.

Function Clustering Self-Organization Maps (FCSOMs) for mining differentially expressed genes in Drosophila and its correlation with the growth medium.

PubMed

Liu, L L; Liu, M J; Ma, M

2015-09-28

The central task of this study was to mine the gene-to-medium relationship. Adequate knowledge of this relationship could potentially improve the accuracy of differentially expressed gene mining. One of the approaches to differentially expressed gene mining uses conventional clustering algorithms to identify the gene-to-medium relationship. Compared to conventional clustering algorithms, self-organization maps (SOMs) identify the nonlinear aspects of the gene-to-medium relationships by mapping the input space into another higher dimensional feature space. However, SOMs are not suitable for huge datasets consisting of millions of samples. Therefore, a new computational model, the Function Clustering Self-Organization Maps (FCSOMs), was developed. FCSOMs take advantage of the theory of granular computing as well as advanced statistical learning methodologies, and are built specifically for each information granule (a function cluster of genes), which are intelligently partitioned by the clustering algorithm provided by the DAVID_6.7 software platform. However, only the gene functions, and not their expression values, are considered in the fuzzy clustering algorithm of DAVID. Compared to the clustering algorithm of DAVID, these experimental results show a marked improvement in the accuracy of classification with the application of FCSOMs. FCSOMs can handle huge datasets and their complex classification problems, as each FCSOM (modeled for each function cluster) can be easily parallelized.

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: modelling the clustering and halo occupation distribution of BOSS CMASS galaxies in the Final Data Release

NASA Astrophysics Data System (ADS)

Rodríguez-Torres, Sergio A.; Chuang, Chia-Hsun; Prada, Francisco; Guo, Hong; Klypin, Anatoly; Behroozi, Peter; Hahn, Chang Hoon; Comparat, Johan; Yepes, Gustavo; Montero-Dorta, Antonio D.; Brownstein, Joel R.; Maraston, Claudia; McBride, Cameron K.; Tinker, Jeremy; Gottlöber, Stefan; Favole, Ginevra; Shu, Yiping; Kitaura, Francisco-Shu; Bolton, Adam; Scoccimarro, Román; Samushia, Lado; Schlegel, David; Schneider, Donald P.; Thomas, Daniel

2016-08-01

We present a study of the clustering and halo occupation distribution of Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxies in the redshift range 0.43 < z < 0.7 drawn from the Final SDSS-III Data Release. We compare the BOSS results with the predictions of a halo abundance matching (HAM) clustering model that assigns galaxies to dark matter haloes selected from the large BigMultiDark N-body simulation of a flat Λ cold dark matter Planck cosmology. We compare the observational data with the simulated ones on a light cone constructed from 20 subsequent outputs of the simulation. Observational effects such as incompleteness, geometry, veto masks and fibre collisions are included in the model, which reproduces within 1σ errors the observed monopole of the two-point correlation function at all relevant scales: from the smallest scales, 0.5 h-1 Mpc, up to scales beyond the baryon acoustic oscillation feature. This model also agrees remarkably well with the BOSS galaxy power spectrum (up to k ˜ 1 h Mpc-1), and the three-point correlation function. The quadrupole of the correlation function presents some tensions with observations. We discuss possible causes that can explain this disagreement, including target selection effects. Overall, the standard HAM model describes remarkably well the clustering statistics of the CMASS sample. We compare the stellar-to-halo mass relation for the CMASS sample measured using weak lensing in the Canada-France-Hawaii Telescope Stripe 82 Survey with the prediction of our clustering model, and find a good agreement within 1σ. The BigMD-BOSS light cone including properties of BOSS galaxies and halo properties is made publicly available.

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

Spatiotemporal clusters of malaria cases at village level, northwest Ethiopia.

PubMed

Alemu, Kassahun; Worku, Alemayehu; Berhane, Yemane; Kumie, Abera

2014-06-06

Malaria attacks are not evenly distributed in space and time. In highland areas with low endemicity, malaria transmission is highly variable and malaria acquisition risk for individuals is unevenly distributed even within a neighbourhood. Characterizing the spatiotemporal distribution of malaria cases in high-altitude villages is necessary to prioritize the risk areas and facilitate interventions. Spatial scan statistics using the Bernoulli method were employed to identify spatial and temporal clusters of malaria in high-altitude villages. Daily malaria data were collected, using a passive surveillance system, from patients visiting local health facilities. Georeference data were collected at villages using hand-held global positioning system devices and linked to patient data. Bernoulli model using Bayesian approaches and Marcov Chain Monte Carlo (MCMC) methods were used to identify the effects of factors on spatial clusters of malaria cases. The deviance information criterion (DIC) was used to assess the goodness-of-fit of the different models. The smaller the DIC, the better the model fit. Malaria cases were clustered in both space and time in high-altitude villages. Spatial scan statistics identified a total of 56 spatial clusters of malaria in high-altitude villages. Of these, 39 were the most likely clusters (LLR = 15.62, p < 0.00001) and 17 were secondary clusters (LLR = 7.05, p < 0.03). The significant most likely temporal malaria clusters were detected between August and December (LLR = 17.87, p < 0.001). Travel away home, males and age above 15 years had statistically significant effect on malaria clusters at high-altitude villages. The study identified spatial clusters of malaria cases occurring at high elevation villages within the district. A patient who travelled away from home to a malaria-endemic area might be the most probable source of malaria infection in a high-altitude village. Malaria interventions in high altitude villages should address factors associated with malaria clustering.

Constraints on the richness-mass relation and the optical-SZE positional offset distribution for SZE-selected clusters

DOE PAGES

Saro, A.

2015-10-12

In this study, we cross-match galaxy cluster candidates selected via their Sunyaev–Zel'dovich effect (SZE) signatures in 129.1 deg 2 of the South Pole Telescope 2500d SPT-SZ survey with optically identified clusters selected from the Dark Energy Survey science verification data. We identify 25 clusters between 0.1 ≲ z ≲ 0.8 in the union of the SPT-SZ and redMaPPer (RM) samples. RM is an optical cluster finding algorithm that also returns a richness estimate for each cluster. We model the richness λ-mass relation with the following function 500> ∝ B λlnM 500 + C λlnE(z) and use SPT-SZ cluster masses andmore » RM richnesses λ to constrain the parameters. We find B λ = 1.14 +0.21 –0.18 and C λ = 0.73 +0.77 –0.75. The associated scatter in mass at fixed richness is σ lnM|λ = 0.18 +0.08 –0.05 at a characteristic richness λ = 70. We demonstrate that our model provides an adequate description of the matched sample, showing that the fraction of SPT-SZ-selected clusters with RM counterparts is consistent with expectations and that the fraction of RM-selected clusters with SPT-SZ counterparts is in mild tension with expectation. We model the optical-SZE cluster positional offset distribution with the sum of two Gaussians, showing that it is consistent with a dominant, centrally peaked population and a subdominant population characterized by larger offsets. We also cross-match the RM catalogue with SPT-SZ candidates below the official catalogue threshold significance ξ = 4.5, using the RM catalogue to provide optical confirmation and redshifts for 15 additional clusters with ξ ϵ [4, 4.5].« less

Modelling Catalyst Surfaces Using DFT Cluster Calculations

PubMed Central

Czekaj, Izabela; Wambach, Jörg; Kröcher, Oliver

2009-01-01

We review our recent theoretical DFT cluster studies of a variety of industrially relevant catalysts such as TiO2, γ-Al2O3, V2O5-WO3-TiO2 and Ni/Al2O3. Aspects of the metal oxide surface structure and the stability and structure of metal clusters on the support are discussed as well as the reactivity of surfaces, including their behaviour upon poisoning. It is exemplarily demonstrated how such theoretical considerations can be combined with DRIFT and XPS results from experimental studies. PMID:20057947

Halo Intrinsic Alignment: Dependence on Mass, Formation Time, and Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xia, Qianli; Kang, Xi; Wang, Peng

In this paper we use high-resolution cosmological simulations to study halo intrinsic alignment and its dependence on mass, formation time, and large-scale environment. In agreement with previous studies using N -body simulations, it is found that massive halos have stronger alignment. For the first time, we find that for a given halo mass older halos have stronger alignment and halos in cluster regions also have stronger alignment than those in filaments. To model these dependencies, we extend the linear alignment model with inclusion of halo bias and find that the halo alignment with its mass and formation time dependence canmore » be explained by halo bias. However, the model cannot account for the environment dependence, as it is found that halo bias is lower in clusters and higher in filaments. Our results suggest that halo bias and environment are independent factors in determining halo alignment. We also study the halo alignment correlation function and find that halos are strongly clustered along their major axes and less clustered along the minor axes. The correlated halo alignment can extend to scales as large as 100 h {sup −1} Mpc, where its feature is mainly driven by the baryon acoustic oscillation effect.« less

Deducing the Milky Way's Massive Cluster Population

NASA Astrophysics Data System (ADS)

Hanson, M. M.; Popescu, B.; Larsen, S. S.; Ivanov, V. D.

2010-11-01

Recent near-infrared surveys of the galactic plane have been used to identify new massive cluster candidates. Follow up study indicates about half are not true, gravitationally-bound clusters. These false positives are created by high density fields of unassociated stars, often due to a sight-line of reduced extinction. What is not so easy to estimate is the number of false negatives, clusters which exist but are not currently being detected by our surveys. In order to derive critical characteristics of the Milky Way's massive cluster population, such as cluster mass function and cluster lifetimes, one must be able to estimate the characteristics of these false negatives. Our group has taken on the daunting task of attempting such an estimate by first creating the stellar cluster imaging simulation program, MASSCLEAN. I will present our preliminary models and methods for deriving the biases of current searches.

Mixture modelling for cluster analysis.

PubMed

McLachlan, G J; Chang, S U

2004-10-01

Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to which it has the highest estimated posterior probability of belonging; that is, the ith cluster consists of those observations assigned to the ith component (i = 1,..., g). The focus is on the use of mixtures of normal components for the cluster analysis of data that can be regarded as being continuous. But attention is also given to the case of mixed data, where the observations consist of both continuous and discrete variables.

Cluster dynamics and cluster size distributions in systems of self-propelled particles

NASA Astrophysics Data System (ADS)

Peruani, F.; Schimansky-Geier, L.; Bär, M.

2010-12-01

Systems of self-propelled particles (SPP) interacting by a velocity alignment mechanism in the presence of noise exhibit rich clustering dynamics. Often, clusters are responsible for the distribution of (local) information in these systems. Here, we investigate the properties of individual clusters in SPP systems, in particular the asymmetric spreading behavior of clusters with respect to their direction of motion. In addition, we formulate a Smoluchowski-type kinetic model to describe the evolution of the cluster size distribution (CSD). This model predicts the emergence of steady-state CSDs in SPP systems. We test our theoretical predictions in simulations of SPP with nematic interactions and find that our simple kinetic model reproduces qualitatively the transition to aggregation observed in simulations.

An algebraic cluster model based on the harmonic oscillator basis

NASA Technical Reports Server (NTRS)

Levai, Geza; Cseh, J.

1995-01-01

We discuss the semimicroscopic algebraic cluster model introduced recently, in which the internal structure of the nuclear clusters is described by the harmonic oscillator shell model, while their relative motion is accounted for by the Vibron model. The algebraic formulation of the model makes extensive use of techniques associated with harmonic oscillators and their symmetry group, SU(3). The model is applied to some cluster systems and is found to reproduce important characteristics of nuclei in the sd-shell region. An approximate SU(3) dynamical symmetry is also found to hold for the C-12 + C-12 system.

Statistical mechanics of the cluster Ising model

NASA Astrophysics Data System (ADS)

Smacchia, Pietro; Amico, Luigi; Facchi, Paolo; Fazio, Rosario; Florio, Giuseppe; Pascazio, Saverio; Vedral, Vlatko

2011-08-01

We study a Hamiltonian system describing a three-spin-1/2 clusterlike interaction competing with an Ising-like antiferromagnetic interaction. We compute free energy, spin-correlation functions, and entanglement both in the ground and in thermal states. The model undergoes a quantum phase transition between an Ising phase with a nonvanishing magnetization and a cluster phase characterized by a string order. Any two-spin entanglement is found to vanish in both quantum phases because of a nontrivial correlation pattern. Nevertheless, the residual multipartite entanglement is maximal in the cluster phase and dependent on the magnetization in the Ising phase. We study the block entropy at the critical point and calculate the central charge of the system, showing that the criticality of the system is beyond the Ising universality class.

Cluster Lensing with the BTC

NASA Astrophysics Data System (ADS)

Fischer, P.

1997-12-01

Weak distortions of background galaxies are rapidly emerging as a powerful tool for the measurement of galaxy cluster mass distributions. Lensing based studies have the advantage of being direct measurements of mass and are not model-dependent as are other techniques (X-ray, radial velocities). To date studies have been limited by CCD field size meaning that full coverage of the clusters out to the virial radii and beyond has not been possible. Probing this large radius region is essential for testing models of large scale structure formation. New wide field CCD mosaics, for the first time, allow mass measurements out to very large radius. We have obtained images for a sample of clusters with the ``Big Throughput Camera'' (BTC) on the CTIO 4m. This camera comprises four thinned SITE 2048(2) CCDs, each 15arcmin on a side for a total area of one quarter of a square degree. We have developed an automated reduction pipeline which: 1) corrects for spatial distortions, 2) corrects for PSF anisotropy, 3) determines relative scaling and background levels, and 4) combines multiple exposures. In this poster we will present some preliminary results of our cluster lensing study. This will include radial mass and light profiles and 2-d mass and galaxy density maps.

Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

PubMed

Bowd, Christopher; Weinreb, Robert N; Balasubramanian, Madhusudhanan; Lee, Intae; Jang, Giljin; Yousefi, Siamak; Zangwill, Linda M; Medeiros, Felipe A; Girkin, Christopher A; Liebmann, Jeffrey M; Goldbaum, Michael H

2014-01-01

The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

Clustering consumers based on trust, confidence and giving behaviour: data-driven model building for charitable involvement in the Australian not-for-profit sector.

PubMed

de Vries, Natalie Jane; Reis, Rodrigo; Moscato, Pablo

2015-01-01

Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in today's competitive environment.

Exponents of non-linear clustering in scale-free one-dimensional cosmological simulations

NASA Astrophysics Data System (ADS)

Benhaiem, David; Joyce, Michael; Sicard, François

2013-03-01

One-dimensional versions of dissipationless cosmological N-body simulations have been shown to share many qualitative behaviours of the three-dimensional problem. Their interest lies in the fact that they can resolve a much greater range of time and length scales, and admit exact numerical integration. We use such models here to study how non-linear clustering depends on initial conditions and cosmology. More specifically, we consider a family of models which, like the three-dimensional Einstein-de Sitter (EdS) model, lead for power-law initial conditions to self-similar clustering characterized in the strongly non-linear regime by power-law behaviour of the two-point correlation function. We study how the corresponding exponent γ depends on the initial conditions, characterized by the exponent n of the power spectrum of initial fluctuations, and on a single parameter κ controlling the rate of expansion. The space of initial conditions/cosmology divides very clearly into two parts: (1) a region in which γ depends strongly on both n and κ and where it agrees very well with a simple generalization of the so-called stable clustering hypothesis in three dimensions; and (2) a region in which γ is more or less independent of both the spectrum and the expansion of the universe. The boundary in (n, κ) space dividing the `stable clustering' region from the `universal' region is very well approximated by a `critical' value of the predicted stable clustering exponent itself. We explain how this division of the (n, κ) space can be understood as a simple physical criterion which might indeed be expected to control the validity of the stable clustering hypothesis. We compare and contrast our findings to results in three dimensions, and discuss in particular the light they may throw on the question of `universality' of non-linear clustering in this context.

Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector

PubMed Central

de Vries, Natalie Jane; Reis, Rodrigo; Moscato, Pablo

2015-01-01

Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that `trust' is the cornerstone of the not-for-profit sector's survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profit's research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1,562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each cluster's most salient features are identified using the CM1 score. Furthermore, symbolic regression modelling is employed to find cluster-specific models to predict `low' or `high' involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labelled as: the `non-institutionalist charities supporters', the `resource allocation critics', the `information-seeking financial sceptics', the `non-questioning charity supporters', the `non-trusting sceptics', the `charity management believers' and the `institutionalist charity believers'. Each cluster exhibits their own characteristics as well as different drivers of `involvement'. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in today's competitive environment. PMID:25849547

Hydrogen bonding interaction of small acetaldehyde clusters studied with core-electron excitation spectroscopy in the oxygen K-edge region

NASA Astrophysics Data System (ADS)

Tabayashi, K.; Chohda, M.; Yamanaka, T.; Tsutsumi, Y.; Takahashi, O.; Yoshida, H.; Taniguchi, M.

2010-06-01

In order to examine inner-shell electron excitation spectra of molecular clusters with strong multipole interactions, excitation spectra and time-of-flight (TOF) fragment-mass spectra of small acetaldehyde (AA) clusters have been studied under the beam conditions. The TOF spectra at the oxygen K-edge region showed an intense growth of the protonated clusters, MnH+ (M=CH3CHO) in the cluster beams. "cluster-specific" excitation spectra could be generated by monitoring partial-ion-yields of the protonated clusters. The most intense band of O1s→π*CO was found to shift to a higher energy by 0.15 eV relative to the monomer band upon clusterization. X-ray absorption spectra (XAS) were also calculated for the representative dimer configurations using a computer modelling program based on the density functional theory. The XAS prediction for the most stable (non-planar) configuration was found to give a close comparison with the cluster-band shift observed. The band shift was interpreted as being due to the HOMO-LUMO interaction within the complex where a contribution of vibrationally blue-shifting hydrogen bonding could be identified.

Wiggler magnetic field assisted third harmonic generation in expanding clusters

NASA Astrophysics Data System (ADS)

Vij, Shivani

2018-04-01

A simple theoretical model is constructed to study the wiggler magnetic field assisted third harmonic generation of intense short pulse laser in a cluster in its expanding phase. The ponderomotive force of laser causes density perturbations in cluster electron density which couples with wiggler magnetic field to produce a nonlinear current that generates transverse third harmonic. An intense short pulse laser propagating through a gas embedded with atomic clusters, converts it into hot plasma balls via tunnel ionization. Initially, the electron plasma frequency inside the clusters ω pe > \\sqrt{3}{ω }1 (with ω 1 being the frequency of the laser). As the cluster expands under Coulomb force and hydrodynamic pressure, ω pe decreases to \\sqrt{3}{ω }1. At this time, there is resonant enhancement in the efficiency of the third harmonic generation. The efficiency of third harmonic generation is enhanced due to cluster plasmon resonance and by phase matching due to wiggler magnetic field. The effect of cluster size on the expansion rate is studied to observe that the clusters of different radii would expand differently. The impact of laser intensity and wiggler magnetic field on the efficiency of third harmonic generation is also explored.

A distance of 133-137 parsecs to the Pleiades star cluster.

PubMed

Pan, Xiaopei; Shao, M; Kulkarni, S R

2004-01-22

Nearby 'open' clusters of stars (those that are not gravitationally bound) have played a crucial role in the development of stellar astronomy because, as a consequence of the stars having a common age, they provide excellent natural laboratories to test theoretical stellar models. Clusters also play a fundamental part in determining distance scales. The satellite Hipparcos surprisingly found that an extensively studied open cluster--the Pleiades (also known as the Seven Sisters)--had a distance of D = 118 +/- 4 pc (refs 2, 3), about ten per cent smaller than the accepted value. The discrepancy generated a spirited debate because the implication was that either current stellar models were incorrect by a surprising amount or Hipparcos was giving incorrect distances. Here we report the orbital parameters of the bright double star Atlas in the Pleiades, using long-baseline optical/infrared interferometry. From the data we derive a firm lower bound of D > 127 pc, with the most likely range being 133 < D < 137 pc. Our result reaffirms the fidelity of current stellar models.

Cooperativity in self-limiting equilibrium self-associating systems

NASA Astrophysics Data System (ADS)

Freed, Karl F.

2012-11-01

A wide variety of highly cooperative self-assembly processes in biological and synthetic systems involve the assembly of a large number (m) of units into clusters, with m narrowly peaked about a large size m0 ≫ 1 and with a second peak centered about the m = 1 unassembled monomers. While very specific models have been proposed for the assembly of, for example, viral capsids and core-shell micelles of ß-casein, no available theory describes a thermodynamically general mechanism for this double peaked, highly cooperative equilibrium assembly process. This study provides a general mechanism for these cooperative processes by developing a minimal Flory-Huggins type theory. Beginning from the simplest non-cooperative, free association model in which the equilibrium constant for addition of a monomer to a cluster is independent of cluster size, the new model merely allows more favorable growth for clusters of intermediate sizes. The theory is illustrated by computing the phase diagram for cases of self-assembly on cooling or heating and for the mass distribution of the two phases.

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

PubMed Central

2012-01-01

Background Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. Results We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that our model outperforms existing models to provide more reliable and robust clustering of time-course data. Our model provides superior results when genetic profiles are correlated. It also gives comparable results when the correlation between the gene profiles is weak. In the applications to real time-course data, relevant clusters of coregulated genes are obtained, which are supported by gene-function annotation databases. Conclusions Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clustering time-course data because it adopts a random effects model that allows for the correlation among observations at different time points. It postulates gene-specific random effects with an autocorrelation variance structure that models coregulation within the clusters. The developed R package is flexible in its specification of the random effects through user-input parameters that enables improved modelling and consequent clustering of time-course data. PMID:23151154

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

NASA Astrophysics Data System (ADS)

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.

New particle formation from sulfuric acid and amines: Comparison of monomethylamine, dimethylamine, and trimethylamine

NASA Astrophysics Data System (ADS)

Olenius, Tinja; Halonen, Roope; Kurtén, Theo; Henschel, Henning; Kupiainen-Määttä, Oona; Ortega, Ismael K.; Jen, Coty N.; Vehkamäki, Hanna; Riipinen, Ilona

2017-07-01

Amines are bases that originate from both anthropogenic and natural sources, and they are recognized as candidates to participate in atmospheric aerosol particle formation together with sulfuric acid. Monomethylamine, dimethylamine, and trimethylamine (MMA, DMA, and TMA, respectively) have been shown to enhance sulfuric acid-driven particle formation more efficiently than ammonia, but both theory and laboratory experiments suggest that there are differences in their enhancing potentials. However, as quantitative concentrations and thermochemical properties of different amines remain relatively uncertain, and also for computational reasons, the compounds have been treated as a single surrogate amine species in large-scale modeling studies. In this work, the differences and similarities of MMA, DMA, and TMA are studied by simulations of molecular cluster formation from sulfuric acid, water, and each of the three amines. Quantum chemistry-based cluster evaporation rate constants are applied in a cluster population dynamics model to yield cluster concentrations and formation rates at boundary layer conditions. While there are differences, for instance, in the clustering mechanisms and cluster hygroscopicity for the three amines, DMA and TMA can be approximated as a lumped species. Formation of nanometer-sized particles and its dependence on ambient conditions is roughly similar for these two: both efficiently form clusters with sulfuric acid, and cluster formation is rather insensitive to changes in temperature and relative humidity. Particle formation from sulfuric acid and MMA is weaker and significantly more sensitive to ambient conditions. Therefore, merging MMA together with DMA and TMA introduces inaccuracies in sulfuric acid-amine particle formation schemes.

Identification of spatiotemporal nutrient patterns in a coastal bay via an integrated k-means clustering and gravity model.

PubMed

Chang, Ni-Bin; Wimberly, Brent; Xuan, Zhemin

2012-03-01

This study presents an integrated k-means clustering and gravity model (IKCGM) for investigating the spatiotemporal patterns of nutrient and associated dissolved oxygen levels in Tampa Bay, Florida. By using a k-means clustering analysis to first partition the nutrient data into a user-specified number of subsets, it is possible to discover the spatiotemporal patterns of nutrient distribution in the bay and capture the inherent linkages of hydrodynamic and biogeochemical features. Such patterns may then be combined with a gravity model to link the nutrient source contribution from each coastal watershed to the generated clusters in the bay to aid in the source proportion analysis for environmental management. The clustering analysis was carried out based on 1 year (2008) water quality data composed of 55 sample stations throughout Tampa Bay collected by the Environmental Protection Commission of Hillsborough County. In addition, hydrological and river water quality data of the same year were acquired from the United States Geological Survey's National Water Information System to support the gravity modeling analysis. The results show that the k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa Bay had the minimum values of total nitrogen (TN) concentrations, chlorophyll a (Chl-a) concentrations, and ocean color values in every season as well as the minimum concentration of total phosphorus (TP) in three consecutive seasons in 2008. The datasets indicate that Lower Tampa Bay is an area with limited nutrient input throughout the year. Cluster 5, located in Middle Tampa Bay, displayed elevated TN concentrations, ocean color values, and Chl-a concentrations, suggesting that high values of colored dissolved organic matter are linked with some nutrient sources. The data presented by the gravity modeling analysis indicate that the Alafia River Basin is the major contributor of nutrients in terms of both TP and TN values in all seasons. With this new integration, improvements for environmental monitoring and assessment were achieved to advance our understanding of sea-land interactions and nutrient cycling in a critical coastal bay, the Gulf of Mexico. This journal is © The Royal Society of Chemistry 2012

Partially supervised speaker clustering.

PubMed

Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S

2012-05-01

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance.

Exploring the Dynamics of Exoplanetary Systems in a Young Stellar Cluster

NASA Astrophysics Data System (ADS)

Thornton, Jonathan Daniel; Glaser, Joseph Paul; Wall, Joshua Edward

2018-01-01

I describe a dynamical simulation of planetary systems in a young star cluster. One rather arbitrary aspect of cluster simulations is the choice of initial conditions. These are typically chosen from some standard model, such as Plummer or King, or from a “fractal” distribution to try to model young clumpy systems. Here I adopt the approach of realizing an initial cluster model directly from a detailed magnetohydrodynamical model of cluster formation from a 1000-solar-mass interstellar gas cloud, with magnetic fields and radiative and wind feedback from massive stars included self-consistently. The N-body simulation of the stars and planets starts once star formation is largely over and feedback has cleared much of the gas from the region where the newborn stars reside. It continues until the cluster dissolves in the galactic field. Of particular interest is what would happen to the free-floating planets created in the gas cloud simulation. Are they captured by a star or are they ejected from the cluster? This method of building a dynamical cluster simulation directly from the results of a cluster formation model allows us to better understand the evolution of young star clusters and enriches our understanding of extrasolar planet development in them. These simulations were performed within the AMUSE simulation framework, and combine N-body, multiples and background potential code.

Super Star Clusters and H II Regions in Nuclear Rings

NASA Astrophysics Data System (ADS)

Filippenko, Alex

1996-07-01

We propose to obtain WFPC2 optical broad-band {F547M and F814W} and narrow-band Halpha+ionN2 {F658N} images of nuclear starburst rings in four nearby galaxies for which we already have ultraviolet {F220W} FOC data. Nuclear rings {or ``hot- spot'' regions} in barred spirals are some of the nearest and least obscured starburst regions, and HST images of nuclear rings in several galaxies show that the rings contain large populations of super star clusters similar to those recently discovered in other types of starburst systems. These compact clusters, many having luminosities exceeding that of the R136 cluster in 30 Doradus, represent a violent mode of star formation distinct from that seen in ordinary disk ionH2 regions, and the nuclear rings present us with an opportunity to study large numbers of these extreme clusters in relatively unobscured starburst environments. It has been suggested that super star clusters are present-day versions of young globular clusters. To evaluate this hypothesis, it is important to understand the physical properties and stellar contents of the clusters, but previous HST studies of nuclear ring galaxies have only used single-filter observations. Together with our UV data, new WFPC2 images will enable us to determine the H II region and cluster luminosity functions within nuclear rings, measure cluster radii, derive age and mass estimates for the clusters by comparison with evolutionary synthesis models, and study the structure and evolution of nuclear rings.

Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

ERIC Educational Resources Information Center

Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2008-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

Effects of cosmic string velocities and the origin of globular clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Ling; Yamanouchi, Shoma; Brandenberger, Robert, E-mail: ling.lin2@mail.mcgill.ca, E-mail: shoma.yamanouchi@mail.mcgill.ca, E-mail: rhb@physics.mcgill.ca

2015-12-01

With the hypothesis that cosmic string loops act as seeds for globular clusters in mind, we study the role that velocities of these strings will play in determining the mass distribution of globular clusters. Loops with high enough velocities will not form compact and roughly spherical objects and can hence not be the seeds for globular clusters. We compute the expected number density and mass function of globular clusters as a function of both the string tension and the peak loop velocity, and compare the results with the observational data on the mass distribution of globular clusters in our Milkymore » Way. We determine the critical peak string loop velocity above which the agreement between the string loop model for the origin of globular clusters (neglecting loop velocities) and observational data is lost.« less

2-Way k-Means as a Model for Microbiome Samples.

PubMed

Jackson, Weston J; Agarwal, Ipsita; Pe'er, Itsik

2017-01-01

Motivation . Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k -means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.

2-Way k-Means as a Model for Microbiome Samples

PubMed Central

2017-01-01

Motivation. Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project. PMID:29177026

On aggregation in CA models in biology

NASA Astrophysics Data System (ADS)

Alber, Mark S.; Kiskowski, Audi

2001-12-01

Aggregation of randomly distributed particles into clusters of aligned particles is modeled using a cellular automata (CA) approach. The CA model accounts for interactions between more than one type of particle, in which pressures for angular alignment with neighbors compete with pressures for grouping by cell type. In the case of only one particle type clusters tend to unite into one big cluster. In the case of several types of particles the dynamics of clusters is more complicated and for specific choices of parameters particle sorting occurs simultaneously with the formation of clusters of aligned particles.

On the multi-scale description of micro-structured fluids composed of aggregating rods

NASA Astrophysics Data System (ADS)

Perez, Marta; Scheuer, Adrien; Abisset-Chavanne, Emmanuelle; Ammar, Amine; Chinesta, Francisco; Keunings, Roland

2018-05-01

When addressing the flow of concentrated suspensions composed of rods, dense clusters are observed. Thus, the adequate modelling and simulation of such a flow requires addressing the kinematics of these dense clusters and their impact on the flow in which they are immersed. In a former work, we addressed a first modelling framework of these clusters, assumed so dense that they were considered rigid and their kinematics (flow-induced rotation) were totally defined by a symmetric tensor c with unit trace representing the cluster conformation. Then, the rigid nature of the clusters was relaxed, assuming them deformable, and a model giving the evolution of both the cluster shape and its microstructural orientation descriptor (the so-called shape and orientation tensors) was proposed. This paper compares the predictions coming from those models with finer-scale discrete simulations inspired from molecular dynamics modelling.

Systematic Study on the Self-Assembled Hexagonal Au Voids, Nano-Clusters and Nanoparticles on GaN (0001).

PubMed

Pandey, Puran; Sui, Mao; Li, Ming-Yu; Zhang, Quanzhen; Kim, Eun-Soo; Lee, Jihoon

2015-01-01

Au nano-clusters and nanoparticles (NPs) have been widely utilized in various electronic, optoelectronic, and bio-medical applications due to their great potentials. The size, density and configuration of Au NPs play a vital role in the performance of these devices. In this paper, we present a systematic study on the self-assembled hexagonal Au voids, nano-clusters and NPs fabricated on GaN (0001) by the variation of annealing temperature and deposition amount. At relatively low annealing temperatures between 400 and 600°C, the fabrication of hexagonal shaped Au voids and Au nano-clusters are observed and discussed based on the diffusion limited aggregation model. The size and density of voids and nano-clusters can systematically be controlled. The self-assembled Au NPs are fabricated at comparatively high temperatures from 650 to 800°C based on the Volmer-Weber growth model and also the size and density can be tuned accordingly. The results are symmetrically analyzed and discussed in conjunction with the diffusion theory and thermodynamics by utilizing AFM and SEM images, EDS maps and spectra, FFT power spectra, cross-sectional line-profiles and size and density plots.

Systematic Study on the Self-Assembled Hexagonal Au Voids, Nano-Clusters and Nanoparticles on GaN (0001)

PubMed Central

Pandey, Puran; Sui, Mao; Li, Ming-Yu; Zhang, Quanzhen; Kim, Eun-Soo; Lee, Jihoon

2015-01-01

Au nano-clusters and nanoparticles (NPs) have been widely utilized in various electronic, optoelectronic, and bio-medical applications due to their great potentials. The size, density and configuration of Au NPs play a vital role in the performance of these devices. In this paper, we present a systematic study on the self-assembled hexagonal Au voids, nano-clusters and NPs fabricated on GaN (0001) by the variation of annealing temperature and deposition amount. At relatively low annealing temperatures between 400 and 600°C, the fabrication of hexagonal shaped Au voids and Au nano-clusters are observed and discussed based on the diffusion limited aggregation model. The size and density of voids and nano-clusters can systematically be controlled. The self-assembled Au NPs are fabricated at comparatively high temperatures from 650 to 800°C based on the Volmer-Weber growth model and also the size and density can be tuned accordingly. The results are symmetrically analyzed and discussed in conjunction with the diffusion theory and thermodynamics by utilizing AFM and SEM images, EDS maps and spectra, FFT power spectra, cross-sectional line-profiles and size and density plots. PMID:26285135

A Dynamical N-body model for the central region of ω Centauri

NASA Astrophysics Data System (ADS)

Jalali, B.; Baumgardt, H.; Kissler-Patig, M.; Gebhardt, K.; Noyola, E.; Lützgendorf, N.; de Zeeuw, P. T.

2012-02-01

Context. Supermassive black holes (SMBHs) are fundamental keys to understand the formation and evolution of their host galaxies. However, the formation and growth of SMBHs are not yet well understood. One of the proposed formation scenarios is the growth of SMBHs from seed intermediate-mass black holes (IMBHs, 102 to 105 M⊙) formed in star clusters. In this context, and also with respect to the low mass end of the M• - σ relation for galaxies, globular clusters are in a mass range that make them ideal systems to look for IMBHs. Among Galactic star clusters, the massive cluster ω Centauri is a special target due to its central high velocity dispersion and also its multiple stellar populations. Aims: We study the central structure and dynamics of the star cluster ω Centauri to examine whether an IMBH is necessary to explain the observed velocity dispersion and surface brightness profiles. Methods: We perform direct N-body simulations on GPU and GRAPE special purpose computers to follow the dynamical evolution of ω Centauri. The simulations are compared to the most recent data-sets in order to explain the present-day conditions of the cluster and to constrain the initial conditions leading to the observed profiles. Results: We find that starting from isotropic spherical multi-mass King models and within our canonical assumptions, a model with a central IMBH mass of 2% of the cluster stellar mass, i.e. a 5. × 104 M⊙ IMBH, provides a satisfactory fit to both the observed shallow cusp in surface brightness and the continuous rise towards the center of the radial velocity dispersion profile. In our isotropic spherical models, the predicted proper motion dispersion for the best-fit model is the same as the radial velocity dispersion one. Conclusions: We conclude that with the presence of a central IMBH in our models, we reproduce consistently the rise in the radial velocity dispersion. Furthermore, we always end up with a shallow cusp in the projected surface brightness of our model clusters containing an IMBH. In addition, we find that the M/L ratio seems to be constant in the central region, and starts to rise slightly from the core radius outwards for all models independent of the presence of a black hole. Considering our initial parameter space, it is not possible to explain the observations without a central IMBH for ω Centauri. To further strengthen the presence of an IMBH as a unique explanation of the observed light and kinematics more detailed analysis such as investigating the contribution of primordial binaries and different anisotropy profiles should be studied.

Determining the Ages and Distances of 4 Open Clusters

NASA Astrophysics Data System (ADS)

Sawczynec, Erica A.; James D. Armstrong, Joe M. Ritter, Jeff Kuhn

2018-01-01

The study of nearby young open clusters can give insight into star formation and potentially the local rate of metal enrichment. Presented is a BVRI photometric analysis of 4 open clusters; NGC 2509, NGC 2483, NGC 2482, and NGC 6705, in order to reevaluate previously published ages and distances using modern CCD photometry, and newer stellar models. Observations were obtained from the Cerro Tololo node of the Las Cumbres Observatory 1.0 meter network. Color magnitude diagrams were compared to modeled isochrones and the updated ages and distances determined. An interesting stellar association was found in the color magnitude diagram of NGC 6705. The structure is suggestive of two epochs of stellar formation. Members of this structure were evaluated using the Gaia Archive in order to explore the possibility of a heterogeneous population. The status of NGC 2483 as an open cluster has been debated; however, it has been noted that there is a high concentration of Be stars found in the region. It is concluded that NGC 2483 is an open cluster.

Pattern selection and super-patterns in the bounded confidence model

DOE PAGES

Ben-Naim, E.; Scheel, A.

2015-10-26

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes ofmore » the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. Furthermore, the spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.« less

Visualization of T Cell-Regulated Monocyte Clusters Mediating Keratinocyte Death in Acquired Cutaneous Immunity.

PubMed

Liu, Zheng; Yang, Fei; Zheng, Hao; Fan, Zhan; Qiao, Sha; Liu, Lei; Tao, Juan; Luo, Qingming; Zhang, Zhihong

2018-06-01

It remains unclear how monocytes are mobilized to amplify inflammatory reactions in T cell-mediated adaptive immunity. Here, we investigate dynamic cellular events in the cascade of inflammatory responses through intravital imaging of a multicolor-labeled murine contact hypersensitivity model. We found that monocytes formed clusters around hair follicles in the contact hypersensitivity model. In this process, effector T cells encountered dendritic cells under regions of monocyte clusters and secreted IFN-γ, which mobilizes CCR2-dependent monocyte interstitial migration and CXCR2-dependent monocyte cluster formation. We showed that hair follicles shaped the inflammatory microenvironment for communication among the monocytes, keratinocytes, and effector T cells. After disrupting the T cell-mobilized monocyte clusters through CXCR2 antagonization, monocyte activation and keratinocyte apoptosis were significantly inhibited. Our study provides a new perspective on effector T cell-regulated monocyte behavior, which amplifies the inflammatory reaction in acquired cutaneous immunity. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Pattern selection and super-patterns in the bounded confidence model

NASA Astrophysics Data System (ADS)

Ben-Naim, E.; Scheel, A.

2015-10-01

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes of the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. The spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.

Formation and emission mechanisms of Ag nanoclusters in the Ar matrix assembly cluster source

NASA Astrophysics Data System (ADS)

Zhao, Junlei; Cao, Lu; Palmer, Richard E.; Nordlund, Kai; Djurabekova, Flyura

2017-11-01

In this paper, we study the mechanisms of growth of Ag nanoclusters in a solid Ar matrix and the emission of these nanoclusters from the matrix by a combination of experimental and theoretical methods. The molecular dynamics simulations show that the cluster growth mechanism can be described as "thermal spike-enhanced clustering" in multiple sequential ion impact events. We further show that experimentally observed large sputtered metal clusters cannot be formed by direct sputtering of Ag mixed in the Ar. Instead, we describe the mechanism of emission of the metal nanocluster that, at first, is formed in the cryogenic matrix due to multiple ion impacts, and then is emitted as a result of the simultaneous effects of interface boiling and spring force. We also develop an analytical model describing this size-dependent cluster emission. The model bridges the atomistic simulations and experimental time and length scales, and allows increasing the controllability of fast generation of nanoclusters in experiments with a high production rate.

Cooling rate dependence of structural order in Al90Sm10 metallic glass

NASA Astrophysics Data System (ADS)

Sun, Yang; Zhang, Yue; Zhang, Feng; Ye, Zhuo; Ding, Zejun; Wang, Cai-Zhuang; Ho, Kai-Ming

2016-07-01

The atomic structure of Al90Sm10 metallic glass is studied using molecular dynamics simulations. By performing a long sub-Tg annealing, we developed a glass model closer to the experiments than the models prepared by continuous cooling. Using the cluster alignment method, we found that "3661" cluster is the dominating short-range order in the glass samples. The connection and arrangement of "3661" clusters, which define the medium-range order in the system, are enhanced significantly in the sub-Tg annealed sample as compared with the fast cooled glass samples. Unlike some strong binary glass formers such as Cu64.5Zr35.5, the clusters representing the short-range order do not form an interconnected interpenetrating network in Al90Sm10, which has only marginal glass formability.

Search for α-Cluster Structure in Exotic Nuclei with the Prototype Active-Target Time-Projection Chamber

NASA Astrophysics Data System (ADS)

Fritsch, A.; Ayyad, Y.; Bazin, D.; Beceiro-Novo, S.; Bradt, J.; Carpenter, L.; Cortesi, M.; Mittig, W.; Suzuki, D.; Ahn, T.; Kolata, J. J.; Becchetti, F. D.; Howard, A. M.

2016-03-01

Some exotic nuclei appear to exhibit α-cluster structure. While various theoretical models currently describe such clustering, more experimental data are needed to constrain model predictions. The Prototype Active-Target Time-Projection Chamber (PAT-TPC) has low-energy thresholds for charged-particle decay and a high luminosity due to its thick gaseous active target volume, making it well-suited to search for low-energy α-cluster reactions. Radioactive-ion beams produced by the TwinSol facility at the University of Notre Dame were delivered to the PAT-TPC to study nuclei including 14C and 14O via α-resonant scattering. Differential cross sections and excitation functions were measured. Preliminary results from our recent experiments will be presented. This work is supported by the U.S. National Science Foundation.

Using Self-Organizing Neural Network Map Combined with Ward's Clustering Algorithm for Visualization of Students' Cognitive Structural Models about Aliveness Concept

PubMed Central

Ugulu, Ilker; Aydin, Halil

2016-01-01

We propose an approach to clustering and visualization of students' cognitive structural models. We use the self-organizing map (SOM) combined with Ward's clustering to conduct cluster analysis. In the study carried out on 100 subjects, a conceptual understanding test consisting of open-ended questions was used as a data collection tool. The results of analyses indicated that students constructed the aliveness concept by associating it predominantly with human. Motion appeared as the most frequently associated term with the aliveness concept. The results suggest that the aliveness concept has been constructed using anthropocentric and animistic cognitive structures. In the next step, we used the data obtained from the conceptual understanding test for training the SOM. Consequently, we propose a visualization method about cognitive structure of the aliveness concept. PMID:26819579

Is gender policy related to the gender gap in external cause and circulatory disease mortality? A mixed effects model of 22 OECD countries 1973–2008

PubMed Central

2012-01-01

Background Gender differences in mortality vary widely between countries and over time, but few studies have examined predictors of these variations, apart from smoking. The aim of this study is to investigate the link between gender policy and the gender gap in cause-specific mortality, adjusted for economic factors and health behaviours. Methods 22 OECD countries were followed 1973–2008 and the outcomes were gender gaps in external cause and circulatory disease mortality. A previously found country cluster solution was used, which includes indicators on taxes, parental leave, pensions, social insurances and social services in kind. Male breadwinner countries were made reference group and compared to earner-carer, compensatory breadwinner, and universal citizen countries. Specific policies were also analysed. Mixed effect models were used, where years were the level 1-units, and countries were the level 2-units. Results Both the earner-carer cluster (ns after adjustment for GDP) and policies characteristic of that cluster are associated with smaller gender differences in external causes, particularly due to an association with increased female mortality. Cluster differences in the gender gap in circulatory disease mortality are the result of a larger relative decrease of male mortality in the compensatory breadwinner cluster and the earner-carer cluster. Policies characteristic of those clusters were however generally related to increased mortality. Conclusion Results for external cause mortality are in concordance with the hypothesis that women become more exposed to risks of accident and violence when they are economically more active. For circulatory disease mortality, results differ depending on approach – cluster or indicator. Whether cluster differences not explained by specific policies reflect other welfare policies or unrelated societal trends is an open question. Recommendations for further studies are made. PMID:23145477

Fascioliasis risk factors and space-time clusters in domestic ruminants in Bangladesh.

PubMed

Rahman, A K M Anisur; Islam, S K Shaheenur; Talukder, Md Hasanuzzaman; Hassan, Md Kumrul; Dhand, Navneet K; Ward, Michael P

2017-05-08

A retrospective observational study was conducted to identify fascioliasis hotspots, clusters, potential risk factors and to map fascioliasis risk in domestic ruminants in Bangladesh. Cases of fascioliasis in cattle, buffalo, sheep and goats from all districts in Bangladesh between 2011 and 2013 were identified via secondary surveillance data from the Department of Livestock Services' Epidemiology Unit. From each case report, date of report, species affected and district data were extracted. The total number of domestic ruminants in each district was used to calculate fascioliasis cases per ten thousand animals at risk per district, and this was used for cluster and hotspot analysis. Clustering was assessed with Moran's spatial autocorrelation statistic, hotspots with the local indicator of spatial association (LISA) statistic and space-time clusters with the scan statistic (Poisson model). The association between district fascioliasis prevalence and climate (temperature, precipitation), elevation, land cover and water bodies was investigated using a spatial regression model. A total of 1,723,971 cases of fascioliasis were reported in the three-year study period in cattle (1,164,560), goats (424,314), buffalo (88,924) and sheep (46,173). A total of nine hotspots were identified; one of these persisted in each of the three years. Only two local clusters were found. Five space-time clusters located within 22 districts were also identified. Annual risk maps of fascioliasis cases correlated with the hotspots and clusters detected. Cultivated and managed (P < 0.001) and artificial surface (P = 0.04) land cover areas, and elevation (P = 0.003) were positively and negatively associated with fascioliasis in Bangladesh, respectively. Results indicate that due to land use characteristics some areas of Bangladesh are at greater risk of fascioliasis. The potential risk factors, hot spots and clusters identified in this study can be used to guide science-based treatment and control decisions for fascioliasis in Bangladesh and in other similar geo-climatic zones throughout the world.

Atomic Cluster Ionization and Attosecond Generation at Long Wavelengths

DTIC Science & Technology

2015-10-31

fellowships for further studies in science, mathematics, engineering or technology fields: Student Metrics This section only applies to graduating...order to investigate this we use a modified Lewenstein quantum model in which the cluster is represented by a 1D Coulomb potential for the parent ion

Study of Electron Ionization and Fragmentation of Non-hydrated and Hydrated Tetrahydrofuran Clusters

NASA Astrophysics Data System (ADS)

Neustetter, Michael; Mahmoodi-Darian, Masoomeh; Denifl, Stephan

2017-05-01

Mass spectroscopic investigations on tetrahydrofuran (THF, C4H8O), a common model molecule of the DNA-backbone, have been carried out. We irradiated isolated THF and (hydrated) THF clusters with low energy electrons (electron energy 70 eV) in order to study electron ionization and ionic fragmentation. For elucidation of fragmentation pathways, deuterated TDF (C4D8O) was investigated as well. One major observation is that the cluster environment shows overall a protective behavior on THF. However, also new fragmentation channels open in the cluster. In this context, we were able to solve a discrepancy in the literature about the fragment ion peak at mass 55 u in the electron ionization mass spectrum of THF. We ascribe this ion yield to the fragmentation of ionized THF clusters.

Weak lensing magnification of SpARCS galaxy clusters

NASA Astrophysics Data System (ADS)

Tudorica, A.; Hildebrandt, H.; Tewes, M.; Hoekstra, H.; Morrison, C. B.; Muzzin, A.; Wilson, G.; Yee, H. K. C.; Lidman, C.; Hicks, A.; Nantais, J.; Erben, T.; van der Burg, R. F. J.; Demarco, R.

2017-12-01

Context. Measuring and calibrating relations between cluster observables is critical for resource-limited studies. The mass-richness relation of clusters offers an observationally inexpensive way of estimating masses. Its calibration is essential for cluster and cosmological studies, especially for high-redshift clusters. Weak gravitational lensing magnification is a promising and complementary method to shear studies, that can be applied at higher redshifts. Aims: We aim to employ the weak lensing magnification method to calibrate the mass-richness relation up to a redshift of 1.4. We used the Spitzer Adaptation of the Red-Sequence Cluster Survey (SpARCS) galaxy cluster candidates (0.2 < z < 1.4) and optical data from the Canada France Hawaii Telescope (CFHT) to test whether magnification can be effectively used to constrain the mass of high-redshift clusters. Methods: Lyman-break galaxies (LBGs) selected using the u-band dropout technique and their colours were used as a background sample of sources. LBG positions were cross-correlated with the centres of the sample of SpARCS clusters to estimate the magnification signal, which was optimally-weighted using an externally-calibrated LBG luminosity function. The signal was measured for cluster sub-samples, binned in both redshift and richness. Results: We measured the cross-correlation between the positions of galaxy cluster candidates and LBGs and detected a weak lensing magnification signal for all bins at a detection significance of 2.6-5.5σ. In particular, the significance of the measurement for clusters with z> 1.0 is 4.1σ; for the entire cluster sample we obtained an average M200 of 1.28 -0.21+0.23 × 1014 M⊙. Conclusions: Our measurements demonstrated the feasibility of using weak lensing magnification as a viable tool for determining the average halo masses for samples of high redshift galaxy clusters. The results also established the success of using galaxy over-densities to select massive clusters at z > 1. Additional studies are necessary for further modelling of the various systematic effects we discussed.

PANCHROMATIC HUBBLE ANDROMEDA TREASURY. XVI. STAR CLUSTER FORMATION EFFICIENCY AND THE CLUSTERED FRACTION OF YOUNG STARS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, L. Clifton; Sandstrom, Karin; Seth, Anil C.

We use the Panchromatic Hubble Andromeda Treasury survey data set to perform spatially resolved measurements of star cluster formation efficiency (Γ), the fraction of stellar mass formed in long-lived star clusters. We use robust star formation history and cluster parameter constraints, obtained through color–magnitude diagram analysis of resolved stellar populations, to study Andromeda’s cluster and field populations over the last ∼300 Myr. We measure Γ of 4%–8% for young, 10–100 Myr-old populations in M31. We find that cluster formation efficiency varies systematically across the M31 disk, consistent with variations in mid-plane pressure. These Γ measurements expand the range of well-studiedmore » galactic environments, providing precise constraints in an H i-dominated, low-intensity star formation environment. Spatially resolved results from M31 are broadly consistent with previous trends observed on galaxy-integrated scales, where Γ increases with increasing star formation rate surface density (Σ{sub SFR}). However, we can explain observed scatter in the relation and attain better agreement between observations and theoretical models if we account for environmental variations in gas depletion time ( τ {sub dep}) when modeling Γ, accounting for the qualitative shift in star formation behavior when transitioning from a H{sub 2}-dominated to a H i-dominated interstellar medium. We also demonstrate that Γ measurements in high Σ{sub SFR} starburst systems are well-explained by τ {sub dep}-dependent fiducial Γ models.« less

The Halo Boundary of Galaxy Clusters in the SDSS

NASA Astrophysics Data System (ADS)

Baxter, Eric; Chang, Chihway; Jain, Bhuvnesh; Adhikari, Susmita; Dalal, Neal; Kravtsov, Andrey; More, Surhud; Rozo, Eduardo; Rykoff, Eli; Sheth, Ravi K.

2017-05-01

Analytical models and simulations predict a rapid decline in the halo density profile associated with the transition from the “infalling” regime outside the halo to the “collapsed” regime within the halo. Using data from SDSS, we explore evidence for such a feature in the density profiles of galaxy clusters using several different approaches. We first estimate the steepening of the outer galaxy density profile around clusters, finding evidence for truncation of the halo profile. Next, we measure the galaxy density profile around clusters using two sets of galaxies selected on color. We find evidence of an abrupt change in galaxy colors that coincides with the location of the steepening of the density profile. Since galaxies that have completed orbits within the cluster are more likely to be quenched of star formation and thus appear redder, this abrupt change in galaxy color can be associated with the transition from single-stream to multi-stream regimes. We also use a standard model comparison approach to measure evidence for a “splashback”-like feature, but find that this approach is very sensitive to modeling assumptions. Finally, we perform measurements using an independent cluster catalog to test for potential systematic errors associated with cluster selection. We identify several avenues for future work: improved understanding of the small-scale galaxy profile, lensing measurements, identification of proxies for the halo accretion rate, and other tests. With upcoming data from the DES, KiDS, and HSC surveys, we can expect significant improvements in the study of halo boundaries.

A Study of Nine High-Redshift Clusters of Galaxies. II. Photometry, Spectra, and Ages of Clusters 0023+0423 and 1604+4304

NASA Astrophysics Data System (ADS)

Postman, Marc; Lubin, Lori M.; Oke, J. B.

1998-08-01

We present an extensive photometric and spectroscopic study of two high-redshift clusters of galaxies based on data obtained from the Keck 10 m telescopes and the Hubble Space Telescope. The clusters Cl 0023+0423 (z = 0.84) and Cl 1604+4304 (z = 0.90) are part of a multiwavelength program of Oke, Postman & Lubin to study nine candidate clusters at z >~ 0.6. Based on these observations, we study in detail both the field and cluster populations. From the confirmed cluster members, we find that Cl 0023+0423 actually consists of two components separated by ~2900 km s^-1. A kinematic analysis indicates that the two components are a poor cluster with ~3 x 10^14 M_⊙ and a less massive group with ~10^13 M_⊙. Cl 1604+4304 is a centrally concentrated, rich cluster at z = 0.8967 with a velocity dispersion of 1226 km s^-1 and a mass of ~3 x 10^15 M_⊙. A large percentage of the cluster members show high levels of star formation activity. Approximately 57% and 50% of the galaxies are active in Cl 0023+0423 and Cl 1604+4304, respectively. These numbers are significantly larger than those found in intermediate-redshift clusters. We also observe many old, red galaxies. Found mainly in Cl 1604+4304, they have spectra consistent with passive stellar evolution, typical of the populations of early-type galaxies in low- and intermediate-redshift clusters. We have calculated their ages by comparing their spectral energy distributions to standard Bruzual & Charlot evolutionary models. We find that their colors are consistent with models having an exponentially decreasing star formation rate with a time constant of 0.6 Gyr. We also observe a significant luminosity brightening in our brightest cluster galaxies. Compared with brightest cluster galaxies at z ~ 0.1, we find a luminosity increase of ~1 mag in the rest M_B and ~0.8 mag in the rest M_V. In the field, we find that ~76% of the galaxies with z > 0.4 show emission-line activity. These numbers are consistent with previous studies. We find that an exponentially decaying star formation rate is required to produce the observed amount of star formation for the majority of the galaxies in our sample. A time constant of tau = 0.6 Gyr appears to be optimal. We also detect several interesting galaxies at z > 1. Two of these galaxies are extremely luminous, with strong Mg ii lambda2800 absorption and Fe ii resonance-line absorption. These lines are so strong that we conclude that they must be generated within the atmospheres of a large population of young, hot stars.

A West Virginia case study: does erosion differ between streambanks clustered by the bank assessment of nonpoint source consequences of sediment (BANCS) model parameters?

Treesearch

Abby L. McQueen; Nicolas P. Zegre; Danny L. Welsch

2013-01-01

The integration of factors and processes responsible for streambank erosion is complex. To explore the influence of physical variables on streambank erosion, parameters for the bank assessment of nonpoint source consequences of sediment (BANCS) model were collected on a 1-km reach of Horseshoe Run in Tucker County, West Virginia. Cluster analysis was used to establish...

Reconstruction of sediment transport pathways in modern microtidal sand flat by multiple classification analysis

NASA Astrophysics Data System (ADS)

Yamashita, S.; Nakajo, T.; Naruse, H.

2009-12-01

In this study, we statistically classified the grain size distribution of the bottom surface sediment on a microtidal sand flat to analyze the depositional processes of the sediment. Multiple classification analysis revealed that two types of sediment populations exist in the bottom surface sediment. Then, we employed the sediment trend model developed by Gao and Collins (1992) for the estimation of sediment transport pathways. As a result, we found that statistical discrimination of the bottom surface sediment provides useful information for the sediment trend model while dealing with various types of sediment transport processes. The microtidal sand flat along the Kushida River estuary, Ise Bay, central Japan, was investigated, and 102 bottom surface sediment samples were obtained. Then, their grain size distribution patterns were measured by the settling tube method, and each grain size distribution parameter (mud and gravel contents, mean grain size, coefficient of variance (CV), skewness, kurtosis, 5, 25, 50, 75, and 95 percentile) was calculated. Here, CV is the normalized sorting value divided by the mean grain size. Two classical statistical methods—principal component analysis (PCA) and fuzzy cluster analysis—were applied. The results of PCA showed that the bottom surface sediment of the study area is mainly characterized by grain size (mean grain size and 5-95 percentile) and the CV value, indicating predominantly large absolute values of factor loadings in primal component (PC) 1. PC1 is interpreted as being indicative of the grain-size trend, in which a finer grain-size distribution indicates better size sorting. The frequency distribution of PC1 has a bimodal shape and suggests the existence of two types of sediment populations. Therefore, we applied fuzzy cluster analysis, the results of which revealed two groupings of the sediment (Cluster 1 and Cluster 2). Cluster 1 shows a lower value of PC1, indicating coarse and poorly sorted sediments. Cluster 1 sediments are distributed around the branched channel from Kushida River and show an expanding distribution from the river mouth toward the northeast direction. Cluster 2 shows a higher value of PC1, indicating fine and well-sorted sediments; this cluster is distributed in a distant area from the river mouth, including the offshore region. Therefore, Cluster 1 and Cluster 2 are interpreted as being deposited by fluvial and wave processes, respectively. Finally, on the basis of this distribution pattern, the sediment trend model was applied in areas dominated separately by fluvial and wave processes. Resultant sediment transport patterns showed good agreement with those obtained by field observations. The results of this study provide an important insight into the numerical models of sediment transport.

Extrinsic Sources of Scatter in the Richness-mass Relation of Galaxy Clusters

NASA Astrophysics Data System (ADS)

Rozo, Eduardo; Rykoff, Eli; Koester, Benjamin; Nord, Brian; Wu, Hao-Yi; Evrard, August; Wechsler, Risa

2011-10-01

Maximizing the utility of upcoming photometric cluster surveys requires a thorough understanding of the richness-mass relation of galaxy clusters. We use Monte Carlo simulations to study the impact of various sources of observational scatter on this relation. Cluster ellipticity, photometric errors, photometric redshift errors, and cluster-to-cluster variations in the properties of red-sequence galaxies contribute negligible noise. Miscentering, however, can be important, and likely contributes to the scatter in the richness-mass relation of galaxy maxBCG clusters at the low-mass end, where centering is more difficult. We also investigate the impact of projection effects under several empirically motivated assumptions about cluster environments. Using Sloan Digital Sky Survey data and the maxBCG cluster catalog, we demonstrate that variations in cluster environments can rarely (≈1%-5% of the time) result in significant richness boosts. Due to the steepness of the mass/richness function, the corresponding fraction of optically selected clusters that suffer from these projection effects is ≈5%-15%. We expect these numbers to be generic in magnitude, but a precise determination requires detailed, survey-specific modeling.

A Study of Pupil Control Ideology: A Person-Oriented Approach to Data Analysis

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2010-01-01

Responses of urban school teachers to the Pupil Control Ideology questionnaire were studied using Latent Class Analysis. The results of the analysis suggest that the best fitting model to the data is a two-cluster solution. In particular, the pupil control ideology of the sample delineates into two clusters of teachers, those with humanistic and…

Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection

PubMed Central

Liu, Wenfen

2017-01-01

Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets. Meanwhile, a scalable semisupervised cluster ensemble algorithm is also proposed via the combination of our fast CSC algorithm and dimensionality reduction with random projection in the process of spectral ensemble clustering. We demonstrate by presenting theoretical analysis and empirical results that the new cluster ensemble algorithm has advantages in terms of efficiency and effectiveness. Furthermore, the approximate preservation of random projection in clustering accuracy proved in the stage of consensus clustering is also suitable for the weighted k-means clustering and thus gives the theoretical guarantee to this special kind of k-means clustering where each point has its corresponding weight. PMID:29312447