Single-cluster dynamics for the random-cluster model
Deng, Y.; Qian, X.; Blöte, H.W.J.
2009-01-01
We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those
Single-cluster dynamics for the random-cluster model
Deng, Y.; Qian, X.; Blöte, H.W.J.
2009-01-01
We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those
Single-cluster dynamics for the random-cluster model
Deng, Youjin; Qian, Xiaofeng; Blöte, Henk W. J.
2009-09-01
We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q -state Potts model to noninteger values q>1 . Its results for static quantities are in a satisfactory agreement with those of the existing Swendsen-Wang-Chayes-Machta (SWCM) algorithm, which involves a full-cluster decomposition of random-cluster configurations. We explore the critical dynamics of this algorithm for several two-dimensional Potts and random-cluster models. For integer q , the single-cluster algorithm can be reduced to the Wolff algorithm, for which case we find that the autocorrelation functions decay almost purely exponentially, with dynamic exponents zexp=0.07 (1), 0.521 (7), and 1.007 (9) for q=2 , 3, and 4, respectively. For noninteger q , the dynamical behavior of the single-cluster algorithm appears to be very dissimilar to that of the SWCM algorithm. For large critical systems, the autocorrelation function displays a range of power-law behavior as a function of time. The dynamic exponents are relatively large. We provide an explanation for this peculiar dynamic behavior.
Bridges in the random-cluster model
Eren Metin Elçi
2016-02-01
Full Text Available The random-cluster model, a correlated bond percolation model, unifies a range of important models of statistical mechanics in one description, including independent bond percolation, the Potts model and uniform spanning trees. By introducing a classification of edges based on their relevance to the connectivity we study the stability of clusters in this model. We prove several exact relations for general graphs that allow us to derive unambiguously the finite-size scaling behavior of the density of bridges and non-bridges. For percolation, we are also able to characterize the point for which clusters become maximally fragile and show that it is connected to the concept of the bridge load. Combining our exact treatment with further results from conformal field theory, we uncover a surprising behavior of the (normalized variance of the number of (non-bridges, showing that it diverges in two dimensions below the value 4cos2(π/3=0.2315891⋯ of the cluster coupling q. Finally, we show that a partial or complete pruning of bridges from clusters enables estimates of the backbone fractal dimension that are much less encumbered by finite-size corrections than more conventional approaches.
Cluster-size dependent randomization traffic flow model
Gao Kun; Wang Bing-Hong; Fu Chuan-Ji; Lu Yu-Feng
2007-01-01
In order to exhibit the meta-stable states, several slow-to-start rules have been investigated as modification to Nagel-Schreckenberg (NS) model. These models can reproduce some realistic phenomena which are absent in the original NS model. But in these models, the size of cluster is still not considered as a useful parameter. In real traffic,the slow-to-start motion of a standing vehicle often depends on the degree of congestion which can be measured by the clusters'size. According to this idea, we propose a cluster-size dependent slow-to-start model based on the speeddependent slow-to-start rule (VDR) model. It gives expected results through simulations. Comparing with the VDR model, our new model has a better traffic efficiency and shows richer complex characters.
Single-cluster-update Monte Carlo method for the random anisotropy model
Rößler, U. K.
1999-06-01
A Wolff-type cluster Monte Carlo algorithm for random magnetic models is presented. The algorithm is demonstrated to reduce significantly the critical slowing down for planar random anisotropy models with weak anisotropy strength. Dynamic exponents zcluster algorithms are estimated for models with ratio of anisotropy to exchange constant D/J=1.0 on cubic lattices in three dimensions. For these models, critical exponents are derived from a finite-size scaling analysis.
A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos
Wu, Baoyuan
2016-10-25
Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-of-the-art results in face clustering and tracking on several videos.
Potts Model with Invisible Colors : Random-Cluster Representation and Pirogov–Sinai Analysis
Enter, Aernout C.D. van; Iacobelli, Giulio; Taati, Siamak
We study a recently introduced variant of the ferromagnetic Potts model consisting of a ferromagnetic interaction among q “visible” colors along with the presence of r non-interacting “invisible” colors. We introduce a random-cluster representation for the model, for which we prove the existence of
Potts q-color field theory and scaling random cluster model
Delfino, Gesualdo
2011-01-01
We study structural properties of the q-color Potts field theory which, for real values of q, describes the scaling limit of the random cluster model. We show that the number of independent n-point Potts spin correlators coincides with that of independent n-point cluster connectivities and is given by generalized Bell numbers. Only a subset of these spin correlators enters the determination of the Potts magnetic properties for q integer. The structure of the operator product expansion of the spin fields for generic q is also identified. For the two-dimensional case, we analyze the duality relation between spin and kink field correlators, both for the bulk and boundary cases, obtaining in particular a sum rule for the kink-kink elastic scattering amplitudes.
Zhang, Yifan
2016-08-18
For face naming in TV series or movies, a typical way is using subtitles/script alignment to get the time stamps of the names, and tagging them to the faces. We study the problem of face naming in videos when subtitles are not available. To this end, we divide the problem into two tasks: face clustering which groups the faces depicting a certain person into a cluster, and name assignment which associates a name to each face. Each task is formulated as a structured prediction problem and modeled by a hidden conditional random field (HCRF) model. We argue that the two tasks are correlated problems whose outputs can provide prior knowledge of the target prediction for each other. The two HCRFs are coupled in a unified graphical model called coupled HCRF where the joint dependence of the cluster labels and face name association is naturally embedded in the correlation between the two HCRFs. We provide an effective algorithm to optimize the two HCRFs iteratively and the performance of the two tasks on real-world data set can be both improved.
Continuity of the Phase Transition for Planar Random-Cluster and Potts Models with {1 ≤ q ≤ 4}
Duminil-Copin, Hugo; Sidoravicius, Vladas; Tassion, Vincent
2017-01-01
This article studies the planar Potts model and its random-cluster representation. We show that the phase transition of the nearest-neighbor ferromagnetic q-state Potts model on Z^2 is continuous for {q in {2,3,4}}, in the sense that there exists a unique Gibbs state, or equivalently that there is no ordering for the critical Gibbs states with monochromatic boundary conditions. The proof uses the random-cluster model with cluster-weight {q ≥ 1} (note that q is not necessarily an integer) and is based on two ingredients: The fact that the two-point function for the free state decays sub-exponentially fast for cluster-weights {1≤ q≤ 4}, which is derived studying parafermionic observables on a discrete Riemann surface.
Ma, Jinhui; Raina, Parminder; Beyene, Joseph; Thabane, Lehana
2013-01-23
The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE)) and cluster-specific (i.e. random-effects logistic regression (RELR)) models for analyzing data from cluster randomized trials (CRTs) with missing binary responses. In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI) and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE), and coverage probability. GEE performs well on all four measures--provided the downward bias of the standard error (when the number of clusters per arm is small) is adjusted appropriately--under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF) cluster MI for CRTs with VIF≥3 and cluster size>50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied. GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.
Cluster randomization and political philosophy.
Chwang, Eric
2012-11-01
In this paper, I will argue that, while the ethical issues raised by cluster randomization can be challenging, they are not new. My thesis divides neatly into two parts. In the first, easier part I argue that many of the ethical challenges posed by cluster randomized human subjects research are clearly present in other types of human subjects research, and so are not novel. In the second, more difficult part I discuss the thorniest ethical challenge for cluster randomized research--cases where consent is genuinely impractical to obtain. I argue that once again these cases require no new analytic insight; instead, we should look to political philosophy for guidance. In other words, the most serious ethical problem that arises in cluster randomized research also arises in political philosophy.
Random matrix improved subspace clustering
Couillet, Romain
2017-03-06
This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.
Ma Jinhui
2013-01-01
Full Text Available Abstracts Background The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE and cluster-specific (i.e. random-effects logistic regression (RELR models for analyzing data from cluster randomized trials (CRTs with missing binary responses. Methods In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE, and coverage probability. Results GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF 50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied. Conclusion GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.
How Clustering Affects Epidemics in Random Networks
Coupechoux, Emilie
2012-01-01
Motivated by the analysis of social networks, we study a model of random networks that has both a given degree distribution and a tunable clustering coefficient. We consider two types of growth processes on these graphs: diffusion and symmetric threshold model. The diffusion process is inspired from epidemic models. It is characterized by an infection probability, each neighbor transmitting the epidemic independently. In the symmetric threshold process, the interactions are still local but the propagation rule is governed by a threshold (that might vary among the different nodes). An interesting example of symmetric threshold process is the contagion process, which is inspired by a simple coordination game played on the network. Both types of processes have been used to model spread of new ideas, technologies, viruses or worms and results have been obtained for random graphs with no clustering. In this paper, we are able to analyze the impact of clustering on the growth processes. While clustering inhibits th...
Local and cluster critical dynamics of the 3d random-site Ising model
Ivaneyko, D.; Ilnytskyi, J.; Berche, B.; Holovatch, Yu.
2006-10-01
We present the results of Monte Carlo simulations for the critical dynamics of the three-dimensional site-diluted quenched Ising model. Three different dynamics are considered, these correspond to the local update Metropolis scheme as well as to the Swendsen-Wang and Wolff cluster algorithms. The lattice sizes of L=10-96 are analysed by a finite-size-scaling technique. The site dilution concentration p=0.85 was chosen to minimize the correction-to-scaling effects. We calculate numerical values of the dynamical critical exponents for the integrated and exponential autocorrelation times for energy and magnetization. As expected, cluster algorithms are characterized by lower values of dynamical critical exponent than the local one: also in the case of dilution critical slowing down is more pronounced for the Metropolis algorithm. However, the striking feature of our estimates is that they suggest that dilution leads to decrease of the dynamical critical exponent for the cluster algorithms. This phenomenon is quite opposite to the local dynamics, where dilution enhances critical slowing down.
The Design of Cluster Randomized Crossover Trials
Rietbergen, Charlotte; Moerbeek, Mirjam
2011-01-01
The inefficiency induced by between-cluster variation in cluster randomized (CR) trials can be reduced by implementing a crossover (CO) design. In a simple CO trial, each subject receives each treatment in random order. A powerful characteristic of this design is that each subject serves as its own control. In a CR CO trial, clusters of subjects…
Cluster banding heat source model
Zhang Liguo; Ji Shude; Yang Jianguo; Fang Hongyuan; Li Yafan
2006-01-01
Concept of cluster banding heat source model is put forward for the problem of overmany increment steps in the process of numerical simulation of large welding structures, and expression of cluster banding heat source model is deduced based on energy conservation law.Because the expression of cluster banding heat source model deduced is suitable for random weld width, quantitative analysis of welding stress field for large welding structures which have regular welds can be made quickly.
Reich, Nicholas G; Myers, Jessica A; Obeng, Daniel; Milstone, Aaron M; Perl, Trish M
2012-01-01
In recent years, the number of studies using a cluster-randomized design has grown dramatically. In addition, the cluster-randomized crossover design has been touted as a methodological advance that can increase efficiency of cluster-randomized studies in certain situations. While the cluster-randomized crossover trial has become a popular tool, standards of design, analysis, reporting and implementation have not been established for this emergent design. We address one particular aspect of cluster-randomized and cluster-randomized crossover trial design: estimating statistical power. We present a general framework for estimating power via simulation in cluster-randomized studies with or without one or more crossover periods. We have implemented this framework in the clusterPower software package for R, freely available online from the Comprehensive R Archive Network. Our simulation framework is easy to implement and users may customize the methods used for data analysis. We give four examples of using the software in practice. The clusterPower package could play an important role in the design of future cluster-randomized and cluster-randomized crossover studies. This work is the first to establish a universal method for calculating power for both cluster-randomized and cluster-randomized clinical trials. More research is needed to develop standardized and recommended methodology for cluster-randomized crossover studies.
Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials
Sanders, Elizabeth A.
2011-01-01
This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…
The design of cluster randomized crossover trials
Rietbergen, C.; Moerbeek, M.
2011-01-01
The inefficiency induced by between-cluster variation in cluster randomized (CR) trials can be reduced by implementing a crossover (CO) design. In a simple CO trial, each subject receives each treatment in random order. A powerful characteristic of this design is that each subject serves as its own
Altarelli, Fabrizio; Zamponi, Francesco
2007-01-01
We study the performances of stochastic heuristic search algorithms on Uniquely Extendible Constraint Satisfaction Problems with random inputs. We show that, for any heuristic preserving the Poissonian nature of the underlying instance, the (heuristic-dependent) largest ratio $\\alpha_a$ of constraints per variables for which a search algorithm is likely to find solutions is smaller than the critical ratio $\\alpha_d$ above which solutions are clustered and highly correlated. In addition we show that the clustering ratio can be reached when the number k of variables per constraints goes to infinity by the so-called Generalized Unit Clause heuristic.
Phase transitions for information diffusion in random clustered networks
Lim, Sungsu; Shin, Joongbo; Kwak, Namju; Jung, Kyomin
2016-09-01
We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks.
General Framework for Effect Sizes in Cluster Randomized Experiments
VanHoudnos, Nathan
2016-01-01
Cluster randomized experiments are ubiquitous in modern education research. Although a variety of modeling approaches are used to analyze these data, perhaps the most common methodology is a normal mixed effects model where some effects, such as the treatment effect, are regarded as fixed, and others, such as the effect of group random assignment…
Lawson, Andrew B
2002-01-01
Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...
Imputation strategies for missing binary outcomes in cluster randomized trials
Akhtar-Danesh Noori
2011-02-01
Full Text Available Abstract Background Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs, where groups of patients rather than individuals are randomized. Standard multiple imputation (MI strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate dependent missing, we compared six MI strategies which account for the intra-cluster correlation for missing binary outcomes in CRTs with the standard imputation strategies and complete case analysis approach using a simulation study. Method We considered three within-cluster and three across-cluster MI strategies for missing binary outcomes in CRTs. The three within-cluster MI strategies are logistic regression method, propensity score method, and Markov chain Monte Carlo (MCMC method, which apply standard MI strategies within each cluster. The three across-cluster MI strategies are propensity score method, random-effects (RE logistic regression approach, and logistic regression with cluster as a fixed effect. Based on the community hypertension assessment trial (CHAT which has complete data, we designed a simulation study to investigate the performance of above MI strategies. Results The estimated treatment effect and its 95% confidence interval (CI from generalized estimating equations (GEE model based on the CHAT complete dataset are 1.14 (0.76 1.70. When 30% of binary outcome are missing completely at random, a simulation study shows that the estimated treatment effects and the corresponding 95% CIs from GEE model are 1.15 (0.76 1.75 if complete case analysis is used, 1.12 (0.72 1.73 if within-cluster MCMC method is used, 1.21 (0.80 1.81 if across-cluster RE logistic regression is used, and 1.16 (0.82 1.64 if standard logistic regression which does not account for clustering is used. Conclusion When the percentage of missing data is low or intra-cluster
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…
Methods for analyzing cost effectiveness data from cluster randomized trials
Clark Allan
2007-09-01
Full Text Available Abstract Background Measurement of individuals' costs and outcomes in randomized trials allows uncertainty about cost effectiveness to be quantified. Uncertainty is expressed as probabilities that an intervention is cost effective, and confidence intervals of incremental cost effectiveness ratios. Randomizing clusters instead of individuals tends to increase uncertainty but such data are often analysed incorrectly in published studies. Methods We used data from a cluster randomized trial to demonstrate five appropriate analytic methods: 1 joint modeling of costs and effects with two-stage non-parametric bootstrap sampling of clusters then individuals, 2 joint modeling of costs and effects with Bayesian hierarchical models and 3 linear regression of net benefits at different willingness to pay levels using a least squares regression with Huber-White robust adjustment of errors, b a least squares hierarchical model and c a Bayesian hierarchical model. Results All five methods produced similar results, with greater uncertainty than if cluster randomization was not accounted for. Conclusion Cost effectiveness analyses alongside cluster randomized trials need to account for study design. Several theoretically coherent methods can be implemented with common statistical software.
Guoying Liu
2015-01-01
Full Text Available This paper presents a variation of the fuzzy local information c-means clustering (FLICM algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.
Blanchet, Juliette; Vignes, Matthieu
2009-03-01
The different measurement techniques that interrogate biological systems provide means for monitoring the behavior of virtually all cell components at different scales and from complementary angles. However, data generated in these experiments are difficult to interpret. A first difficulty arises from high-dimensionality and inherent noise of such data. Organizing them into meaningful groups is then highly desirable to improve our knowledge of biological mechanisms. A more accurate picture can be obtained when accounting for dependencies between components (e.g., genes) under study. A second difficulty arises from the fact that biological experiments often produce missing values. When it is not ignored, the latter issue has been solved by imputing the expression matrix prior to applying traditional analysis methods. Although helpful, this practice can lead to unsound results. We propose in this paper a statistical methodology that integrates individual dependencies in a missing data framework. More explicitly, we present a clustering algorithm dealing with incomplete data in a Hidden Markov Random Field context. This tackles the missing value issue in a probabilistic framework and still allows us to reconstruct missing observations a posteriori without imposing any pre-processing of the data. Experiments on synthetic data validate the gain in using our method, and analysis of real biological data shows its potential to extract biological knowledge.
Kersten, F A M; Nelen, W L D M; van den Boogaard, N M; van Rumste, M M; Koks, C A; IntHout, J; Verhoeve, H R; Pelinck, M J; Boks, D E S; Gianotten, J; Broekmans, F J M; Goddijn, M; Braat, D D M; Mol, B W J; Hermens, R P G M
2017-08-01
What is the effectiveness of a multifaceted implementation strategy compared to usual care on improving the adherence to guideline recommendations on expectant management for couples with unexplained infertility? The multifaceted implementation strategy did not significantly increase adherence to guideline recommendations on expectant management compared to care as usual. Intrauterine insemination (IUI) with or without ovarian hyperstimulation has no beneficial effect compared to no treatment for 6 months after the fertility work-up for couples with unexplained infertility and a good prognosis of natural conception. Therefore, various professionals and policy makers have advocated the use of prognostic profiles and expectant management in guideline recommendations. A cluster randomized controlled trial in 25 clinics in the Netherlands was conducted between March 2013 and May 2014. Clinics were randomized between the implementation strategy (intervention, n = 13) and care as usual (control, n = 12). The effect of the implementation strategy was evaluated by comparing baseline and effect measurement data. Data collection was retrospective and obtained from medical record research and a patient questionnaire. A total of 544 couples were included at baseline and 485 at the effect measurement (247 intervention group/238 control group). Guideline adherence increased from 49 to 69% (OR 2.66; 95% CI 1.45-4.89) in the intervention group, and from 49 to 61% (OR 2.03; 95% CI 1.38-3.00) in the control group. Multilevel analysis with case-mix adjustment showed that the difference of 8% was not statistically significant (OR 1.31; 95% CI 0.67-2.59). The ongoing pregnancy rate within six months after fertility work-up did not significantly differ between intervention and control group (25% versus 27%: OR 0.72; 95% CI 0.40-1.27). There is a possible selection bias, couples included in the study had a higher socio-economic status than non-responders. How this affects guideline
Cluster Based Text Classification Model
2011-01-01
We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases th...... datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset....
Random Projections for $k$-means Clustering
Boutsidis, Christos; Drineas, Petros
2010-01-01
This paper discusses the topic of dimensionality reduction for $k$-means clustering. We prove that any set of $n$ points in $d$ dimensions (rows in a matrix $A \\in \\RR^{n \\times d}$) can be projected into $t = \\Omega(k / \\eps^2)$ dimensions, for any $\\eps \\in (0,1/3)$, in $O(n d \\lceil \\eps^{-2} k/ \\log(d) \\rceil )$ time, such that with constant probability the optimal $k$-partition of the point set is preserved within a factor of $2+\\eps$. The projection is done by post-multiplying $A$ with a $d \\times t$ random matrix $R$ having entries $+1/\\sqrt{t}$ or $-1/\\sqrt{t}$ with equal probability. A numerical implementation of our technique and experiments on a large face images dataset verify the speed and the accuracy of our theoretical results.
Deke, John
2016-10-25
Cluster randomized controlled trials (CRCTs) often require a large number of clusters in order to detect small effects with high probability. However, there are contexts where it may be possible to design a CRCT with a much smaller number of clusters (10 or fewer) and still detect meaningful effects. The objective is to offer recommendations for best practices in design and analysis for small CRCTs. I use simulations to examine alternative design and analysis approaches. Specifically, I examine (1) which analytic approaches control Type I errors at the desired rate, (2) which design and analytic approaches yield the most power, (3) what is the design effect of spurious correlations, and (4) examples of specific scenarios under which impacts of different sizes can be detected with high probability. I find that (1) mixed effects modeling and using Ordinary Least Squares (OLS) on data aggregated to the cluster level both control the Type I error rate, (2) randomization within blocks is always recommended, but how best to account for blocking through covariate adjustment depends on whether the precision gains offset the degrees of freedom loss, (3) power calculations can be accurate when design effects from small sample, spurious correlations are taken into account, and (4) it is very difficult to detect small effects with just four clusters, but with six or more clusters, there are realistic circumstances under which small effects can be detected with high probability. © The Author(s) 2016.
Anne Cori
Full Text Available BACKGROUND: The HPTN 052 trial confirmed that antiretroviral therapy (ART can nearly eliminate HIV transmission from successfully treated HIV-infected individuals within couples. Here, we present the mathematical modeling used to inform the design and monitoring of a new trial aiming to test whether widespread provision of ART is feasible and can substantially reduce population-level HIV incidence. METHODS AND FINDINGS: The HPTN 071 (PopART trial is a three-arm cluster-randomized trial of 21 large population clusters in Zambia and South Africa, starting in 2013. A combination prevention package including home-based voluntary testing and counseling, and ART for HIV positive individuals, will be delivered in arms A and B, with ART offered universally in arm A and according to national guidelines in arm B. Arm C will be the control arm. The primary endpoint is the cumulative three-year HIV incidence. We developed a mathematical model of heterosexual HIV transmission, informed by recent data on HIV-1 natural history. We focused on realistically modeling the intervention package. Parameters were calibrated to data previously collected in these communities and national surveillance data. We predict that, if targets are reached, HIV incidence over three years will drop by >60% in arm A and >25% in arm B, relative to arm C. The considerable uncertainty in the predicted reduction in incidence justifies the need for a trial. The main drivers of this uncertainty are possible community-level behavioral changes associated with the intervention, uptake of testing and treatment, as well as ART retention and adherence. CONCLUSIONS: The HPTN 071 (PopART trial intervention could reduce HIV population-level incidence by >60% over three years. This intervention could serve as a paradigm for national or supra-national implementation. Our analysis highlights the role mathematical modeling can play in trial development and monitoring, and more widely in evaluating the
A Framework for Designing Cluster Randomized Trials with Binary Outcomes
Spybrook, Jessaca; Martinez, Andres
2011-01-01
The purpose of this paper is to provide a frame work for approaching a power analysis for a CRT (cluster randomized trial) with a binary outcome. The authors suggest a framework in the context of a simple CRT and then extend it to a blocked design, or a multi-site cluster randomized trial (MSCRT). The framework is based on proportions, an…
Kim, Hongsoo; Park, Yeon-Hwan; Jung, Young-Il; Choi, Hyoungshim; Lee, Seyune; Kim, Gi-Soo; Yang, Dong-Wook; Paik, Myunghee Cho; Lee, Tae-Jin
2017-04-18
Limited evidence exists on the effectiveness of the chronic care model for people with multimorbidity. This study aims to evaluate the effectiveness of an information and communication technology- (ICT-)enhanced integrated care model, called Systems for Person-centered Elder Care (SPEC), for frail older adults at nursing homes. SPEC is a prospective stepped-wedge cluster randomized trial conducted at 10 nursing homes in South Korea. Residents aged 65 or older meeting the inclusion/exclusion criteria in all the homes are eligible to participate. The multifaceted SPEC intervention, a geriatric care model guided by the chronic care model, consists of five components: comprehensive geriatric assessment for need/risk profiling, individual need-based care planning, interdisciplinary case conferences, person-centered care coordination, and a cloud-based information and communications technology (ICT) tool supporting the intervention process. The primary outcome is quality of care for older residents using a composite measure of quality indicators from the interRAI LTCF assessment system. Outcome assessors and data analysts will be blinded to group assignment. Secondary outcomes include quality of life, healthcare utilization, and cost. Process evaluation will be also conducted. This study is expected to provide important new evidence on the effectiveness, cost-effectiveness, and implementation process of an ICT-supported chronic care model for older persons with multiple chronic illnesses. The SPEC intervention is also unique as the first registered trial implementing an integrated care model using technology to promote person-centered care for frail older nursing home residents in South Korea, where formal LTC was recently introduced. ISRCTN11972147.
Ribbe Miel W
2008-07-01
Full Text Available Abstract Background The objective of this article is to describe the design of a study to evaluate the clinical and economic effects of a Disease Management model on functional health, quality of care and quality of life of persons living in homes for the elderly. Methods This study concerns a cluster randomized controlled clinical trial among five intervention homes and five usual care homes in the North-West of the Netherlands with a total of over 500 residents. All persons who are not terminally ill, are able to be interviewed and sign informed consent are included. For cognitively impaired persons family proxies will be approached to provide outcome information. The Disease Management Model consists of several elements: (1 Trained staff carries out a multidimensional assessment of the patients functional health and care needs with the interRAI Long Term Care Facilities instrument (LTCF. Computerization of the LTCF produces immediate identification of problem areas and thereby guides individualized care planning. (2 The assessment outcomes are discussed in a Multidisciplinary Meeting (MM with the nurse, primary care physician, nursing home physician and Psychotherapist and if necessary other members of the care team. The MM presents individualized care plans to manage or treat modifiable disabilities and risk factors. (3 Consultation by an nursing home physician and psychotherapist is offered to the frailest residents at risk for nursing home admission (according to the interRAI LTCF. Outcome measures are Quality of Care indicators (LTCF based, Quality Adjusted Life Years (Euroqol, Functional health (SF12, COOP-WONCA, Disability (GARS, Patients care satisfaction (QUOTE, hospital and nursing home days and mortality, health care utilization and costs. Discussion This design is unique because no earlier studies were performed to evaluate the effects and costs of this Disease Management Model for disabled persons in homes for the elderly on
Boorsma, Marijke; van Hout, Hein P J; Frijters, Dinnus H; Ribbe, Miel W; Nijpels, Giel
2008-07-07
The objective of this article is to describe the design of a study to evaluate the clinical and economic effects of a Disease Management model on functional health, quality of care and quality of life of persons living in homes for the elderly. This study concerns a cluster randomized controlled clinical trial among five intervention homes and five usual care homes in the North-West of the Netherlands with a total of over 500 residents. All persons who are not terminally ill, are able to be interviewed and sign informed consent are included. For cognitively impaired persons family proxies will be approached to provide outcome information. The Disease Management Model consists of several elements: (1) Trained staff carries out a multidimensional assessment of the patients functional health and care needs with the interRAI Long Term Care Facilities instrument (LTCF). Computerization of the LTCF produces immediate identification of problem areas and thereby guides individualized care planning. (2) The assessment outcomes are discussed in a Multidisciplinary Meeting (MM) with the nurse, primary care physician, nursing home physician and Psychotherapist and if necessary other members of the care team. The MM presents individualized care plans to manage or treat modifiable disabilities and risk factors. (3) Consultation by an nursing home physician and psychotherapist is offered to the frailest residents at risk for nursing home admission (according to the interRAI LTCF). Outcome measures are Quality of Care indicators (LTCF based), Quality Adjusted Life Years (Euroqol), Functional health (SF12, COOP-WONCA), Disability (GARS), Patients care satisfaction (QUOTE), hospital and nursing home days and mortality, health care utilization and costs. This design is unique because no earlier studies were performed to evaluate the effects and costs of this Disease Management Model for disabled persons in homes for the elderly on functional health and quality of care. TRAIL
Habteyes Hailu Tola
Full Text Available Treatment non-adherence results in treatment failure, prolonged transmission of disease and emergence of drug resistance. Although the problem widely investigated, there remains an information gap on the effectiveness of different methods to improve treatment adherence and the predictors of non-adherence in resource limited countries based on theoretical models. This study aimed to evaluate the impact of psychological counseling and educational intervention on tuberculosis (TB treatment adherence based on Health Belief Model (HBM.A cluster randomized control trial was conducted in Addis Ababa from May to December, 2014. Patients were enrolled into study consecutively from 30 randomly selected Health Centers (HCs (14 HCs intervention and 16 HCs control groups. A total of 698 TB patients, who were on treatment for one month to two months were enrolled. A structured questionnaire was administered to both groups of patients at baseline and endpoint of study. Control participants received routine directly-observed anti-TB therapy and the intervention group additionally received combined psychological counseling and adherence education. Treatment non-adherence level was the main outcome of the study, and multilevel logistic regression was employed to assess the impact of intervention on treatment adherence.At enrollment, the level of non-adherence among intervention (19.4% and control (19.6% groups was almost the same. However, after intervention, non-adherence level decreased among intervention group from 19.4 (at baseline to 9.5% (at endpoint, while it increased among control group from 19.4% (baseline to 25.4% (endpoint. Psychological counseling and educational interventions resulted in significant difference with regard to non-adherence level between intervention and control groups (Adjusted OR = 0.31, 95% Confidence Interval (CI (0.18-0.53, p < 0.001.Psychological counseling and educational interventions, which were guided by HBM, significantly
Incorporating Contact Network Structure in Cluster Randomized Trials
Staples, Patrick C; Onnela, Jukka-Pekka
2015-01-01
Whenever possible, the efficacy of a new treatment, such as a drug or behavioral intervention, is investigated by randomly assigning some individuals to a treatment condition and others to a control condition, and comparing the outcomes between the two groups. Often, when the treatment aims to slow an infectious disease, groups or clusters of individuals are assigned en masse to each treatment arm. The structure of interactions within and between clusters can reduce the power of the trial, i.e. the probability of correctly detecting a real treatment effect. We investigate the relationships among power, within-cluster structure, between-cluster mixing, and infectivity by simulating an infectious process on a collection of clusters. We demonstrate that current power calculations may be conservative for low levels of between-cluster mixing, but failing to account for moderate or high amounts can result in severely underpowered studies. Power also depends on within-cluster network structure for certain kinds of i...
Topics in modelling of clustered data
Aerts, Marc; Ryan, Louise M; Geys, Helena
2002-01-01
Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and social science studies. It focuses on providing a comprehensive treatment of marginal, conditional, and random effects models using, among others, likelihood, pseudo-likelihood, and generalized estimating equations methods. The authors motivate and illustrate all aspects of these models in a variety of real applications. They discuss several variations and extensions, including individual-level covariates and combined continuous and discrete outcomes. Flexible modelling with fractional and local polynomials, omnibus lack-of-fit tests, robustification against misspecification, exact, and bootstrap inferential procedures all receive extensive treatment. The application...
Power Calculations for Binary Moderator in Cluster Randomized Trials
Spybrook, Jessaca; Kelcey, Ben
2014-01-01
Cluster randomized trials (CRTs), or studies in which intact groups of individuals are randomly assigned to a condition, are becoming more common in the evaluation of educational programs, policies, and practices. The website for the National Center for Education Evaluation and Regional Assistance (NCEE) reveals they have launched over 30…
Hsu, Hsiao-Ping; Lin, Simon C.; Hu, Chin-Kun
2001-01-01
Percolation models with multiple percolating clusters have attracted much attention in recent years. Here we use Monte Carlo simulations to study bond percolation on $L_{1}\\times L_{2}$ planar random lattices, duals of random lattices, and square lattices with free and periodic boundary conditions, in vertical and horizontal directions, respectively, and with various aspect ratio $L_{1}/L_{2}$. We calculate the probability for the appearance of $n$ percolating clusters, $W_{n},$ the percolati...
Sun, Xu; Yang, Lina; Gao, Lianru; Zhang, Bing; Li, Shanshan; Li, Jun
2015-01-01
Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC-MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC-MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC-MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC-MRF-cluster showed good stability.
Continuum modeling of myxobacteria clustering
Harvey, Cameron W.; Alber, Mark; Tsimring, Lev S.; Aranson, Igor S.
2013-03-01
In this paper we develop a continuum theory of clustering in ensembles of self-propelled inelastically colliding rods with applications to collective dynamics of common gliding bacteria Myxococcus xanthus. A multi-phase hydrodynamic model that couples densities of oriented and isotropic phases is described. This model is used for the analysis of an instability that leads to spontaneous formation of directionally moving dense clusters within initially dilute isotropic ‘gas’ of myxobacteria. Numerical simulations of this model confirm the existence of stationary dense moving clusters and also elucidate the properties of their collisions. The results are shown to be in a qualitative agreement with experiments.
Limited Random Walk Algorithm for Big Graph Data Clustering
Zhang, Honglei; Kiranyaz, Serkan; Gabbouj, Moncef
2016-01-01
Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach of the walking agent using an inflation function and a normalization function. We analyze the behavior of the limited random walk procedure and propose a novel algorithm for both global and local graph clustering problems. Previous random-walk-based algorithms depend on the chosen fitness function to find the clusters around a seed vertex. The proposed algorithm tackles the problem in an entirely different manner. We use the limited random walk procedure to find attracting vertices in a graph and use them as features to cluster the vertices. According to the experimental results on the simulated graph data and the real-world big graph data, the proposed method is superior to the state-of-the-art methods in solving graph clustering problems. Since the proposed method uses the embarrass...
王志同
2016-01-01
Big data clustering process is a gaussian random process, thus in large-scale data classification, build sound data classification model, is very important to improve the ability of mathematical statistics. Binomial, poisson model with global solution of the convex optimization random clustering performance, using binomial, poisson model, the superiority of gaussian random data processing in finite dimensional space, for data clustering analysis. Build the KKT conditions of binomial, poisson model, obtains the binomial, poisson model polynomial kernel, the boundary value of periodic solution of a gaussian clustering feature decomposition, draw Schur complement functional criterion, binomial, poisson model of large-scale data classification system of mathematical statistics, eventually improve the accuracy of the large data clustering. Results show that the derived using binomial, poisson model in the process of gaussian random big data classification is of stable convergence, effectively improves the big data statistics and analysis ability.%大数据的聚类过程是高斯随机过程，因此在大数据分类中，构建稳健的数据分类模型，提高数理统计能力至关重要。二项-泊松模型具有全局解的凸优化随机聚类性能，利用二项-泊松模型对高斯随机性数据处理的优势，在有限维空间中，进行数据聚类分析。构建二项-泊松模型的KKT条件，取得二项-泊松模型的边值周期解多项式核，进行高斯聚类特征分解，得出Schur complement泛函准则，建立二项-泊松模型的数理统计大数据分类系统，最终验证了稳定性。推导结果表明，利用二项-泊松模型在高斯随机大数据分类过程中是稳定收敛的，有效提高了大数据的数理统计和分析能力。
Fuzzy c-Means and Cluster Ensemble with Random Projection for Big Data Clustering
Mao Ye
2016-01-01
Full Text Available Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzy c-means (FCM clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.
Exponential random graph models
Fronczak, Agata
2012-01-01
Nowadays, exponential random graphs (ERGs) are among the most widely-studied network models. Different analytical and numerical techniques for ERG have been developed that resulted in the well-established theory with true predictive power. An excellent basic discussion of exponential random graphs addressed to social science students and researchers is given in [Anderson et al., 1999][Robins et al., 2007]. This essay is intentionally designed to be more theoretical in comparison with the well-known primers just mentioned. Given the interdisciplinary character of the new emerging science of complex networks, the essay aims to give a contribution upon which network scientists and practitioners, who represent different research areas, could build a common area of understanding.
The effect of cluster size variability on statistical power in cluster-randomized trials.
Stephen A Lauer
Full Text Available The frequency of cluster-randomized trials (CRTs in peer-reviewed literature has increased exponentially over the past two decades. CRTs are a valuable tool for studying interventions that cannot be effectively implemented or randomized at the individual level. However, some aspects of the design and analysis of data from CRTs are more complex than those for individually randomized controlled trials. One of the key components to designing a successful CRT is calculating the proper sample size (i.e. number of clusters needed to attain an acceptable level of statistical power. In order to do this, a researcher must make assumptions about the value of several variables, including a fixed mean cluster size. In practice, cluster size can often vary dramatically. Few studies account for the effect of cluster size variation when assessing the statistical power for a given trial. We conducted a simulation study to investigate how the statistical power of CRTs changes with variable cluster sizes. In general, we observed that increases in cluster size variability lead to a decrease in power.
Sample size calculations for 3-level cluster randomized trials
Teerenstra, S.; Moerbeek, M.; Achterberg, T. van; Pelzer, B.J.; Borm, G.F.
2008-01-01
BACKGROUND: The first applications of cluster randomized trials with three instead of two levels are beginning to appear in health research, for instance, in trials where different strategies to implement best-practice guidelines are compared. In such trials, the strategy is implemented in health
Sample size calculations for 3-level cluster randomized trials
Teerenstra, S.; Moerbeek, M.; Achterberg, T. van; Pelzer, B.J.; Borm, G.F.
2008-01-01
Background The first applications of cluster randomized trials with three instead of two levels are beginning to appear in health research, for instance, in trials where different strategies to implement best-practice guidelines are compared. In such trials, the strategy is implemented in health
Sample size calculations for 3-level cluster randomized trials
Teerenstra, S.; Moerbeek, M.; Achterberg, T. van; Pelzer, B.J.; Borm, G.F.
2008-01-01
BACKGROUND: The first applications of cluster randomized trials with three instead of two levels are beginning to appear in health research, for instance, in trials where different strategies to implement best-practice guidelines are compared. In such trials, the strategy is implemented in health ca
Sample size calculations for 3-level cluster randomized trials
Teerenstra, S.; Moerbeek, M.; Achterberg, T. van; Pelzer, B.J.; Borm, G.F.
2008-01-01
Background The first applications of cluster randomized trials with three instead of two levels are beginning to appear in health research, for instance, in trials where different strategies to implement best-practice guidelines are compared. In such trials, the strategy is implemented in health car
Ravaud Philippe
2006-04-01
Full Text Available Abstract Background Cluster randomization design is increasingly used for the evaluation of health-care, screeening or educational interventions. At the planning stage, sample size calculations usually consider an average cluster size without taking into account any potential imbalance in cluster size. However, there may exist high discrepancies in cluster sizes. Methods We performed simulations to study the impact of an imbalance in cluster size on power. We determined by simulations to which extent four methods proposed to adapt the sample size calculations to a pre-specified imbalance in cluster size could lead to adequately powered trials. Results We showed that an imbalance in cluster size can be of high influence on the power in the case of severe imbalance, particularly if the number of clusters is low and/or the intraclass correlation coefficient is high. In the case of a severe imbalance, our simulations confirmed that the minimum variance weights correction of the variation inflaction factor (VIF used in the sample size calculations has the best properties. Conclusion Publication of cluster sizes is important to assess the real power of the trial which was conducted and to help designing future trials. We derived an adaptation of the VIF from the minimum variance weights correction to be used in case the imbalance can be a priori formulated such as "a proportion (γ of clusters actually recruit a proportion (τ of subjects to be included (γ ≤ τ".
Bayesian network meta-analysis for cluster randomized trials with binary outcomes.
Uhlmann, Lorenz; Jensen, Katrin; Kieser, Meinhard
2017-06-01
Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Ethical issues posed by cluster randomized trials in health research
Donner Allan
2011-04-01
Full Text Available Abstract The cluster randomized trial (CRT is used increasingly in knowledge translation research, quality improvement research, community based intervention studies, public health research, and research in developing countries. However, cluster trials raise difficult ethical issues that challenge researchers, research ethics committees, regulators, and sponsors as they seek to fulfill responsibly their respective roles. Our project will provide a systematic analysis of the ethics of cluster trials. Here we have outlined a series of six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation: 1. Who is a research subject? 2. From whom, how, and when must informed consent be obtained? 3. Does clinical equipoise apply to CRTs? 4. How do we determine if the benefits outweigh the risks of CRTs? 5. How ought vulnerable groups be protected in CRTs? 6. Who are gatekeepers and what are their responsibilities? Subsequent papers in this series will address each of these areas, clarifying the ethical issues at stake and, where possible, arguing for a preferred solution. Our hope is that these papers will serve as the basis for the creation of international ethical guidelines for the design and conduct of cluster randomized trials.
Power Calculations for Moderators in Multi-Site Cluster Randomized Trials
Spybrook, Jessaca; Kelcey, Ben; Dong, Nianbo
2016-01-01
Cluster randomized trials (CRTs), or studies in which intact groups of individuals are randomly assigned to a condition, are becoming more common in evaluation studies of educational programs. A specific type of CRT in which clusters are randomly assigned to treatment within blocks or sites, known as multisite cluster randomized trials (MSCRTs),…
Randomness versus deterministic chaos: Effect on invasion percolation clusters
Peng, Chung-Kang; Prakash, Sona; Herrmann, Hans J.; Stanley, H. Eugene
1990-10-01
What is the difference between randomness and chaos \\? Although one can define randomness and one can define chaos, one cannot easily assess the difference in a practical situation. Here we compare the results of these two antipodal approaches on a specific example. Specifically, we study how well the logistic map in its chaotic regime can be used as quasirandom number generator by calculating pertinent properties of a well-known random process: invasion percolation. Only if λ>λ*1 (the first reverse bifurcation point) is a smooth extrapolation in system size possible, and percolation exponents are retrieved. If λ≠1, a sequential filling of the lattice with the random numbers generates a measurable anisotropy in the growth sequence of the clusters, due to short-range correlations.
Detecting Clusters in Atom Probe Data with Gaussian Mixture Models.
Zelenty, Jennifer; Dahl, Andrew; Hyde, Jonathan; Smith, George D W; Moody, Michael P
2017-04-01
Accurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.
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.
Cluster models and other topics
Akaishi, Yoshinori; Horiuchi, Hisashi; Ikeda, Kiyomi
1986-01-01
This volume consists of contributions from some of Japan's most eminent nuclear theorists. The cluster model of the nucleus is discussed pedagogically and the current status of the field is surveyed. A contribution on Monte Carlo Methods and Lattice Gauge Theories gives nuclear theorists a glimpse of related developments in QCD and Gauge Theories. Few Body Systems are reviewed by Y Akaishi, paying special attention to the ATMS Multiple Scattering Method.
Jiangxiu Zhou
2014-09-01
Full Text Available The purpose of this study is to demonstrate a way of dealing with missing data in clustered randomized trials by doing multiple imputation (MI with the PAN package in R through SAS. The procedure for doing MI with PAN through SAS is demonstrated in detail in order for researchers to be able to use this procedure with their own data. An illustration of the technique with empirical data was also included. In this illustration thePAN results were compared with pairwise deletion and three types of MI: (1 Normal Model (NM-MI ignoring the cluster structure; (2 NM-MI with dummy-coded cluster variables (fixed cluster structure; and (3 a hybrid NM-MI which imputes half the time ignoring the cluster structure, and the other half including the dummy-coded cluster variables. The empirical analysis showed that using PAN and the other strategies produced comparable parameter estimates. However, the dummy-coded MI overestimated the intraclass correlation, whereas MI ignoring the cluster structure and the hybrid MI underestimated the intraclass correlation. When compared with PAN, the p-value and standard error for the treatment effect were higher with dummy-coded MI, and lower with MI ignoring the clusterstructure, the hybrid MI approach, and pairwise deletion. Previous studies have shown that NM-MI is not appropriate for handling missing data in clustered randomized trials. This approach, in addition to the pairwise deletion approach, leads to a biased intraclass correlation and faultystatistical conclusions. Imputation in clustered randomized trials should be performed with PAN. We have demonstrated an easy way for using PAN through SAS.
FORMATION OF A INNOVATION REGIONAL CLUSTER MODEL
G. S. Merzlikina
2015-01-01
Full Text Available Summary. As a result of investigation of science and methodical approaches related problems of building and development of innovation clusters there were some issues in functional assignments of innovation and production clusters. Because of those issues, article’s authors differ conceptions of innovation cluster and production cluster, as they explain notion of innovation-production cluster. The main goal of this article is to reveal existing organizational issues in cluster building and its successful development. Based on regional clusters building analysis carried out there was typical practical structure of cluster members interaction revealed. This structure also have its cons, as following: absence cluster orientation to marketing environment, lack of members’ prolonged relations’ building and development system, along with ineffective management of information, financial and material streams within cluster, narrow competence difference and responsibility zones between cluster members, lack of transparence of cluster’s action, low environment changes adaptivity, hard to use cluster members’ intellectual property, and commercialization of hi-tech products. When all those issues listed above come together, it reduces life activity of existing models of innovative cluster-building along with practical opportunity of cluster realization. Because of that, authors offer an upgraded innovative-productive cluster building model with more efficient business processes management system, which includes advanced innovative cluster structure, competence matrix and subcluster responsibility zone. Suggested model differs from other ones by using unified innovative product development control center, which also controls production and marketing realization.
Whitehead, Alfred J; Vesperini, Enrico; Zwart, Simon Portegies
2013-01-01
We perform a series of simulations of evolving star clusters using AMUSE (the Astrophysical Multipurpose Software Environment), 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 cut-off. They are evolved using collisional stellar dynamics and include mass loss due to stellar evolution. After determining that the differences between AMUSE results and prior publications are understood, we explored the variation in cluster lifetimes due to the random realization noise introduced by transforming a King model to specific initial conditions. This random realization noise can affect the lifetime of a simulated star cluster by up to 30%. Two modes of star cluster dissolution were identified: a mass evolution curve that contains a run-away cluster dissolution with a sudden loss of mass, and a dissolution mode that does n...
Eldridge, Sandra M; Ashby, Deborah; Kerry, Sally
2006-10-01
Cluster randomized trials are increasingly popular. In many of these trials, cluster sizes are unequal. This can affect trial power, but standard sample size formulae for these trials ignore this. Previous studies addressing this issue have mostly focused on continuous outcomes or methods that are sometimes difficult to use in practice. We show how a simple formula can be used to judge the possible effect of unequal cluster sizes for various types of analyses and both continuous and binary outcomes. We explore the practical estimation of the coefficient of variation of cluster size required in this formula and demonstrate the formula's performance for a hypothetical but typical trial randomizing UK general practices. The simple formula provides a good estimate of sample size requirements for trials analysed using cluster-level analyses weighting by cluster size and a conservative estimate for other types of analyses. For trials randomizing UK general practices the coefficient of variation of cluster size depends on variation in practice list size, variation in incidence or prevalence of the medical condition under examination, and practice and patient recruitment strategies, and for many trials is expected to be approximately 0.65. Individual-level analyses can be noticeably more efficient than some cluster-level analyses in this context. When the coefficient of variation is <0.23, the effect of adjustment for variable cluster size on sample size is negligible. Most trials randomizing UK general practices and many other cluster randomized trials should account for variable cluster size in their sample size calculations.
A review of R-packages for random-intercept probit regression in small clusters
Haeike Josephy
2016-10-01
Full Text Available Generalized Linear Mixed Models (GLMMs are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based inference. As these seldom yield satisfactory results when analyzing binary outcomes from small clusters, estimation within the Structural Equation Modeling (SEM framework is proposed as an alternative. We compare the performance of R-packages for random-intercept probit regression relying on: the Laplace approximation, adaptive Gaussian quadrature (AGQ, penalized quasi-likelihood, an MCMC-implementation, and integrated nested Laplace approximation within the GLMM-framework, and a robust diagonally weighted least squares estimation within the SEM-framework. In terms of bias for the fixed and random effect estimators, SEM usually performs best for cluster size two, while AGQ prevails in terms of precision (mainly because of SEM's robust standard errors. As the cluster size increases, however, AGQ becomes the best choice for both bias and precision.
Co-clustering models, algorithms and applications
Govaert, Gérard
2013-01-01
Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixture
Complementary feeding: a Global Network cluster randomized controlled trial
Pasha Omrana
2011-01-01
Full Text Available Abstract Background Inadequate and inappropriate complementary feeding are major factors contributing to excess morbidity and mortality in young children in low resource settings. Animal source foods in particular are cited as essential to achieve micronutrient requirements. The efficacy of the recommendation for regular meat consumption, however, has not been systematically evaluated. Methods/Design A cluster randomized efficacy trial was designed to test the hypothesis that 12 months of daily intake of beef added as a complementary food would result in greater linear growth velocity than a micronutrient fortified equi-caloric rice-soy cereal supplement. The study is being conducted in 4 sites of the Global Network for Women's and Children's Health Research located in Guatemala, Pakistan, Democratic Republic of the Congo (DRC and Zambia in communities with toddler stunting rates of at least 20%. Five clusters per country were randomized to each of the food arms, with 30 infants in each cluster. The daily meat or cereal supplement was delivered to the home by community coordinators, starting when the infants were 6 months of age and continuing through 18 months. All participating mothers received nutrition education messages to enhance complementary feeding practices delivered by study coordinators and through posters at the local health center. Outcome measures, obtained at 6, 9, 12, and 18 months by a separate assessment team, included anthropometry; dietary variety and diversity scores; biomarkers of iron, zinc and Vitamin B12 status (18 months; neurocognitive development (12 and 18 months; and incidence of infectious morbidity throughout the trial. The trial was supervised by a trial steering committee, and an independent data monitoring committee provided oversight for the safety and conduct of the trial. Discussion Findings from this trial will test the efficacy of daily intake of meat commencing at age 6 months and, if beneficial, will
Clusters of classical water models
Kiss, Péter T.; Baranyai, András
2009-11-01
The properties of clusters can be used as tests of models constructed for molecular simulation of water. We searched for configurations with minimal energies for a small number of molecules. We identified topologically different structures close to the absolute energy minimum of the system by calculating overlap integrals and enumerating hydrogen bonds. Starting from the dimer, we found increasing number of topologically different, low-energy arrangements for the trimer(3), the tetramer(6), the pentamer(6), and the hexamer(9). We studied simple models with polarizable point dipole. These were the BSV model [J. Brodholt et al., Mol. Phys. 86, 149 (1995)], the DC model [L. X. Dang and T. M. Chang, J. Chem. Phys. 106, 8149 (1997)], and the GCP model [P. Paricaud et al., J. Chem. Phys. 122, 244511 (2005)]. As an alternative the SWM4-DP and the SWM4-NDP charge-on-spring models [G. Lamoureux et al., Chem. Phys. Lett. 418, 245 (2006)] were also investigated. To study the impact of polarizability restricted to the plane of the molecule we carried out calculations for the SPC-FQ and TIP4P-FQ models, too [S. W. Rick et al., J. Chem. Phys. 101, 6141 (1994)]. In addition to them, justified by their widespread use even for near critical or surface behavior calculations, we identified clusters for five nonpolarizable models of ambient water, SPC/E [H. J. C. Berendsen et al., J. Phys. Chem. 91, 6269 (1987)], TIP4P [W. L. Jorgensen et al., J. Chem. Phys. 79, 926 (1983)], TIP4P-EW [H. W. Horn et al., J. Chem. Phys. 120, 9665 (2004)], and TIP4P/2005 [J. L. F. Abascal and C. Vega, J. Chem. Phys. 123, 234505 (2005)]. The fifth was a five-site model named TIP5P [M. W. Mahoney and W. L. Jorgensen, J. Chem. Phys. 112, 8910 (2000)]. To see the impact of the vibrations we studied the flexible SPC model. [K. Toukan and A. Rahman, Phys. Rev. B 31, 2643 (1985)]. We evaluated the results comparing them with experimental data and quantum chemical calculations. The position of the negative
Are Earthquake Clusters/Supercycles Real or Random?
Salditch, L.; Brooks, E. M.; Stein, S.; Spencer, B. D.
2016-12-01
Long records of earthquakes at plate boundaries such as the San Andreas or Cascadia often show that large earthquakes occur in temporal clusters, also termed supercycles, separated by less active intervals. These are intriguing because the boundary is presumably being loaded by steady plate motion. If so, earthquakes resulting from seismic cycles - in which their probability is small shortly after the past one, and then increases with time - should occur quasi-periodically rather than be more frequent in some intervals than others. We are exploring this issue with two approaches. One is to assess whether the clusters result purely by chance from a time-independent process that has no "memory." Thus a future earthquake is equally likely immediately after the past one and much later, so earthquakes can cluster in time. We analyze the agreement between such a model and inter-event times for Parkfield, Pallet Creek, and other records. A useful tool is transformation by the inverse cumulative distribution function, so the inter-event times have a uniform distribution when the memorylessness property holds. The second is via a time-variable model in which earthquake probability increases with time between earthquakes and decreases after an earthquake. The probability of an event increases with time until one happens, after which it decreases, but not to zero. Hence after a long period of quiescence, the probability of an earthquake can remain higher than the long-term average for several cycles. Thus the probability of another earthquake is path dependent, i.e. depends on the prior earthquake history over multiple cycles. Time histories resulting from simulations give clusters with properties similar to those observed. The sequences of earthquakes result from both the model parameters and chance, so two runs with the same parameters look different. The model parameters control the average time between events and the variation of the actual times around this average, so
Calculating sample sizes for cluster randomized trials: we can keep it simple and efficient !
van Breukelen, Gerard J.P.; Candel, Math J.J.M.
2012-01-01
Objective: Simple guidelines for efficient sample sizes in cluster randomized trials with unknown intraclass correlation and varying cluster sizes. Methods: A simple equation is given for the optimal number of clusters and sample size per cluster. Here, optimal means maximizing power for a given
Cluster randomized clinical trials in orthodontics: design, analysis and reporting issues.
Pandis, Nikolaos; Walsh, Tanya; Polychronopoulou, Argy; Eliades, Theodore
2013-10-01
Cluster randomized trials (CRTs) use as the unit of randomization clusters, which are usually defined as a collection of individuals sharing some common characteristics. Common examples of clusters include entire dental practices, hospitals, schools, school classes, villages, and towns. Additionally, several measurements (repeated measurements) taken on the same individual at different time points are also considered to be clusters. In dentistry, CRTs are applicable as patients may be treated as clusters containing several individual teeth. CRTs require certain methodological procedures during sample calculation, randomization, data analysis, and reporting, which are often ignored in dental research publications. In general, due to similarity of the observations within clusters, each individual within a cluster provides less information compared with an individual in a non-clustered trial. Therefore, clustered designs require larger sample sizes compared with non-clustered randomized designs, and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this article to highlight with relevant examples the important methodological characteristics of cluster randomized designs as they may be applied in orthodontics and to explain the problems that may arise if clustered observations are erroneously treated and analysed as independent (non-clustered).
Design of a cluster-randomized minority recruitment trial: RECRUIT.
Tilley, Barbara C; Mainous, Arch G; Smith, Daniel W; McKee, M Diane; Amorrortu, Rossybelle P; Alvidrez, Jennifer; Diaz, Vanessa; Ford, Marvella E; Fernandez, Maria E; Hauser, Robert A; Singer, Carlos; Landa, Veronica; Trevino, Aron; DeSantis, Stacia M; Zhang, Yefei; Daniels, Elvan; Tabor, Derrick; Vernon, Sally W
2017-06-01
Racial/ethnic minority groups remain underrepresented in clinical trials. Many strategies to increase minority recruitment focus on minority communities and emphasize common diseases such as hypertension. Scant literature focuses on minority recruitment to trials of less common conditions, often conducted in specialty clinics and dependent on physician referrals. We identified trust/mistrust of specialist physician investigators and institutions conducting medical research and consequent participant reluctance to participate in clinical trials as key-shared barriers across racial/ethnic groups. We developed a trust-based continuous quality improvement intervention to build trust between specialist physician investigators and community minority-serving physicians and ultimately potential trial participants. To avoid the inherent biases of non-randomized studies, we evaluated the intervention in the national Randomized Recruitment Intervention Trial (RECRUIT). This report presents the design of RECRUIT. Specialty clinic follow-up continues through April 2017. We hypothesized that specialist physician investigators and coordinators trained in the trust-based continuous quality improvement intervention would enroll a greater proportion of minority participants in their specialty clinics than specialist physician investigators in control specialty clinics. Specialty clinic was the unit of randomization. Using continuous quality improvement, the specialist physician investigators and coordinators tailored recruitment approaches to their specialty clinic characteristics and populations. Primary analyses were adjusted for clustering by specialty clinic within parent trial and matching covariates. RECRUIT was implemented in four multi-site clinical trials (parent trials) supported by three National Institutes of Health institutes and included 50 associated specialty clinics from these parent trials. Using current data, we have 88% power or greater to detect a 0.15 or
Making birthing safe for Pakistan women: a cluster randomized trial
Khan Muhammad
2012-07-01
Full Text Available Abstract Background Two out of three neonatal deaths occur in just 10 countries and Pakistan stands third among them. Maternal mortality is also high with most deaths occurring during labor, birth, and first few hours after birth. Enhanced access and utilization of skilled delivery and emergency obstetric care is the demonstrated strategy in reducing maternal and neonatal mortality. This trial aims to compare reduction in neonate mortality and utilization of available safe birthing and Emergency Obstetric and Neonatal Care services among pregnant mothers receiving ‘structured birth planning’, and/or ‘transport facilitation’ compared to routine care. Methods A pragmatic cluster randomized trial, with qualitative and economic studies, will be conducted in Jhang, Chiniot and Khanewal districts of Punjab, Pakistan, from February 2011 to May 2013. At least 29,295 pregnancies will be registered in the three arms, seven clusters per arm; 1 structured birth planning and travel facilitation, 2 structured birth planning, and 3 control arm. Trial will be conducted through the Lady Health Worker program. Main outcomes are difference in neonatal mortality and service utilization; maternal mortality being the secondary outcome. Cluster level analysis will be done according to intention-to-treat. Discussion A nationwide network of about 100,000 lady health workers is already involved in antenatal and postnatal care of pregnant women. They also act as “gatekeepers” for the child birthing services. This gate keeping role mainly includes counseling and referral for skill birth attendance and travel arrangements for emergency obstetric care (if required. The review of current arrangements and practices show that the care delivery process needs enhancement to include adequate information provision as well as informed “decision” making and planned “action” by the pregnant women. The proposed three-year research is to develop, through national
3D simulation of the Cluster-Cluster Aggregation model
Li, Chao; Xiong, Hailing
2014-12-01
We write a program to implement the Cluster-Cluster Aggregation (CCA) model with java programming language. By using the simulation program, the fractal aggregation growth process can be displayed dynamically in the form of a three-dimensional (3D) figure. Meanwhile, the related kinetics data of aggregation simulation can be also recorded dynamically. Compared to the traditional programs, the program has better real-time performance and is more helpful to observe the fractal growth process, which contributes to the scientific study in fractal aggregation. Besides, because of adopting java programming language, the program has very good cross-platform performance.
Spybrook, Jessaca; Hedges, Larry; Borenstein, Michael
2014-01-01
Research designs in which clusters are the unit of randomization are quite common in the social sciences. Given the multilevel nature of these studies, the power analyses for these studies are more complex than in a simple individually randomized trial. Tools are now available to help researchers conduct power analyses for cluster randomized…
Preventing diabetes in primary care: a feasibility cluster randomized trial.
Dawes, Diana; Ashe, Maureen; Campbell, Kristin; Cave, Douglas; Elley, C Raina; Kaczorowski, Janusz; Sohal, Parmjit; Ur, Ehud; Dawes, Martin
2015-04-01
To determine the feasibility of implementing a large-scale primary care-based diabetes prevention trial. A feasibility cluster randomized controlled trial was conducted in British Columbia, Canada, amongst adults with prediabetes using the Facilitated Lifestyle Intervention Prescription (FLIP) vs. usual care. FLIP included lifestyle advice, a pedometer, and telephone support from a lifestyle facilitator for 6 months. Indicators of feasibility included recruitment rates of family practices, participants and facilitators, as well as feasibility and retention rates in the FLIP program and study protocols. Six family practices participated; 59 patients were enrolled between October 2012 and March 2013. The trial protocol was acceptable to practices and participants and had a 95% participant retention rate over the 6 months (56/59). Adherence to the intervention was high (97%), with 34 of 35 patients continuing to receive telephone calls from the facilitator for 6 months. The mean cost of the intervention was C$144 per person. Compared with control, intervention participants significantly reduced weight by 3.2 kg (95% CI, 1.7 to 4.6); body mass index by 1.2 (95% CI, 0.7 to 1.7) and waist circumference by 3 cm (95% CI, 0.3 to 5.7). It is feasible to implement FLIP and to conduct a trial to assess effectiveness. A larger trial with longer follow up to assess progression to diabetes is warranted. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.
Manju, Md Abu; Candel, Math J J M; Berger, Martijn P F
2014-07-10
In this paper, the optimal sample sizes at the cluster and person levels for each of two treatment arms are obtained for cluster randomized trials where the cost-effectiveness of treatments on a continuous scale is studied. The optimal sample sizes maximize the efficiency or power for a given budget or minimize the budget for a given efficiency or power. Optimal sample sizes require information on the intra-cluster correlations (ICCs) for effects and costs, the correlations between costs and effects at individual and cluster levels, the ratio of the variance of effects translated into costs to the variance of the costs (the variance ratio), sampling and measuring costs, and the budget. When planning, a study information on the model parameters usually is not available. To overcome this local optimality problem, the current paper also presents maximin sample sizes. The maximin sample sizes turn out to be rather robust against misspecifying the correlation between costs and effects at the cluster and individual levels but may lose much efficiency when misspecifying the variance ratio. The robustness of the maximin sample sizes against misspecifying the ICCs depends on the variance ratio. The maximin sample sizes are robust under misspecification of the ICC for costs for realistic values of the variance ratio greater than one but not robust under misspecification of the ICC for effects. Finally, we show how to calculate optimal or maximin sample sizes that yield sufficient power for a test on the cost-effectiveness of an intervention.
Random Intercept and Random Slope 2-Level Multilevel Models
Rehan Ahmad Khan
2012-11-01
Full Text Available Random intercept model and random intercept & random slope model carrying two-levels of hierarchy in the population are presented and compared with the traditional regression approach. The impact of students’ satisfaction on their grade point average (GPA was explored with and without controlling teachers influence. The variation at level-1 can be controlled by introducing the higher levels of hierarchy in the model. The fanny movement of the fitted lines proves variation of student grades around teachers.
The XXZ Heisenberg model on random surfaces
Ambjørn, J., E-mail: ambjorn@nbi.dk [The Niels Bohr Institute, Copenhagen University, Blegdamsvej 17, DK-2100 Copenhagen (Denmark); Institute for Mathematics, Astrophysics and Particle Physics (IMAPP), Radbaud University Nijmegen, Heyendaalseweg 135, 6525 AJ, Nijmegen (Netherlands); Sedrakyan, A., E-mail: sedrak@nbi.dk [The Niels Bohr Institute, Copenhagen University, Blegdamsvej 17, DK-2100 Copenhagen (Denmark); Yerevan Physics Institute, Br. Alikhanyan str. 2, Yerevan-36 (Armenia)
2013-09-21
We consider integrable models, or in general any model defined by an R-matrix, on random surfaces, which are discretized using random Manhattan lattices. The set of random Manhattan lattices is defined as the set dual to the lattice random surfaces embedded on a regular d-dimensional lattice. They can also be associated with the random graphs of multiparticle scattering nodes. As an example we formulate a random matrix model where the partition function reproduces the annealed average of the XXZ Heisenberg model over all random Manhattan lattices. A technique is presented which reduces the random matrix integration in partition function to an integration over their eigenvalues.
The XXZ Heisenberg model on random surfaces
Ambjorn, J
2013-01-01
We consider integrable models, or in general any model defined by an $R$-matrix, on random surfaces, which are discretized using random Manhattan lattices. The set of random Manhattan lattices is defined as the set dual to the lattice random surfaces embedded on a regular d-dimensional lattice. They can also be associated with the random graphs of multiparticle scattering nodes. As an example we formulate a random matrix model where the partition function reproduces the annealed average of the XXZ Heisenberg model over all random Manhattan lattices. A technique is presented which reduces the random matrix integration in partition function to an integration over their eigenvalues.
Precise Asymptotics for Random Matrices and Random Growth Models
Zhong Gen SU
2008-01-01
The author considers the largest eigenvalues of random matrices from Gaussian unitary ensemble and Laguerre unitary ensemble, and the rightmost charge in certain random growth models.We obtain some precise asymptotics results, which are in a sense similar to the precise asymptotics for sums of independent random variables in the context of the law of large numbers and complete convergence. Our proofs depend heavily upon the upper and lower tail estimates for random matrices and random growth models. The Tracy-Widom distribution plays a central role as well.
Modelling the Milky Way's globular cluster system
Binney, James; Wong, Leong Khim
2017-05-01
We construct a model for the Galactic globular cluster system based on a realistic gravitational potential and a distribution function (DF) analytic in the action integrals. The DF comprises disc and halo components whose functional forms resemble those recently used to describe the stellar discs and stellar halo. We determine the posterior distribution of our model parameters using a Bayesian approach. This gives us an understanding of how well the globular cluster data constrain our model. The favoured parameter values of the disc and halo DFs are similar to values previously obtained from fits to the stellar disc and halo, although the cluster halo system shows clearer rotation than does the stellar halo. Our model reproduces the generic features of the globular cluster system, namely the density profile, the mean rotation velocity and the fraction of metal-rich clusters. However, the data indicate either incompatibility between catalogued cluster distances and current estimates of distance to the Galactic Centre, or failure to identify clusters behind the bulge. As the data for our Galaxy's components increase in volume and precision over the next few years, it will be rewarding to revisit the present analysis.
Effect of Random Clustering on Surface Damage Density Estimates
Matthews, M J; Feit, M D
2007-10-29
Identification and spatial registration of laser-induced damage relative to incident fluence profiles is often required to characterize the damage properties of laser optics near damage threshold. Of particular interest in inertial confinement laser systems are large aperture beam damage tests (>1cm{sup 2}) where the number of initiated damage sites for {phi}>14J/cm{sup 2} can approach 10{sup 5}-10{sup 6}, requiring automatic microscopy counting to locate and register individual damage sites. However, as was shown for the case of bacteria counting in biology decades ago, random overlapping or 'clumping' prevents accurate counting of Poisson-distributed objects at high densities, and must be accounted for if the underlying statistics are to be understood. In this work we analyze the effect of random clumping on damage initiation density estimates at fluences above damage threshold. The parameter {psi} = a{rho} = {rho}/{rho}{sub 0}, where a = 1/{rho}{sub 0} is the mean damage site area and {rho} is the mean number density, is used to characterize the onset of clumping, and approximations based on a simple model are used to derive an expression for clumped damage density vs. fluence and damage site size. The influence of the uncorrected {rho} vs. {phi} curve on damage initiation probability predictions is also discussed.
Yan, Donghui; Jordan, Michael I
2011-01-01
Inspired by Random Forests (RF) in the context of classification, we propose a new clustering ensemble method---Cluster Forests (CF). Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure $\\kappa$. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis shows that the $\\kappa$ criterion is shown to grow each local clustering in a desirable way---it is "noise-resistant." A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.
Angular momentum in cluster Spherical Collapse Model
Cupani, Guido; Mardirossian, Fabio
2011-01-01
Our new formulation of the Spherical Collapse Model (SCM-L) takes into account the presence of angular momentum associated with the motion of galaxy groups infalling towards the centre of galaxy clusters. The angular momentum is responsible for an additional term in the dynamical equation which is useful to describe the evolution of the clusters in the non-equilibrium region which is investigated in the present paper. Our SCM-L can be used to predict the profiles of several strategic dynamical quantities as the radial and tangential velocities of member galaxies, and the total cluster mass. A good understanding of the non-equilibrium region is important since it is the natural scenario where to study the infall in galaxy clusters and the accretion phenomena present in these objects. Our results corroborate previous estimates and are in very good agreement with the analysis of recent observations and of simulated clusters.
Spijker, A.; Wollersheim, H.C.H.; Teerenstra, S.; Graff, M.J.L.; Adang, E.M.M.; Verhey, F.; Vernooij-Dassen, M.J.F.J.
2011-01-01
OBJECTIVE: To evaluate the effectiveness of the Systematic Care Program for Dementia (SCPD) on patient institutionalization and to determine the predictors of institutionalization. DESIGN: Single-blind, multicenter, cluster-randomized, controlled trial. SETTING: Six community mental health services
Neumann, Charlotte G; Bwibo, Nimrod O; Jiang, Luohua; Weiss, Robert E
2013-01-01
.... There were three schools per group in this cluster randomized trial. Children in feeding group schools received school snacks of a local plant-based dish, githeri, with meat, milk or extra oil added...
Initialization of Markov Random Field Clustering of Large Remote Sensing Images
Tran, T.N.; Wehrens, H.R.M.J.; Hoekman, D.H.; Buydens, L.M.C.
2005-01-01
Markov random field (MRF) clustering, utilizing both spectral and spatial interpixel dependency information, often improves classification accuracy for remote sensing images, such as multichannel polarimetric synthetic aperture radar (SAR) images. However, it is heavily sensitive to initial conditio
Parabolic Anderson Model in a Dynamic Random Environment: Random Conductances
Erhard, D.; den Hollander, F.; Maillard, G.
2016-06-01
The parabolic Anderson model is defined as the partial differential equation ∂ u( x, t)/ ∂ t = κ Δ u( x, t) + ξ( x, t) u( x, t), x ∈ ℤ d , t ≥ 0, where κ ∈ [0, ∞) is the diffusion constant, Δ is the discrete Laplacian, and ξ is a dynamic random environment that drives the equation. The initial condition u( x, 0) = u 0( x), x ∈ ℤ d , is typically taken to be non-negative and bounded. The solution of the parabolic Anderson equation describes the evolution of a field of particles performing independent simple random walks with binary branching: particles jump at rate 2 d κ, split into two at rate ξ ∨ 0, and die at rate (- ξ) ∨ 0. In earlier work we looked at the Lyapunov exponents λ p(κ ) = limlimits _{tto ∞} 1/t log {E} ([u(0,t)]p)^{1/p}, quad p in {N} , qquad λ 0(κ ) = limlimits _{tto ∞} 1/2 log u(0,t). For the former we derived quantitative results on the κ-dependence for four choices of ξ : space-time white noise, independent simple random walks, the exclusion process and the voter model. For the latter we obtained qualitative results under certain space-time mixing conditions on ξ. In the present paper we investigate what happens when κΔ is replaced by Δ𝓚, where 𝓚 = {𝓚( x, y) : x, y ∈ ℤ d , x ˜ y} is a collection of random conductances between neighbouring sites replacing the constant conductances κ in the homogeneous model. We show that the associated annealed Lyapunov exponents λ p (𝓚), p ∈ ℕ, are given by the formula λ p({K} ) = {sup} {λ p(κ ) : κ in {Supp} ({K} )}, where, for a fixed realisation of 𝓚, Supp(𝓚) is the set of values taken by the 𝓚-field. We also show that for the associated quenched Lyapunov exponent λ 0(𝓚) this formula only provides a lower bound, and we conjecture that an upper bound holds when Supp(𝓚) is replaced by its convex hull. Our proof is valid for three classes of reversible ξ, and for all 𝓚
Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects.
Dreyhaupt, Jens; Mayer, Benjamin; Keis, Oliver; Öchsner, Wolfgang; Muche, Rainer
2017-01-01
An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called "cluster randomization"). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies. Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.
Enhancing Digital Book Clustering by LDAC Model
Wang, Lidong; Jie, Yuan
In Digital Library (DL) applications, digital book clustering is an important and urgent research task. However, it is difficult to conduct effectively because of the great length of digital books. To do the correct clustering for digital books, a novel method based on probabilistic topic model is proposed. Firstly, we build a topic model named LDAC. The main goal of LDAC topic modeling is to effectively extract topics from digital books. Subsequently, Gibbs sampling is applied for parameter inference. Once the model parameters are learned, each book is assigned to the cluster which maximizes the posterior probability. Experimental results demonstrate that our approach based on LDAC is able to achieve significant improvement as compared to the related methods.
Modeling the Blue Stragglers in Globular Clusters
Chatterjee, Sourav
2012-10-01
Blue stragglers {BS} have been extensively observed in Galactic globular clusters {GGC}. primarily with HST. Many theoretical studies have identified BS formation channels and it is understood that dynamics in GCs modifies formation and distribution of the BSs. Despite the wealth of observational data, comprehensive theoretical models including all relevant physical processes in dynamically evolving GCs do not exist. Our dynamical cluster modeling code, developed over the past decade, includes all relevant physical processes in a GC including two-body relaxation, strong scattering, physical collisions, and stellar-evolution {single and binary}. We can model GCs with realistic N and provide star-by-star models for GCs directly comparable with the observed data. This proposed study will create realistic GC models with initial conditions from a grid spanning a large range in the multidimensional parameter space including cluster mass, binary fraction, concentration, and Galactic position. Our numerical models combined with observational constraints from existing HST data will for the first time provide explanations for the observed trends in the BS populations in GGCs, the dominant formation channel for these BSs, typical dynamical ages of the BSs, and find detailed dynamical histories of the BSs in GGCs. These models will yield valuable insight on the correlations between the BS properties and a number of cluster dynamical properties {central density, binary fraction, and binary orbital properties} which will potentially help constrain a GC's past evolutionary history. As a bonus a large set of realistic theoretical GC models will be constructed.
Clustering Effects Within the Dinuclear Model
Adamian, Gurgen; Antonenko, Nikolai; Scheid, Werner
The clustering of two nuclei in a nuclear system creates configurations denoted in literature as nuclear molecular structures. A nuclear molecule or a dinuclear system (DNS) as named by Volkov consists of two touching nuclei (clusters) which keep their individuality. Such a system has two main degrees of freedom of collective motions which govern its dynamics: (i) the relative motion between the clusters leading to molecular resonances in the internuclear potential and to the decay of the dinuclear system (separation of the clusters) which is called quasifission since no compound system like in fission is first formed. (ii) the transfer of nucleons or light constituents between the two clusters of the dinuclear system leading to a special dynamics of the mass and charge asymmetries between the clusters in fusion and fission reactions. In this article we discuss the essential aspects of the diabatic internuclear potential used by the di-nuclear system concept and present applications to nuclear structure and reactions. We show applications of the dinuclear model to superdeformed and hyperdeformed bands. An extended discussion is given to the problems of fusion dynamics in the production of superheavy nuclei, to the quasifission process and to multi-nucleon transfer between nuclei. Also the binary and ternary fission processes are discussed within the scission-point model and the dinuclear system concept.
Clustering of Galaxies in Brane World Models
Hameeda, Mir; Ali, Ahmed Farag
2015-01-01
In this paper, we analyze the clustering of galaxies using a modified Newtonian potential. This modification of the Newtonian potential occurs due to the existence of extra dimensions in brane world models. We will analyze a system of galaxies interacting with each other through this modified Newtonian potential. The partition function for this system of galaxies will be calculated, and this partition function will be used to calculate the free energy of this system of galaxies. The entropy and the chemical potential for this system will also be calculated. We will derive an explicit expression for the clustering parameter for this system. This parameter will determine the behavior of this system, and we will be able to express various thermodynamic quantities using this clustering parameter. Thus, we will be able to explicitly analyze the effect that modifying the Newtonian potential can have on the clustering of galaxies.
Clustering of galaxies in brane world models
Hameeda, Mir; Faizal, Mir; Ali, Ahmed Farag
2016-04-01
In this paper, we analyze the clustering of galaxies using a modified Newtonian potential. This modification of the Newtonian potential occurs due to the existence of extra dimensions in brane world models. We will analyze a system of galaxies interacting with each other through this modified Newtonian potential. The partition function for this system of galaxies will be calculated, and this partition function will be used to calculate the free energy of this system of galaxies. The entropy and the chemical potential for this system will also be calculated. We will derive explicit expression for the clustering parameter for this system. This parameter will determine the behavior of this system, and we will be able to express various thermodynamic quantities using this clustering parameter. Thus, we will be able to explicitly analyze the effect that modifying the Newtonian potential can have on the clustering of galaxies. We also analyse the effect of extra dimensions on the two-point functions between galaxies.
A Mixed Effects Randomized Item Response Model
Fox, J.-P.; Wyrick, Cheryl
2008-01-01
The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear…
Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects
Dreyhaupt, Jens
2017-05-01
Full Text Available An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called “cluster randomization”. Compared with studies with individual randomization, studies with cluster randomization normally require (significantly larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies.Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.
Hox, Joop J.; Moerbeek, Mirjam; Kluytmans, Anouck; van de Schoot, Rens
2014-01-01
Cluster randomized trials assess the effect of an intervention that is carried out at the group or cluster level. Ajzen's theory of planned behavior is often used to model the effect of the intervention as an indirect effect mediated in turn by attitude, norms and behavioral intention. Structural
Wang, Rui; De Gruttola, Victor
2017-08-15
We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster-randomized trial evaluating the effect of an intervention to reduce the incidence of hospital-acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped-wedge designs and compare its performance with mixed-effect modeling and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Wu, Sheng; Crespi, Catherine M; Wong, Weng Kee
2012-09-01
The intraclass correlation coefficient (ICC) is a fundamental parameter of interest in cluster randomized trials as it can greatly affect statistical power. We compare common methods of estimating the ICC in cluster randomized trials with binary outcomes, with a specific focus on their application to community-based cancer prevention trials with primary outcome of self-reported cancer screening. Using three real data sets from cancer screening intervention trials with different numbers and types of clusters and cluster sizes, we obtained point estimates and 95% confidence intervals for the ICC using five methods: the analysis of variance estimator, the Fleiss-Cuzick estimator, the Pearson estimator, an estimator based on generalized estimating equations and an estimator from a random intercept logistic regression model. We compared estimates of the ICC for the overall sample and by study condition. Our results show that ICC estimates from different methods can be quite different, although confidence intervals generally overlap. The ICC varied substantially by study condition in two studies, suggesting that the common practice of assuming a common ICC across all clusters in the trial is questionable. A simulation study confirmed pitfalls of erroneously assuming a common ICC. Investigators should consider using sample size and analysis methods that allow the ICC to vary by study condition.
Handlos, Line Neerup; Chakraborty, Hrishikesh; Sen, Pranab Kumar
2009-01-01
To summarize and evaluate all publications including cluster-randomized trials used for maternal and child health research in developing countries during the last 10 years. METHODS: All cluster-randomized trials published between 1998 and 2008 were reviewed, and those that met our criteria...... for inclusion were evaluated further. The criteria for inclusion were that the trial should have been conducted in maternal and child health care in a developing country and that the conclusions should have been made on an individual level. Methods of accounting for clustering in design and analysis were......, and the trials generally improved in quality. CONCLUSIONS: Shortcomings exist in the sample-size calculations and in the analysis of cluster-randomized trials conducted during maternal and child health research in developing countries. Even though there has been improvement over time, further progress in the way...
Handlos, Line Neerup; Chakraborty, Hrishikesh; Sen, Pranab Kumar
2009-01-01
To summarize and evaluate all publications including cluster-randomized trials used for maternal and child health research in developing countries during the last 10 years. METHODS: All cluster-randomized trials published between 1998 and 2008 were reviewed, and those that met our criteria...... for inclusion were evaluated further. The criteria for inclusion were that the trial should have been conducted in maternal and child health care in a developing country and that the conclusions should have been made on an individual level. Methods of accounting for clustering in design and analysis were......, and the trials generally improved in quality. CONCLUSIONS: Shortcomings exist in the sample-size calculations and in the analysis of cluster-randomized trials conducted during maternal and child health research in developing countries. Even though there has been improvement over time, further progress in the way...
CNEM: Cluster Based Network Evolution Model
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
Climate Modeling with a Linux Cluster
Renold, M.; Beyerle, U.; Raible, C. C.; Knutti, R.; Stocker, T. F.; Craig, T.
2004-08-01
Until recently, computationally intensive calculations in many scientific disciplines have been limited to institutions which have access to supercomputing centers. Today, the computing power of PC processors permits the assembly of inexpensive PC clusters that nearly approach the power of supercomputers. Moreover, the combination of inexpensive network cards and Open Source software provides an easy linking of standard computer equipment to enlarge such clusters. Universities and other institutions have taken this opportunity and built their own mini-supercomputers on site. Computing power is a particular issue for the climate modeling and impacts community. The purpose of this article is to make available a Linux cluster version of the Community Climate System Model developed by the National Center for Atmospheric Research (NCAR; http://www.cgd.ucar.edu/csm).
Dynamic exponents for potts model cluster algorithms
Coddington, Paul D.; Baillie, Clive F.
We have studied the Swendsen-Wang and Wolff cluster update algorithms for the Ising model in 2, 3 and 4 dimensions. The data indicate simple relations between the specific heat and the Wolff autocorrelations, and between the magnetization and the Swendsen-Wang autocorrelations. This implies that the dynamic critical exponents are related to the static exponents of the Ising model. We also investigate the possibility of similar relationships for the Q-state Potts model.
Evaluating Mixture Modeling for Clustering: Recommendations and Cautions
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,…
Headache cessation by an educational intervention in grammar schools: a cluster randomized trial.
Albers, L; Heinen, F; Landgraf, M; Straube, A; Blum, B; Filippopulos, F; Lehmann, S; Mansmann, U; Berger, U; Akboga, Y; von Kries, R
2015-02-01
Headache is a common health problem in adolescents. There are a number of risk factors for headache in adolescents that are amenable to intervention. The aim of the study was to assess the effectiveness of a low-level headache prevention programme in the classroom setting to prevent these risk factors. In all, 1674 students in 8th-10th grade at 12 grammar schools in greater Munich, Germany, were cluster randomized into intervention and control groups. A standardized 60-min prevention lesson focusing on preventable risk factors for headache (physical inactivity, coffee consumption, alcohol consumption and smoking) and providing instructions on stress management and neck and shoulder muscle relaxation exercises was given in a classroom setting. Seven months later, students were reassessed. The main outcome parameter was headache cessation. Logistic regression models with random effects for cluster and adjustment for baseline risk factors were calculated. Nine hundred students (intervention group N = 450, control group N = 450) with headache at baseline and complete data for headache and confounders were included in the analysis. Headache cessation was observed in 9.78% of the control group compared with 16.22% in the intervention group (number needed to treat = 16). Accounting for cluster effects and confounders, the probability of headache cessation in the intervention group was 1.77 (95% confidence interval = [1.08; 2.90]) higher than in the control group. The effect was most pronounced in adolescents with tension-type headache: odds ratio = 2.11 (95% confidence interval = [1.15; 3.80]). Our study demonstrates the effectiveness of a one-time, classroom-based headache prevention programme. © 2014 EAN.
The Chronic CARe for diAbeTes study (CARAT: a cluster randomized controlled trial
Birnbaum Beatrice
2010-06-01
Full Text Available Abstract Background Diabetes is a major challenge for the health care system and especially for the primary care provider. The Chronic Care Model represents an evidence-based framework for the care for chronically ill. An increasing number of studies showed that implementing elements of the Chronic Care Model improves patient relevant outcomes and process parameters. However, most of these findings have been performed in settings different from the Swiss health care system which is dominated by single handed practices. Methods/Design CARAT is a cluster randomized controlled trial with general practitioners as the unit of randomization (trial registration: ISRCTN05947538. The study challenges the hypothesis that implementing several elements of the Chronic Care Model via a specially trained practice nurse improves the HbA1c level of diabetes type II patients significantly after one year (primary outcome. Furthermore, we assume that the intervention increases the proportion of patients who achieve the recommended targets regarding blood pressure ( Discussion This study challenges the hypothesis that the Chronic Care Model can be easily implemented by a practice nurse focused approach. If our results will confirm this hypothesis the suggestion arises whether this approach should be implemented in other chronic diseases and multimorbid patients and how to redesign care in Switzerland.
Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields
Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.
1994-01-01
To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.
Managing Clustered Data Using Hierarchical Linear Modeling
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…
Managing Clustered Data Using Hierarchical Linear Modeling
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…
Generalization of Random Intercept Multilevel Models
Rehan Ahmad Khan
2013-10-01
Full Text Available The concept of random intercept models in a multilevel model developed by Goldstein (1986 has been extended for k-levels. The random variation in intercepts at individual level is marginally split into components by incorporating higher levels of hierarchy in the single level model. So, one can control the random variation in intercepts by incorporating the higher levels in the model.
Towards Realistic Modeling of Massive Star Clusters
Gnedin, O.; Li, H.
2016-06-01
Cosmological simulations of galaxy formation are rapidly advancing towards smaller scales. Current models can now resolve giant molecular clouds in galaxies and predict basic properties of star clusters forming within them. I will describe new theoretical simulations of the formation of the Milky Way throughout cosmic time, with the adaptive mesh refinement code ART. However, many challenges - physical and numerical - still remain. I will discuss how observations of massive star clusters and star forming regions can help us overcome some of them. Video of the talk is available at https://goo.gl/ZoZOfX
Survival analysis of the CEAwatch multicentre clustered randomized trial
Verberne, C. J.; Zhan, Z.; van den Heuvel, E. R.; Oppers, F.; de Jong, A. M.; Grossmann, Irene; Klaase, J. M.; de Bock, G. H.; Wiggers, T.
2017-01-01
Background: The CEAwatch randomized trial showed that follow-up with intensive carcinoembryonic antigen (CEA) monitoring (CEAwatch protocol) was better than care as usual (CAU) for early postoperative detection of colorectal cancer recurrence. The aim of this study was to calculate overall survival
Gravothermal Star Clusters - Theory and Computer Modelling
Spurzem, Rainer
2010-11-01
In the George Darwin lecture, delivered to the British Royal Astronomical Society in 1960 by Viktor A. Ambartsumian he wrote on the evolution of stellar systems that it can be described by the "dynamic evolution of a gravitating gas" complemented by "a statistical description of the changes in the physical states of stars". This talk will show how this physical concept has inspired theoretical modeling of star clusters in the following decades up to the present day. The application of principles of thermodynamics shows, as Ambartsumian argued in his 1960 lecture, that there is no stable state of equilibrium of a gravitating star cluster. The trend to local thermodynamic equilibrium is always disturbed by escaping stars (Ambartsumian), as well as by gravothermal and gravogyro instabilities, as it was detected later. Here the state-of-the-art of modeling the evolution of dense stellar systems based on principles of thermodynamics and statistical mechanics (Fokker-Planck approximation) will be reviewed. Recent progress including rotation and internal correlations (primordial binaries) is presented. The models have also very successfully been used to study dense star clusters around massive black holes in galactic nuclei and even (in a few cases) relativistic supermassive dense objects in centres of galaxies (here again briefly touching one of the many research fields of V.A. Ambartsumian). For the modern present time of high-speed supercomputing, where we are tackling direct N-body simulations of star clusters, we will show that such direct modeling supports and proves the concept of the statistical models based on the Fokker-Planck theory, and that both theoretical concepts and direct computer simulations are necessary to support each other and make scientific progress in the study of star cluster evolution.
Modelling nano-clusters and nucleation.
Catlow, C Richard A; Bromley, Stefan T; Hamad, Said; Mora-Fonz, Miguel; Sokol, Alexey A; Woodley, Scott M
2010-01-28
We review the growing role of computational techniques in modelling the structures and properties of nano-particulate oxides and sulphides. We describe the main methods employed, including those based on both electronic structure and interatomic potential approaches. Particular attention is paid to the techniques used in searching for global minima in the energy landscape defined by the nano-particle cluster. We summarise applications to the widely studied ZnO and ZnS systems, to silica nanochemistry and to group IV oxides including TiO(2). We also consider the special case of silica cluster chemistry in solution and its importance in understanding the hydrothermal synthesis of microporous materials. The work summarised, together with related experimental studies, demonstrates a rich and varied nano-cluster chemistry for these materials.
Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao
2015-01-01
Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383
Nan Lin
Full Text Available Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis.
Critical Interfaces in the Random-Bond Potts Model
Jacobsen, Jesper L.; Le Doussal, Pierre; Picco, Marco; Santachiara, Raoul; Wiese, Kay Jörg
2009-02-01
We study geometrical properties of interfaces in the random-temperature q-states Potts model as an example of a conformal field theory weakly perturbed by quenched disorder. Using conformal perturbation theory in q-2 we compute the fractal dimension of Fortuin-Kasteleyn (FK) domain walls. We also compute it numerically both via the Wolff cluster algorithm for q=3 and via transfer-matrix evaluations. We also obtain numerical results for the fractal dimension of spin clusters interfaces for q=3. These are found numerically consistent with the duality κspinκFK=16 as expressed in putative SLE parameters.
Modeling blue stragglers in young clusters
Pin Lu; Li-Cai Deng; Xiao-Bin Zhang
2011-01-01
A grid of binary evolution models are calculated for the study of a blue straggler (BS) population in intermediate age (log Age =7.85 - 8.95) star clusters.The BS formation via mass transfer and merging is studied systematically using our models.Both Case A and B close binary evolutionary tracks are calculated for a large range of parameters.The results show that BSs formed via Case B are generally bluer and even more luminous than those produced by Case A.Furthermore,the larger range in orbital separations of Case B models provides a probability of producing more BSs than in Case A.Based on the grid of models,several Monte-Carlo simulations of BS populations in the clusters in the age range are carried out.The results show that BSs formed via different channels populate different areas in the color magnitude diagram (CMD).The locations of BSs in CMD for a number of clusters are compared to our simulations as well.In order to investigate the influence of mass transfer efficiency in the models and simulations,a set of models is also calculated by implementing a constant mass transfer efficiency,β =0.5,during Roche lobe overflow (Case A binary evolution excluded).The result shows BSs can be formed via mass transfer at any given age in both cases.However,the distributions of the BS populations on CMD are different.
Complex Contagions and hybrid phase transitions in unclustered and clustered random networks
Miller, Joel C
2015-01-01
A complex contagion is an infectious process in which an individual may require multiple transmissions. We typically think of individuals beginning inactive and becoming active once they are contacted by sufficient numbers of active partners. These have been studied in a number of contexts, but the analytic models for dynamic spread of complex contagions are typically complex. Here we study the dynamics of a generalized Watts Threshold Model (gWTM). We first show that a wide range of other processes can be thought of as a special case of this gWTM. Then we adapt an "edge-based compartmental modeling" approach used for infectious diseases in networks to develop and analyze analytic models for the dynamics the gWTM in configuration model and a class of random clustered (triangle-based) networks. The resulting model is relatively simple and compact, and we use this model to gain insights into the dynamics. Under some conditions a cascade can happen with an arbitrarily small initial proportion active, and we deri...
Nonlinear random resistor diode networks and fractal dimensions of directed percolation clusters.
Stenull, O; Janssen, H K
2001-07-01
We study nonlinear random resistor diode networks at the transition from the nonpercolating to the directed percolating phase. The resistor-like bonds and the diode-like bonds under forward bias voltage obey a generalized Ohm's law V approximately I(r). Based on general grounds such as symmetries and relevance we develop a field theoretic model. We focus on the average two-port resistance, which is governed at the transition by the resistance exponent straight phi(r). By employing renormalization group methods we calculate straight phi(r) for arbitrary r to one-loop order. Then we address the fractal dimensions characterizing directed percolation clusters. Via considering distinct values of the nonlinearity r, we determine the dimension of the red bonds, the chemical path, and the backbone to two-loop order.
Taveras, Elsie M; Marshall, Richard; Kleinman, Ken P; Gillman, Matthew W; Hacker, Karen; Horan, Christine M; Smith, Renata L; Price, Sarah; Sharifi, Mona; Rifas-Shiman, Sheryl L; Simon, Steven R
2015-06-01
Evidence of effective treatment of childhood obesity in primary care settings is limited. To examine the extent to which computerized clinical decision support (CDS) delivered to pediatric clinicians at the point of care of obese children, with or without individualized family coaching, improved body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and quality of care. We conducted a cluster-randomized, 3-arm clinical trial. We enrolled 549 children aged 6 to 12 years with a BMI at the 95% percentile or higher from 14 primary care practices in Massachusetts from October 1, 2011, through June 30, 2012. Patients were followed up for 1 year (last follow-up, August 30, 2013). In intent-to-treat analyses, we used linear mixed-effects models to account for clustering by practice and within each person. In 5 practices randomized to CDS, pediatric clinicians received decision support on obesity management, and patients and their families received an intervention for self-guided behavior change. In 5 practices randomized to CDS + coaching, decision support was augmented by individualized family coaching. The remaining 4 practices were randomized to usual care. Smaller age-associated change in BMI and the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures for obesity during the 1-year follow-up. At baseline, mean (SD) patient age and BMI were 9.8 (1.9) years and 25.8 (4.3), respectively. At 1 year, we obtained BMI from 518 children (94.4%) and HEDIS measures from 491 visits (89.4%). The 3 randomization arms had different effects on BMI over time (P = .04). Compared with the usual care arm, BMI increased less in children in the CDS arm during 1 year (-0.51 [95% CI, -0.91 to -0.11]). The CDS + coaching arm had a smaller magnitude of effect (-0.34 [95% CI, -0.75 to 0.07]). We found substantially greater achievement of childhood obesity HEDIS measures in the CDS arm (adjusted odds ratio, 2.28 [95% CI, 1
Finding Non-overlapping Clusters for Generalized Inference Over Graphical Models
Vats, Divyanshu
2011-01-01
Graphical models compactly capture stochastic dependencies amongst a collection of random variables using a graph. Inference over graphical models corresponds to finding marginal probability distributions given joint probability distributions. Several inference algorithms rely on iterative message passing between nodes. Although these algorithms can be generalized so that the message passing occurs between clusters of nodes, there are limited frameworks for finding such clusters. Moreover, current frameworks rely on finding clusters that are overlapping. This increases the computational complexity of finding clusters since the edges over a graph with overlapping clusters must be chosen carefully to avoid inconsistencies in the marginal distribution computations. In this paper, we propose a framework for finding clusters in a graph for generalized inference so that the clusters are \\emph{non-overlapping}. Given an undirected graph, we first derive a linear time algorithm for constructing a block-tree, a tree-s...
The parabolic Anderson model random walk in random potential
König, Wolfgang
2016-01-01
This is a comprehensive survey on the research on the parabolic Anderson model – the heat equation with random potential or the random walk in random potential – of the years 1990 – 2015. The investigation of this model requires a combination of tools from probability (large deviations, extreme-value theory, e.g.) and analysis (spectral theory for the Laplace operator with potential, variational analysis, e.g.). We explain the background, the applications, the questions and the connections with other models and formulate the most relevant results on the long-time behavior of the solution, like quenched and annealed asymptotics for the total mass, intermittency, confinement and concentration properties and mass flow. Furthermore, we explain the most successful proof methods and give a list of open research problems. Proofs are not detailed, but concisely outlined and commented; the formulations of some theorems are slightly simplified for better comprehension.
Ionisation clusters at DNA level: experimental modelling
Pszona, S.; Kula, J
2002-07-01
The importance of initial clustered damage to DNA is a hypothesis, which has to be approached also from physical modelling of the initial products of single charged particle interaction with DNA. A new tool for such studies, presented here, is based on modelling of the ionisation patterns resulting from a single charged particle crossing a nitrogen cavity of nanometre size. The nanometre size sites equivalent in unit density to DNA and nucleosome, have been modelled in a device, called a Jet Counter, consisting of a pulse operated valve which inject nitrogen in the form of an expansion jet into a interaction chamber. The distributions of the number of ions in a cluster created by a single alpha particle of 4.6 MeV along 0.15 nm to 13 nm size in nitrogen have been measured. A new descriptor of radiation action at DNA level is proposed. (author)
NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel
2017-08-01
Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.
Random Effect and Latent Variable Model Selection
Dunson, David B
2008-01-01
Presents various methods for accommodating model uncertainty in random effects and latent variable models. This book focuses on frequentist likelihood ratio and score tests for zero variance components. It also focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models
Infinite Random Graphs as Statistical Mechanical Models
Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria
2011-01-01
We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe...
Pieper, M.J.C.; Francke, A.L.; Steen, J.T. van der; Scherder, E.J.A.; Twisk, J.W.R.; Kovach, C.R.; Achterberg, W.P.
2016-01-01
Objectives: To assess whether implementation of a stepwise multicomponent intervention (STA OP!) is effective in reducing challenging behavior and depression in nursing home residents with advanced dementia. Design: Cluster randomized controlled trial. Setting: Twenty-one clusters (single
Pieper, M.J.; Francke, A.L.; Steen, J.T. van der; Scherder, E.J.; Twisk, J.W.; Kovach, C.R.; Achterberg, W.P.
2016-01-01
OBJECTIVES: To assess whether implementation of a stepwise multicomponent intervention (STA OP!) is effective in reducing challenging behavior and depression in nursing home residents with advanced dementia. DESIGN: Cluster randomized controlled trial. SETTING: Twenty-one clusters (single
Improving antibiotic prescribing in primary care: a cluster-randomized controlled trial
Teixeira Rodrigues, António; Roque,Fátima; Soares, Sara; Figueiras, Adolfo; Herdeiro, Maria Teresa
2016-01-01
Aiming to improve antibiotic prescribing and to diminish the misuse of antibiotics, an educational intervention was performed targeting physicians’ attitudes, knowledge and perceptions about antibiotic prescribing and antimicrobial resistances. Methods The educational intervention was developed in the Centre Health Region of Portugal, with a sample size of 1168 primary care physicians. Clusters were randomly selected as control (4 clusters, 35 primary care facilities, n=862) and interv...
When is informed consent required in cluster randomized trials in health research?
Boruch Robert
2011-09-01
Full Text Available Abstract This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs in health research. In the introductory paper in this series, we set out six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation. This paper addresses the second of the questions posed, namely, from whom, when, and how must informed consent be obtained in CRTs in health research? The ethical principle of respect for persons implies that researchers are generally obligated to obtain the informed consent of research subjects. Aspects of CRT design, including cluster randomization, cluster level interventions, and cluster size, present challenges to obtaining informed consent. Here we address five questions related to consent and CRTs: How can a study proceed if informed consent is not possible? Is consent to randomization always required? What information must be disclosed to potential subjects if their cluster has already been randomized? Is passive consent a valid substitute for informed consent? Do health professionals have a moral obligation to participate as subjects in CRTs designed to improve professional practice? We set out a framework based on the moral foundations of informed consent and international regulatory provisions to address each of these questions. First, when informed consent is not possible, a study may proceed if a research ethics committee is satisfied that conditions for a waiver of consent are satisfied. Second, informed consent to randomization may not be required if it is not possible to approach subjects at the time of randomization. Third, when potential subjects are approached after cluster randomization, they must be provided with a detailed description of the interventions in the trial arm to which their cluster has been randomized; detailed information on interventions in other trial arms need not be provided. Fourth, while passive consent may serve a
Clustering statistics, roughness feedbacks, and randomness in experimental step-pool morphodynamics
Johnson, Joel P. L.
2017-04-01
Step pools are a common bed morphology in boulder-rich gravel streams, but predicting how mountainous landscapes will respond to environmental perturbations such as climate-related hydrological changes requires a better understanding of channel morphodynamics and factors that influence bed stability. Flume experiments exploring bed stabilization demonstrate feedbacks among surface roughness, coarse grain clustering, and surface grain size. Clustering is quantified by using a novel normalization of Ripley's K statistic designed for use in power law functions. At 95% confidence, many but not all beds stabilized with coarse grains becoming more clustered than complete spatial randomness. The clustering statistic predicts hydraulic roughness better than D84 does (the diameter at which 84% of grains are smaller), suggesting that the spatial organization of the bed can be a stronger control than grain size on flow hydraulics. Initial conditions affect the degree of clustering at stability, indicating sensitivity to history.
Magnetic susceptibilities of cluster-hierarchical models
McKay, Susan R.; Berker, A. Nihat
1984-02-01
The exact magnetic susceptibilities of hierarchical models are calculated near and away from criticality, in both the ordered and disordered phases. The mechanism and phenomenology are discussed for models with susceptibilities that are physically sensible, e.g., nondivergent away from criticality. Such models are found based upon the Niemeijer-van Leeuwen cluster renormalization. A recursion-matrix method is presented for the renormalization-group evaluation of response functions. Diagonalization of this matrix at fixed points provides simple criteria for well-behaved densities and response functions.
DUAL RANDOM MODEL OF INCREASING ANNUITY
HeWenjiong; ZhangYi
2001-01-01
The dual random models about the life insurance and social pension insurance have received considerable attention in the recent articles on actuarial theory and applications. This paper discusses a general kind of increasing annuity based on its force of interest accumulationfunction as a general random process. The dual random model of the present value of the benefits of the increasing annuity has been set, and their moments have been calculated under certainconditions.
Reich NG; Milstone AM
2013-01-01
Nicholas G Reich,1 Aaron M Milstone2,3 1Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, UMass-Amherst, Amherst, MA, USA; 2Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; 3Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA Abstract: While individually randomized trials have long provided the gold standard of clinical evidence, the use of cluster-randomized tr...
Scale-free random graphs and Potts model
D-S Lee; K-I Goh; B Kahng; D Kim
2005-06-01
We introduce a simple algorithm that constructs scale-free random graphs efficiently: each vertex has a prescribed weight − (0 < < 1) and an edge can connect vertices and with rate . Corresponding equilibrium ensemble is identified and the problem is solved by the → 1 limit of the -state Potts model with inhomogeneous interactions for all pairs of spins. The number of loops as well as the giant cluster size and the mean cluster size are obtained in the thermodynamic limit as a function of the edge density. Various critical exponents associated with the percolation transition are also obtained together with finite-size scaling forms. The process of forming the giant cluster is qualitatively different between the cases of > 3 and 2 < < 3, where = 1 + -1 is the degree distribution exponent. While for the former, the giant cluster forms abruptly at the percolation transition, for the latter, however, the formation of the giant cluster is gradual and the mean cluster size for finite shows double peaks.
Cluster infall in the concordance LCDM model
Pivato, M C; Lambas, D G; Pivato, Maximiliano C.; Padilla, Nelson D.; Lambas, Diego G.
2005-01-01
We perform statistical analyses of the infall of dark-matter onto clusters in numerical simulations within the concordance LCDM model. By studying the infall profile around clusters of different mass, we find a linear relation between the maximum infall velocity and mass which reach 900km/s for the most massive groups. The maximum infall velocity and the group mass follow a suitable power law fit of the form, V_{inf}^{max} = (M/m_0)^{gamma}. By comparing the measured infall velocity to the linear infall model with an exponential cutoff introduced by Croft et al., we find that the best agreement is obtained for a critical overdensity delta_c = 45. We study the dependence of the direction of infall with respect to the cluster centres, and find that in the case of massive groups, the maximum alignment occurs at scales r ~ 6Mpc/h. We obtain a logarithmic power-law relation between the average infall angle and the group mass. We also study the dependence of the results on the local dark-matter density, finding a r...
RRW: repeated random walks on genome-scale protein networks for local cluster discovery
Can Tolga
2009-09-01
Full Text Available Abstract Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL, and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.
Ahn, Chul; Hu, Fan; Skinner, Celette Sugg; Ahn, Daniel
2009-07-01
In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability (kappa) and the intracluster correlation coefficient (rho) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case (kappa=1.0), i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group (k*) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group (k*) is at least 20 and the imbalance parameter (kappa) is at least 0.8. Empirical powers were close to the nominal level when (rho or =0.8, and k*=10) or (rho< or =0.02, kappa=0.8, and k*=20).
A pilot cluster randomized controlled trial of structured goal-setting following stroke.
Taylor, William J; Brown, Melanie; William, Levack; McPherson, Kathryn M; Reed, Kirk; Dean, Sarah G; Weatherall, Mark
2012-04-01
To determine the feasibility, the cluster design effect and the variance and minimal clinical importance difference in the primary outcome in a pilot study of a structured approach to goal-setting. A cluster randomized controlled trial. Inpatient rehabilitation facilities. People who were admitted to inpatient rehabilitation following stroke who had sufficient cognition to engage in structured goal-setting and complete the primary outcome measure. Structured goal elicitation using the Canadian Occupational Performance Measure. Quality of life at 12 weeks using the Schedule for Individualised Quality of Life (SEIQOL-DW), Functional Independence Measure, Short Form 36 and Patient Perception of Rehabilitation (measuring satisfaction with rehabilitation). Assessors were blinded to the intervention. Four rehabilitation services and 41 patients were randomized. We found high values of the intraclass correlation for the outcome measures (ranging from 0.03 to 0.40) and high variance of the SEIQOL-DW (SD 19.6) in relation to the minimally importance difference of 2.1, leading to impractically large sample size requirements for a cluster randomized design. A cluster randomized design is not a practical means of avoiding contamination effects in studies of inpatient rehabilitation goal-setting. Other techniques for coping with contamination effects are necessary.
The Effectiveness of Healthy Start Home Visit Program: Cluster Randomized Controlled Trial
Leung, Cynthia; Tsang, Sandra; Heung, Kitty
2015-01-01
Purpose: The study reported the effectiveness of a home visit program for disadvantaged Chinese parents with preschool children, using cluster randomized controlled trial design. Method: Participants included 191 parents and their children from 24 preschools, with 84 dyads (12 preschools) in the intervention group and 107 dyads (12 preschools) in…
Efficacy of a Universal Parent Training Program (HOPE-20): Cluster Randomized Controlled Trial
Leung, Cynthia; Tsang, Sandra; Kwan, H. W.
2017-01-01
Objective: This study examined the efficacy of Hands-On Parent Empowerment-20 (HOPE-20) program. Methods: Eligible participants were parents residing in Hong Kong with target children aged 2 years attending nursery schools. Cluster randomized control trial was adopted, with 10 schools (110 participants) assigned to intervention group and 8 schools…
Standardized Effect Size Measures for Mediation Analysis in Cluster-Randomized Trials
Stapleton, Laura M.; Pituch, Keenan A.; Dion, Eric
2015-01-01
This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the…
Improving comfort in people with dementia and pneumonia: a cluster randomized trial
Maaden, T. van der; Vet, H.C. de; Achterberg, W.P.; Boersma, F.; Schols, J.M.; Mehr, D.R.; Galindo-Garre, F.; Hertogh, C.M.; Koopmans, R.T.C.M.; Steen, J.T. van der
2016-01-01
BACKGROUND: Pneumonia in people with dementia has been associated with severe discomfort. We sought to assess the effectiveness of a practice guideline for optimal symptom relief for nursing home residents with dementia and pneumonia. METHODS: A single-blind, multicenter, cluster randomized controll
Improving comfort in people with dementia and pneumonia : a cluster randomized trial
van der Maaden, Tessa; de Vet, Henrica C. W.; Achterberg, Wilco P.; Boersma, Froukje; Schols, Jos M. G. A.; Mehr, David R.; Galindo-Garre, Francisca; Hertogh, Cees M. P. M.; Koopmans, Raymond T. C. M.; van der Steen, Jenny T.
2016-01-01
Background: Pneumonia in people with dementia has been associated with severe discomfort. We sought to assess the effectiveness of a practice guideline for optimal symptom relief for nursing home residents with dementia and pneumonia. Methods: A single-blind, multicenter, cluster randomized controll
Dregan, Alex; van Staa, Tjeerd P; McDermott, Lisa; McCann, Gerard; Ashworth, Mark; Charlton, Judith; Wolfe, Charles D A; Rudd, Anthony; Yardley, Lucy; Gulliford, Martin C
BACKGROUND AND PURPOSE: The aim of this study was to evaluate whether the remote introduction of electronic decision support tools into family practices improves risk factor control after first stroke. This study also aimed to develop methods to implement cluster randomized trials in stroke using
Muskens, Esther; Eveleigh, Rhona; Lucassen, Peter; van Weel, Chris; Spijker, Jan; Verhaak, Peter; Speckens, Anne; Voshaar, Richard Oude
2013-01-01
Background: Inappropriate use of antidepressants (AD), defined as either continuation in the absence of a proper indication or continuation despite the lack of therapeutic efficacy, applies to approximately half of all long term AD users. Methods/design: We have designed a cluster randomized control
Hierarchical modeling of cluster size in wildlife surveys
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).
3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS
C. Zhang
2016-10-01
Full Text Available 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.
A Cluster-Based Random Key Revocation Protocol for Wireless Sensor Networks
Yi Jiang; Hao-Shan Shi
2008-01-01
In recent years, several random key pre distribution schemes have been proposed to bootstrap keys for encryption, but the problem of key and node revocation has received relatively little attention. In this paper, based on a random key pre-distribution scheme using clustering, we present a novel random key revocation protocol, which is suitable for large scale networks greatly and removes compromised information efficiently. The revocation protocol can guarantee network security by using less memory consumption and communication load, and combined by centralized and distributed revocation, having virtues of timeliness and veracity for revocation at the same time.
Testing a workplace physical activity intervention: a cluster randomized controlled trial
Jackson Cath
2011-04-01
Full Text Available Abstract Background Increased physical activity levels benefit both an individuals' health and productivity at work. The purpose of the current study was to explore the impact and cost-effectiveness of a workplace physical activity intervention designed to increase physical activity levels. Methods A total of 1260 participants from 44 UK worksites (based within 5 organizations were recruited to a cluster randomized controlled trial with worksites randomly allocated to an intervention or control condition. Measurement of physical activity and other variables occurred at baseline, and at 0 months, 3 months and 9 months post-intervention. Health outcomes were measured during a 30 minute health check conducted in worksites at baseline and 9 months post intervention. The intervention consisted of a 3 month tool-kit of activities targeting components of the Theory of Planned Behavior, delivered in-house by nominated facilitators. Self-reported physical activity (measured using the IPAQ short-form and health outcomes were assessed. Results and discussion Multilevel modelling found no significant effect of the intervention on MET minutes of activity (from the IPAQ at any of the follow-up time points controlling for baseline activity. However, the intervention did significantly reduce systolic blood pressure (B = -1.79 mm/Hg and resting heart rate (B = -2.08 beats and significantly increased body mass index (B = .18 units compared to control. The intervention was found not to be cost-effective, however the substantial variability round this estimate suggested that further research is warranted. Conclusions The current study found mixed support for this worksite physical activity intervention. The paper discusses some of the tensions involved in conducting rigorous evaluations of large-scale randomized controlled trials in real-world settings. Trial registration Current controlled trials ISRCTN08807396
Random Walk Smooth Transition Autoregressive Models
2004-01-01
This paper extends the family of smooth transition autoregressive (STAR) models by proposing a specification in which the autoregressive parameters follow random walks. The random walks in the parameters can capture structural change within a regime switching framework, but in contrast to the time varying STAR (TV-STAR) speciifcation recently introduced by Lundbergh et al (2003), structural change in our random walk STAR (RW-STAR) setting follows a stochastic process rather than a determinist...
A randomized algorithm for two-cluster partition of a set of vectors
Kel'manov, A. V.; Khandeev, V. I.
2015-02-01
A randomized algorithm is substantiated for the strongly NP-hard problem of partitioning a finite set of vectors of Euclidean space into two clusters of given sizes according to the minimum-of-the sum-of-squared-distances criterion. It is assumed that the centroid of one of the clusters is to be optimized and is determined as the mean value over all vectors in this cluster. The centroid of the other cluster is fixed at the origin. For an established parameter value, the algorithm finds an approximate solution of the problem in time that is linear in the space dimension and the input size of the problem for given values of the relative error and failure probability. The conditions are established under which the algorithm is asymptotically exact and runs in time that is linear in the space dimension and quadratic in the input size of the problem.
Vlemmix, F.; Rosman, A.N.; Rijnders, M.E.; Beuckens, A.; Opmeer, B.C.; Mol, B.W.J.; Kok, M.; Fleuren, M.A.H.
2015-01-01
Onjective: To determine the effectiveness of a client or care-provider strategy to improve the implementation of external cephalic version. Design: Cluster randomized controlled trial.Setting: Twenty-five clusters; hospitals and their referring midwifery practices randomly selected in the Netherland
A cluster-randomized evaluation of a responsive stimulation and feeding intervention in bangladesh.
Aboud, Frances E; Akhter, Sadika
2011-05-01
The goal of this study was to determine if a responsive stimulation and feeding intervention improved developmental and nutritional outcomes compared with a regular information-based parenting program. The hypothesis was that mothers in the intervention would exhibit better parenting skills and children would exhibit better developmental and nutritional outcomes than controls. A cluster-randomized field trial was conducted with 302 children aged 8 to 20 months and their mothers in rural Bangladesh who were randomly assigned according to village to 1 of 3 groups. The control mothers received 12 informational sessions on health and nutrition. The intervention groups received an additional 6 sessions delivered by peer educators who included modeling and coached practice in self-feeding and verbal responsiveness with the child during play. A second intervention group received, along with the sessions, 6 months of a food powder fortified with minerals and vitamins. Developmental outcomes included the Home Observation for Measurement of the Environment (HOME) Inventory, mother-child responsive talk, and language development. Nutritional outcomes included weight, height, self-feeding, and mouthfuls eaten. We used analysis of covariance to compare the 3 groups at the posttest and at follow-up, covarying the pretest levels and confounders. At follow-up, responsive stimulation-feeding groups had better HOME inventory scores, responsive talking, language, mouthfuls eaten, and hand-washing. Micronutrient fortification resulted in more weight gain. A brief behavior-change program that focused on modeling and practice in stimulation and feeding was found to benefit children's nutrition and language development. Micronutrients benefited children's weight but not length.
Random field distributed Heisenberg model on a thin film geometry
Akıncı, Ümit, E-mail: umit.akinci@deu.edu.tr
2014-11-15
The effects of the bimodal random field distribution on the thermal and magnetic properties of the Heisenberg thin film have been investigated by making use of a two spin cluster with the decoupling approximation. Particular attention has been devoted to the obtaining of phase diagrams and magnetization behaviors. The physical behaviors of special as well as tricritical points are discussed for a wide range of selected Hamiltonian parameters. For example, it is found that when the strength of a magnetic field increases, the locations of the special point (which is the ratio of the surface exchange interaction and the exchange interaction of the inner layers that makes the critical temperature of the film independent of the thickness) in the related plane decrease. Moreover, tricritical behavior has been obtained for higher values of the magnetic field, and influences of the varying Hamiltonian parameters on its behavior have been elucidated in detail in order to have a better understanding of the mechanism underlying the considered system. - Highlights: • Effect of bimodal random field distribution within the Heisenberg model is investigated. • Phase diagrams of the random field Heisenberg model in a thin film geometry are obtained. • Effect of the random field on the magnetic properties is obtained. • Variation of the special point with random field is determined. • Variation of the tricritical point with random field is determined.
Joop eHox
2014-02-01
Full Text Available Cluster randomized trials assess the effect of an intervention that is carried out at the group or cluster level. Ajzen’s theory of planned behaviour is often used to model the effect of the intervention as an indirect effect mediated in turn by attitude, norms and behavioural intention. Structural equation modelling (SEM is the technique of choice to estimate indirect effects and their significance. However, this is a large sample technique, and its application in a cluster randomized trial assumes a relatively large number of clusters. In practice, the number of clusters in these studies tends to be relatively small, e.g. much less than fifty. This study uses simulation methods to find the lowest number of clusters needed when multilevel SEM is used to estimate the indirect effect. Maximum likelihood estimation is compared to Bayesian analysis, with the central quality criteria being accuracy of the point estimate and the confidence interval. We also investigate the power of the test for the indirect effect. We conclude that Bayes estimation works well with much smaller cluster level sample sizes such as 20 cases than maximum likelihood estimation; although the bias is larger the coverage is much better. When only 5 to 10 clusters are available per treatment condition even with Bayesian estimation problems occur.
Carrozza, Sylvain; Tanasa, Adrian
2016-11-01
We define in this paper a class of three-index tensor models, endowed with {O(N)^{⊗ 3}} invariance ( N being the size of the tensor). This allows to generate, via the usual QFT perturbative expansion, a class of Feynman tensor graphs which is strictly larger than the class of Feynman graphs of both the multi-orientable model (and hence of the colored model) and the U( N) invariant models. We first exhibit the existence of a large N expansion for such a model with general interactions. We then focus on the quartic model and we identify the leading and next-to-leading order (NLO) graphs of the large N expansion. Finally, we prove the existence of a critical regime and we compute the critical exponents, both at leading order and at NLO. This is achieved through the use of various analytic combinatorics techniques.
The Baltimore and Utrecht models for cluster dissolution
Lamers, H.J.G.L.M.
2009-01-01
The analysis of the age distributions of star cluster samples of different galaxies has resulted in two very different empirical models for the dissolution of star clusters: the Baltimore model and the Utrecht model. I describe these two models and their differences. The Baltimore model implies that
Recent progress on the Random Conductance Model
Biskup, Marek
2011-01-01
Recent progress on the understanding of the Random Conductance Model is reviewed and commented. A particular emphasis is on the results on the scaling limit of the random walk among random conductances for almost every realization of the environment, observations on the behavior of the effective resistance as well as the scaling limit of certain models of gradient fields with non-convex interactions. The text is an expanded version of the lecture notes for a course delivered at the 2011 Cornell Summer School on Probability.
Information Filtering via Collaborative User Clustering Modeling
Zhang, Chu-Xu; Yu, Lu; Liu, Chuang; Liu, Hao; Yan, Xiao-Yong
2013-01-01
The past few years have witnessed the great success of recommender systems, which can significantly help users find out personalized items for them from the information era. One of the most widely applied recommendation methods is the Matrix Factorization (MF). However, most of researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information, but also takes into account the user interest. We compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on a real-world dataset, MovieLens, show that our method performs much better than other three methods in the accuracy of recommendation.
Information filtering via collaborative user clustering modeling
Zhang, Chu-Xu; Zhang, Zi-Ke; Yu, Lu; Liu, Chuang; Liu, Hao; Yan, Xiao-Yong
2014-02-01
The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information but also the user information. In addition, we compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on two real-world datasets, MovieLens 1M and MovieLens 100k, show that our method performs better than other three methods in the accuracy of recommendation.
Dupont, Corinne; Winer, Norbert; Rabilloud, Muriel; Touzet, Sandrine; Branger, Bernard; Lansac, Jacques; Gaucher, Laurent; Duclos, Antoine; Huissoud, Cyril; Boutitie, Florent; Rudigoz, René-Charles; Colin, Cyrille
2017-08-01
Suboptimal care contributes to perinatal morbidity and mortality. We investigated the effects of a multifaceted program designed to improve obstetric practices and outcomes. A cluster-randomized trial was conducted from October 2008 to November 2010 in 95 French maternity units randomized either to receive an information intervention about published guidelines or left to apply them freely. The intervention combined an outreach visit with a morbidity/mortality conference (MMC) to review perinatal morbidity/mortality cases. Within the intervention group, the units were randomized to have MMCs with or without clinical psychologists. The primary outcome was the rate of suboptimal care among perinatal morbidity/mortality cases. The secondary outcomes included the rate of suboptimal care among cases of morbidity, the rate of suboptimal care among cases of mortality, the rate of avoidable morbidity and/or mortality cases, and the incidence of, morbidity and/or mortality. A mixed logistic regression model with random intercept was used to quantify the effect of the intervention on the main outcome. The study reviewed 2459 cases of morbidity or mortality among 165,353 births. The rate of suboptimal care among morbidity plus mortality cases was not significantly lower in the intervention than in the control group (8.1% vs. 10.6%, OR [95% CI]: 0.75 [0.50-1.12], p=0.15. However, the cases of suboptimal care among morbidity cases were significantly lower in the intervention group (7.6% vs. 11.5%, 0.62 [0.40-0.94], p=0.02); the incidence of perinatal morbidity was also lower (7.0 vs. 8.1‰, p=0.01). No differences were found between psychologist-backed and the other units. The intervention reduced the rate of suboptimal care mainly in morbidity cases and the incidence of morbidity but did not succeed in improving morbidity plus mortality combined. More clear-cut results regarding mortality require a longer study period and the inclusion of structures that intervene before and
caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data
Xuan Jianhua
2008-09-01
Full Text Available Abstract Background The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample
caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.
Zhu, Yitan; Li, Huai; Miller, David J; Wang, Zuyi; Xuan, Jianhua; Clarke, Robert; Hoffman, Eric P; Wang, Yue
2008-09-18
The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenotype clustering
Ab initio random structure search for 13-atom clusters of fcc elements.
Chou, J P; Hsing, C R; Wei, C M; Cheng, C; Chang, C M
2013-03-27
The 13-atom metal clusters of fcc elements (Al, Rh, Ir, Ni, Pd, Pt, Cu, Ag, Au) were studied by density functional theory calculations. The global minima were searched for by the ab initio random structure searching method. In addition to some new lowest-energy structures for Pd13 and Au13, we found that the effective coordination numbers of the lowest-energy clusters would increase with the ratio of the dimer-to-bulk bond length. This correlation, together with the electronic structures of the lowest-energy clusters, divides the 13-atom clusters of these fcc elements into two groups (except for Au13, which prefers a two-dimensional structure due to the relativistic effect). Compact-like clusters that are composed exclusively of triangular motifs are preferred for elements without d-electrons (Al) or with (nearly) filled d-band electrons (Ni, Pd, Cu, Ag). Non-compact clusters composed mainly of square motifs connected by some triangular motifs (Rh, Ir, Pt) are favored for elements with unfilled d-band electrons.
A one-dimensional toy model of globular clusters
Fanelli, D; Ruffo, S; Fanelli, Duccio; Merafina, Marco; Ruffo, Stefano
2001-01-01
We introduce a one-dimensional toy model of globular clusters. The model is a version of the well-known gravitational sheets system, where we take additionally into account mass and energy loss by evaporation of stars at the boundaries. Numerical integration by the "exact" event-driven dynamics is performed, for initial uniform density and Gaussian random velocities. Two distinct quasi-stationary asymptotic regimes are attained, depending on the initial energy of the system. We guess the forms of the density and velocity profiles which fit numerical data extremely well and allow to perform an independent calculation of the self-consistent gravitational potential. Some power-laws for the asymptotic number of stars and for the collision times are suggested.
Modelling population processes with random initial conditions.
Pollett, P K; Dooley, A H; Ross, J V
2010-02-01
Population dynamics are almost inevitably associated with two predominant sources of variation: the first, demographic variability, a consequence of chance in progenitive and deleterious events; the second, initial state uncertainty, a consequence of partial observability and reporting delays and errors. Here we outline a general method for incorporating random initial conditions in population models where a deterministic model is sufficient to describe the dynamics of the population. Additionally, we show that for a large class of stochastic models the overall variation is the sum of variation due to random initial conditions and variation due to random dynamics, and thus we are able to quantify the variation not accounted for when random dynamics are ignored. Our results are illustrated with reference to both simulated and real data.
Update Legal Documents Using Hierarchical Ranking Models and Word Clustering
Pham, Minh Quang Nhat; Nguyen, Minh Le; Shimazu, Akira
2010-01-01
Our research addresses the task of updating legal documents when newinformation emerges. In this paper, we employ a hierarchical ranking model tothe task of updating legal documents. Word clustering features are incorporatedto the ranking models to exploit semantic relations between words. Experimentalresults on legal data built from the United States Code show that the hierarchicalranking model with word clustering outperforms baseline methods using VectorSpace Model, and word cluster-based ...
Guohua Zou
2016-12-01
Full Text Available New medical imaging technology, such as Computed Tomography and Magnetic Resonance Imaging (MRI, has been widely used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and accurately, and provide correct digital information for diagnosis, treatment planning and evaluation after surgery. MR has a good imaging diagnostic advantage for brain diseases. However, as the requirements of the brain image definition and quantitative analysis are always increasing, it is necessary to have better segmentation of MR brain images. The FCM (Fuzzy C-means algorithm is widely applied in image segmentation, but it has some shortcomings, such as long computation time and poor anti-noise capability. In this paper, firstly, the Ant Colony algorithm is used to determine the cluster centers and the number of FCM algorithm so as to improve its running speed. Then an improved Markov random field model is used to improve the algorithm, so that its antinoise ability can be improved. Experimental results show that the algorithm put forward in this paper has obvious advantages in image segmentation speed and segmentation effect.
Westine, Carl D; Spybrook, Jessaca; Taylor, Joseph A
2013-12-01
Prior research has focused primarily on empirically estimating design parameters for cluster-randomized trials (CRTs) of mathematics and reading achievement. Little is known about how design parameters compare across other educational outcomes. This article presents empirical estimates of design parameters that can be used to appropriately power CRTs in science education and compares them to estimates using mathematics and reading. Estimates of intraclass correlations (ICCs) are computed for unconditional two-level (students in schools) and three-level (students in schools in districts) hierarchical linear models of science achievement. Relevant student- and school-level pretest and demographic covariates are then considered, and estimates of variance explained are computed. Subjects: Five consecutive years of Texas student-level data for Grades 5, 8, 10, and 11. Science, mathematics, and reading achievement raw scores as measured by the Texas Assessment of Knowledge and Skills. Results: Findings show that ICCs in science range from .172 to .196 across grades and are generally higher than comparable statistics in mathematics, .163-.172, and reading, .099-.156. When available, a 1-year lagged student-level science pretest explains the most variability in the outcome. The 1-year lagged school-level science pretest is the best alternative in the absence of a 1-year lagged student-level science pretest. Science educational researchers should utilize design parameters derived from science achievement outcomes. © The Author(s) 2014.
Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods
Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne
studies have used non-valid analysis of skewed data. We propose two different methods to compare mean cost in two groups. Firstly, we use a non-parametric bootstrap method where the re-sampling takes place on two levels in order to take into account the cluster effect. Secondly, we proceed with a log...... We consider health care data from a cluster-randomized intervention study in primary care to test whether the average health care costs among study patients differ between the two groups. The problems of analysing cost data are that most data are severely skewed. Median instead of mean...... is commonly used for skewed distributions. For health care data, however, we need to recover the total cost in a given patient population. Thus, we focus, on making inferences on population means. Furthermore, a problem of clustered data is added as data related to patients in primary care are organized...
Topological Structures of Cluster Spins for Ising Models
Feng, You-gang
2010-01-01
We discussed hierarchies and rescaling rule of the self similar transformations in Ising models, and define a fractal dimension of an ordered cluster, which minimum corresponds to a fixed point of the transformations. By the fractal structures we divide the clusters into two types: irreducible and reducible. A relationship of cluster spin with its coordination number and fractal dimension is obtained.
Fuzzy Clustering Using the Convex Hull as Geometrical Model
Luca Liparulo
2015-01-01
Full Text Available A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.
A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image
Fei Wang
2017-01-01
Full Text Available An Intensified Charge-Coupled Device (ICCD image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a Different from the independent identically distributed (i.i.d. noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.
A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image.
Wang, Fei; Wang, Yibin; Yang, Meng; Zhang, Xuetao; Zheng, Nanning
2017-01-26
An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.
Random walks on finite lattices with multiple traps: Application to particle-cluster aggregation
Evans, J.W.; Nord, R.S.
1985-11-01
For random walks on finite lattices with multiple (completely adsorbing) traps, one is interested in the mean walk length until trapping and in the probability of capture for the various traps (either for a walk with a specific starting site, or for an average over all nontrap sites). We develop the formulation of Montroll to enable determination of the large-lattice-size asymptotic behavior of these quantities. (Only the case of a single trap has been analyzed in detail previously.) Explicit results are given for the case of symmetric nearest-neighbor random walks on two-dimensional (2D) square and triangular lattices. Procedures for exact calculation of walk lengths on a finite lattice with a single trap are extended to the multiple-trap case to determine all the above quantities. We examine convergence to asymptotic behavior as the lattice size increases. Connection with Witten-Sander irreversible particle-cluster aggregation is made by noting that this process corresponds to designating all sites adjacent to the cluster as traps. Thus capture probabilities for different traps determine the proportions of the various shaped clusters formed. (Reciprocals of) associated average walk lengths relate to rates for various irreversible aggregation processes involving a gas of walkers and clusters. Results are also presented for some of these quantities.
Spielberg, Freya; Crookston, Benjamin T; Chanani, Sheila; Kim, Jaewhan; Kline, Sean; Gray, Bobbi L
2013-01-01
Microfinance can be used to reach women and adolescent girls with HIV prevention education. We report findings from a cluster-randomized control trial among 55 villages in West Bengal to determine the impact of non-formal education on knowledge, attitudes and behaviors for HIV prevention and savings. Multilevel regression models were used to evaluate differences between groups for key outcomes while adjusting for cluster correlation and differences in baseline characteristics. Women and girls who received HIV education showed significant gains in HIV knowledge, awareness that condoms can prevent HIV, self-efficacy for HIV prevention, and confirmed use of clean needles, as compared to the control group. Condom use was rare and did not improve for women. While HIV testing was uncommon, knowledge of HIV-testing resources significantly increased among girls, and trended in the positive direction among women in intervention groups. Conversely, the savings education showed no impact on financial knowledge or behavior change.
Random matrix model approach to chiral symmetry
Verbaarschot, J J M
1996-01-01
We review the application of random matrix theory (RMT) to chiral symmetry in QCD. Starting from the general philosophy of RMT we introduce a chiral random matrix model with the global symmetries of QCD. Exact results are obtained for universal properties of the Dirac spectrum: i) finite volume corrections to valence quark mass dependence of the chiral condensate, and ii) microscopic fluctuations of Dirac spectra. Comparisons with lattice QCD simulations are made. Most notably, the variance of the number of levels in an interval containing $n$ levels on average is suppressed by a factor $(\\log n)/\\pi^2 n$. An extension of the random matrix model model to nonzero temperatures and chemical potential provides us with a schematic model of the chiral phase transition. In particular, this elucidates the nature of the quenched approximation at nonzero chemical potential.
Computer simulations of the random barrier model
Schrøder, Thomas; Dyre, Jeppe
2002-01-01
A brief review of experimental facts regarding ac electronic and ionic conduction in disordered solids is given followed by a discussion of what is perhaps the simplest realistic model, the random barrier model (symmetric hopping model). Results from large scale computer simulations are presented......, focusing on universality of the ac response in the extreme disorder limit. Finally, some important unsolved problems relating to hopping models for ac conduction are listed....
Outlier Identification in Model-Based Cluster Analysis.
Evans, Katie; Love, Tanzy; Thurston, Sally W
2015-04-01
In model-based clustering based on normal-mixture models, a few outlying observations can influence the cluster structure and number. This paper develops a method to identify these, however it does not attempt to identify clusters amidst a large field of noisy observations. We identify outliers as those observations in a cluster with minimal membership proportion or for which the cluster-specific variance with and without the observation is very different. Results from a simulation study demonstrate the ability of our method to detect true outliers without falsely identifying many non-outliers and improved performance over other approaches, under most scenarios. We use the contributed R package MCLUST for model-based clustering, but propose a modified prior for the cluster-specific variance which avoids degeneracies in estimation procedures. We also compare results from our outlier method to published results on National Hockey League data.
Outlier Identification in Model-Based Cluster Analysis
Evans, Katie; Love, Tanzy; Thurston, Sally W.
2015-01-01
In model-based clustering based on normal-mixture models, a few outlying observations can influence the cluster structure and number. This paper develops a method to identify these, however it does not attempt to identify clusters amidst a large field of noisy observations. We identify outliers as those observations in a cluster with minimal membership proportion or for which the cluster-specific variance with and without the observation is very different. Results from a simulation study demonstrate the ability of our method to detect true outliers without falsely identifying many non-outliers and improved performance over other approaches, under most scenarios. We use the contributed R package MCLUST for model-based clustering, but propose a modified prior for the cluster-specific variance which avoids degeneracies in estimation procedures. We also compare results from our outlier method to published results on National Hockey League data. PMID:26806993
A Dexterous Optional Randomized Response Model
Tarray, Tanveer A.; Singh, Housila P.; Yan, Zaizai
2017-01-01
This article addresses the problem of estimating the proportion Pi[subscript S] of the population belonging to a sensitive group using optional randomized response technique in stratified sampling based on Mangat model that has proportional and Neyman allocation and larger gain in efficiency. Numerically, it is found that the suggested model is…
Mardoukhi, Yousof; Jeon, Jae-Hyung; Metzler, Ralf
2015-11-28
We investigate the ergodic properties of a random walker performing (anomalous) diffusion on a random fractal geometry. Extensive Monte Carlo simulations of the motion of tracer particles on an ensemble of realisations of percolation clusters are performed for a wide range of percolation densities. Single trajectories of the tracer motion are analysed to quantify the time averaged mean squared displacement (MSD) and to compare this with the ensemble averaged MSD of the particle motion. Other complementary physical observables associated with ergodicity are studied, as well. It turns out that the time averaged MSD of individual realisations exhibits non-vanishing fluctuations even in the limit of very long observation times as the percolation density approaches the critical value. This apparent non-ergodic behaviour concurs with the ergodic behaviour on the ensemble averaged level. We demonstrate how the non-vanishing fluctuations in single particle trajectories are analytically expressed in terms of the fractal dimension and the cluster size distribution of the random geometry, thus being of purely geometrical origin. Moreover, we reveal that the convergence scaling law to ergodicity, which is known to be inversely proportional to the observation time T for ergodic diffusion processes, follows a power-law ∼T(-h) with h fractal structure of the accessible space. These results provide useful measures for differentiating the subdiffusion on random fractals from an otherwise closely related process, namely, fractional Brownian motion. Implications of our results on the analysis of single particle tracking experiments are provided.
Oduwo, Elizabeth; Edwards, Sarah J L
2014-07-16
Following the South African case, Treatment Action Campaign and Others v Minister of Health and Others, the use of 'pilot' studies to investigate interventions already proven efficacious, offered free of charge to government, but confined by the government to a small part of the population, may violate children's right to health, and the negative duty on governments not to prevent access to treatment. The applicants challenged a government decision to offer Nevirapine in a few pilot sites when evidence showed Nevirapine significantly reduced HIV transmission rates and despite donor offers of a free supply. The government refused to expand access, arguing they needed to collect more information, and citing concerns about long-term hazards, side effects, resistance and inadequate infrastructure. The court ruled this violated children's right to health and asked the government to immediately expand access. Cluster randomized trials involving children are increasingly popular, and are often used to reduce 'contamination': the possibility that members of a cluster adopt behavior of other clusters. However, they raise unique issues insufficiently addressed in literature and ethical guidelines. This case provides additional crucial guidance, based on a common human rights framework, for the Kenyan government and other involved stakeholders. Children possess special rights, often represent a 'captive' group, and so motivate extra consideration. In a systematic review, we therefore investigated whether cluster trial designs are used to prevent or delay children's access to treatment in Kenya or otherwise inconsistently with children's right to health as outlined in the above case. Although we did not find state sponsored cluster trials, most had significant public sector involvement. Core obligations under children's right to health were inadequately addressed across trials. Few cluster trials reported rationale for cluster randomization, offered post- trial access or
Tigers on trails: occupancy modeling for cluster sampling.
Hines, J E; Nichols, J D; Royle, J A; MacKenzie, D I; Gopalaswamy, A M; Kumar, N Samba; Karanth, K U
2010-07-01
Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasingly popular sampling design based on spatial replicates that are not selected randomly and that are expected to exhibit Markovian dependence. We develop two new occupancy models for data collected under this sort of design, one based on an underlying Markov model for spatial dependence and the other based on a trap response model with Markovian detections. We then simulated data under the model for Markovian spatial dependence and fit the data to standard occupancy models and to the two new models. Bias of occupancy estimates was substantial for the standard models, smaller for the new trap response model, and negligible for the new spatial process model. We also fit these models to data from a large-scale tiger occupancy survey recently conducted in Karnataka State, southwestern India. In addition to providing evidence of a positive relationship between tiger occupancy and habitat, model selection statistics and estimates strongly supported the use of the model with Markovian spatial dependence. This new model provides another tool for the decomposition of the detection process, which is sometimes needed for proper estimation and which may also permit interesting biological inferences. In addition to designs employing spatial replication, we note the likely existence of temporal Markovian dependence in many designs using temporal replication. The models developed here will be useful either directly, or with minor extensions, for these designs as well. We believe that these new models represent important additions to the suite of modeling tools now available for occupancy
Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model
Li, X. L.; Zhao, Q. H.; Li, Y.
2017-09-01
Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.
A model for globular cluster extreme anomalies
D'Antona, F.; Ventura, P.
2007-08-01
In spite of the efforts made in recent years, there is still no comprehensive explanation for the chemical anomalies of globular cluster (GC) stars. Among these anomalies, the most striking is oxygen depletion, which reaches values down to [O/Fe] ~ -0.4 in most clusters, but in M13 it goes down to less than [O/Fe] ~ -1. In this work we suggest that the anomalies are due to the superposition of two different events, as follows. (i) Primordial self-enrichment; this is required to explain the oxygen depletion down to a minimum value [O/Fe] ~ -0.4. (ii) Extra mixing in a fraction of the stars already born with anomalous composition; these objects, starting with already low [O/Fe], will reduce the oxygen abundance down to the most extreme values. Contrary to other models that invoke extra mixing to explain the chemical anomalies, we suggest that this mixing is active only if there is a fraction of the stars in which the primordial composition is not only oxygen-depleted, but also extremely helium-rich (Y ~ 0.4), as found in a few GCs from their main-sequence multiplicity. We propose that the rotational evolution (and an associated extra mixing) of extremely helium-rich stars may be affected by the fact that they develop a very small or non-existent molecular weight barrier during the evolution. We show that extra mixing in these stars, having initial chemistry that has already been CNO processed, affects mainly the oxygen abundance, as well as (to a much smaller extent) the sodium abundance. The model also predicts a large fluorine depletion concomitant with the oxygen depletion, and a further enhancement of the surface helium abundance, which reaches values close to Y = 0.5 in the computed models. We stress that, in this tentative explanation, those stars that are primordially oxygen-depleted, but are not extremely helium-rich, do not suffer deep extra mixing.
fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data.
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.
PENERAPAN PENGOLAHAN PARALEL MODEL CLUSTER SEBAGAI WEB SERVER
Maman Somantri
2009-06-01
Full Text Available engolahan paralel merupakan suatu cara yang dilakukan untuk meningkatkan kecepatan pengolahandata dengan melakukan lebih dari satu pengolahan data tersebut secara bersamaan. Salah satu bentuk pengolahanparalel adalah model cluster. Pengolahan paralel model cluster ini akan digunakan untuk mengolah data Web,dengan membangun server Web yang di-cluster. Cluster server Web ini menggunakan teknologi Linux VirtualServer (LVS yang dapat dilakukan dengan NAT, IP tunneling, dan direct routing yang memiliki empat algoritmapenjadwalan.Pada penelitian ini akan digunakan teknologi LVS untuk membuat cluster Web Server denganmenggunakan NAT, diterapkannya teknologi Network File System, dan Network Block Device yang digunakansebagai media penyimpanan dalam jaringan. Dalam pengujian sistem cluster ini, pertama dilakukan pengujianjaringan yang digunakan untuk mengetahui kinerja sistem, dan pengujian sistem cluster dalam mengolah data Webdengan perangkat lunak WebBench dan script benchmark.
Randomly Stopped Sums: Models and Psychological Applications
Michael eSmithson
2014-11-01
Full Text Available This paper describes an approach to modeling the sums of a continuous random variable over a number of measurement occasions when the number of occasions also is a random variable. A typical example is summing the amounts of time spent attending to pieces of information in an information search task leading to a decision to obtain the total time taken to decide. Although there is a large literature on randomly stopped sums in financial statistics, it is largely absent from psychology. The paper begins with the standard modeling approaches used in financial statistics, and then extends them in two ways. First, the randomly stopped sums are modeled as ``life distributions'' such as the gamma or log-normal distribution. A simulation study investigates Type I error rate accuracy and power for gamma and log-normal versions of this model. Second, a Bayesian hierarchical approach is used for constructing an appropriate general linear model of the sums. Model diagnostics are discussed, and three illustrations are presented from real datasets.
Critical properties of random Potts models
Kinzel, Wolfgang; Domany, Eytan
1981-04-01
The critical properties of Potts models with random bonds are considered in two dimensions. A position-space renormalization-group procedure, based on the Migdal-Kadanoff method, is developed. While all previous position-space calculations satisfied the Harris criterion and the resulting scaling relation only approximately, we found conditions under which these relations are exactly satisfied, and constructed our renormalization-group procedure accordingly. Numerical results for phase diagrams and thermodynamic functions for various random-bond Potts models are presented. In addition, some exact results obtained using a duality transformation, as well as an heuristic derivation of scaling properties that correspond to the percolation problem are given.
Duality between random trap and barrier models
Jack, Robert L [Department of Chemistry, University of California at Berkeley, Berkeley, CA 94720 (United States); Sollich, Peter [Department of Mathematics, King' s College London, London WC2R 2LS (United Kingdom)
2008-08-15
We discuss the physical consequences of a duality between two models with quenched disorder, in which particles propagate in one dimension among random traps or across random barriers. We derive an exact relation between their diffusion fronts at fixed disorder and deduce from this that their disorder-averaged diffusion fronts are exactly equal. We use effective dynamics schemes to isolate the different physical processes by which particles propagate in the models and discuss how the duality arises from a correspondence between the rates for these different processes.
Fuzzy Clustering Methods and their Application to Fuzzy Modeling
Kroszynski, Uri; Zhou, Jianjun
1999-01-01
Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate...... prediction of outputs. This article presents an overview of some of the most popular clustering methods, namely Fuzzy Cluster-Means (FCM) and its generalizations to Fuzzy C-Lines and Elliptotypes. The algorithms for computing cluster centers and principal directions from a training data-set are described....... A method to obtain an optimized number of clusters is outlined. Based upon the cluster's characteristics, a behavioural model is formulated in terms of a rule-base and an inference engine. The article reviews several variants for the model formulation. Some limitations of the methods are listed...
Analysing star cluster populations with stochastic models: the HST/WFC3 sample of clusters in M83
Fouesneau, Morgan; Chandar, Rupali; Whitmore, Bradley C
2012-01-01
The majority of clusters in the Universe have masses well below 10^5 Msun. Hence their integrated fluxes and colors can be affected by the random presence of a few bright stars introduced by stochastic sampling of the stellar mass function. Specific methods are being developed to extend the analysis of cluster SEDs into the low-mass regime. In this paper, we apply such a method to observations of star clusters, in the nearby spiral galaxy M83. We reassess ages and masses of a sample of 1242 objects for which UBVIHalpha fluxes were obtained with the HST/WFC3 images. Synthetic clusters with known properties are used to characterize the limitations of the method. The ensemble of color predictions of the discrete cluster models are in good agreement with the distribution of observed colors. We emphasize the important role of the Halpha data in the assessment of the fraction of young objects, particularly in breaking the age-extinction degeneracy that hampers an analysis based on UBVI only. We find the mass distri...
A cluster randomized trial evaluating electronic prescribing in an ambulatory care setting
Quan Sherman
2007-10-01
Full Text Available Abstract Background Medication errors, adverse drug events and potential adverse drug events are common and serious in terms of the harms and costs that they impose on the health system and those who use it. Errors resulting in preventable adverse drug events have been shown to occur most often at the stages of ordering and administration. This paper describes the protocol for a pragmatic trial of electronic prescribing to reduce prescription error. The trial was designed to overcome the limitations associated with traditional study design. Design This study was designed as a 65-week, cluster randomized, parallel study. Methods The trial was conducted within ambulatory outpatient clinics in an academic tertiary care centre in Ontario, Canada. The electronic prescribing software for the study is a Canadian electronic prescribing software package which provides physician prescription entry with decision support at the point of care. Using a handheld computer (PDA the physician selects medications using an error minimising menu-based pick list from a comprehensive drug database, create specific prescription instructions and then transmit the prescription directly and electronically to a participating pharmacy via facsimile or to the physician's printer using local area wireless technology. The unit of allocation and randomization is by 'week', i.e. the system is "on" or "off" according to the randomization scheme and the unit of analysis is the prescription, with adjustment for clustering of patients within practitioners. Discussion This paper describes the protocol for a pragmatic cluster randomized trial of point-of-care electronic prescribing, which was specifically designed to overcome the limitations associated with traditional study design. Trial Registration This trial has been registered with clinicaltrials.gov (ID: NCT00252395
Cluster statistics and quasisoliton dynamics in microscopic optimal-velocity models
Yang, Bo; Xu, Xihua; Pang, John Z. F.; Monterola, Christopher
2016-04-01
Using the non-linear optimal velocity models as an example, we show that there exists an emergent intrinsic scale that characterizes the interaction strength between multiple clusters appearing in the solutions of such models. The interaction characterizes the dynamics of the localized quasisoliton structures given by the time derivative of the headways, and the intrinsic scale is analogous to the "charge" of the quasisolitons, leading to non-trivial cluster statistics from the random perturbations to the initial steady states of uniform headways. The cluster statistics depend both on the quasisoliton charge and the density of the traffic. The intrinsic scale is also related to an emergent quantity that gives the extremum headways in the cluster formation, as well as the coexistence curve separating the absolute stable phase from the metastable phase. The relationship is qualitatively universal for general optimal velocity models.
Kalia, Sumeet; Klar, Neil; Donner, Allan
2016-12-30
Cluster randomized trials (CRTs) involve the random assignment of intact social units rather than independent subjects to intervention groups. Time-to-event outcomes often are endpoints in CRTs. Analyses of such data need to account for the correlation among cluster members. The intracluster correlation coefficient (ICC) is used to assess the similarity among binary and continuous outcomes that belong to the same cluster. However, estimating the ICC in CRTs with time-to-event outcomes is a challenge because of the presence of censored observations. The literature suggests that the ICC may be estimated using either censoring indicators or observed event times. A simulation study explores the effect of administrative censoring on estimating the ICC. Results show that ICC estimators derived from censoring indicators or observed event times are negatively biased. Analytic work further supports these results. Observed event times are preferred to estimate the ICC under minimum frequency of administrative censoring. To our knowledge, the existing literature provides no practical guidance on the estimation of ICC when substantial amount of administrative censoring is present. The results from this study corroborate the need for further methodological research on estimating the ICC for correlated time-to-event outcomes. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Interaction of Fanaroff-Riley class II radio jets with a randomly magnetised intra-cluster medium
Huarte-Espinosa, Martín; Alexander, Paul
2011-01-01
A combination of three-dimensional (3D) magnetohydrodynamics (MHD) and synthetic numerical simulations are presented to follow the evolution of a randomly magnetised plasma that models the intra-cluster medium (ICM), under the isolated effects of powerful, light, hypersonic and bipolar Fanaroff-Riley class II (FR II) jets. We prescribe the cluster magnetic field (CMF) as a Gaussian random field with a Kolmogorov-like energy spectrum. Both the power of the jets and the viewing angle that is used for the synthetic Rotation Measure (RM) observations are investigated. We find the model radio sources introduce and amplify fluctuations on the RM statistical properties which we analyse as a function of time as well as the viewing angle. The average RM and the RM standard deviation are increased by the action of the jets. Energetics, RM statistics and magnetic power spectral analysis consistently show that the effects also correlate with the jets' power, and that the lightest, fastest jets produce the strongest chang...
Two-Stage Modelling Of Random Phenomena
Barańska, Anna
2015-12-01
The main objective of this publication was to present a two-stage algorithm of modelling random phenomena, based on multidimensional function modelling, on the example of modelling the real estate market for the purpose of real estate valuation and estimation of model parameters of foundations vertical displacements. The first stage of the presented algorithm includes a selection of a suitable form of the function model. In the classical algorithms, based on function modelling, prediction of the dependent variable is its value obtained directly from the model. The better the model reflects a relationship between the independent variables and their effect on the dependent variable, the more reliable is the model value. In this paper, an algorithm has been proposed which comprises adjustment of the value obtained from the model with a random correction determined from the residuals of the model for these cases which, in a separate analysis, were considered to be the most similar to the object for which we want to model the dependent variable. The effect of applying the developed quantitative procedures for calculating the corrections and qualitative methods to assess the similarity on the final outcome of the prediction and its accuracy, was examined by statistical methods, mainly using appropriate parametric tests of significance. The idea of the presented algorithm has been designed so as to approximate the value of the dependent variable of the studied phenomenon to its value in reality and, at the same time, to have it "smoothed out" by a well fitted modelling function.
Improving randomness characterization through Bayesian model selection
R., Rafael Díaz-H; Martínez, Alí M Angulo; U'Ren, Alfred B; Hirsch, Jorge G; Marsili, Matteo; Castillo, Isaac Pérez
2016-01-01
Nowadays random number generation plays an essential role in technology with important applications in areas ranging from cryptography, which lies at the core of current communication protocols, to Monte Carlo methods, and other probabilistic algorithms. In this context, a crucial scientific endeavour is to develop effective methods that allow the characterization of random number generators. However, commonly employed methods either lack formality (e.g. the NIST test suite), or are inapplicable in principle (e.g. the characterization derived from the Algorithmic Theory of Information (ATI)). In this letter we present a novel method based on Bayesian model selection, which is both rigorous and effective, for characterizing randomness in a bit sequence. We derive analytic expressions for a model's likelihood which is then used to compute its posterior probability distribution. Our method proves to be more rigorous than NIST's suite and the Borel-Normality criterion and its implementation is straightforward. We...
Van Ness, Peter H.; Peduzzi, Peter N.; Quagliarello, Vincent J.
2012-01-01
This report discusses how methodological aspects of study efficacy and effectiveness combine in cluster randomized trials in nursing homes. Discussion focuses on the relationships between these study aspects in the Pneumonia Reduction in Institutionalized Disabled Elders (PRIDE) trial, an ongoing cluster randomized clinical trial of pneumonia prevention among nursing home residents launched in October 2009 in Greater New Haven, Connecticut. This clinical trial has enrolled long-term care nurs...
The Evolution of Galaxy Clustering in Hierarchical Models
1999-01-01
The main ingredients of recent semi-analytic models of galaxy formation are summarised. We present predictions for the galaxy clustering properties of a well specified LCDM model whose parameters are constrained by observed local galaxy properties. We present preliminary predictions for evolution of clustering that can be probed with deep pencil beam surveys.
The Baltimore and Utrecht models for cluster dissolution
Lamers, Henny J G L M
2008-01-01
The analysis of the age distributions of star cluster samples of different galaxies has resulted in two very different empirical models for the dissolution of star clusters: the Baltimore model and the Utrecht model. I describe these two models and their differences. The Baltimore model implies that the dissolution of star clusters is mass independent and that about 90% of the clusters are destroyed each age dex, up to an age of about a Gyr, after which point mass-dependent dissolution from two-body relaxation becomes the dominant mechanism. In the Utrecht model, cluster dissolution occurs in three stages: (i) mass-independent infant mortality due to the expulsion of gas up to about 10 Myr; (ii) a phase of slow dynamical evolution with strong evolutionary fading of the clusters lasting up to about a Gyr; and (iii) a phase dominated by mass dependent-dissolution, as predicted by dynamical models. I describe the cluster age distributions for mass-limited and magnitude-limited cluster samples for both models. I ...
KMEANS CLUSTERING FOR HIDDEN MARKOV MODEL
Perrone, M.P.; Connell, S.D.
2004-01-01
An unsupervised kmeans clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of as
The quasicrystal model of cluster systems in condensed matter
Melnikov, G.
2017-01-01
The paper proposes a quasicrystal model of the structure of clusters. The model is based on the similarity of the structure of clusters and macroscopic structure of quasicrystals. It offers a formula to calculate the radii of successive coordination spheres in quasicrystalline films. The formula is based on the properties of Fibonacci sequence and characteristics of the power potential of interaction between particles.
Lemme, Francesca; van Breukelen, Gerard J P; Berger, Martijn P F
2016-01-01
Typically, clusters and individuals in cluster randomized trials are allocated across treatment conditions in a balanced fashion. This is optimal under homogeneous costs and outcome variances. However, both the costs and the variances may be heterogeneous. Then, an unbalanced allocation is more effi
Random effect selection in generalised linear models
Denwood, Matt; Houe, Hans; Forkman, Björn;
We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...
Tak, Yuli R; Lichtwarck-Aschoff, Anna; Gillham, Jane E; Van Zundert, Rinka M P; Engels, Rutger C M E
2016-07-01
The longitudinal effectiveness of a universal, adolescent school-based depression prevention program Op Volle Kracht (OVK) was evaluated by means of a cluster randomized controlled trial with intervention and control condition (school as usual). OVK was based on the Penn Resiliency Program (PRP) (Gillham et al. Psychological Science, 6, 343-351, 1995). Depressive symptoms were assessed with the Child Depression Inventory (Kovacs 2001). In total, 1341 adolescents participated, Mage = 13.91, SD = 0.55, 47.3 % girls, 83.1 % Dutch ethnicity; intervention group n = 655, four schools; control group n = 735, five schools. Intent-to-treat analyses revealed that OVK did not prevent depressive symptoms, β = -0.01, SE = 0.05, p = .829, Cohen's d = 0.02, and the prevalence of an elevated level of depressive symptoms was not different between groups at 1 year follow-up, OR = 1.00, 95 % CI = 0.60-1.65, p = .992, NNT = 188. Latent Growth Curve Modeling over the 2 year follow-up period showed that OVK did not predict differences in depressive symptoms immediately following intervention, intercept: β = 0.02, p = .642, or changes in depressive symptoms, slope: β = -0.01, p = .919. No moderation by gender or baseline depressive symptoms was found. To conclude, OVK was not effective in preventing depressive symptoms across the 2 year follow-up. The implications of these findings are discussed.
Dregan, Alex; van Staa, Tjeerd P; McDermott, Lisa; McCann, Gerard; Ashworth, Mark; Charlton, Judith; Wolfe, Charles D A; Rudd, Anthony; Yardley, Lucy; Gulliford, Martin C; Trial Steering Committee
2014-07-01
The aim of this study was to evaluate whether the remote introduction of electronic decision support tools into family practices improves risk factor control after first stroke. This study also aimed to develop methods to implement cluster randomized trials in stroke using electronic health records. Family practices were recruited from the UK Clinical Practice Research Datalink and allocated to intervention and control trial arms by minimization. Remotely installed, electronic decision support tools promoted intensified secondary prevention for 12 months with last measure of systolic blood pressure as the primary outcome. Outcome data from electronic health records were analyzed using marginal models. There were 106 Clinical Practice Research Datalink family practices allocated (intervention, 53; control, 53), with 11 391 (control, 5516; intervention, 5875) participants with acute stroke ever diagnosed. Participants at trial practices had similar characteristics as 47,887 patients with stroke at nontrial practices. During the intervention period, blood pressure values were recorded in the electronic health records for 90% and cholesterol values for 84% of participants. After intervention, the latest mean systolic blood pressure was 131.7 (SD, 16.8) mm Hg in the control trial arm and 131.4 (16.7) mm Hg in the intervention trial arm, and adjusted mean difference was -0.56 mm Hg (95% confidence interval, -1.38 to 0.26; P=0.183). The financial cost of the trial was approximately US $22 per participant, or US $2400 per family practice allocated. Large pragmatic intervention studies may be implemented at low cost by using electronic health records. The intervention used in this trial was not found to be effective, and further research is needed to develop more effective intervention strategies. http://www.controlled-trials.com. Current Controlled Trials identifier: ISRCTN35701810. © 2014 American Heart Association, Inc.
Singh, Debra; Negin, Joel; Orach, Christopher Garimoi; Cumming, Robert
2016-10-03
Community Health Volunteers (CHVs) can be effective in improving pregnancy and newborn outcomes through community education. Inadequate supervision of CHVs, whether due to poor planning, irregular visits, or ineffective supervisory methods, is, however, recognized as a weakness in many programs. There has been little research on best practice supervisory or accompaniment models. From March 2014 to February 2015 a proof of concept study was conducted to compare training alone versus training and supportive supervision by paid CHWs (n = 4) on the effectiveness of CHVs (n = 82) to deliver education about pregnancy, newborn care, family planning and hygiene. The pair-matched cluster randomized trial was conducted in eight villages (four intervention and four control) in Budondo sub-county in Jinja, Uganda. Increases in desired behaviors were seen in both the intervention and control arms over the study period. Both arms showed high retention rates of CHVs (95 %). At 1 year follow-up there was a significantly higher prevalence of installed and functioning tippy taps for hand washing (p reproductive health care by addressing cultural norms and scientific misconceptions. Having a team of 2 CHWs to 40 CHVs enables close to community access to information, conversation and services. Supportive supervision involves creating a non-threatening, empowering environment in which both the CHV and the supervising CHW learn together and overcome obstacles that might otherwise demotivate the CHV. While the results seem promising for added value with supportive supervision for CHVs undertaking reproductive health activities, further research on a larger scale will be needed to substantiate the effect.
Infinite Random Graphs as Statistical Mechanical Models
Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria
2011-01-01
We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe...... a relation to the so-called uniform infinite tree and results on the Hausdorff and spectral dimension of two-dimensional space-time obtained in B. Durhuus, T. Jonsson, J.F. Wheater, J. Stat. Phys. 139, 859 (2010) are briefly outlined. For the latter we discuss results on the absence of spontaneous...... magnetization and argue that, in the generic case, the values of the Hausdorff and spectral dimension of the underlying infinite trees are not influenced by the coupling to an Ising model in a constant magnetic field (B. Durhuus, G.M. Napolitano, in preparation)...
SUCCESSFUL INNOVATIVE CLUSTERS IN ROMANIA – A POSSIBLE MODEL
Liliana SCUTARU
2016-08-01
Full Text Available The present study proposes the construction of a successful innovative cluster model which will help creating strategies and policies to support the Romanian economic growth and development in the medium and long term. One such architecture designed for supporting innovative clusters, including by attracting foreign capital within clusters order to increase their competitiveness, addresses some concrete measures both in terms of organizational system and management strategy as well as the funding system of clusters. The paper is also emphasizing the multiplicity of factors that are contributing to the creation, to the progressive development and to the success of clusters, the activities developed and the relationships established internationally, so as to ensure that the clusters remain on the market and have a good visibility at national and international levels, essentially contributing to the success of cluster.
Tests of Hypotheses Arising In the Correlated Random Coefficient Model.
Heckman, James J; Schmierer, Daniel
2010-11-01
This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance of the instrumental variable estimator for that model. We develop tests of the validity of the correlated random coefficient model against the null hypothesis of the uncorrelated random coefficient model.
Microscopic three-cluster model of 10Be
Lashko, Yu. A.; Filippov, G. F.; Vasilevsky, V. S.
2017-02-01
We investigate spectrum of bound and resonance states in 10Be, and scattering of alpha-particles on 6He. For this aim we make use of a three-cluster microscopic model. This model incorporates Gaussian and oscillator basis functions and reduces three-cluster Schrödinger equation to a two-body like many-channel problem with the two-cluster subsystem being in a bound or a pseudo-bound state. Much attention is given to the effects of cluster polarization on spectrum of bound and resonance states in 10Be, and on elastic and inelastic 6He + α scattering.
Alison H Skalet
Full Text Available BACKGROUND: It is widely thought that widespread antibiotic use selects for community antibiotic resistance, though this has been difficult to prove in the setting of a community-randomized clinical trial. In this study, we used a randomized clinical trial design to assess whether macrolide resistance was higher in communities treated with mass azithromycin for trachoma, compared to untreated control communities. METHODS AND FINDINGS: In a cluster-randomized trial for trachoma control in Ethiopia, 12 communities were randomized to receive mass azithromycin treatment of children aged 1-10 years at months 0, 3, 6, and 9. Twelve control communities were randomized to receive no antibiotic treatments until the conclusion of the study. Nasopharyngeal swabs were collected from randomly selected children in the treated group at baseline and month 12, and in the control group at month 12. Antibiotic susceptibility testing was performed on Streptococcus pneumoniae isolated from the swabs using Etest strips. In the treated group, the mean prevalence of azithromycin resistance among all monitored children increased from 3.6% (95% confidence interval [CI] 0.8%-8.9% at baseline, to 46.9% (37.5%-57.5% at month 12 (p = 0.003. In control communities, azithromycin resistance was 9.2% (95% CI 6.7%-13.3% at month 12, significantly lower than the treated group (p < 0.0001. Penicillin resistance was identified in 0.8% (95% CI 0%-4.2% of isolates in the control group at 1 year, and in no isolates in the children-treated group at baseline or 1 year. CONCLUSIONS: This cluster-randomized clinical trial demonstrated that compared to untreated control communities, nasopharyngeal pneumococcal resistance to macrolides was significantly higher in communities randomized to intensive azithromycin treatment. Mass azithromycin distributions were given more frequently than currently recommended by the World Health Organization's trachoma program. Azithromycin use in this setting
Simonsen, Katherina Beltoft
2016-01-01
based on DDKM version 2. Construction of the DDKM: 3-year cycle One announced on-site accreditation survey One announced periodic midterm survey 82 accreditation standards divided in three themes: organizational standards, continuity of care standards, and disease-specific standards 473......: Nationwide block and cluster RCT 23 public hospitals (3 university hospitals, 15 general hospitals, and 5 psychiatric hospitals 11 hospitals received announced surveys (control group) 12 hospitals received unannounced surveys (intervention group) 9 surveyors randomly allocated in surveyor teams...
Electron-gas clusters: the ultimate jellium model
Koskinen, M.; Lipas, P. O.; Manninen, M.
1995-12-01
The local spin-density approximation is used to calculate ground- and isomeric-state geometries of jellium clusters with 2 to 22 electrons. The positive background charge of the model is completely deformable, both in shape and in density. The model has no input parameters. The resulting shapes of the clusters exhibit breaking of axial and inversion symmetries; in general the shapes are far from ellipsoidal. Those clusters which lack inversion symmetry are extremely soft against odd-multipole deformations. Some clusters can be interpreted as molecules built from magic clusters. The deformation produces a gap at the Fermi level. This results in a regular odd-even staggering of the total energy per electron and of the HOMO level. The strongly deformed 14-electron cluster is semimagic. Stable isomers are predicted. The splitting of the plasmon resonance due to deformation is estimated on a classical argument.
An Extended Clustering Algorithm for Statistical Language Models
Ueberla, J P
1994-01-01
Statistical language models frequently suffer from a lack of training data. This problem can be alleviated by clustering, because it reduces the number of free parameters that need to be trained. However, clustered models have the following drawback: if there is ``enough'' data to train an unclustered model, then the clustered variant may perform worse. On currently used language modeling corpora, e.g. the Wall Street Journal corpus, how do the performances of a clustered and an unclustered model compare? While trying to address this question, we develop the following two ideas. First, to get a clustering algorithm with potentially high performance, an existing algorithm is extended to deal with higher order N-grams. Second, to make it possible to cluster large amounts of training data more efficiently, a heuristic to speed up the algorithm is presented. The resulting clustering algorithm can be used to cluster trigrams on the Wall Street Journal corpus and the language models it produces can compete with exi...
Testing the Correlated Random Coefficient Model*
Heckman, James J.; Schmierer, Daniel; Urzua, Sergio
2010-01-01
The recent literature on instrumental variables (IV) features models in which agents sort into treatment status on the basis of gains from treatment as well as on baseline-pretreatment levels. Components of the gains known to the agents and acted on by them may not be known by the observing economist. Such models are called correlated random coe cient models. Sorting on unobserved components of gains complicates the interpretation of what IV estimates. This paper examines testable implications of the hypothesis that agents do not sort into treatment based on gains. In it, we develop new tests to gauge the empirical relevance of the correlated random coe cient model to examine whether the additional complications associated with it are required. We examine the power of the proposed tests. We derive a new representation of the variance of the instrumental variable estimator for the correlated random coefficient model. We apply the methods in this paper to the prototypical empirical problem of estimating the return to schooling and nd evidence of sorting into schooling based on unobserved components of gains. PMID:21057649
Schmidt, Lukas; Holzner, Markus
2016-01-01
This work considers the distribution of inertial particles in turbulence using the point-particle approximation. We demonstrate that the random point process formed by the positions of particles in space is a Poisson point process with log-normal random intensity ("log Gaussian Cox process" or LGCP). The probability of having a finite number of particles in a small volume is given in terms of the characteristic function of a log-normal distribution. Corrections due to discreteness of the number of particles to the previously derived statistics of particle concentration in the continuum limit are provided. These are relevant for dealing with experimental or numerical data. The probability of having regions without particles, i.e. voids, is larger for inertial particles than for tracer particles where voids are distributed according to Poisson processes. Further, the probability of having large voids decays only log-normally with size. This shows that particles cluster, leaving voids behind. At scales where the...
Exercise program for prevention of groin pain in football players: a cluster-randomized trial
Hölmich, P; Larsen, K; Krogsgaard, Kim
2010-01-01
Groin injuries cause major problems in sports and particularly in football. Exercise is effective in treating adductor-related groin pain, but no trials have been published regarding the specific prevention of groin pain or prevention specifically targeting overuse injuries in sport using exercise...... programs. We performed a cluster-randomized trial including 55 football clubs representing 1211 players. The clubs were randomized to an exercise program aimed at preventing groin injuries (n=27) or to a control group training as usual (n=28). The intervention program consisted of six exercises including...... strengthening (concentric and eccentric), coordination, and core stability exercises for the muscles related to the pelvis. Physiotherapists assigned to each club registered all groin injuries. Twenty-two clubs in each group completed the study, represented by 977 players. There was no significant effect...
Partition-associated incompatibility caused by random assortment of pure plasmid clusters
Ebersbach, Gitte; Sherratt, David J; Gerdes, Kenn;
2005-01-01
Summary Bacterial plasmids and chromosomes encode centromere-like partition loci that actively segregate DNA before cell division. The molecular mechanism behind DNA segregation in bacteria is largely unknown. Here we analyse the mechanism of partition-associated incompatibility for plasmid pB171......-lived pairing of plasmids. Instead, pure R1 and F foci were positioned along the length of the cell, and in a random order. Thus, our results raise the possibility that partition-mediated plasmid incompatibility is not caused by pairing of heterologous plasmids but instead by random positioning of pure plasmid...... clusters along the long axis of the cell. The strength of the incompatibility was correlated with the capability of the plasmids to compete for the mid-cell position....
Charlier, J-C; Zanolli, Z [Unite de Physico-Chimie et de Physique des Materiaux (PCPM), European Theoretical Spectroscopy Facility (ETSF), Universite Catholique de Louvain, Place Croix du Sud 1, B-1348 Louvain-la-Neuve (Belgium); Arnaud, L; Avilov, I V; Felten, A; Pireaux, J-J [Centre de Recherche en Physique de la Matiere et du Rayonnement (PMR-LISE), Facultes Universitaires Notre-Dame de la Paix, 61 Rue de Bruxelles, B-5000 Namur (Belgium); Delgado, M [Sensotran, s.l., Avenida Remolar 31, E-08820 El Prat de Llobregat, Barcelona (Spain); Demoisson, F; Reniers, F [Service de Chimie Analytique et Chimie des Interfaces (CHANI), Universite Libre de Bruxelles, Faculte des Sciences, CP255, Boulevard du Triomphe 2, B-1050 Bruxelles (Belgium); Espinosa, E H; Ionescu, R; Leghrib, R; Llobet, E [Department of Electronic Engineering, Universitat Rovira i Virgili, Avenida Paisos Catalans 26, E-43007 Tarragona (Spain); Ewels, C P; Suarez-Martinez, I [Institut des Materiaux Jean Rouxel (IMN), Universite de Nantes, 2 rue de la Houssiniere-BP 32229, F-44322 Nantes Cedex 3 (France); Guillot, J; Mansour, A; Migeon, H-N [Departement Science et Analyse des Materiaux, Centre de Recherche Public-Gabriel Lippmann, rue du Brill 41, L-4422 Belvaux (Luxembourg); Watson, G E, E-mail: jean-jacques.pireaux@fundp.ac.b [Vega Science Trust, Unit 118, Science Park SQ, Brighton, BN1 9SB (United Kingdom)
2009-09-16
Carbon nanotube surfaces, activated and randomly decorated with metal nanoclusters, have been studied in uniquely combined theoretical and experimental approaches as prototypes for molecular recognition. The key concept is to shape metallic clusters that donate or accept a fractional charge upon adsorption of a target molecule, and modify the electron transport in the nanotube. The present work focuses on a simple system, carbon nanotubes with gold clusters. The nature of the gold-nanotube interaction is studied using first-principles techniques. The numerical simulations predict the binding and diffusion energies of gold atoms at the tube surface, including realistic atomic models for defects potentially present at the nanotube surface. The atomic structure of the gold nanoclusters and their effect on the intrinsic electronic quantum transport properties of the nanotube are also predicted. Experimentally, multi-wall CNTs are decorated with gold clusters using (1) vacuum evaporation, after activation with an RF oxygen plasma and (2) colloid solution injected into an RF atmospheric plasma; the hybrid systems are accurately characterized using XPS and TEM techniques. The response of gas sensors based on these nano{sup 2}hybrids is quantified for the detection of toxic species like NO{sub 2}, CO, C{sub 2}H{sub 5}OH and C{sub 2}H{sub 4}.
Delayed Random Walks: Modeling Human Posture Control
Ohira, Toru
1998-03-01
We consider a phenomenological description of a noisy trajectory which appears on a stabiliogram platform during human postural sway. We hypothesize that this trajectory arises due to a mixture of uncontrollable noise and a corrective delayed feedback to an upright position. Based on this hypothesis, we model the process with a biased random walk whose transition probability depends on its position at a fixed time delay in the past, which we call a delayed random walk. We first introduce a very simple model (T. Ohira and J. G. Milton, Phys.Rev.E. 52), 3277, (1995), which can nevertheless capture the rough qualitative features of the two--point mean square displacement of experimental data with reasonable estimation of delay time. Then, we discuss two approaches toward better capturing and understanding of the experimental data. The first approach is an extension of the model to include a spatial displacement threshold from the upright position below which no or only weak corrective feedback motion takes place. This can be incorporated into an extended delayed random walk model. Numerical simulations show that this extended model can better capture the three scaling region which appears in the two--point mean square displacement. The other approach studied the autocorrelation function of the experimental data, which shows oscillatory behavior. We recently investigated a delayed random walk model whose autocorrelation function has analytically tractable oscillatory behavior (T. Ohira, Phys.Rev.E. 55), R1255, (1997). We discuss how this analytical understanding and its application to delay estimation (T. Ohira and R. Sawatari, Phys.Rev.E. 55), R2077, (1997) could possibly be used to further understand the postural sway data.
Guillaume Marrelec
Full Text Available The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity, provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.
Marrelec, Guillaume; Messé, Arnaud; Bellec, Pierre
2015-01-01
The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.
Verdurmen, Jacqueline E E; Koning, Ina M.; Vollebergh, Wilma A M; van den Eijnden, Regina J J M; Engels, Rutger C M E
2014-01-01
Objective: To examine risk moderation of an alcohol intervention targeting parents and adolescents. Design: A cluster randomized trial including 2937 Dutch early adolescents (m=12.68. years, SD=0.51) and their parents randomized over four conditions: parent intervention, student intervention, combin
Krabbenborg, M.A.M.; Boersma, S.N.; Veld, W.M. van der; Hulst, B. van; Vollebergh, W.A.M.; Wolf, J.R.L.M.
2017-01-01
Objective: To test the effectiveness of Houvast: a strengths-based intervention for homeless young adults. Method: A cluster randomized controlled trial was conducted with 10 Dutch shelter facilities randomly allocated to an intervention and a control group. Homeless young adults were interviewed
A novel configuration model for random graphs with given degree sequence
Xu Xin-Ping; Liu Feng
2007-01-01
Recently, random graphs in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices have attracted much attention. This paper presents a specific realization of a class of random network models in which the connection probability between two vertices (i, j) is a specific function of degrees ki and kj. In the framework of the configuration model of random graphs, we find the analytical expressions for the degree correlation and clustering as a function of the variance of the desired degree distribution. The obtained expressions are checked by means of numerical simulations. Possible applications of our model are discussed.
Fuller, Daniel; Potvin, Louise
2012-06-01
Cluster randomized controlled trials are increasingly used in population health intervention research. Through randomization, researchers attempt to isolate the treatment effect and remove all other effects, including any effects of social context. In many cases, the constant effect assumption cannot be satisfied in cluster randomized controlled trials. We argue that when studying population health interventions, the effective mechanism of intervention lies in the interaction between the treatment and social context. Researchers should be cognizant that attempts to remove the effect of social context using CRTC may fail. The interaction between the treatment and social context should be the primary object of study in population health intervention research.
Alpha-cluster model of atomic nuclei
Sosin, Zbigniew; Kallunkathariyil, Jinesh [Jagiellonian University, M. Smoluchowski Institute of Physics, Krakow (Poland); Blocki, Jan [NCBJ, Theoretical Physics Division (BP2), Swierk (Poland); Lukasik, Jerzy; Pawlowski, Piotr [IFJ PAN, Krakow (Poland)
2016-05-15
The description of a nuclear system in its ground state and at low excitations based on the equation of state (EoS) around normal density is presented. In the expansion of the EoS around the saturation point, additional spin polarization terms are taken into account. These terms, together with the standard symmetry term, are responsible for the appearance of the α-like clusters in the ground-state configurations of the N=Z even-even nuclei. At the nuclear surface these clusters can be identified as alpha particles. A correction for the surface effects is introduced for atomic nuclei. Taking into account an additional interaction between clusters the binding energies and sizes of the considered nuclei are very accurately described. The limits of the EoS parameters are established from the properties of the α, {sup 3}He and t particles. (orig.)
Osrin, David; Azad, Kishwar; Fernandez, Armida; Manandhar, Dharma S; Mwansambo, Charles W; Tripathy, Prasanta; Costello, Anthony M
2009-10-01
Public health interventions usually operate at the level of groups rather than individuals, and cluster randomized controlled trials (RCTs) are one means of evaluating their effectiveness. Using examples from six such trials in Bangladesh, India, Malawi and Nepal, we discuss our experience of the ethical issues that arise in their conduct. We set cluster RCTs in the broader context of public health research, highlighting debates about the need to reconcile individual autonomy with the common good and about the ethics of public health research in low-income settings in general. After a brief introduction to cluster RCTs, we discuss particular challenges we have faced. These include the nature of - and responsibility for - group consent, and the need for consent by individuals within groups to intervention and data collection. We discuss the timing of consent in relation to the implementation of public health strategies, and the problem of securing ethical review and approval in a complex domain. Finally, we consider the debate about benefits to control groups and the standard of care that they should receive, and the issue of post-trial adoption of the intervention under test.
Scuseria, Gustavo E; Henderson, Thomas M; Bulik, Ireneusz W
2013-09-14
We establish a formal connection between the particle-particle (pp) random phase approximation (RPA) and the ladder channel of the coupled cluster doubles (CCD) equations. The relationship between RPA and CCD is best understood within a Bogoliubov quasiparticle (qp) RPA formalism. This work is a follow-up to our previous formal proof on the connection between particle-hole (ph) RPA and ring-CCD. Whereas RPA is a quasibosonic approximation, CC theory is a "correct bosonization" in the sense that the wavefunction and Hilbert space are exactly fermionic, yet the amplitude equations can be interpreted as adding different quasibosonic RPA channels together. Coupled cluster theory achieves this goal by interacting the ph (ring) and pp (ladder) diagrams via a third channel that we here call "crossed-ring" whose presence allows for full fermionic antisymmetry. Additionally, coupled cluster incorporates what we call "mosaic" terms which can be absorbed into defining a new effective one-body Hamiltonian. The inclusion of these mosaic terms seems to be quite important. The pp-RPA and qp-RPA equations are textbook material in nuclear structure physics but are largely unknown in quantum chemistry, where particle number fluctuations and Bogoliubov determinants are rarely used. We believe that the ideas and connections discussed in this paper may help design improved ways of incorporating RPA correlation into density functionals based on a CC perspective.
Scuseria, Gustavo E; Bulik, Ireneusz W
2013-01-01
We establish a formal connection between the particle-particle (pp) random phase approximation (RPA) and the ladder channel of the coupled cluster doubles (CCD) equations. The relationship between RPA and CCD is best understood within a Bogoliubov quasiparticle (qp) RPA formalism. This work is a follow-up to our previous formal proof on the connection between particle-hole (ph) RPA and ring-CCD. Whereas RPA is a quasibosonic approximation, CC theory is a correct bosonization in the sense that the wavefunction and Hilbert space are exactly fermionic. Coupled cluster theory achieves this goal by interacting the ph (ring) and pp (ladder) diagrams via a third channel that we here call "crossed-ring" whose presence allows for full fermionic antisymmetry. Additionally, coupled cluster incorporates what we call "mosaic" terms which can be absorbed into defining a new effective one-body Hamiltonian. The inclusion of these mosaic terms seems to be quite important. The pp-RPA an d qp-RPA equations are textbook material...
An Intervention to Enhance Obstetric and Newborn Care in India: A cluster randomized-trial
Goudar, Shivaprasad S.; Derman, Richard J.; Honnungar, Narayan V.; Patil, Kamal P.; Swamy, Mallaiah K.; Moore, Janet; Wallace, Dennis D.; McClure, Elizabeth M.; Kodkany, Bhalchandra S.; Pasha, Omrana; Sloan, Nancy L.; Wright, Linda L.; Goldenberg, Robert L.
2016-01-01
Objectives This study assessed whether community mobilization and interventions to improve emergency obstetric and newborn care (EmONC) reduced perinatal mortality (PMR) and neonatal mortality rates (NMR) in Belgaum, India. Methods The cluster-randomised controlled trial was conducted in Belgaum District, Karnataka State, India. Twenty geographic clusters were randomized to control or the intervention. The intervention engaged and mobilized community and health authorities to leverage support; strengthened community-based stabilization, referral, and transportation; and aimed to improve quality of care at facilities. Results 17,754 intervention births and 15,954 control births weighing ≥1000 g, respectively, were enrolled and analysed. Comparing the baseline period to the last 6 months period, the NMR was lower in the intervention vs. control clusters (OR=0.60, 95% CI 0.34–1.06, p=.076) as was the PMR (OR = 0.74, 95% CI 0.46–1.19, p=.20) although neither reached statistical significance. Rates of facility birth and caesarean section increased among both groups. There was limited influence on quality of care measures. Conclusions The intervention had large but not statistically significant effects on neonatal and perinatal mortality. Community mobilization and increased facility care may ultimately improve neonatal and perinatal survival, and are important in the context of the global transition towards institutional delivery. PMID:26205277
On scaling properties of cluster distributions in Ising models
Ruge, C.; Wagner, F.
1992-01-01
Scaling relations of cluster distributions for the Wolff algorithm are derived. We found them to be well satisfied for the Ising model in d=3 dimensions. Using scaling and a parametrization of the cluster distribution, we determine the critical exponent β/ν=0.516(6) with moderate effort in computing time.
Ab initio calculations and modelling of atomic cluster structure
Solov'yov, Ilia; Lyalin, Andrey G.; Greiner, Walter
2004-01-01
framework for modelling the fusion process of noble gas clusters is presented. We report the striking correspondence of the peaks in the experimentally measured abundance mass spectra with the peaks in the size-dependence of the second derivative of the binding energy per atom calculated for the chain...... of the noble gas clusters up to 150 atoms....
Fitting Latent Cluster Models for Networks with latentnet
Pavel N. Krivitsky
2007-12-01
Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoﬀ, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.
Modelling Catalyst Surfaces Using DFT Cluster Calculations
Oliver Kröcher
2009-09-01
Full Text Available 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.
Modelling catalyst surfaces using DFT cluster calculations.
Czekaj, Izabela; Wambach, Jörg; Kröcher, Oliver
2009-11-20
We review our recent theoretical DFT cluster studies of a variety of industrially relevant catalysts such as TiO(2), gamma-Al(2)O(3), V(2)O(5)-WO(3)-TiO(2) and Ni/Al(2)O(3). 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.
Structures and components in galaxy clusters: observations and models
Bykov, A M; Ferrari, C; Forman, W R; Kaastra, J S; Klein, U; Markevitch, M; de Plaa, J
2015-01-01
Clusters of galaxies are the largest gravitationally bounded structures in the Universe dominated by dark matter. We review the observational appearance and physical models of plasma structures in clusters of galaxies. Bubbles of relativistic plasma which are inflated by supermassive black holes of AGNs, cooling and heating of the gas, large scale plasma shocks, cold fronts, non-thermal halos and relics are observed in clusters. These constituents are reflecting both the formation history and the dynamical properties of clusters of galaxies. We discuss X-ray spectroscopy as a tool to study the metal enrichment in clusters and fine spectroscopy of Fe X-ray lines as a powerful diagnostics of both the turbulent plasma motions and the energetics of the non-thermal electron populations. The knowledge of the complex dynamical and feedback processes is necessary to understand the energy and matter balance as well as to constrain the role of the non-thermal components of clusters.
Mock Observations of Blue Stragglers in Globular Cluster Models
Sills, Alison; Chatterjee, Sourav; Rasio, Frederic A
2013-01-01
We created artificial color-magnitude diagrams of Monte Carlo dynamical models of globular clusters, and then used observational methods to determine the number of blue stragglers in those clusters. We compared these blue stragglers to various cluster properties, mimicking work that has been done for blue stragglers in Milky Way globular clusters to determine the dominant formation mechanism(s) of this unusual stellar population. We find that a mass-based prescription for selecting blue stragglers will choose approximately twice as many blue stragglers than a selection criterion that was developed for observations of real clusters. However, the two numbers of blue stragglers are well-correlated, so either selection criterion can be used to characterize the blue straggler population of a cluster. We confirm previous results that the simplified prescription for the evolution of a collision or merger product in the BSE code overestimates the lifetime of collision products. Because our observationally-motivated s...
Jochems EC
2015-12-01
Full Text Available Eline C Jochems,1,2 Christina M van der Feltz-Cornelis,1–3 Arno van Dam,3,4 Hugo J Duivenvoorden,5 Cornelis L Mulder1,6 1Department of Psychiatry, Epidemiological and Social Psychiatric Research Institute, Erasmus MC University Medical Center, Rotterdam, the Netherlands; 2GGz Breburg, Top Clinical Center for Body, Mind and Health, Tilburg, the Netherlands; 3Tilburg University, Faculty of Social Sciences, Tranzo Department, Tilburg, the Netherlands; 4GGZ Westelijk Noord Brabant, Bergen op Zoom, the Netherlands; 5Erasmus MC University Medical Center, Rotterdam, the Netherlands; 6BavoEuropoort, Parnassia Psychiatric Institute, Rotterdam, the Netherlands Objective: To evaluate the effectiveness of providing clinicians with regular feedback on the patient’s motivation for treatment in increasing treatment engagement in patients with severe mental illness.Methods: Design: cluster randomized controlled trial (Dutch Trials Registry NTR2968. Participants: adult outpatients with a primary diagnosis of a psychotic disorder or a personality disorder and their clinicians, treated in 12 community mental health teams (the clusters of two mental health institutions in the Netherlands. Interventions: monthly motivation feedback (MF generated by clinicians additional to treatment as usual (TAU and TAU by the community mental health teams. Primary outcome: treatment engagement at patient level, assessed at 12 months by clinicians. Randomization: teams were allocated to MF or TAU by a computerized randomization program that randomized each team to a single treatment by blocks of varying size. All participants within these teams received similar treatment. Clinicians and patients were not blind to treatment allocation at the 12-month assessment.Results: The 294 randomized patients (148 MF, 146 TAU and 57 clinicians (29 MF, 28 TAU of 12 teams (6 MF, 6 TAU were analyzed according to the intention-to-treat principle. No statistically significant differences
Batra, Priya; Mangione, Carol M; Cheng, Eric; Steers, W Neil; Nguyen, Tina A; Bell, Douglas; Kuo, Alice A; Gregory, Kimberly D
2017-01-01
To evaluate whether exposure to MyFamilyPlan-a web-based preconception health education module-changes the proportion of women discussing reproductive health with providers at well-woman visits. Cluster randomized controlled trial. One hundred thirty participants per arm distributed among 34 clusters (physicians) required to detect a 20% change in the primary outcome. Urban academic medical center (California). Eligible women were 18 to 45 years old, were English speaking, were nonpregnant, were able to access the Internet, and had an upcoming well-woman visit. E-mail and phone recruitment between September 2015 and May 2016; 292 enrollees randomized. Intervention participants completed the MyFamilyPlan module online 7 to 10 days before a scheduled well-woman visit; control participants reviewed standard online preconception health education materials. The primary outcome was self-reported discussion of reproductive health with the physician at the well-woman visit. Self-reported secondary outcomes were folic acid use, contraceptive method initiation/change, and self-efficacy score. Multilevel multivariate logistic regression. After adjusting for covariates and cluster, exposure to MyFamilyPlan was the only variable significantly associated with an increase in the proportion of women discussing reproductive health with providers (odds ratio: 1.97, 95% confidence interval: 1.22-3.19). Prespecified secondary outcomes were unaffected. MyFamilyPlan exposure was associated with a significant increase in the proportion of women who reported discussing reproductive health with providers and may promote preconception health awareness; more work is needed to affect associated behaviors.
Kenaszchuk Chris
2007-09-01
Full Text Available Abstract Background Despite a burgeoning interest in using interprofessional approaches to promote effective collaboration in health care, systematic reviews find scant evidence of benefit. This protocol describes the first cluster randomized controlled trial (RCT to design and evaluate an intervention intended to improve interprofessional collaborative communication and patient-centred care. Objectives The objective is to evaluate the effects of a four-component, hospital-based staff communication protocol designed to promote collaborative communication between healthcare professionals and enhance patient-centred care. Methods The study is a multi-centre mixed-methods cluster randomized controlled trial involving twenty clinical teaching teams (CTTs in general internal medicine (GIM divisions of five Toronto tertiary-care hospitals. CTTs will be randomly assigned either to receive an intervention designed to improve interprofessional collaborative communication, or to continue usual communication practices. Non-participant naturalistic observation, shadowing, and semi-structured, qualitative interviews were conducted to explore existing patterns of interprofessional collaboration in the CTTs, and to support intervention development. Interviews and shadowing will continue during intervention delivery in order to document interactions between the intervention settings and adopters, and changes in interprofessional communication. The primary outcome is the rate of unplanned hospital readmission. Secondary outcomes are length of stay (LOS; adherence to evidence-based prescription drug therapy; patients' satisfaction with care; self-report surveys of CTT staff perceptions of interprofessional collaboration; and frequency of calls to paging devices. Outcomes will be compared on an intention-to-treat basis using adjustment methods appropriate for data from a cluster randomized design. Discussion Pre-intervention qualitative analysis revealed that a
Model-based clustering in networks with Stochastic Community Finding
McDaid, Aaron F; Friel, Nial; Hurley, Neil J
2012-01-01
In the model-based clustering of networks, blockmodelling may be used to identify roles in the network. We identify a special case of the Stochastic Block Model (SBM) where we constrain the cluster-cluster interactions such that the density inside the clusters of nodes is expected to be greater than the density between clusters. This corresponds to the intuition behind community-finding methods, where nodes tend to clustered together if they link to each other. We call this model Stochastic Community Finding (SCF) and present an efficient MCMC algorithm which can cluster the nodes, given the network. The algorithm is evaluated on synthetic data and is applied to a social network of interactions at a karate club and at a monastery, demonstrating how the SCF finds the 'ground truth' clustering where sometimes the SBM does not. The SCF is only one possible form of constraint or specialization that may be applied to the SBM. In a more supervised context, it may be appropriate to use other specializations to guide...
Kinetic models with randomly perturbed binary collisions
Bassetti, Federico; Toscani, Giuseppe
2010-01-01
We introduce a class of Kac-like kinetic equations on the real line, with general random collisional rules, which include as particular cases models for wealth redistribution in an agent-based market or models for granular gases with a background heat bath. Conditions on these collisional rules which guarantee both the existence and uniqueness of equilibrium profiles and their main properties are found. We show that the characterization of these stationary solutions is of independent interest, since the same profiles are shown to be solutions of different evolution problems, both in the econophysics context and in the kinetic theory of rarefied gases.
VOODB: A Generic Discrete-Event Random Simulation Model to Evaluate the Performances of OODBs
Darmont, Jérôme
1999-01-01
Performance of object-oriented database systems (OODBs) is still an issue to both designers and users nowadays. The aim of this paper is to propose a generic discrete-event random simulation model, called VOODB, in order to evaluate the performances of OODBs in general, and the performances of optimization methods like clustering in particular. Such optimization methods undoubtedly improve the performances of OODBs. Yet, they also always induce some kind of overhead for the system. Therefore, it is important to evaluate their exact impact on the overall performances. VOODB has been designed as a generic discrete-event random simulation model by putting to use a modelling approach, and has been validated by simulating the behavior of the O2 OODB and the Texas persistent object store. Since our final objective is to compare object clustering algorithms, some experiments have also been conducted on the DSTC clustering technique, which is implemented in Texas. To validate VOODB, performance results obtained by si...
Allen, Kelli D; Oddone, Eugene Z; Coffman, Cynthia J; Jeffreys, Amy S; Bosworth, Hayden B; Chatterjee, Ranee; McDuffie, Jennifer; Strauss, Jennifer L; Yancy, William S; Datta, Santanu K; Corsino, Leonor; Dolor, Rowena J
2017-03-21
A single-site study showed that a combined patient and provider intervention improved outcomes for patients with knee osteoarthritis, but it did not assess separate effects of the interventions. To examine whether patient-based, provider-based, and patient-provider interventions improve osteoarthritis outcomes. Cluster randomized trial with assignment to patient, provider, and patient-provider interventions or usual care. (ClinicalTrials.gov: NCT01435109). 10 Duke University Health System community-based primary care clinics. 537 outpatients with symptomatic hip or knee osteoarthritis. The telephone-based patient intervention focused on weight management, physical activity, and cognitive behavioral pain management. The provider intervention involved electronic delivery of patient-specific osteoarthritis treatment recommendations to providers. The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score at 12 months. Secondary outcomes were objective physical function (Short Physical Performance Battery) and depressive symptoms (Patient Health Questionnaire). Linear mixed models assessed the difference in improvement among groups. No difference was observed in WOMAC score changes from baseline to 12 months in the patient (-1.5 [95% CI, -5.1 to 2.0]; P = 0.40), provider (2.5 [CI, -0.9 to 5.9]; P = 0.152), or patient-provider (-0.7 [CI, -4.2 to 2.8]; P = 0.69) intervention groups compared with usual care. All groups had improvements in WOMAC scores at 12 months (range, -3.7 to -7.7). In addition, no differences were seen in objective physical function or depressive symptoms at 12 months in any of the intervention groups compared with usual care. The study involved 1 health care network. Data on provider referrals were not collected. Contrary to a previous study of a combined patient and provider intervention for osteoarthritis in a Department of Veterans Affairs medical center, this study found no statistically
Willging, Cathleen E; Green, Amy E; Ramos, Mary M
2016-10-22
Reducing youth suicide in the United States (U.S.) is a national public health priority, and lesbian, gay, bisexual, transgender, and queer or questioning (LGBTQ) youth are at elevated risk. The Centers for Disease Control and Prevention (CDC) endorses six evidence-based (EB) strategies that center on meeting the needs of LGBTQ youth in schools; however, fewer than 6 % of U.S. schools implement all of them. The proposed intervention model, "RLAS" (Implementing School Nursing Strategies to Reduce LGBTQ Adolescent Suicide), builds on the Exploration, Preparation, Implementation, and Sustainment (EPIS) conceptual framework and the Dynamic Adaptation Process (DAP) to implement EB strategies in U.S. high schools. The DAP accounts for the multilevel context of school settings and uses Implementation Resource Teams (IRTs) to facilitate appropriate expertise, advise on acceptable adaptations, and provide data feedback to make schools implementation ready and prepared to sustain changes. Mixed methods will be used to examine individual, school, and community factors influencing both implementation process and youth outcomes. A cluster randomized controlled trial will assess whether LGBTQ students and their peers in RLAS intervention schools (n = 20) report reductions in suicidality, depression, substance use, bullying, and truancy related to safety concerns compared to those in usual care schools (n = 20). Implementation progress and fidelity for each EB strategy in RLAS intervention schools will be examined using a modified version of the Stages of Implementation Completion checklist. During the implementation and sustainment phases, annual focus groups will be conducted with the 20 IRTs to document their experiences identifying and advancing adaptation supports to facilitate use of EB strategies and their perceptions of the DAP. The DAP represents a data-informed, collaborative, multiple stakeholder approach to progress from exploration to sustainment and obtain
Percolation properties of the Wolff clusters in planar triangular spin models
Leung, P. W.; Henley, Christopher L.
1991-01-01
We formulate the Wolff algorithm as a site-bond percolation problem, apply it to the ferromagnetic and antiferromagnetic planar triangular spin models, and study the percolation critical behavior using finite-size scaling. In the former case the Wolff algorithm is successful as an accelerating algorithm, whereas in the latter case it is not. We found the percolation temperatures and the cluster exponents for both models. In the antiferromagnetic model, the percolation temperature is higher than the critical temperature of the spin system. The cluster exponents are found to be the same as the random two-dimensional (2D) percolation. In the ferromagnetic model, the percolation temperature agrees with the critical temperature, and the cluster exponents are different from the random 2D percolation, meaning that they are in different universal classes. For the ferromagnetic model we discuss the mechanism of the cluster growth in the regime of the Kosterlitz-Thouless transition. We also note a relation between the dynamic exponent and the percolation exponents.
Particle filters for random set models
Ristic, Branko
2013-01-01
“Particle Filters for Random Set Models” presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. The resulting algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from navigation and autonomous vehicles to bio-informatics and finance. While particle filters have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. This book...
Random graph models for dynamic networks
Zhang, Xiao; Newman, M E J
2016-01-01
We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.
Ma, Jinhui; Thabane, Lehana; Kaczorowski, Janusz; Chambers, Larry; Dolovich, Lisa; Karwalajtys, Tina; Levitt, Cheryl
2009-06-16
Cluster randomized trials (CRTs) are increasingly used to assess the effectiveness of interventions to improve health outcomes or prevent diseases. However, the efficiency and consistency of using different analytical methods in the analysis of binary outcome have received little attention. We described and compared various statistical approaches in the analysis of CRTs using the Community Hypertension Assessment Trial (CHAT) as an example. The CHAT study was a cluster randomized controlled trial aimed at investigating the effectiveness of pharmacy-based blood pressure clinics led by peer health educators, with feedback to family physicians (CHAT intervention) against Usual Practice model (Control), on the monitoring and management of BP among older adults. We compared three cluster-level and six individual-level statistical analysis methods in the analysis of binary outcomes from the CHAT study. The three cluster-level analysis methods were: i) un-weighted linear regression, ii) weighted linear regression, and iii) random-effects meta-regression. The six individual level analysis methods were: i) standard logistic regression, ii) robust standard errors approach, iii) generalized estimating equations, iv) random-effects meta-analytic approach, v) random-effects logistic regression, and vi) Bayesian random-effects regression. We also investigated the robustness of the estimates after the adjustment for the cluster and individual level covariates. Among all the statistical methods assessed, the Bayesian random-effects logistic regression method yielded the widest 95% interval estimate for the odds ratio and consequently led to the most conservative conclusion. However, the results remained robust under all methods - showing sufficient evidence in support of the hypothesis of no effect for the CHAT intervention against Usual Practice control model for management of blood pressure among seniors in primary care. The individual-level standard logistic regression is the
Dolovich Lisa
2009-06-01
Full Text Available Abstract Background Cluster randomized trials (CRTs are increasingly used to assess the effectiveness of interventions to improve health outcomes or prevent diseases. However, the efficiency and consistency of using different analytical methods in the analysis of binary outcome have received little attention. We described and compared various statistical approaches in the analysis of CRTs using the Community Hypertension Assessment Trial (CHAT as an example. The CHAT study was a cluster randomized controlled trial aimed at investigating the effectiveness of pharmacy-based blood pressure clinics led by peer health educators, with feedback to family physicians (CHAT intervention against Usual Practice model (Control, on the monitoring and management of BP among older adults. Methods We compared three cluster-level and six individual-level statistical analysis methods in the analysis of binary outcomes from the CHAT study. The three cluster-level analysis methods were: i un-weighted linear regression, ii weighted linear regression, and iii random-effects meta-regression. The six individual level analysis methods were: i standard logistic regression, ii robust standard errors approach, iii generalized estimating equations, iv random-effects meta-analytic approach, v random-effects logistic regression, and vi Bayesian random-effects regression. We also investigated the robustness of the estimates after the adjustment for the cluster and individual level covariates. Results Among all the statistical methods assessed, the Bayesian random-effects logistic regression method yielded the widest 95% interval estimate for the odds ratio and consequently led to the most conservative conclusion. However, the results remained robust under all methods – showing sufficient evidence in support of the hypothesis of no effect for the CHAT intervention against Usual Practice control model for management of blood pressure among seniors in primary care. The
Old star clusters: Bench tests of low mass stellar models
Salaris M.
2013-03-01
Full Text Available Old star clusters in the Milky Way and external galaxies have been (and still are traditionally used to constrain the age of the universe and the timescales of galaxy formation. A parallel avenue of old star cluster research considers these objects as bench tests of low-mass stellar models. This short review will highlight some recent tests of stellar evolution models that make use of photometric and spectroscopic observations of resolved old star clusters. In some cases these tests have pointed to additional physical processes efficient in low-mass stars, that are not routinely included in model computations. Moreover, recent results from the Kepler mission about the old open cluster NGC6791 are adding new tight constraints to the models.
A Collaboration Service Model for a Global Port Cluster
Toh, Keith K.T; Welsh, Karyn; Hassall, Kim
2010-01-01
... between business entities within the cluster. The maturity of technologies providing portals, web and middleware services provides an opportunity to push the boundaries of contemporary service reference models and service catalogues to what...
Some Abnormal Properties of Water in the Cluster Model
G.A. Melnikov
2013-12-01
Full Text Available In the framework of the cluster model developed by the structure of liquids for the anomalous dependences of the speed of sound and thermal conductivity of water temperature along the liquid-vapor equilibrium are explained.
Wind farms model aggregation using probabilistic clustering
Fernandes, Paula Odete; Ferreira, Ángela Paula
2013-10-01
The main objective of this research is the identification of homogeneous groups within wind farms of a major operator playing in the energy sector in Portugal, based on two multivariate analyses: Hierarchical Cluster Analysis and Discriminant Analysis, by using two independent variables: annual liquid hours and net production. From the produced outputs there were identified three homogenous groups of wind farms: (1) medium Installed Capacity and Induction Generator based Technology, (2) high Installed Capacity and Synchronous Generator based Technology and (3) medium Installed Capacity and Synchronous Generator based Technology, which includes the wind farms with the higher annual liquid hours. It has been found that the results obtained by cluster analysis are well classified, with a total percentage of correct classification of 97,1%, which can be considered excellent.
Noella Angele Piquette
2014-12-01
Full Text Available The present paper reports a cluster randomized control trial evaluation of teaching using ABRACADABRA (ABRA, an evidence-based and web-based literacy intervention (http://abralite.concordia.ca with 107 kindergarten and 96 grade 1 children in 24 classes (12 intervention 12 control classes from all 12 elementary schools in one school district in Canada. Children in the intervention condition received 10-12 hours of whole class instruction using ABRA between pre- and post-test. Hierarchical linear modeling of post-test results showed significant gains in letter-sound knowledge for intervention classrooms over control classrooms. In addition, medium effect sizes were evident for three of five outcome measures favoring the intervention: letter-sound knowledge (d = +.66, phonological blending (d = +.52, and word reading (d = +.52, over effect sizes for regular teaching. It is concluded that regular teaching with ABRA technology adds significantly to literacy in the early elementary years.
Earnshaw, Valerie; Lewis, Jessica B.; Kershaw, Trace S.; Magriples, Urania; Stasko, Emily; Rising, Sharon Schindler; Cassells, Andrea; Cunningham, Shayna; Bernstein, Peter; Tobin, Jonathan N.
2016-01-01
Objectives. We compared an evidence-based model of group prenatal care to traditional individual prenatal care on birth, neonatal, and reproductive health outcomes. Methods. We performed a multisite cluster randomized controlled trial in 14 health centers in New York City (2008–2012). We analyzed 1148 pregnant women aged 14 to 21 years, at less than 24 weeks of gestation, and not at high obstetrical risk. We assessed outcomes via medical records and surveys. Results. In intention-to-treat analyses, women at intervention sites were significantly less likely to have infants small for gestational age (prenatal care resulted in more favorable birth, neonatal, and reproductive outcomes. Successful translation of clinical innovations to enhance care, improve outcomes, and reduce cost requires strategies that facilitate patient adherence and support organizational change. PMID:26691105
A liquid drop model for embedded atom method cluster energies
Finley, C. W.; Abel, P. B.; Ferrante, J.
1996-01-01
Minimum energy configurations for homonuclear clusters containing from two to twenty-two atoms of six metals, Ag, Au, Cu, Ni, Pd, and Pt have been calculated using the Embedded Atom Method (EAM). The average energy per atom as a function of cluster size has been fit to a liquid drop model, giving estimates of the surface and curvature energies. The liquid drop model gives a good representation of the relationship between average energy and cluster size. As a test the resulting surface energies are compared to EAM surface energy calculations for various low-index crystal faces with reasonable agreement.
Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions
Nedialkova, Lilia V.; Amat, Miguel A. [Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544 (United States); Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544 (United States); Hummer, Gerhard, E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438 Frankfurt am Main (Germany)
2014-09-21
Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.
Fractal dimension of critical clusters in the Φ44 model
Jansen, K.; Lang, C. B.
1991-06-01
We study the d=4 O(4) symmetric nonlinear sigma model at the pseudocritical points for 84-284 lattices. The Fortuin-Kasteleyn-Coniglio-Klein clusters are shown to have fractal dimension df~=3-in accordance with the conjectured scaling relation involving the odd critical exponent δ. For the one cluster algorithm introduced recently by Wolff the dynamical critical exponent z comes out to be compatible with zero in this model.
Clustering of European winter storms: A multi-model perspective
Renggli, Dominik; Buettner, Annemarie; Scherb, Anke; Straub, Daniel; Zimmerli, Peter
2016-04-01
The storm series over Europe in 1990 (Daria, Vivian, Wiebke, Herta) and 1999 (Anatol, Lothar, Martin) are very well known. Such clusters of severe events strongly affect the seasonally accumulated damage statistics. The (re)insurance industry has quantified clustering by using distribution assumptions deduced from the historical storm activity of the last 30 to 40 years. The use of storm series simulated by climate models has only started recently. Climate model runs can potentially represent 100s to 1000s of years, allowing a more detailed quantification of clustering than the history of the last few decades. However, it is unknown how sensitive the representation of clustering is to systematic biases. Using a multi-model ensemble allows quantifying that uncertainty. This work uses CMIP5 decadal ensemble hindcasts to study clustering of European winter storms from a multi-model perspective. An objective identification algorithm extracts winter storms (September to April) in the gridded 6-hourly wind data. Since the skill of European storm predictions is very limited on the decadal scale, the different hindcast runs are interpreted as independent realizations. As a consequence, the available hindcast ensemble represents several 1000 simulated storm seasons. The seasonal clustering of winter storms is quantified using the dispersion coefficient. The benchmark for the decadal prediction models is the 20th Century Reanalysis. The decadal prediction models are able to reproduce typical features of the clustering characteristics observed in the reanalysis data. Clustering occurs in all analyzed models over the North Atlantic and European region, in particular over Great Britain and Scandinavia as well as over Iberia (i.e. the exit regions of the North Atlantic storm track). Clustering is generally weaker in the models compared to reanalysis, although the differences between different models are substantial. In contrast to existing studies, clustering is driven by weak
Estimation in Dirichlet random effects models
Kyung, Minjung; Casella, George; 10.1214/09-AOS731
2010-01-01
We develop a new Gibbs sampler for a linear mixed model with a Dirichlet process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet distributions, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms. We also investigate methods for the estimation of the precision parameter of the Dirichlet process, finding that maximum likelihood may not be desirable, but a posterior mode is a reasonable approach. Examples are given to show how these models perform on real data. Our results complement both the theoretical basis of the Dirichlet process nonparametric prior and the computational work that has been done to date.
Effective Transparency: A Test of Atomistic Laser-Cluster Models
Pandit, Rishi; Teague, Thomas; Hartwick, Zachary; Bigaouette, Nicolas; Ramunno, Lora; Ackad, Edward
2016-01-01
The effective transparency of rare-gas clusters, post-interaction with an extreme ultraviolet (XUV) pump pulse, is studied by using an atomistic hybrid quantum-classical molecular dynamics model. We find there is an intensity range in which an XUV probe pulse has no lasting effect on the average charge state of a cluster after being saturated by an XUV pump pulse: the cluster is effectively transparent to the probe pulse. The range of this phenomena increases with the size of the cluster and thus provides an excellent candidate for an experimental test of the effective transparency effect. We present predictions for the clusters at the peak of the laser pulse as well as the experimental time-of-flight signal expected along with trends which can be compared with. Significant deviations from these predictions would provide evidence for enhanced photoionization mechanism(s).
Modeling the Tenuous Intracluster Medium in Globular Clusters
Naiman, J; Ramirez-Ruiz, E
2013-01-01
We employ hydrodynamical simulations to investigate the underlying mechanism responsible for the low levels of gas and dust in globular clusters. Our models examine the competing effects of mass supply from the evolved stellar population and energy injection from the main sequence stellar members for globular clusters 47 Tucanae, M15, NGC 6440, and NGC 6752. Disregarding all other gas evacuation processes, we find that the energy output from the main sequence stellar population alone is capable of effectively clearing the evolved stellar ejecta and producing intracluster gas densities consistent with current observational constraints. This result distinguishes a viable ubiquitous gas and dust evacuation mechanism for globular clusters. In addition, we extend our analysis to probe the efficiency of pulsar wind feedback in globular clusters. The detection of intracluster ionized gas in cluster 47 Tucanae allows us to place particularly strict limits on pulsar wind thermalization efficiency, which must be extrem...
CLUSTERS AS A MODEL OF ECONOMIC DEVELOPMENT OF SERBIA
Marko Laketa
2013-12-01
Full Text Available Insufficient competitiveness of small and medium enterprises in Serbia can be significantly improved by a system of business associations through clusters, business incubators and technology parks. This connection contributes to the growth and development of not only the cluster members, but has a regional and national dimension as well because without it there is no significant breakthrough on the international market. The process of association of small and medium enterprises in clusters and other forms of interconnection in Serbia is far from the required and potential level.The awareness on the importance of clusters in a local economic development through contributions to the advancement of small and medium sized enterprises is not yet sufficiently mature. Support to associating into clusters and usage of their benefits after the model of highly developed countries is the basis for leading a successful economic policy and in Serbia there are all necessary prerequisites for it.
Emergence of Clustering in an Acquaintance Model without Homophily
Bhat, Uttam; Redner, S
2014-01-01
We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of this constraint, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. That is, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them is controllable. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition and the network consists of a collection of well-defined communities close to the transition. As a function of time, the network can undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, albeit less dramatically, in Facebook networks.
Multi-mode clustering model for hierarchical wireless sensor networks
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.
Korshøj, Mette; Krustrup, Peter; Jørgensen, Marie Birk;
2012-01-01
Prevalence of cardiovascular risk factors is unevenly distributed among occupational groups. The working environment, as well as lifestyle and socioeconomic status contribute to the disparity and variation in prevalence of these risk factors. High physical work demands have been shown to increase...... the risk for cardiovascular disease and mortality, contrary to leisure time physical activity. High physical work demands in combination with a low cardiorespiratory fitness infer a high relative workload and an excessive risk for cardiovascular mortality. Therefore, the aim of this study is to examine...... and cardiovascular risk factors among cleaners. Cleaners are eligible if they are employed ≥ 20 hours/week, at one of the enrolled companies. In the randomization, strata are formed according to the manager the participant reports to. The clusters will be balanced on the following criteria: Geographical work...
Guiteras, Raymond; Levinsohn, James; Mobarak, Ahmed Mushfiq
2015-05-22
Poor sanitation contributes to morbidity and mortality in the developing world, but there is disagreement on what policies can increase sanitation coverage. To measure the effects of alternative policies on investment in hygienic latrines, we assigned 380 communities in rural Bangladesh to different marketing treatments-community motivation and information; subsidies; a supply-side market access intervention; and a control-in a cluster-randomized trial. Community motivation alone did not increase hygienic latrine ownership (+1.6 percentage points, P = 0.43), nor did the supply-side intervention (+0.3 percentage points, P = 0.90). Subsidies to the majority of the landless poor increased ownership among subsidized households (+22.0 percentage points, P < 0.001) and their unsubsidized neighbors (+8.5 percentage points, P = 0.001), which suggests that investment decisions are interlinked across neighbors. Subsidies also reduced open defecation by 14 percentage points (P < 0.001).
林金官; 韦博成
2004-01-01
In this paper, it is discussed that two tests for varying dispersion of binomial data in the framework of nonlinear logistic models with random effects, which are widely used in analyzing longitudinal binomial data. One is the individual test and power calculation for varying dispersion through testing the randomness of cluster effects, which is extensions of Dean(1992) and Commenges et al (1994). The second test is the composite test for varying dispersion through simultaneously testing the randomness of cluster effects and the equality of random-effect means. The score test statistics are constructed and expressed in simple, easy to use, matrix formulas. The authors illustrate their test methods using the insecticide data (Giltinan, Capizzi & Malani (1988)).
Aerosol cluster impact and break-up : II. Atomic and Cluster Scale Models.
Lechman, Jeremy B.; Takato, Yoichi (State University of New York at Buffalo, Buffalo, NY)
2010-09-01
Understanding the interaction of aerosol particle clusters/flocs with surfaces is an area of interest for a number of processes in chemical, pharmaceutical, and powder manufacturing as well as in steam-tube rupture in nuclear power plants. Developing predictive capabilities for these applications involves coupled phenomena on multiple length and timescales from the process macroscopic scale ({approx}1m) to the multi-cluster interaction scale (1mm-0.1m) to the single cluster scale ({approx}1000 - 10000 particles) to the particle scale (10nm-10{micro}m) interactions, and on down to the sub-particle, atomic scale interactions. The focus of this report is on the single cluster scale; although work directed toward developing better models of particle-particle interactions by considering sub-particle scale interactions and phenomena is also described. In particular, results of mesoscale (i.e., particle to single cluster scale) discrete element method (DEM) simulations for aerosol cluster impact with rigid walls are presented. The particle-particle interaction model is based on JKR adhesion theory and is implemented as an enhancement to the granular package in the LAMMPS code. The theory behind the model is outlined and preliminary results are shown. Additionally, as mentioned, results from atomistic classical molecular dynamics simulations are also described as a means of developing higher fidelity models of particle-particle interactions. Ultimately, the results from these and other studies at various scales must be collated to provide systems level models with accurate 'sub-grid' information for design, analysis and control of the underlying systems processes.
Collard, Dorine C M; Verhagen, Evert A L M; Chinapaw, Mai J M; Knol, Dirk L; van Mechelen, Willem
2010-02-01
To study the effects of a school-based injury prevention program on physical activity injury incidence and severity. Cluster randomized controlled trial performed from January 1, 2006, through July 31, 2007. Forty Dutch primary schools. A total of 2210 children (aged 10-12 years). Schools were randomized to receive either the regular curriculum or an intervention program that targeted physical activity injuries. Incidence and severity of physical activity injuries per 1000 hours of physical activity participation. A total of 100 injuries in the intervention group and 104 injuries in the control group were registered. Nonresponse at baseline or follow-up was minimal (8.7%). The Cox regression analyses adjusted for clustering showed a small nonsignificant intervention effect on total (HR, 0.81; 95% confidence interval [CI], 0.41-1.59), sports club (0.69; 0.28-1.68), and leisure time injuries (0.75; 0.36-1.55). However, physical activity appeared to be an effect modifier. In those who were less physically active, the intervention had a larger effect. The intervention reduced the total and leisure time injury incidence (HR, 0.47; 95% CI, 0.21-1.06; and 0.43; 0.16-1.14; respectively). Sports club injury incidence was significantly reduced (HR, 0.23; 95% CI, 0.07-0.75). We found a substantial and relevant reduction in physical activity injuries, especially in children in the low active group, because of the intervention. This school-based injury prevention program is promising, but future large-scale research is needed.
Cluster-randomized xylitol toothpaste trial for early childhood caries prevention.
Chi, Donald L; Tut, Ohnmar; Milgrom, Peter
2014-01-01
The purpose of this study was to assess the efficacy of supervised tooth-brushing with xylitol toothpaste to prevent early childhood caries (ECC) and reduce mutans streptococci. In this cluster-randomized efficacy trial, 196 four- to five-year-old children in four Head Start classrooms in the Marshall Islands were randomly assigned to supervised toothbrushing with 1,400 ppm/31 percent fluoride xylitol or 1,450 ppm fluoride sorbitol toothpaste. We hypothesized that there would be no difference in efficacy between the two types of toothpaste. The primary outcome was the surface-level primary molar caries increment (d(2-3)mfs) after six months. A single examiner was blinded to classroom assignments. Two classrooms were assigned to the fluoride-xylitol group (85 children), and two classrooms were assigned to the fluoride-sorbitol group (83 children). The child-level analyses accounted for clustering. There was no difference between the two groups in baseline or end-of-trial mean d(2-3)mfs. The mean d(2-3)mfs increment was greater in the fluoride-xylitol group compared to the fluoride-sorbitol group (2.5 and 1.4 d(2-3)mfs, respectively), but the difference was not significant (95% confidence interval: -0.17, 2.37; P=.07). No adverse effects were reported. After six months, brushing with a low-strength xylitol/fluoride tooth-paste is no more efficacious in reducing ECC than a fluoride-only toothpaste in a high caries-risk child population.
Reducing RANS Model Error Using Random Forest
Wang, Jian-Xun; Wu, Jin-Long; Xiao, Heng; Ling, Julia
2016-11-01
Reynolds-Averaged Navier-Stokes (RANS) models are still the work-horse tools in the turbulence modeling of industrial flows. However, the model discrepancy due to the inadequacy of modeled Reynolds stresses largely diminishes the reliability of simulation results. In this work we use a physics-informed machine learning approach to improve the RANS modeled Reynolds stresses and propagate them to obtain the mean velocity field. Specifically, the functional forms of Reynolds stress discrepancies with respect to mean flow features are trained based on an offline database of flows with similar characteristics. The random forest model is used to predict Reynolds stress discrepancies in new flows. Then the improved Reynolds stresses are propagated to the velocity field via RANS equations. The effects of expanding the feature space through the use of a complete basis of Galilean tensor invariants are also studied. The flow in a square duct, which is challenging for standard RANS models, is investigated to demonstrate the merit of the proposed approach. The results show that both the Reynolds stresses and the propagated velocity field are improved over the baseline RANS predictions. SAND Number: SAND2016-7437 A
Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.
1985-01-01
The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.
Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.
1985-08-01
The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references.
Suzuki, Y; Kato, T A; Sato, R; Fujisawa, D; Aoyama-Uehara, K; Hashimoto, N; Yonemoto, N; Fukasawa, M; Otsuka, K
2014-06-01
Aims. To evaluate the effectiveness of a brief suicide management training programme for Japanese medical residents compared with the usual lecture on suicidality. Methods. In this multi-center, clustered randomized controlled trial, the intervention group attended a structured suicide management programme and the control group, the usual lecture on depression and suicidality. The primary outcome was the difference in residents' cumulative competency score to manage suicidal persons from baseline (T0) to 1 month after the intervention (T2), determined using the Suicide Intervention Response Inventory (SIRI-1) score, at individual level. Results. Analysis of 114 residents (intervention group n = 65, control group n = 49) assigned to two clusters in each group revealed no change in SIRI-1 score from T0 to T2 or immediately after the intervention (T1) between the two groups. As a secondary analysis, discrepancy in judgement between the participants and Japanese suicidologists was examined immediately after the intervention in the adjusted model, with a mean difference in score of 9.98 (95% confidence interval: 4.39-15.56; p = 0.001). Conclusions. The structured programme was not proven to improve competency in suicide management when measured by the SIRI-1 score. Further elaboration of the programme and valid measurement of its outcome would be needed to show the program's effectiveness.
Liu, Baoshun; Li, Ziqiang; Zhao, Xiujian
2015-02-21
In this research, Monte-Carlo Continuity Random Walking (MC-RW) model was used to study the relation between electron transport and photocatalysis of nano-crystalline (nc) clusters. The effects of defect energy disorder, spatial disorder of material structure, electron density, and interfacial transfer/recombination on the electron transport and the photocatalysis were studied. Photocatalytic activity is defined as 1/τ from a statistical viewpoint with τ being the electron average lifetime. Based on the MC-RW simulation, a clear physical and chemical "picture" was given for the photocatalytic kinetic analysis of nc-clusters. It is shown that the increase of defect energy disorder and material spatial structural disorder, such as the decrease of defect trap number, the increase of crystallinity, the increase of particle size, and the increase of inter-particle connection, can enhance photocatalytic activity through increasing electron transport ability. The increase of electron density increases the electron Fermi level, which decreases the activation energy for electron de-trapping from traps to extending states, and correspondingly increases electron transport ability and photocatalytic activity. Reducing recombination of electrons and holes can increase electron transport through the increase of electron density and then increases the photocatalytic activity. In addition to the electron transport, the increase of probability for electrons to undergo photocatalysis can increase photocatalytic activity through the increase of the electron interfacial transfer speed.
The cluster beam route to model catalysts and beyond.
Ellis, Peter R; Brown, Christopher M; Bishop, Peter T; Yin, Jinlong; Cooke, Kevin; Terry, William D; Liu, Jian; Yin, Feng; Palmer, Richard E
2016-07-01
The generation of beams of atomic clusters in the gas phase and their subsequent deposition (in vacuum) onto suitable catalyst supports, possibly after an intermediate mass filtering step, represents a new and attractive approach for the preparation of model catalyst particles. Compared with the colloidal route to the production of pre-formed catalytic nanoparticles, the nanocluster beam approach offers several advantages: the clusters produced in the beam have no ligands, their size can be selected to arbitrarily high precision by the mass filter, and metal particles containing challenging combinations of metals can be readily produced. However, until now the cluster approach has been held back by the extremely low rates of metal particle production, of the order of 1 microgram per hour. This is more than sufficient for surface science studies but several orders of magnitude below what is desirable even for research-level reaction studies under realistic conditions. In this paper we describe solutions to this scaling problem, specifically, the development of two new generations of cluster beam sources, which suggest that cluster beam yields of grams per hour may ultimately be feasible. Moreover, we illustrate the effectiveness of model catalysts prepared by cluster beam deposition onto agitated powders in the selective hydrogenation of 1-pentyne (a gas phase reaction) and 3-hexyn-1-ol (a liquid phase reaction). Our results for elemental Pd and binary PdSn and PdTi cluster catalysts demonstrate favourable combinations of yield and selectivity compared with reference materials synthesised by conventional methods.
Cluster Development of Zhengzhou Urban Agriculture Based on Diamond Model
2012-01-01
Based on basic theory of Diamond Model,this paper analyzes the competitive power of Zhengzhou urban agriculture from production factors,demand conditions,related and supporting industries,business strategies and structure,and horizontal competition.In line with these situations,it introduces that the cluster development is an effective approach to lifting competitive power of Zhengzhou urban agriculture.Finally,it presents following countermeasures and suggestions:optimize spatial distribution for cluster development of urban agriculture;cultivate leading enterprises and optimize organizational form of urban agriculture;energetically develop low-carbon agriculture to create favorable ecological environment for cluster development of urban agriculture.
Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis
Fu, Pei-hua; Yin, Hong-bo
In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.
Muskens Esther
2013-01-01
Full Text Available Abstract Background Inappropriate use of antidepressants (AD, defined as either continuation in the absence of a proper indication or continuation despite the lack of therapeutic efficacy, applies to approximately half of all long term AD users. Methods/design We have designed a cluster randomized controlled clinical trial to assess the (cost- effectiveness of an antidepressant cessation advice in the absence of a proper indication for maintenance treatment with antidepressants in primary care. We will select all patients using antidepressants for over 9 months from 45 general practices. Patients will be diagnosed using the Composite International Diagnostic Interview (CIDI version 3.0, extended with questions about the psychiatric history and previous treatment strategies. General practices will be randomized to either the intervention or the control group. In case of overtreatment, defined as the absence of a proper indication according to current guidelines, a cessation advice is given to the general practitioner. In the control groups no specific information is given. The primary outcome measure will be the proportion of patients that successfully discontinue their antidepressants at one-year follow-up. Secondary outcomes are dimensional measures of psychopathology and costs. Discussion This study protocol provides a detailed overview of the design of the trial. Study results will be of importance for refining current guidelines. If the intervention is effective it can be used in managed care programs. Trial registration NTR2032
Encouraging Health Insurance for the Informal Sector: A Cluster Randomized Experiment in Vietnam.
Wagstaff, Adam; Nguyen, Ha Thi Hong; Dao, Huyen; Bales, Sarah
2016-06-01
Subsidized voluntary enrollment in government-run health insurance schemes is often proposed as a way of increasing coverage among informal sector workers and their families. We report the results of a cluster randomized experiment, in which 3000 households in 20 communes in Vietnam were randomly assigned at baseline to a control group or one of three treatments: an information leaflet about Vietnam's government-run scheme and the benefits of health insurance, a voucher entitling eligible household members to 25% off their annual premium, and both. At baseline, the four groups had similar enrollment rates (4%) and were balanced on plausible enrollment determinants. The interventions all had small and insignificant effects (around 1 percentage point or ppt). Among those reporting sickness in the 12 months prior to the baseline survey the subsidy-only intervention raised enrollment by 3.5 ppts (p = 0.08) while the combined intervention raised enrollment by 4.5 ppts (p = 0.02); however, the differences in the effect sizes between the sick and non-sick were just shy of being significant. Our results suggest that information campaigns and subsidies may have limited effects on voluntary health insurance enrollment in Vietnam and that such interventions might exacerbate adverse selection. Copyright © The World Bank Health Economics © 2015 John Wiley & Sons, Ltd.
Hagen, Åste M; Melby-Lervåg, Monica; Lervåg, Arne
2017-10-01
Children with language comprehension difficulties are at risk of educational and social problems, which in turn impede employment prospects in adulthood. However, few randomized trials have examined how such problems can be ameliorated during the preschool years. We conducted a cluster randomized trial in 148 preschool classrooms. Our intervention targeted language comprehension skills and lasted 1 year and 1 month, with five blocks of 6 weeks and intervention three times per week (about 75 min per week). Effects were assessed on a range of measures of language performance. Immediately after the intervention, there were moderate effects on both near, intermediate and distal measures of language performance. At delayed follow-up (7 months after the intervention), these reliable effects remained for the distal measures. It is possible to intervene in classroom settings to improve the language comprehension skills of children with language difficulties. However, it appears that such interventions need to be intensive and prolonged. © 2017 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.
Gulpers Math JM
2006-10-01
Full Text Available Abstract Background Physical restraints are still frequently used in nursing home residents despite growing evidence for the ineffectiveness and negative consequences of these methods. Therefore, reduction in the use of physical restraints in psycho-geriatric nursing home residents is very important. The aim of this study was to investigate the short-term effects of an educational intervention on the use of physical restraints in psycho-geriatric nursing home residents. Methods A cluster randomized trial was applied to 5 psycho-geriatric nursing home wards (n = 167 residents with dementia. The wards were assigned at random to either educational intervention (3 wards or control status (2 wards. The restraint status was observed and residents' characteristics, such as cognitive status, were determined by using the Minimum Data Set (MDS at baseline and 1 month after intervention. Results Restraint use did not change significantly over time in the experimental group (55%–56%, compared to a significant increased use (P Conclusion An educational programme for nurses combined with consultation with a nurse specialist did not decrease the use of physical restraints in psycho-geriatric nursing home residents in the short term. However, the residents in the control group experienced more restraint use during the study period compared to the residents in the experimental group. Whether the intervention will reduce restraint use in the long term could not be inferred from these results. Further research is necessary to gain insight into the long-term effects of this educational intervention.
Variable cluster analysis method for building neural network model
王海东; 刘元东
2004-01-01
To address the problems that input variables should be reduced as much as possible and explain output variables fully in building neural network model of complicated system, a variable selection method based on cluster analysis was investigated. Similarity coefficient which describes the mutual relation of variables was defined. The methods of the highest contribution rate, part replacing whole and variable replacement are put forwarded and deduced by information theory. The software of the neural network based on cluster analysis, which can provide many kinds of methods for defining variable similarity coefficient, clustering system variable and evaluating variable cluster, was developed and applied to build neural network forecast model of cement clinker quality. The results show that all the network scale, training time and prediction accuracy are perfect. The practical application demonstrates that the method of selecting variables for neural network is feasible and effective.
Gas phase metal cluster model systems for heterogeneous catalysis.
Lang, Sandra M; Bernhardt, Thorsten M
2012-07-14
Since the advent of intense cluster sources, physical and chemical properties of isolated metal clusters are an active field of research. In particular, gas phase metal clusters represent ideal model systems to gain molecular level insight into the energetics and kinetics of metal-mediated catalytic reactions. Here we summarize experimental reactivity studies as well as investigations of thermal catalytic reaction cycles on small gas phase metal clusters, mostly in relation to the surprising catalytic activity of nanoscale gold particles. A particular emphasis is put on the importance of conceptual insights gained through the study of gas phase model systems. Based on these concepts future perspectives are formulated in terms of variation and optimization of catalytic materials e.g. by utilization of bimetals and metal oxides. Furthermore, the future potential of bio-inspired catalytic material systems are highlighted and technical developments are discussed.
Talukder, Shamim; Farhana, Dina; Vitta, Bineti; Greiner, Ted
2017-01-01
In rural Bangladesh, most births take place at home. There is little evidence regarding the influence of traditional birth attendants (TBAs) or community volunteers (CVs) on early infant feeding practices. We conducted a pragmatic cluster randomized controlled trial in Panchagarh District to examine the effects of training and post-training supervision of TBAs/CVs on early breastfeeding practices. Nine unions were randomized into three groups of three unions. We compared outcomes between mothers in a control group (CG), those living in unions where TBAs/CVs had received a 5-day training in early feeding practices (TG) and those living in unions where TBAs/CVs were both trained and supervised (SG). A total of 1182 mothers of infants aged 0-6 months were interviewed at baseline. After 6 months of intervention, an endline survey was conducted on a different sample of 1148 mothers of infants aged 0-6 months in the same areas. In both intervention areas, TBAs/CVs made regular home visits and attended births whenever possible. Rates of early initiation of breastfeeding, avoidance of prelacteal feeds and exclusive breastfeeding were compared between groups using cluster-controlled mixed model logistic regression. At endline, both intervention groups had significantly higher proportions of mothers who reported early initiation of breastfeeding (CG: 88%, TG: 96%, SG: 96%) and avoidance of prelacteal feeds (CG: 48%, TG: 80%, SG: 88%) compared with the control group; there were no significant differences between the two intervention groups. The endline rates of reported exclusive breastfeeding were not significantly different among groups (CG: 67%, TG: 76%, SG: 83%). © 2016 John Wiley & Sons Ltd.
A random effects epidemic-type aftershock sequence model.
Lin, Feng-Chang
2011-04-01
We consider an extension of the temporal epidemic-type aftershock sequence (ETAS) model with random effects as a special case of a well-known doubly stochastic self-exciting point process. The new model arises from a deterministic function that is randomly scaled by a nonnegative random variable, which is unobservable but assumed to follow either positive stable or one-parameter gamma distribution with unit mean. Both random effects models are of interest although the one-parameter gamma random effects model is more popular when modeling associated survival times. Our estimation is based on the maximum likelihood approach with marginalized intensity. The methods are shown to perform well in simulation experiments. When applied to an earthquake sequence on the east coast of Taiwan, the extended model with positive stable random effects provides a better model fit, compared to the original ETAS model and the extended model with one-parameter gamma random effects.
Schmidt, Lukas; Fouxon, Itzhak; Holzner, Markus
2017-07-01
inertia, described by the Poisson distribution, the typical void size is of the order of the mean interparticle distance. However, at small but finite inertia, the void size is larger than the mean interparticle distance by a factor that diverges in the continuum limit of infinite number of particles, manifesting strong deviations from the Poisson distribution. Thus, voids are very sensitive to inertia and limits of zero inertia and continuum may not commute. Further, the tail of the probability distribution of the void size is log normal, in contrast with faster than exponential decay of the Poisson distribution. Remarkably, at scales where there is no clustering (so pair-correlation function of concentration splits in the product of averages), there can still be an increase of the void probability so that turbulent voiding is stronger than clustering. These results can find applications in the long-time survival probability of reacting particles where the particle survives in the void of predators. The demonstrated double stochasticity (Poisson with random intensity) of the distribution originates in the two-step formation of fluctuations of the number of particles inside the volume under observation. First, turbulence randomly brings uncorrelated particles below the viscous scale with Poisson-type probability. Then, turbulence compresses the particles inside the observation volume. We confirm the predictions by numerical observations of inertial particle motion in a chaotic Arnold-Beltrami-Childress flow. Our work implies that the particle distribution in arbitrary weakly compressible flow with finite time correlations is a LGCP.
Pai Menaka
2013-01-01
Full Text Available Abstract Background Venous thromboembolism (VTE is a common preventable cause of mortality in hospitalized medical patients. Despite rigorous randomized trials generating strong recommendations for anticoagulant use to prevent VTE, nearly 40% of medical patients receive inappropriate thromboprophylaxis. Knowledge-translation strategies are needed to bridge this gap. Methods We conducted a 16-week pilot cluster randomized controlled trial (RCT to determine the proportion of medical patients that were appropriately managed for thromboprophylaxis (according to the American College of Chest Physician guidelines within 24 hours of admission, through the use of a multicomponent knowledge-translation intervention. Our primary goal was to determine the feasibility of conducting this study on a larger scale. The intervention comprised clinician education, a paper-based VTE risk assessment algorithm, printed physicians’ orders, and audit and feedback sessions. Medical wards at six hospitals (representing clusters in Ontario, Canada were included; three were randomized to the multicomponent intervention and three to usual care (i.e., no active strategies for thromboprophylaxis in place. Blinding was not used. Results A total of 2,611 patients (1,154 in the intervention and 1,457 in the control group were eligible and included in the analysis. This multicomponent intervention did not lead to a significant difference in appropriate VTE prophylaxis rates between intervention and control hospitals (appropriate management rate odds ratio = 0.80; 95% confidence interval: 0.50, 1.28; p = 0.36; intra-class correlation coefficient: 0.022, and thus was not considered feasible. Major barriers to effective knowledge translation were poor attendance by clinical staff at education and feedback sessions, difficulty locating preprinted orders, and lack of involvement by clinical and administrative leaders. We identified several factors that may increase uptake of a VTE
Korshøj Mette
2012-08-01
Full Text Available Abstract Background Prevalence of cardiovascular risk factors is unevenly distributed among occupational groups. The working environment, as well as lifestyle and socioeconomic status contribute to the disparity and variation in prevalence of these risk factors. High physical work demands have been shown to increase the risk for cardiovascular disease and mortality, contrary to leisure time physical activity. High physical work demands in combination with a low cardiorespiratory fitness infer a high relative workload and an excessive risk for cardiovascular mortality. Therefore, the aim of this study is to examine whether a worksite aerobic exercise intervention will reduce the relative workload and cardiovascular risk factors by an increased cardiorespiratory fitness. Methods/design A cluster-randomized controlled trial is performed to evaluate the effect of the worksite aerobic exercise intervention on cardiorespiratory fitness and cardiovascular risk factors among cleaners. Cleaners are eligible if they are employed ≥ 20 hours/week, at one of the enrolled companies. In the randomization, strata are formed according to the manager the participant reports to. The clusters will be balanced on the following criteria: Geographical work location, gender, age and seniority. Cleaners are randomized to either I a reference group, receiving lectures concerning healthy living, or II an intervention group, performing worksite aerobic exercise “60 min per week”. Data collection will be conducted at baseline, four months and 12 months after baseline, at the worksite during working hours. The data collection will consist of a questionnaire-based interview, physiological testing of health and capacity-related measures, and objective diurnal measures of heart rate, physical activity and blood pressure. Primary outcome is cardiorespiratory fitness. Discussion Information is lacking about whether an improved cardiorespiratory fitness will affect
Engineering practice variation through provider agreement: a cluster-randomized feasibility trial
McCarren M
2014-10-01
Full Text Available Madeline McCarren,1 Elaine L Twedt,1 Faizmohamed M Mansuri,2 Philip R Nelson,3 Brian T Peek3 1Pharmacy Benefits Management Services, Department of Veterans Affairs, Hines, IL, 2Wilkes-Barre VA Medical Center, Wilkes-Barre, PA, 3Charles George VA Medical Center, Asheville, NC, USA Purpose: Minimal-risk randomized trials that can be embedded in practice could facilitate learning health-care systems. A cluster-randomized design was proposed to compare treatment strategies by assigning clusters (eg, providers to “favor” a particular drug, with providers retaining autonomy for specific patients. Patient informed consent might be waived, broadening inclusion. However, it is not known if providers will adhere to the assignment or whether institutional review boards will waive consent. We evaluated the feasibility of this trial design.Subjects and methods: Agreeable providers were randomized to “favor” either hydrochlorothiazide or chlorthalidone when starting patients on thiazide-type therapy for hypertension. The assignment applied when the provider had already decided to start a thiazide, and providers could deviate from the strategy as needed. Prescriptions were aggregated to produce a provider strategy-adherence rate.Results: All four institutional review boards waived documentation of patient consent. Providers (n=18 followed their assigned strategy for most of their new thiazide prescriptions (n=138 patients. In the “favor hydrochlorothiazide” group, there was 99% adherence to that strategy. In the “favor chlorthalidone” group, chlorthalidone comprised 77% of new thiazide starts, up from 1% in the pre-study period. When the assigned strategy was followed, dosing in the recommended range was 48% for hydrochlorothiazide (25–50 mg/day and 100% for chlorthalidone (12.5–25.0 mg/day. Providers were motivated to participate by a desire to contribute to a comparative effectiveness study. A study promotional mug, provider information
Modeling the Color Magnitude Relation for Galaxy Clusters
Jimenez, Noelia; Castelli, Analia Smith; Bassino, Lilia P
2011-01-01
We investigate the origin of the colour-magnitude relation (CMR) observed in cluster galaxies by using a combination of a cosmological N-body simulation of a cluster of galaxies and a semi-analytic model of galaxy formation. The departure of galaxies in the bright end of the CMR with respect to the trend denoted by less luminous galaxies could be explained by the influence of minor mergers
Hasselaar Jeroen
2011-08-01
Full Text Available Abstract Background Due to the growing number of elderly with advanced chronic conditions, healthcare services will come under increasing pressure. Teleconsultation is an innovative approach to deliver quality of care for palliative patients at home. Quantitative studies assessing the effect of teleconsultation on clinical outcomes are scarce. The aim of this present study is to investigate the effectiveness of teleconsultation in complex palliative homecare. Methods/Design During a 2-year recruitment period, GPs are invited to participate in this cluster randomized controlled trial. When a GP refers an eligible patient for the study, the GP is randomized to the intervention group or the control group. Patients in the intervention group have a weekly teleconsultation with a nurse practitioner and/or a physician of the palliative consultation team. The nurse practitioner, in cooperation with the palliative care specialist of the palliative consultation team, advises the GP on treatment policy of the patient. The primary outcome of patient symptom burden is assessed at baseline and weekly using the Edmonton Symptom Assessment Scale (ESAS and at baseline and every four weeks using the Hospital Anxiety and Depression Scale (HADS. Secondary outcomes are self-perceived burden from informal care (EDIZ, patient experienced continuity of medical care (NCQ, patient and caregiver satisfaction with the teleconsultation (PSQ, the experienced problems and needs in palliative care (PNPC-sv and the number of hospital admissions. Discussion This is one of the first randomized controlled trials in palliative telecare. Our data will verify whether telemedicine positively affects palliative homecare. Trial registration The Netherlands National Trial Register NTR2817
Tong, Elisa K; Nguyen, Tung T; Lo, Penny; Stewart, Susan L; Gildengorin, Ginny L; Tsoh, Janice Y; Jo, Angela M; Kagawa-Singer, Marjorie L; Sy, Angela U; Cuaresma, Charlene; Lam, Hy T; Wong, Ching; Tran, Mi T; Chen, Moon S
2017-01-01
Asian Americans have lower colorectal cancer (CRC) screening rates than non-Hispanic white individuals. Hmong Americans have limited socioeconomic resources and literacy. The current randomized controlled trial was conducted to determine whether bilingual/bicultural lay health educator (LHE) education could increase CRC screening among Hmong Americans. A cluster randomized controlled trial was conducted among Hmong Americans in Sacramento, California. LHEs and recruited participants were randomized to intervention or control groups. The intervention group received CRC education over 3 months delivered by an LHE. The control group received education regarding nutrition and physical activity delivered by a health educator. The outcomes were changes in self-reported ever-screening and up-to-date CRC screening after 6 months. All 329 participants were foreign-born with mostly no formal education, limited English proficiency, and no employment. The majority of the participants were insured and had a regular source of health care. The intervention group experienced greater changes after the intervention than the control group for ever-screening (P = .068) and being up-to-date with screening (Pscreening (adjusted odds ratio, 1.73; 95% confidence interval, 1.07-2.79) and being up-to-date with screening (adjusted odds ratio, 1.71; 95% confidence interval, 1.26-2.32). Individuals who had health insurance were found to have >4 times the odds of receiving screening, both ever-screening and up-to-date screening. A higher CRC knowledge score mediated the intervention effect for both screening outcomes. A culturally and linguistically appropriate educational intervention delivered by trained LHEs was found to increase CRC screening in an immigrant population with low levels of education, employment, English proficiency, and literacy. Cancer 2017;98-106. © 2016 American Cancer Society. © 2016 American Cancer Society.
Chaudhry Shazia H
2009-07-01
Full Text Available Abstract Background Cluster randomized trials are an increasingly important methodological tool in health research. In cluster randomized trials, intact social units or groups of individuals, such as medical practices, schools, or entire communities – rather than individual themselves – are randomly allocated to intervention or control conditions, while outcomes are then observed on individual cluster members. The substantial methodological differences between cluster randomized trials and conventional randomized trials pose serious challenges to the current conceptual framework for research ethics. The ethical implications of randomizing groups rather than individuals are not addressed in current research ethics guidelines, nor have they even been thoroughly explored. The main objectives of this research are to: (1 identify ethical issues arising in cluster trials and learn how they are currently being addressed; (2 understand how ethics reviews of cluster trials are carried out in different countries (Canada, the USA and the UK; (3 elicit the views and experiences of trial participants and cluster representatives; (4 develop well-grounded guidelines for the ethical conduct and review of cluster trials by conducting an extensive ethical analysis and organizing a consensus process; (5 disseminate the guidelines to researchers, research ethics boards (REBs, journal editors, and research funders. Methods We will use a mixed-methods (qualitative and quantitative approach incorporating both empirical and conceptual work. Empirical work will include a systematic review of a random sample of published trials, a survey and in-depth interviews with trialists, a survey of REBs, and in-depth interviews and focus group discussions with trial participants and gatekeepers. The empirical work will inform the concurrent ethical analysis which will lead to a guidance document laying out principles, policy options, and rationale for proposed guidelines. An
Ivanova Anna
2008-10-01
Full Text Available Abstract Background Most quality improvement programs in diabetes care incorporate aspects of clinician education, performance feedback, patient education, care management, and diabetes care teams to support primary care physicians. Few studies have applied all of these dimensions to address clinical inertia. Aim To evaluate interventions to improve adherence to evidence-based guidelines for diabetes and reduce clinical inertia in primary care physicians. Design Two-arm cluster randomized controlled trial. Participants Primary care physicians in Belgium. Interventions Primary care physicians will be randomly allocated to 'Usual' (UQIP or 'Advanced' (AQIP Quality Improvement Programs. Physicians in the UQIP will receive interventions addressing the main physician, patient, and office system factors that contribute to clinical inertia. Physicians in the AQIP will receive additional interventions that focus on sustainable behavior changes in patients and providers. Outcomes Primary endpoints are the proportions of patients within targets for three clinical outcomes: 1 glycosylated hemoglobin Primary and secondary analysis Statistical analyses will be performed using an intent-to-treat approach with a multilevel model. Linear and generalized linear mixed models will be used to account for the clustered nature of the data, i.e., patients clustered withinimary care physicians, and repeated assessments clustered within patients. To compare patient characteristics at baseline and between the intervention arms, the generalized estimating equations (GEE approach will be used, taking the clustered nature of the data within physicians into account. We will also use the GEE approach to test for differences in evolution of the primary and secondary endpoints for all patients, and for patients in the two interventions arms, accounting for within-patient clustering. Trial Registration number: NTR 1369.
de Lijster, Gaby P A; Felten, Hanneke; Kok, Gerjo; Kocken, Paul L
2016-05-01
Many adolescents experience sexual harassment and victims of sexual harassment have higher risks regarding well-being and health behaviors such as higher risks of suicidal thoughts, suicidal ideation and feeling unsafe at school. A peer-performed play and school lessons on preventing sexual harassment behavior were presented to secondary school students. We evaluated its effectiveness, using a cluster-randomized controlled design to assign schools to an experimental condition [n = 14 schools; 431 students (51 % female)] and a control condition [n = 11 schools; 384 students (51 % female)]. To measure the effects of the intervention at first post-test and 6-month follow-up, our multilevel analyses used a two-level random intercept model. Outcome measures were sexual harassment behaviors, behavioral determinants and distal factors influencing these behaviors. At post-test, students in the experimental group reported a reduced intention to commit sexual harassment behavior and higher self-efficacy in rejecting it. At post-test and follow-up there was a significant positive effect on social norms for rejecting sexual harassment behavior. At follow-up, sexual self-esteem was higher in students in the experimental group than in the control group. Effects on these determinants will benefit adolescents' future sexual behaviors. In combination, the play and lessons, possibly together with continued sexual health education and skills programs on social-emotional learning in subsequent school years, have potential for preventing sexual harassment behavior.
Marie-Claude Boily
Full Text Available The rigorous evaluation of the impact of combination HIV prevention packages at the population level will be critical for the future of HIV prevention. In this review, we discuss important considerations for the design and interpretation of cluster randomized controlled trials (C-RCTs of combination prevention interventions. We focus on three large C-RCTs that will start soon and are designed to test the hypothesis that combination prevention packages, including expanded access to antiretroviral therapy, can substantially reduce HIV incidence. Using a general framework to integrate mathematical modelling analysis into the design, conduct, and analysis of C-RCTs will complement traditional statistical analyses and strengthen the evaluation of the interventions. Importantly, even with combination interventions, it may be challenging to substantially reduce HIV incidence over the 2- to 3-y duration of a C-RCT, unless interventions are scaled up rapidly and key populations are reached. Thus, we propose the innovative use of mathematical modelling to conduct interim analyses, when interim HIV incidence data are not available, to allow the ongoing trials to be modified or adapted to reduce the likelihood of inconclusive outcomes. The preplanned, interactive use of mathematical models during C-RCTs will also provide a valuable opportunity to validate and refine model projections.
Study of nuclear clustering using the modern shell model approach
Volya, Alexander; Tchuvil'Sky, Yury
2014-03-01
Nuclear clustering, alpha decays, and multi-particle correlations are important components of nuclear dynamics. In this work we use the modern configuration-interaction approach with most advanced realistic shell-model Hamiltonians to study these questions. We utilize the algebraic many-nucleon structures and the corresponding fractional parentage coefficients to build the translationally invariant wave functions of the alpha-cluster channels. We explore the alpha spectroscopic factors, study the distribution of clustering strength, and discuss the structure of an effective 4-body operator describing the in-medium alpha dynamics in the multi-shell valence configuration space. Sensitivity of alpha clustering to the components of an effective Hamiltonian, which includes its collective and many-body components, as well as isospin symmetry breaking terms, are of interest. We offer effective techniques for evaluation of the cluster spectroscopic factors satisfying the orthogonality conditions of the respective cluster channels. We present a study of clustering phenomena, single-particle dynamics, and electromagnetic transitions for a number of nuclei in p-sd shells and compare our results with the experimentally available data. This work is supported by the U.S. Department of Energy under contract number DE-SC0009883.
Molecular dynamics modelling of EGCG clusters on ceramide bilayers
Yeo, Jingjie; Cheng, Yuan; Li, Weifeng; Zhang, Yong-Wei [Institute of High Performance Computing, A*STAR, 138632 (Singapore)
2015-12-31
A novel method of atomistic modelling and characterization of both pure ceramide and mixed lipid bilayers is being developed, using only the General Amber ForceField. Lipid bilayers modelled as pure ceramides adopt hexagonal packing after equilibration, and the area per lipid and bilayer thickness are consistent with previously reported theoretical results. Mixed lipid bilayers are modelled as a combination of ceramides, cholesterol, and free fatty acids. This model is shown to be stable after equilibration. Green tea extract, also known as epigallocatechin-3-gallate, is introduced as a spherical cluster on the surface of the mixed lipid bilayer. It is demonstrated that the cluster is able to bind to the bilayers as a cluster without diffusing into the surrounding water.
Parameterization of geophysical inversion model using particle clustering
Yang, Dikun
2015-01-01
This paper presents a new method of constructing physical models in a geophysical inverse problem, when there are only a few possible physical property values in the model and they are reasonably known but the geometry of the target is sought. The model consists of a fixed background and many small "particles" as building blocks that float around in the background to resemble the target by clustering. This approach contrasts the conventional geometric inversions requiring the target to be regularly shaped bodies, since here the geometry of the target can be arbitrary and does not need to be known beforehand. Because of the lack of resolution in the data, the particles may not necessarily cluster when recovering compact targets. A model norm, called distribution norm, is introduced to quantify the spread of particles and incorporated into the objective function to encourage further clustering of the particles. As proof of concept, 1D magnetotelluric inversion is used as example. My experiments reveal that the ...
Hamann, Johannes; Holzhüter, Fabian; Stecher, Lynne; Heres, Stephan
2017-02-23
Shared decision making (SDM) is a model of how doctors and patients interact with each other. It aims at changing the traditional power asymmetry between doctors and patients by strengthening the exchange of information and the decisional position of the patient. Although SDM is generally welcomed by mental health patients as well as by mental health professionals its implementation in routine care, especially in the more acute settings, is still lacking. SDM-PLUS has been developed as an approach that addresses both patients and mental health professionals and aims at implementing SDM even for the very acutely ill patients. The SDM-PLUS study will be performed as a matched-pair cluster-randomized trial in acute psychiatric wards. On wards allocated to the intervention group personnel will receive communication training (addressing how to implement SDM for various scenarios) and patients will receive a group intervention addressing patient skills for SDM. Wards allocated to the control condition will continue treatment as usual. A total sample size of 276 patients suffering from schizophrenia or schizoaffective disorder on 12 wards is planned. The main outcome parameter will be patients' perceived involvement in decision making during the inpatient stay measured with the SDM-Q-9 questionnaire. Secondary objectives include the therapeutic relationship and long term outcomes such as medication adherence and rehospitalization rates. In addition, process measures and qualitative data will be obtained to allow for the analysis of potential barriers and facilitators of SDM-PLUS. The primary analysis will be a comparison of SDM-Q-9 sum scores 3 weeks after study inclusion (or discharge, if earlier) between the intervention and control groups. To assess the effect of the intervention on this continuous primary outcome, a random effects linear regression model will be fitted with ward (cluster) as a random effect term and intervention group as a fixed effect. This will be
A dynamical $\\alpha$-cluster model of $^{16}$O
Halcrow, C J; Manton, N S
2016-01-01
We calculate the low-lying spectrum of the $^{16}$O nucleus using an $\\alpha$-cluster model which includes the important tetrahedral and square configurations. Our approach is motivated by the dynamics of $\\alpha$-particle scattering in the Skyrme model. We are able to replicate the large energy splitting that is observed between states of identical spin but opposite parities, as well as introduce states that were previously not found in other cluster models, such as a $0^-$ state. We also provide a novel interpretation of the first excited state of $^{16}$O and make predictions for the energies of $6^-$ states that have yet to be observed experimentally.
Models of epidemics: when contact repetition and clustering should be included
Scholz Roland W
2009-06-01
Full Text Available Abstract Background The spread of infectious disease is determined by biological factors, e.g. the duration of the infectious period, and social factors, e.g. the arrangement of potentially contagious contacts. Repetitiveness and clustering of contacts are known to be relevant factors influencing the transmission of droplet or contact transmitted diseases. However, we do not yet completely know under what conditions repetitiveness and clustering should be included for realistically modelling disease spread. Methods We compare two different types of individual-based models: One assumes random mixing without repetition of contacts, whereas the other assumes that the same contacts repeat day-by-day. The latter exists in two variants, with and without clustering. We systematically test and compare how the total size of an outbreak differs between these model types depending on the key parameters transmission probability, number of contacts per day, duration of the infectious period, different levels of clustering and varying proportions of repetitive contacts. Results The simulation runs under different parameter constellations provide the following results: The difference between both model types is highest for low numbers of contacts per day and low transmission probabilities. The number of contacts and the transmission probability have a higher influence on this difference than the duration of the infectious period. Even when only minor parts of the daily contacts are repetitive and clustered can there be relevant differences compared to a purely random mixing model. Conclusion We show that random mixing models provide acceptable estimates of the total outbreak size if the number of contacts per day is high or if the per-contact transmission probability is high, as seen in typical childhood diseases such as measles. In the case of very short infectious periods, for instance, as in Norovirus, models assuming repeating contacts will also behave
Characteristic Polynomials of Complex Random Matrix Models
Akemann, G
2003-01-01
We calculate the expectation value of an arbitrary product of characteristic polynomials of complex random matrices and their hermitian conjugates. Using the technique of orthogonal polynomials in the complex plane our result can be written in terms of a determinant containing these polynomials and their kernel. It generalizes the known expression for hermitian matrices and it also provides a generalization of the Christoffel formula to the complex plane. The derivation we present holds for complex matrix models with a general weight function at finite-N, where N is the size of the matrix. We give some explicit examples at finite-N for specific weight functions. The characteristic polynomials in the large-N limit at weak and strong non-hermiticity follow easily and they are universal in the weak limit. We also comment on the issue of the BMN large-N limit.
Cluster variation studies of the anisotropic exchange interaction model
King, T. C.; Chen, H. H.
The cluster variation method is applied to study critical properties of the Potts-like ferromagnetic anisotropic exchange interaction model. Phase transition temperatures, order parameter discontinuities and latent heats of the model on the triangular and the fcc lattices are determined by the triangle approximation; and those on the square and the sc lattices are determined by the square approximation.
Constraining Galaxy Formation Models with Dwarf Ellipticals in Clusters
Conselice, C J
2005-01-01
Recent observations demonstrate that dwarf elliptical (dE) galaxies in clusters, despite their faintness, are likely a critical galaxy type for understanding the processes behind galaxy formation. Dwarf ellipticals are the most common galaxy type, and are particularly abundant in rich galaxy clusters. The dwarf to giant ratio is in fact highest in rich clusters of galaxies, suggesting that cluster dEs do not form in groups that later merge to form clusters. Dwarf ellipticals are potentially the only galaxy type whose formation is sensitive to global, rather than local, environment. The dominant idea for explaining the formation of these systems, through Cold Dark Matter models, is that dEs form early and within their present environments. Recent results suggest that some dwarfs appear in clusters after the bulk of massive galaxies form, a scenario not predicted in standard hierarchical structure formation models. Many dEs have younger and more metal rich stellar populations than dwarfs in lower density enviro...
Malmberg, M.; Kleinjan, M.; Overbeek, G.; Vermulst, A.; Monshouwer, K.; Lammers, J.; Vollebergh, W.A.M.; Engels, R.C.M.E.
2014-01-01
Aim: To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design: Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting: General population of 11-15-year-old adolesce
Malmberg, Monique; Kleinjan, Marloes; Overbeek, Geertjan; Vermulst, Ad; Monshouwer, Karin; Lammers, Jeroen; Vollebergh, Wilma A M; Engels, Rutger C M E
2014-01-01
Aim: To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design: Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting: General population of 11-15-year-old adolesce
Lennox, Nicholas; Bain, Chris; Rey-Conde, Therese; Taylor, Miriam; Boyle, Frances M.; Purdie, David M.; Ware, Robert S.
2010-01-01
Background: People with intellectual disability who live in the community often have poor health and healthcare, partly as a consequence of poor communication, recall difficulties and incomplete patient health information. Materials and Methods: A cluster randomized-controlled trial with 2 x 2 factorial design was conducted with adults with…
Dong, Nianbo; Spybrook, Jessaca; Kelcey, Ben
2016-01-01
The purpose of this study is to propose a general framework for power analyses to detect the moderator effects in two- and three-level cluster randomized trials (CRTs). The study specifically aims to: (1) develop the statistical formulations for calculating statistical power, minimum detectable effect size (MDES) and its confidence interval to…
Hedges, Larry V.; Hedberg, E. C.
2013-01-01
Background: Cluster-randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels.…
Hesselink, Arlette E.; Rutten, Guy E. H.; Slootmaker, Sander M.; de Weerdt, Inge; Raaijmakers, Lieke G. M.; Jonkers, Ruud; Martens, Marloes K.; Bilo, Henk J. G.
2015-01-01
Background: The worldwide epidemic of type 2 diabetes (T2DM) underlines the need for diabetes prevention strategies. In this study the feasibility and effectiveness of a nurse led lifestyle program for subjects with impaired fasting glucose (IFG) is assessed. Methods: A cluster randomized clinical t
Hesselink, Arlette E.; Rutten, Guy E. H.; Slootmaker, Sander M.; de Weerdt, Inge; Raaijmakers, Lieke G. M.; Jonkers, Ruud; Martens, Marloes K.; Bilo, Henk J. G.
2015-01-01
Background: The worldwide epidemic of type 2 diabetes (T2DM) underlines the need for diabetes prevention strategies. In this study the feasibility and effectiveness of a nurse led lifestyle program for subjects with impaired fasting glucose (IFG) is assessed. Methods: A cluster randomized clinical
Malmberg, M.; Kleinjan, M.; Overbeek, G.J.; Vermulst, A.A.; Monshouwer, K.; Lammers, J.; Vollebergh, W.A.M.; Engels, R.C.M.E.
2014-01-01
Aim To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting General population of 11-15-year-old adolescents
Malmberg, M.; Kleinjan, M.; Overbeek, G.; Vermulst, A.; Monshouwer, K.; Lammers, J.; Vollebergh, W.A.M.; Engels, R.C.M.E.
2014-01-01
Aim: To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design: Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting: General population of 11-15-year-old adolesce
Malmberg, Monique; Kleinjan, Marloes; Overbeek, Geertjan|info:eu-repo/dai/nl/25233602X; Vermulst, Ad; Monshouwer, Karin|info:eu-repo/dai/nl/202651967; Lammers, Jeroen; Vollebergh, Wilma A M|info:eu-repo/dai/nl/090632893; Engels, Rutger C M E
2014-01-01
Aim: To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design: Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting: General population of 11-15-year-old adolesce
Malmberg, M.; Kleinjan, M.; Overbeek, G.J.; Vermulst, A.A.; Monshouwer, K.; Lammers, J.; Vollebergh, W.A.M.; Engels, R.C.M.E.
2014-01-01
Aim To evaluate the effectiveness of the Healthy School and Drugs programme on alcohol, tobacco and marijuana use among Dutch early adolescents. Design Randomized clustered trial with two intervention conditions (i.e. e-learning and integral). Setting General population of 11-15-year-old adolescents
Cleveringa, F. G. W.; Minkman, M. H.; Gorter, K. J.; van den Donk, M.; Rutten, G. E. H. M.
2010-01-01
P>Aims The Diabetes Care Protocol (DCP) combines task delegation, intensification of diabetes treatment and feedback. It reduces cardiovascular risk in Type 2 diabetes (T2DM) patients. This study determines the effects of DCP on patient-important outcomes. Methods A cluster randomized, non-inferiori
Hedges, Larry V.; Hedberg, Eric C.
2013-01-01
Background: Cluster randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels.…
Evolutionary Synthesis Modelling of Young Star Clusters in Merging Galaxies
Anders, P; De Grijs, R; Anders, Peter; Alvensleben, Uta Fritze - v.; Grijs, Richard de
2003-01-01
The observational properties of globular cluster systems (GCSs) are vital tools to investigate the violent star formation histories of their host galaxies. This violence is thought to have been triggered by galaxy interactions or mergers. The most basic properties of a GCS are its luminosity function (number of clusters per luminosity bin) and color distributions. A large number of observed GCS show bimodal color distributions, which can be translated into a bimodality in either metallicity and/or age. An additional uncertainty comes into play when one considers extinction. These effects can be disentangled either by obtaining spectroscopic data for the clusters or by imaging observations in at least four passbands. This allows us then to discriminate between various formation scenarios of GCSs, e.g. the merger scenario by Ashman & Zepf, and the multi-phase collapse model by Forbes et. al.. Young and metal-rich star cluster populations are seen to form in interacting and merging galaxies. We analyse multi...
A Collaboration Service Model for a Global Port Cluster
Keith K.T. Toh
2010-03-01
Full Text Available The importance of port clusters to a global city may be viewed from a number of perspectives. The development of port clusters and economies of agglomeration and their contribution to a regional economy is underpinned by information and physical infrastructure that facilitates collaboration between business entities within the cluster. The maturity of technologies providing portals, web and middleware services provides an opportunity to push the boundaries of contemporary service reference models and service catalogues to what the authors propose to be "collaboration services". Servicing port clusters, portal engineers of the future must consider collaboration services to benefit a region. Particularly, service orchestration through a "public user portal" must gain better utilisation of publically owned infrastructure, to share knowledge and collaborate among organisations through information systems.
Young, Sean D.; Cumberland, William G.; Nianogo, Roch; Menacho, Luis A.; Galea, Jerome T.; Coates, Thomas
2015-01-01
Background Social media technologies are newly emerging tools that can be used for HIV prevention and testing in low- and middle-income countries, such as Peru. This study examined the efficacy of using the Harnessing Online Peer Education (HOPE) social media intervention to increase HIV testing among men who have sex with men (MSM) in Peru. Methods In a cluster randomized controlled trial with concealed allocation, Peruvian MSM from Greater Lima/Callao (N = 556) were randomly assigned to join private intervention or control groups on Facebook for 12 weeks. In the intervention condition, forty-nine Peruvian MSM were trained and randomly assigned to be HIV prevention mentors to participants via Facebook groups over 12 weeks. Control participants received an enhanced standard of care, including standard offline HIV prevention available in Peru as well as participation in Facebook groups (without peer leaders) that provided study updates and HIV testing information. After accepting a request to join the groups, continued participation was voluntary. Participants could request a free HIV test at a local community clinic, and completed questionnaires on HIV risk behaviors and social media use at baseline and 12-week follow-up. Findings Between March 19, 2012, and June 11, 2012, and Sept 26, 2012, and Dec 19, 2012, 556 participants were randomly assigned to intervention groups (N=278) or control groups (N=278); we analyse data for 252 and 246. 43 participants (17%) in the intervention group and 16 (7%) in the control groups got tested for HIV (adjusted odds ratio 2.61, 95% CI 1.55–4.38). No adverse events were reported. Retention at 12-week follow-up was 90%. Across conditions, 7 (87.5%) of the 8 participants who tested positive were linked to care at a local clinic. Interpretation Development of peer-mentored social media communities seemed to be an effective method to increase HIV testing among high-risk populations in Peru.: Results suggest that the HOPE social
Does clinical equipoise apply to cluster randomized trials in health research?
Brehaut Jamie C
2011-05-01
Full Text Available Abstract This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs in health research. In the introductory paper in this series, Weijer and colleagues set out six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation. This paper addresses the third of the questions posed, namely, does clinical equipoise apply to CRTs in health research? The ethical principle of beneficence is the moral obligation not to harm needlessly and, when possible, to promote the welfare of research subjects. Two related ethical problems have been discussed in the CRT literature. First, are control groups that receive only usual care unduly disadvantaged? Second, when accumulating data suggests the superiority of one intervention in a trial, is there an ethical obligation to act? In individually randomized trials involving patients, similar questions are addressed by the concept of clinical equipoise, that is, the ethical requirement that, at the start of a trial, there be a state of honest, professional disagreement in the community of expert practitioners as to the preferred treatment. Since CRTs may not involve physician-researchers and patient-subjects, the applicability of clinical equipoise to CRTs is uncertain. Here we argue that clinical equipoise may be usefully grounded in a trust relationship between the state and research subjects, and, as a result, clinical equipoise is applicable to CRTs. Clinical equipoise is used to argue that control groups receiving only usual care are not disadvantaged so long as the evidence supporting the experimental and control interventions is such that experts would disagree as to which is preferred. Further, while data accumulating during the course of a CRT may favor one intervention over another, clinical equipoise supports continuing the trial until the results are likely to be broadly convincing, often coinciding with the planned
Design, rationale, and baseline demographics of SEARCH I: a prospective cluster-randomized study
Albers F
2012-07-01
Full Text Available Frank Albers,1 Asif Shaikh,2 Ahmar Iqbal,31Medical Affairs Respiratory, 2Clinical Development and Medical Affairs, Field Based Medicine-Respiratory, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA; 3Respiratory Medical Affairs, Pfizer Inc, New York, NY, USAAbstract: Questionnaires are available to identify patients at risk for several chronic diseases, including COPD, but are infrequently utilized in primary care. COPD is often underdiagnosed, while at the same time the US Preventive Services Task Force recommends against spirometric screening for COPD in asymptomatic adults. Use of a symptom-based questionnaire and subsequent handheld spirometric device depending on the answers to the questionnaire is a promising approach to identify patients at risk for COPD. Screening, Evaluating and Assessing Rate CHanges of diagnosing respiratory conditions in primary care 1 (SEARCH I was a prospective cluster-randomized study in 168 US primary care practices evaluating the effect of the COPD-Population Screener (COPD-PS™ questionnaire. The effect of this questionnaire alone or sequentially with the handheld copd-6TM device was evaluated on new diagnoses of COPD and on respiratory diagnostic practice patterns (including referrals for pulmonary function testing, referrals to pulmonologists, new diagnoses of COPD, and new respiratory medication prescriptions. Participating practices entered a total of 9704 consecutive consenting subjects aged ≥ 40 years attending primary care clinics. Study arm results were compared for new COPD diagnosis rates between usual care and (1 COPD-PS plus copd-6 and (2 COPD-PS alone. A cluster-randomization design allowed comparison of the intervention effects at the practice level instead of individuals being the subjects of the intervention. Regional principal investigators controlled the flow of study information to sub-investigators at participating practices to reduce observation bias (Hawthorne effect. The
Ha, Amy S; Lonsdale, Chris; Lubans, David R; Ng, Johan Y Y
2017-07-11
The Self-determined Exercise and Learning For FITness (SELF-FIT) is a multi-component school-based intervention based on tenets of self-determination theory. SELF-FIT aims to increase students' moderate-to-vigorous physical activity (MVPA) during physical education lessons, and enhance their autonomous motivation towards fitness activities. Using a cluster randomized controlled trial, we aim to examine the effects of the intervention on students' MVPA during school physical education. Secondary 2 students (approximately aged 14 years) from 26 classes in 26 different schools will be recruited. After baseline assessments, students will be randomized into either the experimental group or wait-list control group using a matched-pair randomization. Teachers allocated to the experimental group will attend two half-day workshops and deliver the SELF-FIT intervention for 8 weeks. The main intervention components include training teachers to teach in more need supportive ways, and conducting fitness exercises using a fitness dice with interchangeable faces. Other motivational components, such as playing music during classes, are also included. The primary outcome of the trial is students' MVPA during PE lessons. Secondary outcomes include students' leisure-time MVPA, perceived need support from teachers, need satisfaction, autonomous motivation towards physical education, intention to engage in physical activity, psychological well-being, and health-related fitness (cardiorespiratory and muscular fitness). Quantitative data will be analyzed using multilevel modeling approaches. Focus group interviews will also be conducted to assess students' perceptions of the intervention. The SELF-FIT intervention has been designed to improve students' health and well-being by using high-intensity activities in classes delivered by teachers who have been trained to be autonomy needs supportive. If successful, scalable interventions based on SELF-FIT could be applied in physical
Van Ness, Peter H; Peduzzi, Peter N; Quagliarello, Vincent J
2012-11-01
This report discusses how methodological aspects of study efficacy and effectiveness combine in cluster randomized trials in nursing homes. Discussion focuses on the relationships between these study aspects in the Pneumonia Reduction in Institutionalized Disabled Elders (PRIDE) trial, an ongoing cluster randomized clinical trial of pneumonia prevention among nursing home residents launched in October 2009 in Greater New Haven, Connecticut. This clinical trial has enrolled long-term care nursing home residents, over 65years in age, who have either inadequate oral care or swallowing difficulty, previously identified risk factors for pneumonia. It has used a multicomponent intervention consisting of manual tooth/gum brushing, 0.12% chlorhexidine oral rinse administered twice daily by nurses, and upright feeding positioning at meals to reduce rates of radiographically documented pneumonia. Cluster randomization is attractive for nursing home intervention studies because physical proximity and administrative arrangements make it difficult to deliver different interventions to residents of the same nursing home. Implementing an intervention in an entire home requires integration into the daily life of residents and into the administrative procedures of the nursing home. This characteristic of nursing home cluster randomized trials makes them approximate "real-world" research contexts, but implementation can be challenging. The PRIDE trial of pneumonia prevention utilized specific methodological choices that include both efficacy and effectiveness elements. Cluster randomized trials in nursing homes having elements of both efficacy and effectiveness (i.e., hybrid designs) can address some of the methodological challenges of conducting clinical research in nursing homes; they have distinctive advantages and some limitations.
Power Analysis for Models of Change in Cluster Randomized Designs
Li, Wei; Konstantopoulos, Spyros
2017-01-01
Field experiments in education frequently assign entire groups such as schools to treatment or control conditions. These experiments incorporate sometimes a longitudinal component where for example students are followed over time to assess differences in the average rate of linear change, or rate of acceleration. In this study, we provide methods…
Power Analysis for Models of Change in Cluster Randomized Designs
Li, Wei; Konstantopoulos, Spyros
2017-01-01
Field experiments in education frequently assign entire groups such as schools to treatment or control conditions. These experiments incorporate sometimes a longitudinal component where for example students are followed over time to assess differences in the average rate of linear change, or rate of acceleration. In this study, we provide methods…
Huizing, Anna R; Hamers, Jan PH; Gulpers, Math JM; Berger, Martijn PF
2006-01-01
Background Physical restraints are still frequently used in nursing home residents despite growing evidence for the ineffectiveness and negative consequences of these methods. Therefore, reduction in the use of physical restraints in psycho-geriatric nursing home residents is very important. The aim of this study was to investigate the short-term effects of an educational intervention on the use of physical restraints in psycho-geriatric nursing home residents. Methods A cluster randomized trial was applied to 5 psycho-geriatric nursing home wards (n = 167 residents with dementia). The wards were assigned at random to either educational intervention (3 wards) or control status (2 wards). The restraint status was observed and residents' characteristics, such as cognitive status, were determined by using the Minimum Data Set (MDS) at baseline and 1 month after intervention. Results Restraint use did not change significantly over time in the experimental group (55%–56%), compared to a significant increased use (P < 0.05) in the control group (56%–70%). The mean restraint intensity and mean multiple restraint use in residents increased in the control group but no changes were shown in the experimental group. Logistic regression analysis showed that residents in the control group were more likely to experience increased restraint use than residents in the experimental group. Conclusion An educational programme for nurses combined with consultation with a nurse specialist did not decrease the use of physical restraints in psycho-geriatric nursing home residents in the short term. However, the residents in the control group experienced more restraint use during the study period compared to the residents in the experimental group. Whether the intervention will reduce restraint use in the long term could not be inferred from these results. Further research is necessary to gain insight into the long-term effects of this educational intervention. PMID:17067376
Surgical mask to prevent influenza transmission in households: a cluster randomized trial.
Laetitia Canini
Full Text Available BACKGROUND: Facemasks and respirators have been stockpiled during pandemic preparedness. However, data on their effectiveness for limiting transmission are scarce. We evaluated the effectiveness of facemask use by index cases for limiting influenza transmission by large droplets produced during coughing in households. METHODOLOGY AND PRINCIPAL FINDINGS: A cluster randomized intervention trial was conducted in France during the 2008-2009 influenza season. Households were recruited during a medical visit of a household member with a positive rapid influenza A test and symptoms lasting less than 48 hours. Households were randomized either to the mask or control group for 7 days. In the intervention arm, the index case had to wear a surgical mask from the medical visit and for a period of 5 days. The trial was initially intended to include 372 households but was prematurely interrupted after the inclusion of 105 households (306 contacts following the advice of an independent steering committee. We used generalized estimating equations to test the association between the intervention and the proportion of household contacts who developed an influenza-like illness during the 7 days following the inclusion. Influenza-like illness was reported in 24/148 (16.2% of the contacts in the intervention arm and in 25/158 (15.8% of the contacts in the control arm and the difference between arms was 0.40% (95%CI: -10% to 11%, P = 1.00. We observed a good adherence to the intervention. In various sensitivity analyses, we did not identify any trend in the results suggesting effectiveness of facemasks. CONCLUSION: This study should be interpreted with caution since the lack of statistical power prevents us to draw formal conclusion regarding effectiveness of facemasks in the context of a seasonal epidemic. TRIAL REGISTRATION: clinicaltrials.gov NCT00774774.
Who is the research subject in cluster randomized trials in health research?
Brehaut Jamie C
2011-07-01
Full Text Available Abstract This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs in health research. In the introductory paper in this series, we set out six areas of inquiry that must be addressed if the CRT is to be set on a firm ethical foundation. This paper addresses the first of the questions posed, namely, who is the research subject in a CRT in health research? The identification of human research subjects is logically prior to the application of protections as set out in research ethics and regulation. Aspects of CRT design, including the fact that in a single study the units of randomization, experimentation, and observation may differ, complicate the identification of human research subjects. But the proper identification of human research subjects is important if they are to be protected from harm and exploitation, and if research ethics committees are to review CRTs efficiently. We examine the research ethics literature and international regulations to identify the core features of human research subjects, and then unify these features under a single, comprehensive definition of human research subject. We define a human research subject as any person whose interests may be compromised as a result of interventions in a research study. Individuals are only human research subjects in CRTs if: (1 they are directly intervened upon by investigators; (2 they interact with investigators; (3 they are deliberately intervened upon via a manipulation of their environment that may compromise their interests; or (4 their identifiable private information is used to generate data. Individuals who are indirectly affected by CRT study interventions, including patients of healthcare providers participating in knowledge translation CRTs, are not human research subjects unless at least one of these conditions is met.
Selby Joe
2010-10-01
Full Text Available Abstract Background Many patients with diabetes have poor blood pressure (BP control. Pharmacological therapy is the cornerstone of effective BP treatment, yet there are high rates both of poor medication adherence and failure to intensify medications. Successful medication management requires an effective partnership between providers who initiate and increase doses of effective medications and patients who adhere to the regimen. Methods In this cluster-randomized controlled effectiveness study, primary care teams within sites were randomized to a program led by a clinical pharmacist trained in motivational interviewing-based behavioral counseling approaches and authorized to make BP medication changes or to usual care. This study involved the collection of data during a 14-month intervention period in three Department of Veterans Affairs facilities and two Kaiser Permanente Northern California facilities. The clinical pharmacist was supported by clinical information systems that enabled proactive identification of, and outreach to, eligible patients identified on the basis of poor BP control and either medication refill gaps or lack of recent medication intensification. The primary outcome is the relative change in systolic blood pressure (SBP measurements over time. Secondary outcomes are changes in Hemoglobin A1c, low-density lipoprotein cholesterol (LDL, medication adherence determined from pharmacy refill data, and medication intensification rates. Discussion Integration of the three intervention elements - proactive identification, adherence counseling and medication intensification - is essential to achieve optimal levels of control for high-risk patients. Testing the effectiveness of this intervention at the team level allows us to study the program as it would typically be implemented within a clinic setting, including how it integrates with other elements of care. Trial Registration The ClinicalTrials.gov registration number is NCT
George Christine
2012-06-01
Full Text Available Abstract Objective To reduce arsenic (As exposure, we evaluated the effectiveness of training community members to perform water arsenic (WAs testing and provide As education compared to sending representatives from outside communities to conduct these tasks. Methods We conducted a cluster based randomized controlled trial of 20 villages in Singair, Bangladesh. Fifty eligible respondents were randomly selected in each village. In 10 villages, a community member provided As education and WAs testing. In a second set of 10 villages an outside representative performed these tasks. Results Overall, 53% of respondents using As contaminated wells, relative to the Bangladesh As standard of 50 μg/L, at baseline switched after receiving the intervention. Further, when there was less than 60% arsenic contaminated wells in a village, the classification used by the Bangladeshi and UNICEF, 74% of study households in the community tester villages, and 72% of households in the outside tester villages reported switching to an As safe drinking water source . Switching was more common in the outside-tester (63% versus community-tester villages (44%. However, after adjusting for the availability of arsenic safe drinking water sources, well switching did not differ significantly by type of As tester (Odds ratio =0.86[95% confidence interval 0.42-1.77. At follow-up, among those using As contaminated wells who switched to safe wells, average urinary As concentrations significantly decreased. Conclusion The overall intervention was effective in reducing As exposure provided there were As-safe drinking water sources available. However, there was not a significant difference observed in the ability of the community and outside testers to encourage study households to use As-safe water sources. The findings of this study suggest that As education and WAs testing programs provided by As testers, irrespective of their residence, could be used as an effective, low cost
Micronutrient-Fortified Rice Can Increase Hookworm Infection Risk: A Cluster Randomized Trial.
Brechje de Gier
Full Text Available Fortification of staple foods is considered an effective and safe strategy to combat micronutrient deficiencies, thereby improving health. While improving micronutrient status might be expected to have positive effects on immunity, some studies have reported increases in infections or inflammation after iron supplementation.To study effects of micronutrient-fortified rice on hookworm infection in Cambodian schoolchildren.A double-blinded, cluster-randomized trial was conducted in 16 Cambodian primary schools partaking in the World Food Program school meal program. Three types of multi-micronutrient fortified rice were tested against placebo rice within the school meal program: UltraRice_original, UltraRice_improved and NutriRice. Four schools were randomly assigned to each study group (placebo n = 492, UltraRice_original n = 479, UltraRice_improved n = 500, NutriRice n = 506. Intestinal parasite infection was measured in fecal samples by Kato-Katz method at baseline and after three and seven months. In a subgroup (N = 330, fecal calprotectin was measured by ELISA as a marker for intestinal inflammation.Baseline prevalence of hookworm infection was 18.6%, but differed considerably among schools (range 0%- 48.1%.Micronutrient-fortified rice significantly increased risk of new hookworm infection. This effect was modified by baseline hookworm prevalence at the school; hookworm infection risk was increased by all three types of fortified rice in schools where baseline prevalence was high (>15%, and only by UltraRice_original in schools with low baseline prevalence. Neither hookworm infection nor fortified rice was related to fecal calprotectin.Consumption of rice fortified with micronutrients can increase hookworm prevalence, especially in environments with high infection pressure. When considering fortification of staple foods, a careful risk-benefit analysis is warranted, taking into account severity of micronutrient deficiencies and local
van Gelder, Vincent A; Scherpbier-de Haan, Nynke D; van Berkel, Saskia; Akkermans, Reinier P; de Grauw, Inge S; Adang, Eddy M; Assendelft, Pim J; de Grauw, Wim J C; Biermans, Marion C J; Wetzels, Jack F M
2017-08-01
Consultation of a nephrologist is important in aligning care for patients with chronic kidney disease (CKD) at the primary-secondary care interface. However, current consultation methods come with practical difficulties that can lead to postponed consultation or patient referral instead. This study aimed to investigate whether a web-based consultation platform, telenephrology, led to a lower referral rate of indicated patients. Furthermore, we assessed consultation rate, quality of care, costs and general practitioner (GPs') experiences with telenephrology. Cluster randomized controlled trial with 47 general practices in the Netherlands was randomized to access to telenephrology or to enhanced usual care. A total of 3004 CKD patients aged 18 years or older who were under primary care were included (intervention group n = 1277, control group n = 1727) and 2693 completed the trial. All practices participated in a CKD management course and were given an overview of their CKD patients. The referral rates amounted to 2.3% (n = 29) in the intervention group and 3.0% (n = 52) in the control group, which was a non-significant difference, OR 0.61; 95% CI 0.31 to 1.23. The intervention group's consultation rate was 6.3% (n = 81) against 5.0% (n = 87) (OR 2.00; 95% CI 0.75-5.33). We found no difference in quality of care or costs. The majority of GPs had a positive opinion about telenephrology. The data in our study do not allow for conclusions on the effect of telenephrology on the rate of patient referrals and provider-to-provider consultations, compared to conventional methods. It was positively evaluated by GPs and was non-inferior in terms of quality of care and costs.
Efficient Cluster Algorithm for CP(N-1) Models
Beard, B B; Riederer, S; Wiese, U J
2006-01-01
Despite several attempts, no efficient cluster algorithm has been constructed for CP(N-1) models in the standard Wilson formulation of lattice field theory. In fact, there is a no-go theorem that prevents the construction of an efficient Wolff-type embedding algorithm. In this paper, we construct an efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin systems. Such systems provide a regularization for CP(N-1) models in the framework of D-theory. We present detailed studies of the autocorrelations and find a dynamical critical exponent that is consistent with z = 0.
Efficient cluster algorithm for CP(N-1) models
Beard, B. B.; Pepe, M.; Riederer, S.; Wiese, U.-J.
2006-11-01
Despite several attempts, no efficient cluster algorithm has been constructed for CP(N-1) models in the standard Wilson formulation of lattice field theory. In fact, there is a no-go theorem that prevents the construction of an efficient Wolff-type embedding algorithm. In this paper, we construct an efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin systems. Such systems provide a regularization for CP(N-1) models in the framework of D-theory. We present detailed studies of the autocorrelations and find a dynamical critical exponent that is consistent with z=0.
Topic Modeling Based Image Clustering by Events in Social Media
Bin Xu
2016-01-01
Full Text Available Social event detection in large photo collections is very challenging and multimodal clustering is an effective methodology to deal with the problem. Geographic information is important in event detection. This paper proposed a topic model based approach to estimate the missing geographic information for photos. The approach utilizes a supervised multimodal topic model to estimate the joint distribution of time, geographic, content, and attached textual information. Then we annotate the missing geographic photos with a predicted geographic coordinate. Experimental results indicate that the clustering performance improved by annotated geographic information.
Cluster model of social partnership in municipal education
Romanova Oksana
2016-03-01
Full Text Available This article discusses the model of educational clusters that are based on social interaction between educational institutions and public-private partnerships. Particular attention is paid to methods of creating such educational network, which allows not only to educational organizations to obtain the missing for the implementation of educational activities and resources to achieve certain educational outcomes, but also to meet the needs of customers of educational services. Different approaches to the formation of a model educational cluster, based on partnerships.
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.
Modeling and clustering users with evolving profiles in usage streams
Zhang, Chongsheng
2012-09-01
Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users\\' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users\\' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.
Ponomarev, A. L.; Brenner, D.; Hlatky, L. R.; Sachs, R. K.
2000-01-01
DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.
Analysis of Massive Emigration from Poland: The Model-Based Clustering Approach
Witek, Ewa
The model-based approach assumes that data is generated by a finite mixture of probability distributions such as multivariate normal distributions. In finite mixture models, each component of probability distribution corresponds to a cluster. The problem of determining the number of clusters and choosing an appropriate clustering method becomes the problem of statistical model choice. Hence, the model-based approach provides a key advantage over heuristic clustering algorithms, because it selects both the correct model and the number of clusters.
Spybrook, Jessaca; Shi, Ran; Kelcey, Benjamin
2016-01-01
This article examines the statistical precision of cluster randomized trials (CRTs) funded by the Institute of Education Sciences (IES). Specifically, it compares the total number of clusters randomized and the minimum detectable effect size (MDES) of two sets of studies, those funded in the early years of IES (2002-2004) and those funded in the…
Turner, Rebecca M; Prevost, A Toby; Thompson, Simon G
2004-04-30
The sample size required for a cluster randomized trial depends on the magnitude of the intracluster correlation coefficient (ICC). The usual sample size calculation makes no allowance for the fact that the ICC is not known precisely in advance. We develop methods which allow for the uncertainty in a previously observed ICC, using a variety of distributional assumptions. Distributions for the power are derived, reflecting this uncertainty. Further, the observed ICC in a future study will not equal its true value, and we consider the impact of this on power. We implement calculations within a Bayesian simulation approach, and provide one simplification that can be performed using simple simulation within spreadsheet software. In our examples, recognizing the uncertainty in a previous ICC estimate decreases expected power, especially when the power calculated naively from the ICC estimate is high. To protect against the possibility of low power, sample sizes may need to be very substantially increased. Recognizing the variability in the future observed ICC has little effect if prior uncertainty has already been taken into account. We show how our method can be extended to the case in which multiple prior ICC estimates are available. The methods presented in this paper can be used by applied researchers to protect against loss of power, or to choose a design which reduces the impact of uncertainty in the ICC. Copyright 2004 John Wiley & Sons, Ltd.
Ahlen, Johan; Hursti, Timo; Tanner, Lindsey; Tokay, Zelal; Ghaderi, Ata
2017-07-20
Our study aimed at evaluating FRIENDS for Life, an intervention to prevent anxiety and depression in Swedish school children. A total of 695 children between the ages of 8 and 11 were recruited from 17 schools in Stockholm, Sweden, and cluster-randomized to either the intervention or control group. Teachers in the intervention group received a full day of training and administered FRIENDS for Life in their classrooms. We assessed the children's anxiety and depressive symptoms, general mental health, and academic performance at pre- and post-intervention as well as at the 12-month follow-up. A multi-informant approach was used with data collected from children, parents, and teachers. Assessment was done with the Spence Children's Anxiety Scale, Children's Depression Inventory, and the Strengths and Difficulties Questionnaire. Children's baseline symptoms, gender, and age as well as their teacher's use of supervision were examined as moderators of effect. Our study found no short- or long-term effects of the intervention for any outcome with regard to the entire sample. We found an enhanced effect of the intervention regarding children with elevated depressive symptoms at baseline. We found a decrease in anxiety symptoms among children whose teachers attended a larger number of supervision sessions, compared to children whose teachers attended fewer supervised sessions or the control group. Mediation analyses showed that this effect was driven by change in the last phase of the intervention, suggesting that supervision might play an important role in enhancing teachers' ability to administer the intervention effectively.
Schonfeld, David J; Adams, Ryan E; Fredstrom, Bridget K; Weissberg, Roger P; Gilman, Richard; Voyce, Charlene; Tomlin, Ricarda; Speese-Linehan, Dee
2015-09-01
This study evaluated the results of a social and emotional learning (SEL) program on academic achievement among students attending a large, urban, high-risk school district. Using a cluster-randomized design, 24 elementary schools were assigned to receive either the intervention curriculum (Promoting Alternative Thinking Strategies, or PATHS) or a curriculum that delivered few if any SEL topics (i.e., the control group). In addition to state mastery test scores, demographic data, school attendance, and dosage information were obtained from 705 students who remained in the same group from the 3rd to the 6th grade. Analyses of odds ratios revealed that students enrolled in the intervention schools demonstrated higher levels of basic proficiency in reading, writing, and math at some grade levels. Although these between-groups differences held for race/ethnicity, gender, and socioeconomic status, significant within-group differences also were noted across these variables. Collectively, these findings indicated that social development instruction may be a promising approach to promote acquisition of academic proficiency, especially among youth attending high-risk school settings. Implications of these findings with respect to SEL programs conclude the article.
Researchers’ perceptions of ethical challenges in cluster randomized trials: a qualitative analysis
McRae Andrew D
2013-01-01
Full Text Available Abstract Background Cluster randomized trials (CRTs pose ethical challenges for investigators and ethics committees. This study describes the views and experiences of CRT researchers with respect to: (1 ethical challenges in CRTs; (2 the ethics review process for CRTs; and (3 the need for comprehensive ethics guidelines for CRTs. Methods Descriptive qualitative analysis of interviews conducted with a purposive sample of 20 experienced CRT researchers. Results Informants expressed concern over the potential for bias that may result from requirements to obtain informed consent from research participants in CRTs. Informants suggested that the need for informed consent ought to be related to the type of intervention under study in a CRT. Informants rarely expressed concern regarding risks to research participants in CRTs, other than risks to privacy. Important issues identified in the research ethics literature, including fair subject selection and other justice issues, were not mentioned by informants. The ethics review process has had positive and negative impacts on CRT conduct. Informants stated that variability in ethics review between jurisdictions, and increasingly stringent ethics review in recent years, have hampered their ability to conduct CRTs. Many informants said that comprehensive ethics guidelines for CRTs would be helpful to researchers and research ethics committees. Conclusions Informants identified key ethical challenges in the conduct of CRTs, specifically relating to identifying subjects, seeking informed consent, and the use of gatekeepers. These data have since been used to identify topics for in-depth ethical analysis and to guide the development of comprehensive ethics guidelines for CRTs.
Group Music Therapy as a Preventive Intervention for Young People at Risk: Cluster-Randomized Trial.
Gold, Christian; Saarikallio, Suvi; Crooke, Alexander Hew Dale; McFerran, Katrina Skewes
2017-07-01
Music forms an important part of the lives and identities of adolescents and may have positive or negative mental health implications. Music therapy can be effective for mental disorders such as depression, but its preventive potential is unknown. The aim of this study was to examine whether group music therapy (GMT) is an effective intervention for young people who may be at risk of developing mental health problems, as indicated via unhealthy music use. The main question was whether GMT can reduce unhealthy uses of music and increase potentials for healthy uses of music, compared to self-directed music listening (SDML). We were also interested in effects of GMT on depressive symptoms, psychosocial well-being, rumination, and reflection. In an exploratory cluster-randomized trial in Australian schools, 100 students with self-reported unhealthy music use were invited to GMT (weekly sessions over 8 weeks) or SDML. Changes in the Healthy-Unhealthy Music Scale (HUMS) and mental health outcomes were measured over 3 months. Both interventions were well accepted. No effects were found between GMT and SDML (all p > 0.05); both groups tended to show small improvements over time. Younger participants benefited more from GMT, and older ones more from SDML (p = 0.018). GMT was associated with similar changes as SDML. Further research is needed to improve the processes of selecting participants for targeted interventions; to determine optimal dosage; and to provide more reliable evidence of effects of music-based interventions for adolescents.
Roumie, Christianne L; Elasy, Tom A; Greevy, Robert; Griffin, Marie R; Liu, Xulei; Stone, William J; Wallston, Kenneth A; Dittus, Robert S; Alvarez, Vincent; Cobb, Janice; Speroff, Theodore
2006-08-01
Inadequate blood pressure control is a persistent gap in quality care. To evaluate provider and patient interventions to improve blood pressure control. Cluster randomized, controlled trial. 2 hospital-based and 8 community-based clinics in the Veterans Affairs Tennessee Valley Healthcare System. 1341 veterans with essential hypertension cared for by 182 providers. Eligible patients had 2 or more blood pressure measurements greater than 140/90 mm Hg in a 6-month period and were taking a single antihypertensive agent. Providers who cared for eligible patients were randomly assigned to receive an e-mail with a Web-based link to the Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC 7) guidelines (provider education); provider education and a patient-specific hypertension computerized alert (provider education and alert); or provider education, hypertension alert, and patient education, in which patients were sent a letter advocating drug adherence, lifestyle modification, and conversations with providers (patient education). Proportion of patients with a systolic blood pressure less than 140 mm Hg at 6 months; intensification of antihypertensive medication. Mean baseline blood pressure was 157/83 mm Hg with no differences between groups (P = 0.105). Six-month follow-up data were available for 975 patients (73%). Patients of providers who were randomly assigned to the patient education group had better blood pressure control (138/75 mm Hg) than those in the provider education and alert or provider education alone groups (146/76 mm Hg and 145/78 mm Hg, respectively). More patients in the patient education group had a systolic blood pressure of 140 mm Hg or less compared with those in the provider education or provider education and alert groups (adjusted relative risk for the patient education group compared with the provider education alone group, 1.31 [95% CI, 1.06 to 1.62]; P = 0
Three-Dimensional Modeling of Fracture Clusters in Geothermal Reservoirs
Ghassemi, Ahmad [Univ. of Oklahoma, Norman, OK (United States)
2017-08-11
The objective of this is to develop a 3-D numerical model for simulating mode I, II, and III (tensile, shear, and out-of-plane) propagation of multiple fractures and fracture clusters to accurately predict geothermal reservoir stimulation using the virtual multi-dimensional internal bond (VMIB). Effective development of enhanced geothermal systems can significantly benefit from improved modeling of hydraulic fracturing. In geothermal reservoirs, where the temperature can reach or exceed 350oC, thermal and poro-mechanical processes play an important role in fracture initiation and propagation. In this project hydraulic fracturing of hot subsurface rock mass will be numerically modeled by extending the virtual multiple internal bond theory and implementing it in a finite element code, WARP3D, a three-dimensional finite element code for solid mechanics. The new constitutive model along with the poro-thermoelastic computational algorithms will allow modeling the initiation and propagation of clusters of fractures, and extension of pre-existing fractures. The work will enable the industry to realistically model stimulation of geothermal reservoirs. The project addresses the Geothermal Technologies Office objective of accurately predicting geothermal reservoir stimulation (GTO technology priority item). The project goal will be attained by: (i) development of the VMIB method for application to 3D analysis of fracture clusters; (ii) development of poro- and thermoelastic material sub-routines for use in 3D finite element code WARP3D; (iii) implementation of VMIB and the new material routines in WARP3D to enable simulation of clusters of fractures while accounting for the effects of the pore pressure, thermal stress and inelastic deformation; (iv) simulation of 3D fracture propagation and coalescence and formation of clusters, and comparison with laboratory compression tests; and (v) application of the model to interpretation of injection experiments (planned by our
Wang, Yao; Xiao, Lily Dongxia; Ullah, Shahid; He, Guo-Ping; De Bellis, Anita
2017-02-01
The lack of dementia education programmes for health professionals in primary care is one of the major factors contributing to the unmet demand for dementia care services. To determine the effectiveness of a nurse-led dementia education and knowledge translation programme for health professionals in primary care; participants' satisfaction with the programme; and to understand participants' perceptions of and experiences in the programme. A cluster randomized controlled trial was used as the main methodology to evaluate health professionals' knowledge, attitudes and care approach. Focus groups were used at the end of the project to understand health professionals' perceptions of and experiences in the programme. Fourteen community health service centres in a province in China participated in the study. Seven centres were randomly assigned to the intervention or control group respectively and 85 health professionals in each group completed the programme. A train-the-trainer model was used to implement a dementia education and knowledge translation programme. Outcome variables were measured at baseline, on the completion of the programme and at 3-month follow-up. A mixed effect linear regression model was applied to compare the significant differences of outcome measures over time between the two groups. Focus groups were guided by four semi-structured questions and analysed using content analysis. Findings revealed significant effects of the education and knowledge translation programme on participants' knowledge, attitudes and a person-centred care approach. Focus groups confirmed that the programme had a positive impact on dementia care practice. A dementia education and knowledge translation programme for health professionals in primary care has positive effects on their knowledge, attitudes, care approach and care practice. Copyright © 2016 Elsevier Ltd. All rights reserved.
A grand unified model for liganded gold clusters
Xu, Wen Wu; Zhu, Beien; Zeng, Xiao Cheng; Gao, Yi
2016-12-01
A grand unified model (GUM) is developed to achieve fundamental understanding of rich structures of all 71 liganded gold clusters reported to date. Inspired by the quark model by which composite particles (for example, protons and neutrons) are formed by combining three quarks (or flavours), here gold atoms are assigned three `flavours' (namely, bottom, middle and top) to represent three possible valence states. The `composite particles' in GUM are categorized into two groups: variants of triangular elementary block Au3(2e) and tetrahedral elementary block Au4(2e), all satisfying the duet rule (2e) of the valence shell, akin to the octet rule in general chemistry. The elementary blocks, when packed together, form the cores of liganded gold clusters. With the GUM, structures of 71 liganded gold clusters and their growth mechanism can be deciphered altogether. Although GUM is a predictive heuristic and may not be necessarily reflective of the actual electronic structure, several highly stable liganded gold clusters are predicted, thereby offering GUM-guided synthesis of liganded gold clusters by design.
ICA Model Order Estimation Using Clustering Method
P. Sovka
2007-12-01
Full Text Available In this paper a novel approach for independent component analysis (ICA model order estimation of movement electroencephalogram (EEG signals is described. The application is targeted to the brain-computer interface (BCI EEG preprocessing. The previous work has shown that it is possible to decompose EEG into movement-related and non-movement-related independent components (ICs. The selection of only movement related ICs might lead to BCI EEG classification score increasing. The real number of the independent sources in the brain is an important parameter of the preprocessing step. Previously, we used principal component analysis (PCA for estimation of the number of the independent sources. However, PCA estimates only the number of uncorrelated and not independent components ignoring the higher-order signal statistics. In this work, we use another approach - selection of highly correlated ICs from several ICA runs. The ICA model order estimation is done at significance level ÃŽÂ± = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters.
Armstrong Natalie
2012-09-01
Full Text Available Abstract Background World-wide healthcare systems are faced with an epidemic of type 2 diabetes. In the United Kingdom, clinical care is primarily provided by general practitioners (GPs rather than hospital specialists. Intermediate care clinics for diabetes (ICCD potentially provide a model for supporting GPs in their care of people with poorly controlled type 2 diabetes and in their management of cardiovascular risk factors. This study aims to (1 compare patients with type 2 diabetes registered with practices that have access to an ICCD service with those that have access only to usual hospital care; (2 assess the cost-effectiveness of the intervention; and (3 explore the views and experiences of patients, health professionals and other stakeholders. Methods/Design This two-arm cluster randomized controlled trial (with integral economic evaluation and qualitative study is set in general practices in three UK Primary Care Trusts. Practices are randomized to one of two groups with patients referred to either an ICCD (intervention or to hospital care (control. Intervention group: GP practices in the intervention arm have the opportunity to refer patients to an ICCD - a multidisciplinary team led by a specialist nurse and a diabetologist. Patients are reviewed and managed in the ICCD for a short period with a goal of improving diabetes and cardiovascular risk factor control and are then referred back to practice. or Control group: Standard GP care, with referral to secondary care as required, but no access to ICCD. Participants are adults aged 18 years or older who have type 2 diabetes that is difficult for their GPs to control. The primary outcome is the proportion of participants reaching three risk factor targets: HbA1c (≤7.0%; blood pressure ( Discussion Forty-nine practices have been randomized, 1,997 patients have been recruited to the trial, and 20 patients have been recruited to the qualitative study. Results will be available late 2012
Metal cluster fission: jellium model and Molecular dynamics simulations
Lyalin, Andrey G.; Obolensky, Oleg I.; Solov'yov, Ilia;
2004-01-01
Fission of doubly charged sodium clusters is studied using the open-shell two-center deformed jellium model approximation and it ab initio molecular dynamic approach accounting for all electrons in the system. Results of calculations of fission reactions Na_10^2+ --> Na_7^+ + Na_3^+ and Na_18^2+ ...
A new efficient Cluster Algorithm for the Ising Model
Nyffeler, M; Wiese, U J; Nyfeler, Matthias; Pepe, Michele; Wiese, Uwe-Jens
2005-01-01
Using D-theory we construct a new efficient cluster algorithm for the Ising model. The construction is very different from the standard Swendsen-Wang algorithm and related to worm algorithms. With the new algorithm we have measured the correlation function with high precision over a surprisingly large number of orders of magnitude.
nIFTy galaxy cluster simulations II: radiative models
Sembolini, F
2016-04-01
Full Text Available We have simulated the formation of a massive galaxy cluster (M(supcrit)(sub200) = 1.1×10(sup15)h(sup-1)M) in a CDM universe using 10 different codes (RAMSES, 2 incarnations of AREPO and 7 of GADGET), modeling hydrodynamics with full radiative...
Analytical model for non-thermal pressure in galaxy clusters
Shi, Xun; Komatsu, Eiichiro
2014-07-01
Non-thermal pressure in the intracluster gas has been found ubiquitously in numerical simulations, and observed indirectly. In this paper we develop an analytical model for intracluster non-thermal pressure in the virial region of relaxed clusters. We write down and solve a first-order differential equation describing the evolution of non-thermal velocity dispersion. This equation is based on insights gained from observations, numerical simulations, and theory of turbulence. The non-thermal energy is sourced, in a self-similar fashion, by the mass growth of clusters via mergers and accretion, and dissipates with a time-scale determined by the turnover time of the largest turbulence eddies. Our model predicts a radial profile of non-thermal pressure for relaxed clusters. The non-thermal fraction increases with radius, redshift, and cluster mass, in agreement with numerical simulations. The radial dependence is due to a rapid increase of the dissipation time-scale with radii, and the mass and redshift dependence comes from the mass growth history. Combing our model for the non-thermal fraction with the Komatsu-Seljak model for the total pressure, we obtain thermal pressure profiles, and compute the hydrostatic mass bias. We find typically 10 per cent bias for the hydrostatic mass enclosed within r500.
Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection.
Al-Jarrah, Omar Y; Alhussein, Omar; Yoo, Paul D; Muhaidat, Sami; Taha, Kamal; Kim, Kwangjo
2016-08-01
Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-inspection-based IDS (PI-IDS) identifies active intrusion attempts by inspecting transmission control protocol and user datagram protocol packet's payload and comparing it with previously seen attacks signatures. However, the PI-IDS abilities to detect intrusions might be incapacitated by packet encryption. Traffic-based IDS (T-IDS) alleviates the shortcomings of PI-IDS, as it does not inspect packet payload; however, it analyzes packet header to identify intrusions. As the network's traffic grows rapidly, not only the detection-rate is critical, but also the efficiency and the scalability of IDS become more significant. In this paper, we propose a state-of-the-art T-IDS built on a novel randomized data partitioned learning model (RDPLM), relying on a compact network feature set and feature selection techniques, simplified subspacing and a multiple randomized meta-learning technique. The proposed model has achieved 99.984% accuracy and 21.38 s training time on a well-known benchmark botnet dataset. Experiment results demonstrate that the proposed methodology outperforms other well-known machine-learning models used in the same detection task, namely, sequential minimal optimization, deep neural network, C4.5, reduced error pruning tree, and randomTree.
Gonzales, Ralph; Anderer, Tammy; McCulloch, Charles E.; Maselli, Judith H.; Bloom, Frederick J; Graf, Thomas R; Stahl, Melissa; Yefko, Michelle; Molecavage, Julie; Metlay, Joshua P
2013-01-01
Background National quality indicators show little change in the overuse of antibiotics for uncomplicated acute bronchitis. We compared the impact of two decision support strategies on antibiotic treatment of uncomplicated acute bronchitis. Methods We conducted a three-arm, cluster-randomized trial among 33 primary care practices belonging to an integrated health care system in central Pennsylvania. The printed intervention arm (n=11 practices) received decision support for acute cough illness through a print-based strategy, the computerized intervention group (n=11) received decision support through an electronic medical record-based strategy, and third group of practices (n=11) served as the control arm. Both intervention groups also received provider education and feedback on prescribing practices, and patient education brochures at check-in. Antibiotic prescription rates for uncomplicated acute bronchitis in the winter period (October 2009 – March 2010) following introduction of the intervention were compared with the previous three winter periods in an intent-to-treat analysis. Results Compared with the baseline period, the percentage of adolescents and adults prescribed antibiotics during the intervention period decreased at the printed (from 80.0% to 68.3%) and computerized intervention sites (from 74.0% to 60.7%), but increased slightly at the control sites (from 72.5% to 74.3%). After controlling for patient and provider characteristics, and clustering of observations by provider and practice site, the differences for the intervention groups were statistically significant from control (control vs. printed P=0.003; control vs. computerized P=0.014) but no among themselves (printed vs. computerized P=0.67). Changes in total visits, proportion diagnosed as uncomplicated acute bronchitis and thirty-day return visit rates were similar between study groups. Conclusions Implementation of a decision support strategy for acute bronchitis can help reduce overuse
Gonzales, Ralph; Anderer, Tammy; McCulloch, Charles E; Maselli, Judith H; Bloom, Frederick J; Graf, Thomas R; Stahl, Melissa; Yefko, Michelle; Molecavage, Julie; Metlay, Joshua P
2013-02-25
National quality indicators show little change in the overuse of antibiotics for uncomplicated acute bronchitis. We compared the effect of 2 decision support strategies on antibiotic treatment of uncomplicated acute bronchitis. We conducted a 3-arm cluster randomized trial among 33 primary care practices belonging to an integrated health care system in central Pennsylvania. The printed decision support intervention sites (11 practices) received decision support for acute cough illness through a print-based strategy, the computer-assisted decision support intervention sites (11 practices) received decision support through an electronic medical record-based strategy, and the control sites (11 practices) served as a control arm. Both intervention sites also received clinician education and feedback on prescribing practices, as well as patient education brochures at check-in. Antibiotic prescription rates for uncomplicated acute bronchitis in the winter period (October 1, 2009, through March 31, 2010) following introduction of the intervention were compared with the previous 3 winter periods in an intent-to-treat analysis. Compared with the baseline period, the percentage of adolescents and adults prescribed antibiotics during the intervention period decreased at the printed decision support intervention sites (from 80.0% to 68.3%) and at the computer-assisted decision support intervention sites (from 74.0% to 60.7%) but increased slightly at the control sites (from 72.5% to 74.3%). After controlling for patient and clinician characteristics, as well as clustering of observations by clinician and practice site, the differences for the intervention sites were statistically significant from the control sites (P = .003 for control sites vs printed decision support intervention sites and P = .01 for control sites vs computer-assisted decision support intervention sites) but not between themselves (P = .67 for printed decision support intervention sites vs computer
Self-Organized Criticality in a Random Network Model
Nirei, Makoto
1998-01-01
A new model of self-organized criticality is defined by incorporating a random network model in order to explain endogenous complex fluctuations of economic aggregates. The model can feature many globally interactive systems such as economies or societies.
Traffic Accident, System Model and Cluster Analysis in GIS
Veronika Vlčková
2015-07-01
Full Text Available One of the many often frequented topics as normal journalism, so the professional public, is the problem of traffic accidents. This article illustrates the orientation of considerations to a less known context of accidents, with the help of constructive systems theory and its methods, cluster analysis and geoinformation engineering. Traffic accident is reframing the space-time, and therefore it can be to study with tools of technology of geographic information systems. The application of system approach enabling the formulation of the system model, grabbed by tools of geoinformation engineering and multicriterial and cluster analysis.
Latent Clustering Models for Outlier Identification in Telecom Data
Ye Ouyang
2016-01-01
Full Text Available Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar high-speed machines. The ability to quickly identify unexpected traffic data in this stream is critical for mobile carriers, as it can be caused by either fraudulent intrusion or technical problems. Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers. In this article, we develop and compare clustering models for telecom data, focusing on those that include time-stamp information management. Two main models are introduced, solved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA and time-dependent Gaussian Mixture Models (time-GMM. These models are then compared with other different clustering models, such as Gaussian model and GMM (which do not contain time-stamp information. We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low tuning parameters or expertise requirement.
Effects of tidal gravitational fields in clustering dark energy models
Pace, Francesco; Reischke, Robert; Meyer, Sven; Schäfer, Björn Malte
2017-04-01
We extend a previous work by Reischke et al. by studying the effects of tidal shear on clustering dark energy models within the framework of the extended spherical collapse model and using the Zel'dovich approximation. As in previous works on clustering dark energy, we assumed a vanishing effective sound speed describing the perturbations in dark energy models. To be self-consistent, our treatment is valid only on linear scales since we do not intend to introduce any heuristic models. This approach makes the linear overdensity δc mass dependent and similarly to the case of smooth dark energy, its effects are predominant at small masses and redshifts. Tidal shear has effects of the order of per cent or less, regardless of the model and preserves a well-known feature of clustering dark energy: When dark energy perturbations are included, the models resemble better the Lambda cold dark matter evolution of perturbations. We also showed that effects on the comoving number density of haloes are small and qualitatively and quantitatively in agreement with what were previously found for smooth dark energy models.
Concept Association and Hierarchical Hamming Clustering Model in Text Classification
Su Gui-yang; Li Jian-hua; Ma Ying-hua; Li Sheng-hong; Yin Zhong-hang
2004-01-01
We propose two models in this paper. The concept of association model is put forward to obtain the co-occurrence relationships among keywords in the documents and the hierarchical Hamming clustering model is used to reduce the dimensionality of the category feature vector space which can solve the problem of the extremely high dimensionality of the documents' feature space. The results of experiment indicate that it can obtain the co-occurrence relations among keywords in the documents which promote the recall of classification system effectively. The hierarchical Hamming clustering model can reduce the dimensionality of the category feature vector efficiently, the size of the vector space is only about 10% of the primary dimensionality.
Modelling autophagy selectivity by receptor clustering on peroxisomes
Brown, Aidan I
2016-01-01
When subcellular organelles are degraded by autophagy, typically some, but not all, of each targeted organelle type are degraded. Autophagy selectivity must not only select the correct type of organelle, but must discriminate between individual organelles of the same kind. In the context of peroxisomes, we use computational models to explore the hypothesis that physical clustering of autophagy receptor proteins on the surface of each organelle provides an appropriate all-or-none signal for degradation. The pexophagy receptor proteins NBR1 and p62 are well characterized, though only NBR1 is essential for pexophagy (Deosaran {\\em et al.}, 2013). Extending earlier work by addressing the initial nucleation of NBR1 clusters on individual peroxisomes, we find that larger peroxisomes nucleate NBR1 clusters first and lose them due to competitive coarsening last, resulting in significant size-selectivity favouring large peroxisomes. This effect can explain the increased catalase signal that results from experimental s...
Competitive growth model involving random deposition and random deposition with surface relaxation
Horowitz, Claudio M.; Monetti, Roberto A.; Albano, Ezequiel V.
2001-06-01
A deposition model that considers a mixture of random deposition with surface relaxation and a pure random deposition is proposed and studied. As the system evolves, random deposition with surface relaxation (pure random deposition) take place with probability p and (1{minus}p), respectively. The discrete (microscopic) approach to the model is studied by means of extensive numerical simulations, while continuous equations are used in order to investigate the mesoscopic properties of the model. A dynamic scaling ansatz for the interface width W(L,t,p) as a function of the lattice side L, the time t and p is formulated and tested. Three exponents, which can be linked to the standard growth exponent of random deposition with surface relaxation by means of a scaling relation, are identified. In the continuous limit, the model can be well described by means of a phenomenological stochastic growth equation with a p-dependent effective surface tension.
α-α folding cluster model for α-radioactivity
Soylu, A.; Bayrak, O.
2015-04-01
The -decay half-lives are calculated for heavy and superheavy nuclei for and from the ground state to ground state transitions within the framework of the Wentzel-Kramers-Brillouin (WKB) method and the Bohr-Sommerfeld quantization. In the calculations, the - single folding cluster potential obtained with the folded integral of the - potential with the -cluster density distributions is used in order to model the nuclear interaction between the -particle and core nucleus. While the results show very good agreement with the experimental ones in the heavy-nuclei region, especially for even-even nuclei, smaller values than the experimental ones are obtained for superheavy nuclei. As both the density of the core and the interaction term in the folding integral include the -clustering effects and, in this way, all cluster effects are taken into account in the model, the results of calculations are more physical and reasonable than the calculations done in the other models. The present method could be applied to light nuclei with different types of nuclear densities.
α-α folding cluster model for α-radioactivity
Soylu, A. [Nigde University, Department of Physics, Nigde (Turkey); Bayrak, O. [Akdeniz University, Department of Physics, Antalya (Turkey)
2015-04-01
The α-decay half-lives are calculated for heavy and superheavy nuclei for 52 ≤ Z ≤ 112 and 108 ≤ A ≤ 285 from the ground state to ground state α transitions within the framework of the Wentzel-Kramers-Brillouin (WKB) method and the Bohr-Sommerfeld quantization. In the calculations, the α-α single folding cluster potential obtained with the folded integral of the α-α potential with the α-cluster density distributions is used in order to model the nuclear interaction between the α-particle and core nucleus. While the results show very good agreement with the experimental ones in the heavy-nuclei region, especially for even-even nuclei, smaller values than the experimental ones are obtained for superheavy nuclei. As both the density of the core and the interaction term in the folding integral include the α-clustering effects and, in this way, all cluster effects are taken into account in the model, the results of calculations are more physical and reasonable than the calculations done in the other models. The present method could be applied to light nuclei with different types of nuclear densities. (orig.)
Enhancing a sustainable healthy working life: design of a clustered randomized controlled trial.
Koolhaas, Wendy; Brouwer, Sandra; Groothoff, Johan W; van der Klink, Jac Jl
2010-08-06
To improve a sustainable healthy working life, we have developed the intervention 'Staying healthy at work', which endeavours to enhance work participation of employees aged 45 years and older by increasing their problem-solving capacity and stimulating their awareness of their role and responsibility towards a healthy working life. This research study aims to evaluate the process and the effectiveness of the intervention compared with care as usual. The study is a cluster-randomized controlled trial design (randomized at the supervisor level), with a 1-year follow-up. Workers aged 45 years and older have been enrolled in the study. Workers in the intervention group are receiving the intervention 'Staying healthy at work'. The main focus of the intervention is to promote a healthy working life of ageing workers by: (1) changing workers awareness and behaviour, by emphasizing their own decisive role in attaining goals; (2) improving the supervisors' ability to support workers in taking the necessary action, by means of enhancing knowledge and competence; and (3) enhancing the use of the human resource professionals and the occupational health tools available within the organization. The supervisors in the intervention group have been trained how to present themselves as a source of support for the worker. Workers in the control group are receiving care as usual; supervisors in the control group have not participated in the training. Measurements have been taken at baseline and will be followed up at 3, 6 and 12 months. The primary outcome measures are vitality, work ability and productivity. The secondary outcomes measures include fatigue, job strain, work attitude, self-efficacy and work engagement. A process evaluation will be conducted at both the supervisor and the worker levels, and satisfaction with the content of the intervention will be assessed. The intervention 'Staying healthy at work' has the potential to provide evidence-based knowledge of an innovative
Enhancing a sustainable healthy working life: design of a clustered randomized controlled trial
Koolhaas Wendy
2010-08-01
Full Text Available Abstract Background To improve a sustainable healthy working life, we have developed the intervention 'Staying healthy at work', which endeavours to enhance work participation of employees aged 45 years and older by increasing their problem-solving capacity and stimulating their awareness of their role and responsibility towards a healthy working life. This research study aims to evaluate the process and the effectiveness of the intervention compared with care as usual. Methods/design The study is a cluster-randomized controlled trial design (randomized at the supervisor level, with a 1-year follow-up. Workers aged 45 years and older have been enrolled in the study. Workers in the intervention group are receiving the intervention 'Staying healthy at work'. The main focus of the intervention is to promote a healthy working life of ageing workers by: (1 changing workers awareness and behaviour, by emphasizing their own decisive role in attaining goals; (2 improving the supervisors' ability to support workers in taking the necessary action, by means of enhancing knowledge and competence; and (3 enhancing the use of the human resource professionals and the occupational health tools available within the organization. The supervisors in the intervention group have been trained how to present themselves as a source of support for the worker. Workers in the control group are receiving care as usual; supervisors in the control group have not participated in the training. Measurements have been taken at baseline and will be followed up at 3, 6 and 12 months. The primary outcome measures are vitality, work ability and productivity. The secondary outcomes measures include fatigue, job strain, work attitude, self-efficacy and work engagement. A process evaluation will be conducted at both the supervisor and the worker levels, and satisfaction with the content of the intervention will be assessed. Discussion The intervention 'Staying healthy at work' has the
López-García-Franco Alberto
2012-05-01
Full Text Available Abstract Background Medically unexplained symptoms are an important mental health problem in primary care and generate a high cost in health services. Cognitive behavioral therapy and psychodynamic therapy have proven effective in these patients. However, there are few studies on the effectiveness of psychosocial interventions by primary health care. The project aims to determine whether a cognitive-behavioral group intervention in patients with medically unexplained symptoms, is more effective than routine clinical practice to improve the quality of life measured by the SF-12 questionary at 12 month. Methods/design This study involves a community based cluster randomized trial in primary healthcare centres in Madrid (Spain. The number of patients required is 242 (121 in each arm, all between 18 and 65 of age with medically unexplained symptoms that had seeked medical attention in primary care at least 10 times during the previous year. The main outcome variable is the quality of life measured by the SF-12 questionnaire on Mental Healthcare. Secondary outcome variables include number of consultations, number of drug (prescriptions and number of days of sick leave together with other prognosis and descriptive variables. Main effectiveness will be analyzed by comparing the percentage of patients that improve at least 4 points on the SF-12 questionnaire between intervention and control groups at 12 months. All statistical tests will be performed with intention to treat. Logistic regression with random effects will be used to adjust for prognostic factors. Confounding factors or factors that might alter the effect recorded will be taken into account in this analysis. Discussion This study aims to provide more insight to address medically unexplained symptoms, highly prevalent in primary care, from a quantitative methodology. It involves intervention group conducted by previously trained nursing staff to diminish the progression to the chronicity
Topic Modeling Based Image Clustering by Events in Social Media
2016-01-01
Social event detection in large photo collections is very challenging and multimodal clustering is an effective methodology to deal with the problem. Geographic information is important in event detection. This paper proposed a topic model based approach to estimate the missing geographic information for photos. The approach utilizes a supervised multimodal topic model to estimate the joint distribution of time, geographic, content, and attached textual information. Then we annotate the missi...
Model study in chemisorption: atomic hydrogen on beryllium clusters
Bauschlicher, C.W. Jr.
1976-08-01
The interaction between atomic hydrogen and the (0001) surface of Be metal has been studied by ab initio electronic structure theory. Self-consistent-field (SCF) calculations have been performed using minimum, optimized minimum, double zeta and mixed basis sets for clusters as large as 22 Be atoms. The binding energy and equilibrium geometry (the distance to the surface) were determined for 4 sites. Both spatially restricted (the wavefunction was constrained to transform as one of the irreducible representations of the molecular point group) and unrestricted SCF calculations were performed. Using only the optimized minimum basis set, clusters containing as many as 22 beryllium atoms have been investigated. From a variety of considerations, this cluster is seen to be nearly converged within the model used, providing the most reliable results for chemisorption. The site dependence of the frequency is shown to be a geometrical effect depending on the number and angle of the bonds. The diffusion of atomic hydrogen through a perfect beryllium crystal is predicted to be energetically unfavorable. The cohesive energy, the ionization energy and the singlet-triplet separation were computed for the clusters without hydrogen. These quantities can be seen as a measure of the total amount of edge effects. The chemisorptive properties are not related to the total amount of edge effects, but rather the edge effects felt by the adsorbate bonding berylliums. This lack of correlation with the total edge effects illustrates the local nature of the bonding, further strengthening the cluster model for chemisorption. A detailed discussion of the bonding and electronic structure is included. The remaining edge effects for the Be/sub 22/ cluster are discussed.
Hoffmann Kristina
2010-01-01
Full Text Available Abstract Background With rates of childhood obesity increasing, physical activity (PA promotion especially in young children has assumed greater importance. Given the limited effectiveness of most interventions to date, new approaches are needed. The General Systems theory suggests that involving parents as intervention targets may be effective in fostering healthier life styles in children. We describe the development of a parent-focused participatory intervention and the procedures used to evaluate its effectiveness in increasing daily PA in preschoolers. Methods/Design Thirty-seven South German preschools were identified for this study and agreed to participate. Using a two-armed, controlled cluster-randomized trial design we test a participatory intervention with parents as the primary target group and potential agents of behavioural change. Specifically, the intervention is designed to engage parents in the development, refinement and selection of project ideas to promote PA and in incorporating these ideas into daily routines within the preschool community, consisting of children, teachers and parents. Our study is embedded within an existing state-sponsored programme providing structured gym lessons to preschool children. Thus, child-based PA outcomes from the study arm with the parent-focused intervention and the state-sponsored programme are compared with those from the study arm with the state-sponsored programme alone. The evaluation entails baseline measurements of study outcomes as well as follow-up measurements at 6 and 12 months. Accelerometry measures PA intensity over a period of six days, with the mean over six days used as the primary outcome measure. Secondary outcomes include childrens' BMI, a sum of averaged skin fold thickness measurements across multiple sites, and PA behaviour. Longitudinal multilevel models are used to assess within-subject change and between-group differences in study outcomes, adjusted for covariates
Schoonhoven, Lisette; van Gaal, Betsie G I; Teerenstra, Steven; Adang, Eddy; van der Vleuten, Carine; van Achterberg, Theo
2015-01-01
No-rinse disposable wash gloves are increasingly implemented in health care to replace traditional soap and water bed baths without proper evaluation of (cost) effectiveness. To compare bed baths for effects on skin integrity and resistance against bathing and costs. Cluster randomized trial. Fifty six nursing home wards in the Netherlands. Participants: Five hundred adult care-dependent residents and 275 nurses from nursing home wards. The experimental condition 'washing without water' consists of a bed bath with disposable wash gloves made of non-woven waffled fibers, saturated with a no-rinse, quickly vaporizing skin cleaning and caring lotion. The control condition is a traditional bed bath using soap, water, washcloths and towels. Both conditions were continued for 6 weeks. Outcome measures were prevalence of skin damage distinguished in two levels of severity: any skin abnormality/lesion and significant skin lesions. Additional outcomes: resistance during bed baths, costs. Any skin abnormalities/lesions over time decreased slightly in the experimental group, and increased slightly in the control group, resulting in 72.7% vs 77.6% of residents having any skin abnormalities/lesions after 6 weeks, respectively (p=0.04). There were no differences in significant skin lesions or resistance after 6 weeks. Mean costs for bed baths during 6 weeks per resident were estimated at €218.30 (95%CI 150.52-286.08) in the experimental group and €232.20 (95%CI: 203.80-260.60) in the control group (difference €13.90 (95%CI: -25.61-53.42). Washing without water mildly protects from skin abnormalities/lesions, costs for preparing and performing bed baths do not differ from costs for traditional bed bathing. Thus, washing without water can be considered the more efficient alternative. Copyright © 2014 Elsevier Ltd. All rights reserved.
Ettl, Florian; Testori, Christoph; Weiser, Christoph; Fleischhackl, Sabine; Mayer-Stickler, Monika; Herkner, Harald; Schreiber, Wolfgang; Fleischhackl, Roman
2011-06-01
The first-aid training necessary for obtaining a drivers license in Austria has a regulated and predefined curriculum but has been targeted for the implementation of a new course structure with less theoretical input, repetitive training in cardiopulmonary resuscitation (CPR) and structured presentations using innovative media. The standard and a new course design were compared with a prospective, participant- and observer-blinded, cluster-randomized controlled study. Six months after the initial training, we evaluated the confidence of the 66 participants in their skills, CPR effectiveness parameters and correctness of their actions. The median self-confidence was significantly higher in the interventional group [IG, visual analogue scale (VAS:"0" not-confident at all,"100" highly confident):57] than in the control group (CG, VAS:41). The mean chest compression rate in the IG (98/min) was closer to the recommended 100 bpm than in the CG (110/min). The time to the first chest compression (IG:25s, CG:36s) and time to first defibrillator shock (IG:86s, CG:92s) were significantly shorter in the IG. Furthermore, the IG participants were safer in their handling of the defibrillator and started with countermeasures against developing shock more often. The management of an unconscious person and of heavy bleeding did not show a difference between the two groups even after shortening the lecture time. Motivation and self-confidence as well as skill retention after six months were shown to be dependent on the teaching methods and the time for practical training. Courses may be reorganized and content rescheduled, even within predefined curricula, to improve course outcomes. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Mental health first aid training for high school teachers: a cluster randomized trial
Jorm Anthony F
2010-06-01
Full Text Available Abstract Background Mental disorders often have their first onset during adolescence. For this reason, high school teachers are in a good position to provide initial assistance to students who are developing mental health problems. To improve the skills of teachers in this area, a Mental Health First Aid training course was modified to be suitable for high school teachers and evaluated in a cluster randomized trial. Methods The trial was carried out with teachers in South Australian high schools. Teachers at 7 schools received training and those at another 7 were wait-listed for future training. The effects of the training on teachers were evaluated using questionnaires pre- and post-training and at 6 months follow-up. The questionnaires assessed mental health knowledge, stigmatizing attitudes, confidence in providing help to others, help actually provided, school policy and procedures, and teacher mental health. The indirect effects on students were evaluated using questionnaires at pre-training and at follow-up which assessed any mental health help and information received from school staff, and also the mental health of the student. Results The training increased teachers' knowledge, changed beliefs about treatment to be more like those of mental health professionals, reduced some aspects of stigma, and increased confidence in providing help to students and colleagues. There was an indirect effect on students, who reported receiving more mental health information from school staff. Most of the changes found were sustained 6 months after training. However, no effects were found on teachers' individual support towards students with mental health problems or on student mental health. Conclusions Mental Health First Aid training has positive effects on teachers' mental health knowledge, attitudes, confidence and some aspects of their behaviour. Trial registration ACTRN12608000561381
A cluster randomized trial to evaluate a health education programme "Living with Sun at School".
Sancho-Garnier, Hélène; Pereira, Bruno; Césarini, Pierre
2012-07-01
Over-exposure to sunlight increases the risk of skin cancers, particularly when exposure occurs during childhood. School teachers can play an active role in providing an education programme that can help prevent this. "Living with the Sun," (LWS) is a sun safety education program for school children based on a handy guide for classroom activities designed to improve children's knowledge, but moreover to positively modify their sun safety attitudes and behaviours. The goal of our study was to determine the effectiveness of this programme by examining children's knowledge, attitude and sun exposure behaviours prior to and after the completion of the programme. We carried out a cluster randomised trial in which the classes were randomly assigned to one of two groups; one using the LWS programme and another that didn't, serving as the control. Data was collected before completion of the programme and an additional three times in the year after completion. The 70 participating classes (1,365 schoolchildren) were distributed throughout France. Statistical analysis confirmed that knowledge of sun risk increased significantly in the LWS classes (p < 0.001). Both groups positively changed their attitudes when considering the best sun protection, but the LWS group proved to consistently be more convinced (p = 0.04). After the summer holidays, differences between the two groups decreased throughout the year but stayed globally significant. We also observed some significant behaviour modification during the holidays. For instance, the LWS group applied sunscreen more frequently than the control group, and were more likely to wear a hat (72% versus 59%) and use a sun umbrella on the beach (75% versus 64%).
Elvira Nouwens
Full Text Available BACKGROUND: Accreditation of healthcare organizations is a widely used method to assess and improve quality of healthcare. Our aim was to determine the effectiveness of improvement plans in practice accreditation of primary care practices, focusing on cardiovascular risk management (CVRM. METHOD: A two-arm cluster randomized controlled trial with a block design was conducted with measurements at baseline and follow-up. Primary care practices allocated to the intervention group (n = 22 were instructed to focus improvement plans during the intervention period on CVRM, while practices in the control group (n = 23 could focus on any domain except on CVRM and diabetes mellitus. Primary outcomes were systolic blood pressure <140 mmHg, LDL cholesterol <2.5 mmol/l and prescription of antiplatelet drugs. Secondary outcomes were 17 indicators of CVRM and physician's perceived goal attainment for the chosen improvement project. RESULTS: No effect was found on the primary outcomes. Blood pressure targets were reached in 39.8% of patients in the intervention and 38.7% of patients in the control group; cholesterol target levels were reached in 44.5% and 49.0% respectively; antiplatelet drugs were prescribed in 82.7% in both groups. Six secondary outcomes improved: smoking status, exercise control, diet control, registration of alcohol intake, measurement of waist circumference, and fasting glucose. Participants' perceived goal attainment was high in both arms: mean scores of 7.9 and 8.2 on the 10-point scale. CONCLUSIONS: The focus of improvement plans on CVRM in the practice accreditation program led to some improvements of CVRM, but not on the primary outcomes. ClinicalTrials.gov NCT00791362.
Effects of the X:IT smoking intervention: a school-based cluster randomized trial.
Andersen, Anette; Krølner, Rikker; Bast, Lotus Sofie; Thygesen, Lau Caspar; Due, Pernille
2015-12-01
Uptake of smoking in adolescence is still of major public health concern. Evaluations of school-based programmes for smoking prevention show mixed results. The aim of this study was to examine the effect of X:IT, a multi-component school-based programme to prevent adolescent smoking. Data from a Danish cluster randomized trial included 4041 year-7 students (mean age: 12.5) from 51 intervention and 43 control schools. Outcome measure 'current smoking' was dichotomized into smoking daily, weekly, monthly or more seldom vs do not smoke. Analyses were adjusted for baseline covariates: sex, family socioeconomic position (SEP), best friend's smoking and parental smoking. We performed multilevel, logistic regression analyses of available cases and intention-to-treat (ITT) analyses, replacing missing outcome values by multiple imputation. At baseline, 4.7% and 6.8% of the students at the intervention and the control schools smoked, respectively. After 1 year of the intervention, the prevalence was 7.9% and 10.7%, respectively. At follow-up, 553 students (13.7%) did not answer the question on smoking. Available case analyses: crude odds ratios (OR) for smoking at intervention schools compared with control schools: 0.65 (0.48-0.88) and adjusted: 0.70 (0.47-1.04). ITT analyses: crude OR for smoking at intervention schools compared with control schools: 0.67 (0.50-0.89) and adjusted: 0.61 (0.45-0.82). Students at intervention schools had a lower risk of smoking after a year of intervention in year 7. This multi-component intervention involving educational, parental and context-related intervention components seems to be efficient in lowering or postponing smoking uptake in Danish adolescents. © The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
Vellinga, Akke; Galvin, Sandra; Duane, Sinead; Callan, Aoife; Bennett, Kathleen; Cormican, Martin; Domegan, Christine; Murphy, Andrew W
2016-02-02
Overuse of antimicrobial therapy in the community adds to the global spread of antimicrobial resistance, which is jeopardizing the treatment of common infections. We designed a cluster randomized complex intervention to improve antimicrobial prescribing for urinary tract infection in Irish general practice. During a 3-month baseline period, all practices received a workshop to promote consultation coding for urinary tract infections. Practices in intervention arms A and B received a second workshop with information on antimicrobial prescribing guidelines and a practice audit report (baseline data). Practices in intervention arm B received additional evidence on delayed prescribing of antimicrobials for suspected urinary tract infection. A reminder integrated into the patient management software suggested first-line treatment and, for practices in arm B, delayed prescribing. Over the 6-month intervention, practices in arms A and B received monthly audit reports of antimicrobial prescribing. The proportion of antimicrobial prescribing according to guidelines for urinary tract infection increased in arms A and B relative to control (adjusted overall odds ratio [OR] 2.3, 95% confidence interval [CI] 1.7 to 3.2; arm A adjusted OR 2.7, 95% CI 1.8 to 4.1; arm B adjusted OR 2.0, 95% CI 1.3 to 3.0). An unintended increase in antimicrobial prescribing was observed in the intervention arms relative to control (arm A adjusted OR 2.2, 95% CI 1.2 to 4.0; arm B adjusted OR 1.4, 95% CI 0.9 to 2.1). Improvements in guideline-based prescribing were sustained at 5 months after the intervention. A complex intervention, including audit reports and reminders, improved the quality of prescribing for urinary tract infection in Irish general practice. ClinicalTrials.gov, no. NCT01913860. © 2016 Canadian Medical Association or its licensors.
Jakobsen, Markus Due; Sundstrup, Emil; Brandt, Mikkel; Jay, Kenneth; Aagaard, Per; Andersen, Lars L
2015-11-01
High physical exertion during work is a risk factor for musculoskeletal pain and long-term sickness absence. Physical exertion (RPE) reflects the balance between physical work demands and physical capacity of the individual. Thus, increasing the physical capacity through physical exercise may decrease physical exertion during work. This study investigates the effect of workplace-based versus home-based physical exercise on physical exertion during work (WRPE) among healthcare workers. 200 female healthcare workers (age: 42.0, body mass index: 24.1, average pain intensity: 3.1 on a scale of 0 to 10, average WRPE: 3.6 on a scale of 0 to 10) from 18 departments at three participating hospitals. Participants were randomly allocated at the cluster level to 10 weeks of: (1) workplace physical exercise (WORK) performed in groups during working hours for 5×10 minutes per week and up to five group-based coaching sessions on motivation for regular physical exercise, or (2) home-based physical exercise (HOME) performed during leisure time for 5×10 minutes per week. Physical exertion was assessed at baseline and at 10-week follow-up. 2.2 (SD: 1.1) and 1.0 (SD: 1.2) training sessions were performed per week in WORK and HOME, respectively. Physical exertion was reduced more in WORK than HOME (pworkplace appears more effective than home-based exercise in reducing physical exertion during daily work tasks in healthcare workers. © 2015 the Nordic Societies of Public Health.
Haleem Abdul
2012-12-01
Full Text Available Abstract Background Oral health education (OHE in schools has largely been imparted by dental professionals. Considering the substantial cost of this expert-led approach, the strategies relying on teachers, peer-leaders and learners themselves have also been utilized. However the evidence for comparative effectiveness of these strategies is lacking in the dental literature. The present study was conducted to compare the effectiveness of dentist-led, teacher-led, peer-led and self-learning strategies of oral health education. Methods A two-year cluster randomized controlled trial following a parallel design was conducted. It involved five groups of adolescents aged 10-11 years at the start of the study. The trial involved process as well as four outcome evaluations. The present paper discusses the findings of the study pertaining to the baseline and final outcome evaluation, both comprising of a self-administered questionnaire, a structured interview and clinical oral examination. The data were analyzed using Generalized Estimating Equations. Results All the three educator-led strategies of OHE had statistically higher mean oral health knowledge (OHK, oral health behavior (OHB, oral hygiene status (OHS and combined knowledge, behavior and oral hygiene status (KBS scores than the self-learning and control groups (p Conclusions The dentist-led, teacher-led and peer-led strategies of oral health education are equally effective in improving the oral health knowledge and oral hygiene status of adolescents. The peer-led strategy, however, is almost as effective as the dentist-led strategy and comparatively more effective than the teacher-led and self-learning strategies in improving their oral health behavior. Trail registration SRCTN39391017
Jian-Gao Fan; Xiao-Bu Cai; Lui Li; Xing-Jian Li; Fei Dai; Jun Zhu
2008-01-01
AIM: To examine the relations of alcohol consumption to the prevalence of metabolic syndrome in Shanghai adults.METHODS: We performed a cross-sectional analysis of data from the randomized multistage stratified cluster sampling of Shanghai adults, who were evaluated for alcohol consumption and each component of metabolic syndrome, using the adapted U.S. National Cholesterol Education Program criteria. Current alcohol consumption was defined as more than once of alcohol drinking per month.RESULTS: The study population consisted of 3953participants (1524 men) with a mean age of 54.3 ± 12.1years. Among them, 448 subjects (11.3%) were current alcohol drinkers, including 405 males and 43 females.After adjustment for age and sex, the prevalence of current alcohol drinking and metabolic syndrome in the general population of Shanghai was 13.0% and 15.3%,respectively. Compared with nondrinkers, the prevalence of hypertriglyceridemia and hypertension was higher while the prevalence of abdominal obesity, low serum high-density-lipoprotein cholesterol (HDL-C) and diabetes mellitus was lower in subjects who consumed alcohol twice or more per month, with a trend toward reducing the prevalence of metabolic syndrome. Among the current alcohol drinkers, systolic blood pressure, HDL-C, fasting plasma glucose, and prevalence of hypertriglyceridemia tended to increase with increased alcohol consumption.However, Iow-density-lipoprotein cholesterol concentration,prevalence of abdominal obesity, low serum HDL-C andmetabolic syndrome showed the tendency to decrease.Moreover, these statistically significant differences were independent of gender and age.CONCLUSION: Current alcohol consumption is associatedwith a lower prevalence of metabolic syndrome irrespe-ctive of alcohol intake (g/d), and has a favorable influence on HDL-C, waist circumference, and possible diabetes mellitus. However, alcohol intake increases the likelihoodof hypertension, hypertriglyceridemia and hyperglycemia
Zimmerman, Richard K.; Nowalk, Mary Patricia; Lin, Chyongchiou Jeng; Hannibal, Kristin; Moehling, Krissy K.; Huang, Hsin-Hui; Matambanadzo, Annamore; Troy, Judith; Allred, Norma J.; Gallik, Greg; Reis, Evelyn C.
2014-01-01
Purpose To increase childhood influenza vaccination rates using a toolkit and early vaccine delivery in a randomized cluster trial. Methods Twenty primary care practices treating children (range for n=536-8,183) were randomly assigned to Intervention and Control arms to test the effectiveness of an evidence-based practice improvement toolkit (4 Pillars Toolkit) and early vaccine supplies for use among disadvantaged children on influenza vaccination rates among children 6 months-18 years. Follow-up staff meetings and surveys were used to assess use and acceptability of the intervention strategies in the Intervention arm. Rates for the 2010-2011 and 2011-2012 influenza seasons were compared. Two-level generalized linear mixed modeling was used to evaluate outcomes. Results Overall increases in influenza vaccination rates were significantly greater in the Intervention arm (7.9 percentage points) compared with the Control arm (4.4 percentage points; P58% did not significantly increase. In regression analyses, a child's likelihood of being vaccinated was significantly higher with: younger age, white race (Odds ratio [OR]=1.29; 95% confidence interval [CI]=1.23-1.34), having commercial insurance (OR=1.30; 95%CI=1.25-1.35), higher pre-intervention practice vaccination rate (OR=1.25; 95%CI=1.16-1.34), and being in the Intervention arm (OR=1.23; 95%CI=1.01-1.50). Early delivery of influenza vaccine was rated by Intervention practices as an effective strategy for raising rates. Conclusions Implementation of a multi-strategy toolkit and early vaccine supplies can significantly improve influenza vaccination rates among children in primary care practices but the effect may be less pronounced in practices with moderate to high existing vaccination rates. PMID:24793941
Juthani-Mehta, Manisha; Van Ness, Peter H.; McGloin, Joanne; Argraves, Stephanie; Chen, Shu; Charpentier, Peter; Miller, Laura; Williams, Kathleen; Wall, Diane; Baker, Dorothy; Tinetti, Mary; Peduzzi, Peter; Quagliarello, Vincent J.
2015-01-01
Background. Pneumonia remains an important public health problem among elderly nursing home residents. This clinical trial sought to determine if a multicomponent intervention protocol, including manual tooth/gum brushing plus 0.12% chlorhexidine oral rinse, twice per day, plus upright positioning during feeding, could reduce the incidence of radiographically documented pneumonia among nursing home residents, compared with usual care. Methods. This cluster-randomized clinical trial was conducted in 36 nursing homes in Connecticut. Eligible residents >65 years with at least 1 of 2 modifiable risk factors for pneumonia (ie, impaired oral hygiene, swallowing difficulty) were enrolled. Nursing homes were randomized to the multicomponent intervention protocol or usual care. Participants were followed for up to 2.5 years for development of the primary outcome, a radiographically documented pneumonia, and secondary outcome, a lower respiratory tract infection (LRTI) without radiographic documentation. Results. A total of 834 participants were enrolled: 434 to intervention and 400 to usual care. The trial was terminated for futility. The number of participants in the intervention vs control arms with first pneumonia was 119 (27.4%) vs 94 (23.5%), respectively, and with first LRTI, 125 (28.8%) vs 100 (25.0%), respectively. In a multivariable Cox regression model, the hazard ratio in the intervention vs control arms, respectively, was 1.12 (95% confidence interval [CI], .84–1.50; P = .44) for first pneumonia and 1.07 (95% CI, .79–1.46, P = .65) for first LRTI. Conclusions. The multicomponent intervention protocol did not significantly reduce the incidence of first radiographically confirmed pneumonia or LRTI compared with usual care in nursing home residents. Clinical Trials Registration. NCT00975780. PMID:25520333
Critical behavior of the random-bond Ashkin-Teller model: A Monte Carlo study
Wiseman, Shai; Domany, Eytan
1995-04-01
The critical behavior of a bond-disordered Ashkin-Teller model on a square lattice is investigated by intensive Monte Carlo simulations. A duality transformation is used to locate a critical plane of the disordered model. This critical plane corresponds to the line of critical points of the pure model, along which critical exponents vary continuously. Along this line the scaling exponent corresponding to randomness φ=(α/ν) varies continuously and is positive so that the randomness is relevant, and different critical behavior is expected for the disordered model. We use a cluster algorithm for the Monte Carlo simulations based on the Wolff embedding idea, and perform a finite size scaling study of several critical models, extrapolating between the critical bond-disordered Ising and bond-disordered four-state Potts models. The critical behavior of the disordered model is compared with the critical behavior of an anisotropic Ashkin-Teller model, which is used as a reference pure model. We find no essential change in the order parameters' critical exponents with respect to those of the pure model. The divergence of the specific heat C is changed dramatically. Our results favor a logarithmic type divergence at Tc, C~lnL for the random-bond Ashkin-Teller and four-state Potts models and C~ln lnL for the random-bond Ising model.
Modeling of Random Delays in Networked Control Systems
Yuan Ge
2013-01-01
Full Text Available In networked control systems (NCSs, the presence of communication networks in control loops causes many imperfections such as random delays, packet losses, multipacket transmission, and packet disordering. In fact, random delays are usually the most important problems and challenges in NCSs because, to some extent, other problems are often caused by random delays. In order to compensate for random delays which may lead to performance degradation and instability of NCSs, it is necessary to establish the mathematical model of random delays before compensation. In this paper, four major delay models are surveyed including constant delay model, mutually independent stochastic delay model, Markov chain model, and hidden Markov model. In each delay model, some promising compensation methods of delays are also addressed.
Lee Martin
2011-10-01
Full Text Available Abstract Background Meta-analyses show collaborative care models (CCMs with nurse care management are effective for improving primary care for depression. This study aimed to develop CCM approaches that could be sustained and spread within Veterans Affairs (VA. Evidence-based quality improvement (EBQI uses QI approaches within a research/clinical partnership to redesign care. The study used EBQI methods for CCM redesign, tested the effectiveness of the locally adapted model as implemented, and assessed the contextual factors shaping intervention effectiveness. Methods The study intervention is EBQI as applied to CCM implementation. The study uses a cluster randomized design as a formative evaluation tool to test and improve the effectiveness of the redesign process, with seven intervention and three non-intervention VA primary care practices in five different states. The primary study outcome is patient antidepressant use. The context evaluation is descriptive and uses subgroup analysis. The primary context evaluation measure is naturalistic primary care clinician (PCC predilection to adopt CCM. For the randomized evaluation, trained telephone research interviewers enrolled consecutive primary care patients with major depression in the evaluation, referred enrolled patients in intervention practices to the implemented CCM, and re-surveyed at seven months. Results Interviewers enrolled 288 CCM site and 258 non-CCM site patients. Enrolled intervention site patients were more likely to receive appropriate antidepressant care (66% versus 43%, p = 0.01, but showed no significant difference in symptom improvement compared to usual care. In terms of context, only 40% of enrolled patients received complete care management per protocol. PCC predilection to adopt CCM had substantial effects on patient participation, with patients belonging to early adopter clinicians completing adequate care manager follow-up significantly more often than patients of
Ersbøll, Annette Kjær; Ersbøll, Bjarne Kjær
2009-01-01
The K-function is often used to detect spatial clustering in spatial point processes, e.g. clustering of infected herds. Clustering is identified by testing the observed K-function for complete spatial randomness modelled, e.g. by a homogeneous Poisson process. The approach provides information a...... of the herd locations in general. The approach also overcomes edge effects and problems with complex shapes of the study region. An application to bovine virus diarrhoea virus (BVDV) infection in Denmark is described....
Breakup reaction models for two- and three-cluster projectiles
Baye, D
2010-01-01
Breakup reactions are one of the main tools for the study of exotic nuclei, and in particular of their continuum. In order to get valuable information from measurements, a precise reaction model coupled to a fair description of the projectile is needed. We assume that the projectile initially possesses a cluster structure, which is revealed by the dissociation process. This structure is described by a few-body Hamiltonian involving effective forces between the clusters. Within this assumption, we review various reaction models. In semiclassical models, the projectile-target relative motion is described by a classical trajectory and the reaction properties are deduced by solving a time-dependent Schroedinger equation. We then describe the principle and variants of the eikonal approximation: the dynamical eikonal approximation, the standard eikonal approximation, and a corrected version avoiding Coulomb divergence. Finally, we present the continuum-discretized coupled-channel method (CDCC), in which the Schroed...
Critical dynamics of cluster algorithms in the dilute Ising model
Hennecke, M.; Heyken, U.
1993-08-01
Autocorrelation times for thermodynamic quantities at T C are calculated from Monte Carlo simulations of the site-diluted simple cubic Ising model, using the Swendsen-Wang and Wolff cluster algorithms. Our results show that for these algorithms the autocorrelation times decrease when reducing the concentration of magnetic sites from 100% down to 40%. This is of crucial importance when estimating static properties of the model, since the variances of these estimators increase with autocorrelation time. The dynamical critical exponents are calculated for both algorithms, observing pronounced finite-size effects in the energy autocorrelation data for the algorithm of Wolff. We conclude that, when applied to the dilute Ising model, cluster algorithms become even more effective than local algorithms, for which increasing autocorrelation times are expected.
Modeling the Formation of Globular Cluster Systems in the Virgo Cluster
Li, Hui
2014-01-01
Globular cluster (GC) systems are some of the oldest and most unique building blocks of galaxies. The mass and chemical composition of GCs preserve the fossil record of the early stages of formation of their host galaxies. The observed distribution of GC colors within massive early-type galaxies in the ACS Virgo Cluster Survey (ACSVCS) reveals a multi-modal shape, which likely corresponds to a multi-modal metallicity distribution. In this paper, we present a simple model for the formation and dynamical disruption of globular clusters that aims to match the ACSVCS data. We test the hypothesis that GCs are formed during major mergers of gas-rich galaxies and inherit the metallicity of their hosts. To trace merger events, we use halo merger trees extracted from a large cosmological N-body simulation. We select 20 halos in the mass range 2*10^{12}-7*10^{13} M_sun and match them to 18 Virgo galaxies with K-band luminosity between 3*10^{10} and 3*10^{11}L_sun. To set the Iron abundances, we use an empirical galaxy ...
Sugama Junko; Sanada Hiromi; Shigeta Yoshie; Nakagami Gojiro; Konya Chizuko
2012-01-01
Abstract Background Most older adults with urinary incontinence use absorbent pads. Because of exposure to moisture and chemical irritating substances in urine, the perineal skin region is always at risk for development of incontinence-associated dermatitis (IAD). The aim of this study was to examine the efficacy of an improved absorbent pad against IAD. Methods A cluster randomized controlled design was used to compare the efficacy of two absorbent pads. Female inpatients aged ≥65 years who ...
Image modeling of compact starburst clusters: I. R136
Khorrami, Zeinab; Chesneau, Olivier
2016-01-01
Continuous progress in data quality from HST, recent multiwavelength high resolution spectroscopy and high contrast imaging from ground adaptive optics on large telescopes need modeling of R136 to understand its nature and evolutionary stage. To produce the best synthesized multiwavelength images of R136 we need to simulate the effect of dynamical and stellar evolution, mass segregation and binary stars fraction on the survival of young massive clusters with the initial parameters of R136 in the LMC, being set to the present knowledge of this famous cluster. We produced a series of 32 young massive clusters using the NBODY6 code. Each cluster was tracked with adequate temporal samples to follow the evolution of R136 during its early stages. To compare the NBODY6 simulations with observational data, we created the synthetic images from the output of the code. We used the TLUSTY and KURUCZ model atmospheres to produce the fluxes in HST/ WFPC2 filters. GENEVA isochrones were used to track the evolution of stars....
Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable
du Toit, Stephen H. C.; Cudeck, Robert
2009-01-01
A method is presented for marginal maximum likelihood estimation of the nonlinear random coefficient model when the response function has some linear parameters. This is done by writing the marginal distribution of the repeated measures as a conditional distribution of the response given the nonlinear random effects. The resulting distribution…
Comper, Maria Luiza Caires; Padula, Rosimeire Simprini
2014-05-22
Job rotation has often been used in situations where the level of exposure cannot be reduced due to the characteristics of the job or through physical measures. However, the effectiveness of the job rotation strategy at preventing musculoskeletal complaints lacks adequate scientific data. A cluster randomized controlled trial will be used to investigate the effectiveness of job rotation to prevent musculoskeletal disorders in industrial workers. The randomized cluster was based in characteristics of production sectors. A total cluster will be 4 sectors, and 957 workers will be recruited from a textile industry and randomly allocated into intervention or control groups. Both groups will receive training on ergonomics guidelines. In addition, the intervention group will perform job rotation, switching between tasks with low, moderate, and high risk for musculoskeletal complaints. The primary outcome will be the number of working hours lost due to sick leave by musculoskeletal injuries recorded in employee administrative data bases. Secondary outcomes measured via survey include: body parts with musculoskeletal pain, the intensity of this pain, physical workload, fatigue, general health status, physical activity level, and work productivity. Secondary outcome measures will be assessed at baseline and after 3, 6, 9, and 12 months. The cost-effectiveness analysis will be performed from the societal and company perspective. Prevention of work-related musculoskeletal disorders is beneficial for workers, employers, and society. The results of this study will provide new information about the effectiveness of job rotation as a strategy to reduce work-related musculoskeletal disorders. NCT01979731, November 3, 2013.
Bello, Nora M; Steibel, Juan P; Tempelman, Robert J
2010-06-01
Bivariate mixed effects models are often used to jointly infer upon covariance matrices for both random effects (u) and residuals (e) between two different phenotypes in order to investigate the architecture of their relationship. However, these (co)variances themselves may additionally depend upon covariates as well as additional sets of exchangeable random effects that facilitate borrowing of strength across a large number of clusters. We propose a hierarchical Bayesian extension of the classical bivariate mixed effects model by embedding additional levels of mixed effects modeling of reparameterizations of u-level and e-level (co)variances between two traits. These parameters are based upon a recently popularized square-root-free Cholesky decomposition and are readily interpretable, each conveniently facilitating a generalized linear model characterization. Using Markov Chain Monte Carlo methods, we validate our model based on a simulation study and apply it to a joint analysis of milk yield and calving interval phenotypes in Michigan dairy cows. This analysis indicates that the e-level relationship between the two traits is highly heterogeneous across herds and depends upon systematic herd management factors.
Efficient speaker verification using Gaussian mixture model component clustering.
De Leon, Phillip L. (New Mexico State University, Las Cruces, NM); McClanahan, Richard D.
2012-04-01
In speaker verification (SV) systems that employ a support vector machine (SVM) classifier to make decisions on a supervector derived from Gaussian mixture model (GMM) component mean vectors, a significant portion of the computational load is involved in the calculation of the a posteriori probability of the feature vectors of the speaker under test with respect to the individual component densities of the universal background model (UBM). Further, the calculation of the sufficient statistics for the weight, mean, and covariance parameters derived from these same feature vectors also contribute a substantial amount of processing load to the SV system. In this paper, we propose a method that utilizes clusters of GMM-UBM mixture component densities in order to reduce the computational load required. In the adaptation step we score the feature vectors against the clusters and calculate the a posteriori probabilities and update the statistics exclusively for mixture components belonging to appropriate clusters. Each cluster is a grouping of multivariate normal distributions and is modeled by a single multivariate distribution. As such, the set of multivariate normal distributions representing the different clusters also form a GMM. This GMM is referred to as a hash GMM which can be considered to a lower resolution representation of the GMM-UBM. The mapping that associates the components of the hash GMM with components of the original GMM-UBM is referred to as a shortlist. This research investigates various methods of clustering the components of the GMM-UBM and forming hash GMMs. Of five different methods that are presented one method, Gaussian mixture reduction as proposed by Runnall's, easily outperformed the other methods. This method of Gaussian reduction iteratively reduces the size of a GMM by successively merging pairs of component densities. Pairs are selected for merger by using a Kullback-Leibler based metric. Using Runnal's method of reduction, we
Connolly, Stuart J; Philippon, Francois; Longtin, Yves; Casanova, Amparo; Birnie, David H; Exner, Derek V; Dorian, Paul; Prakash, Ratika; Alings, Marco; Krahn, Andrew D
2013-06-01
Randomized clinical trials are a major advance in clinical research methodology. However, there are myriad important questions about the effectiveness of treatments used in daily practice that are not informed by the results of randomized trials. This is in part because of important limitations inherent in the methodology of randomized efficacy trials which are performed with tight control of inclusion, exclusion, treatment, and follow-up. This approach enhances evaluation of clinical efficacy (performance in controlled situations) but increases complexity and is not well suited to test clinical effectiveness (performance under conditions of actual use). The cluster crossover trial is a new concept for efficient comparative effectiveness testing. Deep tissue infection occurs in 2% of patients after arrhythmia device implantation, usually requires system extraction, and increases mortality. There is variation in antibiotic prophylaxis used to reduce implanted device infections. To efficiently evaluate the comparative effectiveness of antibiotic strategies now in use, we designed a cluster crossover clinical trial, which randomized implanting centres to 1 of 2 prophylactic antibiotic strategies, which became the standard care at the centre for 6 months, followed by crossover to the other strategy, rerandomization, and second crossover. This method greatly reduces trial complexity because it aligns study procedures with usual clinical care and increases generalizability. Pilot studies have tested the feasibility and an 10,800-patient trial, funded by the Canadian Institutes of Health Research, is now under way. The cluster crossover randomized trial design is well suited to efficiently test comparative effectiveness of existing treatments where there is variability of practice, clinical equipoise, and minimal risk.
A Note on the Correlated Random Coefficient Model
Kolodziejczyk, Christophe
In this note we derive the bias of the OLS estimator for a correlated random coefficient model with one random coefficient, but which is correlated with a binary variable. We provide set-identification to the parameters of interest of the model. We also show how to reduce the bias of the estimator...
Compact Sets without Converging Sequences in the Random Real Model
D. Fremlin
2007-10-01
Full Text Available It is shown that in the model obtained by adding any number of random reals to a model of CH, there is a compact Hausdorff space of weight w1 which contains no non-trivial converging sequences. It is shown that for certain spaces with noconverging sequences, the addition of random reals will not add any converging sequences.
A random energy model for size dependence : recurrence vs. transience
Külske, Christof
1998-01-01
We investigate the size dependence of disordered spin models having an infinite number of Gibbs measures in the framework of a simplified 'random energy model for size dependence'. We introduce two versions (involving either independent random walks or branching processes), that can be seen as gener
Random non-Hermitian tight-binding models
Marinello, G.; Pato, M. P.
2016-08-01
For a one dimensional system tight binding models are described by sparse tridiagonal matrices which describe interactions between nearest neighbors. In this report, we construct open and closed random tight-binding models based in the tridiagonal matrices of the so-called,β-ensembles of random matrix theory.
Trapping in the random conductance model
Biskup, M; Rozinov, A; Vandenberg-Rodes, A
2012-01-01
We consider random walks on $\\Z^d$ among nearest-neighbor random conductances which are i.i.d., positive, bounded uniformly from above but whose support extends all the way to zero. Our focus is on the detailed properties of the paths of the random walk conditioned to return back to the starting point at time $2n$. We show that in the situations when the heat kernel exhibits subdiffusive decay --- which is known to occur in dimensions $d\\ge4$ --- the walk gets trapped for a time of order $n$ in a small spatial region. This shows that the strategy used earlier to infer subdiffusive lower bounds on the heat kernel in specific examples is in fact dominant. In addition, we settle a conjecture concerning the worst possible subdiffusive decay in four dimensions.
Trapping in the Random Conductance Model
Biskup, M.; Louidor, O.; Rozinov, A.; Vandenberg-Rodes, A.
2013-01-01
We consider random walks on ℤ d among nearest-neighbor random conductances which are i.i.d., positive, bounded uniformly from above but whose support extends all the way to zero. Our focus is on the detailed properties of the paths of the random walk conditioned to return back to the starting point at time 2 n. We show that in the situations when the heat kernel exhibits subdiffusive decay—which is known to occur in dimensions d≥4—the walk gets trapped for a time of order n in a small spatial region. This shows that the strategy used earlier to infer subdiffusive lower bounds on the heat kernel in specific examples is in fact dominant. In addition, we settle a conjecture concerning the worst possible subdiffusive decay in four dimensions.
On competitive Lotka–Volterra model in random environments
Zhu, C; Yin, G
2009-01-01
Focusing on competitive Lotka-Volterra model in random environments, this paper uses regime-switching diffusions to model the dynamics of the population sizes of n different species in an ecosystem...
Margolis, Karen L; Asche, Stephen E; Bergdall, Anna R; Dehmer, Steven P; Groen, Sarah E; Kadrmas, Holly M; Kerby, Tessa J; Klotzle, Krissa J; Maciosek, Michael V; Michels, Ryan D; O'Connor, Patrick J; Pritchard, Rachel A; Sekenski, Jaime L; Sperl-Hillen, JoAnn M; Trower, Nicole K
2013-07-03
Only about half of patients with high blood pressure (BP) in the United States have their BP controlled. Practical, robust, and sustainable models are needed to improve BP control in patients with uncontrolled hypertension. To determine whether an intervention combining home BP telemonitoring with pharmacist case management improves BP control compared with usual care and to determine whether BP control is maintained after the intervention is stopped. A cluster randomized clinical trial of 450 adults with uncontrolled BP recruited from 14,692 patients with electronic medical records across 16 primary care clinics in an integrated health system in Minneapolis-St Paul, Minnesota, with 12 months of intervention and 6 months of postintervention follow-up. Eight clinics were randomized to provide usual care to patients (n = 222) and 8 clinics were randomized to provide a telemonitoring intervention (n = 228). Intervention patients received home BP telemonitors and transmitted BP data to pharmacists who adjusted antihypertensive therapy accordingly. Control of systolic BP to less than 140 mm Hg and diastolic BP to less than 90 mm Hg (telemonitoring intervention group vs 30.0% (95% CI, 23.2% to 37.8%) of patients in the usual care group (P = .001). At 18 months (6 months of postintervention follow-up), BP was controlled in 71.8% (95% CI, 65.0% to 77.8%) of patients in the telemonitoring intervention group vs 57.1% (95% CI, 51.5% to 62.6%) of patients in the usual care group (P = .003). Compared with the usual care group, systolic BP decreased more from baseline among patients in the telemonitoring intervention group at 6 months (-10.7 mm Hg [95% CI, -14.3 to -7.3 mm Hg]; Ptelemonitoring intervention group at 6 months (-6.0 mm Hg [95% CI, -8.6 to -3.4 mm Hg]; Ptelemonitoring and pharmacist case management achieved better BP control compared with usual care during 12 months of intervention that persisted during 6 months of postintervention follow
Aarons, Gregory A; Ehrhart, Mark G; Moullin, Joanna C; Torres, Elisa M; Green, Amy E
2017-03-03
Evidence-based practice (EBP) implementation represents a strategic change in organizations that requires effective leadership and alignment of leadership and organizational support across organizational levels. As such, there is a need for combining leadership development with organizational strategies to support organizational climate conducive to EBP implementation. The leadership and organizational change for implementation (LOCI) intervention includes leadership training for workgroup leaders, ongoing implementation leadership coaching, 360° assessment, and strategic planning with top and middle management regarding how they can support workgroup leaders in developing a positive EBP implementation climate. This test of the LOCI intervention will take place in conjunction with the implementation of motivational interviewing (MI) in 60 substance use disorder treatment programs in California, USA. Participants will include agency executives, 60 program leaders, and approximately 360 treatment staff. LOCI will be tested using a multiple cohort, cluster randomized trial that randomizes workgroups (i.e., programs) within agency to either LOCI or a webinar leadership training control condition in three consecutive cohorts. The LOCI intervention is 12 months, and the webinar control intervention takes place in months 1, 5, and 8, for each cohort. Web-based surveys of staff and supervisors will be used to collect data on leadership, implementation climate, provider attitudes, and citizenship. Audio recordings of counseling sessions will be coded for MI fidelity. The unit of analysis will be the workgroup, randomized by site within agency and with care taken that co-located workgroups are assigned to the same condition to avoid contamination. Hierarchical linear modeling (HLM) will be used to analyze the data to account for the nested data structure. LOCI has been developed to be a feasible and effective approach for organizations to create a positive climate and
WHIM emission and the cluster soft excess: a model comparison
Mittaz, J; Cen, R; Bonamente, M
2004-01-01
The confirmation of the cluster soft excess (CSE) by XMM-Newton has rekindled interest as to its origin. The recent detections of CSE emission at large cluster radii together with reports of OVII line emission associated with the CSE has led many authors to conjecture that the CSE is, in fact, a signature of the warm-hot intergalactic medium (WHIM). In this paper we test the scenario by comparing the observed properties of the CSE with predictions based on models of the WHIM. We find that emission from the WHIM in current models is 3 to 4 orders of magnitude too faint to explain the CSE. We discuss different possibilities for this discrepancy including issues of simulation resolution and scale, and the role of small density enhancements or galaxy groups. Our final conclusion is that the WHIM alone is unlikely to be able to accout for the observed flux of the CSE.
An Efficient Cluster Algorithm for CP(N-1) Models
Beard, B B; Riederer, S; Wiese, U J
2005-01-01
We construct an efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin systems. Such systems provide a new regularization for CP(N-1) models in the framework of D-theory, which is an alternative non-perturbative approach to quantum field theory formulated in terms of discrete quantum variables instead of classical fields. Despite several attempts, no efficient cluster algorithm has been constructed for CP(N-1) models in the standard formulation of lattice field theory. In fact, there is even a no-go theorem that prevents the construction of an efficient Wolff-type embedding algorithm. We present various simulations for different correlation lengths, couplings and lattice sizes. We have simulated correlation lengths up to 250 lattice spacings on lattices as large as 640x640 and we detect no evidence for critical slowing down.
Interloper treatment in dynamical modelling of galaxy clusters
Wojtak, R; Mamon, G A; Gottlöber, S; Prada, F; Moles, M; Wojtak, Radoslaw; Lokas, Ewa L.; Mamon, Gary A.; Gottloeber, Stefan; Prada, Francisco; Moles, Mariano
2006-01-01
The aim of this paper is to study the efficiency of different approaches to interloper treatment in dynamical modelling of galaxy clusters. Using cosmological N-body simulation of standard LCDM model we select 10 massive dark matter haloes and use their particles to emulate mock kinematic data in terms of projected galaxy positions and velocities as they would be measured by a distant observer. Taking advantage of the full 3D information available from the simulation we select samples of interlopers defined with different criteria. The interlopers thus selected provide means to assess the efficiency of different interloper removal schemes. We study direct methods of interloper removal based on dynamical or statistical restrictions imposed on ranges of positions and velocities available to cluster members. In determining these ranges we use either the velocity dispersion criterion or a maximum velocity profile. We find that the direct methods exclude on average 60-70 percent of unbound particles producing a sa...
Yli-Harja Olli
2009-05-01
Full Text Available Abstract Background Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with multiple data sources nowadays, and these heterogeneous information sources should be made as efficient use of as possible. Results This paper presents a novel Beta-Gaussian mixture model (BGMM for clustering genes based on Gaussian distributed and beta distributed data. The proposed BGMM can be viewed as a natural extension of the beta mixture model (BMM and the Gaussian mixture model (GMM. The proposed BGMM method differs from other mixture model based methods in its integration of two different data types into a single and unified probabilistic modeling framework, which provides a more efficient use of multiple data sources than methods that analyze different data sources separately. Moreover, BGMM provides an exceedingly flexible modeling framework since many data sources can be modeled as Gaussian or beta distributed random variables, and it can also be extended to integrate data that have other parametric distributions as well, which adds even more flexibility to this model-based clustering framework. We developed three types of estimation algorithms for BGMM, the standard expectation maximization (EM algorithm, an approximated EM and a hybrid EM, and propose to tackle the model selection problem by well-known model selection criteria, for which we test the Akaike information criterion (AIC, a modified AIC (AIC3, the Bayesian information criterion (BIC, and the integrated classification likelihood-BIC (ICL-BIC. Conclusion Performance tests with simulated data show that combining two different data sources into a single mixture joint model greatly improves the clustering
A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery
WANG Shaoyu
2016-12-01
Full Text Available Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.
Aslam, Muhammad Zaheer
2011-01-01
Mobile Adhoc Network is a kind of wireless ad hoc network where nodes are connected wirelessly and the network is self configuring. MANET may work in a standalone manner or may be a part of another network. In this paper we have compared Random Walk Mobility Model and Random Waypoint Mobility Model over two reactive routing protocols Dynamic Source Routing (DSR) and Adhoc On-Demand Distance Vector Routing (AODV) protocol and one Proactive routing protocol Distance Sequenced Distance Vector Routing (DSDV) Our analysis showed that DSR, AODV & DSDV under Random Walk and Random Way Point Mobility models have similar results for similar inputs however as the pause time increases so does the difference in performance rises. They show that their motion, direction, angle of direction, speed is same under both mobility models. We have made their analysis on packet delivery ratio, throughput and routing overhead. We have tested them with different criteria like different number of nodes, speed and different maximum...
Clustering Multivariate Time Series Using Hidden Markov Models
Shima Ghassempour
2014-03-01
Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.
Some random models in traffic science
Hjorth, U.
1996-06-01
We give an overview of stochastic models for the following traffic phenomena. Models for traffic flow including gaps and capacities for lanes, crossings and roundabouts. Models for wanted and achieved speed distributions. Mode selection models including dispersed equilibrium models and traffic accident models. Also some statistical questions are discussed. 60 refs, 1 tab
The Statistical Power of the Cluster Randomized Block Design with Matched Pairs--A Simulation Study
Dong, Nianbo; Lipsey, Mark
2010-01-01
This study uses simulation techniques to examine the statistical power of the group- randomized design and the matched-pair (MP) randomized block design under various parameter combinations. Both nearest neighbor matching and random matching are used for the MP design. The power of each design for any parameter combination was calculated from…
A Model for Random Student Drug Testing
Nelson, Judith A.; Rose, Nancy L.; Lutz, Danielle
2011-01-01
The purpose of this case study was to examine random student drug testing in one school district relevant to: (a) the perceptions of students participating in competitive extracurricular activities regarding drug use and abuse; (b) the attitudes and perceptions of parents, school staff, and community members regarding student drug involvement; (c)…
Consistent estimators in random censorship semiparametric models
王启华
1996-01-01
For the fixed design regression modelwhen Y, are randomly censored on the right, the estimators of unknown parameter and regression function g from censored observations are defined in the two cases .where the censored distribution is known and unknown, respectively. Moreover, the sufficient conditions under which these estimators are strongly consistent and pth (p>2) mean consistent are also established.
Cluster Dynamics Modeling with Bubble Nucleation, Growth and Coalescence
de Almeida, Valmor F. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Blondel, Sophie [Univ. of Tennessee, Knoxville, TN (United States); Bernholdt, David E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Wirth, Brian D. [Univ. of Tennessee, Knoxville, TN (United States)
2017-06-01
The topic of this communication pertains to defect formation in irradiated solids such as plasma-facing tungsten submitted to helium implantation in fusion reactor com- ponents, and nuclear fuel (metal and oxides) submitted to volatile ssion product generation in nuclear reactors. The purpose of this progress report is to describe ef- forts towards addressing the prediction of long-time evolution of defects via continuum cluster dynamics simulation. The di culties are twofold. First, realistic, long-time dynamics in reactor conditions leads to a non-dilute di usion regime which is not accommodated by the prevailing dilute, stressless cluster dynamics theory. Second, long-time dynamics calls for a large set of species (ideally an in nite set) to capture all possible emerging defects, and this represents a computational bottleneck. Extensions beyond the dilute limit is a signi cant undertaking since no model has been advanced to extend cluster dynamics to non-dilute, deformable conditions. Here our proposed approach to model the non-dilute limit is to monitor the appearance of a spatially localized void volume fraction in the solid matrix with a bell shape pro le and insert an explicit geometrical bubble onto the support of the bell function. The newly cre- ated internal moving boundary provides the means to account for the interfacial ux of mobile species into the bubble, and the growth of bubbles allows for coalescence phenomena which captures highly non-dilute interactions. We present a preliminary interfacial kinematic model with associated interfacial di usion transport to follow the evolution of the bubble in any number of spatial dimensions and any number of bubbles, which can be further extended to include a deformation theory. Finally we comment on a computational front-tracking method to be used in conjunction with conventional cluster dynamics simulations in the non-dilute model proposed.
Xu, Lizhen; Paterson, Andrew D; Xu, Wei
2017-04-01
Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational taxonomic units within a single taxonomic cluster. This novel method can incorporate both negative binomial and zero-inflated negative binomial responses, and can account for serial and familial correlations. We develop a Markov chain Monte Carlo algorithm that is built on a data augmentation scheme using Pólya-Gamma random variables. Hierarchical centering and parameter expansion techniques are also used to improve the convergence of the Markov chain. We evaluate the performance of our proposed method through extensive simulations. We also apply our method to a human microbiome study.
Genetic Modeling of GIS-Based Cell Clusters and Its Application in Mineral Resources Prediction
无
2003-01-01
This paper presents a synthetic analysis method for multi-sourced geological data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been statistical analysis of cells delimitated based on thoughts of random sampiing. That might lead to insufficient utilization of local spatial information, for a cell is treated as a point without internal structure. We now take "cell clusters", i. e. , spatial associations of cells, as basic units of statistics, thus the spatial configuration information of geological variables is easier to be detected and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi-discriminating model for the clusters via genetic algorithm. Both the right-judgment rates and the in-class vs. between-class distance ratios are considered to form the evolutional adaptive values of the population. An application of the method in gold mineral resources prediction in east Xinjiang, China is presented.
Versatility and robustness of Gaussian random fields for modelling random media
Quintanilla, John A.; Chen, Jordan T.; Reidy, Richard F.; Allen, Andrew J.
2007-06-01
One of the authors (JAQ) has recently introduced a method of modelling random materials using excursion sets of Gaussian random fields. This method uses convex quadratic programming to find the optimal admissible field autocorrelation function, providing both theoretical and computational advantages over other techniques such as simulated annealing. In this paper, we discuss the application of this algorithm to model various aerogel systems given small-angle neutron scattering data. We also present new results concerning the robustness of this method.
Fall injuries in Baghdad from 2003 to 2014: results of a randomized household cluster survey
Stewart, Barclay T; Lafta, Riyadh; Shatari, Sahar A Esa Al; Cherewick, Megan; Flaxman, Abraham; Hagopian, Amy; Burnham, Gilbert; Kushner, Adam L
2015-01-01
Introduction Falls incur nearly 35 million disability-adjusted life-years annually; 75% of which occur in low- and middle-income countries. The epidemiology of civilian injuries during conflict is relatively unknown, yet important for planning prevention initiatives, health policy and humanitarian assistance. This study aimed to determine the death and disability and household consequences of fall injuries in post-invasion Baghdad. Methods A two-stage, cluster randomized, community-based household survey was performed in May of 2014 to determine the civilian burden of injury from 2003 to 2014 in Baghdad. In addition to questions about household member death, households were interviewed regarding injury specifics, healthcare required, disability, relatedness to conflict and resultant financial hardship. Results Nine hundred households totaling 5,148 individuals were interviewed. There were 138 fall injuries (25% of all injuries reported); fall was the most common mechanism of civilian injury in Baghdad. The rate of serious fall injuries increased from 78 to 466 per 100,000 persons in 2003 and 2013, respectively. Fall was the most common mechanism among the injured elderly (i.e. ≥65 years; 15/24 elderly unintentional injuries; 63%). However, 46 fall injuries were children aged injuries) and 77 were respondents aged 15 - 64 years (36%). Respondents who spent significant time within the home (i.e. unemployed, retired, homemaker) had three times greater odds of having suffered a fall injury than student referents (aOR 3.34; 95%CI 1.30 – 8.60). Almost 80% of fall injured were left with life-limiting disability. Affected households often borrowed substantial sums of money (34 households; 30% of affected households) and/or suffered food insecurity after a family member's fall (52; 46%). Conclusion Falls were the most common cause of civilian injury in Baghdad. In part due to the effect of prolonged insecurity on a fragile health system, many injuries resulted in life
Mette Toftager
Full Text Available BACKGROUND: Multicomponent school-based interventions have the potential to reduce the age-related decline in adolescents' physical activity (PA, yet there is not consistent evidence to guide non-curricular and school environment interventions. The aim of this study was to assess the effectiveness of a multicomponent environmental school-based intervention, designed to reduce the age-related decline in PA among adolescents. METHODS: A cluster randomized controlled trial was conducted with 7 intervention and 7 control schools. Baseline measurements were carried out in spring 2010 with 2 years of follow-up. A total of 1,348 students (11-13 years, in grade 5 and 6 enrolled in the study at baseline. The 14 schools included in the study were located in the Region of Southern Denmark. The intervention consisted of organizational and physical changes in the school environment with a total of 11 intervention components. The primary outcome measure was overall PA (cpm, counts per minute and was supported by analyses of time spent in MVPA, and time spent sedentary. Furthermore, a secondary outcome measure was PA in school time and during recess. PA was measured using accelerometer (Actigraph GT3X. RESULTS: A total of 797 students completed the trial and had valid accelerometer data. No significant difference was found for overall PA with an adjusted difference of -19.1 cpm (95% CI: -93, 53 or for school time activity with an adjusted difference of 6 cpm (95% CI: -73, 85. A sensitivity analysis revealed a positive significant intervention effect of PA in recess with an adjusted difference of 95 cpm. CONCLUSIONS: No evidence was found of the overall effect of a non-curricular multicomponent school-based intervention on PA among Danish adolescents. The intervention was positively associated with PA during school time and recess, however, with small estimates. Lack of effect on overall PA could be due to both program theory and different degrees of
Analysis of Two-Layered Random Interfaces for Two Dimensional Widom-Rowlinson's Model
Jun Wang
2011-01-01
Full Text Available The statistical behaviors of two-layered random-phase interfaces in two-dimensional Widom-Rowlinson's model are investigated. The phase interfaces separate two coexisting phases of the lattice Widom-Rowlinson model; when the chemical potential μ of the model is large enough, the convergence of the probability distributions which describe the fluctuations of the phase interfaces is studied. In this paper, the backbones of interfaces are introduced in the model, and the corresponding polymer chains and cluster expansions are developed and analyzed for the polymer weights. And the existence of the free energy for two-layered random-phase interfaces of the two-dimensional Widom-Rowlinson model is given.
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2017-01-18
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.
Faggiano Fabrizio
2011-05-01
Full Text Available Abstract Background Although social environments may influence alcohol-related behaviours in youth, the relationship between neighbourhood socioeconomic context and effectiveness of school-based prevention against underage drinking has been insufficiently investigated. We study whether the social environment affects the impact of a new school-based prevention programme on alcohol use among European students. Methods During the school year 2004-2005, 7079 students 12-14 years of age from 143 schools in nine European centres participated in this cluster randomised controlled trial. Schools were randomly assigned to either control or a 12-session standardised curriculum based on the comprehensive social influence model. Randomisation was blocked within socioeconomic levels of the school environment. Alcohol use and alcohol-related problem behaviours were investigated through a self-completed anonymous questionnaire at baseline and 18 months thereafter. Data were analysed using multilevel models, separately by socioeconomic level. Results At baseline, adolescents in schools of low socioeconomic level were more likely to report problem drinking than other students. Participation in the programme was associated in this group with a decreased odds of reporting episodes of drunkenness (OR = 0.60, 95% CI = 0.44-0.83, intention to get drunk (OR = 0.60, 95% CI = 0.45-0.79, and marginally alcohol-related problem behaviours (OR = 0.70, 95% CI = 0.46-1.06. No significant programme's effects emerged for students in schools of medium or high socioeconomic level. Effects on frequency of alcohol consumption were also stronger among students in disadvantaged schools, although the estimates did not attain statistical significance in any subgroup. Conclusions It is plausible that comprehensive social influence programmes have a more favourable effect on problematic drinking among students in underprivileged social environments. Trial registration ISRCTN: ISRCTN
David A Rolls
Full Text Available We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR epidemic dynamics. The types of network models are exponential random graph models (ERGMs and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a "hidden population". In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdős-Rényi model and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example, although we would expect the ability to model cliques to be helpful for contact networks exhibiting household structure.
Neuro-fuzzy system modeling based on automatic fuzzy clustering
Yuangang TANG; Fuchun SUN; Zengqi SUN
2005-01-01
A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.
Random matrix model for disordered conductors
Zafar Ahmed; Sudhir R Jain
2000-03-01
We present a random matrix ensemble where real, positive semi-deﬁnite matrix elements, , are log-normal distributed, $\\exp[-\\log^{2}(x)]$. We show that the level density varies with energy, , as 2/(1 + ) for large , in the unitary family, consistent with the expectation for disordered conductors. The two-level correlation function is studied for the unitary family and found to be largely of the universal form despite the fact that the level density has a non-compact support. The results are based on the method of orthogonal polynomials (the Stieltjes-Wigert polynomials here). An interesting random walk problem associated with the joint probability distribution of the ensuing ensemble is discussed and its connection with level dynamics is brought out. It is further proved that Dyson's Coulomb gas analogy breaks down whenever the conﬁning potential is given by a transcendental function for which there exist orthogonal polynomials.
STRUCTURAL MODELING OF INNOVATION CLUSTER INNOVATION CLUSTER’S INSTITUTIONAL ENVIRONMENT
D. L. Napolskikh
2012-01-01
Full Text Available The modern state of the problem of modeling the internal and external environment of the innovation cluster is considered. The proposed organizational model of interaction between the institutions of the cluster and the environment, as well as model and institutional infrastructure component of the cluster are offered. A hypothesis on the need for the organic model of the institutional environment of innovation cluster is offered.
Beskind, Daniel L; Stolz, Uwe; Thiede, Rebecca; Hoyer, Riley; Burns, Whitney; Brown, Jeffrey; Ludgate, Melissa; Tiutan, Timothy; Shane, Romy; McMorrow, Deven; Pleasants, Michael; Panchal, Ashish R
2016-07-01
CPR training in schools is a public health initiative to improve out of hospital cardiac arrest (OHCA) survival. It is unclear whether brief video training in students improves CPR quality and responsiveness and skills retention. Determine if a brief video is as effective as classroom instruction for chest compression-only (CCO) CPR training in high school students. This was a prospective cluster-randomized controlled trial with three study arms: control (sham video), brief video (BV), and CCO-CPR class. Students were randomized and clustered based on their classrooms and evaluated using a standardized OHCA scenario measuring CPR quality (compression rate, depth, hands-off time) and responsiveness (calling 911, time to calling 911, starting compressions within 2min). Data was collected at baseline, post-intervention and 2 months. Generalized linear mixed models were used to analyze outcome data, accounting for repeated measures for each individual and clustering by class. 179 students (14-18 years) were consented in 7 classrooms (clusters). At post-intervention and 2 months, BV and CCO class students called 911 more frequently and sooner, started chest compressions earlier, and had improved chest compression rates and hands-off time compared to baseline. Chest compression depth improved significantly from baseline in the CCO class, but not in the BV group post-intervention and at 2 months. Brief CPR video training resulted in improved CPR quality and responsiveness in high school students. Compression depth only improved with traditional class training. This suggests brief educational interventions are beneficial to improve CPR responsiveness but psychomotor training is important for CPR quality. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Modelling clustering of vertically aligned carbon nanotube arrays
Schaber, Clemens F.; Filippov, Alexander E.; Heinlein, Thorsten; Schneider, Jörg J.; Gorb, Stanislav N.
2015-01-01
Previous research demonstrated that arrays of vertically aligned carbon nanotubes (VACNTs) exhibit strong frictional properties. Experiments indicated a strong decrease of the friction coefficient from the first to the second sliding cycle in repetitive measurements on the same VACNT spot, but stable values in consecutive cycles. VACNTs form clusters under shear applied during friction tests, and self-organization stabilizes the mechanical properties of the arrays. With increasing load in the range between 300 µN and 4 mN applied normally to the array surface during friction tests the size of the clusters increases, while the coefficient of friction decreases. To better understand the experimentally obtained results, we formulated and numerically studied a minimalistic model, which reproduces the main features of the system with a minimum of adjustable parameters. We calculate the van der Waals forces between the spherical friction probe and bunches of the arrays using the well-known Morse potential function to predict the number of clusters, their size, instantaneous and mean friction forces and the behaviour of the VACNTs during consecutive sliding cycles and at different normal loads. The data obtained by the model calculations coincide very well with the experimental data and can help in adapting VACNT arrays for biomimetic applications. PMID:26464787
Analytical model for non-thermal pressure in galaxy clusters
Shi, Xun
2014-01-01
Non-thermal pressure in the intracluster gas has been found ubiquitously in numerical simulations, and observed indirectly. In this paper we develop, for the first time, an analytical model for intracluster non-thermal pressure. We write down and solve a first-order differential equation describing the evolution of non-thermal velocity dispersion. This equation is based on insights gained from observations, numerical simulations, and theory of turbulence. The non-thermal energy is sourced, in a self-similar fashion, by the mass growth of clusters via mergers and accretion, and dissipates with a time scale determined by the turnover time of the largest turbulence eddies. Our model predicts a radial profile of non-thermal pressure for relaxed clusters. The non-thermal fraction increases with radius, redshift, and cluster mass, in agreement with numerical simulations. The radial dependence is due to a rapid increase of the dissipation time scale with radii, and the mass and redshift dependence comes from the mas...
A halo model for cosmological neutral hydrogen : abundances and clustering
Padmanabhan, Hamsa; Amara, Adam
2016-01-01
We extend the results of previous analyses towards constraining the abundance and clustering of post-reionization ($z \\sim 0-5$) neutral hydrogen (HI) systems using a halo model framework. We work with a comprehensive HI dataset including the small-scale clustering, column density and mass function of HI galaxies at low redshifts, intensity mapping measurements at intermediate redshifts and the UV/optical observations of Damped Lyman Alpha (DLA) systems at higher redshifts. We use a Markov Chain Monte Carlo (MCMC) approach to constrain the parameters of the best-fitting models, both for the HI-halo mass relation and the HI radial density profile. We find that a radial exponential profile results in a good fit to the low-redshift HI observations, including the clustering and the column density distribution. The form of the profile is also found to match the high-redshift DLA observations, when used in combination with a three-parameter HI-halo mass relation and a redshift evolution in the HI concentration. The...
nIFTy galaxy cluster simulations II: radiative models
Sembolini, Federico; Pearce, Frazer R; Power, Chris; Knebe, Alexander; Kay, Scott T; Cui, Weiguang; Yepes, Gustavo; Beck, Alexander M; Borgani, Stefano; Cunnama, Daniel; Davé, Romeel; February, Sean; Huang, Shuiyao; Katz, Neal; McCarthy, Ian G; Murante, Giuseppe; Newton, Richard D A; Perret, Valentin; Saro, Alexandro; Schaye, Joop; Teyssier, Romain
2015-01-01
We have simulated the formation of a massive galaxy cluster (M$_{200}^{\\rm crit}$ = 1.1$\\times$10$^{15}h^{-1}M_{\\odot}$) in a $\\Lambda$CDM universe using 10 different codes (RAMSES, 2 incarnations of AREPO and 7 of GADGET), modeling hydrodynamics with full radiative subgrid physics. These codes include Smoothed-Particle Hydrodynamics (SPH), spanning traditional and advanced SPH schemes, adaptive mesh and moving mesh codes. Our goal is to study the consistency between simulated clusters modeled with different radiative physical implementations - such as cooling, star formation and AGN feedback. We compare images of the cluster at $z=0$, global properties such as mass, and radial profiles of various dynamical and thermodynamical quantities. We find that, with respect to non-radiative simulations, dark matter is more centrally concentrated, the extent not simply depending on the presence/absence of AGN feedback. The scatter in global quantities is substantially higher than for non-radiative runs. Intriguingly, a...
Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support
S. Parkash Kumar
2011-01-01
Full Text Available Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Approach: The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. The knowledge hidden among the educational data set is extracted through Fuzzy k-means cluster an unsupervised learning depends on certain initiation values to define the subgroups present in the data set. Results: Based on the features of the dataset and input parameters cluster formation vary, which motivates the clarification of cluster validity. The results of quality indexed fuzzy k-means shows better cluster validation compared to that of traditional k-family algorithm. Conclusion: The experimental results of cluster validation scheme confirm the reliability of validity index showing that it performs better than other k-family clusters.
A random walk evolution model of wireless sensor networks and virus spreading
Wang Ya-Qi; Yang Xiao-Yuan
2013-01-01
In this paper,considering both cluster heads and sensor nodes,we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure,and discuss the spreading dynamic behavior of viruses in the evolution model.A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance,but also can dynamically balance the energy loss of the entire network.It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency,it can effectively inhibit the spread of viruses.In addition,the heterogeneity of the network improves the network efficiency and enhances the virus prevalence.We confirm all the theoretical results with sufficient numerical simulations.
Cha, Seungman; Kang, Douk; Tuffuor, Benedict; Lee, Gyuhong; Cho, Jungmyung; Chung, Jihye; Kim, Myongjin; Lee, Hoonsang; Lee, Jaeeun; Oh, Chunghyeon
2015-01-01
.... We report the results of a matched cluster randomized trial investigating the effect of improved water supply on diarrheal prevalence of children under five living in rural areas of the Volta Region in Ghana...
nIFTy galaxy cluster simulations - II. Radiative models
Sembolini, Federico; Elahi, Pascal Jahan; Pearce, Frazer R.; Power, Chris; Knebe, Alexander; Kay, Scott T.; Cui, Weiguang; Yepes, Gustavo; Beck, Alexander M.; Borgani, Stefano; Cunnama, Daniel; Davé, Romeel; February, Sean; Huang, Shuiyao; Katz, Neal; McCarthy, Ian G.; Murante, Giuseppe; Newton, Richard D. A.; Perret, Valentin; Puchwein, Ewald; Saro, Alexandro; Schaye, Joop; Teyssier, Romain
2016-07-01
We have simulated the formation of a massive galaxy cluster (M_{200}^crit = 1.1 × 1015 h-1 M⊙) in a Λ cold dark matter universe using 10 different codes (RAMSES, 2 incarnations of AREPO and 7 of GADGET), modelling hydrodynamics with full radiative subgrid physics. These codes include smoothed-particle hydrodynamics (SPH), spanning traditional and advanced SPH schemes, adaptive mesh and moving mesh codes. Our goal is to study the consistency between simulated clusters modelled with different radiative physical implementations - such as cooling, star formation and thermal active galactic nucleus (AGN) feedback. We compare images of the cluster at z = 0, global properties such as mass, and radial profiles of various dynamical and thermodynamical quantities. We find that, with respect to non-radiative simulations, dark matter is more centrally concentrated, the extent not simply depending on the presence/absence of AGN feedback. The scatter in global quantities is substantially higher than for non-radiative runs. Intriguingly, adding radiative physics seems to have washed away the marked code-based differences present in the entropy profile seen for non-radiative simulations in Sembolini et al.: radiative physics + classic SPH can produce entropy cores, at least in the case of non cool-core clusters. Furthermore, the inclusion/absence of AGN feedback is not the dividing line -as in the case of describing the stellar content - for whether a code produces an unrealistic temperature inversion and a falling central entropy profile. However, AGN feedback does strongly affect the overall stellar distribution, limiting the effect of overcooling and reducing sensibly the stellar fraction.
Anisotropic Models for Globular Clusters, Galactic Bulges and Dark Halos
Nguyen, P H
2013-01-01
Spherical systems with a polytropic equation of state are of great interest in astrophysics. They are widely used to describe neutron stars, red giants, white dwarfs, brown dwarfs, main sequence stars, galactic halos and globular clusters of diverse sizes. In this paper we construct analytically a family of self-gravitating spherical models in the post-Newtonian approximation of general relativity. These models present interesting cusps in their density profiles which are appropriate for the modeling of galaxies and dark matter halos. The systems described here are anisotropic in the sense that their equiprobability surfaces in velocity space are non-spherical, leading to an overabundance of radial or circular orbits, depending on the parameters of the model in consideration. Among the family, we find the post-Newtonian generalization of the Plummer and Hernquist models. A close inspection of their equation of state reveals that these solutions interpolate smoothly between a polytropic sphere in the asymptoti...
Bi-Spectrum Scattering Model for Conducting Randomly Rough Surface
刘宁; 李宗谦
2002-01-01
A scattering model is developed to predict the scattering coefficient of a conducting randomly rough surface by analyzing the randomly rough surface in the spectral domain using the bi-spectrum method. For common randomly rough surfaces without obvious two-scale characteristics, a scale-compression filter can divide the auto-correlation spectrum into two parts with different correlation lengths. The Kirchhoff approximation and the small perturbation method are used to obtain the surface field, then a bistatic scattering model, the bi-spectrum model (BSM), is used to derive an explicit expression from the surface field. Examples using the integral equation model (IEM), finite difference of the time domain (FDTD) method, and BSM show that the BSM accuracy is acceptable and its range of validity is similar to IEM. BSM can also be extended to a scattering model for dielectric randomly rough surfaces.
Parameter estimation of hidden periodic model in random fields
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
A note on moving average models for Gaussian random fields
Hansen, Linda Vadgård; Thorarinsdottir, Thordis L.
The class of moving average models offers a flexible modeling framework for Gaussian random fields with many well known models such as the Matérn covariance family and the Gaussian covariance falling under this framework. Moving average models may also be viewed as a kernel smoothing of a Lévy...
Dopp, C.M.E.; Graff, M.J.L.; Teerenstra, S.; Sanden, M.W.; Olde Rikkert, M.G.M.; Vernooij-Dassen, M.J.F.J.
2013-01-01
BACKGROUND: To evaluate the effectiveness of a multifaceted implementation strategy on physicians' referral rate to and knowledge on the community occupational therapy in dementia program (COTiD program). METHODS: A cluster randomized controlled trial with 28 experimental and 17 control clusters was
Dear, R F; Barratt, A L; Askie, L M; Butow, P N; McGeechan, K; Crossing, S; Currow, D C; Tattersall, M H N
2012-07-01
Cancer patients want access to reliable information about currently recruiting clinical trials. Oncologists and their patients were randomly assigned to access a consumer-friendly cancer clinical trials web site [Australian Cancer Trials (ACT), www.australiancancertrials.gov.au] or to usual care in a cluster randomized controlled trial. The primary outcome, measured from audio recordings of oncologist-patient consultations, was the proportion of patients with whom participation in any clinical trial was discussed. Analysis was by intention-to-treat accounting for clustering and stratification. Thirty medical oncologists and 493 patients were recruited. Overall, 46% of consultations in the intervention group compared with 34% in the control group contained a discussion about clinical trials (P=0.08). The mean consultation length in both groups was 29 min (P=0.69). The proportion consenting to a trial was 10% in both groups (P=0.65). Patients' knowledge about randomized trials was lower in the intervention than the control group (mean score 3.0 versus 3.3, P=0.03) but decisional conflict scores were similar (mean score 42 versus 43, P=0.83). Good communication between patients and physicians is essential. Within this context, a web site such as Australian Cancer Trials may be an important tool to encourage discussion about clinical trial participation.
Walther, Birte; Hanewinkel, Reiner; Morgenstern, Matthis
2014-09-01
The aim of this study was to evaluate the effects of a four-session school-based media literacy curriculum on adolescent computer gaming and Internet use behavior. The study comprised a cluster randomized controlled trial with three assessments (baseline, posttest, and 12-month follow-up). At baseline, a total of 2,303 sixth and seventh grade adolescents from 27 secondary schools were assessed. Of these, 1,843 (80%) could be reached at all three assessments (Mage=12.0 years; SD=0.83). Students of the intervention group received the media literacy program Vernetzte www.Welten ("Connected www.Worlds ") implemented by trained teachers during class time. The control group attended regular class. Main outcome measures were adolescents' computer gaming and Internet use: days per month, hours per day, and addictive use patterns. Parental media monitoring and rules at home were assessed as secondary outcomes. Results of multilevel growth-curve models revealed a significant intervention effect in terms of a lower increase in self-reported gaming frequency (β = -1.10 [95% CI -2.06, -0.13]), gaming time (β = -0.27 [95% CI -0.40, -0.14]), and proportion of excessive gamers (AOR=0.21 [95% CI 0.08, 0.57]) in the intervention group. There were also significant group-time interactions for the addictive gaming scale (β=-0.08 [95% CI -0.12, -0.04]), and the Internet Addiction Scale (β = -0.06 [95% CI -0.10, -0.01]). No effect was found for days and hours of Internet use or parental media behavior. The study shows that the program Vernetzte www.Welten can influence adolescents' media use behavior. Future research should address mediating and moderating variables of program effects.
Al Otaiba, Stephanie; Connor, Carol M; Folsom, Jessica Sidler; Greulich, Luana; Meadows, Jane; Li, Zhi
2011-06-01
The purpose of this cluster-randomized control field trial was to was to examine the extent to which kindergarten teachers could learn a promising instructional strategy, wherein kindergarten reading instruction was differentiated based upon students' ongoing assessments of language and literacy skills and documented child characteristic by instruction (CXI) interactions; and to test the efficacy of this differentiated reading instruction on the reading outcomes of students from culturally diverse backgrounds. The study involved 14 schools and included 23 treatment (n = 305 students) and 21 contrast teacher (n = 251 students). Teachers in the contrast condition received only a baseline professional development that included a researcher-delivered summer day-long workshop on individualized instruction. Data sources included parent surveys, individually administered child assessments of language, cognitive, and reading skills and videotapes of classroom instruction. Using Hierarchical Multivariate Linear Modeling (HMLM), we found students in treatment classrooms outperformed students in the contrast classrooms on a latent measure of reading skills, comprised of letter-word reading, decoding, alphabetic knowledge, and phonological awareness (ES = .52). Teachers in both conditions provided small group instruction, but teachers in the treatment condition provided significantly more individualized instruction. Our findings extend research on the efficacy of teachers using Individualized Student Instruction to individualize instruction based upon students' language and literacy skills in first through third grade. Findings are discussed regarding the value of professional development related to differentiating core reading instruction and the challenges of using Response to Intervention approaches to address students' needs in the areas of reading in general education contexts.
McDermott, Robyn A; Schmidt, Barbara; Preece, Cilla; Owens, Vickie; Taylor, Sean; Li, Ming; Esterman, Adrian
2015-02-19
Health outcomes for Indigenous Australians with diabetes in remote areas remain poor, including high rates of avoidable complications which could be reduced with better primary level care. We aimed to evaluate the effectiveness of a community-based health-worker led case management approach to the care of Indigenous adults with poorly controlled type 2 diabetes in primary care services in remote northern Australia. Two hundred and thirteen adults with poorly controlled diabetes (HbA1c > 8.5%) and significant comorbidities in 12 remote communities were randomly assigned by service cluster to receive chronic care co-ordination from a community-based health worker supported by a clinical outreach team, or to a waitlist control group which received usual care. At baseline, mean age of participants was 47.9 years, 62.4% were female, half were Aboriginal and half identified as Torres Strait Islander, 67% had less than 12 years of education, 39% were smokers, median income was $18,200 and 47% were unemployed. Mean HbA1c was 10.7% (93 mmol/mol) and BMI 32.5. At follow-up after 18 months, HbA1c reduction was significantly greater in the intervention group (-1.0% vs -0.2%, SE (diff) = 0.2, p = 0.02). There were no significant differences between the groups for blood pressure, lipid profile, BMI or renal function. Intervention group participants were more likely to receive nutrition and dental services according to scheduled care plans. Smoking rates were unchanged. A culturally safe, community level health-worker led model of diabetes care for high risk patients can be effective in improving diabetes control in remote Indigenous Australian communities where there is poor access to mainstream services. This approach can be effective in other remote settings, but requires longer term evaluation to capture accrued benefits. ANZCTR 12610000812099, Registered 29 September 2010.
Carnes, Molly; Devine, Patricia G.; Manwell, Linda Baier; Byars-Winston, Angela; Fine, Eve; Ford, Cecilia E.; Forscher, Patrick; Isaac, Carol; Kaatz, Anna; Magua, Wairimu; Palta, Mari; Sheridan, Jennifer
2014-01-01
Purpose Despite sincere commitment to egalitarian, meritocratic principles, subtle gender bias persists, constraining women’s opportunities for academic advancement. The authors implemented a pair-matched, single-blind, cluster-randomized, controlled study of a gender bias habit-changing intervention at a large public university. Method Participants were faculty in 92 departments or divisions at the University of Wisconsin-Madison. Between September 2010 and March 2012, experimental departments were offered a gender bias habit-changing intervention as a 2.5 hour workshop. Surveys measured gender bias awareness; motivation, self-efficacy, and outcome expectations to reduce bias; and gender equity action. A timed word categorization task measured implicit gender/leadership bias. Faculty completed a worklife survey before and after all experimental departments received the intervention. Control departments were offered workshops after data were collected. Results Linear mixed-effects models showed significantly greater changes post-intervention for faculty in experimental vs. control departments on several outcome measures, including self-efficacy to engage in gender equity promoting behaviors (P = .013). When ≥ 25% of a department’s faculty attended the workshop (26 of 46 departments), significant increases in self-reported action to promote gender equity occurred at 3 months (P = .007). Post-intervention, faculty in experimental departments expressed greater perceptions of fit (P = .024), valuing of their research (P = .019), and comfort in raising personal and professional conflicts (P = .025). Conclusions An intervention that facilitates intentional behavioral change can help faculty break the gender bias habit and change department climate in ways that should support the career advancement of women in academic medicine, science, and engineering. PMID:25374039
Nørskov, A K; Wetterslev, J; Rosenstock, C V
2016-01-01
' heterogeneous individual airway assessments. Preoperative prediction of difficult intubation and actual intubation difficulties were registered in the Danish Anaesthesia Database for both groups. Patients who were preoperatively scheduled for intubation by advanced techniques (e.g. video laryngoscopy; flexible......BACKGROUND: Unanticipated difficult intubation remains a challenge in anaesthesia. The Simplified Airway Risk Index (SARI) is a multivariable risk model consisting of seven independent risk factors for difficult intubation. Our aim was to compare preoperative airway assessment based on the SARI...... with usual airway assessment. METHODS: From 01.10.2012 to 31.12.2013, 28 departments were cluster-randomized to apply the SARI model or usual airway assessment. The SARI group implemented the SARI model. The Non-SARI group continued usual airway assessment, thus reflecting a group of anaesthetists...
Sums of random matrices and the Potts model on random planar maps
Atkin, Max R.; Niedner, Benjamin; Wheater, John F.
2016-05-01
We compute the partition function of the q-states Potts model on a random planar lattice with p≤slant q allowed, equally weighted colours on a connected boundary. To this end, we employ its matrix model representation in the planar limit, generalising a result by Voiculescu for the addition of random matrices to a situation beyond free probability theory. We show that the partition functions with p and q - p colours on the boundary are related algebraically. Finally, we investigate the phase diagram of the model when 0≤slant q≤slant 4 and comment on the conformal field theory description of the critical points.
Weiss, Michael J.; Lockwood, J. R.; McCaffrey, Daniel F.
2016-01-01
In the "individually randomized group treatment" (IRGT) experimental design, individuals are first randomly assigned to a treatment arm or a control arm, but then within each arm, are grouped together (e.g., within classrooms/schools, through shared case managers, in group therapy sessions, through shared doctors, etc.) to receive…
Witt Udsen F
2017-07-01
Full Text Available Flemming Witt Udsen,1 Pernille H Lilholt,2 Ole K Hejlesen,2 Lars H Ehlers1 1Danish Centre for Healthcare Improvements, Aalborg University, Aalborg, Denmark; 2Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Purpose: Results from the Danish cluster-randomized trial of telehealthcare to 1,225 patients with chronic obstructive pulmonary disease (COPD, the Danish Telecare North Trial, concluded that the telehealthcare solution was unlikely to be cost-effective, by applying international willingness-to-pay threshold values. The purpose of this article was to assess potential sources of variation across subgroups, which could explain overall cost-effectiveness results or be utilized in future economic studies in telehealthcare research. Methods: First, the cost-structures and cost-effectiveness across COPD severities were analyzed. Second, five additional subgroup analyses were conducted, focusing on differences in cost-effectiveness across a set of comorbidities, age-groups, genders, resource patterns (resource use in the social care sector prior to randomization, and delivery sites. All subgroups were investigated post hoc. In analyzing cost-effectiveness, two separate linear mixed-effects models with treatment-by-covariate interactions were applied: one for quality-adjusted life-year (QALY gain and one for total healthcare and social sector costs. Probabilistic sensitivity analysis was used for each subgroup result in order to quantify the uncertainty around the cost-effectiveness results. Results: The study concludes that, across the COPD severities, patients with severe COPD (GOLD 3 classification are likely to be the most cost-effective group. This is primarily due to lower hospital-admission and primary-care costs. Telehealthcare for patients younger than 60 years is also more likely to be cost-effective than for older COPD patients. Overall, results indicate that existing resource patterns of patients and
Roura Pilar
2010-02-01
Full Text Available Abstract Background It is a priority to achieve smoking cessation in diabetic smokers, given that this is a group of patients with elevated cardiovascular risk. Furthermore, tobacco has a multiplying effect on micro and macro vascular complications. Smoking abstinence rates increase as the intensity of the intervention, length of the intervention and number and diversity of contacts with the healthcare professional during the intervention increases. However, there are few published studies about smoking cessation in diabetics in primary care, a level of healthcare that plays an essential role in these patients. Therefore, the aim of the present study is to evaluate the effectiveness of an intensive smoking cessation intervention in diabetic patients in primary care. Methods/Design Cluster randomized trial, controlled and multicentric. Randomization unit: Primary Care Team. Study population: 546 diabetic smokers older than 14 years of age whose disease is controlled by one of the primary care teams in the study. Outcome Measures: Continuous tobacco abstinence (a person who has not smoked for at least six months and with a CO level of less than 6 ppm measured by a cooximeter , evolution in the Prochaska and DiClemente's Transtheoretical Model of Change, number of cigarettes/day, length of the visit. Point of assessment: one- year post- inclusion in the study. Intervention: Brief motivational interview for diabetic smokers at the pre-contemplation and contemplation stage, intensive motivational interview with pharmacotherapy for diabetic smokers in the preparation-action stage and reinforcing intevention in the maintenance stage. Statistical Analysis: A descriptive analysis of all variables will be done, as well as a multilevel logistic regression and a Poisson regression. All analyses will be done with an intention to treatment basis and will be fitted for potential confounding factors and variables of clinical importance. Statistical packages
Abdou Amza
Full Text Available Trachoma control programs utilize mass azithromycin distributions to treat ocular Chlamydia trachomatis as part of an effort to eliminate this disease world-wide. But it remains unclear what the community-level risk factors are for infection.This cluster-randomized, controlled trial entered 48 randomly selected communities in a 2×2 factorial design evaluating the effect of different treatment frequencies and treatment coverage levels. A pretreatment census and examination established the prevalence of risk factors for clinical trachoma and ocular chlamydia infection including years of education of household head, distance to primary water source, presence of household latrine, and facial cleanliness (ocular discharge, nasal discharge, and presence of facial flies. Univariate and multivariate associations were tested using linear regression and Bayes model averaging.There were a total of 24,536 participants (4,484 children aged 0-5 years in 6,235 households in the study. Before treatment in May to July 2010, the community-level prevalence of active trachoma (TF or TI utilizing the World Health Organization [WHO] grading system was 26.0% (95% CI: 21.9% to 30.0% and the mean community-level prevalence of chlamydia infection by Amplicor PCR was 20.7% (95% CI: 16.5% to 24.9% in children aged 0-5 years. Univariate analysis showed that nasal discharge (0.29, 95% CI: 0.04 to 0.54; P = 0.03, presence of flies on the face (0.40, 95% CI: 0.17 to 0.64; P = 0.001, and years of formal education completed by the head of household (0.07, 95% CI: 0.07 to 0.13; P = 0.03 were independent risk factors for chlamydia infection. In multivariate analysis, facial flies (0.26, 95% CI: 0.02 to 0.49; P = 0.03 and years of formal education completed by the head of household (0.06, 95% CI: 0.008 to 0.11; P = 0.02 were associated risk factors for ocular chlamydial infection.We have found that the presence of facial flies and years of education of the head
A voxelation-corrected non-stationary 3D cluster-size test based on random field theory.
Li, Huanjie; Nickerson, Lisa D; Zhao, Xuna; Nichols, Thomas E; Gao, Jia-Hong
2015-09-01
Cluster-size tests (CSTs) based on random field theory (RFT) are commonly adopted to identify significant differences in brain images. However, the use of RFT in CSTs rests on the assumption of uniform smoothness (stationarity). When images are non-stationary, CSTs based on RFT will likely lead to increased false positives in smooth regions and reduced power in rough regions. An adjustment to the cluster size according to the local smoothness at each voxel has been proposed for the standard test based on RFT to address non-stationarity, however, this technique requires images with a large degree of spatial smoothing, large degrees of freedom and high intensity thresholding. Recently, we proposed a voxelation-corrected 3D CST based on Gaussian random field theory that does not place constraints on the degree of spatial smoothness. However, this approach is only applicable to stationary images, requiring further modification to enable use for non-stationary images. In this study, we present modifications of this method to develop a voxelation-corrected non-stationary 3D CST based on RFT. Both simulated and real data were used to compare the voxelation-corrected non-stationary CST to the standard cluster-size adjusted non-stationary CST based on RFT and the voxelation-corrected stationary CST. We found that voxelation-corrected stationary CST is liberal for non-stationary images and the voxelation-corrected non-stationary CST performs better than cluster-size adjusted non-stationary CST based on RFT under low smoothness, low intensity threshold and low degrees of freedom. Published by Elsevier Inc.
Adang Eddy
2011-03-01
Full Text Available Abstract Background Even effective interventions for people with dementia and their caregivers require specific implementation efforts. A pilot study showed that the highly effective community occupational therapy in dementia (COTiD program was not implemented optimally due to various barriers. To decrease these barriers and make implementation of the program more effective a combined implementation (CI strategy was developed. In our study we will compare the effectiveness of this CI strategy with the usual educational (ED strategy. Methods In this cluster randomized, single-blinded, controlled trial, each cluster consists of at least two occupational therapists, a manager, and a physician working at Dutch healthcare organizations that deliver community occupational therapy. Forty-five clusters, stratified by healthcare setting (nursing home, hospital, mental health service, have been allocated randomly to either the intervention group (CI strategy or the control group (ED strategy. The study population consists of the professionals included in each cluster and community-dwelling people with dementia and their caregivers. The primary outcome measures are the use of community OT, the adherence of OTs to the COTiD program, and the cost effectiveness of implementing the COTiD program in outpatient care. Secondary outcome measures are patient and caregiver outcomes and knowledge of managers, physicians and OTs about the COTiD program. Discussion Implementation research is fairly new in the field of occupational therapy, making this a unique study. This study does not only evaluate the effects of the CI-strategy on professionals, but also the effects of professionals' degree of implementation on client and caregiver outcomes. Clinical trials registration NCT01117285
Random walk models for top-N recommendation task
Yin ZHANG; Jiang-qin WU; Yue-ting ZHUANG
2009-01-01
Recently there has been an increasing interest in applying random walk based methods to recommender systems.We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem.taking into account the degree of each node on the user-item bipartite graph,and induce an effective absorbing random walk (ARW) algorithm for the top-N recommendation task.Our random walk approach directly generates the top-N recommendations for individuals,rather than predicting the ratings of the recommendations.Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.
Modeling, clustering, and segmenting video with mixtures of dynamic textures.
Chan, Antoni B; Vasconcelos, Nuno
2008-05-01
A dynamic texture is a spatio-temporal generative model for video, which represents video sequences as observations from a linear dynamical system. This work studies the mixture of dynamic textures, a statistical model for an ensemble of video sequences that is sampled from a finite collection of visual processes, each of which is a dynamic texture. An expectationmaximization (EM) algorithm is derived for learning the parameters of the model, and the model is related to previous works in linear systems, machine learning, time-series clustering, control theory, and computer vision. Through experimentation, it is shown that the mixture of dynamic textures is a suitable representation for both the appearance and dynamics of a variety of visual processes that have traditionally been challenging for computer vision (e.g. fire, steam, water, vehicle and pedestrian traffic, etc.). When compared with state-of-the-art methods in motion segmentation, including both temporal texture methods and traditional representations (e.g. optical flow or other localized motion representations), the mixture of dynamic textures achieves superior performance in the problems of clustering and segmenting video of such processes.
A Global Model for Circumgalactic and Cluster-core Precipitation
Voit, G. Mark; Meece, Greg; Li, Yuan; O'Shea, Brian W.; Bryan, Greg L.; Donahue, Megan
2017-08-01
We provide an analytic framework for interpreting observations of multiphase circumgalactic gas that is heavily informed by recent numerical simulations of thermal instability and precipitation in cool-core galaxy clusters. We start by considering the local conditions required for the formation of multiphase gas via two different modes: (1) uplift of ambient gas by galactic outflows, and (2) condensation in a stratified stationary medium in which thermal balance is explicitly maintained. Analytic exploration of these two modes provides insights into the relationships between the local ratio of the cooling and freefall timescales (i.e., {t}{cool}/{t}{ff}), the large-scale gradient of specific entropy, and the development of precipitation and multiphase media in circumgalactic gas. We then use these analytic findings to interpret recent simulations of circumgalactic gas in which global thermal balance is maintained. We show that long-lasting configurations of gas with 5≲ \\min ({t}{cool}/{t}{ff})≲ 20 and radial entropy profiles similar to observations of cool cores in galaxy clusters are a natural outcome of precipitation-regulated feedback. We conclude with some observational predictions that follow from these models. This work focuses primarily on precipitation and AGN feedback in galaxy-cluster cores, because that is where the observations of multiphase gas around galaxies are most complete. However, many of the physical principles that govern condensation in those environments apply to circumgalactic gas around galaxies of all masses.
Fiber modeling and clustering based on neuroanatomical features.
Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang
2011-01-01
DTI tractography allows unprecedented understanding of brain neural connectivity in-vivo by capturing water diffusion patterns in brain white-matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering the computation needed for subsequent data analysis intractable. A remedy is to group the fibers into bundles using fiber clustering techniques. Most existing fiber clustering methods, however, rely on fiber geometrical information only by viewing fibers as curves in the 3D Euclidean space. The important neuroanatomical aspect of the fibers is mostly ignored. In this paper, neuroanatomical information is encapsulated in a feature vector called the associativity vector, which functions as the "fingerprint" for each fiber and depicts the connectivity of the fiber with respect to individual anatomies. Using the associativity vectors of fibers, we model the fibers as observations sampled from multivariate Gaussian mixtures in the feature space. An expectation-maximization clustering approach is then employed to group the fibers into 16 major bundles. Experimental results indicate that the proposed method groups the fibers into anatomically meaningful bundles, which are highly consistent across subjects.
A spectrophotometric model applied to cluster galaxies: the WINGS dataset
Fritz, J; Bettoni, D; Cava, A; Couch, W J; D'Onofrio, M; Dressler, A; Fasano, G; Kjaergaard, P; Moles, M; Varela, J
2007-01-01
[Abridged] The WIde-field Nearby Galaxy-cluster Survey (WINGS) is a project aiming at the study of the galaxy populations in clusters in the local universe (0.04
Cluster model of s- and p-shell hypernuclei
Mohammad Shoeb; Alemiye Mamo; Amanuel Fessahatsion
2007-06-01
The binding energy ( ) of the s- and p-shell hypernuclei are calculated variationally in the cluster model and multidimensional integrations are performed using Monte Carlo. A variety of phenomenological -core potentials consistent with the -core energies and a wide range of simulated s-state potentials are taken as input. The of $_{ }^{6}$He is explained and $_{ }^{5}$He and $_{ }^{5}$H are predicted to be particle stable in the -core model. The results for s-shell hypernuclei are in excellent agreement with those of non-VMC calculations. The $_{}^{10}$Be in model is overbound for combinations of and potentials. A phenomenological dispersive three-body force, , consistent with the of $_{}^{9}$Be in the model underbinds $_{ }^{10}$Be. The incremental values for the s- and p-shell cannot be reconciled, consistent with the finding of earlier analyses.
The non-random clustering of non-synonymous substitutions and its relationship to evolutionary rate
Stone Eric A
2011-08-01
Full Text Available Abstract Background Protein sequences are subject to a mosaic of constraint. Changes to functional domains and buried residues, for example, are more apt to disrupt protein structure and function than are changes to residues participating in loops or exposed to solvent. Regions of constraint on the tertiary structure of a protein often result in loose segmentation of its primary structure into stretches of slowly- and rapidly-evolving amino acids. This clustering can be exploited, and existing methods have done so by relying on local sequence conservation as a signature of selection to help identify functionally important regions within proteins. We invert this paradigm by leveraging the regional nature of protein structure and function to both illuminate and make use of genome-wide patterns of local sequence conservation. Results Our hypothesis is that the regional nature of structural and functional constraints will assert a positive autocorrelation on the evolutionary rates of neighboring sites, which, in a pairwise comparison of orthologous proteins, will manifest itself as the clustering of non-synonymous changes across the amino acid sequence. We introduce a dispersion ratio statistic to test this and related hypotheses. Using genome-wide interspecific comparisons of orthologous protein pairs, we reveal a strong log-linear relationship between the degree of clustering and the intensity of constraint. We further demonstrate how this relationship varies with the evolutionary distance between the species being compared. We provide some evidence that proteins with a history of positive selection deviate from genome-wide trends. Conclusions We find a significant association between the evolutionary rate of a protein and the degree to which non-synonymous changes cluster along its primary sequence. We show that clustering is a non-redundant predictor of evolutionary rate, and we speculate that conflicting signals of clustering and constraint may
Berdahl, Andrew; Shreim, Amer; Sood, Vishal; Davidsen, Joern; Paczuski, Maya [Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 1N4 (Canada)], E-mail: aberdahl@phas.ucalgary.ca
2008-06-15
We discuss basic features of emergent complexity in dynamical systems far from equilibrium by focusing on the network structure of their state space. We start by measuring the distributions of avalanche and transient times in random Boolean networks (RBNs) and in the Drosophila polarity network by exact enumeration. A transient time is the duration of the transient from a starting state to an attractor. An avalanche is a special transient which starts as a single Boolean element perturbation of an attractor state. Significant differences at short times between the avalanche and the transient times for RBNs with small connectivity K-compared to the number of elements N-indicate that attractors tend to cluster in configuration space. In addition, one bit flip has a non-negligible chance to put an attractor state directly onto another attractor. This clustering is also present in the segment polarity gene network of Drosophila melanogaster, suggesting that this may be a robust feature of biological regulatory networks. We also define and measure a branching ratio for the state space networks and find evidence for a new timescale that diverges roughly linearly with N for 2{<=}K<
Gulliford, Martin C; van Staa, Tjeerd P; McDermott, Lisa; McCann, Gerard; Charlton, Judith; Dregan, Alex
2014-06-11
There is growing interest in conducting clinical and cluster randomized trials through electronic health records. This paper reports on the methodological issues identified during the implementation of two cluster randomized trials using the electronic health records of the Clinical Practice Research Datalink (CPRD). Two trials were completed in primary care: one aimed to reduce inappropriate antibiotic prescribing for acute respiratory infection; the other aimed to increase physician adherence with secondary prevention interventions after first stroke. The paper draws on documentary records and trial datasets to report on the methodological experience with respect to research ethics and research governance approval, general practice recruitment and allocation, sample size calculation and power, intervention implementation, and trial analysis. We obtained research governance approvals from more than 150 primary care organizations in England, Wales, and Scotland. There were 104 CPRD general practices recruited to the antibiotic trial and 106 to the stroke trial, with the target number of practices being recruited within six months. Interventions were installed into practice information systems remotely over the internet. The mean number of participants per practice was 5,588 in the antibiotic trial and 110 in the stroke trial, with the coefficient of variation of practice sizes being 0.53 and 0.56 respectively. Outcome measures showed substantial correlations between the 12 months before, and after intervention, with coefficients ranging from 0.42 for diastolic blood pressure to 0.91 for proportion of consultations with antibiotics prescribed, defining practice and participant eligibility for analysis requires careful consideration. Cluster randomized trials may be performed efficiently in large samples from UK general practices using the electronic health records of a primary care database. The geographical dispersal of trial sites presents a difficulty for
Humphrey, Neil; Barlow, Alexandra; Wigelsworth, Michael; Lendrum, Ann; Pert, Kirsty; Joyce, Craig; Stephens, Emma; Wo, Lawrence; Squires, Garry; Woods, Kevin; Calam, Rachel; Turner, Alex
2016-10-01
This randomized controlled trial (RCT) evaluated the efficacy of the Promoting Alternative Thinking Strategies curriculum (PATHS; Kusche & Greenberg, 1994) as a means to improve children's social-emotional competence (assessed via the Social Skills Improvement System (SSIS); Gresham & Elliot, 2008) and mental health outcomes (assessed via the Strengths and Difficulties Questionnaire (SDQ); Goodman, 1997). Forty-five schools in Greater Manchester, England, were randomly assigned to treatment and control groups. Allocation was balanced by proportions of children eligible for free school meals and speaking English as an additional language via minimization. Children (N=4516) aged 7-9years at baseline in the participating schools were the target cohort. During the two-year trial period, teachers of this cohort in schools allocated to the intervention group delivered the PATHS curriculum, while their counterparts in the control group continued their usual provision. Teachers in PATHS schools received initial training and on-going support and assistance from trained coaches. Hierarchical linear modeling of outcome data was undertaken to identify both primary (e.g., for all children) and secondary (e.g., for children classified as "at-risk") intervention effects. A primary effect of the PATHS curriculum was found, demonstrating increases in teacher ratings of changes in children's social-emotional competence. Additionally, secondary effects of PATHS were identified, showing reductions in teacher ratings of emotional symptoms and increases in pro-social behavior and child ratings of engagement among children identified as at-risk at baseline. However, our analyses also identified primary effects favoring the usual provision group, showing reductions in teacher ratings of peer problems and emotional symptoms, and secondary effects demonstrating reductions in teacher ratings of conduct problems and child ratings of co-operation among at-risk children. Effect sizes were small
Weighted Hybrid Decision Tree Model for Random Forest Classifier
Kulkarni, Vrushali Y.; Sinha, Pradeep K.; Petare, Manisha C.
2016-06-01
Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.
Carrozza, Sylvain
2015-01-01
We define in this paper a class of three indices tensor models, endowed with $O(N)^{\\otimes 3}$ invariance ($N$ being the size of the tensor). This allows to generate, via the usual QFT perturbative expansion, a class of Feynman tensor graphs which is strictly larger than the class of Feynman graphs of both the multi-orientable model (and hence of the colored model) and the $U(N)$ invariant models. We first exhibit the existence of a large $N$ expansion for such a model with general interactions. We then focus on the quartic model and we identify the leading and next-to-leading order (NLO) graphs of the large $N$ expansion. Finally, we prove the existence of a critical regime and we compute the critical exponents, both at leading order and at NLO. This is achieved through the use of various analytic combinatorics techniques.
Carrozza, Sylvain; Tanasa, Adrian
2016-08-01
We define in this paper a class of three-index tensor models, endowed with {O(N)^{⊗ 3}} invariance (N being the size of the tensor). This allows to generate, via the usual QFT perturbative expansion, a class of Feynman tensor graphs which is strictly larger than the class of Feynman graphs of both the multi-orientable model (and hence of the colored model) and the U(N) invariant models. We first exhibit the existence of a large N expansion for such a model with general interactions. We then focus on the quartic model and we identify the leading and next-to-leading order (NLO) graphs of the large N expansion. Finally, we prove the existence of a critical regime and we compute the critical exponents, both at leading order and at NLO. This is achieved through the use of various analytic combinatorics techniques.
Multilevel random effect and marginal models for longitudinal data ...
Multilevel random effect and marginal models for longitudinal data. ... Ethiopian Journal of Science and Technology ... the occurrence of specific adverse events than children injected with licensed vaccine, and if so, to quantify the difference.
Phase diagram of the classical Heisenberg model in a trimodal random field distribution
Santos-Filho, A.; Albuquerque, D. F. de; Santos-Filho, J. B.; Batista, T. S. Araujo
2016-11-01
The classical spin 1 / 2 Heisenberg model on a simple cubic lattice, with fluctuating bond interactions between nearest neighbors and in the presence of a random magnetic field, is investigated by effective field theory based on two-spin cluster. The random field is drawn from the asymmetric and anisotropic trimodal probability distribution. The fluctuating bond is extracted from the symmetric and anisotropic bimodal probability. We estimate the transition temperatures, and the phase diagram in the Tc- h, Tc- p and Tc - α planes. We observe that the temperature of the tricritical point decreases with the increase of disorder in exchange interactions until the system ceases to display tricritical behavior. The disorder of the interactions and reentrant phenomena depends on the trimodal distribution of the random field.
Random recursive trees and the elephant random walk
Kürsten, Rüdiger
2016-03-01
One class of random walks with infinite memory, so-called elephant random walks, are simple models describing anomalous diffusion. We present a surprising connection between these models and bond percolation on random recursive trees. We use a coupling between the two models to translate results from elephant random walks to the percolation process. We calculate, besides other quantities, exact expressions for the first and the second moment of the root cluster size and of the number of nodes in child clusters of the first generation. We further introduce another model, the skew elephant random walk, and calculate the first and second moment of this process.
A Gompertzian model with random effects to cervical cancer growth
Mazlan, Mazma Syahidatul Ayuni; Rosli, Norhayati [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia)
2015-05-15
In this paper, a Gompertzian model with random effects is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via maximum likehood estimation. We apply 4-stage Runge-Kutta (SRK4) for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of the cervical cancer growth. Low values of root mean-square error (RMSE) of Gompertzian model with random effect indicate good fits.
Multicritical tensor models and hard dimers on spherical random lattices
Bonzom, Valentin
2012-01-01
Random tensor models which display multicritical behaviors in a remarkably simple fashion are presented. They come with entropy exponents \\gamma = (m-1)/m, similarly to multicritical random branched polymers. Moreover, they are interpreted as models of hard dimers on a set of random lattices for the sphere in dimension three and higher. Dimers with their exclusion rules are generated by the different interactions between tensors, whose coupling constants are dimer activities. As an illustration, we describe one multicritical point, which is interpreted as a transition between the dilute phase and a crystallized phase, though with negative activities.
Peng, Degao; van Aggelen, Helen; Steinmann, Stephan; Yang, Yang; Yang, Weitao; Duke University Team
2014-03-01
The particle-particle random-phase approximation (pp-RPA) recently attracts extensive interests in quantum chemistry recently. Pp-RPA is a versatile model to calculate ground-state correlation energies, and double ionization potential/double electron affinity. We inspect particle-particle random-phase approximation in different perspectives to further understand its theoretical fundamentals. Viewed as summation of all ladder diagrams, the pp-RPA correlation energy is proved to be analytically equivalent to the ladder coupled-cluster doubles (ladder-CCD) theory. With this equivalence, we can make use of various well-established coupled-cluster techniques to study pp-RPA. Furthermore, we establish linear-response time-dependent density-functional theory with pairing fields (TDDFT-PF), where pp-RPA can be interpreted as the mean-field approximation to a general theory. TDDFT-PF is closely related to the density-functional theory of superconductors, but is applied to normal systems to capture exact N plus/minus 2 excitations. In the linear-response regime, both the adiabatic and non-adiabatic TDDFT-PF equations are established. This sets the fundamentals for further density-functional developments aiming for pp-RPA. These theoretical perspectives will be very helpful for future study.
Zhou, Huan; Sun, Shuai; Luo, Renfu; Sylvia, Sean; Yue, Ai; Shi, Yaojiang; Zhang, Linxiu; Medina, Alexis; Rozelle, Scott
2016-07-01
To test whether text message reminders sent to caregivers improve the effectiveness of a home micronutrient fortification program in western China. We carried out a cluster-randomized controlled trial in 351 villages (clusters) in Shaanxi Province in 2013 and 2014, enrolling children aged 6 to 12 months. We randomly assigned each village to 1 of 3 groups: free delivery group, text messaging group, or control group. We collected information on compliance with treatments and hemoglobin concentrations from all children at baseline and 6-month follow-up. We estimated the intent-to-treat effects on compliance and child anemia using a logistic regression model. There were 1393 eligible children. We found that assignment to the text messaging group led to an increase in full compliance (marginal effect = 0.10; 95% confidence interval [CI] = 0.03, 0.16) compared with the free delivery group and decrease in the rate of anemia at end line relative to the control group (marginal effect = -0.07; 95% CI = -0.12, -0.01), but not relative to the free delivery group (marginal effect = -0.03; 95% CI = -0.09, 0.03). Text messages improved compliance of caregivers to a home fortification program and children's nutrition.
Leonhardt, Corinna; Keller, Stefan; Chenot, Jean-François; Luckmann, Judith; Basler, Heinz-Dieter; Wegscheider, Karl; Baum, Erika; Donner-Banzhoff, Norbert; Pfingsten, Michael; Hildebrandt, Jan; Kochen, Michael M; Becker, Annette
2008-01-01
To investigate the effectiveness of a TTM-based motivational counselling approach by trained practice nurses to promote physical activity of low back pain patients in a German primary care setting. Data were collected in a cluster-randomized controlled trial with three study arms via questionnaires and patient interviews at baseline and after 6 and 12 months. We analysed total physical activity and self-efficacy by using random effect models to allow for clustering. A total of 1378 low back pain patients, many with acute symptoms, were included in the study. Nearly 40% of all patients reported sufficient physical activity at baseline. While there were significant improvements in patients' physical activity behaviour in all study arms, there was no evidence for an intervention effect. The outcome may be explained by insufficient performance of the practice nurses, implementation barriers caused by the German health care system and the heterogenous sample. Given the objective to incorporate practice nurses into patient education, there is a need for a better basic training of the nurses and for a change towards an organizational structure that facilitates patient-nurse communication. Counselling for low back pain patients has to consider more specificated aims for different subgroups.
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
Crean, Hugh F; Johnson, Deborah B
2013-09-01
This study reports on aggressive outcomes from a cluster randomized trial of the Promoting Alternative Thinking Strategies (PATHS) curriculum. Fourteen elementary schools were randomly assigned to intervention or control condition and third grade students were followed through the fifth grade. Teacher and self-reports of student aggression, conduct problems, delinquency, acting out problems, and social information processing (SIP) variables were collected. Linear change for each of the SIP variables was noted with control students demonstrating increased normative beliefs about aggression, increased aggressive social problem solving, increased hostile attribution bias, and increased aggressive interpersonal negotiation strategies over time while PATHS students remained relatively stable. Teachers reported significant curvilinear change in student aggression, conduct problems, and acting out behavior problems; all favoring PATHS students.