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

Science.gov (United States)

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

2010-06-30

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

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

NARCIS (Netherlands)

van Schie, Sander; Moerbeek, Mirjam

2014-01-01

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

The energetics and structure of nickel clusters: Size dependence

International Nuclear Information System (INIS)

Cleveland, C.L.; Landman, U.

1991-01-01

The energetics of nickel clusters over a broad size range are explored within the context of the many-body potentials obtained via the embedded atom method. Unconstrained local minimum energy configurations are found for single crystal clusters consisting of various truncations of the cube or octahedron, with and without (110) faces, as well as some monotwinnings of these. We also examine multitwinned structures such as icosahedra and various truncations of the decahedron, such as those of Ino and Marks. These clusters range in size from 142 to over 5000 atoms. As in most such previous studies, such as those on Lennard-Jones systems, we find that icosahedral clusters are favored for the smallest cluster sizes and that Marks' decahedra are favored for intermediate sizes (all our atomic systems larger than about 2300 atoms). Of course very large clusters will be single crystal face-centered-cubic (fcc) polyhedra: the onset of optimally stable single-crystal nickel clusters is estimated to occur at 17 000 atoms. We find, via comparisons to results obtained via atomistic calculations, that simple macroscopic expressions using accurate surface, strain, and twinning energies can usefully predict energy differences between different structures even for clusters of much smaller size than expected. These expressions can be used to assess the relative energetic merits of various structural motifs and their dependence on cluster size

Modified genetic algorithms to model cluster structures in medium-size silicon clusters

International Nuclear Information System (INIS)

Bazterra, Victor E.; Ona, Ofelia; Caputo, Maria C.; Ferraro, Marta B.; Fuentealba, Patricio; Facelli, Julio C.

2004-01-01

This paper presents the results obtained using a genetic algorithm (GA) to search for stable structures of medium size silicon clusters. In this work the GA uses a semiempirical energy function to find the best cluster structures, which are further optimized using density-functional theory. For small clusters our results agree well with previously reported structures, but for larger ones different structures appear. This is the case of Si 36 where we report a different structure, with significant lower energy than those previously found using limited search approaches on common structural motifs. This demonstrates the need for global optimization schemes when searching for stable structures of medium-size silicon clusters

Control of cluster ion sizes for efficient injection heating

International Nuclear Information System (INIS)

Enjoji, Hiroshi; Be, S.H.; Yano, Katsuki; Okamoto, Kosuke

1976-01-01

For heating of plasmas by injection of hydrogen cluster ions, the specific size (N/Z) approximately 10 2 molecules/charge is believed to be most desirable. A fundamental research to develop a practical method for tailoring large cluster ions into small suitable sizes has been carried out by using nitrogen cluster ions of the initial mean specific size (N/Z) 0 approximately 10 5 . The beam of neutral large clusters of total intensity 20 mAsub(eq) was led to an ionizer and then the large cluster ions are accelerated to 8.9 keV before entering the divider which disintegrates them into small fragments by multiple ionization. The mean specific size of disintegrated cluster ions (N/Z)' becomes smaller with increase in ionizing electron current of the divider. (N/Z)' becomes 10 3 approximately 10 4 at an electron current of 140 mA and an accelerating voltage of 680 V of the divider with its efficiency of 20 approximately 60%. Thus, the original large cluster ions are divided into small fragments of which the mean specific size is 1/20 approximately 1/100 of the initial value without much decrease in total intensity of the cluster ion beam

Structural stability of nano-sized clusters

NARCIS (Netherlands)

De Hosson, JTM; Palasantzas, G; Vystavel, T; Koch, S; Ovidko,; Pande, CS; Krishnamoorti, R; Lavernia, E; Skandan, G

2004-01-01

This contribution presents challenges to control the microstructure in nano-structured materials via a relatively new approach, i.e. using a so-called nanocluster source. An important aspect is that the cluster size distribution is monodisperse and that the kinetic energy of the clusters during

Size dependent magnetism of mass selected deposited transition metal clusters

International Nuclear Information System (INIS)

Lau, T.

2002-05-01

The size dependent magnetic properties of small iron clusters deposited on ultrathin Ni/Cu(100) films have been studied with circularly polarised synchrotron radiation. For X-ray magnetic circular dichroism studies, the magnetic moments of size selected clusters were aligned perpendicular to the sample surface. Exchange coupling of the clusters to the ultrathin Ni/Cu(100) film determines the orientation of their magnetic moments. All clusters are coupled ferromagnetically to the underlayer. With the use of sum rules, orbital and spin magnetic moments as well as their ratios have been extracted from X-ray magnetic circular dichroism spectra. The ratio of orbital to spin magnetic moments varies considerably as a function of cluster size, reflecting the dependence of magnetic properties on cluster size and geometry. These variations can be explained in terms of a strongly size dependent orbital moment. Both orbital and spin magnetic moments are significantly enhanced in small clusters as compared to bulk iron, although this effect is more pronounced for the spin moment. Magnetic properties of deposited clusters are governed by the interplay of cluster specific properties on the one hand and cluster-substrate interactions on the other hand. Size dependent variations of magnetic moments are modified upon contact with the substrate. (orig.)

The role of micro size computing clusters for small physics groups

International Nuclear Information System (INIS)

Shevel, A Y

2014-01-01

A small physics group (3-15 persons) might use a number of computing facilities for the analysis/simulation, developing/testing, teaching. It is discussed different types of computing facilities: collaboration computing facilities, group local computing cluster (including colocation), cloud computing. The author discuss the growing variety of different computing options for small groups and does emphasize the role of the group owned computing cluster of micro size.

Computational and experimental study of the cluster size distribution in MAPLE

International Nuclear Information System (INIS)

Leveugle, Elodie; Zhigilei, Leonid V.; Sellinger, Aaron; Fitz-Gerald, James M.

2007-01-01

A combined experimental and computational study is performed to investigate the origin and characteristics of the surface features observed in SEM images of thin polymer films deposited in matrix-assisted pulsed laser evaporation (MAPLE). Analysis of high-resolution SEM images of surface morphologies of the films deposited at different fluences reveals that the mass distributions of the surface features can be well described by a power-law, Y(N) ∝ N -t , with exponent -t ∼ -1.6. Molecular dynamic simulations of the MAPLE process predict a similar size distribution for large clusters observed in the ablation plume. A weak dependence of the cluster size distributions on fluence and target composition suggests that the power-law cluster size distribution may be a general characteristic of the ablation plume generated as a result of an explosive decomposition of a target region overheated above the limit of its thermodynamic stability. Based on the simulation results, we suggest that the ejection of large matrix-polymer clusters, followed by evaporation of the volatile matrix, is responsible for the formation of the surface features observed in the polymer films deposited in MAPLE experiments

Micron-size hydrogen cluster target for laser-driven proton acceleration

Science.gov (United States)

Jinno, S.; Kanasaki, M.; Uno, M.; Matsui, R.; Uesaka, M.; Kishimoto, Y.; Fukuda, Y.

2018-04-01

As a new laser-driven ion acceleration technique, we proposed a way to produce impurity-free, highly reproducible, and robust proton beams exceeding 100 MeV using a Coulomb explosion of micron-size hydrogen clusters. In this study, micron-size hydrogen clusters were generated by expanding the cooled high-pressure hydrogen gas into a vacuum via a conical nozzle connected to a solenoid valve cooled by a mechanical cryostat. The size distributions of the hydrogen clusters were evaluated by measuring the angular distribution of laser light scattered from the clusters. The data were analyzed mathematically based on the Mie scattering theory combined with the Tikhonov regularization method. The maximum size of the hydrogen cluster at 25 K and 6 MPa in the stagnation state was recognized to be 2.15 ± 0.10 μm. The mean cluster size decreased with increasing temperature, and was found to be much larger than that given by Hagena’s formula. This discrepancy suggests that the micron-size hydrogen clusters were formed by the atomization (spallation) of the liquid or supercritical fluid phase of hydrogen. In addition, the density profiles of the gas phase were evaluated for 25 to 80 K at 6 MPa using a Nomarski interferometer. Based on the measurement results and the equation of state for hydrogen, the cluster mass fraction was obtained. 3D particles-in-cell (PIC) simulations concerning the interaction processes of micron-size hydrogen clusters with high power laser pulses predicted the generation of protons exceeding 100 MeV and accelerating in a laser propagation direction via an anisotropic Coulomb explosion mechanism, thus demonstrating a future candidate in laser-driven proton sources for upcoming multi-petawatt lasers.

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

Science.gov (United States)

Liu, Jingxia; Colditz, Graham A

2018-05-01

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

7 CFR 52.1851 - Sizes of raisins with seeds-layer or cluster.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Sizes of raisins with seeds-layer or cluster. 52.1851...-Raisins with Seeds § 52.1851 Sizes of raisins with seeds—layer or cluster. The size of Layer or Cluster... measurement as applicable to layer or cluster raisins with seeds are: (a) 3 Crown size or larger. “3 Crown...

Size and composition dependence of the frozen structures in Co-based bimetallic clusters

International Nuclear Information System (INIS)

Li, Guojian; Wang, Qiang; Cao, Yongze; Du, Jiaojiao; He, Jicheng

2012-01-01

This Letter studies the size-dependent freezing of Co, Co–Ni, and Co–Cu clusters by using molecular dynamics with embedded atom method. Size effect occurs in these three types of clusters. The clusters with large sizes always freeze to form their bulk-like structures. However, the frozen structures for small sizes are generally related to their compositions. The icosahedral clusters are formed for Co clusters (for ⩽3.2 nm diameter) and also for Co–Ni clusters but at a larger size range (for ⩽4.08 nm). Upon the Co–Cu clusters, decahedral structure is obtained for small size (for 2.47 nm). The released energy induced the structural transformation plays a key role in the frozen structures. These results indicate that the preformed clusters with special structures can be tuned by controlling their compositions and sizes. -- Highlights: ► The size effect occurs in the Co, Co–Ni, and Co–Cu clusters. ► The clusters with large sizes always freeze to form their bulk-like structures. ► The frozen structures for small sizes are generally related to their compositions. ► Icosahedron is formed for Co and also for Co–Ni but at a larger size range. ► Upon the Co–Cu clusters, decahedral structure is obtained for small size.

Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

Directory of Open Access Journals (Sweden)

Sami Ullah

2017-11-01

Full Text Available Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space–time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend.

Experimental equivalent cluster-size distributions in nano-metric volumes of liquid water

International Nuclear Information System (INIS)

Grosswendt, B.; De Nardo, L.; Colautti, P.; Pszona, S.; Conte, V.; Tornielli, G.

2004-01-01

Ionisation cluster-size distributions in nano-metric volumes of liquid water were determined for alpha particles at 4.6 and 5.4 MeV by measuring cluster-size frequencies in small gaseous volumes of nitrogen or propane at low gas pressure as well as by applying a suitable scaling procedure. This scaling procedure was based on the mean free ionisation lengths of alpha particles in water and in the gases measured. For validation, the measurements of cluster sizes in gaseous volumes and the cluster-size formation in volumes of liquid water of equivalent size were simulated by Monte Carlo methods. The experimental water-equivalent cluster-size distributions in nitrogen and propane are compared with those in liquid water and show that cluster-size formation by alpha particles in nitrogen or propane can directly be related to those in liquid water. (authors)

Characterization of micron-size hydrogen clusters using Mie scattering.

Science.gov (United States)

Jinno, S; Tanaka, H; Matsui, R; Kanasaki, M; Sakaki, H; Kando, M; Kondo, K; Sugiyama, A; Uesaka, M; Kishimoto, Y; Fukuda, Y

2017-08-07

Hydrogen clusters with diameters of a few micrometer range, composed of 10 8-10 hydrogen molecules, have been produced for the first time in an expansion of supercooled, high-pressure hydrogen gas into a vacuum through a conical nozzle connected to a cryogenic pulsed solenoid valve. The size distribution of the clusters has been evaluated by measuring the angular distribution of laser light scattered from the clusters. The data were analyzed based on the Mie scattering theory combined with the Tikhonov regularization method including the instrumental functions, the validity of which was assessed by performing a calibration study using a reference target consisting of standard micro-particles with two different sizes. The size distribution of the clusters was found discrete peaked at 0.33 ± 0.03, 0.65 ± 0.05, 0.81 ± 0.06, 1.40 ± 0.06 and 2.00 ± 0.13 µm in diameter. The highly reproducible and impurity-free nature of the micron-size hydrogen clusters can be a promising target for laser-driven multi-MeV proton sources with the currently available high power lasers.

Finite-size modifications of the magnetic properties of clusters

DEFF Research Database (Denmark)

Hendriksen, Peter Vang; Linderoth, Søren; Lindgård, Per-Anker

1993-01-01

relative to the bulk, and the consequent neutron-scattering cross section exhibits discretely spaced wave-vector-broadened eigenstates. The implications of the finite size on thermodynamic properties, like the temperature dependence of the magnetization and the critical temperature, are also elucidated. We...... find the temperature dependence of the cluster magnetization to be well described by an effective power law, M(mean) is-proportional-to 1 - BT(alpha), with a size-dependent, but structure-independent, exponent larger than the bulk value. The critical temperature of the clusters is calculated from...... the spin-wave spectrum by a method based on the correlation theory and the spherical approximation generalized to the case of finite systems. A size-dependent reduction of the critical temperature by up to 50% for the smallest clusters is found. The trends found for the model clusters are extrapolated...

Mesophase Formation Stabilizes High-purity Magic-sized Clusters

KAUST Repository

Nevers, Douglas R.; Williamson, Curtis B.; Savitzky, Benjamin H; Hadar, Ido; Banin, Uri; Kourkoutis, Lena F.; Hanrath, Tobias; Robinson, Richard D.

2018-01-01

Magic-sized clusters (MSCs) are renowned for their identical size and closed-shell stability that inhibit conventional nanoparticle (NP) growth processes. Though MSCs have been of increasing interest, understanding the reaction pathways toward their nucleation and stabilization is an outstanding issue. In this work, we demonstrate that high concentration synthesis (1000 mM) promotes a well-defined reaction pathway to form high-purity MSCs (>99.9%). The MSCs are resistant to typical growth and dissolution processes. Based on insights from in-situ X-ray scattering analysis, we attribute this stability to the accompanying production of a large, hexagonal organic-inorganic mesophase (>100 nm grain size) that arrests growth of the MSCs and prevents NP growth. At intermediate concentrations (500 mM), the MSC mesophase forms, but is unstable, resulting in NP growth at the expense of the assemblies. These results provide an alternate explanation for the high stability of MSCs. Whereas the conventional mantra has been that the stability of MSCs derives from the precise arrangement of the inorganic structures (i.e., closed-shell atomic packing), we demonstrate that anisotropic clusters can also be stabilized by self-forming fibrous mesophase assemblies. At lower concentration (<200 mM or >16 acid-to-metal), MSCs are further destabilized and NPs formation dominates that of MSCs. Overall, the high concentration approach intensifies and showcases inherent concentration-dependent surfactant phase behavior that is not accessible in conventional (i.e., dilute) conditions. This work provides not only a robust method to synthesize, stabilize, and study identical MSC products, but also uncovers an underappreciated stabilizing interaction between surfactants and clusters.

Mesophase Formation Stabilizes High-purity Magic-sized Clusters

KAUST Repository

Nevers, Douglas R.

2018-01-27

Magic-sized clusters (MSCs) are renowned for their identical size and closed-shell stability that inhibit conventional nanoparticle (NP) growth processes. Though MSCs have been of increasing interest, understanding the reaction pathways toward their nucleation and stabilization is an outstanding issue. In this work, we demonstrate that high concentration synthesis (1000 mM) promotes a well-defined reaction pathway to form high-purity MSCs (>99.9%). The MSCs are resistant to typical growth and dissolution processes. Based on insights from in-situ X-ray scattering analysis, we attribute this stability to the accompanying production of a large, hexagonal organic-inorganic mesophase (>100 nm grain size) that arrests growth of the MSCs and prevents NP growth. At intermediate concentrations (500 mM), the MSC mesophase forms, but is unstable, resulting in NP growth at the expense of the assemblies. These results provide an alternate explanation for the high stability of MSCs. Whereas the conventional mantra has been that the stability of MSCs derives from the precise arrangement of the inorganic structures (i.e., closed-shell atomic packing), we demonstrate that anisotropic clusters can also be stabilized by self-forming fibrous mesophase assemblies. At lower concentration (<200 mM or >16 acid-to-metal), MSCs are further destabilized and NPs formation dominates that of MSCs. Overall, the high concentration approach intensifies and showcases inherent concentration-dependent surfactant phase behavior that is not accessible in conventional (i.e., dilute) conditions. This work provides not only a robust method to synthesize, stabilize, and study identical MSC products, but also uncovers an underappreciated stabilizing interaction between surfactants and clusters.

Small angle neutron scattering measurements of magnetic cluster sizes in magnetic recorging disks

CERN Document Server

Toney, M

2003-01-01

We describe Small Angle Neutron Scattering measurements of the magnetic cluster size distributions for several longitudinal magnetic recording media. We find that the average magnetic cluster size is slightly larger than the average physical grain size, that there is a broad distribution of cluster sizes, and that the cluster size is inversely correlated to the media signal-to-noise ratio. These results show that intergranular magnetic coupling in these media is small and they provide empirical data for the cluster-size distribution that can be incorporated into models of magnetic recording.

Chemisorption on size-selected metal clusters: activation barriers and chemical reactions for deuterium and aluminum cluster ions

International Nuclear Information System (INIS)

Jarrold, M.F.; Bower, J.E.

1988-01-01

The authors describe a new approach to investigating chemisorption on size-selected metal clusters. This approach involves investigating the collision-energy dependence of chemisorption using low-energy ion beam techniques. The method provides a direct measure of the activation barrier for chemisorption and in some cases an estimate of the desorption energy as well. They describe the application of this technique to chemisorption of deuterium on size-selected aluminum clusters. The activation barriers increase with cluster size (from a little over 1 eV for Al 10 + to around 2 eV for Al 27 + ) and show significant odd-even oscillations. The activation barriers for the clusters with an odd number of atoms are larger than those for the even-numbered clusters. In addition to chemisorption of deuterium onto the clusters, chemical reactions were observed, often resulting in cluster fragmentation. The main products observed were Al/sub n-1/D + , Al/sub n-2/ + , and Al + for clusters with n + and Al/sub n-1/D + for the larger clusters

Size estimates of nobel gas clusters by Rayleigh scattering experiments

Institute of Scientific and Technical Information of China (English)

Pinpin Zhu (朱频频); Guoquan Ni (倪国权); Zhizhan Xu (徐至展)

2003-01-01

Noble gases (argon, krypton, and xenon) are puffed into vacuum through a nozzle to produce clusters for studying laser-cluster interactions. Good estimates of the average size of the argon, krypton and xenon clusters are made by carrying out a series of Rayleigh scattering experiments. In the experiments, we have found that the scattered signal intensity varied greatly with the opening area of the pulsed valve. A new method is put forward to choose the appropriate scattered signal and measure the size of Kr cluster.

Self-selection in size and structure in argon clusters formed on amorphous carbon

Energy Technology Data Exchange (ETDEWEB)

Krainyukova, Nina V.; Waal, Benjamin W. van de

2004-07-01

Argon clusters formed on an amorphous carbon substrate as deposited from the vapor phase were studied by means of transmission high energy electron diffraction using the liquid helium cryostat. Electron diffractograms were analysed on the basis of assumption that there exist a cluster size distribution in samples formed on substrate and multi-shell structures such as icosahedra, decahedra, fcc and hcp were probed for different sizes up to {approx}15 000 atoms. The experimental data were considered as a result of a superposition of diffracted intensities from clusters of different sizes and structures. The comparative analysis was based on the R-factor minimization that was found to be equal to 0.014 for the best fit between experiment and modelling. The total size and structure distribution function shows the presence of 'non-crystallographic' structures such as icosahedra and decahedra with five-fold symmetry that was found to prevail and a smaller amount of fcc and hcp structures. Possible growth mechanisms as well as observed general tendency to self-selection in sizes and structures are presumably governed by confined pore-like geometry in an amorphous carbon substrate.

[Electronic and structural properties of individual nanometer-size supported metallic clusters

International Nuclear Information System (INIS)

Reifenberger, R.

1993-01-01

This report summarizes the work performed under contract DOE-FCO2-84ER45162. During the past ten years, our study of electron emission from laser-illuminated field emission tips has taken on a broader scope by addressing problems of direct interest to those concerned with the unique physical and chemical properties of nanometer-size clusters. The work performed has demonstrated that much needed data can be obtained on individual nanometer-size clusters supported on a wide-variety of different substrates. The work was performed in collaboration with R.P. Andres in the School of Chemical Engineering at Purdue University. The Multiple Expansion Cluster Source developed by Andres and his students was essential for producing the nanometer-size clusters studied. The following report features a discussion of these results. This report provides a motivation for studying the properties of nanometer-size clusters and summarizes the results obtained

Subtypes of autism by cluster analysis based on structural MRI data.

Science.gov (United States)

Hrdlicka, Michal; Dudova, Iva; Beranova, Irena; Lisy, Jiri; Belsan, Tomas; Neuwirth, Jiri; Komarek, Vladimir; Faladova, Ludvika; Havlovicova, Marketa; Sedlacek, Zdenek; Blatny, Marek; Urbanek, Tomas

2005-05-01

The aim of our study was to subcategorize Autistic Spectrum Disorders (ASD) using a multidisciplinary approach. Sixty four autistic patients (mean age 9.4+/-5.6 years) were entered into a cluster analysis. The clustering analysis was based on MRI data. The clusters obtained did not differ significantly in the overall severity of autistic symptomatology as measured by the total score on the Childhood Autism Rating Scale (CARS). The clusters could be characterized as showing significant differences: Cluster 1: showed the largest sizes of the genu and splenium of the corpus callosum (CC), the lowest pregnancy order and the lowest frequency of facial dysmorphic features. Cluster 2: showed the largest sizes of the amygdala and hippocampus (HPC), the least abnormal visual response on the CARS, the lowest frequency of epilepsy and the least frequent abnormal psychomotor development during the first year of life. Cluster 3: showed the largest sizes of the caput of the nucleus caudatus (NC), the smallest sizes of the HPC and facial dysmorphic features were always present. Cluster 4: showed the smallest sizes of the genu and splenium of the CC, as well as the amygdala, and caput of the NC, the most abnormal visual response on the CARS, the highest frequency of epilepsy, the highest pregnancy order, abnormal psychomotor development during the first year of life was always present and facial dysmorphic features were always present. This multidisciplinary approach seems to be a promising method for subtyping autism.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

Science.gov (United States)

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

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

Science.gov (United States)

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

2017-08-01

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

Colloidal solutions of luminescent porous silicon clusters with different cluster sizes

Czech Academy of Sciences Publication Activity Database

Herynková, Kateřina; Podkorytov, E.; Šlechta, Miroslav; Cibulka, Ondřej; Leitner, J.; Pelant, Ivan

2014-01-01

Roč. 9, č. 1 (2014), 1-5 ISSN 1931-7573 Institutional support: RVO:68378271 Keywords : nanocrystalline silicon * porous silicon * cluster size * luminescent markers Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 2.524, year: 2012

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

Science.gov (United States)

Kim, Sehwi; Jung, Inkyung

2017-01-01

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

7 CFR 52.1850 - Sizes of raisins with seeds-except layer or cluster.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Sizes of raisins with seeds-except layer or cluster... Raisins 1 Type III-Raisins with Seeds § 52.1850 Sizes of raisins with seeds—except layer or cluster. The sizes of Raisins with Seeds—except for Layer or Cluster Raisins with Seeds, are not incorporated in the...

Location and Size Planning of Distributed Photovoltaic Generation in Distribution network System Based on K-means Clustering Analysis

Science.gov (United States)

Lu, Siqi; Wang, Xiaorong; Wu, Junyong

2018-01-01

The paper presents a method to generate the planning scenarios, which is based on K-means clustering analysis algorithm driven by data, for the location and size planning of distributed photovoltaic (PV) units in the network. Taken the power losses of the network, the installation and maintenance costs of distributed PV, the profit of distributed PV and the voltage offset as objectives and the locations and sizes of distributed PV as decision variables, Pareto optimal front is obtained through the self-adaptive genetic algorithm (GA) and solutions are ranked by a method called technique for order preference by similarity to an ideal solution (TOPSIS). Finally, select the planning schemes at the top of the ranking list based on different planning emphasis after the analysis in detail. The proposed method is applied to a 10-kV distribution network in Gansu Province, China and the results are discussed.

Modulation aware cluster size optimisation in wireless sensor networks

Science.gov (United States)

Sriram Naik, M.; Kumar, Vinay

2017-07-01

Wireless sensor networks (WSNs) play a great role because of their numerous advantages to the mankind. The main challenge with WSNs is the energy efficiency. In this paper, we have focused on the energy minimisation with the help of cluster size optimisation along with consideration of modulation effect when the nodes are not able to communicate using baseband communication technique. Cluster size optimisations is important technique to improve the performance of WSNs. It provides improvement in energy efficiency, network scalability, network lifetime and latency. We have proposed analytical expression for cluster size optimisation using traditional sensing model of nodes for square sensing field with consideration of modulation effects. Energy minimisation can be achieved by changing the modulation schemes such as BPSK, 16-QAM, QPSK, 64-QAM, etc., so we are considering the effect of different modulation techniques in the cluster formation. The nodes in the sensing fields are random and uniformly deployed. It is also observed that placement of base station at centre of scenario enables very less number of modulation schemes to work in energy efficient manner but when base station placed at the corner of the sensing field, it enable large number of modulation schemes to work in energy efficient manner.

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

Science.gov (United States)

Shen, Chung-Wei; Chen, Yi-Hau

2018-03-13

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

Cluster analysis of HZE particle tracks as applied to space radiobiology problems

International Nuclear Information System (INIS)

Batmunkh, M.; Bayarchimeg, L.; Lkhagva, O.; Belov, O.

2013-01-01

A cluster analysis is performed of ionizations in tracks produced by the most abundant nuclei in the charge and energy spectra of the galactic cosmic rays. The frequency distribution of clusters is estimated for cluster sizes comparable to the DNA molecule at different packaging levels. For this purpose, an improved K-mean-based algorithm is suggested. This technique allows processing particle tracks containing a large number of ionization events without setting the number of clusters as an input parameter. Using this method, the ionization distribution pattern is analyzed depending on the cluster size and particle's linear energy transfer

Model catalysis by size-selected cluster deposition

Energy Technology Data Exchange (ETDEWEB)

Anderson, Scott [Univ. of Utah, Salt Lake City, UT (United States)

2015-11-20

This report summarizes the accomplishments during the last four years of the subject grant. Results are presented for experiments in which size-selected model catalysts were studied under surface science and aqueous electrochemical conditions. Strong effects of cluster size were found, and by correlating the size effects with size-dependent physical properties of the samples measured by surface science methods, it was possible to deduce mechanistic insights, such as the factors that control the rate-limiting step in the reactions. Results are presented for CO oxidation, CO binding energetics and geometries, and electronic effects under surface science conditions, and for the electrochemical oxygen reduction reaction, ethanol oxidation reaction, and for oxidation of carbon by water.

An imbalance in cluster sizes does not lead to notable loss of power in cross-sectional, stepped-wedge cluster randomised trials with a continuous outcome.

Science.gov (United States)

Kristunas, Caroline A; Smith, Karen L; Gray, Laura J

2017-03-07

The current methodology for sample size calculations for stepped-wedge cluster randomised trials (SW-CRTs) is based on the assumption of equal cluster sizes. However, as is often the case in cluster randomised trials (CRTs), the clusters in SW-CRTs are likely to vary in size, which in other designs of CRT leads to a reduction in power. The effect of an imbalance in cluster size on the power of SW-CRTs has not previously been reported, nor what an appropriate adjustment to the sample size calculation should be to allow for any imbalance. We aimed to assess the impact of an imbalance in cluster size on the power of a cross-sectional SW-CRT and recommend a method for calculating the sample size of a SW-CRT when there is an imbalance in cluster size. The effect of varying degrees of imbalance in cluster size on the power of SW-CRTs was investigated using simulations. The sample size was calculated using both the standard method and two proposed adjusted design effects (DEs), based on those suggested for CRTs with unequal cluster sizes. The data were analysed using generalised estimating equations with an exchangeable correlation matrix and robust standard errors. An imbalance in cluster size was not found to have a notable effect on the power of SW-CRTs. The two proposed adjusted DEs resulted in trials that were generally considerably over-powered. We recommend that the standard method of sample size calculation for SW-CRTs be used, provided that the assumptions of the method hold. However, it would be beneficial to investigate, through simulation, what effect the maximum likely amount of inequality in cluster sizes would be on the power of the trial and whether any inflation of the sample size would be required.

Melting of size-selected gallium clusters with 60-183 atoms.

Science.gov (United States)

Pyfer, Katheryne L; Kafader, Jared O; Yalamanchali, Anirudh; Jarrold, Martin F

2014-07-10

Heat capacities have been measured as a function of temperature for size-selected gallium cluster cations with between 60 and 183 atoms. Almost all clusters studied show a single peak in the heat capacity that is attributed to a melting transition. The peaks can be fit by a two-state model incorporating only fully solid-like and fully liquid-like species, and hence no partially melted intermediates. The exceptions are Ga90(+), which does not show a peak, and Ga80(+) and Ga81(+), which show two peaks. For the clusters with two peaks, the lower temperature peak is attributed to a structural transition. The melting temperatures for clusters with less than 50 atoms have previously been shown to be hundreds of degrees above the bulk melting point. For clusters with more than 60 atoms the melting temperatures decrease, approaching the bulk value (303 K) at around 95 atoms, and then show several small upward excursions with increasing cluster size. A plot of the latent heat against the entropy change for melting reveals two groups of clusters: the latent heats and entropy changes for clusters with less than 94 atoms are distinct from those for clusters with more than 93 atoms. This observation suggests that a significant change in the nature of the bonding or the structure of the clusters occurs at 93-94 atoms. Even though the melting temperatures are close to the bulk value for the larger clusters studied here, the latent heats and entropies of melting are still far from the bulk values.

Manipulating cluster size of polyanion-stabilized Fe3O4 magnetic nanoparticle clusters via electrostatic-mediated assembly for tunable magnetophoresis behavior

International Nuclear Information System (INIS)

Yeap, Swee Pin; Ahmad, Abdul Latif; Ooi, Boon Seng; Lim, JitKang

2015-01-01

We report in this article an approach for manipulating the size of magnetic nanoparticle clusters (MNCs) via electrostatic-mediated assembly technique using an electrolyte as a clustering agent. The clusters were surface-tethered with poly(sodium 4-styrenesulfonate) (PSS) through electrostatic compensation to enhance their colloidal stability. Dynamic light scattering was employed to trace the evolution of cluster size. Simultaneously, electrophoretic mobility and Fourier transform infrared spectroscopy analyses were conducted to investigate the possible schemes involved in both cluster formation and PSS grafting. Results showed that the average hydrodynamic cluster size of the PSS/MNCs and their corresponding size distributions were successfully shifted by means of manipulating the suspension pH, the ionic nature of the electrolyte, and the electrolyte concentration. More specifically, the electrokinetic behavior of the particles upon interaction with the electrolyte plays a profound role in the formation of the PSS/MNCs. Nonetheless, the solubility of the polymer in electrolyte solution and the purification of the particles from residual ions should not be omitted in determining the effectiveness of this clustering approach. The PSS adlayer makes the resultant entities highly water-dispersible and provides electrosteric stabilization to shield the PSS/MNCs from aggregation. In this study, the experimental observations were analyzed and discussed on the basis of existing fundamental colloidal theories. The strategy of cluster size manipulation proposed here is simple and convenient to implement. Furthermore, manipulating the size of the MNCs also facilitates the tuning of magnetophoresis kinetics on exposure to low magnetic field gradient, which makes this nano-entity useful for engineering applications, specifically in separation processes.

Superresolution Imaging of Aquaporin-4 Cluster Size in Antibody-Stained Paraffin Brain Sections.

Science.gov (United States)

Smith, Alex J; Verkman, Alan S

2015-12-15

The water channel aquaporin-4 (AQP4) forms supramolecular clusters whose size is determined by the ratio of M1- and M23-AQP4 isoforms. In cultured astrocytes, differences in the subcellular localization and macromolecular interactions of small and large AQP4 clusters results in distinct physiological roles for M1- and M23-AQP4. Here, we developed quantitative superresolution optical imaging methodology to measure AQP4 cluster size in antibody-stained paraffin sections of mouse cerebral cortex and spinal cord, human postmortem brain, and glioma biopsy specimens. This methodology was used to demonstrate that large AQP4 clusters are formed in AQP4(-/-) astrocytes transfected with only M23-AQP4, but not in those expressing only M1-AQP4, both in vitro and in vivo. Native AQP4 in mouse cortex, where both isoforms are expressed, was enriched in astrocyte foot-processes adjacent to microcapillaries; clusters in perivascular regions of the cortex were larger than in parenchymal regions, demonstrating size-dependent subcellular segregation of AQP4 clusters. Two-color superresolution imaging demonstrated colocalization of Kir4.1 with AQP4 clusters in perivascular areas but not in parenchyma. Surprisingly, the subcellular distribution of AQP4 clusters was different between gray and white matter astrocytes in spinal cord, demonstrating regional specificity in cluster polarization. Changes in AQP4 subcellular distribution are associated with several neurological diseases and we demonstrate that AQP4 clustering was preserved in a postmortem human cortical brain tissue specimen, but that AQP4 was not substantially clustered in a human glioblastoma specimen despite high-level expression. Our results demonstrate the utility of superresolution optical imaging for measuring the size of AQP4 supramolecular clusters in paraffin sections of brain tissue and support AQP4 cluster size as a primary determinant of its subcellular distribution. Copyright © 2015 Biophysical Society

Reversing Size-Dependent Trends in the Oxidation of Copper Clusters through Support Effects: Reversing Size-Dependent Trends in the Oxidation of Copper Clusters through Support Effects

Energy Technology Data Exchange (ETDEWEB)

Mammen, Nisha [Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, -560064 Bangalore India; Spanu, Leonardo [Shell Technology Center, Shell India Markets Private Limited, -560048 Bangalore India; Tyo, Eric C. [Materials Science Division, Argonne National Laboratory, 60439 Argonne IL USA; Yang, Bing [Materials Science Division, Argonne National Laboratory, 60439 Argonne IL USA; Halder, Avik [Materials Science Division, Argonne National Laboratory, 60439 Argonne IL USA; Seifert, Sönke [X-ray Science Division, Argonne National Laboratory, 60439 Argonne IL USA; Pellin, Michael J. [Materials Science Division, Argonne National Laboratory, 60439 Argonne IL USA; Vajda, Stefan [Materials Science Division, Argonne National Laboratory, 60439 Argonne IL USA; Institute for Molecular Engineering, The University of Chicago, 60637 Chicago IL USA; Narasimhan, Shobhana [Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, -560064 Bangalore India

2017-12-22

Having the ability to tune the oxidation state of Cu nanoparticles is essential for their utility as catalysts. The degree of oxidation that maximizes product yield and selectivity is known to vary, depending on the particular reaction. Using first principles calculations and XANES measurements, we show that for subnanometer sizes in the gas phase, smaller Cu clusters are more resistant to oxidation. However, this trend is reversed upon deposition on an alumina support. We are able to explain this result in terms of strong cluster-support interactions, which differ significantly for the oxidized and elemental clusters. The stable cluster phases also feature novel oxygen stoichiometries. Our results suggest that one can tune the degree of oxidation of Cu catalysts by optimizing not just their size, but also the support they are deposited on.

Cluster analysis for applications

CERN Document Server

Anderberg, Michael R

1973-01-01

Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

Effect of laser spot size on fusion neutron yield in laser–deuterium cluster interactions

International Nuclear Information System (INIS)

Chen Guanglong; Lu Haiyang; Wang Cheng; Liu Jiansheng; Li Ruxin; Ni Guoquan; Xu Zhizhan

2008-01-01

The effect of the laser spot size on the neutron yield of table-top nuclear fusion from explosions of a femtosecond intense laser pulse heated deuterium clusters is investigated by using a simplified model, in which the cluster size distribution and the energy attenuation of the laser as it propagates through the cluster jet are taken into account. It has been found that there exists a proper laser spot size for the maximum fusion neutron yield for a given laser pulse and a specific deuterium gas cluster jet. The proper spot size, which is dependent on the laser parameters and the cluster jet parameters, has been calculated and compared with the available experimental data. A reasonable agreement between the calculated results and the published experimental results is found

Ionic cluster size distributions of swollen nafion/sulfated beta-cyclodextrin membranes characterized by nuclear magnetic resonance cryoporometry.

Science.gov (United States)

Jeon, Jae-Deok; Kwak, Seung-Yeop

2007-08-16

Nafion/sb-CD membranes were prepared by mixing 5 wt% Nafion solution with H+-form sulfated beta-cyclodextrin (sb-CD), and their water uptakes, ion exchange capacities (IECs), and ionic cluster size distributions were measured. Gravimetric and thermogravimetric measurements showed that the water uptake of the membranes increased with increases in their sb-CD content. The IECs of the membrane were measured with acid-base titration and found to increase with increases in the sb-CD content, reaching 0.96 mequiv/g for NC5 ("NCx" denotes a Nafion/sb-CD composite membrane containing x wt% of sb-CD). The cluster-correlation peaks and ionic cluster size distributions of the water-swollen membranes were determined using small-angle X-ray scattering (SAXS) and 1H nuclear magnetic resonance (NMR) cryoporometry, respectively. The SAXS experiments confirmed that increases in the sb-CD content of the membranes shifted the maximum SAXS peaks to lower angles, indicating an increase in the cluster correlation peak. NMR cryoporometry is based on the theory of the melting point depression, Delta Tm, of a liquid confined within a pore, which is dependent on the pore diameter. The melting point depression was determined by analyzing the variation of the NMR signal intensity with temperature. Our analysis of the intensity-temperature (IT) curves showed that the ionic cluster size distribution gradually became broader with increases in the membrane sb-CD content due to the increased water content, indicating an increase in the ionic cluster size. This result indicates that the presence of sb-CD with its many sulfonic acid sites in the Nafion membranes results in increases in the ionic cluster size as well as in the water uptake and the IEC. We conclude that NMR cryoporometry provides a method for determining the ionic cluster size on the nanometer scale in an aqueous environment, which cannot be obtained using other methods.

Pronounced cluster-size effects: gas-phase reactivity of bare vanadium cluster cations V(n)+ (n = 1-7) toward methanol.

Science.gov (United States)

Feyel, Sandra; Schröder, Detlef; Schwarz, Helmut

2009-05-14

Mass spectrometric experiments are used to examine the size-dependent interactions of bare vanadium cluster cations V(n)(+) (n = 1-7) with methanol. The reactivity patterns exhibit enormous size effects throughout the range of clusters investigated. For example, dehydrogenation of methanol to produce V(n)OC(+) is only brought about by clusters with n > or = 3. Atomic vanadium cation V(+) also is reactive, but instead of dehydrogenation of the alcohol, expulsions of either methane or a methyl radical take place. In marked contrast, the reaction efficiency of the dinuclear cluster V(2)(+) is extremely low. For the cluster cations V(n)(+) (n = 3-7), complete and efficient dehydrogenation of methanol to produce V(n)OC(+) and two hydrogen molecules prevails. DFT calculations shed light on the mechanism of the dehydrogenation of methanol by the smallest reactive cluster cation V(3)(+) and propose the occurrence of chemisorption concomitant with C-O bond cleavage rather than adsorption of an intact carbon monoxide molecule by the cluster.

Clustering analysis

International Nuclear Information System (INIS)

Romli

1997-01-01

Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

Cluster analysis

CERN Document Server

Everitt, Brian S; Leese, Morven; Stahl, Daniel

2011-01-01

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

Size-based emphysema cluster analysis on low attenuation area in 3D volumetric CT: comparison with pulmonary functional test

Science.gov (United States)

Lee, Minho; Kim, Namkug; Lee, Sang Min; Seo, Joon Beom; Oh, Sang Young

2015-03-01

To quantify low attenuation area (LAA) of emphysematous regions according to cluster size in 3D volumetric CT data of chronic obstructive pulmonary disease (COPD) patients and to compare these indices with their pulmonary functional test (PFT). Sixty patients with COPD were scanned by a more than 16-multi detector row CT scanner (Siemens Sensation 16 and 64) within 0.75mm collimation. Based on these LAA masks, a length scale analysis to estimate each emphysema LAA's size was performed as follows. At first, Gaussian low pass filter from 30mm to 1mm kernel size with 1mm interval on the mask was performed from large to small size, iteratively. Centroid voxels resistant to the each filter were selected and dilated by the size of the kernel, which was regarded as the specific size emphysema mask. The slopes of area and number of size based LAA (slope of semi-log plot) were analyzed and compared with PFT. PFT parameters including DLco, FEV1, and FEV1/FVC were significantly (all p-value< 0.002) correlated with the slopes (r-values; -0.73, 0.54, 0.69, respectively) and EI (r-values; -0.84, -0.60, -0.68, respectively). In addition, the D independently contributed regression for FEV1 and FEV1/FVC (adjust R sq. of regression study: EI only, 0.70, 0.45; EI and D, 0.71, 0.51, respectively). By the size based LAA segmentation and analysis, we evaluated the Ds of area, number, and distribution of size based LAA, which would be independent factors for predictor of PFT parameters.

[Electronic and structural properties of individual nanometer-size supported metallic clusters]. Final performance report

Energy Technology Data Exchange (ETDEWEB)

Reifenberger, R.

1993-09-01

This report summarizes the work performed under contract DOE-FCO2-84ER45162. During the past ten years, our study of electron emission from laser-illuminated field emission tips has taken on a broader scope by addressing problems of direct interest to those concerned with the unique physical and chemical properties of nanometer-size clusters. The work performed has demonstrated that much needed data can be obtained on individual nanometer-size clusters supported on a wide-variety of different substrates. The work was performed in collaboration with R.P. Andres in the School of Chemical Engineering at Purdue University. The Multiple Expansion Cluster Source developed by Andres and his students was essential for producing the nanometer-size clusters studied. The following report features a discussion of these results. This report provides a motivation for studying the properties of nanometer-size clusters and summarizes the results obtained.

Diffusion of two-dimensional epitaxial clusters on metal (100) surfaces: Facile versus nucleation-mediated behavior and their merging for larger sizes

Science.gov (United States)

Lai, King C.; Liu, Da-Jiang; Evans, James W.

2017-12-01

For diffusion of two-dimensional homoepitaxial clusters of N atoms on metal (100) surfaces mediated by edge atom hopping, macroscale continuum theory suggests that the diffusion coefficient scales like DN˜ N-β with β =3 /2 . However, we find quite different and diverse behavior in multiple size regimes. These include: (i) facile diffusion for small sizes N mediated diffusion with small β 2 for N =Np+1 and Np+2 also for moderate sizes 9 ≤N ≤O (102) ; (iv) merging of the above distinct branches and subsequent anomalous scaling with 1 ≲β analysis must account for a strong enhancement of diffusivity for short time increments due to back correlation in the cluster motion. Further understanding of this enhancement, of anomalous size scaling behavior, and of the merging of various branches, is facilitated by combinatorial analysis of the number of the ground-state and low-lying excited state cluster configurations, and also of kink populations.

Clustering for high-dimension, low-sample size data using distance vectors

OpenAIRE

Terada, Yoshikazu

2013-01-01

In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is not the closeness, but the "values" of distance that contain information of the cluster structure in high-dimensional space. Based on this fact, we propose an efficient and simple clustering approach, called distance vector clustering, for HDLSS data. Under the assumptions given in the work of Hall et al. (2005), w...

Manipulating cluster size of polyanion-stabilized Fe{sub 3}O{sub 4} magnetic nanoparticle clusters via electrostatic-mediated assembly for tunable magnetophoresis behavior

Energy Technology Data Exchange (ETDEWEB)

Yeap, Swee Pin, E-mail: sweepin0727@hotmail.com; Ahmad, Abdul Latif; Ooi, Boon Seng; Lim, JitKang, E-mail: chjitkangl@usm.my [Universiti Sains Malaysia, School of Chemical Engineering (Malaysia)

2015-10-15

We report in this article an approach for manipulating the size of magnetic nanoparticle clusters (MNCs) via electrostatic-mediated assembly technique using an electrolyte as a clustering agent. The clusters were surface-tethered with poly(sodium 4-styrenesulfonate) (PSS) through electrostatic compensation to enhance their colloidal stability. Dynamic light scattering was employed to trace the evolution of cluster size. Simultaneously, electrophoretic mobility and Fourier transform infrared spectroscopy analyses were conducted to investigate the possible schemes involved in both cluster formation and PSS grafting. Results showed that the average hydrodynamic cluster size of the PSS/MNCs and their corresponding size distributions were successfully shifted by means of manipulating the suspension pH, the ionic nature of the electrolyte, and the electrolyte concentration. More specifically, the electrokinetic behavior of the particles upon interaction with the electrolyte plays a profound role in the formation of the PSS/MNCs. Nonetheless, the solubility of the polymer in electrolyte solution and the purification of the particles from residual ions should not be omitted in determining the effectiveness of this clustering approach. The PSS adlayer makes the resultant entities highly water-dispersible and provides electrosteric stabilization to shield the PSS/MNCs from aggregation. In this study, the experimental observations were analyzed and discussed on the basis of existing fundamental colloidal theories. The strategy of cluster size manipulation proposed here is simple and convenient to implement. Furthermore, manipulating the size of the MNCs also facilitates the tuning of magnetophoresis kinetics on exposure to low magnetic field gradient, which makes this nano-entity useful for engineering applications, specifically in separation processes.

Differences in Flower Transcriptome between Grapevine Clones Are Related to Their Cluster Compactness, Fruitfulness, and Berry Size

Directory of Open Access Journals (Sweden)

Jérôme Grimplet

2017-04-01

Full Text Available Grapevine cluster compactness has a clear impact on fruit quality and health status, as clusters with greater compactness are more susceptible to pests and diseases and ripen more asynchronously. Different parameters related to inflorescence and cluster architecture (length, width, branching, etc., fruitfulness (number of berries, number of seeds and berry size (length, width contribute to the final level of compactness. From a collection of 501 clones of cultivar Garnacha Tinta, two compact and two loose clones with stable differences for cluster compactness-related traits were selected and phenotyped. Key organs and developmental stages were selected for sampling and transcriptomic analyses. Comparison of global gene expression patterns in flowers at the end of bloom allowed identification of potential gene networks with a role in determining the final berry number, berry size and ultimately cluster compactness. A large portion of the differentially expressed genes were found in networks related to cell division (carbohydrates uptake, cell wall metabolism, cell cycle, nucleic acids metabolism, cell division, DNA repair. Their greater expression level in flowers of compact clones indicated that the number of berries and the berry size at ripening appear related to the rate of cell replication in flowers during the early growth stages after pollination. In addition, fluctuations in auxin and gibberellin signaling and transport related gene expression support that they play a central role in fruit set and impact berry number and size. Other hormones, such as ethylene and jasmonate may differentially regulate indirect effects, such as defense mechanisms activation or polyphenols production. This is the first transcriptomic based analysis focused on the discovery of the underlying gene networks involved in grapevine traits of grapevine cluster compactness, berry number and berry size.

Differences in Flower Transcriptome between Grapevine Clones Are Related to Their Cluster Compactness, Fruitfulness, and Berry Size.

Science.gov (United States)

Grimplet, Jérôme; Tello, Javier; Laguna, Natalia; Ibáñez, Javier

2017-01-01

Grapevine cluster compactness has a clear impact on fruit quality and health status, as clusters with greater compactness are more susceptible to pests and diseases and ripen more asynchronously. Different parameters related to inflorescence and cluster architecture (length, width, branching, etc.), fruitfulness (number of berries, number of seeds) and berry size (length, width) contribute to the final level of compactness. From a collection of 501 clones of cultivar Garnacha Tinta, two compact and two loose clones with stable differences for cluster compactness-related traits were selected and phenotyped. Key organs and developmental stages were selected for sampling and transcriptomic analyses. Comparison of global gene expression patterns in flowers at the end of bloom allowed identification of potential gene networks with a role in determining the final berry number, berry size and ultimately cluster compactness. A large portion of the differentially expressed genes were found in networks related to cell division (carbohydrates uptake, cell wall metabolism, cell cycle, nucleic acids metabolism, cell division, DNA repair). Their greater expression level in flowers of compact clones indicated that the number of berries and the berry size at ripening appear related to the rate of cell replication in flowers during the early growth stages after pollination. In addition, fluctuations in auxin and gibberellin signaling and transport related gene expression support that they play a central role in fruit set and impact berry number and size. Other hormones, such as ethylene and jasmonate may differentially regulate indirect effects, such as defense mechanisms activation or polyphenols production. This is the first transcriptomic based analysis focused on the discovery of the underlying gene networks involved in grapevine traits of grapevine cluster compactness, berry number and berry size.

Electron impact ionization of size selected hydrogen clusters (H2)N: ion fragment and neutral size distributions.

Science.gov (United States)

Kornilov, Oleg; Toennies, J Peter

2008-05-21

Clusters consisting of normal H2 molecules, produced in a free jet expansion, are size selected by diffraction from a transmission nanograting prior to electron impact ionization. For each neutral cluster (H2)(N) (N=2-40), the relative intensities of the ion fragments Hn+ are measured with a mass spectrometer. H3+ is found to be the most abundant fragment up to N=17. With a further increase in N, the abundances of H3+, H5+, H7+, and H9+ first increase and, after passing through a maximum, approach each other. At N=40, they are about the same and more than a factor of 2 and 3 larger than for H11+ and H13+, respectively. For a given neutral cluster size, the intensities of the ion fragments follow a Poisson distribution. The fragmentation probabilities are used to determine the neutral cluster size distribution produced in the expansion at a source temperature of 30.1 K and a source pressure of 1.50 bar. The distribution shows no clear evidence of a magic number N=13 as predicted by theory and found in experiments with pure para-H2 clusters. The ion fragment distributions are also used to extract information on the internal energy distribution of the H3+ ions produced in the reaction H2+ + H2-->H3+ +H, which is initiated upon ionization of the cluster. The internal energy is assumed to be rapidly equilibrated and to determine the number of molecules subsequently evaporated. The internal energy distribution found in this way is in good agreement with data obtained in an earlier independent merged beam scattering experiment.

Van der Waals coefficients for alkali metal clusters and their size

Indian Academy of Sciences (India)

In this paper we employ the hydrodynamic formulation of time-dependent density functional theory to obtain the van der Waals coefficients 6 and 8 of alkali metal clusters of various sizes including very large clusters. Such calculations become computationally very demanding in the orbital-based Kohn-Sham formalism, ...

Using cluster analysis to organize and explore regional GPS velocities

Science.gov (United States)

Simpson, Robert W.; Thatcher, Wayne; Savage, James C.

2012-01-01

Cluster analysis offers a simple visual exploratory tool for the initial investigation of regional Global Positioning System (GPS) velocity observations, which are providing increasingly precise mappings of actively deforming continental lithosphere. The deformation fields from dense regional GPS networks can often be concisely described in terms of relatively coherent blocks bounded by active faults, although the choice of blocks, their number and size, can be subjective and is often guided by the distribution of known faults. To illustrate our method, we apply cluster analysis to GPS velocities from the San Francisco Bay Region, California, to search for spatially coherent patterns of deformation, including evidence of block-like behavior. The clustering process identifies four robust groupings of velocities that we identify with four crustal blocks. Although the analysis uses no prior geologic information other than the GPS velocities, the cluster/block boundaries track three major faults, both locked and creeping.

Marketing research cluster analysis

OpenAIRE

Marić Nebojša

2002-01-01

One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

Marketing research cluster analysis

Directory of Open Access Journals (Sweden)

Marić Nebojša

2002-01-01

Full Text Available One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

Sizing the star cluster population of the Large Magellanic Cloud

Science.gov (United States)

Piatti, Andrés E.

2018-04-01

The number of star clusters that populate the Large Magellanic Cloud (LMC) at deprojected distances knowledge of the LMC cluster formation and dissolution histories, we closely revisited such a compilation of objects and found that only ˜35 per cent of the previously known catalogued clusters have been included. The remaining entries are likely related to stellar overdensities of the LMC composite star field, because there is a remarkable enhancement of objects with assigned ages older than log(t yr-1) ˜ 9.4, which contrasts with the existence of the LMC cluster age gap; the assumption of a cluster formation rate similar to that of the LMC star field does not help to conciliate so large amount of clusters either; and nearly 50 per cent of them come from cluster search procedures known to produce more than 90 per cent of false detections. The lack of further analyses to confirm the physical reality as genuine star clusters of the identified overdensities also glooms those results. We support that the actual size of the LMC main body cluster population is close to that previously known.

Lack of Dependence of the Sizes of the Mesoscopic Protein Clusters on Electrostatics.

Science.gov (United States)

Vorontsova, Maria A; Chan, Ho Yin; Lubchenko, Vassiliy; Vekilov, Peter G

2015-11-03

Protein-rich clusters of steady submicron size and narrow size distribution exist in protein solutions in apparent violation of the classical laws of phase equilibrium. Even though they contain a minor fraction of the total protein, evidence suggests that they may serve as essential precursors for the nucleation of ordered solids such as crystals, sickle-cell hemoglobin polymers, and amyloid fibrils. The cluster formation mechanism remains elusive. We use the highly basic protein lysozyme at nearly neutral and lower pH as a model and explore the response of the cluster population to the electrostatic forces, which govern numerous biophysical phenomena, including crystallization and fibrillization. We tune the strength of intermolecular electrostatic forces by varying the solution ionic strength I and pH and find that despite the weaker repulsion at higher I and pH, the cluster size remains constant. Cluster responses to the presence of urea and ethanol demonstrate that cluster formation is controlled by hydrophobic interactions between the peptide backbones, exposed to the solvent after partial protein unfolding that may lead to transient protein oligomers. These findings reveal that the mechanism of the mesoscopic clusters is fundamentally different from those underlying the two main classes of ordered protein solid phases, crystals and amyloid fibrils, and partial unfolding of the protein chain may play a significant role. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

An Experimental Observation of Axial Variation of Average Size of Methane Clusters in a Gas Jet

International Nuclear Information System (INIS)

Ji-Feng, Han; Chao-Wen, Yang; Jing-Wei, Miao; Jian-Feng, Lu; Meng, Liu; Xiao-Bing, Luo; Mian-Gong, Shi

2010-01-01

Axial variation of average size of methane clusters in a gas jet produced by supersonic expansion of methane through a cylindrical nozzle of 0.8 mm in diameter is observed using a Rayleigh scattering method. The scattered light intensity exhibits a power scaling on the backing pressure ranging from 16 to 50 bar, and the power is strongly Z dependent varying from 8.4 (Z = 3 mm) to 5.4 (Z = 11 mm), which is much larger than that of the argon cluster. The scattered light intensity versus axial position shows that the position of 5 mm has the maximum signal intensity. The estimation of the average cluster size on axial position Z indicates that the cluster growth process goes forward until the maximum average cluster size is reached at Z = 9 mm, and the average cluster size will decrease gradually for Z > 9 mm

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

Science.gov (United States)

Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

Science.gov (United States)

Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

2017-12-01

Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

Energy Technology Data Exchange (ETDEWEB)

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

International Nuclear Information System (INIS)

Lalonde, Michel; Wassenaar, Richard; Wells, R. Glenn; Birnie, David; Ruddy, Terrence D.

2014-01-01

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

Comprehensive cluster analysis with Transitivity Clustering.

Science.gov (United States)

Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

2011-03-01

Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2006-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2008-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Size selected metal clusters

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. The Optical Absorption Spectra of Small Silver Clusters (5-11) ... Soft Landing and Fragmentation of Small Clusters Deposited in Noble-Gas Films. Harbich, W.; Fedrigo, S.; Buttet, J. Phys. Rev. B 1998, 58, 7428. CO combustion on supported gold clusters. Arenz M ...

[Cluster analysis in biomedical researches].

Science.gov (United States)

Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

2013-01-01

Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

Advanced analysis of forest fire clustering

Science.gov (United States)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index

Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

Cluster cosmological analysis with X ray instrumental observables: introduction and testing of AsPIX method

International Nuclear Information System (INIS)

Valotti, Andrea

2016-01-01

Cosmology is one of the fundamental pillars of astrophysics, as such it contains many unsolved puzzles. To investigate some of those puzzles, we analyze X-ray surveys of galaxy clusters. These surveys are possible thanks to the bremsstrahlung emission of the intra-cluster medium. The simultaneous fit of cluster counts as a function of mass and distance provides an independent measure of cosmological parameters such as Ω m , σ s , and the dark energy equation of state w0. A novel approach to cosmological analysis using galaxy cluster data, called top-down, was developed in N. Clerc et al. (2012). This top-down approach is based purely on instrumental observables that are considered in a two-dimensional X-ray color-magnitude diagram. The method self-consistently includes selection effects and scaling relationships. It also provides a means of bypassing the computation of individual cluster masses. My work presents an extension of the top-down method by introducing the apparent size of the cluster, creating a three-dimensional X-ray cluster diagram. The size of a cluster is sensitive to both the cluster mass and its angular diameter, so it must also be included in the assessment of selection effects. The performance of this new method is investigated using a Fisher analysis. In parallel, I have studied the effects of the intrinsic scatter in the cluster size scaling relation on the sample selection as well as on the obtained cosmological parameters. To validate the method, I estimate uncertainties of cosmological parameters with MCMC method Amoeba minimization routine and using two simulated XMM surveys that have an increasing level of complexity. The first simulated survey is a set of toy catalogues of 100 and 10000 deg 2 , whereas the second is a 1000 deg 2 catalogue that was generated using an Aardvark semi-analytical N-body simulation. This comparison corroborates the conclusions of the Fisher analysis. In conclusion, I find that a cluster diagram that accounts

Detection of secondary structure elements in proteins by hydrophobic cluster analysis.

Science.gov (United States)

Woodcock, S; Mornon, J P; Henrissat, B

1992-10-01

Hydrophobic cluster analysis (HCA) is a protein sequence comparison method based on alpha-helical representations of the sequences where the size, shape and orientation of the clusters of hydrophobic residues are primarily compared. The effectiveness of HCA has been suggested to originate from its potential ability to focus on the residues forming the hydrophobic core of globular proteins. We have addressed the robustness of the bidimensional representation used for HCA in its ability to detect the regular secondary structure elements of proteins. Various parameters have been studied such as those governing cluster size and limits, the hydrophobic residues constituting the clusters as well as the potential shift of the cluster positions with respect to the position of the regular secondary structure elements. The following results have been found to support the alpha-helical bidimensional representation used in HCA: (i) there is a positive correlation (clearly above background noise) between the hydrophobic clusters and the regular secondary structure elements in proteins; (ii) the hydrophobic clusters are centred on the regular secondary structure elements; (iii) the pitch of the helical representation which gives the best correspondence is that of an alpha-helix. The correspondence between hydrophobic clusters and regular secondary structure elements suggests a way to implement variable gap penalties during the automatic alignment of protein sequences.

A Binary System in the Hyades Cluster Hosting a Neptune-Sized Planet

Science.gov (United States)

Feinstein, Adina; Ciardi, David; Crossfield, Ian; Schlieder, Joshua; Petigura, Erik; David, Trevor J.; Bristow, Makennah; Patel, Rahul; Arnold, Lauren; Benneke, Björn; Christiansen, Jessie; Dressing, Courtney; Fulton, Benjamin; Howard, Andrew; Isaacson, Howard; Sinukoff, Evan; Thackeray, Beverly

2018-01-01

We report the discovery of a Neptune-size planet (Rp = 3.0Rearth) in the Hyades Cluster. The host star is in a binary system, comprising a K5V star and M7/8V star with a projected separation of 40 AU. The planet orbits the primary star with an orbital period of 17.3 days and a transit duration of 3 hours. The host star is bright (V = 11.2, J = 9.1) and so may be a good target for precise radial velocity measurements. The planet is the first Neptune-sized planet to be found orbiting in a binary system within an open cluster. The Hyades is the nearest star cluster to the Sun, has an age of 625-750 Myr, and forms one of the fundamental rungs in the distance ladder; understanding the planet population in such a well-studied cluster can help us understand and set contraints on the formation and evolution of planetary systems.

Atomic size effect on the formation of ionized cluster beam epitaxy in Lennard-Jones systems

International Nuclear Information System (INIS)

Hsieh Horngming; Averback, R.S.

1991-01-01

Ionized cluster beam deposition is studied by molecular dynamics simulations in which the atomic size of incident cluster atoms is different from the size of substrate atoms. Lennard-Jones interatomic potentials are used for the two-component system. The results provide the morphologies of the overlayers for various atomic sizes and are compared to simulation results of molecular beam epitaxy. (orig.)

Diffusion of two-dimensional epitaxial clusters on metal (100) surfaces: Facile versus nucleation-mediated behavior and their merging for larger sizes

International Nuclear Information System (INIS)

Lai, King C.; Liu, Da-Jiang; Evans, James W.

2017-01-01

For diffusion of two-dimensional homoepitaxial clusters of N atoms on metal(100) surfaces mediated by edge atom hopping, macroscale continuum theory suggests that the diffusion coefficient scales like DN ~ N -β with β = 3/2. However, we find quite different and diverse behavior in multiple size regimes. These include: (i) facile diffusion for small sizes N < 9; (ii) slow nucleation-mediated diffusion with small β < 1 for “perfect” sizes N = N p = L 2 or L(L+1), for L = 3, 4,… having unique ground state shapes, for moderate sizes 9 ≤ N ≤ O(10 2 ); the same also applies for N = N p +3, N p + 4,… (iii) facile diffusion but with large β > 2 for N = Np + 1 and N p + 2 also for moderate sizes 9 ≤ N ≤ O(10 2 ); (iv) merging of the above distinct branches and subsequent anomalous scaling with 1 ≲ β < 3/2, reflecting the quasi-facetted structure of clusters, for larger N = O(10 2 ) to N = O(10 3 ); and (v) classic scaling with β = 3/2 for very large N = O(103) and above. The specified size ranges apply for typical model parameters. We focus on the moderate size regime where show that diffusivity cycles quasi-periodically from the slowest branch for N p + 3 (not Np) to the fastest branch for Np + 1. Behavior is quantified by Kinetic Monte Carlo simulation of an appropriate stochastic lattice-gas model. However, precise analysis must account for a strong enhancement of diffusivity for short time increments due to back-correlation in the cluster motion. Further understanding of this enhancement, of anomalous size scaling behavior, and of the merging of various branches, is facilitated by combinatorial analysis of the number of the ground state and low-lying excited state cluster configurations, and also of kink populations.

Fischer–Tropsch Synthesis at a Low Pressure on Subnanometer Cobalt Oxide Clusters: The Effect of Cluster Size and Support on Activity and Selectivity

Energy Technology Data Exchange (ETDEWEB)

Lee, Sungsik; Lee, Byeongdu; Seifert, Sönke; Winans, Randall E.; Vajda, Stefan

2015-05-21

In this study, the catalytic activity and changes in the oxidation state during the Fischer Tropsch (FT) reaction was investigated on subnanometer size-selected cobalt clusters deposited on oxide (Al2O3, MgO) and carbon-based (ultrananocrystalline diamond UNCD) supports by temperature programmed reaction (TPRx) combined with in-situ grazing-incidence X-ray absorption characterization (GIXAS). The activity and selectivity of ultrasmall cobalt clusters exhibits a very strong dependence on cluster size and support. The evolution of the oxidation state of metal cluster during the reaction reveals that metal-support interaction plays a key role in the reaction.

A DFT study of arsine adsorption on palladium doped graphene: Effects of palladium cluster size

International Nuclear Information System (INIS)

Kunaseth, Manaschai; Mudchimo, Tanabat; Namuangruk, Supawadee; Kungwan, Nawee; Promarak, Vinich; Jungsuttiwong, Siriporn

2016-01-01

Graphical abstract: The relationship between charge difference and adsorption strength demonstrates that charge migration from Pd_n-SDG to AsH_x significantly enhanced adsorption strength, the Pd_6 clusters doped SDG with a steep slope is recommended as a superior adsorbent material for AsH_3 removal from gas stream. - Highlights: • Pd atom and Pd clusters bind strongly onto the defective graphene surface. • Larger size of Pd cluster adsorbs arsine and its hydrogenated products stronger. • Order of adsorption strength on Pd_n doped graphene: As > AsH > AsH_2 > > AsH_3. • Charge migration characterizes the strong adsorption of AsH_2, AsH, and As. • Pd cluster doped graphene is thermodynamically preferable for arsine removal. - Abstract: In this study, we have investigated the size effects of palladium (Pd) doped single-vacancy defective graphene (SDG) surface to the adsorption of AsH_3 and its dehydrogenated products on Pd using density functional theory calculations. Here, Pd cluster binding study revealed that Pd_6 nanocluster bound strongest to the SDG surface, while adsorption of AsH_x (x = 0–3) on the most stable Pd_n doped SDG showed that dehydrogenated arsine compounds adsorbed onto the surface stronger than the pristine AsH_3 molecule. Charge analysis revealed that considerable amount of charge migration from Pd to dehydrogenated arsine molecules after adsorption may constitute strong adsorption for dehydrogenated arsine. In addition, study of thermodynamic pathways of AsH_3 dehydrogenation on Pd_n doped SDG adsorbents indicated that Pd cluster doping on SDG adsorbent tends to be thermodynamically favorable for AsH_3 decomposition than the single-Pd atom doped SDG. Hence, our study has indicated that Pd_6 clusters doped SDG is more advantageous as adsorbent material for AsH_3 removal.

Observation of propane cluster size distributions during nucleation and growth in a Laval expansion

Energy Technology Data Exchange (ETDEWEB)

Ferreiro, Jorge J.; Chakrabarty, Satrajit; Schläppi, Bernhard; Signorell, Ruth [Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog Weg 2, CH-8093 Zürich (Switzerland)

2016-12-07

We report on molecular-level studies of the condensation of propane gas and propane/ethane gas mixtures in the uniform (constant pressure and temperature) postnozzle flow of Laval expansions using soft single-photon ionization by vacuum ultraviolet light and mass spectrometric detection. The whole process, from the nucleation to the growth to molecular aggregates of sizes of several nanometers (∼5 nm), can be monitored at the molecular level with high time-resolution (∼3 μs) for a broad range of pressures and temperatures. For each time, pressure, and temperature, a whole mass spectrum is recorded, which allows one to determine the critical cluster size range for nucleation as well as the kinetics and mechanisms of cluster-size specific growth. The detailed information about the size, composition, and population of individual molecular clusters upon condensation provides unique experimental data for comparison with future molecular-level simulations.

THE SIZE DIFFERENCE BETWEEN RED AND BLUE GLOBULAR CLUSTERS IS NOT DUE TO PROJECTION EFFECTS

International Nuclear Information System (INIS)

Webb, Jeremy J.; Harris, William E.; Sills, Alison

2012-01-01

Metal-rich (red) globular clusters in massive galaxies are, on average, smaller than metal-poor (blue) globular clusters. One of the possible explanations for this phenomenon is that the two populations of clusters have different spatial distributions. We test this idea by comparing clusters observed in unusually deep, high signal-to-noise images of M87 with a simulated globular cluster population in which the red and blue clusters have different spatial distributions, matching the observations. We compare the overall distribution of cluster effective radii as well as the relationship between effective radius and galactocentric distance for both the observed and simulated red and blue sub-populations. We find that the different spatial distributions does not produce a significant size difference between the red and blue sub-populations as a whole or at a given galactocentric distance. These results suggest that the size difference between red and blue globular clusters is likely due to differences during formation or later evolution.

The Size Difference between Red and Blue Globular Clusters is not due to Projection Effects

Science.gov (United States)

Webb, Jeremy J.; Harris, William E.; Sills, Alison

2012-11-01

Metal-rich (red) globular clusters in massive galaxies are, on average, smaller than metal-poor (blue) globular clusters. One of the possible explanations for this phenomenon is that the two populations of clusters have different spatial distributions. We test this idea by comparing clusters observed in unusually deep, high signal-to-noise images of M87 with a simulated globular cluster population in which the red and blue clusters have different spatial distributions, matching the observations. We compare the overall distribution of cluster effective radii as well as the relationship between effective radius and galactocentric distance for both the observed and simulated red and blue sub-populations. We find that the different spatial distributions does not produce a significant size difference between the red and blue sub-populations as a whole or at a given galactocentric distance. These results suggest that the size difference between red and blue globular clusters is likely due to differences during formation or later evolution.

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

Science.gov (United States)

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.

Exchange bias in finite sized NiO nanoparticles with Ni clusters

International Nuclear Information System (INIS)

Gandhi, Ashish Chhaganlal; Lin, Jauyn Grace

2017-01-01

Structural and magnetic properties of finite sized NiO nanoparticles are investigated with synchrotron X-ray diffraction (XRD), transmission electron microscopy, magnetometer and ferromagnetic resonance (FMR) spectroscopy. A minor Ni phase is detected with synchrotron XRD, attributed to the oxygen defects in the NiO core. A considerable exchange bias of ~100 Oe is observed at 50 K and it drops abruptly and vanishes above 150 K, in association with the reduction of frozen spins. FMR data indicate a strong interaction between ferromagnetic (FM) and antiferromagnetic (AFM) phases below 150 K, consistent with the picture of isolated FM clusters in AFM matrix. - Highlights: • Structural and magnetic properties of finite sized NiO nanoparticles are systematically investigated with several advanced techniques. • A strong interaction between ferromagnetic and antiferromagnetic phases is found below 150 K. • Exchange bias field in finite sized NiO nanoparticles is due to anisotropy energy of Ni clusters over riding the domain wall energy of NiO.

Exchange bias in finite sized NiO nanoparticles with Ni clusters

Energy Technology Data Exchange (ETDEWEB)

Gandhi, Ashish Chhaganlal; Lin, Jauyn Grace, E-mail: jglin@ntu.edu.tw

2017-02-15

Structural and magnetic properties of finite sized NiO nanoparticles are investigated with synchrotron X-ray diffraction (XRD), transmission electron microscopy, magnetometer and ferromagnetic resonance (FMR) spectroscopy. A minor Ni phase is detected with synchrotron XRD, attributed to the oxygen defects in the NiO core. A considerable exchange bias of ~100 Oe is observed at 50 K and it drops abruptly and vanishes above 150 K, in association with the reduction of frozen spins. FMR data indicate a strong interaction between ferromagnetic (FM) and antiferromagnetic (AFM) phases below 150 K, consistent with the picture of isolated FM clusters in AFM matrix. - Highlights: • Structural and magnetic properties of finite sized NiO nanoparticles are systematically investigated with several advanced techniques. • A strong interaction between ferromagnetic and antiferromagnetic phases is found below 150 K. • Exchange bias field in finite sized NiO nanoparticles is due to anisotropy energy of Ni clusters over riding the domain wall energy of NiO.

Common Factor Analysis Versus Principal Component Analysis: Choice for Symptom Cluster Research

Directory of Open Access Journals (Sweden)

Hee-Ju Kim, PhD, RN

2008-03-01

Conclusion: If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research, CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

Arrays of Size-Selected Metal Nanoparticles Formed by Cluster Ion Beam Technique

DEFF Research Database (Denmark)

Ceynowa, F. A.; Chirumamilla, Manohar; Zenin, Volodymyr

2018-01-01

Deposition of size-selected copper and silver nanoparticles (NPs) on polymers using cluster beam technique is studied. It is shown that ratio of particle embedment in the film can be controlled by simple thermal annealing. Combining electron beam lithography, cluster beam deposition, and heat...... with required configurations which can be applied for wave-guiding, resonators, in sensor technologies, and surface enhanced Raman scattering....

Effects of solvation shells and cluster size on the reaction of aluminum clusters with water

Directory of Open Access Journals (Sweden)

Weiwei Mou

2011-12-01

Full Text Available Reaction of aluminum clusters, Aln (n = 16, 17 and 18, with liquid water is investigated using quantum molecular dynamics simulations, which show rapid production of hydrogen molecules assisted by proton transfer along a chain of hydrogen bonds (H-bonds between water molecules, i.e. Grotthuss mechanism. The simulation results provide answers to two unsolved questions: (1 What is the role of a solvation shell formed by non-reacting H-bonds surrounding the H-bond chain; and (2 whether the high size-selectivity observed in gas-phase Aln-water reaction persists in liquid phase? First, the solvation shell is found to play a crucial role in facilitating proton transfer and hence H2 production. Namely, it greatly modifies the energy barrier, generally to much lower values (< 0.1 eV. Second, we find that H2 production by Aln in liquid water does not depend strongly on the cluster size, in contrast to the existence of magic numbers in gas-phase reaction. This paper elucidates atomistic mechanisms underlying these observations.

Size effects in van der Waals clusters studied by spin and angle-resolved electron spectroscopy and multi-coincidence ion imaging

International Nuclear Information System (INIS)

Rolles, D; Pesic, Z D; Zhang, H; Bilodeau, R C; Bozek, J D; Berrah, N

2007-01-01

We have studied the valence and inner-shell photoionization of free rare-gas clusters by means of angle and spin resolved photoelectron spectroscopy and momentum resolving electron-multi-ion coincidence spectroscopy. The electron measurements probe the evolution of the photoelectron angular distribution and spin polarization parameters as a function of photon energy and cluster size, and reveal a strong cluster size dependence of the photoelectron angular distributions in certain photon energy regions. In contrast, the spin polarization parameter of the cluster photoelectrons is found to be very close to the atomic value for all covered photon energies and cluster sizes. The ion imaging measurements, which probe the fragmentation dynamics of multiply charged van der Waals clusters, also exhibit a pronounced cluster size dependence

A DFT study of arsine adsorption on palladium doped graphene: Effects of palladium cluster size

Energy Technology Data Exchange (ETDEWEB)

Kunaseth, Manaschai, E-mail: manaschai@nanotec.or.th [National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA) , Pathum Thani 12120 (Thailand); Mudchimo, Tanabat [Department of Chemistry and Center of Excellence for Innovation in Chemistry, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani 34190 (Thailand); Namuangruk, Supawadee [National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA) , Pathum Thani 12120 (Thailand); Kungwan, Nawee [Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai 50200 (Thailand); Promarak, Vinich [Department of Material Science and Engineering, School of Molecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology, Rayong 21201 (Thailand); Jungsuttiwong, Siriporn, E-mail: siriporn.j@ubu.ac.th [Department of Chemistry and Center of Excellence for Innovation in Chemistry, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani 34190 (Thailand)

2016-03-30

Graphical abstract: The relationship between charge difference and adsorption strength demonstrates that charge migration from Pd{sub n}-SDG to AsH{sub x} significantly enhanced adsorption strength, the Pd{sub 6} clusters doped SDG with a steep slope is recommended as a superior adsorbent material for AsH{sub 3} removal from gas stream. - Highlights: • Pd atom and Pd clusters bind strongly onto the defective graphene surface. • Larger size of Pd cluster adsorbs arsine and its hydrogenated products stronger. • Order of adsorption strength on Pd{sub n} doped graphene: As > AsH > AsH{sub 2} > > AsH{sub 3}. • Charge migration characterizes the strong adsorption of AsH{sub 2}, AsH, and As. • Pd cluster doped graphene is thermodynamically preferable for arsine removal. - Abstract: In this study, we have investigated the size effects of palladium (Pd) doped single-vacancy defective graphene (SDG) surface to the adsorption of AsH{sub 3} and its dehydrogenated products on Pd using density functional theory calculations. Here, Pd cluster binding study revealed that Pd{sub 6} nanocluster bound strongest to the SDG surface, while adsorption of AsH{sub x} (x = 0–3) on the most stable Pd{sub n} doped SDG showed that dehydrogenated arsine compounds adsorbed onto the surface stronger than the pristine AsH{sub 3} molecule. Charge analysis revealed that considerable amount of charge migration from Pd to dehydrogenated arsine molecules after adsorption may constitute strong adsorption for dehydrogenated arsine. In addition, study of thermodynamic pathways of AsH{sub 3} dehydrogenation on Pd{sub n} doped SDG adsorbents indicated that Pd cluster doping on SDG adsorbent tends to be thermodynamically favorable for AsH{sub 3} decomposition than the single-Pd atom doped SDG. Hence, our study has indicated that Pd{sub 6} clusters doped SDG is more advantageous as adsorbent material for AsH{sub 3} removal.

Integrative cluster analysis in bioinformatics

CERN Document Server

Abu-Jamous, Basel; Nandi, Asoke K

2015-01-01

Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review o

Effects of incident cluster size, substrate temperature, and incident energy on bombardment of Ni clusters onto Cu (0 0 1) surface studied using molecular dynamics simulation

International Nuclear Information System (INIS)

Lin, Shiang-Jiun; Wu, Cheng-Da; Fang, Te-Hua; Chen, Guan-Hung

2012-01-01

The bombardment process of a Ni cluster onto a Cu (0 0 1) surface is studied using molecular dynamics (MD) simulations based on the tight-binding second-moment approximation (TB-SMA) many-body potential. The effects of incident cluster size, substrate temperature, and incident energy are evaluated in terms of molecular trajectories, kinetic energy, stress, self-diffusion coefficient, and sputtering yield. The simulation results clearly show that the penetration depth and Cu surface damage increase with increasing incident cluster size for a given incident energy per atom. The self-diffusion coefficient and the penetration depth of a cluster significantly increase with increasing substrate temperature. An incident cluster can be scattered into molecules or atoms that become embedded in the surface after incidence. When the incident energy is increased, the number of volcano-like defects and the penetration depth increase. A high sputtering yield can be obtained by increasing the incident energy at high temperature. The sputtering yield significantly increases with cluster size when the incident energy is above 5 eV/atom.

Mechanistic study on lowering the sensitivity of positive atmospheric pressure photoionization mass spectrometric analyses: size-dependent reactivity of solvent clusters.

Science.gov (United States)

Ahmed, Arif; Choi, Cheol Ho; Kim, Sunghwan

2015-11-15

Understanding the mechanism of atmospheric pressure photoionization (APPI) is important for studies employing APPI liquid chromatography/mass spectrometry (LC/MS). In this study, the APPI mechanism for polyaromatic hydrocarbon (PAH) compounds dissolved in toluene and methanol or water mixture was investigated by use of MS analysis and quantum mechanical simulation. In particular, four different mechanisms that could contribute to the signal reduction were considered based on a combination of MS data and quantum mechanical calculations. The APPI mechanism is clarified by combining MS data and density functional theory (DFT) calculations. To obtain MS data, a positive-mode (+) APPI Q Exactive Orbitrap mass spectrometer was used to analyze each solution. DFT calculations were performed using the general atomic and molecular electronic structure system (GAMESS). The experimental results indicated that methanol significantly reduced the signal in (+) APPI, but no significative signal reduction was observed when water was used as a co-solvent with toluene. The signal reduction is more significant especially for molecular ions than for protonated ions. Therefore, important information about the mechanism of methanol-induced signal reduction in (+) APPI-MS can be gained due its negative impact on APPI efficiency. The size-dependent reactivity of methanol clusters ((CH3 OH)n , n = 1-8) is an important factor in determining the sensitivity of (+) APPI-MS analyses. Clusters can compete with toluene radical ions for electrons. The reactivity increases as the sizes of the methanol clusters increase and this effect can be caused by the size-dependent ionization energy of the solvent clusters. The resulting increase in cluster reactivity explains the flow rate and temperature-dependent signal reduction observed in the analytes. Based on the results presented here, minimizing the sizes of methanol clusters can improve the sensitivity of LC/(+)-APPI-MS. Copyright © 2015 John

Exploring the Potential of Different-Sized Supported Subnanometer Pt Clusters as Catalysts for Wet Chemical Applications

KAUST Repository

Rondelli, Manuel

2017-05-10

The use of physicochemical preparation techniques of metal clusters in the ultrahigh vacuum (UHV) allows for high control of cluster nuclearity and size distribution for fundamental studies in catalysis. Surprisingly, the potential of these systems as catalysts for organic chemistry transformations in solution has not been explored. To this end, single Pt atoms and Pt clusters with two narrow size distributions were prepared in the UHV and applied for the hydrogenation of p-chloronitrobenzene to p-chloroaniline in ethanol. Following the observation of very high catalytic turnovers (approaching the million molecules of p-nitroaniline formed per Pt cluster) and of size-dependent activity, this work addresses fundamental questions with respect to the suitability of these systems as heterogeneous catalysts for the conversion of solution-phase reagents. For this purpose, we employ scanning transmission electron microscopy (STEM) and X-ray photoelectron spectroscopy (XPS) characterization before and after reaction to assess the stability of the clusters on the support and the question of heterogeneity versus homogeneity in the catalytic process.

Sample size estimation to substantiate freedom from disease for clustered binary data with a specific risk profile

DEFF Research Database (Denmark)

Kostoulas, P.; Nielsen, Søren Saxmose; Browne, W. J.

2013-01-01

and power when applied to these groups. We propose the use of the variance partition coefficient (VPC), which measures the clustering of infection/disease for individuals with a common risk profile. Sample size estimates are obtained separately for those groups that exhibit markedly different heterogeneity......, thus, optimizing resource allocation. A VPC-based predictive simulation method for sample size estimation to substantiate freedom from disease is presented. To illustrate the benefits of the proposed approach we give two examples with the analysis of data from a risk factor study on Mycobacterium avium...

Magnetron sputtering cluster apparatus for formation and deposition of size-selected metal nanoparticles

DEFF Research Database (Denmark)

Hanif, Muhammad; Popok, Vladimir

2015-01-01

selection is achieved using an electrostatic quadrupole mass selector. The deposited silver clusters are studied using atomic force microscopy. The height distributions show typical relative standard size deviation of 9-13% for given sizes in the range between 5-23 nm. Thus, the apparatus demonstrates good...... capability in formation of supported size-selected metal nanoparticles with controllable coverage for various practical applications....

Application of a parallel genetic algorithm to the global optimization of medium-sized Au-Pd sub-nanometre clusters

Science.gov (United States)

Hussein, Heider A.; Demiroglu, Ilker; Johnston, Roy L.

2018-02-01

To contribute to the discussion of the high activity and reactivity of Au-Pd system, we have adopted the BPGA-DFT approach to study the structural and energetic properties of medium-sized Au-Pd sub-nanometre clusters with 11-18 atoms. We have examined the structural behaviour and stability as a function of cluster size and composition. The study suggests 2D-3D crossover points for pure Au clusters at 14 and 16 atoms, whereas pure Pd clusters are all found to be 3D. For Au-Pd nanoalloys, the role of cluster size and the influence of doping were found to be extensive and non-monotonic in altering cluster structures. Various stability criteria (e.g. binding energies, second differences in energy, and mixing energies) are used to evaluate the energetics, structures, and tendency of segregation in sub-nanometre Au-Pd clusters. HOMO-LUMO gaps were calculated to give additional information on cluster stability and a systematic homotop search was used to evaluate the energies of the generated global minima of mono-substituted clusters and the preferred doping sites, as well as confirming the validity of the BPGA-DFT approach.

Cluster analysis for the probability of DSB site induced by electron tracks

Energy Technology Data Exchange (ETDEWEB)

Yoshii, Y. [Biological Research, Education and Instrumentation Center, Sapporo Medical University, Sapporo 060-8556 (Japan); Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan); Sasaki, K. [Faculty of Health Sciences, Hokkaido University of Science, Sapporo 006-8585 (Japan); Matsuya, Y. [Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan); Date, H., E-mail: date@hs.hokudai.ac.jp [Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan)

2015-05-01

To clarify the influence of bio-cells exposed to ionizing radiations, the densely populated pattern of the ionization in the cell nucleus is of importance because it governs the extent of DNA damage which may lead to cell lethality. In this study, we have conducted a cluster analysis of ionization and excitation events to estimate the number of double-strand breaks (DSBs) induced by electron tracks. A Monte Carlo simulation for electrons in liquid water was performed to determine the spatial location of the ionization and excitation events. The events were divided into clusters by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The algorithm enables us to sort out the events into the groups (clusters) in which a minimum number of neighboring events are contained within a given radius. For evaluating the number of DSBs in the extracted clusters, we have introduced an aggregation index (AI). The computational results show that a sub-keV electron produces DSBs in a dense formation more effectively than higher energy electrons. The root-mean square radius (RMSR) of the cluster size is below 5 nm, which is smaller than the chromatin fiber thickness. It was found that this size of clustering events has a high possibility to cause lesions in DNA within the chromatin fiber site.

Genome-wide Identification and Expression Analysis of Half-size ABCG Genes in Malus × domestica

Directory of Open Access Journals (Sweden)

Juanjuan MA

2018-03-01

Full Text Available Half-size adenosine triphosphate-binding cassette transporter subgroup G (ABCG genes play crucial roles in regulating the movements of a variety of substrates and have been well studied in several plants. However, half-size ABCGs have not been characterized in detail in apple (Malus × domestica Borkh.. Here, we performed a genome-wide identification and expression analysis of the half-size ABCG gene family in apple. A total of 46 apple half-size ABCGs were identified and divided into six clusters according to the phylogenetic analysis. A gene structural analysis showed that most half-size ABCGs in the same cluster shared a similar exon–intron organization. A gene duplication analysis showed that segmental, tandem and whole-genome duplications could account for the expansion of half-size ABCG transporters in M. domestica. Moreover, a promoter scan, digital expression analysis and RNA-seq revealed that MdABCG21 may be involved in root's cytokinin transport and that ABCG17 may be involved in the lateral bud development of M. spectabilis ‘Bly114’ by mediating cytokinin transport. The data presented here lay the foundation for further investigations into the biological and physiological processes and functions of half-size ABCG genes in apple. Keywords: apple, ABCG gene, duplication, gene expression

Beyond assembly bias: exploring secondary halo biases for cluster-size haloes

Science.gov (United States)

Mao, Yao-Yuan; Zentner, Andrew R.; Wechsler, Risa H.

2018-03-01

Secondary halo bias, commonly known as `assembly bias', is the dependence of halo clustering on a halo property other than mass. This prediction of the Λ Cold Dark Matter cosmology is essential to modelling the galaxy distribution to high precision and interpreting clustering measurements. As the name suggests, different manifestations of secondary halo bias have been thought to originate from halo assembly histories. We show conclusively that this is incorrect for cluster-size haloes. We present an up-to-date summary of secondary halo biases of high-mass haloes due to various halo properties including concentration, spin, several proxies of assembly history, and subhalo properties. While concentration, spin, and the abundance and radial distribution of subhaloes exhibit significant secondary biases, properties that directly quantify halo assembly history do not. In fact, the entire assembly histories of haloes in pairs are nearly identical to those of isolated haloes. In general, a global correlation between two halo properties does not predict whether or not these two properties exhibit similar secondary biases. For example, assembly history and concentration (or subhalo abundance) are correlated for both paired and isolated haloes, but follow slightly different conditional distributions in these two cases. This results in a secondary halo bias due to concentration (or subhalo abundance), despite the lack of assembly bias in the strict sense for cluster-size haloes. Due to this complexity, caution must be exercised in using any one halo property as a proxy to study the secondary bias due to another property.

Preliminary Cluster Size and Efficiencies results of CMS RPC at GIF++

CERN Document Server

Gonzalez Blanco Gonzalez, Genoveva

2016-01-01

A brief description and first preliminary results of the Efficiencies and Cluster Size measurements of the CMS Resistive Plate Chambers, will be presented inside the Gamma Irradiation Facility GIF++ at CERN. Preliminary studies that sets the base performance measurements of CMS RPC for starting aging studies.

Cluster analysis in phenotyping a Portuguese population.

Science.gov (United States)

Loureiro, C C; Sa-Couto, P; Todo-Bom, A; Bousquet, J

2015-09-03

Unbiased cluster analysis using clinical parameters has identified asthma phenotypes. Adding inflammatory biomarkers to this analysis provided a better insight into the disease mechanisms. This approach has not yet been applied to asthmatic Portuguese patients. To identify phenotypes of asthma using cluster analysis in a Portuguese asthmatic population treated in secondary medical care. Consecutive patients with asthma were recruited from the outpatient clinic. Patients were optimally treated according to GINA guidelines and enrolled in the study. Procedures were performed according to a standard evaluation of asthma. Phenotypes were identified by cluster analysis using Ward's clustering method. Of the 72 patients enrolled, 57 had full data and were included for cluster analysis. Distribution was set in 5 clusters described as follows: cluster (C) 1, early onset mild allergic asthma; C2, moderate allergic asthma, with long evolution, female prevalence and mixed inflammation; C3, allergic brittle asthma in young females with early disease onset and no evidence of inflammation; C4, severe asthma in obese females with late disease onset, highly symptomatic despite low Th2 inflammation; C5, severe asthma with chronic airflow obstruction, late disease onset and eosinophilic inflammation. In our study population, the identified clusters were mainly coincident with other larger-scale cluster analysis. Variables such as age at disease onset, obesity, lung function, FeNO (Th2 biomarker) and disease severity were important for cluster distinction. Copyright © 2015. Published by Elsevier España, S.L.U.

Understanding the cluster randomised crossover design: a graphical illustraton of the components of variation and a sample size tutorial.

Science.gov (United States)

Arnup, Sarah J; McKenzie, Joanne E; Hemming, Karla; Pilcher, David; Forbes, Andrew B

2017-08-15

In a cluster randomised crossover (CRXO) design, a sequence of interventions is assigned to a group, or 'cluster' of individuals. Each cluster receives each intervention in a separate period of time, forming 'cluster-periods'. Sample size calculations for CRXO trials need to account for both the cluster randomisation and crossover aspects of the design. Formulae are available for the two-period, two-intervention, cross-sectional CRXO design, however implementation of these formulae is known to be suboptimal. The aims of this tutorial are to illustrate the intuition behind the design; and provide guidance on performing sample size calculations. Graphical illustrations are used to describe the effect of the cluster randomisation and crossover aspects of the design on the correlation between individual responses in a CRXO trial. Sample size calculations for binary and continuous outcomes are illustrated using parameters estimated from the Australia and New Zealand Intensive Care Society - Adult Patient Database (ANZICS-APD) for patient mortality and length(s) of stay (LOS). The similarity between individual responses in a CRXO trial can be understood in terms of three components of variation: variation in cluster mean response; variation in the cluster-period mean response; and variation between individual responses within a cluster-period; or equivalently in terms of the correlation between individual responses in the same cluster-period (within-cluster within-period correlation, WPC), and between individual responses in the same cluster, but in different periods (within-cluster between-period correlation, BPC). The BPC lies between zero and the WPC. When the WPC and BPC are equal the precision gained by crossover aspect of the CRXO design equals the precision lost by cluster randomisation. When the BPC is zero there is no advantage in a CRXO over a parallel-group cluster randomised trial. Sample size calculations illustrate that small changes in the specification of

Finite size effects in the evaporation rate of 3He clusters

International Nuclear Information System (INIS)

Guirao, A.; Pi, M.; Barranco, M.

1991-01-01

We have computed the density of states and the evaporation rate of 3 He clusters, paying special attention to finite size effects which modify the 3 He level density parameter and chemical potential from their bulk values. Ready-to-use liquid-drop expansions of these quantities are given. (orig.)

Forward-backward multiplicity correlations and the clusterization

International Nuclear Information System (INIS)

Kostenko, B.F.; Musul'manbekov, Zh.Zh.

1990-01-01

An analysis of the forward-backward multiplicity correlations for pp- and p-barp-collisions has been fulfilled in the framework of the statistical cluster model. Connection between the strength of correlations and sizes of clusters is investigated. The dependence of masses and sizes of clusters on the energy of colliding hadrons is obtained. 15 refs.; 9 figs.; 1 tab

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

Science.gov (United States)

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

Profitability and efficiency of Italian utilities: cluster analysis of financial statement ratios

International Nuclear Information System (INIS)

Linares, E.

2008-01-01

The last ten years have witnessed conspicuous changes in European and Italian regulation of public utility services and in the strategies of the major players in these fields. In response to these changes Italian utilities have made a variety of choices regarding size, presence in more or less capital-intensive stages of different value chains, and diversification. These choices have been implemented both through internal growth and by means of mergers and acquisitions. In this context it is interesting to try to establish whether there is a nexus between these choices and the performance of Italian utilities in terms of profitability and efficiency. Therefore statistical multivariate analysis techniques (cluster analysis and factor analysis) have been applied to several ratios obtained from the 2005 financial statement of 34 utilities. First, a hierarchical cluster analysis method has been applied to financial statement data in order to identify homogeneous groups based on several indicators of the incidence of costs (external costs, personnel costs, depreciation and amortization), profitability (return on sales, return on assets, return on equity) and efficiency (in the utilization of personnel, of total assets, of property, plant and equipment). Five clusters have been found. Then the clusters have been characterized in terms of the aforementioned indicators, the presence in different stages of the energy value chains (electricity and gas) and other descriptive variables (such as turnover, number of employees, assets, percentage of property, plant and equipment on total assets, sales revenues from electricity, gas, water supply and sanitation, waste collection and treatment and other services). In a second round cluster analysis has been preceded by factor analysis, in order to find a smaller set of variables. This procedure has revealed three not directly observable factors that can be interpreted as follows: i) efficiency in ordinary and financial management

Coordinate based random effect size meta-analysis of neuroimaging studies.

Science.gov (United States)

Tench, C R; Tanasescu, Radu; Constantinescu, C S; Auer, D P; Cottam, W J

2017-06-01

Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely. Copyright © 2017 Elsevier Inc. All rights reserved.

Poly(methyl methacrylate) Composites with Size-selected Silver Nanoparticles Fabricated Using Cluster Beam Technique

DEFF Research Database (Denmark)

Muhammad, Hanif; Juluri, Raghavendra R.; Chirumamilla, Manohar

2016-01-01

based on cluster beam technique allowing the formation of monocrystalline size-selected silver nanoparticles with a ±5–7% precision of diameter and controllable embedment into poly (methyl methacrylate). It is shown that the soft-landed silver clusters preserve almost spherical shape with a slight...... tendency to flattening upon impact. By controlling the polymer hardness (from viscous to soft state) prior the cluster deposition and annealing conditions after the deposition the degree of immersion of the nanoparticles into polymer can be tuned, thus, making it possible to create composites with either...

Linear and nonlinear surface spectroscopy of supported size selected metal clusters and organic adsorbates

Energy Technology Data Exchange (ETDEWEB)

Thaemer, Martin Georg

2012-03-08

The spectroscopic investigation of supported size selected metal clusters over a wide wavelength range plays an important role for understanding their outstanding catalytic properties. The challenge which must be overcome to perform such measurements is the difficult detection of the weak spectroscopic signals from these samples. As a consequence, highly sensitive spectroscopic methods are applied, such as surface Cavity Ringdown Spectroscopy and surface Second Harmonic Generation Spectroscopy. The spectroscopic apparatus developed is shown to have a sensitivity which is high enough to detect sub-monolayer coverages of adsorbates on surfaces. In the measured spectra of small supported silver clusters of the sizes Ag{sub 4}2, Ag{sub 2}1, Ag{sub 9}, and Ag atoms a stepwise transition from particles with purely metallic character to particles with molecule-like properties can be observed within this size range.

Risk factors associated with cluster size of Mycobacterium tuberculosis (Mtb) of different RFLP lineages in Brazil.

Science.gov (United States)

Peres, Renata Lyrio; Vinhas, Solange Alves; Ribeiro, Fabíola Karla Correa; Palaci, Moisés; do Prado, Thiago Nascimento; Reis-Santos, Bárbara; Zandonade, Eliana; Suffys, Philip Noel; Golub, Jonathan E; Riley, Lee W; Maciel, Ethel Leonor

2018-02-08

Tuberculosis (TB) transmission is influenced by patient-related risk, environment and bacteriological factors. We determined the risk factors associated with cluster size of IS6110 RFLP based genotypes of Mycobacterium tuberculosis (Mtb) isolates from Vitoria, Espirito Santo, Brazil. Cross-sectional study of new TB cases identified in the metropolitan area of Vitoria, Brazil between 2000 and 2010. Mtb isolates were genotyped by the IS6110 RFLP, spoligotyping and RD Rio . The isolates were classified according to genotype cluster sizes by three genotyping methods and associated patient epidemiologic characteristics. Regression Model was performed to identify factors associated with cluster size. Among 959 Mtb isolates, 461 (48%) cases had an isolate that belonged to an RFLP cluster, and six clusters with ten or more isolates were identified. Of the isolates spoligotyped, 448 (52%) were classified as LAM and 412 (48%) as non-LAM. Our regression model found that 6-9 isolates/RFLP cluster were more likely belong to the LAM family, having the RD Rio genotype and to be smear-positive (adjusted OR = 1.17, 95% CI 1.08-1.26; adjusted OR = 1.25, 95% CI 1.14-1.37; crude OR = 2.68, 95% IC 1.13-6.34; respectively) and living in a Serra city neighborhood decrease the risk of being in the 6-9 isolates/RFLP cluster (adjusted OR = 0.29, 95% CI, 0.10-0.84), than in the others groups. Individuals aged 21 to 30, 31 to 40 and > 50 years were less likely of belonging the 2-5 isolates/RFLP cluster than unique patterns compared to individuals cluster group (adjustment OR = 0.45, 95% CI 0.24-0.85) than unique patterns. We found that a large proportion of new TB infections in Vitoria is caused by prevalent Mtb genotypes belonging to the LAM family and RD Rio genotypes. Such information demonstrates that some genotypes are more likely to cause recent transmission. Targeting interventions such as screening in specific areas and social risk groups, should be a priority

Chemistry and kinetics of size-selected cobalt cluster cations at thermal energies. I. Reactions with CO

Science.gov (United States)

Guo, B. C.; Kerns, K. P.; Castleman, A. W., Jr.

1992-06-01

The chemistry and kinetics of size-selected Co+n cluster-ion (n=2-8) reactions with CO are studied using a selected ion drift tube affixed with a laser vaporization source operated under well-defined thermal conditions. All reactions studied in the present work are found to be association reactions. Their absolute rate constants, which are determined quantitatively, are found to have a strong dependence on cluster size. Similar to the cases of reactions with many other reactants such as H2 and CH4, Co+4 and Co+5 display a higher reactivity toward the CO molecule than do clusters of neighboring size. The multiple-collision conditions employed in the present work have enabled a determination of the maximum coordination number of CO molecules bound onto each Co+n cluster. It is found that the tetramer tends to bond 12 CO molecules, the pentamer 14 CO, hexamer 16 CO, and so on. The results are interpreted in terms of Lauher's calculation and the polyhedral skeletal electron pair theory. All the measured maximum coordination numbers correlate extremely well with the predictions of these theories, except for the trimer where the measured number is one CO less than the predicted value. The good agreement between experiment and theory enables one to gain some insight into the geometric structure of the clusters. Based on the present findings, the cobalt tetramer cation is interpreted to have a tetrahedral structure, the pentamer a trigonal bipyramid, and the hexamer an octahedral structure. Other cluster structures are also discussed.

A statistical analysis of North East Atlantic (submicron aerosol size distributions

Directory of Open Access Journals (Sweden)

M. Dall'Osto

2011-12-01

Full Text Available The Global Atmospheric Watch research station at Mace Head (Ireland offers the possibility to sample some of the cleanest air masses being imported into Europe as well as some of the most polluted being exported out of Europe. We present a statistical cluster analysis of the physical characteristics of aerosol size distributions in air ranging from the cleanest to the most polluted for the year 2008. Data coverage achieved was 75% throughout the year. By applying the Hartigan-Wong k-Means method, 12 clusters were identified as systematically occurring. These 12 clusters could be further combined into 4 categories with similar characteristics, namely: coastal nucleation category (occurring 21.3 % of the time, open ocean nucleation category (occurring 32.6% of the time, background clean marine category (occurring 26.1% of the time and anthropogenic category (occurring 20% of the time aerosol size distributions. The coastal nucleation category is characterised by a clear and dominant nucleation mode at sizes less than 10 nm while the open ocean nucleation category is characterised by a dominant Aitken mode between 15 nm and 50 nm. The background clean marine aerosol exhibited a clear bimodality in the sub-micron size distribution, with although it should be noted that either the Aitken mode or the accumulation mode may dominate the number concentration. However, peculiar background clean marine size distributions with coarser accumulation modes are also observed during winter months. By contrast, the continentally-influenced size distributions are generally more monomodal (accumulation, albeit with traces of bimodality. The open ocean category occurs more often during May, June and July, corresponding with the North East (NE Atlantic high biological period. Combined with the relatively high percentage frequency of occurrence (32.6%, this suggests that the marine biota is an important source of new nano aerosol particles in NE Atlantic Air.

Cluster analysis of track structure

International Nuclear Information System (INIS)

Michalik, V.

1991-01-01

One of the possibilities of classifying track structures is application of conventional partition techniques of analysis of multidimensional data to the track structure. Using these cluster algorithms this paper attempts to find characteristics of radiation reflecting the spatial distribution of ionizations in the primary particle track. An absolute frequency distribution of clusters of ionizations giving the mean number of clusters produced by radiation per unit of deposited energy can serve as this characteristic. General computation techniques used as well as methods of calculations of distributions of clusters for different radiations are discussed. 8 refs.; 5 figs

Electron impact fragmentation of size-selected krypton clusters

Czech Academy of Sciences Publication Activity Database

Steinbach, Ch.; Fárník, Michal; Buck, U.; Brindle, C. A.; Janda, K. C.

2006-01-01

Roč. 110, č. 29 (2006), s. 9108-9115 ISSN 1089-5639 Grant - others:Deutsche Forschungsgemeinschaft(DE) GRK 782; US National Science Foundation(US) CHE-0213149 Institutional research plan: CEZ:AV0Z40400503 Keywords : rare-gas cluster * bombardment fragmentation * scattering analysis Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.047, year: 2006

Understanding Boron through Size-Selected Clusters: Structure, Chemical Bonding, and Fluxionality

Energy Technology Data Exchange (ETDEWEB)

Sergeeva, Alina P.; Popov, Ivan A.; Piazza, Zachary A.; Li, Wei-Li; Romanescu, Constantin; Wang, Lai S.; Boldyrev, Alexander I.

2014-04-15

��/C analogy. It is believed that the electronic transmutation concept will be effective and valuable in aiding the design of new boride materials with predictable properties. The study of boron clusters with intermediate properties between those of individual atoms and bulk solids has given rise to a unique opportunity to broaden the frontier of boron chemistry. Understanding boron clusters has spurred experimentalists and theoreticians to find new boron-based nanomaterials, such as boron fullerenes, nanotubes, two-dimensional boron, and new compounds containing boron clusters as building blocks. Here, a brief and timely overview is presented addressing the recent progress made on boron clusters and the approaches used in the authors’ laboratories to determine the structure, stability, and chemical bonding of size-selected boron clusters by joint photoelectron spectroscopy and theoretical studies. Specifically, key findings on all-boron hydrocarbon analogues, metal-centered boron wheels, and electronic transmutation in boron clusters are summarized.

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

Science.gov (United States)

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

Science.gov (United States)

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

Gene cluster statistics with gene families.

Science.gov (United States)

Raghupathy, Narayanan; Durand, Dannie

2009-05-01

analysis in genomes of various sizes and illustrate the utility of our methods by applying them to gene clusters recently reported in the literature. Mathematical code to compute cluster probabilities using our methods is available as supplementary material.

MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS

Directory of Open Access Journals (Sweden)

Jana Halčinová

2014-06-01

Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.

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

Science.gov (United States)

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

2011-10-01

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

On the application of the weak-beam technique to the determination of the sizes of small point-defect clusters in ion-irradiated copper

International Nuclear Information System (INIS)

Jenkins, M. L.

1998-01-01

We have made an analysis of the conditions necessary for the successful use of the weak-beam technique for identifying and characterizing small point-defect clusters in ion-irradiated copper. The visibility of small defects was found to depend only weakly on the magnitude of the beam-convergence. In general, the image sizes of small clusters were found to be most sensitive to the magnitude of Sa with the image sizes of some individual defects changing by large amounts with changes as small as 0.025 nm -1 . The most reliable information on the true defect size is likely to be obtained by taking a series of 5-9 micrographs with a systematic variation of deviation parameter from 0.2-0.3 nm -1 . This procedure allows size information to be obtained down to a resolution limit of about 0.5 nm for defects situated throughout a foil thickness of 60 nm. The technique has been applied to the determination of changes in the sizes of small defects produced by a low-temperature in-situ irradiation and annealing experiment

Exact WKB analysis and cluster algebras

International Nuclear Information System (INIS)

Iwaki, Kohei; Nakanishi, Tomoki

2014-01-01

We develop the mutation theory in the exact WKB analysis using the framework of cluster algebras. Under a continuous deformation of the potential of the Schrödinger equation on a compact Riemann surface, the Stokes graph may change the topology. We call this phenomenon the mutation of Stokes graphs. Along the mutation of Stokes graphs, the Voros symbols, which are monodromy data of the equation, also mutate due to the Stokes phenomenon. We show that the Voros symbols mutate as variables of a cluster algebra with surface realization. As an application, we obtain the identities of Stokes automorphisms associated with periods of cluster algebras. The paper also includes an extensive introduction of the exact WKB analysis and the surface realization of cluster algebras for nonexperts. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Cluster algebras in mathematical physics’. (paper)

Performance comparison analysis library communication cluster system using merge sort

Science.gov (United States)

Wulandari, D. A. R.; Ramadhan, M. E.

2018-04-01

Begins by using a single processor, to increase the speed of computing time, the use of multi-processor was introduced. The second paradigm is known as parallel computing, example cluster. The cluster must have the communication potocol for processing, one of it is message passing Interface (MPI). MPI have many library, both of them OPENMPI and MPICH2. Performance of the cluster machine depend on suitable between performance characters of library communication and characters of the problem so this study aims to analyze the comparative performances libraries in handling parallel computing process. The case study in this research are MPICH2 and OpenMPI. This case research execute sorting’s problem to know the performance of cluster system. The sorting problem use mergesort method. The research method is by implementing OpenMPI and MPICH2 on a Linux-based cluster by using five computer virtual then analyze the performance of the system by different scenario tests and three parameters for to know the performance of MPICH2 and OpenMPI. These performances are execution time, speedup and efficiency. The results of this study showed that the addition of each data size makes OpenMPI and MPICH2 have an average speed-up and efficiency tend to increase but at a large data size decreases. increased data size doesn’t necessarily increased speed up and efficiency but only execution time example in 100000 data size. OpenMPI has a execution time greater than MPICH2 example in 1000 data size average execution time with MPICH2 is 0,009721 and OpenMPI is 0,003895 OpenMPI can customize communication needs.

POLYMER COMPOSITE FILMS WITH SIZE-SELECTED METAL NANOPARTICLES FABRICATED BY CLUSTER BEAM TECHNIQUE

DEFF Research Database (Denmark)

Ceynowa, F. A.; Chirumamilla, Manohar; Popok, Vladimir

2017-01-01

Formation of polymer films with size-selected silver and copper nanoparticles (NPs) is studied. Polymers are prepared by spin coating while NPs are fabricated and deposited utilizing a magnetron sputtering cluster apparatus. The particle embedding into the films is provided by thermal annealing...... after the deposition. The degree of immersion can be controlled by the annealing temperature and time. Together with control of cluster coverage the described approach represents an efficient method for the synthesis of thin polymer composite layers with either partially or fully embedded metal NPs....... Combining electron beam lithography, cluster beam deposition and thermal annealing allows to form ordered arrays of metal NPs on polymer films. Plasticity and flexibility of polymer host and specific properties added by coinage metal NPs open a way for different applications of such composite materials...

From virtual clustering analysis to self-consistent clustering analysis: a mathematical study

Science.gov (United States)

Tang, Shaoqiang; Zhang, Lei; Liu, Wing Kam

2018-03-01

In this paper, we propose a new homogenization algorithm, virtual clustering analysis (VCA), as well as provide a mathematical framework for the recently proposed self-consistent clustering analysis (SCA) (Liu et al. in Comput Methods Appl Mech Eng 306:319-341, 2016). In the mathematical theory, we clarify the key assumptions and ideas of VCA and SCA, and derive the continuous and discrete Lippmann-Schwinger equations. Based on a key postulation of "once response similarly, always response similarly", clustering is performed in an offline stage by machine learning techniques (k-means and SOM), and facilitates substantial reduction of computational complexity in an online predictive stage. The clear mathematical setup allows for the first time a convergence study of clustering refinement in one space dimension. Convergence is proved rigorously, and found to be of second order from numerical investigations. Furthermore, we propose to suitably enlarge the domain in VCA, such that the boundary terms may be neglected in the Lippmann-Schwinger equation, by virtue of the Saint-Venant's principle. In contrast, they were not obtained in the original SCA paper, and we discover these terms may well be responsible for the numerical dependency on the choice of reference material property. Since VCA enhances the accuracy by overcoming the modeling error, and reduce the numerical cost by avoiding an outer loop iteration for attaining the material property consistency in SCA, its efficiency is expected even higher than the recently proposed SCA algorithm.

Deposition of size-selected atomic clusters on surfaces

International Nuclear Information System (INIS)

Carroll, S.J.

1999-06-01

This dissertation presents technical developments and experimental and computational investigations concerned with the deposition of atomic clusters onto surfaces. It consists of a collection of papers, in which the main body of results are contained, and four chapters presenting a subject review, computational and experimental techniques and a summary of the results presented in full within the papers. Technical work includes the optimization of an existing gas condensation cluster source based on evaporation, and the design, construction and optimization of a new gas condensation cluster source based on RF magnetron sputtering (detailed in Paper 1). The result of cluster deposition onto surfaces is found to depend on the cluster deposition energy; three impact energy regimes are explored in this work. (1) Low energy: n clusters create a defect in the surface, which pins the cluster in place, inhibiting cluster diffusion at room temperature (Paper V). (3) High energy: > 50 eV/atom. The clusters implant into the surface. For Ag 20 -Ag 200 clusters, the implantation depth is found to scale linearly with the impact energy and inversely with the cross-sectional area of the cluster, with an offset due to energy lost to the elastic compression of the surface (Paper VI). For smaller (Ag 3 ) clusters the orientation of the cluster with respect to the surface and the precise impact site play an important role; the impact energy has to be 'focused' in order for cluster implantation to occur (Paper VII). The application of deposited clusters for the creation of Si nanostructures by plasma etching is explored in Paper VIII. (author)

Size-dependent valence change in small Pr, Nd, and Sm clusters isolated in solid Ar

International Nuclear Information System (INIS)

Luebcke, M.; Sonntag, B.; Niemann, W.; Rabe, P.

1986-01-01

The L/sub III/ absorption thresholds of Pr, Nd, and Sm clusters isolated in solid Ar are marked by prominent white lines. The lines ascribed to divalent and trivalent rare-earth metals are well separated in energy. From the relative intensities of these lines an average valence of the rare-earth atoms in the cluster has been determined. For dimers and trimers the average valence is close to 2, the value for free atoms. For clusters consisting of more than 20 atoms the average valence approaches 3, the value for bulk metals. In between the valence changes abruptly, indicating the existence of a critical cluster size of approximately 5 atoms for Pr and Nd and of 13 atoms for Sm

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

Science.gov (United States)

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

2013-10-26

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

Effects of manganese doping on the structure evolution of small-sized boron clusters

Science.gov (United States)

Zhao, Lingquan; Qu, Xin; Wang, Yanchao; Lv, Jian; Zhang, Lijun; Hu, Ziyu; Gu, Guangrui; Ma, Yanming

2017-07-01

Atomic doping of clusters is known as an effective approach to stabilize or modify the structures and properties of resulting doped clusters. We herein report the effect of manganese (Mn) doping on the structure evolution of small-sized boron (B) clusters. The global minimum structures of both neutral and charged Mn doped B cluster \\text{MnB}nQ (n  =  10-20 and Q  =  0, ±1) have been proposed through extensive first-principles swarm-intelligence based structure searches. It is found that Mn doping has significantly modified the grow behaviors of B clusters, leading to two novel structural transitions from planar to tubular and then to cage-like B structures in both neutral and charged species. Half-sandwich-type structures are most favorable for small \\text{MnB}n-/0/+ (n  ⩽  13) clusters and gradually transform to Mn-centered double-ring tubular structures at \\text{MnB}16-/0/+ clusters with superior thermodynamic stabilities compared with their neighbors. Most strikingly, endohedral cages become the ground-state structures for larger \\text{MnB}n-/0/+ (n  ⩾  19) clusters, among which \\text{MnB}20+ adopts a highly symmetric structure with superior thermodynamic stability and a large HOMO-LUMO gap of 4.53 eV. The unique stability of the endohedral \\text{MnB}\\text{20}+ cage is attributed to the geometric fit and formation of 18-electron closed-shell configuration. The results significantly advance our understanding about the structure and bonding of B-based clusters and strongly suggest transition-metal doping as a viable route to synthesize intriguing B-based nanomaterials.

Size and Site Dependence of the Catalytic Activity of Iridium Clusters toward Ethane Dehydrogenation.

Science.gov (United States)

Ge, Yingbin; Jiang, Hao; Kato, Russell; Gummagatta, Prasuna

2016-12-01

This research focuses on optimizing transition metal nanocatalyst immobilization and activity to enhance ethane dehydrogenation. Ethane dehydrogenation, catalyzed by thermally stable Ir n (n = 8, 12, 18) atomic clusters that exhibit a cuboid structure, was studied using the B3LYP method with triple-ζ basis sets. Relativistic effects and dispersion corrections were included in the calculations. In the dehydrogenation reaction Ir n + C 2 H 6 → H-Ir n -C 2 H 5 → (H) 2 -Ir n -C 2 H 4 , the first H-elimination is the rate-limiting step, primarily because the reaction releases sufficient heat to facilitate the second H-elimination. The catalytic activity of the Ir clusters strongly depends on the Ir cluster size and the specific catalytic site. Cubic Ir 8 is the least reactive toward H-elimination in ethane: Ir 8 + C 2 H 6 → H-Ir 8 -C 2 H 5 has a large (65 kJ/mol) energy barrier, whereas Ir 12 (3 × 2 × 2 cuboid) and Ir 18 (3 × 3 × 2 cuboid) lower this energy barrier to 22 and 3 kJ/mol, respectively. The site dependence is as prominent as the size effect. For example, the energy barrier for the Ir 18 + C 2 H 6 → H-Ir 18 -C 2 H 5 reaction is 3, 48, and 71 kJ/mol at the corner, edge, or face-center sites of the Ir 18 cuboid, respectively. Energy release due to Ir cluster insertion into an ethane C-H bond facilitates hydrogen migration on the Ir cluster surface, and the second H-elimination of ethane. In an oxygen-rich environment, oxygen molecules may be absorbed on the Ir cluster surface. The oxygen atoms bonded to the Ir cluster surface may slightly increase the energy barrier for H-elimination in ethane. However, the adsorption of oxygen and its reaction with H atoms on the Ir cluster releases sufficient heat to yield an overall thermodynamically favored reaction: Ir n + C 2 H 6 + 1 / 2 O 2 → Ir n + C 2 H 4 + H 2 O. These results will be useful toward reducing the energy cost of ethane dehydrogenation in industry.

Differences in Flower Transcriptome between Grapevine Clones Are Related to Their Cluster Compactness, Fruitfulness, and Berry Size

OpenAIRE

Grimplet, Jérôme; Tello, Javier; Laguna Ullán, Natalia; Ibáñez Marcos, Javier

2017-01-01

Grapevine cluster compactness has a clear impact on fruit quality and health status, as clusters with greater compactness are more susceptible to pests and diseases and ripen more asynchronously. Different parameters related to inflorescence and cluster architecture (length, width, branching, etc.), fruitfulness (number of berries, number of seeds) and berry size (length, width) contribute to the final level of compactness. From a collection of 501 clones of cultivar Garnacha Tinta, two compa...

Robust cluster analysis and variable selection

CERN Document Server

Ritter, Gunter

2014-01-01

Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of bot

Cluster analysis

OpenAIRE

Mucha, Hans-Joachim; Sofyan, Hizir

2000-01-01

As an explorative technique, duster analysis provides a description or a reduction in the dimension of the data. It classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of many variables. Its aim is to construct groups in such a way that the profiles of objects in the same groups are relatively homogenous whereas the profiles of objects in different groups are relatively heterogeneous. Clustering is distinct from classification techniques, ...

Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

Energy Technology Data Exchange (ETDEWEB)

Hu, Lin; Maroudas, Dimitrios, E-mail: maroudas@ecs.umass.edu [Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts 01003-9303 (United States); Hammond, Karl D. [Department of Chemical Engineering, University of Missouri, Columbia, Missouri 65211 (United States); Wirth, Brian D. [Department of Nuclear Engineering, University of Tennessee, Knoxville, Tennessee 37996 (United States)

2015-10-28

We report the results of a systematic atomic-scale analysis of the reactions of small mobile helium clusters (He{sub n}, 4 ≤ n ≤ 7) near low-Miller-index tungsten (W) surfaces, aiming at a fundamental understanding of the near-surface dynamics of helium-carrying species in plasma-exposed tungsten. These small mobile helium clusters are attracted to the surface and migrate to the surface by Fickian diffusion and drift due to the thermodynamic driving force for surface segregation. As the clusters migrate toward the surface, trap mutation (TM) and cluster dissociation reactions are activated at rates higher than in the bulk. TM produces W adatoms and immobile complexes of helium clusters surrounding W vacancies located within the lattice planes at a short distance from the surface. These reactions are identified and characterized in detail based on the analysis of a large number of molecular-dynamics trajectories for each such mobile cluster near W(100), W(110), and W(111) surfaces. TM is found to be the dominant cluster reaction for all cluster and surface combinations, except for the He{sub 4} and He{sub 5} clusters near W(100) where cluster partial dissociation following TM dominates. We find that there exists a critical cluster size, n = 4 near W(100) and W(111) and n = 5 near W(110), beyond which the formation of multiple W adatoms and vacancies in the TM reactions is observed. The identified cluster reactions are responsible for important structural, morphological, and compositional features in the plasma-exposed tungsten, including surface adatom populations, near-surface immobile helium-vacancy complexes, and retained helium content, which are expected to influence the amount of hydrogen re-cycling and tritium retention in fusion tokamaks.

Supersonic copper clusters

International Nuclear Information System (INIS)

Powers, D.E.; Hansen, S.G.; Geusic, M.E.; Michalopoulos, D.L.; Smalley, R.E.

1983-01-01

Copper clusters ranging in size from 1 to 29 atoms have been prepared in a supersonic beam by laser vaporization of a rotating copper target rod within the throat of a pulsed supersonic nozzle using helium for the carrier gas. The clusters were cooled extensively in the supersonic expansion [T(translational) 1 to 4 K, T(rotational) = 4 K, T(vibrational) = 20 to 70 K]. These clusters were detected in the supersonic beam by laser photoionization with time-of-flight mass analysis. Using a number of fixed frequency outputs of an exciplex laser, the threshold behavior of the photoionization cross section was monitored as a function of cluster size.nce two-photon ionization (R2PI) with mass selective detection allowed the detection of five new electronic band systems in the region between 2690 and 3200 A, for each of the three naturally occurring isotopic forms of Cu 2 . In the process of scanning the R2PI spectrum of these new electronic states, the ionization potential of the copper dimer was determined to be 7.894 +- 0.015 eV

Quantum-Size Dependence of the Energy for Vacancy Formation in Charged Small Metal Clusters. Drop Model

Science.gov (United States)

Pogosov, V. V.; Reva, V. I.

2018-04-01

Self-consistent computations of the monovacancy formation energy are performed for Na N , Mg N , and Al N (12 < N ≤ 168) spherical clusters in the drop model for stable jelly. Scenarios of the Schottky vacancy formation and "bubble vacancy blowing" are considered. It is shown that the asymptotic behavior of the size dependences of the energy for the vacancy formation by these two mechanisms is different and the difference between the characteristics of a charged and neutral cluster is entirely determined by the difference between the ionization potentials of clusters and the energies of electron attachment to them.

Supercomputer and cluster performance modeling and analysis efforts:2004-2006.

Energy Technology Data Exchange (ETDEWEB)

Sturtevant, Judith E.; Ganti, Anand; Meyer, Harold (Hal) Edward; Stevenson, Joel O.; Benner, Robert E., Jr. (.,; .); Goudy, Susan Phelps; Doerfler, Douglas W.; Domino, Stefan Paul; Taylor, Mark A.; Malins, Robert Joseph; Scott, Ryan T.; Barnette, Daniel Wayne; Rajan, Mahesh; Ang, James Alfred; Black, Amalia Rebecca; Laub, Thomas William; Vaughan, Courtenay Thomas; Franke, Brian Claude

2007-02-01

This report describes efforts by the Performance Modeling and Analysis Team to investigate performance characteristics of Sandia's engineering and scientific applications on the ASC capability and advanced architecture supercomputers, and Sandia's capacity Linux clusters. Efforts to model various aspects of these computers are also discussed. The goals of these efforts are to quantify and compare Sandia's supercomputer and cluster performance characteristics; to reveal strengths and weaknesses in such systems; and to predict performance characteristics of, and provide guidelines for, future acquisitions and follow-on systems. Described herein are the results obtained from running benchmarks and applications to extract performance characteristics and comparisons, as well as modeling efforts, obtained during the time period 2004-2006. The format of the report, with hypertext links to numerous additional documents, purposefully minimizes the document size needed to disseminate the extensive results from our research.

A first-principles investigation of the effect of Pt cluster size on CO and NO oxidation intermediates and energetics

International Nuclear Information System (INIS)

Xu, Ye; Getman, Rachel B; Shelton, William Allison Jr.; Schneider, William F

2008-01-01

As catalysis research strives toward designing structurally and functionally well-defined catalytic centers containing as few active metal atoms as possible, the importance of understanding the reactivity of small metal clusters, and in particular of systematic comparisons of reaction types and cluster sizes, has grown concomitantly. Here we report density functional theory calculations (GGA-PW91) that probe the relationship between particle size, intermediate structures, and energetics of CO and NO oxidation by molecular and atomic oxygen on Ptx clusters (x = 1-5 and 10). The preferred structures, charge distributions, vibrational spectra, and energetics are systematically examined for oxygen (O2, 2O, and O), CO, CO2, NO, and NO2, for CO/NO co-adsorbed with O2, 2O, and O, and for CO2/NO2 co-adsorbed with O. The binding energies of oxygen, CO, NO, and the oxidation products CO2 and NO2 are all markedly enhanced on Ptx compared to Pt(111), and they trend toward the Pt(111) levels as cluster size increases. Because of the strong interaction of both the reactants and products with the Ptx clusters, deep energy sinks develop on the potential energy surfaces of the respective oxidation processes, indicating worse reaction energetics than on Pt(111). Thus the smallest Pt clusters are less effective for catalyzing CO and NO oxidation in their original state than bulk Pt. Our results further suggests that oxidation by molecular O2 is thermodynamically more facile than oxidation by atomic O on Ptx. Conditions and applications in which the Ptx clusters may be effective catalysts are discussed

Properties of an ionised-cluster beam from a vaporised-cluster ion source

International Nuclear Information System (INIS)

Takagi, T.; Yamada, I.; Sasaki, A.

1978-01-01

A new type of ion source vaporised-metal cluster ion source, has been developed for deposition and epitaxy. A cluster consisting of 10 2 to 10 3 atoms coupled loosely together is formed by adiabatic expansion ejecting the vapour of materials into a high-vacuum region through the nozzle of a heated crucible. The clusters are ionised by electron bombardment and accelerated with neutral clusters toward a substrate. In this paper, mechanisms of cluster formation experimental results of the cluster size (atoms/cluster) and its distribution, and characteristics of the cluster ion beams are reported. The size is calculated from the kinetic equation E = (1/2)mNVsub(ej) 2 , where E is the cluster beam energy, Vsub(ej) is the ejection velocity, m is the mass of atom and N is the cluster size. The energy and the velocity of the cluster are measured by an electrostatic 127 0 energy analyser and a rotating disc system, respectively. The cluster size obtained for Ag is about 5 x 10 2 to 2 x 10 3 atoms. The retarding potential method is used to confirm the results for Ag. The same dependence on cluster size for metals such as Ag, Cu and Pb has been obtained in previous experiments. In the cluster state the cluster ion beam is easily produced by electron bombardment. About 50% of ionised clusters are obtained under typical operation conditions, because of the large ionisation cross sections of the clusters. To obtain a uniform spatial distribution, the ionising electrode system is also discussed. The new techniques are termed ionised-cluster beam deposition (ICBD) and epitaxy (ICBE). (author)

Application of cluster and discriminant analyses to diagnose lithological heterogeneity of the parent material according to its particle-size distribution

Science.gov (United States)

Giniyatullin, K. G.; Valeeva, A. A.; Smirnova, E. V.

2017-08-01

Particle-size distribution in soddy-podzolic and light gray forest soils of the Botanical Garden of Kazan Federal University has been studied. The cluster analysis of data on the samples from genetic soil horizons attests to the lithological heterogeneity of the profiles of all the studied soils. It is probable that they are developed from the two-layered sediments with the upper colluvial layer underlain by the alluvial layer. According to the discriminant analysis, the major contribution to the discrimination of colluvial and alluvial layers is that of the fraction >0.25 mm. The results of canonical analysis show that there is only one significant discriminant function that separates alluvial and colluvial sediments on the investigated territory. The discriminant function correlates with the contents of fractions 0.05-0.01, 0.25-0.05, and >0.25 mm. Classification functions making it possible to distinguish between alluvial and colluvial sediments have been calculated. Statistical assessment of particle-size distribution data obtained for the plow horizons on ten plowed fields within the garden indicates that this horizon is formed from colluvial sediments. We conclude that the contents of separate fractions and their ratios cannot be used as a universal criterion of the lithological heterogeneity. However, adequate combination of the cluster and discriminant analyses makes it possible to give a comprehensive assessment of the lithology of soil samples from data on the contents of sand and silt fractions, which considerably increases the information value and reliability of the results.

Towards Cluster-Assembled Materials of True Monodispersity in Size and Chemical Environment: Synthesis, Dynamics and Activity

Science.gov (United States)

2016-10-27

thermodynamic and kinetic cluster size control on periodically wet - table surfaces, new questions came up concerning the link between diffusivity and...from ring-hollow (H,F) to ring-bridge (B). Left: STM annealing series (50×50 nm2), Moiré cell scheme and cluster position evaluation. Pd clus- ters...growth at T>700 K. Below: Measured and theoretically simulated (insets) STM images of various stages in the dehydrogenation process upon annealing

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

Science.gov (United States)

Sauzet, Odile; Peacock, Janet L

2017-07-20

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

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

Directory of Open Access Journals (Sweden)

Odile Sauzet

2017-07-01

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

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

Directory of Open Access Journals (Sweden)

Joop eHox

2014-02-01

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

Quantum size correction to the work function and centroid of excess charge in positively ionized simple metal clusters

International Nuclear Information System (INIS)

Payami, M.

2004-01-01

In this work, we have shown the important role of the finite-size correction to the work function in predicting the correct position of the centroid of excess charge in positively charged simple metal clusters with different r s values (2≤ r s ≥ 7). For this purpose, firstly we have calculated the self-consistent Kohn-Sham energies of neutral and singly-ionized clusters with sizes 2≤ N ≥100 in the framework of local spin-density approximation and stabilized jellium model as well as simple jellium model with rigid jellium. Secondly, we have fitted our results to the asymptotic ionization formulas both with and without the size correction to the work function. The results of fittings show that the formula containing the size correction predict a correct position of the centroid inside the jellium while the other predicts a false position, outside the jellium sphere

Clustering Trajectories by Relevant Parts for Air Traffic Analysis.

Science.gov (United States)

Andrienko, Gennady; Andrienko, Natalia; Fuchs, Georg; Garcia, Jose Manuel Cordero

2018-01-01

Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.

Sample size calculations for cluster randomised crossover trials in Australian and New Zealand intensive care research.

Science.gov (United States)

Arnup, Sarah J; McKenzie, Joanne E; Pilcher, David; Bellomo, Rinaldo; Forbes, Andrew B

2018-06-01

The cluster randomised crossover (CRXO) design provides an opportunity to conduct randomised controlled trials to evaluate low risk interventions in the intensive care setting. Our aim is to provide a tutorial on how to perform a sample size calculation for a CRXO trial, focusing on the meaning of the elements required for the calculations, with application to intensive care trials. We use all-cause in-hospital mortality from the Australian and New Zealand Intensive Care Society Adult Patient Database clinical registry to illustrate the sample size calculations. We show sample size calculations for a two-intervention, two 12-month period, cross-sectional CRXO trial. We provide the formulae, and examples of their use, to determine the number of intensive care units required to detect a risk ratio (RR) with a designated level of power between two interventions for trials in which the elements required for sample size calculations remain constant across all ICUs (unstratified design); and in which there are distinct groups (strata) of ICUs that differ importantly in the elements required for sample size calculations (stratified design). The CRXO design markedly reduces the sample size requirement compared with the parallel-group, cluster randomised design for the example cases. The stratified design further reduces the sample size requirement compared with the unstratified design. The CRXO design enables the evaluation of routinely used interventions that can bring about small, but important, improvements in patient care in the intensive care setting.

Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

Directory of Open Access Journals (Sweden)

I. Crawford

2015-11-01

Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

Science.gov (United States)

Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

2017-07-01

Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

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

Science.gov (United States)

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

2015-01-01

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

Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

Science.gov (United States)

Hwang, Heungsun; Dillon, William R.

2010-01-01

A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…

Energetics of charged metal clusters containing vacancies

Science.gov (United States)

Pogosov, Valentin V.; Reva, Vitalii I.

2018-01-01

We study theoretically large metal clusters containing vacancies. We propose an approach, which combines the Kohn-Sham results for monovacancy in a bulk of metal and analytical expansions in small parameters cv (relative concentration of vacancies) and RN,v -1, RN ,v being cluster radii. We obtain expressions of the ionization potential and electron affinity in the form of corrections to electron work function, which require only the characteristics of 3D defect-free metal. The Kohn-Sham method is used to calculate the electron profiles, ionization potential, electron affinity, electrical capacitance; dissociation, cohesion, and monovacancy-formation energies of the small perfect clusters NaN, MgN, AlN (N ≤ 270) and the clusters containing a monovacancy (N ≥ 12) in the stabilized-jellium model. The quantum-sized dependences for monovacancy-formation energies are calculated for the Schottky scenario and the "bubble blowing" scenario, and their asymptotic behavior is also determined. It is shown that the asymptotical behaviors of size dependences for these two mechanisms differ from each other and weakly depend on the number of atoms in the cluster. The contribution of monovacancy to energetics of charged clusters and the size dependences of their characteristics and asymptotics are discussed. It is shown that the difference between the characteristics for the neutral and charged clusters is entirely determined by size dependences of ionization potential and electron affinity. Obtained analytical dependences may be useful for the analysis of the results of photoionization experiments and for the estimation of the size dependences of the vacancy concentration including the vicinity of the melting point.

Semi-supervised consensus clustering for gene expression data analysis

OpenAIRE

Wang, Yunli; Pan, Youlian

2014-01-01

Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

PREDICTED SIZES OF PRESSURE-SUPPORTED HI CLOUDS IN THE OUTSKIRTS OF THE VIRGO CLUSTER

Energy Technology Data Exchange (ETDEWEB)

Burkhart, Blakesley; Loeb, Abraham [Harvard-Smithsonian Center for Astrophysics, 60 Garden St. Cambridge, MA (United States)

2016-06-10

Using data from the ALFALFA AGES Arecibo HI survey of galaxies and the Virgo cluster X-ray pressure profiles from XMM-Newton , we investigate the possibility that starless dark HI clumps, also known as “dark galaxies,” are supported by external pressure in the surrounding intercluster medium. We find that the starless HI clump masses, velocity dispersions, and positions allow these clumps to be in pressure equilibrium with the X-ray gas near the virial radius of the Virgo cluster. We predict the sizes of these clumps to range from 1 to 10 kpc, in agreement with the range of sizes found for spatially resolved HI starless clumps outside of Virgo. Based on the predicted HI surface density of the Virgo sources, as well as a sample of other similar resolved ALFALFA HI dark clumps with follow-up optical/radio observations, we predict that most of the HI dark clumps are on the cusp of forming stars. These HI sources therefore mark the transition between starless HI clouds and dwarf galaxies with stars.

Allergen Sensitization Pattern by Sex: A Cluster Analysis in Korea.

Science.gov (United States)

Ohn, Jungyoon; Paik, Seung Hwan; Doh, Eun Jin; Park, Hyun-Sun; Yoon, Hyun-Sun; Cho, Soyun

2017-12-01

Allergens tend to sensitize simultaneously. Etiology of this phenomenon has been suggested to be allergen cross-reactivity or concurrent exposure. However, little is known about specific allergen sensitization patterns. To investigate the allergen sensitization characteristics according to gender. Multiple allergen simultaneous test (MAST) is widely used as a screening tool for detecting allergen sensitization in dermatologic clinics. We retrospectively reviewed the medical records of patients with MAST results between 2008 and 2014 in our Department of Dermatology. A cluster analysis was performed to elucidate the allergen-specific immunoglobulin (Ig)E cluster pattern. The results of MAST (39 allergen-specific IgEs) from 4,360 cases were analyzed. By cluster analysis, 39items were grouped into 8 clusters. Each cluster had characteristic features. When compared with female, the male group tended to be sensitized more frequently to all tested allergens, except for fungus allergens cluster. The cluster and comparative analysis results demonstrate that the allergen sensitization is clustered, manifesting allergen similarity or co-exposure. Only the fungus cluster allergens tend to sensitize female group more frequently than male group.

Quantum size correction to the work function and the centroid of excess charge in positively ionized simple metal clusters

Directory of Open Access Journals (Sweden)

M. Payami

2003-12-01

Full Text Available  In this work, we have shown the important role of the finite-size correction to the work function in predicting the correct position of the centroid of excess charge in positively charged simple metal clusters with different values . For this purpose, firstly we have calculated the self-consistent Kohn-Sham energies of neutral and singly-ionized clusters with sizes in the framework of local spin-density approximation and stabilized jellium model (SJM as well as simple jellium model (JM with rigid jellium. Secondly, we have fitted our results to the asymptotic ionization formulas both with and without the size correction to the work function. The results of fittings show that the formula containing the size correction predict a correct position of the centroid inside the jellium while the other predicts a false position, outside the jellium sphere.

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

Science.gov (United States)

Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

2013-03-01

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results.

Science.gov (United States)

Joshi, Vineet K; Freudenberg, Johannes M; Hu, Zhen; Medvedovic, Mario

2011-01-17

Cluster analysis methods have been extensively researched, but the adoption of new methods is often hindered by technical barriers in their implementation and use. WebGimm is a free cluster analysis web-service, and an open source general purpose clustering web-server infrastructure designed to facilitate easy deployment of integrated cluster analysis servers based on clustering and functional annotation algorithms implemented in R. Integrated functional analyses and interactive browsing of both, clustering structure and functional annotations provides a complete analytical environment for cluster analysis and interpretation of results. The Java Web Start client-based interface is modeled after the familiar cluster/treeview packages making its use intuitive to a wide array of biomedical researchers. For biomedical researchers, WebGimm provides an avenue to access state of the art clustering procedures. For Bioinformatics methods developers, WebGimm offers a convenient avenue to deploy their newly developed clustering methods. WebGimm server, software and manuals can be freely accessed at http://ClusterAnalysis.org/.

Cluster analysis of activity-time series in motor learning

DEFF Research Database (Denmark)

Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

2002-01-01

Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

Dense Fe cluster-assembled films by energetic cluster deposition

International Nuclear Information System (INIS)

Peng, D.L.; Yamada, H.; Hihara, T.; Uchida, T.; Sumiyama, K.

2004-01-01

High-density Fe cluster-assembled films were produced at room temperature by an energetic cluster deposition. Though cluster-assemblies are usually sooty and porous, the present Fe cluster-assembled films are lustrous and dense, revealing a soft magnetic behavior. Size-monodispersed Fe clusters with the mean cluster size d=9 nm were synthesized using a plasma-gas-condensation technique. Ionized clusters are accelerated electrically and deposited onto the substrate together with neutral clusters from the same cluster source. Packing fraction and saturation magnetic flux density increase rapidly and magnetic coercivity decreases remarkably with increasing acceleration voltage. The Fe cluster-assembled film obtained at the acceleration voltage of -20 kV has a packing fraction of 0.86±0.03, saturation magnetic flux density of 1.78±0.05 Wb/m 2 , and coercivity value smaller than 80 A/m. The resistivity at room temperature is ten times larger than that of bulk Fe metal

Percolation approach for atomic and molecular cluster formation

International Nuclear Information System (INIS)

Knospe, O.; Seifert, G.

1987-12-01

We apply a percolation approach for the theoretical analysis of mass spectra of molecular microclusters obtained by adiabatic expansion technique. The evolution of the shape of the experimental size distributions as function of stagnation pressure and stagnation temperature are theoretically reproduced by varying the percolation parameter. Remaining discrepancies between theory and experiment are discussed. In addition, the even-odd alternation as well as the 'magic' shell structure within metallic, secondary ion mass spectra are investigated by introducing statistical weights for the cluster formation probabilities. Shell correction energies of atomic clusters as function of cluster-size are deduced from the experimental data. (orig.)

Physicochemical properties of different corn varieties by principal components analysis and cluster analysis

International Nuclear Information System (INIS)

Zeng, J.; Li, G.; Sun, J.

2013-01-01

Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties. The chemical compositions and some properties of corn flour which processed by drying milling were determined. The results showed that the chemical compositions and physicochemical properties were significantly different among twenty six corn varieties. The quality of corn flour was concerned with five principal components from principal component analysis and the contribution rate of starch pasting properties was important, which could account for 48.90%. Twenty six corn varieties could be classified into four groups by cluster analysis. The consistency between principal components analysis and cluster analysis indicated that multivariate analyses were feasible in the study of corn variety properties. (author)

Taxonomical analysis of the Cancer cluster of galaxies

International Nuclear Information System (INIS)

Perea, J.; Olmo, A. del; Moles, M.

1986-01-01

A description is presented of the Cancer cluster of galaxies, based on a taxonomical analysis in (α,delta, Vsub(r)) space. Earlier results by previous authors on the lack of dynamical entity of the cluster are confirmed. The present analysis points out the existence of a binary structure in the most populated region of the complex. (author)

DNA-Protected Silver Clusters for Nanophotonics

Directory of Open Access Journals (Sweden)

Elisabeth Gwinn

2015-02-01

Full Text Available DNA-protected silver clusters (AgN-DNA possess unique fluorescence properties that depend on the specific DNA template that stabilizes the cluster. They exhibit peak emission wavelengths that range across the visible and near-IR spectrum. This wide color palette, combined with low toxicity, high fluorescence quantum yields of some clusters, low synthesis costs, small cluster sizes and compatibility with DNA are enabling many applications that employ AgN-DNA. Here we review what is known about the underlying composition and structure of AgN-DNA, and how these relate to the optical properties of these fascinating, hybrid biomolecule-metal cluster nanomaterials. We place AgN-DNA in the general context of ligand-stabilized metal clusters and compare their properties to those of other noble metal clusters stabilized by small molecule ligands. The methods used to isolate pure AgN-DNA for analysis of composition and for studies of solution and single-emitter optical properties are discussed. We give a brief overview of structurally sensitive chiroptical studies, both theoretical and experimental, and review experiments on bringing silver clusters of distinct size and color into nanoscale DNA assemblies. Progress towards using DNA scaffolds to assemble multi-cluster arrays is also reviewed.

Phenotype Clustering of Breast Epithelial Cells in Confocal Imagesbased on Nuclear Protein Distribution Analysis

Energy Technology Data Exchange (ETDEWEB)

Long, Fuhui; Peng, Hanchuan; Sudar, Damir; Levievre, Sophie A.; Knowles, David W.

2006-09-05

Background: The distribution of the chromatin-associatedproteins plays a key role in directing nuclear function. Previously, wedeveloped an image-based method to quantify the nuclear distributions ofproteins and showed that these distributions depended on the phenotype ofhuman mammary epithelial cells. Here we describe a method that creates ahierarchical tree of the given cell phenotypes and calculates thestatistical significance between them, based on the clustering analysisof nuclear protein distributions. Results: Nuclear distributions ofnuclear mitotic apparatus protein were previously obtained fornon-neoplastic S1 and malignant T4-2 human mammary epithelial cellscultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 andthe number of days in cultured. A probabilistic ensemble approach wasused to define a set of consensus clusters from the results of multipletraditional cluster analysis techniques applied to the nucleardistribution data. Cluster histograms were constructed to show how cellsin any one phenotype were distributed across the consensus clusters.Grouping various phenotypes allowed us to build phenotype trees andcalculate the statistical difference between each group. The resultsshowed that non-neoplastic S1 cells could be distinguished from malignantT4-2 cells with 94.19 percent accuracy; that proliferating S1 cells couldbe distinguished from differentiated S1 cells with 92.86 percentaccuracy; and showed no significant difference between the variousphenotypes of T4-2 cells corresponding to increasing tumor sizes.Conclusion: This work presents a cluster analysis method that canidentify significant cell phenotypes, based on the nuclear distributionof specific proteins, with high accuracy.

Multiscale deep drawing analysis of dual-phase steels using grain cluster-based RGC scheme

International Nuclear Information System (INIS)

Tjahjanto, D D; Eisenlohr, P; Roters, F

2015-01-01

Multiscale modelling and simulation play an important role in sheet metal forming analysis, since the overall material responses at macroscopic engineering scales, e.g. formability and anisotropy, are strongly influenced by microstructural properties, such as grain size and crystal orientations (texture). In the present report, multiscale analysis on deep drawing of dual-phase steels is performed using an efficient grain cluster-based homogenization scheme.The homogenization scheme, called relaxed grain cluster (RGC), is based on a generalization of the grain cluster concept, where a (representative) volume element consists of p  ×  q  ×  r (hexahedral) grains. In this scheme, variation of the strain or deformation of individual grains is taken into account through the, so-called, interface relaxation, which is formulated within an energy minimization framework. An interfacial penalty term is introduced into the energy minimization framework in order to account for the effects of grain boundaries.The grain cluster-based homogenization scheme has been implemented and incorporated into the advanced material simulation platform DAMASK, which purposes to bridge the macroscale boundary value problems associated with deep drawing analysis to the micromechanical constitutive law, e.g. crystal plasticity model. Standard Lankford anisotropy tests are performed to validate the model parameters prior to the deep drawing analysis. Model predictions for the deep drawing simulations are analyzed and compared to the corresponding experimental data. The result shows that the predictions of the model are in a very good agreement with the experimental measurement. (paper)

Assessment of surface water quality using hierarchical cluster analysis

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Dheeraj Kumar Dabgerwal

2016-02-01

Full Text Available This study was carried out to assess the physicochemical quality river Varuna inVaranasi,India. Water samples were collected from 10 sites during January-June 2015. Pearson correlation analysis was used to assess the direction and strength of relationship between physicochemical parameters. Hierarchical Cluster analysis was also performed to determine the sources of pollution in the river Varuna. The result showed quite high value of DO, Nitrate, BOD, COD and Total Alkalinity, above the BIS permissible limit. The results of correlation analysis identified key water parameters as pH, electrical conductivity, total alkalinity and nitrate, which influence the concentration of other water parameters. Cluster analysis identified three major clusters of sampling sites out of total 10 sites, according to the similarity in water quality. This study illustrated the usefulness of correlation and cluster analysis for getting better information about the river water quality.International Journal of Environment Vol. 5 (1 2016,  pp: 32-44

Size exclusion chromatography for semipreparative scale separation of Au38(SR)24 and Au40(SR)24 and larger clusters.

Science.gov (United States)

Knoppe, Stefan; Boudon, Julien; Dolamic, Igor; Dass, Amala; Bürgi, Thomas

2011-07-01

Size exclusion chromatography (SEC) on a semipreparative scale (10 mg and more) was used to size-select ultrasmall gold nanoclusters (<2 nm) from polydisperse mixtures. In particular, the ubiquitous byproducts of the etching process toward Au(38)(SR)(24) (SR, thiolate) clusters were separated and gained in high monodispersity (based on mass spectrometry). The isolated fractions were characterized by UV-vis spectroscopy, MALDI mass spectrometry, HPLC, and electron microscopy. Most notably, the separation of Au(38)(SR)(24) and Au(40)(SR)(24) clusters is demonstrated.

Site-specific fragmentation of polystyrene molecule using size-selected Ar gas cluster ion beam

International Nuclear Information System (INIS)

Moritani, Kousuke; Mukai, Gen; Hashinokuchi, Michihiro; Mochiji, Kozo

2009-01-01

The secondary ion mass spectrum (SIMS) of a polystyrene thin film was investigated using a size-selected Ar gas cluster ion beam (GCIB). The fragmentation in the SIM spectrum varied by kinetic energy per atom (E atom ); the E atom dependence of the secondary ion intensity of the fragment species of polystyrene can be essentially classified into three types based on the relationship between E atom and the dissociation energy of a specific bonding site in the molecule. These results indicate that adjusting E atom of size-selected GCIB may realize site-specific bond breaking within a molecule. (author)

Exploring the Potential of Different-Sized Supported Subnanometer Pt Clusters as Catalysts for Wet Chemical Applications

KAUST Repository

Rondelli, Manuel; Zwaschka, Gregor; Krause, Maximilian; Rö tzer, Marian D.; Hedhili, Mohamed N.; Hogerl, Manuel Peter; D’ Elia, Valerio; Schweinberger, Florian F.; Basset, Jean-Marie; Heiz, Ueli

2017-01-01

as catalysts for organic chemistry transformations in solution has not been explored. To this end, single Pt atoms and Pt clusters with two narrow size distributions were prepared in the UHV and applied for the hydrogenation of p-chloronitrobenzene to p

Macroeconomic Dimensions in the Clusterization Processes: Lithuanian Biomass Cluster Case

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Navickas Valentinas

2017-03-01

Full Text Available The Future production systems’ increasing significance will impose work, which maintains not a competitive, but a collaboration basis, with concentrated resources and expertise, which can help to reach the general purpose. One form of collaboration among medium-size business organizations is work in clusters. Clusterization as a phenomenon has been known from quite a long time, but it offers simple benefits to researches at micro and medium levels. The clusterization process evaluation in macroeconomic dimensions has been comparatively little investigated. Thereby, in this article, the clusterization processes is analysed by concentrating our attention on macroeconomic factor researches. The authors analyse clusterization’s influence on country’s macroeconomic growth; they apply a structure research methodology for clusterization’s macroeconomic influence evaluation and propose that clusterization processes benefit macroeconomic analysis. The theoretical model of clusterization processes was validated by referring to a biomass cluster case. Because biomass cluster case is a new phenomenon, currently there are no other scientific approaches to them. The authors’ accomplished researches show that clusterization allows the achievement of a large positive slip in macroeconomics, which proves to lead to a high value added to creation, a faster country economic growth, and social situation amelioration.

Conformationally averaged vertical detachment energy of finite size NO3(-)·nH2O clusters: a route connecting few to many.

Science.gov (United States)

Pathak, Arup Kumar; Samanta, Alok Kumar; Maity, Dilip Kumar

2011-04-07

We report conformationally averaged VDEs (VDE(w)(n)) for different sizes of NO(3)(-)·nH(2)O clusters calculated by using uncorrelated HF, correlated hybrid density functional (B3LYP, BHHLYP) and correlated ab intio (MP2 and CCSD(T)) theory. It is observed that the VDE(w)(n) at the B3LYP/6-311++G(d,p), B3LYP/Aug-cc-Pvtz and CCSD(T)/6-311++G(d,p) levels is very close to the experimentally measured VDE. It is shown that the use of calculated results of the conformationally averaged VDE for small-sized solvated negatively-charged clusters and a microscopic theory-based general expression for the same provides a route to obtain the VDE for a wide range of cluster sizes, including bulk.

Analysis of Aspects of Innovation in a Brazilian Cluster

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Adriana Valélia Saraceni

2012-09-01

Full Text Available Innovation through clustering has become very important on the increased significance that interaction represents on innovation and learning process concept. This study aims to identify whereas a case analysis on innovation process in a cluster represents on the learning process. Therefore, this study is developed in two stages. First, we used a preliminary case study verifying a cluster innovation analysis and it Innovation Index, for further, exploring a combined body of theory and practice. Further, the second stage is developed by exploring the learning process concept. Both stages allowed us building a theory model for the learning process development in clusters. The main results of the model development come up with a mechanism of improvement implementation on clusters when case studies are applied.

A Lagrangian Analysis of Cold Cloud Clusters and Their Life Cycles With Satellite Observations

Science.gov (United States)

Esmaili, Rebekah Bradley; Tian, Yudong; Vila, Daniel Alejandro; Kim, Kyu-Myong

2016-01-01

Cloud movement and evolution signify the complex water and energy transport in the atmosphere-ocean-land system. Detecting, clustering, and tracking clouds as semi coherent cluster objects enables study of their evolution which can complement climate model simulations and enhance satellite retrieval algorithms, where there are large gaps between overpasses. Using an area-overlap cluster tracking algorithm, in this study we examine the trajectories, horizontal extent, and brightness temperature variations of millions of individual cloud clusters over their lifespan, from infrared satellite observations at 30-minute, 4-km resolution, for a period of 11 years. We found that the majority of cold clouds were both small and short-lived and that their frequency and location are influenced by El Nino. More importantly, this large sample of individually tracked clouds shows their horizontal size and temperature evolution. Longer lived clusters tended to achieve their temperature and size maturity milestones at different times, while these stages often occurred simultaneously in shorter lived clusters. On average, clusters with this lag also exhibited a greater rainfall contribution than those where minimum temperature and maximum size stages occurred simultaneously. Furthermore, by examining the diurnal cycle of cluster development over Africa and the Indian subcontinent, we observed differences in the local timing of the maximum occurrence at different life cycle stages. Over land there was a strong diurnal peak in the afternoon while over the ocean there was a semi-diurnal peak composed of longer-lived clusters in the early morning hours and shorter-lived clusters in the afternoon. Building on regional specific work, this study provides a long-term, high-resolution, and global survey of object-based cloud characteristics.

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

Science.gov (United States)

Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

2017-01-01

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

Phenotypes Determined by Cluster Analysis in Moderate to Severe Bronchial Asthma.

Science.gov (United States)

Youroukova, Vania M; Dimitrova, Denitsa G; Valerieva, Anna D; Lesichkova, Spaska S; Velikova, Tsvetelina V; Ivanova-Todorova, Ekaterina I; Tumangelova-Yuzeir, Kalina D

2017-06-01

Bronchial asthma is a heterogeneous disease that includes various subtypes. They may share similar clinical characteristics, but probably have different pathological mechanisms. To identify phenotypes using cluster analysis in moderate to severe bronchial asthma and to compare differences in clinical, physiological, immunological and inflammatory data between the clusters. Forty adult patients with moderate to severe bronchial asthma out of exacerbation were included. All underwent clinical assessment, anthropometric measurements, skin prick testing, standard spirometry and measurement fraction of exhaled nitric oxide. Blood eosinophilic count, serum total IgE and periostin levels were determined. Two-step cluster approach, hierarchical clustering method and k-mean analysis were used for identification of the clusters. We have identified four clusters. Cluster 1 (n=14) - late-onset, non-atopic asthma with impaired lung function, Cluster 2 (n=13) - late-onset, atopic asthma, Cluster 3 (n=6) - late-onset, aspirin sensitivity, eosinophilic asthma, and Cluster 4 (n=7) - early-onset, atopic asthma. Our study is the first in Bulgaria in which cluster analysis is applied to asthmatic patients. We identified four clusters. The variables with greatest force for differentiation in our study were: age of asthma onset, duration of diseases, atopy, smoking, blood eosinophils, nonsteroidal anti-inflammatory drugs hypersensitivity, baseline FEV1/FVC and symptoms severity. Our results support the concept of heterogeneity of bronchial asthma and demonstrate that cluster analysis can be an useful tool for phenotyping of disease and personalized approach to the treatment of patients.

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

International Nuclear Information System (INIS)

Klassmann, A.

1997-01-01

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

Estimating multi-phase pore-scale characteristics from X-ray tomographic data using cluster analysis-based segmentation

DEFF Research Database (Denmark)

Wildenschild, D.; Culligan, K.A.; Christensen, Britt Stenhøj Baun

2006-01-01

present in grey-scale X-ray tomographic images. The approach is based on a cluster analysis technique, used in combination with various other filtering and skeletonization schemes. We apply this segmentation algorithm to analyze multiphase pore-scale flow subjects such as hysteresis and interfacial...... characterization. The results clearly illustrate the advantage of using X-ray tomography together with cluster analysis-based image processing techniques. We were able to obtain detailed information on pore scale distribution of air and water phases, as well as quantitative measures of air bubble size and air...... of individual pores and interfaces. However, separation of the various phases (fluids and solids) in the grey-scale tomographic images has posed a major problem to quantitative analysis of the data. We present an image processing technique that facilitates identification and separation of the various phases...

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

Science.gov (United States)

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Formation of stable products from cluster-cluster collisions

International Nuclear Information System (INIS)

Alamanova, Denitsa; Grigoryan, Valeri G; Springborg, Michael

2007-01-01

The formation of stable products from copper cluster-cluster collisions is investigated by using classical molecular-dynamics simulations in combination with an embedded-atom potential. The dependence of the product clusters on impact energy, relative orientation of the clusters, and size of the clusters is studied. The structures and total energies of the product clusters are analysed and compared with those of the colliding clusters before impact. These results, together with the internal temperature, are used in obtaining an increased understanding of cluster fusion processes

STM observations of ferromagnetic clusters

International Nuclear Information System (INIS)

Wawro, A.; Kasuya, A.

1998-01-01

Co, Fe and Ni clusters of nanometer size, deposited on silicon and graphite (highly oriented pyrolytic graphite), were observed by a scanning tunneling microscope. Deposition as well as the scanning tunneling microscope measurements were carried out in an ultrahigh vacuum system at room temperature. Detailed analysis of Co cluster height was done with the scanning tunneling microscope equipped with a ferromagnetic tip in a magnetic field up to 70 Oe. It is found that bigger clusters (few nanometers in height) exhibit a dependence of their apparent height on applied magnetic field. We propose that such behaviour originates from the ferromagnetic ordering of cluster and associate this effect to spin polarized tunneling. (author)

Cluster analysis for determining distribution center location

Science.gov (United States)

Lestari Widaningrum, Dyah; Andika, Aditya; Murphiyanto, Richard Dimas Julian

2017-12-01

Determination of distribution facilities is highly important to survive in the high level of competition in today’s business world. Companies can operate multiple distribution centers to mitigate supply chain risk. Thus, new problems arise, namely how many and where the facilities should be provided. This study examines a fast-food restaurant brand, which located in the Greater Jakarta. This brand is included in the category of top 5 fast food restaurant chain based on retail sales. There were three stages in this study, compiling spatial data, cluster analysis, and network analysis. Cluster analysis results are used to consider the location of the additional distribution center. Network analysis results show a more efficient process referring to a shorter distance to the distribution process.

Cluster analysis of behavioural weight management strategies and associations with weight change in young women: a longitudinal analysis.

Science.gov (United States)

Madigan, C D; Daley, A J; Kabir, E; Aveyard, P; Brown, W

2015-11-01

Maintaining a healthy weight is important for the prevention of many chronic diseases. Little is known about the strategies used by young women to manage their weight, or the effectiveness of these in preventing weight gain. We aimed to identify clusters of weight control strategies used by women and to determine the average annual weight change among women in each cluster from 2000 to 2009. Latent cluster analysis of weight control strategies reported by 8125 participants in the Australian Longitudinal Study of Women's Health. Analyses were performed in March-November 2014. Weight control strategies were used by 79% of the women, and four unique clusters were found. The largest cluster group (39.7%) was named dieters as 90% had been on a diet in the past year, and half of these women had lost 5 kg on purpose. Women cut down on size of meals, fats and sugars and took part in vigorous physical activity. Additionally 20% had used a commercial programme. The next largest cluster (30.2%) was the healthy living group who followed the public health messages of 'eat less and move more'. The do nothing group (20%) did not actively control their weight whereas the perpetual dieters group (10.7%) used all strategies, including unhealthy behaviours. On average women gained 700 g per year (over 9 years); however, the perpetual dieters group gained significantly more weight (210 g) than the do nothing group (Phealth guidelines on health eating and physical activity.

Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio

Directory of Open Access Journals (Sweden)

Elena ANDREI (DRAGOMIR

2012-08-01

Full Text Available Methods underlying cluster analysis are very useful in data analysis, especially when the processed volume of data is very large, so that it becomes impossible to extract essential information, unless specific instruments are used to summarize and structure the gross information. In this context, cluster analysis techniques are used particularly, for systematic information analysis. The aim of this article is to build an useful model for banking field, based on data mining techniques, by dividing the groups of borrowers into clusters, in order to obtain a profile of the customers (debtors and good payers. We assume that a class is appropriate if it contains members that have a high degree of similarity and the standard method for measuring the similarity within a group shows the lowest variance. After clustering, data mining techniques are implemented on the cluster with bad debtors, reaching a very high accuracy after implementation. The paper is structured as follows: Section 2 describes the model for data analysis based on a specific scoring model that we proposed. In section 3, we present a cluster analysis using K-means algorithm and the DM models are applied on a specific cluster. Section 4 shows the conclusions.

Cluster Analysis as an Analytical Tool of Population Policy

Directory of Open Access Journals (Sweden)

Oksana Mikhaylovna Shubat

2017-12-01

Full Text Available The predicted negative trends in Russian demography (falling birth rates, population decline actualize the need to strengthen measures of family and population policy. Our research purpose is to identify groups of Russian regions with similar characteristics in the family sphere using cluster analysis. The findings should make an important contribution to the field of family policy. We used hierarchical cluster analysis based on the Ward method and the Euclidean distance for segmentation of Russian regions. Clustering is based on four variables, which allowed assessing the family institution in the region. The authors used the data of Federal State Statistics Service from 2010 to 2015. Clustering and profiling of each segment has allowed forming a model of Russian regions depending on the features of the family institution in these regions. The authors revealed four clusters grouping regions with similar problems in the family sphere. This segmentation makes it possible to develop the most relevant family policy measures in each group of regions. Thus, the analysis has shown a high degree of differentiation of the family institution in the regions. This suggests that a unified approach to population problems’ solving is far from being effective. To achieve greater results in the implementation of family policy, a differentiated approach is needed. Methods of multidimensional data classification can be successfully applied as a relevant analytical toolkit. Further research could develop the adaptation of multidimensional classification methods to the analysis of the population problems in Russian regions. In particular, the algorithms of nonparametric cluster analysis may be of relevance in future studies.

A critical evaluation of the use of cluster analysis to identify contaminated sediments in the Ria de Vigo

Energy Technology Data Exchange (ETDEWEB)

Rubio, B; Nombela, M. A; Vilas, F [Departamento de Geociencias Marinas y Ordenacion del Territorio, Vigo, Espana (Spain)

2001-06-01

The indiscriminate use of cluster analysis to distinguish contaminated and non-contaminated sediments has led us to make a comparative evaluation of different cluster analysis procedures as applied to heavy metal concentrations in subtidal sediments from the Ria de Vigo, NW Spain. The use of different clusters algorithms and other transformations from the same departing set of data lead to the formation of different clusters with a clear inconclusive result about the contamination status of the sediments. The results show that this approach is better suited to identifying groups of samples differing in sedimentological characteristics, such as grain size, rather than in the degree of contamination. Our main aim is to call attention to these aspects in cluster analysis and to suggest that researches should be rigorous with this kind of analysis. Finally, the use of discriminate analysis allows us to find a discriminate function that separates the samples into two clearly differentiated groups, which should not be treated jointly. [Spanish] El uso indiscriminado del analisis cluster para distinguir sedimentos contaminados y no contaminados nos ha llevado a realizar una evaluacion comparativa entre los diferentes procedimientos de estos analisis aplicada a la concentracion de metales pesados en sedimentos submareales de la Ria de Vigo, NW de Espana. La utilizacion de distintos algoritmos de cluster, asi como otras transformaciones de la misma matriz de datos conduce a la formacion de diferentes clusters con un resultado inconcluso sobre el estado de contaminacion de los sedimentos. Los resultados muestran que esta aproximacion se ajusta mejor para identificar grupos de muestras que difieren en caracteristicas sedimentologicas, tal como el tamano de grano, mas que el grado de contaminacion. El principal objetivo es llamar la atencion sobre estos aspectos del analisis cluster y sugerir a los investigadores que sean rigurosos con este tipo de analisis. Finalmente el uso

Clinical Characteristics of Exacerbation-Prone Adult Asthmatics Identified by Cluster Analysis.

Science.gov (United States)

Kim, Mi Ae; Shin, Seung Woo; Park, Jong Sook; Uh, Soo Taek; Chang, Hun Soo; Bae, Da Jeong; Cho, You Sook; Park, Hae Sim; Yoon, Ho Joo; Choi, Byoung Whui; Kim, Yong Hoon; Park, Choon Sik

2017-11-01

Asthma is a heterogeneous disease characterized by various types of airway inflammation and obstruction. Therefore, it is classified into several subphenotypes, such as early-onset atopic, obese non-eosinophilic, benign, and eosinophilic asthma, using cluster analysis. A number of asthmatics frequently experience exacerbation over a long-term follow-up period, but the exacerbation-prone subphenotype has rarely been evaluated by cluster analysis. This prompted us to identify clusters reflecting asthma exacerbation. A uniform cluster analysis method was applied to 259 adult asthmatics who were regularly followed-up for over 1 year using 12 variables, selected on the basis of their contribution to asthma phenotypes. After clustering, clinical profiles and exacerbation rates during follow-up were compared among the clusters. Four subphenotypes were identified: cluster 1 was comprised of patients with early-onset atopic asthma with preserved lung function, cluster 2 late-onset non-atopic asthma with impaired lung function, cluster 3 early-onset atopic asthma with severely impaired lung function, and cluster 4 late-onset non-atopic asthma with well-preserved lung function. The patients in clusters 2 and 3 were identified as exacerbation-prone asthmatics, showing a higher risk of asthma exacerbation. Two different phenotypes of exacerbation-prone asthma were identified among Korean asthmatics using cluster analysis; both were characterized by impaired lung function, but the age at asthma onset and atopic status were different between the two. Copyright © 2017 The Korean Academy of Asthma, Allergy and Clinical Immunology · The Korean Academy of Pediatric Allergy and Respiratory Disease

Cluster growing process and a sequence of magic numbers

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2003-01-01

demonstrate that in this way all known global minimum structures of the Lennard-Jones (LJ) clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence for the clusters of noble gas atoms......We present a new theoretical framework for modeling the cluster growing process. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system, and absorbing its energy at each step, we find cluster growing paths up to the cluster sizes of more than 100 atoms. We...

Automated analysis of organic particles using cluster SIMS

Energy Technology Data Exchange (ETDEWEB)

Gillen, Greg; Zeissler, Cindy; Mahoney, Christine; Lindstrom, Abigail; Fletcher, Robert; Chi, Peter; Verkouteren, Jennifer; Bright, David; Lareau, Richard T.; Boldman, Mike

2004-06-15

Cluster primary ion bombardment combined with secondary ion imaging is used on an ion microscope secondary ion mass spectrometer for the spatially resolved analysis of organic particles on various surfaces. Compared to the use of monoatomic primary ion beam bombardment, the use of a cluster primary ion beam (SF{sub 5}{sup +} or C{sub 8}{sup -}) provides significant improvement in molecular ion yields and a reduction in beam-induced degradation of the analyte molecules. These characteristics of cluster bombardment, along with automated sample stage control and custom image analysis software are utilized to rapidly characterize the spatial distribution of trace explosive particles, narcotics and inkjet-printed microarrays on a variety of surfaces.

Impact of sample size on principal component analysis ordination of an environmental data set: effects on eigenstructure

Directory of Open Access Journals (Sweden)

Shaukat S. Shahid

2016-06-01

Full Text Available In this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50 on the eigenvalues and eigenvectors resulting from principal component analysis (PCA. For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22 of a small data set comprising of 55 samples (stations from where water samples were collected. Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.

Data Clustering

Science.gov (United States)

Wagstaff, Kiri L.

2012-03-01

On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

A cluster analysis study based on profitability and financial indicators in the Italian gas retail market

Energy Technology Data Exchange (ETDEWEB)

Capece, Guendalina; Di Pillo, Francesca; Levialdi, Nathan [Dipartimento di Ingegneria dell' Impresa, Universita di Roma ' Tor Vergata' , Via del Politecnico 1, 00133 Roma (Italy); Cricelli, Livio [Dipartimento di Meccanica, Strutture, Ambiente e Territorio, Universita di Cassino, Via G. Di Biasio 43, 03043 Cassino (Italy)

2010-07-15

In the European Union, the natural gas market is increasingly being liberalized. The liberalization process is aimed at leading to lower prices and higher volumes, and hence higher consumer welfare. This paper focuses on the changes in performance in the natural gas retail market by analyzing the profit and financial position of the companies concerned over the first three years following the market liberalization. The balance sheets of 105 Italian companies in this sector are analyzed, after which a cluster analysis is performed employing the most significant performance indexes. The companies are then analyzed within each cluster with respect to age, size, geographical location and business diversification. The results of our analysis show that the majority of companies attained a high level of performance, although this positive outcome was mitigated by the gradual decrease of the average values of performance indicators during the period concerned. The companies that achieve the best performances belong to longstanding business groups, are medium-large sized and are located in the north of the country. Regarding business diversification, in the first two years, the specialised companies outperformed the diversified companies. (author)

A cluster analysis study based on profitability and financial indicators in the Italian gas retail market

International Nuclear Information System (INIS)

Capece, Guendalina; Di Pillo, Francesca; Levialdi, Nathan; Cricelli, Livio

2010-01-01

In the European Union, the natural gas market is increasingly being liberalized. The liberalization process is aimed at leading to lower prices and higher volumes, and hence higher consumer welfare. This paper focuses on the changes in performance in the natural gas retail market by analyzing the profit and financial position of the companies concerned over the first three years following the market liberalization. The balance sheets of 105 Italian companies in this sector are analyzed, after which a cluster analysis is performed employing the most significant performance indexes. The companies are then analyzed within each cluster with respect to age, size, geographical location and business diversification. The results of our analysis show that the majority of companies attained a high level of performance, although this positive outcome was mitigated by the gradual decrease of the average values of performance indicators during the period concerned. The companies that achieve the best performances belong to longstanding business groups, are medium-large sized and are located in the north of the country. Regarding business diversification, in the first two years, the specialised companies outperformed the diversified companies. (author)

Network Analysis Tools: from biological networks to clusters and pathways.

Science.gov (United States)

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

Performance analysis of clustering techniques over microarray data: A case study

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Dash, Rasmita; Misra, Bijan Bihari

2018-03-01

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

Direct on-strip analysis of size- and time-resolved aerosol impactor samples using laser induced fluorescence spectra excited at 263 and 351 nm

International Nuclear Information System (INIS)

Wang, Chuji; Pan, Yong-Le; James, Deryck; Wetmore, Alan E.; Redding, Brandon

2014-01-01

Highlights: • A dual wavelength UV-LIF spectra-rotating drum impactor (RDI) technique was developed. • The technique was demonstrated by direct on-strip analysis of size- and time-resolved LIF spectra of atmospheric aerosol particles. • More than 2000 LIF spectra of atmospheric aerosol particles collected over three weeks in Djibouti were obtained and assigned to various fluorescence clusters. • The LIF spectra showed size- and time-sensitivity behavior with a time resolution of 3.6 h. - Abstract: We report a novel atmospheric aerosol characterization technique, in which dual wavelength UV laser induced fluorescence (LIF) spectrometry marries an eight-stage rotating drum impactor (RDI), namely UV-LIF-RDI, to achieve size- and time-resolved analysis of aerosol particles on-strip. The UV-LIF-RDI technique measured LIF spectra via direct laser beam illumination onto the particles that were impacted on a RDI strip with a spatial resolution of 1.2 mm, equivalent to an averaged time resolution in the aerosol sampling of 3.6 h. Excited by a 263 nm or 351 nm laser, more than 2000 LIF spectra within a 3-week aerosol collection time period were obtained from the eight individual RDI strips that collected particles in eight different sizes ranging from 0.09 to 10 μm in Djibouti. Based on the known fluorescence database from atmospheric aerosols in the US, the LIF spectra obtained from the Djibouti aerosol samples were found to be dominated by fluorescence clusters 2, 5, and 8 (peaked at 330, 370, and 475 nm) when excited at 263 nm and by fluorescence clusters 1, 2, 5, and 6 (peaked at 390 and 460 nm) when excited at 351 nm. Size- and time-dependent variations of the fluorescence spectra revealed some size and time evolution behavior of organic and biological aerosols from the atmosphere in Djibouti. Moreover, this analytical technique could locate the possible sources and chemical compositions contributing to these fluorescence clusters. Advantages, limitations, and

Direct on-strip analysis of size- and time-resolved aerosol impactor samples using laser induced fluorescence spectra excited at 263 and 351 nm

Energy Technology Data Exchange (ETDEWEB)

Wang, Chuji [U.S. Army Research Laboratory, Adelphi, MD 20783 (United States); Mississippi State University, Starkville, MS, 39759 (United States); Pan, Yong-Le, E-mail: yongle.pan.civ@mail.mil [U.S. Army Research Laboratory, Adelphi, MD 20783 (United States); James, Deryck; Wetmore, Alan E. [U.S. Army Research Laboratory, Adelphi, MD 20783 (United States); Redding, Brandon [Yale University, New Haven, CT 06510 (United States)

2014-04-01

Highlights: • A dual wavelength UV-LIF spectra-rotating drum impactor (RDI) technique was developed. • The technique was demonstrated by direct on-strip analysis of size- and time-resolved LIF spectra of atmospheric aerosol particles. • More than 2000 LIF spectra of atmospheric aerosol particles collected over three weeks in Djibouti were obtained and assigned to various fluorescence clusters. • The LIF spectra showed size- and time-sensitivity behavior with a time resolution of 3.6 h. - Abstract: We report a novel atmospheric aerosol characterization technique, in which dual wavelength UV laser induced fluorescence (LIF) spectrometry marries an eight-stage rotating drum impactor (RDI), namely UV-LIF-RDI, to achieve size- and time-resolved analysis of aerosol particles on-strip. The UV-LIF-RDI technique measured LIF spectra via direct laser beam illumination onto the particles that were impacted on a RDI strip with a spatial resolution of 1.2 mm, equivalent to an averaged time resolution in the aerosol sampling of 3.6 h. Excited by a 263 nm or 351 nm laser, more than 2000 LIF spectra within a 3-week aerosol collection time period were obtained from the eight individual RDI strips that collected particles in eight different sizes ranging from 0.09 to 10 μm in Djibouti. Based on the known fluorescence database from atmospheric aerosols in the US, the LIF spectra obtained from the Djibouti aerosol samples were found to be dominated by fluorescence clusters 2, 5, and 8 (peaked at 330, 370, and 475 nm) when excited at 263 nm and by fluorescence clusters 1, 2, 5, and 6 (peaked at 390 and 460 nm) when excited at 351 nm. Size- and time-dependent variations of the fluorescence spectra revealed some size and time evolution behavior of organic and biological aerosols from the atmosphere in Djibouti. Moreover, this analytical technique could locate the possible sources and chemical compositions contributing to these fluorescence clusters. Advantages, limitations, and

Cluster analysis of typhoid cases in Kota Bharu, Kelantan, Malaysia

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Nazarudin Safian

2008-09-01

Full Text Available Typhoid fever is still a major public health problem globally as well as in Malaysia. This study was done to identify the spatial epidemiology of typhoid fever in the Kota Bharu District of Malaysia as a first step to developing more advanced analysis of the whole country. The main characteristic of the epidemiological pattern that interested us was whether typhoid cases occurred in clusters or whether they were evenly distributed throughout the area. We also wanted to know at what spatial distances they were clustered. All confirmed typhoid cases that were reported to the Kota Bharu District Health Department from the year 2001 to June of 2005 were taken as the samples. From the home address of the cases, the location of the house was traced and a coordinate was taken using handheld GPS devices. Spatial statistical analysis was done to determine the distribution of typhoid cases, whether clustered, random or dispersed. The spatial statistical analysis was done using CrimeStat III software to determine whether typhoid cases occur in clusters, and later on to determine at what distances it clustered. From 736 cases involved in the study there was significant clustering for cases occurring in the years 2001, 2002, 2003 and 2005. There was no significant clustering in year 2004. Typhoid clustering also occurred strongly for distances up to 6 km. This study shows that typhoid cases occur in clusters, and this method could be applicable to describe spatial epidemiology for a specific area. (Med J Indones 2008; 17: 175-82Keywords: typhoid, clustering, spatial epidemiology, GIS

Cluster analysis of Southeastern U.S. climate stations

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Stooksbury, D. E.; Michaels, P. J.

1991-09-01

A two-step cluster analysis of 449 Southeastern climate stations is used to objectively determine general climate clusters (groups of climate stations) for eight southeastern states. The purpose is objectively to define regions of climatic homogeneity that should perform more robustly in subsequent climatic impact models. This type of analysis has been successfully used in many related climate research problems including the determination of corn/climate districts in Iowa (Ortiz-Valdez, 1985) and the classification of synoptic climate types (Davis, 1988). These general climate clusters may be more appropriate for climate research than the standard climate divisions (CD) groupings of climate stations, which are modifications of the agro-economic United States Department of Agriculture crop reporting districts. Unlike the CD's, these objectively determined climate clusters are not restricted by state borders and thus have reduced multicollinearity which makes them more appropriate for the study of the impact of climate and climatic change.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

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Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hierarchical Star Formation in Turbulent Media: Evidence from Young Star Clusters

Energy Technology Data Exchange (ETDEWEB)

Grasha, K.; Calzetti, D. [Astronomy Department, University of Massachusetts, Amherst, MA 01003 (United States); Elmegreen, B. G. [IBM Research Division, T.J. Watson Research Center, Yorktown Heights, NY (United States); Adamo, A.; Messa, M. [Department of Astronomy, The Oskar Klein Centre, Stockholm University, Stockholm (Sweden); Aloisi, A.; Bright, S. N.; Lee, J. C.; Ryon, J. E.; Ubeda, L. [Space Telescope Science Institute, Baltimore, MD (United States); Cook, D. O. [California Institute of Technology, 1200 East California Boulevard, Pasadena, CA (United States); Dale, D. A. [Department of Physics and Astronomy, University of Wyoming, Laramie, WY (United States); Fumagalli, M. [Institute for Computational Cosmology and Centre for Extragalactic Astronomy, Department of Physics, Durham University, Durham (United Kingdom); Gallagher III, J. S. [Department of Astronomy, University of Wisconsin–Madison, Madison, WI (United States); Gouliermis, D. A. [Zentrum für Astronomie der Universität Heidelberg, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2, D-69120 Heidelberg (Germany); Grebel, E. K. [Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr. 12-14, D-69120, Heidelberg (Germany); Kahre, L. [Department of Astronomy, New Mexico State University, Las Cruces, NM (United States); Kim, H. [Gemini Observatory, La Serena (Chile); Krumholz, M. R., E-mail: kgrasha@astro.umass.edu [Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611 (Australia)

2017-06-10

We present an analysis of the positions and ages of young star clusters in eight local galaxies to investigate the connection between the age difference and separation of cluster pairs. We find that star clusters do not form uniformly but instead are distributed so that the age difference increases with the cluster pair separation to the 0.25–0.6 power, and that the maximum size over which star formation is physically correlated ranges from ∼200 pc to ∼1 kpc. The observed trends between age difference and separation suggest that cluster formation is hierarchical both in space and time: clusters that are close to each other are more similar in age than clusters born further apart. The temporal correlations between stellar aggregates have slopes that are consistent with predictions of turbulence acting as the primary driver of star formation. The velocity associated with the maximum size is proportional to the galaxy’s shear, suggesting that the galactic environment influences the maximum size of the star-forming structures.

Cluster analysis by optimal decomposition of induced fuzzy sets

Energy Technology Data Exchange (ETDEWEB)

Backer, E

1978-01-01

Nonsupervised pattern recognition is addressed and the concept of fuzzy sets is explored in order to provide the investigator (data analyst) additional information supplied by the pattern class membership values apart from the classical pattern class assignments. The basic ideas behind the pattern recognition problem, the clustering problem, and the concept of fuzzy sets in cluster analysis are discussed, and a brief review of the literature of the fuzzy cluster analysis is given. Some mathematical aspects of fuzzy set theory are briefly discussed; in particular, a measure of fuzziness is suggested. The optimization-clustering problem is characterized. Then the fundamental idea behind affinity decomposition is considered. Next, further analysis takes place with respect to the partitioning-characterization functions. The iterative optimization procedure is then addressed. The reclassification function is investigated and convergence properties are examined. Finally, several experiments in support of the method suggested are described. Four object data sets serve as appropriate test cases. 120 references, 70 figures, 11 tables. (RWR)

Graph analysis of cell clusters forming vascular networks

Science.gov (United States)

Alves, A. P.; Mesquita, O. N.; Gómez-Gardeñes, J.; Agero, U.

2018-03-01

This manuscript describes the experimental observation of vasculogenesis in chick embryos by means of network analysis. The formation of the vascular network was observed in the area opaca of embryos from 40 to 55 h of development. In the area opaca endothelial cell clusters self-organize as a primitive and approximately regular network of capillaries. The process was observed by bright-field microscopy in control embryos and in embryos treated with Bevacizumab (Avastin), an antibody that inhibits the signalling of the vascular endothelial growth factor (VEGF). The sequence of images of the vascular growth were thresholded, and used to quantify the forming network in control and Avastin-treated embryos. This characterization is made by measuring vessels density, number of cell clusters and the largest cluster density. From the original images, the topology of the vascular network was extracted and characterized by means of the usual network metrics such as: the degree distribution, average clustering coefficient, average short path length and assortativity, among others. This analysis allows to monitor how the largest connected cluster of the vascular network evolves in time and provides with quantitative evidence of the disruptive effects that Avastin has on the tree structure of vascular networks.

application of single-linkage clustering method in the analysis of ...

African Journals Online (AJOL)

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ANALYSIS OF GROWTH RATE OF GROSS DOMESTIC PRODUCT. (GDP) AT ... The end result of the algorithm is a tree of clusters called a dendrogram, which shows how the clusters are ..... Number of cluster sum from from observations of ...

MANOVA, LDA, and FA criteria in clusters parameter estimation

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Stan Lipovetsky

2015-12-01

Full Text Available Multivariate analysis of variance (MANOVA and linear discriminant analysis (LDA apply such well-known criteria as the Wilks’ lambda, Lawley–Hotelling trace, and Pillai’s trace test for checking quality of the solutions. The current paper suggests using these criteria for building objectives for finding clusters parameters because optimizing such objectives corresponds to the best distinguishing between the clusters. Relation to Joreskog’s classification for factor analysis (FA techniques is also considered. The problem can be reduced to the multinomial parameterization, and solution can be found in a nonlinear optimization procedure which yields the estimates for the cluster centers and sizes. This approach for clustering works with data compressed into covariance matrix so can be especially useful for big data.

A Flocking Based algorithm for Document Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

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

2006-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

SIZE OF LIVESTOCK AGRICULTURAL OPERATIONS

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Bazbanela Stere

2015-07-01

Full Text Available The main goal of the paper is to map the performance of Romanian farms from the perspective of livestock agricultural operations using principal component analysis technique (PCA and similarities between Romania and other countries from UE. The empirical results reveal that animal breedings farms are grouped into two categories :small and middle sized farms ; and the fact that Romania , one of Europe’s major forces in the field of livestock husbandry, has come to be one of the biggest importers of food products, although, by tradition, it is one of the continent’s countries with ideal conditions for breeding all species of animals. When clustering the countries we observ that in countries such as Greece, Italy, Portugal, Spain, cow farms, for example, do not exceed 10-16 heads and in Holland, England, Denmark, Belgium and France, the average farm size reaches 30-70 heads of milk cows. The cluster analysis revealed that in livestock operations, animal stock is the one that generates production, while the animal number indicates the size of the livestock unit.

Percolation with multiple giant clusters

International Nuclear Information System (INIS)

Ben-Naim, E; Krapivsky, P L

2005-01-01

We study mean-field percolation with freezing. Specifically, we consider cluster formation via two competing processes: irreversible aggregation and freezing. We find that when the freezing rate exceeds a certain threshold, the percolation transition is suppressed. Below this threshold, the system undergoes a series of percolation transitions with multiple giant clusters ('gels') formed. Giant clusters are not self-averaging as their total number and their sizes fluctuate from realization to realization. The size distribution F k , of frozen clusters of size k, has a universal tail, F k ∼ k -3 . We propose freezing as a practical mechanism for controlling the gel size. (letter to the editor)

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

Science.gov (United States)

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

2014-01-01

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

CLUSTER ANALYSIS UKRAINIAN REGIONAL DISTRIBUTION BY LEVEL OF INNOVATION

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Roman Shchur

2016-07-01

Full Text Available   SWOT-analysis of the threats and benefits of innovation development strategy of Ivano-Frankivsk region in the context of financial support was сonducted. Methodical approach to determine of public-private partnerships potential that is tool of innovative economic development financing was identified. Cluster analysis of possibilities of forming public-private partnership in a particular region was carried out. Optimal set of problem areas that require urgent solutions and financial security is defined on the basis of cluster approach. It will help to form practical recommendations for the formation of an effective financial mechanism in the regions of Ukraine. Key words: the mechanism of innovation development financial provision, innovation development, public-private partnerships, cluster analysis, innovative development strategy.

Multiscale visual quality assessment for cluster analysis with self-organizing maps

Science.gov (United States)

Bernard, Jürgen; von Landesberger, Tatiana; Bremm, Sebastian; Schreck, Tobias

2011-01-01

Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

Analysis of Security Mechanisms Based on Clusters IoT Environments

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Paulo Gaona-García

2017-03-01

Full Text Available Internet of things is based on sensors, communication networks and intelligence that manages the entire process and the generated data. Sensors are the senses of systems, because of this, they can be used in large quantities. Sensors must have low power consumption and cost, small size and great flexibility for its use in all circumstances. Therefore, the security of these network devices, data sensors and other devices, is a major concern as it grows rapidly in terms of nodes interconnected via sensor data. This paper presents an analysis from a systematic review point of view of articles on Internet of Things (IoT, security aspects specifically at privacy level and control access in this type of environment. Finally, it presents an analysis of security issues that must be addressed, from different clusters and identified areas within the fields of application of this technology.

Interplay between experiments and calculations for organometallic clusters and caged clusters

International Nuclear Information System (INIS)

Nakajima, Atsushi

2015-01-01

Clusters consisting of 10-1000 atoms exhibit size-dependent electronic and geometric properties. In particular, composite clusters consisting of several elements and/or components provide a promising way for a bottom-up approach for designing functional advanced materials, because the functionality of the composite clusters can be optimized not only by the cluster size but also by their compositions. In the formation of composite clusters, their geometric symmetry and dimensionality are emphasized to control the physical and chemical properties, because selective and anisotropic enhancements for optical, chemical, and magnetic properties can be expected. Organometallic clusters and caged clusters are demonstrated as a representative example of designing the functionality of the composite clusters. Organometallic vanadium-benzene forms a one dimensional sandwich structure showing ferromagnetic behaviors and anomalously large HOMO-LUMO gap differences of two spin orbitals, which can be regarded as spin-filter components for cluster-based spintronic devices. Caged clusters of aluminum (Al) are well stabilized both geometrically and electronically at Al 12 X, behaving as a “superatom”

Cluster Analysis of Clinical Data Identifies Fibromyalgia Subgroups

Science.gov (United States)

Docampo, Elisa; Collado, Antonio; Escaramís, Geòrgia; Carbonell, Jordi; Rivera, Javier; Vidal, Javier; Alegre, José

2013-01-01

Introduction Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. Material and Methods 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. Results Variables clustered into three independent dimensions: “symptomatology”, “comorbidities” and “clinical scales”. Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity. Conclusions We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment. PMID:24098674

Cluster analysis of clinical data identifies fibromyalgia subgroups.

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Elisa Docampo

Full Text Available INTRODUCTION: Fibromyalgia (FM is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. MATERIAL AND METHODS: 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. RESULTS: VARIABLES CLUSTERED INTO THREE INDEPENDENT DIMENSIONS: "symptomatology", "comorbidities" and "clinical scales". Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1, high symptomatology and comorbidities (Cluster 2, and high symptomatology but low comorbidities (Cluster 3, showing differences in measures of disease severity. CONCLUSIONS: We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment.

METHOD OF CONSTRUCTION OF GENETIC DATA CLUSTERS

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N. A. Novoselova

2016-01-01

Full Text Available The paper presents a method of construction of genetic data clusters (functional modules using the randomized matrices. To build the functional modules the selection and analysis of the eigenvalues of the gene profiles correlation matrix is performed. The principal components, corresponding to the eigenvalues, which are significantly different from those obtained for the randomly generated correlation matrix, are used for the analysis. Each selected principal component forms gene cluster. In a comparative experiment with the analogs the proposed method shows the advantage in allocating statistically significant different-sized clusters, the ability to filter non- informative genes and to extract the biologically interpretable functional modules matching the real data structure.

The cosmological analysis of X-ray cluster surveys - I. A new method for interpreting number counts

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Clerc, N.; Pierre, M.; Pacaud, F.; Sadibekova, T.

2012-07-01

We present a new method aimed at simplifying the cosmological analysis of X-ray cluster surveys. It is based on purely instrumental observable quantities considered in a two-dimensional X-ray colour-magnitude diagram (hardness ratio versus count rate). The basic principle is that even in rather shallow surveys, substantial information on cluster redshift and temperature is present in the raw X-ray data and can be statistically extracted; in parallel, such diagrams can be readily predicted from an ab initio cosmological modelling. We illustrate the methodology for the case of a 100-deg2XMM survey having a sensitivity of ˜10-14 erg s-1 cm-2 and fit at the same time, the survey selection function, the cluster evolutionary scaling relations and the cosmology; our sole assumption - driven by the limited size of the sample considered in the case study - is that the local cluster scaling relations are known. We devote special attention to the realistic modelling of the count-rate measurement uncertainties and evaluate the potential of the method via a Fisher analysis. In the absence of individual cluster redshifts, the count rate and hardness ratio (CR-HR) method appears to be much more efficient than the traditional approach based on cluster counts (i.e. dn/dz, requiring redshifts). In the case where redshifts are available, our method performs similar to the traditional mass function (dn/dM/dz) for the purely cosmological parameters, but constrains better parameters defining the cluster scaling relations and their evolution. A further practical advantage of the CR-HR method is its simplicity: this fully top-down approach totally bypasses the tedious steps consisting in deriving cluster masses from X-ray temperature measurements.

Scale size and life time of energy conversion regions observed by Cluster in the plasma sheet

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M. Hamrin

2009-11-01

Full Text Available In this article, and in a companion paper by Hamrin et al. (2009 [Occurrence and location of concentrated load and generator regions observed by Cluster in the plasma sheet], we investigate localized energy conversion regions (ECRs in Earth's plasma sheet. From more than 80 Cluster plasma sheet crossings (660 h data at the altitude of about 15–20 RE in the summer and fall of 2001, we have identified 116 Concentrated Load Regions (CLRs and 35 Concentrated Generator Regions (CGRs. By examining variations in the power density, E·J, where E is the electric field and J is the current density obtained by Cluster, we have estimated typical values of the scale size and life time of the CLRs and the CGRs. We find that a majority of the observed ECRs are rather stationary in space, but varying in time. Assuming that the ECRs are cylindrically shaped and equal in size, we conclude that the typical scale size of the ECRs is 2 RE≲ΔSECR≲5 RE. The ECRs hence occupy a significant portion of the mid altitude plasma sheet. Moreover, the CLRs appear to be somewhat larger than the CGRs. The life time of the ECRs are of the order of 1–10 min, consistent with the large scale magnetotail MHD simulations of Birn and Hesse (2005. The life time of the CGRs is somewhat shorter than for the CLRs. On time scales of 1–10 min, we believe that ECRs rise and vanish in significant regions of the plasma sheet, possibly oscillating between load and generator character. It is probable that at least some of the observed ECRs oscillate energy back and forth in the plasma sheet instead of channeling it to the ionosphere.

Stability analysis and structural rules of titanium dioxide clusters (TiO2)n with n = 1-9

International Nuclear Information System (INIS)

Zhang Weiwei; Han Ye; Yao Shuyu; Sun Haiqing

2011-01-01

Highlights: · We investigated the structure and stability of (TiO 2 ) n clusters with n = 1-9. · Some initial structures are introduced and proved to be the real global minimum. · We summarized the structural rules for small (TiO 2 ) n clusters. · The bonding features for the energy increment or decrement of the clusters are investigated. · A general shift of stability and reactivity with size for (TiO 2 ) n clusters. - Abstract: Atomic clusters have been considered as models for fundamental mechanistic insight into complex surfaces and catalysts. The structure and stability of (TiO 2 ) n clusters with n = 1-9 are investigated using the b3lyp hybrid density functional method in this paper. Some of the clusters are proposed initially and proved to be the real global minima. The stability and band gap of the clusters as a function of size are also investigated. The structural rules of the clusters are first summarized. The lowest-lying (TiO 2 ) n isomers tend to form some compact rather than quasi-linear or circular structures. The oxygen atom in 4-fold coordination and the titanium atom in 4-fold coordination favor the cluster stability. The 5-fold coordinated Ti-atom, the Ti-Ti bond and the terminal Ti-O bond lead to stability penalty for the clusters. No evidence for a regular variation in stability or reactivity with size of the clusters has shown. The structural rules can serve as guiding factors for formation research and structure design of (TiO 2 ) n and other transition metal oxide clusters.

Development of small scale cluster computer for numerical analysis

Science.gov (United States)

Zulkifli, N. H. N.; Sapit, A.; Mohammed, A. N.

2017-09-01

In this study, two units of personal computer were successfully networked together to form a small scale cluster. Each of the processor involved are multicore processor which has four cores in it, thus made this cluster to have eight processors. Here, the cluster incorporate Ubuntu 14.04 LINUX environment with MPI implementation (MPICH2). Two main tests were conducted in order to test the cluster, which is communication test and performance test. The communication test was done to make sure that the computers are able to pass the required information without any problem and were done by using simple MPI Hello Program where the program written in C language. Additional, performance test was also done to prove that this cluster calculation performance is much better than single CPU computer. In this performance test, four tests were done by running the same code by using single node, 2 processors, 4 processors, and 8 processors. The result shows that with additional processors, the time required to solve the problem decrease. Time required for the calculation shorten to half when we double the processors. To conclude, we successfully develop a small scale cluster computer using common hardware which capable of higher computing power when compare to single CPU processor, and this can be beneficial for research that require high computing power especially numerical analysis such as finite element analysis, computational fluid dynamics, and computational physics analysis.

SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

International Nuclear Information System (INIS)

Harmon, S; Wendelberger, B; Jeraj, R

2014-01-01

Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [ 18 F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI mean = 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI range : 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found

SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Harmon, S; Wendelberger, B [University of Wisconsin-Madison, Madison, WI (United States); Jeraj, R [University of Wisconsin-Madison, Madison, WI (United States); University of Ljubljana (Slovenia)

2014-06-01

Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms

Self-consistent clustering analysis: an efficient multiscale scheme for inelastic heterogeneous materials

Energy Technology Data Exchange (ETDEWEB)

Liu, Z.; Bessa, M. A.; Liu, W.K.

2017-10-25

A predictive computational theory is shown for modeling complex, hierarchical materials ranging from metal alloys to polymer nanocomposites. The theory can capture complex mechanisms such as plasticity and failure that span across multiple length scales. This general multiscale material modeling theory relies on sound principles of mathematics and mechanics, and a cutting-edge reduced order modeling method named self-consistent clustering analysis (SCA) [Zeliang Liu, M.A. Bessa, Wing Kam Liu, “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials,” Comput. Methods Appl. Mech. Engrg. 306 (2016) 319–341]. SCA reduces by several orders of magnitude the computational cost of micromechanical and concurrent multiscale simulations, while retaining the microstructure information. This remarkable increase in efficiency is achieved with a data-driven clustering method. Computationally expensive operations are performed in the so-called offline stage, where degrees of freedom (DOFs) are agglomerated into clusters. The interaction tensor of these clusters is computed. In the online or predictive stage, the Lippmann-Schwinger integral equation is solved cluster-wise using a self-consistent scheme to ensure solution accuracy and avoid path dependence. To construct a concurrent multiscale model, this scheme is applied at each material point in a macroscale structure, replacing a conventional constitutive model with the average response computed from the microscale model using just the SCA online stage. A regularized damage theory is incorporated in the microscale that avoids the mesh and RVE size dependence that commonly plagues microscale damage calculations. The SCA method is illustrated with two cases: a carbon fiber reinforced polymer (CFRP) structure with the concurrent multiscale model and an application to fatigue prediction for additively manufactured metals. For the CFRP problem, a speed up estimated to be about

Using Cluster Analysis for Data Mining in Educational Technology Research

Science.gov (United States)

Antonenko, Pavlo D.; Toy, Serkan; Niederhauser, Dale S.

2012-01-01

Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through…

[Typologies of Madrid's citizens (Spain) at the end-of-life: cluster analysis].

Science.gov (United States)

Ortiz-Gonçalves, Belén; Perea-Pérez, Bernardo; Labajo González, Elena; Albarrán Juan, Elena; Santiago-Sáez, Andrés

2018-03-06

To establish typologies within Madrid's citizens (Spain) with regard to end-of-life by cluster analysis. The SPAD 8 programme was implemented in a sample from a health care centre in the autonomous region of Madrid (Spain). A multiple correspondence analysis technique was used, followed by a cluster analysis to create a dendrogram. A cross-sectional study was made beforehand with the results of the questionnaire. Five clusters stand out. Cluster 1: a group who preferred not to answer numerous questions (5%). Cluster 2: in favour of receiving palliative care and euthanasia (40%). Cluster 3: would oppose assisted suicide and would not ask for spiritual assistance (15%). Cluster 4: would like to receive palliative care and assisted suicide (16%). Cluster 5: would oppose assisted suicide and would ask for spiritual assistance (24%). The following four clusters stood out. Clusters 2 and 4 would like to receive palliative care, euthanasia (2) and assisted suicide (4). Clusters 4 and 5 regularly practiced their faith and their family members did not receive palliative care. Clusters 3 and 5 would be opposed to euthanasia and assisted suicide in particular. Clusters 2, 4 and 5 had not completed an advance directive document (2, 4 and 5). Clusters 2 and 3 seldom practiced their faith. This study could be taken into consideration to improve the quality of end-of-life care choices. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Ionization of nitrogen cluster beam

International Nuclear Information System (INIS)

Yano, Katsuki; Be, S.H.; Enjoji, Hiroshi; Okamoto, Kosuke

1975-01-01

A nitrogen cluster beam (neutral particle intensity of 28.6 mAsub(eq)) is ionized by electron collisions in a Bayard-Alpert gauge type ionizer. The extraction efficiency of about 65% is obtained at an electron current of 10 mA with an energy of 50 eV. The mean cluster size produced at a pressure of 663 Torr and temperature of 77.3 K is 2x10 5 molecules per cluster. By the Coulomb repulsion force, multiply ionized cluster ions are broken up into smaller fragments and the cluster ion size reduces to one-fourth at an electron current of 15 mA. Mean neutral cluster sizes depend strongly on the initial degree of saturation PHI 0 and are 2x10 5 , 7x10 4 and 3x10 4 molecules per cluster at PHI 0 's of 0.87, 0.66 and 0.39, respectively. (auth.)

Cluster Ion Implantation in Graphite and Diamond

DEFF Research Database (Denmark)

Popok, Vladimir

2014-01-01

Cluster ion beam technique is a versatile tool which can be used for controllable formation of nanosize objects as well as modification and processing of surfaces and shallow layers on an atomic scale. The current paper present an overview and analysis of data obtained on a few sets of graphite...... and diamond samples implanted by keV-energy size-selected cobalt and argon clusters. One of the emphases is put on pinning of metal clusters on graphite with a possibility of following selective etching of graphene layers. The other topic of concern is related to the development of scaling law for cluster...... implantation. Implantation of cobalt and argon clusters into two different allotropic forms of carbon, namely, graphite and diamond is analysed and compared in order to approach universal theory of cluster stopping in matter....

Clustering Dycom

KAUST Repository

Minku, Leandro L.

2017-10-06

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.

Scalability analysis of the synchronizability for ring or chain networks with dense clusters

International Nuclear Information System (INIS)

Lu, Jun-An; Zhang, Yong; Chen, Juan; Lü, Jinhu

2014-01-01

It is well known that most real-world complex networks, such as the Internet and the World Wide Web, are evolving networks. An interesting fundamental question is: how do some important functions or dynamical behaviors of complex networks evolve with increasing network scale? This paper aims at investigating the scalability of the synchronizability for ring or chain networks with dense clusters as the network size increases. We discover some interesting phenomena as follows: (i) the synchronizability of ring or chain networks with clusters decreases with increasing network scale regardless of the inner structures of all communities; (ii) for the same network scale, the network synchronizability decreases more quickly with increasing number of cluster blocks than with increasing number of nodes within the cluster block; (iii) the number of rings or chains has a much more significant influence on the network synchronizability than the size of the rings or chains. Our results indicate that network synchronizability can be maintained with increasing network scale by avoiding ring and chain structures. (paper)

Lowest-energy cage structures of medium-sized (ZnO){sub n} clusters with n = 15 − 24

Energy Technology Data Exchange (ETDEWEB)

Tang, Lingli; Sai, Linwei [School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China and College of Advanced Science and Technology, Dalian University of Technology, Dalian 116024 (China); Zhao, Jijun, E-mail: zhaojj@dlut.edu.cn [College of Advanced Science and Technology, Dalian University of Technology, Dalian 116024, China and Key Laboratory of Materials Modification by Laser, Ion and Electron Beams (Dalian University of Technology), Ministry of Education, Dalian 116024 (China); Qiu, Ruifeng [School of Mathematical Sciences, Dalian University of Technology, Dalian 116024 (China)

2015-01-22

Fullerene-like cage structures of medium-sized (ZnO){sub n} clusters with n = 15 − 24 were generated by spiral algorithm and optimized using density functional theory calculations. Most of these lowest-energy cage structures contain only four-membered and six-membered rings, whereas eight-membered rings were found in the lowest-energy cages of (ZnO){sub n} (n = 19, 20, 23, 24). Our best cage configurations either reproduce or prevail the previously reported ones. The size-dependent electronic properties were also discussed.

Fission of Polyanionic Metal Clusters

Science.gov (United States)

König, S.; Jankowski, A.; Marx, G.; Schweikhard, L.; Wolfram, M.

2018-04-01

Size-selected dianionic lead clusters Pbn2 -, n =34 - 56 , are stored in a Penning trap and studied with respect to their decay products upon photoexcitation. Contrary to the decay of other dianionic metal clusters, these lead clusters show a variety of decay channels. The mass spectra of the fragments are compared to the corresponding spectra of the monoanionic precursors. This comparison leads to the conclusion that, in the cluster size region below about n =48 , the fission reaction Pbn2 -→Pbn-10 -+Pb10- is the major decay process. Its disappearance at larger cluster sizes may be an indication of a nonmetal to metal transition. Recently, the pair of Pb10- and Pbn-10 - were observed as pronounced fragments in electron-attachment studies [S. König et al., Int. J. Mass Spectrom. 421, 129 (2017), 10.1016/j.ijms.2017.06.009]. The present findings suggest that this combination is the fingerprint of the decay of doubly charged lead clusters. With this assumption, the dianion clusters have been traced down to Pb212 -, whereas the smallest size for the direct observation was as high as n =28 .

Methodology сomparative statistical analysis of Russian industry based on cluster analysis

Directory of Open Access Journals (Sweden)

Sergey S. Shishulin

2017-01-01

Full Text Available The article is devoted to researching of the possibilities of applying multidimensional statistical analysis in the study of industrial production on the basis of comparing its growth rates and structure with other developed and developing countries of the world. The purpose of this article is to determine the optimal set of statistical methods and the results of their application to industrial production data, which would give the best access to the analysis of the result.Data includes such indicators as output, output, gross value added, the number of employed and other indicators of the system of national accounts and operational business statistics. The objects of observation are the industry of the countrys of the Customs Union, the United States, Japan and Erope in 2005-2015. As the research tool used as the simplest methods of transformation, graphical and tabular visualization of data, and methods of statistical analysis. In particular, based on a specialized software package (SPSS, the main components method, discriminant analysis, hierarchical methods of cluster analysis, Ward’s method and k-means were applied.The application of the method of principal components to the initial data makes it possible to substantially and effectively reduce the initial space of industrial production data. Thus, for example, in analyzing the structure of industrial production, the reduction was from fifteen industries to three basic, well-interpreted factors: the relatively extractive industries (with a low degree of processing, high-tech industries and consumer goods (medium-technology sectors. At the same time, as a result of comparison of the results of application of cluster analysis to the initial data and data obtained on the basis of the principal components method, it was established that clustering industrial production data on the basis of new factors significantly improves the results of clustering.As a result of analyzing the parameters of

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali.

Science.gov (United States)

Minetti, Andrea; Riera-Montes, Margarita; Nackers, Fabienne; Roederer, Thomas; Koudika, Marie Hortense; Sekkenes, Johanne; Taconet, Aurore; Fermon, Florence; Touré, Albouhary; Grais, Rebecca F; Checchi, Francesco

2012-10-12

Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required. We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.

Critical sizes and critical characteristics of nanoclusters, nanostructures and nanomaterials

International Nuclear Information System (INIS)

Suzdalev, I.P.

2005-01-01

Full text: Critical sizes and characteristics of nanoclusters and nanostructures are introduced as the parameters of nanosystems and nanomaterials. The next critical characteristics are considered: atomic and electronic 'magic number', critical size of cluster nucleation, critical size of melting-freezing of cluster, critical size of quantum (laser) radiation, critical sizes for the single electron conductivity, critical energy and magnetic field for the magnetic tunneling, critical cluster sizes for the giant magnetic resistance, critical size of the first order magnetic phase transition. The critical characteristics are estimated by thermodynamic approaches, by Moessbauer spectroscopy, AFM, heat capacity, SQUID magnetometry and other technique, The influence of cluster-cluster interactions, cluster-matrix interactions and cluster defects on cluster atomic dynamics, cluster melting, cluster critical sizes, Curie or Neel points and the character of magnetic phase transitions were investigated. The applications of critical size and critical characteristic parameters for the nanomaterial characterization are considered

Genome-scale analysis of positional clustering of mouse testis-specific genes

Directory of Open Access Journals (Sweden)

Lee Bernett TK

2005-01-01

Full Text Available Abstract Background Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes. Results Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats. Conclusion Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

Pattern recognition in menstrual bleeding diaries by statistical cluster analysis

Directory of Open Access Journals (Sweden)

Wessel Jens

2009-07-01

Full Text Available Abstract Background The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs. Methods We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstrual bleeding diaries. The optimal number of clusters was determined using the semi-partial R2, the cubic cluster criterion, the pseudo-F- and the pseudo-t2-statistic. Finally, the interpretability of the results from a gynecological point of view was assessed. Results The method of Ward yielded distinct clusters of the bleeding diaries. The other methods successively chained the observations into one cluster. The optimal number of distinctive bleeding patterns was six. We found two desirable and four undesirable bleeding patterns. Cyclic and non cyclic bleeding patterns were well separated. Conclusion Using this cluster analysis with the method of Ward medications and devices having an impact on bleeding can be easily compared and categorized.

Comparative analysis of clustering methods for gene expression time course data

Directory of Open Access Journals (Sweden)

Ivan G. Costa

2004-01-01

Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

The Productivity Analysis of Chennai Automotive Industry Cluster

Science.gov (United States)

Bhaskaran, E.

2014-07-01

Chennai, also called the Detroit of India, is India's second fastest growing auto market and exports auto components and vehicles to US, Germany, Japan and Brazil. For inclusive growth and sustainable development, 250 auto component industries in Ambattur, Thirumalisai and Thirumudivakkam Industrial Estates located in Chennai have adopted the Cluster Development Approach called Automotive Component Cluster. The objective is to study the Value Chain, Correlation and Data Envelopment Analysis by determining technical efficiency, peer weights, input and output slacks of 100 auto component industries in three estates. The methodology adopted is using Data Envelopment Analysis of Output Oriented Banker Charnes Cooper model by taking net worth, fixed assets, employment as inputs and gross output as outputs. The non-zero represents the weights for efficient clusters. The higher slack obtained reveals the excess net worth, fixed assets, employment and shortage in gross output. To conclude, the variables are highly correlated and the inefficient industries should increase their gross output or decrease the fixed assets or employment. Moreover for sustainable development, the cluster should strengthen infrastructure, technology, procurement, production and marketing interrelationships to decrease costs and to increase productivity and efficiency to compete in the indigenous and export market.

MMPI profiles of males accused of severe crimes: a cluster analysis

NARCIS (Netherlands)

Spaans, M.; Barendregt, M.; Muller, E.; Beurs, E. de; Nijman, H.L.I.; Rinne, T.

2009-01-01

In studies attempting to classify criminal offenders by cluster analysis of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) data, the number of clusters found varied between 10 (the Megargee System) and two (one cluster indicating no psychopathology and one exhibiting serious

Proteomic properties reveal phyloecological clusters of Archaea.

Directory of Open Access Journals (Sweden)

Nela Nikolic

Full Text Available In this study, we propose a novel way to describe the variety of environmental adaptations of Archaea. We have clustered 57 Archaea by using a non-redundant set of proteomic features, and verified that the clusters correspond to environmental adaptations to the archaeal habitats. The first cluster consists dominantly of hyperthermophiles and hyperthermoacidophilic aerobes. The second cluster joins together halophilic and extremely halophilic Archaea, while the third cluster contains mesophilic (mostly methanogenic Archaea together with thermoacidophiles. The non-redundant subset of proteomic features was found to consist of five features: the ratio of charged residues to uncharged, average protein size, normalized frequency of beta-sheet, normalized frequency of extended structure and number of hydrogen bond donors. We propose this clustering to be termed phyloecological clustering. This approach could give additional insights into relationships among archaeal species that may be hidden by sole phylogenetic analysis.

Using Cluster Analysis to Compartmentalize a Large Managed Wetland Based on Physical, Biological, and Climatic Geospatial Attributes.

Science.gov (United States)

Hahus, Ian; Migliaccio, Kati; Douglas-Mankin, Kyle; Klarenberg, Geraldine; Muñoz-Carpena, Rafael

2018-04-27

Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward's linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.

Influence of stacking fault energies on the size distribution and character of defect clusters formed by collision cascades in face-centered cubic metals

Directory of Open Access Journals (Sweden)

Y. Yang

2016-12-01

Full Text Available Molecular dynamics simulations are performed to evaluate the influence of the stacking fault energy (SFE as a single variable parameter on defect formation by collision cascades in face-centered cubic metals. The simulations are performed for energies of a primary knock-on atom (EPKA up to 50keV at 100K by using six sets of the recently developed embedded atom method–type potentials. Neither the number of residual defects nor their clustering behavior is found to be affected by the SFE, except for the mean size of the vacancy clusters at EPKA=50keV. The mean size increases as the SFE decreases because of the enhanced formation of large vacancy clusters, which prefer to have stacking faults inside them. On the other hand, the ratio of glissile self-interstitial atom (SIA clusters decreases as the SFE increases. At higher SFEs, both the number of Frank loops and number of perfect loops tend to decrease; instead, three-dimensional irregular clusters with higher densities appear, most of which are sessile. The effect of SFE on the number of Frank loops becomes apparent only at a high EPKA of 50keV, where comparably large SIA clusters can be formed with a higher density.

ANALYSIS OF DEVELOPING BATIK INDUSTRY CLUSTER IN BAKARAN VILLAGE CENTRAL JAVA PROVINCE

Directory of Open Access Journals (Sweden)

Hermanto Hermanto

2017-06-01

Full Text Available SMEs grow in a cluster in a certain geographical area. The entrepreneurs grow and thrive through the business cluster. Central Java Province has a lot of business clusters in improving the regional economy, one of which is batik industry cluster. Pati Regency is one of regencies / city in Central Java that has the lowest turnover. Batik industy cluster in Pati develops quite well, which can be seen from the increasing number of batik industry incorporated in the cluster. This research examines the strategy of developing the batik industry cluster in Pati Regency. The purpose of this research is to determine the proper strategy for developing the batik industry clusters in Pati. The method of research is quantitative. The analysis tool of this research is the Strengths, Weakness, Opportunity, Threats (SWOT analysis. The result of SWOT analysis in this research shows that the proper strategy for developing the batik industry cluster in Pati is optimizing the management of batik business cluster in Bakaran Village; the local government provides information of the facility of business capital loans; the utilization of labors from Bakaran Village while improving the quality of labors by training, and marketing the Bakaran batik to the broader markets while maintaining the quality of batik. Advice that can be given from this research is that the parties who have a role in batik industry cluster development in Bakaran Village, Pati Regency, such as the Local Government.

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

Science.gov (United States)

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

A SURVEY ON DOCUMENT CLUSTERING APPROACH FOR COMPUTER FORENSIC ANALYSIS

OpenAIRE

Monika Raghuvanshi*, Rahul Patel

2016-01-01

In a forensic analysis, large numbers of files are examined. Much of the information comprises of in unstructured format, so it’s quite difficult task for computer forensic to perform such analysis. That’s why to do the forensic analysis of document within a limited period of time require a special approach such as document clustering. This paper review different document clustering algorithms methodologies for example K-mean, K-medoid, single link, complete link, average link in accorandance...

Acoustic Cluster Therapy: In Vitro and Ex Vivo Measurement of Activated Bubble Size Distribution and Temporal Dynamics.

Science.gov (United States)

Healey, Andrew John; Sontum, Per Christian; Kvåle, Svein; Eriksen, Morten; Bendiksen, Ragnar; Tornes, Audun; Østensen, Jonny

2016-05-01

Acoustic cluster technology (ACT) is a two-component, microparticle formulation platform being developed for ultrasound-mediated drug delivery. Sonazoid microbubbles, which have a negative surface charge, are mixed with micron-sized perfluoromethylcyclopentane droplets stabilized with a positively charged surface membrane to form microbubble/microdroplet clusters. On exposure to ultrasound, the oil undergoes a phase change to the gaseous state, generating 20- to 40-μm ACT bubbles. An acoustic transmission technique is used to measure absorption and velocity dispersion of the ACT bubbles. An inversion technique computes bubble size population with temporal resolution of seconds. Bubble populations are measured both in vitro and in vivo after activation within the cardiac chambers of a dog model, with catheter-based flow through an extracorporeal measurement flow chamber. Volume-weighted mean diameter in arterial blood after activation in the left ventricle was 22 μm, with no bubbles >44 μm in diameter. After intravenous administration, 24.4% of the oil is activated in the cardiac chambers. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Cluster Analysis in Rapeseed (Brassica Napus L.)

International Nuclear Information System (INIS)

Mahasi, J.M

2002-01-01

With widening edible deficit, Kenya has become increasingly dependent on imported edible oils. Many oilseed crops (e.g. sunflower, soya beans, rapeseed/mustard, sesame, groundnuts etc) can be grown in Kenya. But oilseed rape is preferred because it very high yielding (1.5 tons-4.0 tons/ha) with oil content of 42-46%. Other uses include fitting in various cropping systems as; relay/inter crops, rotational crops, trap crops and fodder. It is soft seeded hence oil extraction is relatively easy. The meal is high in protein and very useful in livestock supplementation. Rapeseed can be straight combined using adjusted wheat combines. The priority is to expand domestic oilseed production, hence the need to introduce improved rapeseed germplasm from other countries. The success of any crop improvement programme depends on the extent of genetic diversity in the material. Hence, it is essential to understand the adaptation of introduced genotypes and the similarities if any among them. Evaluation trials were carried out on 17 rapeseed genotypes (nine Canadian origin and eight of European origin) grown at 4 locations namely Endebess, Njoro, Timau and Mau Narok in three years (1992, 1993 and 1994). Results for 1993 were discarded due to severe drought. An analysis of variance was carried out only on seed yields and the treatments were found to be significantly different. Cluster analysis was then carried out on mean seed yields and based on this analysis; only one major group exists within the material. In 1992, varieties 2,3,8 and 9 didn't fall in the same cluster as the rest. Variety 8 was the only one not classified with the rest of the Canadian varieties. Three European varieties (2,3 and 9) were however not classified with the others. In 1994, varieties 10 and 6 didn't fall in the major cluster. Of these two, variety 10 is of Canadian origin. Varieties were more similar in 1994 than 1992 due to favorable weather. It is evident that, genotypes from different geographical

Small gold clusters on graphene, their mobility and clustering: a DFT study

International Nuclear Information System (INIS)

Amft, Martin; Sanyal, Biplab; Eriksson, Olle; Skorodumova, Natalia V

2011-01-01

Motivated by the experimentally observed high mobility of gold atoms on graphene and their tendency to form nanometer-sized clusters, we present a density functional theory study of the ground state structures of small gold clusters on graphene, their mobility and clustering. Our detailed analysis of the electronic structures identifies the opportunity to form strong gold-gold bonds and the graphene-mediated interaction of the pre-adsorbed fragments as the driving forces behind gold's tendency to aggregate on graphene. While clusters containing up to three gold atoms have one unambiguous ground state structure, both gas phase isomers of a cluster with four gold atoms can be found on graphene. In the gas phase the diamond-shaped Au 4 D cluster is the ground state structure, whereas the Y-shaped Au 4 Y becomes the actual ground state when adsorbed on graphene. As we show, both clusters can be produced on graphene by two distinct clustering processes. We also studied in detail the stepwise formation of a gold dimer out of two pre-adsorbed adatoms, as well as the formation of Au 3 . All reactions are exothermic and no further activation barriers, apart from the diffusion barriers, were found. The diffusion barriers of all studied clusters range from 4 to 36 meV only, and are substantially exceeded by the adsorption energies of - 0.1 to - 0.59 eV. This explains the high mobility of Au 1-4 on graphene along the C-C bonds.

Scale size and life time of energy conversion regions observed by Cluster in the plasma sheet

Directory of Open Access Journals (Sweden)

M. Hamrin

2009-11-01

Full Text Available In this article, and in a companion paper by Hamrin et al. (2009 [Occurrence and location of concentrated load and generator regions observed by Cluster in the plasma sheet], we investigate localized energy conversion regions (ECRs in Earth's plasma sheet. From more than 80 Cluster plasma sheet crossings (660 h data at the altitude of about 15–20 R_E in the summer and fall of 2001, we have identified 116 Concentrated Load Regions (CLRs and 35 Concentrated Generator Regions (CGRs. By examining variations in the power density, E·J, where E is the electric field and J is the current density obtained by Cluster, we have estimated typical values of the scale size and life time of the CLRs and the CGRs. We find that a majority of the observed ECRs are rather stationary in space, but varying in time. Assuming that the ECRs are cylindrically shaped and equal in size, we conclude that the typical scale size of the ECRs is 2 R_E≲ΔS_ECR≲5 R_E. The ECRs hence occupy a significant portion of the mid altitude plasma sheet. Moreover, the CLRs appear to be somewhat larger than the CGRs. The life time of the ECRs are of the order of 1–10 min, consistent with the large scale magnetotail MHD simulations of Birn and Hesse (2005. The life time of the CGRs is somewhat shorter than for the CLRs. On time scales of 1–10 min, we believe that ECRs rise and vanish in significant regions of the plasma sheet, possibly oscillating between load and generator character. It is probable that at least some of the observed ECRs oscillate energy back and forth in the plasma sheet instead of channeling it to the ionosphere.

The Quantitative Analysis of Chennai Automotive Industry Cluster

Science.gov (United States)

Bhaskaran, Ethirajan

2016-07-01

Chennai, also called as Detroit of India due to presence of Automotive Industry producing over 40 % of the India's vehicle and components. During 2001-2002, the Automotive Component Industries (ACI) in Ambattur, Thirumalizai and Thirumudivakkam Industrial Estate, Chennai has faced problems on infrastructure, technology, procurement, production and marketing. The objective is to study the Quantitative Performance of Chennai Automotive Industry Cluster before (2001-2002) and after the CDA (2008-2009). The methodology adopted is collection of primary data from 100 ACI using quantitative questionnaire and analyzing using Correlation Analysis (CA), Regression Analysis (RA), Friedman Test (FMT), and Kruskall Wallis Test (KWT).The CA computed for the different set of variables reveals that there is high degree of relationship between the variables studied. The RA models constructed establish the strong relationship between the dependent variable and a host of independent variables. The models proposed here reveal the approximate relationship in a closer form. KWT proves, there is no significant difference between three locations clusters with respect to: Net Profit, Production Cost, Marketing Costs, Procurement Costs and Gross Output. This supports that each location has contributed for development of automobile component cluster uniformly. The FMT proves, there is no significant difference between industrial units in respect of cost like Production, Infrastructure, Technology, Marketing and Net Profit. To conclude, the Automotive Industries have fully utilized the Physical Infrastructure and Centralised Facilities by adopting CDA and now exporting their products to North America, South America, Europe, Australia, Africa and Asia. The value chain analysis models have been implemented in all the cluster units. This Cluster Development Approach (CDA) model can be implemented in industries of under developed and developing countries for cost reduction and productivity

An approach based on genetic algorithms and DFT for studying clusters: (H2O) n (2 ≤ n ≤ 13) cluster analysis

International Nuclear Information System (INIS)

Sabato de Abreu e Silva, Elcio; Anderson Duarte, Helio; Belchior, Jadson Claudio

2006-01-01

The present work proposes the application of a genetic algorithm (GA) for determining global minima to be used as seeds for a higher level ab initio method analysis such as density function theory (DFT). Water clusters ((H 2 O) n (2 ≤ n ≤ 13)) are used as a test case and for the initial guesses four empirical potentials (TIP3P, TIP4P, TIP5P and ST2) were considered for the GA calculations. Two types of analysis were performed namely rigid (DFT R M) and non rigid (DFT N RM) molecules for the corresponding structures and energies. For the DFT analysis, the PBE exchange correlation functional and the large basis set A-PVTZ have been used. All structures and their respective energies calculated through the GA method, DFT R M and DFT N RM are compared and discussed. The proposed methodology showed to be very efficient in order to have quasi accurate global minima on the level of ab initio calculations and the data are discussed in the light of previously published results with particular attention to ((H 2 O) n (2 ≤ n ≤ 13)) clusters. The results suggest that the stabilization energy error for the empirical potentials used are additive with respect to the cluster size, roughly 0.5 kcal mol -1 per water molecule after ZPE correction. Finally, the approach of using GA/empirical potential structures as starting point for ab initio optimization methods showed to be a computationally manageable strategy to explore the potential energy surface of large systems at quantum level. In conclusion, this work proposes an alternative approach to accurately study properties of larger systems in a very efficient manner

An approach based on genetic algorithms and DFT for studying clusters: (H{sub 2}O) {sub n} (2 {<=} n {<=} 13) cluster analysis

Energy Technology Data Exchange (ETDEWEB)

Sabato de Abreu e Silva, Elcio [Departamento de Quimica - ICEx, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Pampulha (31.270-901) Belo Horizonte, Minas Gerias (Brazil); Anderson Duarte, Helio [Departamento de Quimica - ICEx, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Pampulha (31.270-901) Belo Horizonte, Minas Gerias (Brazil); Belchior, Jadson Claudio [Departamento de Quimica - ICEx, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Pampulha (31.270-901) Belo Horizonte, Minas Gerias (Brazil)], E-mail: jadson@ufmg.br

2006-04-21

The present work proposes the application of a genetic algorithm (GA) for determining global minima to be used as seeds for a higher level ab initio method analysis such as density function theory (DFT). Water clusters ((H{sub 2}O) {sub n} (2 {<=} n {<=} 13)) are used as a test case and for the initial guesses four empirical potentials (TIP3P, TIP4P, TIP5P and ST2) were considered for the GA calculations. Two types of analysis were performed namely rigid (DFT{sub R}M) and non rigid (DFT{sub N}RM) molecules for the corresponding structures and energies. For the DFT analysis, the PBE exchange correlation functional and the large basis set A-PVTZ have been used. All structures and their respective energies calculated through the GA method, DFT{sub R}M and DFT{sub N}RM are compared and discussed. The proposed methodology showed to be very efficient in order to have quasi accurate global minima on the level of ab initio calculations and the data are discussed in the light of previously published results with particular attention to ((H{sub 2}O) {sub n} (2 {<=} n {<=} 13)) clusters. The results suggest that the stabilization energy error for the empirical potentials used are additive with respect to the cluster size, roughly 0.5 kcal mol{sup -1} per water molecule after ZPE correction. Finally, the approach of using GA/empirical potential structures as starting point for ab initio optimization methods showed to be a computationally manageable strategy to explore the potential energy surface of large systems at quantum level. In conclusion, this work proposes an alternative approach to accurately study properties of larger systems in a very efficient manner.

Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability.

Science.gov (United States)

Miller, Christopher B; Bartlett, Delwyn J; Mullins, Anna E; Dodds, Kirsty L; Gordon, Christopher J; Kyle, Simon D; Kim, Jong Won; D'Rozario, Angela L; Lee, Rico S C; Comas, Maria; Marshall, Nathaniel S; Yee, Brendon J; Espie, Colin A; Grunstein, Ronald R

2016-11-01

To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative ( q )-EEG and heart rate variability (HRV). Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q -EEG. Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. © 2016 Associated Professional Sleep Societies, LLC.

Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability

Science.gov (United States)

Miller, Christopher B.; Bartlett, Delwyn J.; Mullins, Anna E.; Dodds, Kirsty L.; Gordon, Christopher J.; Kyle, Simon D.; Kim, Jong Won; D'Rozario, Angela L.; Lee, Rico S.C.; Comas, Maria; Marshall, Nathaniel S.; Yee, Brendon J.; Espie, Colin A.; Grunstein, Ronald R.

2016-01-01

Study Objectives: To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative (q)-EEG and heart rate variability (HRV). Methods: Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. Results: From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q-EEG. Clinical Trial Registration: Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. Citation: Miller CB, Bartlett DJ, Mullins AE, Dodds KL, Gordon CJ, Kyle SD, Kim JW, D'Rozario AL, Lee RS, Comas M, Marshall NS, Yee BJ, Espie CA, Grunstein RR. Clusters of Insomnia Disorder: an exploratory cluster analysis of objective sleep parameters reveals differences in neurocognitive functioning, quantitative EEG, and heart rate variability. SLEEP 2016;39(11):1993–2004. PMID:27568796

Assessment of genetic divergence in tomato through agglomerative hierarchical clustering and principal component analysis

International Nuclear Information System (INIS)

Iqbal, Q.; Saleem, M.Y.; Hameed, A.; Asghar, M.

2014-01-01

For the improvement of qualitative and quantitative traits, existence of variability has prime importance in plant breeding. Data on different morphological and reproductive traits of 47 tomato genotypes were analyzed for correlation,agglomerative hierarchical clustering and principal component analysis (PCA) to select genotypes and traits for future breeding program. Correlation analysis revealed significant positive association between yield and yield components like fruit diameter, single fruit weight and number of fruits plant-1. Principal component (PC) analysis depicted first three PCs with Eigen-value higher than 1 contributing 81.72% of total variability for different traits. The PC-I showed positive factor loadings for all the traits except number of fruits plant-1. The contribution of single fruit weight and fruit diameter was highest in PC-1. Cluster analysis grouped all genotypes into five divergent clusters. The genotypes in cluster-II and cluster-V exhibited uniform maturity and higher yield. The D2 statistics confirmed highest distance between cluster- III and cluster-V while maximum similarity was observed in cluster-II and cluster-III. It is therefore suggested that crosses between genotypes of cluster-II and cluster-V with those of cluster-I and cluster-III may exhibit heterosis in F1 for hybrid breeding and for selection of superior genotypes in succeeding generations for cross breeding programme. (author)

Design and capabilities of an experimental setup based on magnetron sputtering for formation and deposition of size-selected metal clusters on ultra-clean surfaces

DEFF Research Database (Denmark)

Hartmann, Hannes; Popok, Vladimir; Barke, Ingo

2012-01-01

The design and performance of an experimental setup utilizing a magnetron sputtering source for production of beams of ionized size-selected clusters for deposition in ultra-high vacuum is described. For the case of copper cluster formation the influence of different source parameters is studied...

Beams of mass-selected clusters: realization and first experiments

International Nuclear Information System (INIS)

Kamalou, O.

2007-04-01

The main objective of this work concerns the production of beams of mass-selected clusters of metallic and semiconductor materials. Clusters are produced in magnetron sputtering source combined with a gas aggregation chamber, cooled by liquid nitrogen circulation. Downstream of the cluster source, a Wiley-McLaren time-of-flight setup allows to select a given cluster size or a narrow size range. The pulsed mass-selected cluster ion beam is separated from the continuous neutral one by an electrostatic 90-quadrupole deflector. After the deflector, the density of the pulsed beam amounts to about 10 3 particles/cm 3 . Preliminary deposition experiments of mass-selected copper clusters with a deposition energy of about 0.5 eV/atom have ben performed on highly oriented pyrolytic graphite (HOPG) substrates, indicating that copper clusters are evidently mobile on the HOPG-surface until they reach cleavage steps, dislocation lines or other surface defects. In order to lower the cluster mobility on the HOPG-surface, we have first irradiated HOPG samples with slow highly charged ions (high dose) in order to create superficial defects. In a second step we have deposited mass-selected copper clusters on these pre-irradiated samples. The first analysis by AFM (Atomic Force Microscopy) techniques showed that the copper clusters are trapped on the defects produced by the highly charged ions. (author)

Soft landing of size selected clusters in rare gas matrices

International Nuclear Information System (INIS)

Lau, J.T; Wurth, W.; Ehrke, H-U.; Achleitner, A.

2003-01-01

Soft landing of mass selected clusters in rare gas matrices is a technique used to preserve mass selection in cluster deposition. To prevent fragmentation upon deposition, the substrate is covered with rare gas matrices to dissipate the cluster kinetic energy upon impact. Theoretical and experimental studies demonstrate the power of this technique. Besides STM, optical absorption, excitation, and fluorescence experiments, x-ray absorption at core levels can be used as a tool to study soft landing conditions, as will be shown here. X-ray absorption spectroscopy is also well suited to follow diffusion and agglomeration of clusters on surfaces via energy shifts in core level absorption

A Distributed Flocking Approach for Information Stream Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

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

2006-01-01

Intelligence analysts are currently overwhelmed with the amount of information streams generated everyday. There is a lack of comprehensive tool that can real-time analyze the information streams. Document clustering analysis plays an important role in improving the accuracy of information retrieval. However, most clustering technologies can only be applied for analyzing the static document collection because they normally require a large amount of computation resource and long time to get accurate result. It is very difficult to cluster a dynamic changed text information streams on an individual computer. Our early research has resulted in a dynamic reactive flock clustering algorithm which can continually refine the clustering result and quickly react to the change of document contents. This character makes the algorithm suitable for cluster analyzing dynamic changed document information, such as text information stream. Because of the decentralized character of this algorithm, a distributed approach is a very natural way to increase the clustering speed of the algorithm. In this paper, we present a distributed multi-agent flocking approach for the text information stream clustering and discuss the decentralized architectures and communication schemes for load balance and status information synchronization in this approach.

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

Directory of Open Access Journals (Sweden)

Minetti Andrea

2012-10-01

Full Text Available Abstract Background Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS approach has been proposed as an alternative, as smaller sample sizes are required. Methods We explored (i the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. Results VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i health areas not requiring supplemental activities; ii health areas requiring additional vaccination; iii health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3, standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Conclusions Small sample cluster surveys (10 × 15 are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.

Genome cluster database. A sequence family analysis platform for Arabidopsis and rice.

Science.gov (United States)

Horan, Kevin; Lauricha, Josh; Bailey-Serres, Julia; Raikhel, Natasha; Girke, Thomas

2005-05-01

The genome-wide protein sequences from Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) spp. japonica were clustered into families using sequence similarity and domain-based clustering. The two fundamentally different methods resulted in separate cluster sets with complementary properties to compensate the limitations for accurate family analysis. Functional names for the identified families were assigned with an efficient computational approach that uses the description of the most common molecular function gene ontology node within each cluster. Subsequently, multiple alignments and phylogenetic trees were calculated for the assembled families. All clustering results and their underlying sequences were organized in the Web-accessible Genome Cluster Database (http://bioinfo.ucr.edu/projects/GCD) with rich interactive and user-friendly sequence family mining tools to facilitate the analysis of any given family of interest for the plant science community. An automated clustering pipeline ensures current information for future updates in the annotations of the two genomes and clustering improvements. The analysis allowed the first systematic identification of family and singlet proteins present in both organisms as well as those restricted to one of them. In addition, the established Web resources for mining these data provide a road map for future studies of the composition and structure of protein families between the two species.

Cluster analysis of obesity and asthma phenotypes.

Directory of Open Access Journals (Sweden)

E Rand Sutherland

Full Text Available Asthma is a heterogeneous disease with variability among patients in characteristics such as lung function, symptoms and control, body weight, markers of inflammation, and responsiveness to glucocorticoids (GC. Cluster analysis of well-characterized cohorts can advance understanding of disease subgroups in asthma and point to unsuspected disease mechanisms. We utilized an hypothesis-free cluster analytical approach to define the contribution of obesity and related variables to asthma phenotype.In a cohort of clinical trial participants (n = 250, minimum-variance hierarchical clustering was used to identify clinical and inflammatory biomarkers important in determining disease cluster membership in mild and moderate persistent asthmatics. In a subset of participants, GC sensitivity was assessed via expression of GC receptor alpha (GCRα and induction of MAP kinase phosphatase-1 (MKP-1 expression by dexamethasone. Four asthma clusters were identified, with body mass index (BMI, kg/m(2 and severity of asthma symptoms (AEQ score the most significant determinants of cluster membership (F = 57.1, p<0.0001 and F = 44.8, p<0.0001, respectively. Two clusters were composed of predominantly obese individuals; these two obese asthma clusters differed from one another with regard to age of asthma onset, measures of asthma symptoms (AEQ and control (ACQ, exhaled nitric oxide concentration (F(ENO and airway hyperresponsiveness (methacholine PC(20 but were similar with regard to measures of lung function (FEV(1 (% and FEV(1/FVC, airway eosinophilia, IgE, leptin, adiponectin and C-reactive protein (hsCRP. Members of obese clusters demonstrated evidence of reduced expression of GCRα, a finding which was correlated with a reduced induction of MKP-1 expression by dexamethasoneObesity is an important determinant of asthma phenotype in adults. There is heterogeneity in expression of clinical and inflammatory biomarkers of asthma across obese individuals

The size of clusters in a neutrino-dominated universe

International Nuclear Information System (INIS)

White, S.D.M.; Davis, M.; Frenk, C.S.

1984-01-01

Quite soon after the first collapse of structure almost half the matter in a neutrino-dominated universe is expected to reside in clusters. The masses and binding energies of these neutrino clusters are too large for them to be identified with observed galaxy clusters. Even if such objects were able to suppress all galaxy formation, their X-ray emission would, however, make them highly visible if more than 2.5 per cent of their mass was in ordinary matter. Such a low baryon density leads to insufficient cooling for galaxies to form in pancakes. A neutrino-dominated universe appears to conflict with observation irrespective of the details of the processes which govern galaxy formation. (author)

Production and characterization of supersonic carbon cluster beams

International Nuclear Information System (INIS)

Rohlfing, E.A.; Cox, D.M.; Kaldor, A.

1984-01-01

Laser vaporization of a substrate within the throat of a pulsed nozzle is used to generate a supersonic beam of carbon clusters. The neutral cluster beam is probed downstream by UV laser photoionization with time-of-flight mass analysis of the resulting photoions. Using graphite as the substrate, carbon clusters C/sub n/ for n = 1--190 have been produced having a distinctly bimodal cluster size distribution: (i) Both even and odd clusters for C/sub n/, 1 + /sub n/ signals are interpreted on the basis of cluster formation and stability arguments. Ionizing laser power dependences taken at several different photon energies are used to roughly bracket the carbon cluster ionization potentials, and, at high laser intensity, to observe the onset of multiphoton fragmentation. By treating the graphite rod with KOH, a greatly altered carbon cluster distribution with mixed carbon/potassium clusters of formula K 2 C/sub 2n/ is produced

Cluster analysis as a prediction tool for pregnancy outcomes.

Science.gov (United States)

Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

2015-03-01

Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

Interpretation of aerosol trace metal particle size distributions

International Nuclear Information System (INIS)

Johansson, T.B.; Van Grieken, R.E.; Winchester, J.W.

1974-01-01

Proton-induced X-ray emission (PIXE) analysis is capable of rapid routine determination of 10--15 elements present in amounts greater than or equal to 1 ng simultaneously in aerosol size fractions as collected by single orifice impactors over short periods of time. This enables detailed study of complex relationships between elements detected. Since absolute elemental concentrations may be strongly influenced by meteorological and topographical conditions, it is useful to normalize to a reference element. Comparison between the ratios of concentrations with aerosol and corresponding values for anticipated sources may lead to the identification of important sources for the elements. Further geochemical insights may be found through linear correlation coefficients, regression analysis, and cluster analysis. By calculating correlations for elemental pairs, an indication of the degree of covariance between the elements is obtained. Preliminary results indicate that correlations may be particle size dependent. A high degree of covariance may be caused either by a common source or may only reflect the conservative nature of the aerosol. In a regression analysis, by plotting elemental pairs and estimating the regression coefficients, we may be able to conclude if there is more than one source operating for a given element in a certain size range. Analysis of clustering of several elements, previously investigated for aerosol filter samples, can be applied to the analysis of aerosol size fractions. Careful statistical treatment of elemental concentrations as a function of aerosol particle size may thus yield significant information on the generation, transport and deposition of trace metals in the atmosphere

Phenotypes of asthma in low-income children and adolescents: cluster analysis

Directory of Open Access Journals (Sweden)

Anna Lucia Barros Cabral

Full Text Available ABSTRACT Objective: Studies characterizing asthma phenotypes have predominantly included adults or have involved children and adolescents in developed countries. Therefore, their applicability in other populations, such as those of developing countries, remains indeterminate. Our objective was to determine how low-income children and adolescents with asthma in Brazil are distributed across a cluster analysis. Methods: We included 306 children and adolescents (6-18 years of age with a clinical diagnosis of asthma and under medical treatment for at least one year of follow-up. At enrollment, all the patients were clinically stable. For the cluster analysis, we selected 20 variables commonly measured in clinical practice and considered important in defining asthma phenotypes. Variables with high multicollinearity were excluded. A cluster analysis was applied using a twostep agglomerative test and log-likelihood distance measure. Results: Three clusters were defined for our population. Cluster 1 (n = 94 included subjects with normal pulmonary function, mild eosinophil inflammation, few exacerbations, later age at asthma onset, and mild atopy. Cluster 2 (n = 87 included those with normal pulmonary function, a moderate number of exacerbations, early age at asthma onset, more severe eosinophil inflammation, and moderate atopy. Cluster 3 (n = 108 included those with poor pulmonary function, frequent exacerbations, severe eosinophil inflammation, and severe atopy. Conclusions: Asthma was characterized by the presence of atopy, number of exacerbations, and lung function in low-income children and adolescents in Brazil. The many similarities with previous cluster analyses of phenotypes indicate that this approach shows good generalizability.

Reproducibility of Cognitive Profiles in Psychosis Using Cluster Analysis.

Science.gov (United States)

Lewandowski, Kathryn E; Baker, Justin T; McCarthy, Julie M; Norris, Lesley A; Öngür, Dost

2018-04-01

Cognitive dysfunction is a core symptom dimension that cuts across the psychoses. Recent findings support classification of patients along the cognitive dimension using cluster analysis; however, data-derived groupings may be highly determined by sampling characteristics and the measures used to derive the clusters, and so their interpretability must be established. We examined cognitive clusters in a cross-diagnostic sample of patients with psychosis and associations with clinical and functional outcomes. We then compared our findings to a previous report of cognitive clusters in a separate sample using a different cognitive battery. Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, and clinical and community functioning assessments. Cluster analyses were performed on cognitive variables, and clusters were compared on demographic, cognitive, and clinical measures. Results were compared to findings from our previous report. A four-cluster solution provided a good fit to the data; profiles included a neuropsychologically normal cluster, a globally impaired cluster, and two clusters of mixed profiles. Cognitive burden was associated with symptom severity and poorer community functioning. The patterns of cognitive performance by cluster were highly consistent with our previous findings. We found evidence of four cognitive subgroups of patients with psychosis, with cognitive profiles that map closely to those produced in our previous work. Clusters were associated with clinical and community variables and a measure of premorbid functioning, suggesting that they reflect meaningful groupings: replicable, and related to clinical presentation and functional outcomes. (JINS, 2018, 24, 382-390).

Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.

Science.gov (United States)

Horiuchi, Yu; Tanimoto, Shuzou; Latif, A H M Mahbub; Urayama, Kevin Y; Aoki, Jiro; Yahagi, Kazuyuki; Okuno, Taishi; Sato, Yu; Tanaka, Tetsu; Koseki, Keita; Komiyama, Kota; Nakajima, Hiroyoshi; Hara, Kazuhiro; Tanabe, Kengo

2018-07-01

Acute heart failure (AHF) is a heterogeneous disease caused by various cardiovascular (CV) pathophysiology and multiple non-CV comorbidities. We aimed to identify clinically important subgroups to improve our understanding of the pathophysiology of AHF and inform clinical decision-making. We evaluated detailed clinical data of 345 consecutive AHF patients using non-hierarchical cluster analysis of 77 variables, including age, sex, HF etiology, comorbidities, physical findings, laboratory data, electrocardiogram, echocardiogram and treatment during hospitalization. Cox proportional hazards regression analysis was performed to estimate the association between the clusters and clinical outcomes. Three clusters were identified. Cluster 1 (n=108) represented "vascular failure". This cluster had the highest average systolic blood pressure at admission and lung congestion with type 2 respiratory failure. Cluster 2 (n=89) represented "cardiac and renal failure". They had the lowest ejection fraction (EF) and worst renal function. Cluster 3 (n=148) comprised mostly older patients and had the highest prevalence of atrial fibrillation and preserved EF. Death or HF hospitalization within 12-month occurred in 23% of Cluster 1, 36% of Cluster 2 and 36% of Cluster 3 (p=0.034). Compared with Cluster 1, risk of death or HF hospitalization was 1.74 (95% CI, 1.03-2.95, p=0.037) for Cluster 2 and 1.82 (95% CI, 1.13-2.93, p=0.014) for Cluster 3. Cluster analysis may be effective in producing clinically relevant categories of AHF, and may suggest underlying pathophysiology and potential utility in predicting clinical outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

Identification and validation of asthma phenotypes in Chinese population using cluster analysis.

Science.gov (United States)

Wang, Lei; Liang, Rui; Zhou, Ting; Zheng, Jing; Liang, Bing Miao; Zhang, Hong Ping; Luo, Feng Ming; Gibson, Peter G; Wang, Gang

2017-10-01

Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management. To identify and prospectively validate asthma clusters in a Chinese population. Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters. Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries. Copyright © 2017 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

The Multimorbidity Cluster Analysis Tool: Identifying Combinations and Permutations of Multiple Chronic Diseases Using a Record-Level Computational Analysis

Directory of Open Access Journals (Sweden)

Kathryn Nicholson

2017-12-01

Full Text Available Introduction: Multimorbidity, or the co-occurrence of multiple chronic health conditions within an individual, is an increasingly dominant presence and burden in modern health care systems.  To fully capture its complexity, further research is needed to uncover the patterns and consequences of these co-occurring health states.  As such, the Multimorbidity Cluster Analysis Tool and the accompanying Multimorbidity Cluster Analysis Toolkit have been created to allow researchers to identify distinct clusters that exist within a sample of participants or patients living with multimorbidity.  Development: The Tool and Toolkit were developed at Western University in London, Ontario, Canada.  This open-access computational program (JAVA code and executable file was developed and tested to support an analysis of thousands of individual records and up to 100 disease diagnoses or categories.  Application: The computational program can be adapted to the methodological elements of a research project, including type of data, type of chronic disease reporting, measurement of multimorbidity, sample size and research setting.  The computational program will identify all existing, and mutually exclusive, combinations and permutations within the dataset.  An application of this computational program is provided as an example, in which more than 75,000 individual records and 20 chronic disease categories resulted in the detection of 10,411 unique combinations and 24,647 unique permutations among female and male patients.  Discussion: The Tool and Toolkit are now available for use by researchers interested in exploring the complexities of multimorbidity.  Its careful use, and the comparison between results, will be valuable additions to the nuanced understanding of multimorbidity.

Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study

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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.

K-means clustering versus validation measures: a data-distribution perspective.

Science.gov (United States)

Xiong, Hui; Wu, Junjie; Chen, Jian

2009-04-01

K-means is a well-known and widely used partitional clustering method. While there are considerable research efforts to characterize the key features of the K-means clustering algorithm, further investigation is needed to understand how data distributions can have impact on the performance of K-means clustering. To that end, in this paper, we provide a formal and organized study of the effect of skewed data distributions on K-means clustering. Along this line, we first formally illustrate that K-means tends to produce clusters of relatively uniform size, even if input data have varied "true" cluster sizes. In addition, we show that some clustering validation measures, such as the entropy measure, may not capture this uniform effect and provide misleading information on the clustering performance. Viewed in this light, we provide the coefficient of variation (CV) as a necessary criterion to validate the clustering results. Our findings reveal that K-means tends to produce clusters in which the variations of cluster sizes, as measured by CV, are in a range of about 0.3-1.0. Specifically, for data sets with large variation in "true" cluster sizes (e.g., CV > 1.0), K-means reduces variation in resultant cluster sizes to less than 1.0. In contrast, for data sets with small variation in "true" cluster sizes (e.g., CV K-means increases variation in resultant cluster sizes to greater than 0.3. In other words, for the earlier two cases, K-means produces the clustering results which are away from the "true" cluster distributions.

Cluster analysis of radionuclide concentrations in beach sand

NARCIS (Netherlands)

de Meijer, R.J.; James, I.; Jennings, P.J.; Keoyers, J.E.

This paper presents a method in which natural radionuclide concentrations of beach sand minerals are traced along a stretch of coast by cluster analysis. This analysis yields two groups of mineral deposit with different origins. The method deviates from standard methods of following dispersal of

Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis.

Science.gov (United States)

Liao, Minlei; Li, Yunfeng; Kianifard, Farid; Obi, Engels; Arcona, Stephen

2016-03-02

Cluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and "clusters" found in large data sets. However, this method has not been widely used in large healthcare claims databases where the distribution of expenditure data is commonly severely skewed. The purpose of this study was to identify cost change patterns of patients with end-stage renal disease (ESRD) who initiated hemodialysis (HD) by applying different clustering methods. A retrospective, cross-sectional, observational study was conducted using the Truven Health MarketScan® Research Databases. Patients aged ≥18 years with ≥2 ESRD diagnoses who initiated HD between 2008 and 2010 were included. The K-means CA method and hierarchical CA with various linkage methods were applied to all-cause costs within baseline (12-months pre-HD) and follow-up periods (12-months post-HD) to identify clusters. Demographic, clinical, and cost information was extracted from both periods, and then examined by cluster. A total of 18,380 patients were identified. Meaningful all-cause cost clusters were generated using K-means CA and hierarchical CA with either flexible beta or Ward's methods. Based on cluster sample sizes and change of cost patterns, the K-means CA method and 4 clusters were selected: Cluster 1: Average to High (n = 113); Cluster 2: Very High to High (n = 89); Cluster 3: Average to Average (n = 16,624); or Cluster 4: Increasing Costs, High at Both Points (n = 1554). Median cost changes in the 12-month pre-HD and post-HD periods increased from $185,070 to $884,605 for Cluster 1 (Average to High), decreased from $910,930 to $157,997 for Cluster 2 (Very High to High), were relatively stable and remained low from $15,168 to $13,026 for Cluster 3 (Average to Average), and increased from $57,909 to $193,140 for Cluster 4 (Increasing Costs, High at Both Points). Relatively stable costs after starting HD were associated with more stable scores

Low-energy irradiation effects of gas cluster ion beams

International Nuclear Information System (INIS)

Houzumi, Shingo; Takeshima, Keigo; Mochiji, Kozo; Toyoda, Noriaki; Yamada, Isao

2007-01-01

A cluster-ion irradiation system with cluster-size selection has been developed to study the effects of the cluster size for surface processes using cluster ions. A permanent magnet with a magnetic field of 1.2 T is installed for size separation of large cluster ions. Trace formations at HOPG surface by the irradiation with size-selected Ar-cluster ions under acceleration energy of 30 keV were investigated by a scanning tunneling microscopy. Generation behavior of the crater-like traces is strongly affected by the number of constituent atoms (cluster size) of the irradiating cluster ion. When the incident cluster ion is composed of 100-3000 atoms, crater-like traces are observed on the irradiated surfaces. In contrast, such traces are not observed at all with the irradiation of the cluster-ions composed of over 5000 atoms. Such the behavior is discussed on the basis of the kinetic energy per constituent atom of the cluster ion. To study GCIB irradiation effects against macromolecule, GCIB was irradiated on DNA molecules absorbed on graphite surface. By the GCIB irradiation, much more DNA molecules was sputtered away as compared with the monomer-ion irradiation. (author)

Stopping of hypervelocity clusters in solids

International Nuclear Information System (INIS)

Anders, Christian; Ziegenhain, Gerolf; Urbassek, Herbert M; Bringa, Eduardo M

2011-01-01

Using molecular-dynamics simulations, we study the processes underlying the stopping of energetic clusters upon impact in matter. We investigate self-bombardment of both a metallic (Cu) and a van-der-Waals bonded (frozen Ar) target. Clusters with sizes up to N = 10 4 atoms and with energies per atom of E/N = 0.1-1600 eV atom -1 were studied. We find that the stopping force exerted on a cluster follows an N 2/3 -dependence with cluster size N; thus large clusters experience less stopping than equi-velocity atoms. In the course of being stopped, the cluster is strongly deformed and attains a roughly pancake shape. Due to the cluster inertia, maximum deformation occurs later than the maximum stopping force. The time scale of projectile stopping is set by t 0 , the time the cluster needs to cover its own diameter before impacting the target; it thus depends on both cluster size and velocity. The time when the cluster experiences its maximum stopping force is around (0.7-0.8)t 0 . We find that the cluster is deformed with huge strain rates of around 1/2t 0 ; this amounts to 10 11 -10 13 s -1 for the cases studied here. (paper)

Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

Science.gov (United States)

Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

2008-06-18

Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson

Application of microarray analysis on computer cluster and cloud platforms.

Science.gov (United States)

Bernau, C; Boulesteix, A-L; Knaus, J

2013-01-01

Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

GLOBULAR CLUSTER ABUNDANCES FROM HIGH-RESOLUTION, INTEGRATED-LIGHT SPECTROSCOPY. II. EXPANDING THE METALLICITY RANGE FOR OLD CLUSTERS AND UPDATED ANALYSIS TECHNIQUES

Energy Technology Data Exchange (ETDEWEB)

Colucci, Janet E.; Bernstein, Rebecca A.; McWilliam, Andrew [The Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101 (United States)

2017-01-10

We present abundances of globular clusters (GCs) in the Milky Way and Fornax from integrated-light (IL) spectra. Our goal is to evaluate the consistency of the IL analysis relative to standard abundance analysis for individual stars in those same clusters. This sample includes an updated analysis of seven clusters from our previous publications and results for five new clusters that expand the metallicity range over which our technique has been tested. We find that the [Fe/H] measured from IL spectra agrees to ∼0.1 dex for GCs with metallicities as high as [Fe/H] = −0.3, but the abundances measured for more metal-rich clusters may be underestimated. In addition we systematically evaluate the accuracy of abundance ratios, [X/Fe], for Na i, Mg i, Al i, Si i, Ca i, Ti i, Ti ii, Sc ii, V i, Cr i, Mn i, Co i, Ni i, Cu i, Y ii, Zr i, Ba ii, La ii, Nd ii, and Eu ii. The elements for which the IL analysis gives results that are most similar to analysis of individual stellar spectra are Fe i, Ca i, Si i, Ni i, and Ba ii. The elements that show the greatest differences include Mg i and Zr i. Some elements show good agreement only over a limited range in metallicity. More stellar abundance data in these clusters would enable more complete evaluation of the IL results for other important elements.

A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

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Shaoning Li

2017-01-01

Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.

Clustering of users of digital libraries through log file analysis

Directory of Open Access Journals (Sweden)

Juan Antonio Martínez-Comeche

2017-09-01

Full Text Available This study analyzes how users perform information retrieval tasks when introducing queries to the Hispanic Digital Library. Clusters of users are differentiated based on their distinct information behavior. The study used the log files collected by the server over a year and different possible clustering algorithms are compared. The k-means algorithm is found to be a suitable clustering method for the analysis of large log files from digital libraries. In the case of the Hispanic Digital Library the results show three clusters of users and the characteristic information behavior of each group is described.

Caracterización de las PYMES en Costa Rica mediante el empleo del Análisis de Conglomerados o Cluster (Categorization of the Costa Rican SME using cluster analysis

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Ricardo Monge González

2012-12-01

Full Text Available El presente artículo discute los resultados de la aplicación de un análisis de conglomerados o cluster a una muestra representativa de ochocientas nueve micro, pequeñas y medianas empresas costarricenses formales o semiformales, las cuales fueron encuestadas por el Observatorio de Mipymes de Costa Rica en el año 2007. Este enfoque permite estudiar las Mipymes bajo una óptica diferente al enfoque tradicional, que se basa en el tamaño de las empresas (micro, pequeñas o medianas o las actividades productivas a las que pertenecen (agricultura, industria, comercio y servicios. Es decir, permite analizar y clasificar las empresas según su grado de madurez, o bien, de su permanencia y éxito en el mercado en que operan. Tal clasificación es útil a la hora de evaluar el acceso al financiamiento de las empresas o a programas de incentivos por parte de instituciones públicas, entre otras muchas variables. Así, el análisis de cluster se convierte en una valiosa herramienta para el análisis de políticas públicas y la promulgación de recomendaciones de políticas en pro del desarrollo de las Mipymes.   ABSTRACT This article examines results obtained from the cluster analysis of a sample of 809 micro, small and medium sized, formal and semi-formal Costa Rican businesses surveyed by the Observatorio de Mipymes (SME Observatory in 2007. This approach allows the study of SME´s from a different perspective than the one provided by the more traditional approach by size (micro, small or medium, or by productive sector (agricultural, industrial, commercial or service. That is, businesses are studied and classified either according to their maturity status, or by their permanence and success in the market. This classification is useful to evaluate their access to financing or to governmental incentive programs. As a result, cluster analysis becomes a valuable tool to evaluate and recommend public policies for the development of SMEs.

Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

Science.gov (United States)

Muraoka, Masae; Okuda, Hiroshi

With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

Full text clustering and relationship network analysis of biomedical publications.

Directory of Open Access Journals (Sweden)

Renchu Guan

Full Text Available Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

Full text clustering and relationship network analysis of biomedical publications.

Science.gov (United States)

Guan, Renchu; Yang, Chen; Marchese, Maurizio; Liang, Yanchun; Shi, Xiaohu

2014-01-01

Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

Hydration structure and dynamics of a hydroxide ion in water clusters of varying size and temperature: Quantum chemical and ab initio molecular dynamics studies

International Nuclear Information System (INIS)

Bankura, Arindam; Chandra, Amalendu

2012-01-01

Highlights: ► A theoretical study of hydroxide ion-water clusters is carried for varying cluster size and temperature. ► The structures of OH − (H 2 O) n are found out through quantum chemical calculations for n = 4, 8, 16 and 20. ► The finite temperature behavior of the clusters is studied through ab initio dynamical simulations. ► The spectral features of OH modes (deuterated) and their dependence on hydrogen bonding states of water are discussed. ► The mechanism and kinetics of proton transfer processes in these anionic clusters are also investigated. - Abstract: We have investigated the hydration structure and dynamics of OH − (H 2 O) n clusters (n = 4, 8, 16 and 20) by means of quantum chemical and ab initio molecular dynamics calculations. Quantum chemical calculations reveal that the solvation structure of the hydroxide ion transforms from three and four-coordinated surface states to five-coordinated interior state with increase in cluster size. Several other isomeric structures with energies not very different from the most stable isomer are also found. Ab initio simulations show that the most probable configurations at higher temperatures need not be the lowest energy isomeric structure. The rates of proton transfer in these clusters are found to be slower than that in bulk water. The vibrational spectral calculations reveal distinct features for free OH (deuterated) stretch modes of water in different hydrogen bonding states. Effects of temperature on the structural and dynamical properties are also investigated for the largest cluster considered here.

Steady state subchannel analysis of AHWR fuel cluster

International Nuclear Information System (INIS)

Dasgupta, A.; Chandraker, D.K.; Vijayan, P.K.; Saha, D.

2006-09-01

Subchannel analysis is a technique used to predict the thermal hydraulic behavior of reactor fuel assemblies. The rod cluster is subdivided into a number of parallel interacting flow subchannels. The conservation equations are solved for each of these subchannels, taking into account subchannel interactions. Subchannel analysis of AHWR D-5 fuel cluster has been carried out to determine the variations in thermal hydraulic conditions of coolant and fuel temperatures along the length of the fuel bundle. The hottest regions within the AHWR fuel bundle have been identified. The effect of creep on the fuel performance has also been studied. MCHFR has been calculated using Jansen-Levy correlation. The calculations have been backed by sensitivity analysis for parameters whose values are not known accurately. The sensitivity analysis showed the calculations to have a very low sensitivity to these parameters. Apart from the analysis, the report also includes a brief introduction of a few subchannel codes. A brief description of the equations and solution methodology used in COBRA-IIIC and COBRA-IV-I is also given. (author)

Mobility in Europe: Recent Trends from a Cluster Analysis

Directory of Open Access Journals (Sweden)

Ioana Manafi

2017-08-01

Full Text Available During the past decade, Europe was confronted with major changes and events offering large opportunities for mobility. The EU enlargement process, the EU policies regarding youth, the economic crisis affecting national economies on different levels, political instabilities in some European countries, high rates of unemployment or the increasing number of refugees are only a few of the factors influencing net migration in Europe. Based on a set of socio-economic indicators for EU/EFTA countries and cluster analysis, the paper provides an overview of regional differences across European countries, related to migration magnitude in the identified clusters. The obtained clusters are in accordance with previous studies in migration, and appear stable during the period of 2005-2013, with only some exceptions. The analysis revealed three country clusters: EU/EFTA center-receiving countries, EU/EFTA periphery-sending countries and EU/EFTA outlier countries, the names suggesting not only the geographical position within Europe, but the trends in net migration flows during the years. Therewith, the results provide evidence for the persistence of a movement from periphery to center countries, which is correlated with recent flows of mobility in Europe.

OPEN CLUSTERS IN THE MILKY WAY OUTER DISK: NEWLY DISCOVERED AND UNSTUDIED CLUSTERS IN THE SPITZER GLIMPSE-360, CYG-X, AND SMOG SURVEYS

International Nuclear Information System (INIS)

Zasowski, G.; Beaton, R. L.; Hamm, K. K.; Majewski, S. R.; Patterson, R. J.; Babler, B.; Churchwell, E.; Meade, M.; Whitney, B. A.; Benjamin, R. A.; Watson, C.

2013-01-01

Open stellar clusters are extremely valuable probes of Galactic structure, star formation, kinematics, and chemical abundance patterns. Near-infrared (NIR) data have enabled the detection of hundreds of clusters hidden from optical surveys, and mid-infrared (MIR) data are poised to offer an even clearer view into the most heavily obscured parts of the Milky Way. We use new MIR images from the Spitzer GLIMPSE-360, Cyg-X, and SMOG surveys to visually identify a large number of open cluster candidates in the outer disk of the Milky Way (65° < l < 265°). Using NIR color-magnitude diagrams, stellar isochrones, and stellar reddening estimates, we derive cluster parameters (metallicity, distance, reddening) for those objects without previous identification and/or parameters in the literature. In total, we present coordinates and sizes of 20 previously unknown open cluster candidates; for 7 of these we also present metallicity, distance, and reddening values. In addition, we provide the first estimates of these values for nine clusters that had been previously cataloged. We compare our cluster sizes and other derived parameters to those in the open cluster catalog of Dias et al. and find strong similarities except for a higher mean reddening for our objects, which signifies our increased detection sensitivity in regions of high extinction. The results of this cluster search and analysis demonstrate the ability of MIR imaging and photometry to augment significantly the current census of open clusters in the Galaxy

Cluster analysis for portfolio optimization

OpenAIRE

Vincenzo Tola; Fabrizio Lillo; Mauro Gallegati; Rosario N. Mantegna

2005-01-01

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio compositi...

Hierarchical cluster analysis of progression patterns in open-angle glaucoma patients with medical treatment.

Science.gov (United States)

Bae, Hyoung Won; Rho, Seungsoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

2014-04-29

To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had undergone visual field (VF) testing at least once per year for 5 or more years. OAG was classified into subgroups using hierarchical cluster analysis based on the following five variables: baseline mean deviation (MD), baseline visual field index (VFI), MD slope, VFI slope, and Glaucoma Progression Analysis (GPA) printout. After that, other parameters were compared between clusters. Two clusters were made after a hierarchical cluster analysis. Cluster 1 showed -4.06 ± 2.43 dB baseline MD, 92.58% ± 6.27% baseline VFI, -0.28 ± 0.38 dB per year MD slope, -0.52% ± 0.81% per year VFI slope, and all "no progression" cases in GPA printout, whereas cluster 2 showed -8.68 ± 3.81 baseline MD, 77.54 ± 12.98 baseline VFI, -0.72 ± 0.55 MD slope, -2.22 ± 1.89 VFI slope, and seven "possible" and four "likely" progression cases in GPA printout. There were no significant differences in age, sex, mean IOP, central corneal thickness, and axial length between clusters. However, cluster 2 included more high-tension glaucoma patients and used a greater number of antiglaucoma eye drops significantly compared with cluster 1. Hierarchical cluster analysis of progression patterns divided OAG into slow and fast progression groups, evidenced by assessing the parameters of glaucomatous progression in VF testing. In the fast progression group, the prevalence of high-tension glaucoma was greater and the number of antiglaucoma medications administered was increased versus the slow progression group. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Cluster analysis of spontaneous preterm birth phenotypes identifies potential associations among preterm birth mechanisms.

Science.gov (United States)

Esplin, M Sean; Manuck, Tracy A; Varner, Michael W; Christensen, Bryce; Biggio, Joseph; Bukowski, Radek; Parry, Samuel; Zhang, Heping; Huang, Hao; Andrews, William; Saade, George; Sadovsky, Yoel; Reddy, Uma M; Ilekis, John

2015-09-01

We sought to use an innovative tool that is based on common biologic pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB) to enhance investigators' ability to identify and to highlight common mechanisms and underlying genetic factors that are responsible for SPTB. We performed a secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks' gestation. Each woman was assessed for the presence of underlying SPTB causes. A hierarchic cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis with the use of VEGAS software. One thousand twenty-eight women with SPTB were assigned phenotypes. Hierarchic clustering of the phenotypes revealed 5 major clusters. Cluster 1 (n = 445) was characterized by maternal stress; cluster 2 (n = 294) was characterized by premature membrane rupture; cluster 3 (n = 120) was characterized by familial factors, and cluster 4 (n = 63) was characterized by maternal comorbidities. Cluster 5 (n = 106) was multifactorial and characterized by infection (INF), decidual hemorrhage (DH), and placental dysfunction (PD). These 3 phenotypes were correlated highly by χ(2) analysis (PD and DH, P cluster 3 of SPTB. We identified 5 major clusters of SPTB based on a phenotype tool and hierarch clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors that were underlying SPTB. Copyright © 2015 Elsevier Inc. All rights reserved.

A critical cluster analysis of 44 indicators of author-level performance

DEFF Research Database (Denmark)

Wildgaard, Lorna Elizabeth

2016-01-01

-four indicators of individual researcher performance were computed using the data. The clustering solution was supported by continued reference to the researcher’s curriculum vitae, an effect analysis and a risk analysis. Disciplinary appropriate indicators were identified and used to divide the researchers......This paper explores a 7-stage cluster methodology as a process to identify appropriate indicators for evaluation of individual researchers at a disciplinary and seniority level. Publication and citation data for 741 researchers from 4 disciplines was collected in Web of Science. Forty...... of statistics in research evaluation. The strength of the 7-stage cluster methodology is that it makes clear that in the evaluation of individual researchers, statistics cannot stand alone. The methodology is reliant on contextual information to verify the bibliometric values and cluster solution...

Tweets clustering using latent semantic analysis

Science.gov (United States)

Rasidi, Norsuhaili Mahamed; Bakar, Sakhinah Abu; Razak, Fatimah Abdul

2017-04-01

Social media are becoming overloaded with information due to the increasing number of information feeds. Unlike other social media, Twitter users are allowed to broadcast a short message called as `tweet". In this study, we extract tweets related to MH370 for certain of time. In this paper, we present overview of our approach for tweets clustering to analyze the users' responses toward tragedy of MH370. The tweets were clustered based on the frequency of terms obtained from the classification process. The method we used for the text classification is Latent Semantic Analysis. As a result, there are two types of tweets that response to MH370 tragedy which is emotional and non-emotional. We show some of our initial results to demonstrate the effectiveness of our approach.

Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.

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Conley, Samantha

2017-12-01

The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

The composite sequential clustering technique for analysis of multispectral scanner data

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Su, M. Y.

1972-01-01

The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

Cluster-based analysis of multi-model climate ensembles

Science.gov (United States)

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

2018-06-01

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

Undergraduate ALFALFA Team: Analysis of Spatially-Resolved Star-Formation in Nearby Galaxy Groups and Clusters

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Finn, Rose; Collova, Natasha; Spicer, Sandy; Whalen, Kelly; Koopmann, Rebecca A.; Durbala, Adriana; Haynes, Martha P.; Undergraduate ALFALFA Team

2017-01-01

As part of the Undergraduate ALFALFA Team, we are conducting a survey of the gas and star-formation properties of galaxies in 36 groups and clusters in the local universe. The galaxies in our sample span a large range of galactic environments, from the centers of galaxy groups and clusters to the surrounding infall regions. One goal of the project is to map the spatial distribution of star-formation; the relative extent of the star-forming and stellar disks provides important information about the internal and external processes that deplete gas and thus drive galaxy evolution. We obtained wide-field H-alpha observations with the WIYN 0.9m telescope at Kitt Peak National Observatory for galaxies in the vicinity of the MKW11 and NRGb004 galaxy groups and the Abell 1367 cluster. We present a preliminary analysis of the relative size of the star-forming and stellar disks as a function of galaxy morphology and local galaxy density, and we calculate gas depletion times using star-formation rates and HI gas mass. We will combine these results with those from other UAT members to determine if and how environmentally-driven gas depletion varies with the mass and X-ray properties of the host group or cluster. This work has supported by NSF grants AST-0847430, AST-1211005 and AST-1637339.

Clusters of galaxies as tools in observational cosmology : results from x-ray analysis

International Nuclear Information System (INIS)

Weratschnig, J.M.

2009-01-01

Clusters of galaxies are the largest gravitationally bound structures in the universe. They can be used as ideal tools to study large scale structure formation (e.g. when studying merger clusters) and provide highly interesting environments to analyse several characteristic interaction processes (like ram pressure stripping of galaxies, magnetic fields). In this dissertation thesis, we have studied several clusters of galaxies using X-ray observations. To obtain scientific results, we have applied different data reduction and analysis methods. With a combination of morphological and spectral analysis, the merger cluster Abell 514 was studied in much detail. It has a highly interesting morphology and shows signs for an ongoing merger as well as a shock. using a new method to detect substructure, we have analysed several clusters to determine whether any substructure is present in the X-ray image. This hints towards a real structure in the distribution of the intra-cluster medium (ICM) and is evidence for ongoing mergers. The results from this analysis are extensively used with the cluster of galaxies Abell S1136. Here, we study the ICM distribution and compare its structure with the spatial distribution of star forming galaxies. Cluster magnetic fields are another important topic of my thesis. They can be studied in Radio observations, which can be put into relation with results from X-ray observations. using observational data from several clusters, we could support the theory that cluster magnetic fields are frozen into the ICM. (author)

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

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Marco Borri

Full Text Available To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment.The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4. Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters.The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4, determined with cluster validation, produced the best separation between reducing and non-reducing clusters.The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

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Jeban Ganesalingam

2009-09-01

Full Text Available Amyotrophic lateral sclerosis (ALS is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes.Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method.The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001. Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb and time from symptom onset to diagnosis (p<0.00001.The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.

Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

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Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

2001-04-01

Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

Applying Data Clustering Feature to Speed Up Ant Colony Optimization

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Chao-Yang Pang

2014-01-01

Full Text Available Ant colony optimization (ACO is often used to solve optimization problems, such as traveling salesman problem (TSP. When it is applied to TSP, its runtime is proportional to the squared size of problem N so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size N becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.

Application of cluster analysis and unsupervised learning to multivariate tissue characterization

International Nuclear Information System (INIS)

Momenan, R.; Insana, M.F.; Wagner, R.F.; Garra, B.S.; Loew, M.H.

1987-01-01

This paper describes a procedure for classifying tissue types from unlabeled acoustic measurements (data type unknown) using unsupervised cluster analysis. These techniques are being applied to unsupervised ultrasonic image segmentation and tissue characterization. The performance of a new clustering technique is measured and compared with supervised methods, such as a linear Bayes classifier. In these comparisons two objectives are sought: a) How well does the clustering method group the data?; b) Do the clusters correspond to known tissue classes? The first question is investigated by a measure of cluster similarity and dispersion. The second question involves a comparison with a supervised technique using labeled data

Clustering analysis for muon tomography data elaboration in the Muon Portal project

Science.gov (United States)

Bandieramonte, M.; Antonuccio-Delogu, V.; Becciani, U.; Costa, A.; La Rocca, P.; Massimino, P.; Petta, C.; Pistagna, C.; Riggi, F.; Riggi, S.; Sciacca, E.; Vitello, F.

2015-05-01

Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data.

Symptom Clusters in People Living with HIV Attending Five Palliative Care Facilities in Two Sub-Saharan African Countries: A Hierarchical Cluster Analysis.

Science.gov (United States)

Moens, Katrien; Siegert, Richard J; Taylor, Steve; Namisango, Eve; Harding, Richard

2015-01-01

Symptom research across conditions has historically focused on single symptoms, and the burden of multiple symptoms and their interactions has been relatively neglected especially in people living with HIV. Symptom cluster studies are required to set priorities in treatment planning, and to lessen the total symptom burden. This study aimed to identify and compare symptom clusters among people living with HIV attending five palliative care facilities in two sub-Saharan African countries. Data from cross-sectional self-report of seven-day symptom prevalence on the 32-item Memorial Symptom Assessment Scale-Short Form were used. A hierarchical cluster analysis was conducted using Ward's method applying squared Euclidean Distance as the similarity measure to determine the clusters. Contingency tables, X2 tests and ANOVA were used to compare the clusters by patient specific characteristics and distress scores. Among the sample (N=217) the mean age was 36.5 (SD 9.0), 73.2% were female, and 49.1% were on antiretroviral therapy (ART). The cluster analysis produced five symptom clusters identified as: 1) dermatological; 2) generalised anxiety and elimination; 3) social and image; 4) persistently present; and 5) a gastrointestinal-related symptom cluster. The patients in the first three symptom clusters reported the highest physical and psychological distress scores. Patient characteristics varied significantly across the five clusters by functional status (worst functional physical status in cluster one, ppeople living with HIV with longitudinally collected symptom data to test cluster stability and identify common symptom trajectories is recommended.

TURBULENT CLUSTERING OF PROTOPLANETARY DUST AND PLANETESIMAL FORMATION

International Nuclear Information System (INIS)

Pan Liubin; Padoan, Paolo; Scalo, John; Kritsuk, Alexei G.; Norman, Michael L.

2011-01-01

We study the clustering of inertial particles in turbulent flows and discuss its applications to dust particles in protoplanetary disks. Using numerical simulations, we compute the radial distribution function (RDF), which measures the probability of finding particle pairs at given distances, and the probability density function of the particle concentration. The clustering statistics depend on the Stokes number, St, defined as the ratio of the particle friction timescale, τ p , to the Kolmogorov timescale in the flow. In agreement with previous studies, we find that, in the dissipation range, the clustering intensity strongly peaks at St ≅ 1, and the RDF for St ∼ 1 shows a fast power-law increase toward small scales, suggesting that turbulent clustering may considerably enhance the particle collision rate. Clustering at inertial-range scales is of particular interest to the problem of planetesimal formation. At these large scales, the strongest clustering is from particles with τ p in the inertial range. Clustering of these particles occurs primarily around a scale where the eddy turnover time is ∼τ p . We find that particles of different sizes tend to cluster at different locations, leading to flat RDFs between different particles at small scales. In the presence of multiple particle sizes, the overall clustering strength decreases as the particle size distribution broadens. We discuss particle clustering in two recent models for planetesimal formation. We argue that, in the model based on turbulent clustering of chondrule-size particles, the probability of finding strong clusters that can seed planetesimals may have been significantly overestimated. We discuss various clustering mechanisms in simulations of planetesimal formation by gravitational collapse of dense clumps of meter-size particles, in particular the contribution from turbulent clustering due to the limited numerical resolution.

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

Science.gov (United States)

Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

2018-01-01

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

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

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Sorana D. BOLBOACĂ

2011-06-01

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

Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

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Martinez Fernando J

2010-03-01

Full Text Available Abstract Background Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity. Methods We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster. Results We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1 post-bronchodilator FEV1 percent predicted, 2 percent bronchodilator responsiveness, and quantitative CT measurements of 3 apical emphysema and 4 airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1 emphysema predominant, 2 bronchodilator responsive, with higher FEV1; 3 discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4 airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant was associated with TGFB1 SNP rs1800470. Conclusions Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

Respective efficiencies of nuclear collisions and electronic excitations for precipitating Ag clusters in sol-gel films

CERN Document Server

Pivin, J C

2002-01-01

The growth of silver clusters in co-sputtered SiO sub 2 :Ag films under irradiation with increasing fluences of 1.5 MeV He or 3 MeV Au ions is investigated by recording spectra of optical extinction. The analysis of surface plasmon resonances in these very small clusters on basis of Mie theory permits to estimate more precisely their mean size than TEM images. A linear increase of the mean cluster size with the energy deposited by ions in electronic excitations and little effect of collision cascades are observed. The growth kinetics is ascribed to a process of desorption/re-adsorption of Ag atoms at the surface of clusters.

Transcriptional analysis of exopolysaccharides biosynthesis gene clusters in Lactobacillus plantarum.

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Vastano, Valeria; Perrone, Filomena; Marasco, Rosangela; Sacco, Margherita; Muscariello, Lidia

2016-04-01

Exopolysaccharides (EPS) from lactic acid bacteria contribute to specific rheology and texture of fermented milk products and find applications also in non-dairy foods and in therapeutics. Recently, four clusters of genes (cps) associated with surface polysaccharide production have been identified in Lactobacillus plantarum WCFS1, a probiotic and food-associated lactobacillus. These clusters are involved in cell surface architecture and probably in release and/or exposure of immunomodulating bacterial molecules. Here we show a transcriptional analysis of these clusters. Indeed, RT-PCR experiments revealed that the cps loci are organized in five operons. Moreover, by reverse transcription-qPCR analysis performed on L. plantarum WCFS1 (wild type) and WCFS1-2 (ΔccpA), we demonstrated that expression of three cps clusters is under the control of the global regulator CcpA. These results, together with the identification of putative CcpA target sequences (catabolite responsive element CRE) in the regulatory region of four out of five transcriptional units, strongly suggest for the first time a role of the master regulator CcpA in EPS gene transcription among lactobacilli.

Particle coagulation in molten metal based on three-dimensional analysis of cluster by x-ray micro-computer tomography (CT)

International Nuclear Information System (INIS)

Li, Tao; Shimasaki, Shin-ichi; Taniguchi, Shoji; Narita, Shunsuke; Uesugi, Kentaro

2013-01-01

Particle coagulation plays a key role in steel refining process to remove inclusions. Many research works focus on the behaviors of particle coagulation. To reveal its mechanism water model experiments have been performed by some researchers including the authors' group. In this paper, experiments of particle coagulation were carried out with molten Al including SiC particles in a mechanically agitated crucible with two baffles. Particle coagulation and formation of clusters were observed on the microscopy images of as-polished samples. Three-dimensional (3D) analysis of the clusters in solidified Al was implemented by X-ray micro CT available at SPring-8. The methods to distinguish clusters on two-dimensional (2D) cross-sectional images were discussed, which were established in the previous works by the present authors' group. The characteristics of the 3D SiC clusters and their 2D cross-sections were analyzed. The statistical ranges of the parameters for 2D clusters were used as criterions to distinguish the clusters on 2D microscopy images from the as-polished samples. The kinetics of SiC particle coagulation was studied by the measured cluster number density and size using our program to distinguish cluster in 2D cross-sectional images according to 3D information (DC-2D-3D). The calculated and experimental results of the SiC particle coagulation in molten Al agree well with each other. (author)

Similarity transformed coupled cluster response (ST-CCR) theory--a time-dependent similarity transformed equation-of-motion coupled cluster (STEOM-CC) approach.

Science.gov (United States)

Landau, Arie

2013-07-07

This paper presents a new method for calculating spectroscopic properties in the framework of response theory utilizing a sequence of similarity transformations (STs). The STs are preformed using the coupled cluster (CC) and Fock-space coupled cluster operators. The linear and quadratic response functions of the new similarity transformed CC response (ST-CCR) method are derived. The poles of the linear response yield excitation-energy (EE) expressions identical to the ones in the similarity transformed equation-of-motion coupled cluster (STEOM-CC) approach. ST-CCR and STEOM-CC complement each other, in analogy to the complementarity of CC response (CCR) and equation-of-motion coupled cluster (EOM-CC). ST-CCR/STEOM-CC and CCR/EOM-CC yield size-extensive and size-intensive EEs, respectively. Other electronic-properties, e.g., transition dipole strengths, are also size-extensive within ST-CCR, in contrast to STEOM-CC. Moreover, analysis suggests that in comparison with CCR, the ST-CCR expressions may be confined to a smaller subspace, however, the precise scope of the truncation can only be determined numerically. In addition, reformulation of the time-independent STEOM-CC using the same parameterization as in ST-CCR, as well as an efficient truncation scheme, is presented. The shown convergence of the time-dependent and time-independent expressions displays the completeness of the presented formalism.

Cluster Analysis of Maize Inbred Lines

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Jiban Shrestha

2016-12-01

Full Text Available The determination of diversity among inbred lines is important for heterosis breeding. Sixty maize inbred lines were evaluated for their eight agro morphological traits during winter season of 2011 to analyze their genetic diversity. Clustering was done by average linkage method. The inbred lines were grouped into six clusters. Inbred lines grouped into Clusters II had taller plants with maximum number of leaves. The cluster III was characterized with shorter plants with minimum number of leaves. The inbred lines categorized into cluster V had early flowering whereas the group into cluster VI had late flowering time. The inbred lines grouped into the cluster III were characterized by higher value of anthesis silking interval (ASI and those of cluster VI had lower value of ASI. These results showed that the inbred lines having widely divergent clusters can be utilized in hybrid breeding programme.

[Principal component analysis and cluster analysis of inorganic elements in sea cucumber Apostichopus japonicus].

Science.gov (United States)

Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie

2011-11-01

The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.

Electron: Cluster interactions

International Nuclear Information System (INIS)

Scheidemann, A.A.; Knight, W.D.

1994-02-01

Beam depletion spectroscopy has been used to measure absolute total inelastic electron-sodium cluster collision cross sections in the energy range from E ∼ 0.1 to E ∼ 6 eV. The investigation focused on the closed shell clusters Na 8 , Na 20 , Na 40 . The measured cross sections show an increase for the lowest collision energies where electron attachment is the primary scattering channel. The electron attachment cross section can be understood in terms of Langevin scattering, connecting this measurement with the polarizability of the cluster. For energies above the dissociation energy the measured electron-cluster cross section is energy independent, thus defining an electron-cluster interaction range. This interaction range increases with the cluster size

Statistical Techniques Applied to Aerial Radiometric Surveys (STAARS): cluster analysis. National Uranium Resource Evaluation

International Nuclear Information System (INIS)

Pirkle, F.L.; Stablein, N.K.; Howell, J.A.; Wecksung, G.W.; Duran, B.S.

1982-11-01

One objective of the aerial radiometric surveys flown as part of the US Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the regional distribution of near-surface radioelement abundances. Some method for identifying groups of observations with similar radioelement values was therefore required. It is shown in this report that cluster analysis can identify such groups even when no a priori knowledge of the geology of an area exists. A method of convergent k-means cluster analysis coupled with a hierarchical cluster analysis is used to classify 6991 observations (three radiometric variables at each observation location) from the Precambrian rocks of the Copper Mountain, Wyoming, area. Another method, one that combines a principal components analysis with a convergent k-means analysis, is applied to the same data. These two methods are compared with a convergent k-means analysis that utilizes available geologic knowledge. All three methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and one represents outliers (anomalously high 214 Bi). A segmentation of the data corresponding to geologic reality as discovered by other methods has been achieved based solely on analysis of aerial radiometric data. The techniques employed are composites of classical clustering methods designed to handle the special problems presented by large data sets. 20 figures, 7 tables

Gas phase reactivity of thermal metal clusters

Science.gov (United States)

Castleman, A. W., Jr.; Harms, A. C.; Leuchtner, R. E.

1991-03-01

Reaction kinetics of metal cluster ions under well defined thermal conditions were studied using a flow tube reactor in combination with laser vaporization. Aluminum anions and cations were reacted with oxygen, and several species which are predicted jellium shell closings, were found to have special stability. Metal alloy cluster anions comprised of Al, V and Nb were also seen to react with oxygen. Alloy clusters with an even number of electrons reacted more slowly than odd electron species, and certain clusters appeared to be exceptionally unreactive. Copper cation clusters were observed to associate with carbon monoxide with reactivities that approach bulk behavior at surprisingly small cluster size. These reactions demonstrate how the rate of reaction changes with cluster size.

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

Science.gov (United States)

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

1994-01-01

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

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

Science.gov (United States)

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

1994-07-01

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

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

International Nuclear Information System (INIS)

Hozé, Nathanaël; Holcman, David

2012-01-01

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

Fluctuating micro-heterogeneity in water–tert-butyl alcohol mixtures and lambda-type divergence of the mean cluster size with phase transition-like multiple anomalies

International Nuclear Information System (INIS)

Banerjee, Saikat; Furtado, Jonathan; Bagchi, Biman

2014-01-01

Water–tert-butyl alcohol (TBA) binary mixture exhibits a large number of thermodynamic and dynamic anomalies. These anomalies are observed at surprisingly low TBA mole fraction, with x TBA ≈ 0.03–0.07. We demonstrate here that the origin of the anomalies lies in the local structural changes that occur due to self-aggregation of TBA molecules. We observe a percolation transition of the TBA molecules at x TBA ≈ 0.05. We note that “islands” of TBA clusters form even below this mole fraction, while a large spanning cluster emerges above that mole fraction. At this percolation threshold, we observe a lambda-type divergence in the fluctuation of the size of the largest TBA cluster, reminiscent of a critical point. Alongside, the structure of water is also perturbed, albeit weakly, by the aggregation of TBA molecules. There is a monotonic decrease in the tetrahedral order parameter of water, while the dipole moment correlation shows a weak nonlinearity. Interestingly, water molecules themselves exhibit a reverse percolation transition at higher TBA concentration, x TBA ≈ 0.45, where large spanning water clusters now break-up into small clusters. This is accompanied by significant divergence of the fluctuations in the size of largest water cluster. This second transition gives rise to another set of anomalies around. Both the percolation transitions can be regarded as manifestations of Janus effect at small molecular level

Optimal Sizing for Wind/PV/Battery System Using Fuzzy c-Means Clustering with Self-Adapted Cluster Number

Directory of Open Access Journals (Sweden)

Xin Liu

2017-01-01

Full Text Available Integrating wind generation, photovoltaic power, and battery storage to form hybrid power systems has been recognized to be promising in renewable energy development. However, considering the system complexity and uncertainty of renewable energies, such as wind and solar types, it is difficult to obtain practical solutions for these systems. In this paper, optimal sizing for a wind/PV/battery system is realized by trade-offs between technical and economic factors. Firstly, the fuzzy c-means clustering algorithm was modified with self-adapted parameters to extract useful information from historical data. Furthermore, the Markov model is combined to determine the chronological system states of natural resources and load. Finally, a power balance strategy is introduced to guide the optimization process with the genetic algorithm to establish the optimal configuration with minimized cost while guaranteeing reliability and environmental factors. A case of island hybrid power system is analyzed, and the simulation results are compared with the general FCM method and chronological method to validate the effectiveness of the mentioned method.

Performance Evaluation of Hadoop-based Large-scale Network Traffic Analysis Cluster

Directory of Open Access Journals (Sweden)

Tao Ran

2016-01-01

Full Text Available As Hadoop has gained popularity in big data era, it is widely used in various fields. The self-design and self-developed large-scale network traffic analysis cluster works well based on Hadoop, with off-line applications running on it to analyze the massive network traffic data. On purpose of scientifically and reasonably evaluating the performance of analysis cluster, we propose a performance evaluation system. Firstly, we set the execution times of three benchmark applications as the benchmark of the performance, and pick 40 metrics of customized statistical resource data. Then we identify the relationship between the resource data and the execution times by a statistic modeling analysis approach, which is composed of principal component analysis and multiple linear regression. After training models by historical data, we can predict the execution times by current resource data. Finally, we evaluate the performance of analysis cluster by the validated predicting of execution times. Experimental results show that the predicted execution times by trained models are within acceptable error range, and the evaluation results of performance are accurate and reliable.

Using self-consistent Gibbs free energy surfaces to calculate size distributions of neutral and charged clusters for the sulfuric acid-water binary system

Science.gov (United States)

Smith, J. A.; Froyd, K. D.; Toon, O. B.

2012-12-01

We construct tables of reaction enthalpies and entropies for the association reactions involving sulfuric acid vapor, water vapor, and the bisulfate ion. These tables are created from experimental measurements and quantum chemical calculations for molecular clusters and a classical thermodynamic model for larger clusters. These initial tables are not thermodynamically consistent. For example, the Gibbs free energy of associating a cluster consisting of one acid molecule and two water molecules depends on the order in which the cluster was assembled: add two waters and then the acid or add an acid and a water and then the second water. We adjust the values within the tables using the method of Lagrange multipliers to minimize the adjustments and produce self-consistent Gibbs free energy surfaces for the neutral clusters and the charged clusters. With the self-consistent Gibbs free energy surfaces, we calculate size distributions of neutral and charged clusters for a variety of atmospheric conditions. Depending on the conditions, nucleation can be dominated by growth along the neutral channel or growth along the ion channel followed by ion-ion recombination.

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Data Analysis and Visualization (IDAV) and the Department of Computer Science, University of California, Davis, One Shields Avenue, Davis CA 95616, USA,; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,' ' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA; Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA,; Computer Science Division,University of California, Berkeley, CA, USA,; Computer Science Department, University of California, Irvine, CA, USA,; All authors are with the Berkeley Drosophila Transcription Network Project, Lawrence Berkeley National Laboratory,; Rubel, Oliver; Weber, Gunther H.; Huang, Min-Yu; Bethel, E. Wes; Biggin, Mark D.; Fowlkes, Charless C.; Hendriks, Cris L. Luengo; Keranen, Soile V. E.; Eisen, Michael B.; Knowles, David W.; Malik, Jitendra; Hagen, Hans; Hamann, Bernd

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

Analysis of RXTE data on Clusters of Galaxies

Science.gov (United States)

Petrosian, Vahe

2004-01-01

This grant provided support for the reduction, analysis and interpretation of of hard X-ray (HXR, for short) observations of the cluster of galaxies RXJO658--5557 scheduled for the week of August 23, 2002 under the RXTE Cycle 7 program (PI Vahe Petrosian, Obs. ID 70165). The goal of the observation was to search for and characterize the shape of the HXR component beyond the well established thermal soft X-ray (SXR) component. Such hard components have been detected in several nearby clusters. distant cluster would provide information on the characteristics of this radiation at a different epoch in the evolution of the imiverse and shed light on its origin. We (Petrosian, 2001) have argued that thermal bremsstrahlung, as proposed earlier, cannot be the mechanism for the production of the HXRs and that the most likely mechanism is Compton upscattering of the cosmic microwave radiation by relativistic electrons which are known to be present in the clusters and be responsible for the observed radio emission. Based on this picture we estimated that this cluster, in spite of its relatively large distance, will have HXR signal comparable to the other nearby ones. The planned observation of a relatively The proposed RXTE observations were carried out and the data have been analyzed. We detect a hard X-ray tail in the spectrum of this cluster with a flux very nearly equal to our predicted value. This has strengthen the case for the Compton scattering model. We intend the data obtained via this observation to be a part of a larger data set. We have identified other clusters of galaxies (in archival RXTE and other instrument data sets) with sufficiently high quality data where we can search for and measure (or at least put meaningful limits) on the strength of the hard component. With these studies we expect to clarify the mechanism for acceleration of particles in the intercluster medium and provide guidance for future observations of this intriguing phenomenon by instrument