Rank-dependent deactivation in network evolution.

PubMed

Xu, Xin-Jian; Zhou, Ming-Chen

2009-12-01

A rank-dependent deactivation mechanism is introduced to network evolution. The growth dynamics of the network is based on a finite memory of individuals, which is implemented by deactivating one site at each time step. The model shows striking features of a wide range of real-world networks: power-law degree distribution, high clustering coefficient, and disassortative degree correlation.

Properties of highly clustered networks

NASA Astrophysics Data System (ADS)

Newman, M. E.

2003-08-01

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

An Investigation of the Differences and Similarities between Generated Small-World Networks for Right- and Left-Hand Motor Imageries.

PubMed

Zhang, Jiang; Li, Yuyao; Chen, Huafu; Ding, Jurong; Yuan, Zhen

2016-11-04

In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions.

The effects of charge, polymerization, and cluster size on the diffusivity of dissolved Si species in pore water

NASA Astrophysics Data System (ADS)

Yokoyama, Tadashi; Sakuma, Hiroshi

2018-03-01

Silicon (Si) is the most abundant cation in crustal rocks. The charge and degree of polymerization of dissolved Si significantly change depending on solution pH and Si concentration. We used molecular dynamics (MD) simulations to predict the self-diffusion coefficients of dissolved Si, DSi, for 15 monomeric and polymeric species at ambient temperature. The results showed that DSi decreased with increasing negative charge and increasing degree of polymerization. The relationship between DSi and charge (Z) can be expressed by DSi/10-6 = 2.0 + 9.8e0.47Z, and that between DSi and number of polymerization (NSi) by DSi/10-6 = 9.7/NSi0.56. The results also revealed that multiple Si molecules assembled into a cluster and D decreased as the cluster size increased. Experiments to evaluate the diffusivity of Si in pore water revealed that the diffusion coefficient decreased with increasing Si concentration, a result consistent with the MD simulations. Simulation results can now be used to quantitatively assess water-rock interactions and water-concrete reactions over a wide range of environmentally relevant conditions.

Sector Identification in a Set of Stock Return Time Series Traded at the London Stock Exchange

NASA Astrophysics Data System (ADS)

Coronnello, C.; Tumminello, M.; Lillo, F.; Micciche, S.; Mantegna, R. N.

2005-09-01

We compare some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a portfolio of stocks traded at the London Stock Exchange. The investigated time series are recorded both at a daily time horizon and at a 5-minute time horizon. The correlation coefficient matrix is very different at different time horizons confirming that more structured correlation coefficient matrices are observed for long time horizons. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methods present a different degree of sensitivity with respect to different sectors. Our comparative analysis suggests that the application of just a single method could not be able to extract all the economic information present in the correlation coefficient matrix of a stock portfolio.

Optical mapping of prefrontal brain connectivity and activation during emotion anticipation.

PubMed

Wang, Meng-Yun; Lu, Feng-Mei; Hu, Zhishan; Zhang, Juan; Yuan, Zhen

2018-09-17

Accumulated neuroimaging evidence shows that the dorsal lateral prefrontal cortex (dlPFC) is activated during emotion anticipation. The aim of this work is to examine the brain connectivity and activation differences in dlPFC between the positive, neutral and negative emotion anticipation by using functional near-infrared spectroscopy (fNIRS). The hemodynamic responses were first assessed for all subjects during the performance of various emotion anticipation tasks. And then small-world analysis was performed, in which the small-world network indicators including the clustering coefficient, average path length, average node degree, and measure of small-world index were calculated for the functional brain networks associated with the positive, neutral and negative emotion anticipation, respectively. We discovered that compared to negative and neutral emotion anticipation, the positive one exhibited enhanced brain activation in the left dlPFC. Although the functional brain networks for the three emotion anticipation cases manifested the small-world properties regarding the clustering coefficient, average path length, average node degree, and measure of small-world index, the positive one showed significantly higher clustering coefficient and shorter average path length than those from the neutral and negative cases. Consequently, the small-world network indicators and brain activation in dlPPC were able to distinguish well between the positive, neutral and negative emotion anticipation. Copyright © 2018 Elsevier B.V. All rights reserved.

The cluster [Re6Se8I6]3- penetrates biological membranes: drug-like properties for CNS tumor treatment and diagnosis.

PubMed

Estrada, Lisbell D; Duran, Elizabeth; Cisterna, Matias; Echeverria, Cesar; Zheng, Zhiping; Borgna, Vincenzo; Arancibia-Miranda, Nicolas; Ramírez-Tagle, Rodrigo

2018-03-24

Tumorigenic cell lines are more susceptible to [Re 6 Se 8 I 6 ] 3- cluster-induced death than normal cells, becoming a novel candidate for cancer treatment. Still, the feasibility of using this type of molecules in human patients remains unclear and further pharmacokinetics analysis is needed. Using coupled plasma optical emission spectroscopy, we determined the Re-cluster tissue content in injected mice, as a biodistribution measurement. Our results show that the Re-cluster successfully reaches different tissues, accumulating mainly in heart and liver. In order to dissect the mechanism underlying cluster biodistribution, we used three different experimental approaches. First, we evaluate the degree of lipophilicity by determining the octanol/water partition coefficient. The cluster mostly remained in the octanol fraction, with a coefficient of 1.86 ± 0.02, which indicates it could potentially cross cell membranes. Then, we measured the biological membrane penetration through a parallel artificial membrane permeability assays (PAMPA) assay. The Re-cluster crosses the artificial membrane, with a coefficient of 122 nm/s that is considered highly permeable. To evaluate a potential application of the Re-cluster in central nervous system (CNS) tumors, we analyzed the cluster's brain penetration by exposing cultured blood-brain-barrier (BBB) cells to increasing concentrations of the cluster. The Re-cluster effectively penetrates the BBB, reaching nearly 30% of the brain side after 24 h. Thus, our results indicate that the Re-cluster penetrates biological membranes reaching different target organs-most probably due to its lipophilic properties-becoming a promising anti-cancer drug with high potential for CNS cancer's diagnosis and treatment.

Competing Contact Processes on Homogeneous Networks with Tunable Clusterization

NASA Astrophysics Data System (ADS)

Rybak, Marcin; Kułakowski, Krzysztof

2013-03-01

We investigate two homogeneous networks: the Watts-Strogatz network with mean degree ⟨k⟩ = 4 and the Erdös-Rényi network with ⟨k⟩ = 10. In both kinds of networks, the clustering coefficient C is a tunable control parameter. The network is an area of two competing contact processes, where nodes can be in two states, S or D. A node S becomes D with probability 1 if at least two its mutually linked neighbors are D. A node D becomes S with a given probability p if at least one of its neighbors is S. The competition between the processes is described by a phase diagram, where the critical probability pc depends on the clustering coefficient C. For p > pc the rate of state S increases in time, seemingly to dominate in the whole system. Below pc, the majority of nodes is in the D-state. The numerical results indicate that for the Watts-Strogatz network the D-process is activated at the finite value of the clustering coefficient C, close to 0.3. On the contrary, for the Erdös-Rényi network the transition is observed at the whole investigated range of C.

Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China.

PubMed

Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping

2015-01-01

Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China.

Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China

PubMed Central

Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping

2015-01-01

Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China. PMID:26340555

Clustering determines the dynamics of complex contagions in multiplex networks

NASA Astrophysics Data System (ADS)

Zhuang, Yong; Arenas, Alex; Yaǧan, Osman

2017-01-01

We present the mathematical analysis of generalized complex contagions in a class of clustered multiplex networks. The model is intended to understand spread of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is especially useful in problems related to spreading and percolation. The results present nontrivial dependencies between the clustering coefficient of the networks and its average degree. In particular, several phase transitions are shown to occur depending on these descriptors. Generally speaking, our findings reveal that increasing clustering decreases the probability of having global cascades and their size, however, this tendency changes with the average degree. There exists a certain average degree from which on clustering favors the probability and size of the contagion. By comparing the dynamics of complex contagions over multiplex networks and their monoplex projections, we demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.

How networks split when rival leaders emerge

NASA Astrophysics Data System (ADS)

Krawczyk, Malgorzata J.; Kułakowski, Krzysztof

2018-02-01

In a model social network, two hubs are appointed as leaders. Consecutive cutting of links on a shortest path between them splits the network in two. Next, the network is growing again till the initial size. Both processes are cyclically repeated. We investigate the size of a cluster containing the largest hub, the degree, the clustering coefficient, the closeness centrality and the betweenness centrality of the largest hub, as dependent on the number of cycles. The results are interpreted in terms of the leader's benefits from conflicts.

Autoscoring Essays Based on Complex Networks

ERIC Educational Resources Information Center

Ke, Xiaohua; Zeng, Yongqiang; Luo, Haijiao

2016-01-01

This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale-free networks (e.g., a word adjacency model) from which typical network features, such as the in-/out-degrees, clustering coefficient (CC),…

The study of RMB exchange rate complex networks based on fluctuation mode

NASA Astrophysics Data System (ADS)

Yao, Can-Zhong; Lin, Ji-Nan; Zheng, Xu-Zhou; Liu, Xiao-Feng

2015-10-01

In the paper, we research on the characteristics of RMB exchange rate time series fluctuation with methods of symbolization and coarse gaining. First, based on fluctuation features of RMB exchange rate, we define the first type of fluctuation mode as one specific foreign currency against RMB in four days' fluctuating situations, and the second type as four different foreign currencies against RMB in one day's fluctuating situation. With the transforming method, we construct the unique-currency and multi-currency complex networks. Further, through analyzing the topological features including out-degree, betweenness centrality and clustering coefficient of fluctuation-mode complex networks, we find that the out-degree distribution of both types of fluctuation mode basically follows power-law distributions with exponents between 1 and 2. The further analysis reveals that the out-degree and the clustering coefficient generally obey the approximated negative correlation. With this result, we confirm previous observations showing that the RMB exchange rate exhibits a characteristic of long-range memory. Finally, we analyze the most probable transmission route of fluctuation modes, and provide probability prediction matrix. The transmission route for RMB exchange rate fluctuation modes exhibits the characteristics of partially closed loop, repeat and reversibility, which lays a solid foundation for predicting RMB exchange rate fluctuation patterns with large volume of data.

Thermodynamic Behavior of Nano-sized Gold Clusters on the (001) Surface

NASA Technical Reports Server (NTRS)

Paik, Sun M.; Yoo, Sung M.; Namkung, Min; Wincheski, Russell A.; Bushnell, Dennis M. (Technical Monitor)

2001-01-01

We have studied thermal expansion of the surface layers of the hexagonally reconstructed Au (001) surface using a classical Molecular Dynamics (MD) simulation technique with an Embedded Atomic Method (EAM) type many-body potential. We find that the top-most hexagonal layer contracts as temperature increases, whereas the second layer expands or contracts depending on the system size. The magnitude of expansion coefficient of the top layer is much larger than that of the other layers. The calculated thermal expansion coefficients of the top-most layer are about -4.93 x 10(exp -5)Angstroms/Kelvin for the (262 x 227)Angstrom cluster and -3.05 x 10(exp -5)Angstroms/Kelvin for (101 x 87)Angstrom cluster. The Fast Fourier Transform (FFT) image of the atomic density shows that there exists a rotated domain of the top-most hexagonal cluster with rotation angle close to 1 degree at temperature T less than 1000Kelvin. As the temperature increases this domain undergoes a surface orientational phase transition. These predictions are in good agreement with previous phenomenological theories and experimental studies.

Effects of global financial crisis on network structure in a local stock market

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

2014-08-01

This study considers the effects of the 2008 global financial crisis on threshold networks of a local Korean financial market around the time of the crisis. Prices of individual stocks belonging to KOSPI 200 (Korea Composite Stock Price Index 200) are considered for three time periods, namely before, during, and after the crisis. Threshold networks are constructed from fully connected cross-correlation networks, and thresholds of cross-correlation coefficients are assigned to obtain threshold networks. At the high threshold, only one large cluster consisting of firms in the financial sector, heavy industry, and construction is observed during the crisis. However, before and after the crisis, there are several fragmented clusters belonging to various sectors. The power law of the degree distribution in threshold networks is observed within the limited range of thresholds. Threshold networks are fatter during the crisis than before or after the crisis. The clustering coefficient of the threshold network follows the power law in the scaling range.

DARPA ADAMS Project

DTIC Science & Technology

2015-05-11

it means that Mary distrusts John. We showed that it is possible to analyze such trust- distrust relationships within signed social networks in... relationship problems) − Professional Problems (negative changes at workplace, interpersonal conflicts) Furthermore, we encode in 2nd degree variables...a social media forum data. • Processed initial set of Vegas metrics data (clustering coefficient, # similar users, # skip levels) through time

Matrimonial distance, inbreeding coefficient and population size: Dhangar data.

PubMed

Majumder, P P; Malhotra, K C

1979-01-01

Data on the distance between the birthplaces of spouses (matrimonial distance) were collected from 2,260 married individuals belonging to 21 endogamous castes of the Dhangar (shepherd) cast-cluster of Maharashtra, India. The general form of the distribution of matrimonial distances is one which is extremely positively skewed and leptokurtic. The percentage of intra-village marriages generally decreases from the southern areas of Maharashtra to the northern areas of the state, as does the inbreeding coefficient. This situation is in conformity with the socio-cultural norms regulating matrimonial choice in south and north India. An attempt has been made to relate the degree of inbreeding to the mean matrimonial distance and population size. The mean matrimonial distance is more useful in predicting the degree of inbreeding than population size.

Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution.

PubMed

Menezes, Mozart B C; Kim, Seokjin; Huang, Rongbing

2017-01-01

Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.

Properties of a new small-world network with spatially biased random shortcuts

NASA Astrophysics Data System (ADS)

Matsuzawa, Ryo; Tanimoto, Jun; Fukuda, Eriko

2017-11-01

This paper introduces a small-world (SW) network with a power-law distance distribution that differs from conventional models in that it uses completely random shortcuts. By incorporating spatial constraints, we analyze the divergence of the proposed model from conventional models in terms of fundamental network properties such as clustering coefficient, average path length, and degree distribution. We find that when the spatial constraint more strongly prohibits a long shortcut, the clustering coefficient is improved and the average path length increases. We also analyze the spatial prisoner's dilemma (SPD) games played on our new SW network in order to understand its dynamical characteristics. Depending on the basis graph, i.e., whether it is a one-dimensional ring or a two-dimensional lattice, and the parameter controlling the prohibition of long-distance shortcuts, the emergent results can vastly differ.

Comparing Chemistry to Outcome: The Development of a Chemical Distance Metric, Coupled with Clustering and Hierarchal Visualization Applied to Macromolecular Crystallography

PubMed Central

Bruno, Andrew E.; Ruby, Amanda M.; Luft, Joseph R.; Grant, Thomas D.; Seetharaman, Jayaraman; Montelione, Gaetano T.; Hunt, John F.; Snell, Edward H.

2014-01-01

Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications related to crystallography appear promising and the distance coefficient, clustering, and hierarchal visualization of results undoubtedly have applications in wider fields. PMID:24971458

A network model of the interbank market

NASA Astrophysics Data System (ADS)

Li, Shouwei; He, Jianmin; Zhuang, Yaming

2010-12-01

This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.

A character network study of two Sci-Fi TV series

NASA Astrophysics Data System (ADS)

Tan, M. S. A.; Ujum, E. A.; Ratnavelu, K.

2014-03-01

This work is an analysis of the character networks in two science fiction television series: Stargate and Star Trek. These networks are constructed on the basis of scene co-occurrence between characters to indicate the presence of a connection. Global network structure measures such as the average path length, graph density, network diameter, average degree, median degree, maximum degree, and average clustering coefficient are computed as well as individual node centrality scores. The two fictional networks constructed are found to be quite similar in structure which is astonishing given that Stargate only ran for 18 years in comparison to the 48 years for Star Trek.

Higher-order clustering in networks

NASA Astrophysics Data System (ADS)

Yin, Hao; Benson, Austin R.; Leskovec, Jure

2018-05-01

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

How children explore the phonological network in child-directed speech: A survival analysis of children’s first word productions

PubMed Central

Carlson, Matthew T.; Sonderegger, Morgan; Bane, Max

2014-01-01

We explored how phonological network structure influences the age of words’ first appearance in children’s (14–50 months) speech, using a large, longitudinal corpus of spontaneous child-caregiver interactions. We represent the caregiver lexicon as a network in which each word is connected to all of its phonological neighbors, and consider both words’ local neighborhood density (degree), and also their embeddedness among interconnected neighborhoods (clustering coefficient and coreness). The larger-scale structure reflected in the latter two measures is implicated in current theories of lexical development and processing, but its role in lexical development has not yet been explored. Multilevel discrete-time survival analysis revealed that children are more likely to produce new words whose network properties support lexical access for production: high degree, but low clustering coefficient and coreness. These effects appear to be strongest at earlier ages and largely absent from 30 months on. These results suggest that both a word’s local connectivity in the lexicon and its position in the lexicon as a whole influences when it is learned, and they underscore how general lexical processing mechanisms contribute to productive vocabulary development. PMID:25089073

Failure tolerance of spike phase synchronization in coupled neural networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2011-09-01

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.

Spectra of random networks in the weak clustering regime

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Evolution of Cooperation in Social Dilemmas on Complex Networks

PubMed Central

Iyer, Swami; Killingback, Timothy

2016-01-01

Cooperation in social dilemmas is essential for the functioning of systems at multiple levels of complexity, from the simplest biological organisms to the most sophisticated human societies. Cooperation, although widespread, is fundamentally challenging to explain evolutionarily, since natural selection typically favors selfish behavior which is not socially optimal. Here we study the evolution of cooperation in three exemplars of key social dilemmas, representing the prisoner’s dilemma, hawk-dove and coordination classes of games, in structured populations defined by complex networks. Using individual-based simulations of the games on model and empirical networks, we give a detailed comparative study of the effects of the structural properties of a network, such as its average degree, variance in degree distribution, clustering coefficient, and assortativity coefficient, on the promotion of cooperative behavior in all three classes of games. PMID:26928428

Clustering Coefficients for Correlation Networks.

PubMed

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

Clustering Coefficients for Correlation Networks

PubMed Central

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties. PMID:29599714

Tabu Search enhances network robustness under targeted attacks

NASA Astrophysics Data System (ADS)

Sun, Shi-wen; Ma, Yi-lin; Li, Rui-qi; Wang, Li; Xia, Cheng-yi

2016-03-01

We focus on the optimization of network robustness with respect to intentional attacks on high-degree nodes. Given an existing network, this problem can be considered as a typical single-objective combinatorial optimization problem. Based on the heuristic Tabu Search optimization algorithm, a link-rewiring method is applied to reconstruct the network while keeping the degree of every node unchanged. Through numerical simulations, BA scale-free network and two real-world networks are investigated to verify the effectiveness of the proposed optimization method. Meanwhile, we analyze how the optimization affects other topological properties of the networks, including natural connectivity, clustering coefficient and degree-degree correlation. The current results can help to improve the robustness of existing complex real-world systems, as well as to provide some insights into the design of robust networks.

Improved community model for social networks based on social mobility

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Luo, Hao; Wang, Hao-Xian

2015-07-01

This paper proposes an improved community model for social networks based on social mobility. The relationship between the group distribution and the community size is investigated in terms of communication rate and turnover rate. The degree distributions, clustering coefficients, average distances and diameters of networks are analyzed. Experimental results demonstrate that the proposed model possesses the small-world property and can reproduce social networks effectively and efficiently.

Edge union of networks on the same vertex set

NASA Astrophysics Data System (ADS)

Loe, Chuan Wen; Jeldtoft Jensen, Henrik

2013-06-01

Random network generators such as Erdős-Rényi, Watts-Strogatz and Barabási-Albert models are used as models to study real-world networks. Let G1(V, E1) and G2(V, E2) be two such networks on the same vertex set V. This paper studies the degree distribution and clustering coefficient of the resultant networks, G(V, E1∪E2).

The drug target genes show higher evolutionary conservation than non-target genes.

PubMed

Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

2016-01-26

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

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

PubMed

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

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

Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters

PubMed Central

Kim, Jiyu; Jung, Inkyung

2017-01-01

Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters. PMID:28129368

Lack of small-scale clustering in 21-cm intensity maps crossed with 2dF galaxy densities at z ~ 0.08

NASA Astrophysics Data System (ADS)

Anderson, Christopher; Luciw, Nicholas; Li, Yi-Chao; Kuo, Cheng-Yu; Yadav, Jaswant; Masui, Kiyoshi; Chang, Tzu-Ching; Chen, Xuelei; Oppermann, Niels; Pen, Ue-Li; Timbie, Peter T.

2017-06-01

I report results from 21-cm intensity maps acquired from the Parkes radio telescope and cross-correlated with galaxy maps from the 2dF galaxy survey. The data span the redshift range 0.057

Statistics of Weighted Brain Networks Reveal Hierarchical Organization and Gaussian Degree Distribution

PubMed Central

Ivković, Miloš; Kuceyeski, Amy; Raj, Ashish

2012-01-01

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable. PMID:22761649

Statistics of weighted brain networks reveal hierarchical organization and Gaussian degree distribution.

PubMed

Ivković, Miloš; Kuceyeski, Amy; Raj, Ashish

2012-01-01

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable.

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

PubMed

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 correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.

SpectralNET – an application for spectral graph analysis and visualization

PubMed Central

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-01-01

Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request. PMID:16236170

SpectralNET--an application for spectral graph analysis and visualization.

PubMed

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-10-19

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

AFLP analysis of Cynodon dactylon (L.) Pers. var. dactylon genetic variation.

PubMed

Wu, Y Q; Taliaferro, C M; Bai, G H; Anderson, M P

2004-08-01

Cynodon dactylon (L.) Pers. var. dactylon (common bermudagrass) is geographically widely distributed between about lat 45 degrees N and lat 45 degrees S, penetrating to about lat 53 degrees N in Europe. The extensive variation of morphological and adaptive characteristics of the taxon is substantially documented, but information is lacking on DNA molecular variation in geographically disparate forms. Accordingly, this study was conducted to assess molecular genetic variation and genetic relatedness among 28 C. dactylon var. dactylon accessions originating from 11 countries on 4 continents (Africa, Asia, Australia, and Europe). A fluorescence-labeled amplified fragment length polymorphism (AFLP) DNA profiling method was used to detect the genetic diversity and relatedness. On the basis of 443 polymorphic AFLP fragments from 8 primer combinations, the accessions were grouped into clusters and subclusters associating with their geographic origins. Genetic similarity coefficients (SC) for the 28 accessions ranged from 0.53 to 0.98. Accessions originating from Africa, Australia, Asia, and Europe formed major groupings as indicated by cluster and principal coordinate analysis. Accessions from Australia and Asia, though separately clustered, were relatively closely related and most distantly related to accessions of European origin. African accessions formed two distant clusters and had the greatest variation in genetic relatedness relative to accessions from other geographic regions. Sampling the full extent of genetic variation in C. dactylon var. dactylon would require extensive germplasm collection in the major geographic regions of its distributional range.

Identifying hub stations and important lines of bus networks: A case study in Xiamen, China

NASA Astrophysics Data System (ADS)

Zhang, Hui; Zhuge, Chengxiang; Yu, Xiaohua

2018-07-01

Hub stations and important lines play key roles in transfers between stations. In this paper, a node failure model is proposed to identify hub stations. In the model, we introduce two new indicators called neighborhood degree ratio and transfer index to evaluate the importance of stations, which consider neighborhood stations' degree of station and the initial transfer times between stations. Moreover, line accessibility is developed to measure the importance of lines in the bus network. Xiamen bus network in 2016 is utilized to test the model. The results show that the two introduced indicators are more effective to identify hub stations compared with traditional complex network indicators such as degree, clustering coefficient and betweenness.

A novel magnetic resonance imaging segmentation technique for determining diffuse intrinsic pontine glioma tumor volume.

PubMed

Singh, Ranjodh; Zhou, Zhiping; Tisnado, Jamie; Haque, Sofia; Peck, Kyung K; Young, Robert J; Tsiouris, Apostolos John; Thakur, Sunitha B; Souweidane, Mark M

2016-11-01

OBJECTIVE Accurately determining diffuse intrinsic pontine glioma (DIPG) tumor volume is clinically important. The aims of the current study were to 1) measure DIPG volumes using methods that require different degrees of subjective judgment; and 2) evaluate interobserver agreement of measurements made using these methods. METHODS Eight patients from a Phase I clinical trial testing convection-enhanced delivery (CED) of a therapeutic antibody were included in the study. Pre-CED, post-radiation therapy axial T2-weighted images were analyzed using 2 methods requiring high degrees of subjective judgment (picture archiving and communication system [PACS] polygon and Volume Viewer auto-contour methods) and 1 method requiring a low degree of subjective judgment (k-means clustering segmentation) to determine tumor volumes. Lin's concordance correlation coefficients (CCCs) were calculated to assess interobserver agreement. RESULTS The CCCs of measurements made by 2 observers with the PACS polygon and the Volume Viewer auto-contour methods were 0.9465 (lower 1-sided 95% confidence limit 0.8472) and 0.7514 (lower 1-sided 95% confidence limit 0.3143), respectively. Both were considered poor agreement. The CCC of measurements made using k-means clustering segmentation was 0.9938 (lower 1-sided 95% confidence limit 0.9772), which was considered substantial strength of agreement. CONCLUSIONS The poor interobserver agreement of PACS polygon and Volume Viewer auto-contour methods highlighted the difficulty in consistently measuring DIPG tumor volumes using methods requiring high degrees of subjective judgment. k-means clustering segmentation, which requires a low degree of subjective judgment, showed better interobserver agreement and produced tumor volumes with delineated borders.

Food-web structure and network theory: The role of connectance and size

PubMed Central

Dunne, Jennifer A.; Williams, Richard J.; Martinez, Neo D.

2002-01-01

Networks from a wide range of physical, biological, and social systems have been recently described as “small-world” and “scale-free.” However, studies disagree whether ecological networks called food webs possess the characteristic path lengths, clustering coefficients, and degree distributions required for membership in these classes of networks. Our analysis suggests that the disagreements are based on selective use of relatively few food webs, as well as analytical decisions that obscure important variability in the data. We analyze a broad range of 16 high-quality food webs, with 25–172 nodes, from a variety of aquatic and terrestrial ecosystems. Food webs generally have much higher complexity, measured as connectance (the fraction of all possible links that are realized in a network), and much smaller size than other networks studied, which have important implications for network topology. Our results resolve prior conflicts by demonstrating that although some food webs have small-world and scale-free structure, most do not if they exceed a relatively low level of connectance. Although food-web degree distributions do not display a universal functional form, observed distributions are systematically related to network connectance and size. Also, although food webs often lack small-world structure because of low clustering, we identify a continuum of real-world networks including food webs whose ratios of observed to random clustering coefficients increase as a power–law function of network size over 7 orders of magnitude. Although food webs are generally not small-world, scale-free networks, food-web topology is consistent with patterns found within those classes of networks. PMID:12235364

A social network's changing statistical properties and the quality of human innovation

NASA Astrophysics Data System (ADS)

Uzzi, Brian

2008-06-01

We examined the entire network of creative artists that made Broadway musicals, in the post-War period, a collaboration network of international acclaim and influence, with an eye to investigating how the network's structural features condition the relationship between individual artistic talent and the success of their musicals. Our findings show that some of the evolving topographical qualities of degree distributions, path lengths and assortativity are relatively stable with time even as collaboration patterns shift, which suggests their changes are only minimally associated with the ebb and flux of the success of new productions. In contrast, the clustering coefficient changed substantially over time and we found that it had a nonlinear association with the production of financially and artistically successful shows. When the clustering coefficient ratio is low or high, the financial and artistic success of the industry is low, while an intermediate level of clustering is associated with successful shows. We supported these findings with sociological theory on the relationship between social structure and collaboration and with tests of statistical inference. Our discussion focuses on connecting the statistical properties of social networks to their performance and the performance of the actors embedded within them.

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

Coevolving complex networks in the model of social interactions

NASA Astrophysics Data System (ADS)

Raducha, Tomasz; Gubiec, Tomasz

2017-04-01

We analyze Axelrod's model of social interactions on coevolving complex networks. We introduce four extensions with different mechanisms of edge rewiring. The models are intended to catch two kinds of interactions-preferential attachment, which can be observed in scientists or actors collaborations, and local rewiring, which can be observed in friendship formation in everyday relations. Numerical simulations show that proposed dynamics can lead to the power-law distribution of nodes' degree and high value of the clustering coefficient, while still retaining the small-world effect in three models. All models are characterized by two phase transitions of a different nature. In case of local rewiring we obtain order-disorder discontinuous phase transition even in the thermodynamic limit, while in case of long-distance switching discontinuity disappears in the thermodynamic limit, leaving one continuous phase transition. In addition, we discover a new and universal characteristic of the second transition point-an abrupt increase of the clustering coefficient, due to formation of many small complete subgraphs inside the network.

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

PubMed

Kim, Sehwi; Jung, Inkyung

2017-01-01

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

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

PubMed Central

Kim, Sehwi

2017-01-01

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

Features and heterogeneities in growing network models

NASA Astrophysics Data System (ADS)

Ferretti, Luca; Cortelezzi, Michele; Yang, Bin; Marmorini, Giacomo; Bianconi, Ginestra

2012-06-01

Many complex networks from the World Wide Web to biological networks grow taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document such as personal page, thematic website, news, blog, search engine, social network, etc., or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an “effective fitness” for each class of nodes, determining the rate at which nodes acquire new links. The degree distribution exhibits a multiscaling behavior analogous to the the fitness model. This property is robust with respect to variations in the model, as long as links are assigned through effective preferential attachment. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show that it disappears for large network size, a property shared with the Barabási-Albert model. Negative degree correlations are also present in this class of models, along with nontrivial mixing patterns among features. We therefore conclude that both small clustering coefficients and disassortative mixing are outcomes of the preferential attachment mechanism in general growing networks.

Revealing how network structure affects accuracy of link prediction

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations

DOE PAGES

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J.

2018-04-24

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-Molecular Dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients; we use this approach to examine atomic condensation onto 6-56 atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity ( v) between atom and cluster andmore » the initial impact parameter ( b). In all cases there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms, and for 14 atom and 28 atom Mg clusters, as cluster equilibration temperature increases the condensation rate coefficient drops to values below the hard sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (> 1000 m s -1) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). In conclusion, the presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.« less

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations.

PubMed

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J

2018-04-28

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard-sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-molecular dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients. We use this approach to examine atomic condensation onto 6-56-atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity (v) between atom and cluster and the initial impact parameter (b). In all cases, there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard-sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms and for 14- and 28-atom Mg clusters, as cluster equilibration temperature increases, the condensation rate coefficient drops to values below the hard-sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (>1000 m s -1 ) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). The presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations

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

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J.

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-Molecular Dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients; we use this approach to examine atomic condensation onto 6-56 atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity ( v) between atom and cluster andmore » the initial impact parameter ( b). In all cases there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms, and for 14 atom and 28 atom Mg clusters, as cluster equilibration temperature increases the condensation rate coefficient drops to values below the hard sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (> 1000 m s -1) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). In conclusion, the presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.« less

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations

NASA Astrophysics Data System (ADS)

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J.

2018-04-01

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard-sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-molecular dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients. We use this approach to examine atomic condensation onto 6-56-atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity (v) between atom and cluster and the initial impact parameter (b). In all cases, there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard-sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms and for 14- and 28-atom Mg clusters, as cluster equilibration temperature increases, the condensation rate coefficient drops to values below the hard-sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (>1000 m s-1) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). The presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.

Disruptions of brain structural network in end-stage renal disease patients with long-term hemodialysis and normal-appearing brain tissues.

PubMed

Chou, Ming-Chung; Ko, Chih-Hung; Chang, Jer-Ming; Hsieh, Tsyh-Jyi

2018-05-04

End-stage renal disease (ESRD) patients on hemodialysis were demonstrated to exhibit silent and invisible white-matter alterations which would likely lead to disruptions of brain structural networks. Therefore, the purpose of this study was to investigate the disruptions of brain structural network in ESRD patients. Thiry-three ESRD patients with normal-appearing brain tissues and 29 age- and gender-matched healthy controls were enrolled in this study and underwent both cognitive ability screening instrument (CASI) assessment and diffusion tensor imaging (DTI) acquisition. Brain structural connectivity network was constructed using probabilistic tractography with automatic anatomical labeling template. Graph-theory analysis was performed to detect the alterations of node-strength, node-degree, node-local efficiency, and node-clustering coefficient in ESRD patients. Correlational analysis was performed to understand the relationship between network measures, CASI score, and dialysis duration. Structural connectivity, node-strength, node-degree, and node-local efficiency were significantly decreased, whereas node-clustering coefficient was significantly increased in ESRD patients as compared with healthy controls. The disrupted local structural networks were generally associated with common neurological complications of ESRD patients, but the correlational analysis did not reveal significant correlation between network measures, CASI score, and dialysis duration. Graph-theory analysis was helpful to investigate disruptions of brain structural network in ESRD patients with normal-appearing brain tissues. Copyright © 2018. Published by Elsevier Masson SAS.

Local versus global knowledge in the Barabási-Albert scale-free network model.

PubMed

Gómez-Gardeñes, Jesús; Moreno, Yamir

2004-03-01

The scale-free model of Barabási and Albert (BA) gave rise to a burst of activity in the field of complex networks. In this paper, we revisit one of the main assumptions of the model, the preferential attachment (PA) rule. We study a model in which the PA rule is applied to a neighborhood of newly created nodes and thus no global knowledge of the network is assumed. We numerically show that global properties of the BA model such as the connectivity distribution and the average shortest path length are quite robust when there is some degree of local knowledge. In contrast, other properties such as the clustering coefficient and degree-degree correlations differ and approach the values measured for real-world networks.

Network topology and resilience analysis of South Korean power grid

NASA Astrophysics Data System (ADS)

Kim, Dong Hwan; Eisenberg, Daniel A.; Chun, Yeong Han; Park, Jeryang

2017-01-01

In this work, we present topological and resilience analyses of the South Korean power grid (KPG) with a broad voltage level. While topological analysis of KPG only with high-voltage infrastructure shows an exponential degree distribution, providing another empirical evidence of power grid topology, the inclusion of low voltage components generates a distribution with a larger variance and a smaller average degree. This result suggests that the topology of a power grid may converge to a highly skewed degree distribution if more low-voltage data is considered. Moreover, when compared to ER random and BA scale-free networks, the KPG has a lower efficiency and a higher clustering coefficient, implying that highly clustered structure does not necessarily guarantee a functional efficiency of a network. Error and attack tolerance analysis, evaluated with efficiency, indicate that the KPG is more vulnerable to random or degree-based attacks than betweenness-based intentional attack. Cascading failure analysis with recovery mechanism demonstrates that resilience of the network depends on both tolerance capacity and recovery initiation time. Also, when the two factors are fixed, the KPG is most vulnerable among the three networks. Based on our analysis, we propose that the topology of power grids should be designed so the loads are homogeneously distributed, or functional hubs and their neighbors have high tolerance capacity to enhance resilience.

A novel magnetic resonance imaging segmentation technique for determining diffuse intrinsic pontine glioma tumor volume

PubMed Central

Singh, Ranjodh; Zhou, Zhiping; Tisnado, Jamie; Haque, Sofia; Peck, Kyung K.; Young, Robert J.; Tsiouris, Apostolos John; Thakur, Sunitha B.; Souweidane, Mark M.

2017-01-01

OBJECTIVE Accurately determining diffuse intrinsic pontine glioma (DIPG) tumor volume is clinically important. The aims of the current study were to 1) measure DIPG volumes using methods that require different degrees of subjective judgment; and 2) evaluate interobserver agreement of measurements made using these methods. METHODS Eight patients from a Phase I clinical trial testing convection-enhanced delivery (CED) of a therapeutic antibody were included in the study. Pre-CED, post–radiation therapy axial T2-weighted images were analyzed using 2 methods requiring high degrees of subjective judgment (picture archiving and communication system [PACS] polygon and Volume Viewer auto-contour methods) and 1 method requiring a low degree of subjective judgment (k-means clustering segmentation) to determine tumor volumes. Lin’s concordance correlation coefficients (CCCs) were calculated to assess interobserver agreement. RESULTS The CCCs of measurements made by 2 observers with the PACS polygon and the Volume Viewer auto-contour methods were 0.9465 (lower 1-sided 95% confidence limit 0.8472) and 0.7514 (lower 1-sided 95% confidence limit 0.3143), respectively. Both were considered poor agreement. The CCC of measurements made using k-means clustering segmentation was 0.9938 (lower 1-sided 95% confidence limit 0.9772), which was considered substantial strength of agreement. CONCLUSIONS The poor interobserver agreement of PACS polygon and Volume Viewer auto-contour methods high-lighted the difficulty in consistently measuring DIPG tumor volumes using methods requiring high degrees of subjective judgment. k-means clustering segmentation, which requires a low degree of subjective judgment, showed better interob-server agreement and produced tumor volumes with delineated borders. PMID:27391980

Observed intra-cluster correlation coefficients in a cluster survey sample of patient encounters in general practice in Australia

PubMed Central

Knox, Stephanie A; Chondros, Patty

2004-01-01

Background Cluster sample study designs are cost effective, however cluster samples violate the simple random sample assumption of independence of observations. Failure to account for the intra-cluster correlation of observations when sampling through clusters may lead to an under-powered study. Researchers therefore need estimates of intra-cluster correlation for a range of outcomes to calculate sample size. We report intra-cluster correlation coefficients observed within a large-scale cross-sectional study of general practice in Australia, where the general practitioner (GP) was the primary sampling unit and the patient encounter was the unit of inference. Methods Each year the Bettering the Evaluation and Care of Health (BEACH) study recruits a random sample of approximately 1,000 GPs across Australia. Each GP completes details of 100 consecutive patient encounters. Intra-cluster correlation coefficients were estimated for patient demographics, morbidity managed and treatments received. Intra-cluster correlation coefficients were estimated for descriptive outcomes and for associations between outcomes and predictors and were compared across two independent samples of GPs drawn three years apart. Results Between April 1999 and March 2000, a random sample of 1,047 Australian general practitioners recorded details of 104,700 patient encounters. Intra-cluster correlation coefficients for patient demographics ranged from 0.055 for patient sex to 0.451 for language spoken at home. Intra-cluster correlations for morbidity variables ranged from 0.005 for the management of eye problems to 0.059 for management of psychological problems. Intra-cluster correlation for the association between two variables was smaller than the descriptive intra-cluster correlation of each variable. When compared with the April 2002 to March 2003 sample (1,008 GPs) the estimated intra-cluster correlation coefficients were found to be consistent across samples. Conclusions The demonstrated precision and reliability of the estimated intra-cluster correlations indicate that these coefficients will be useful for calculating sample sizes in future general practice surveys that use the GP as the primary sampling unit. PMID:15613248

Macroscopic damping model for structural dynamics with random polycrystalline configurations

NASA Astrophysics Data System (ADS)

Yang, Yantao; Cui, Junzhi; Yu, Yifan; Xiang, Meizhen

2018-06-01

In this paper the macroscopic damping model for dynamical behavior of the structures with random polycrystalline configurations at micro-nano scales is established. First, the global motion equation of a crystal is decomposed into a set of motion equations with independent single degree of freedom (SDOF) along normal discrete modes, and then damping behavior is introduced into each SDOF motion. Through the interpolation of discrete modes, the continuous representation of damping effects for the crystal is obtained. Second, from energy conservation law the expression of the damping coefficient is derived, and the approximate formula of damping coefficient is given. Next, the continuous damping coefficient for polycrystalline cluster is expressed, the continuous dynamical equation with damping term is obtained, and then the concrete damping coefficients for a polycrystalline Cu sample are shown. Finally, by using statistical two-scale homogenization method, the macroscopic homogenized dynamical equation containing damping term for the structures with random polycrystalline configurations at micro-nano scales is set up.

Topological properties of complex networks in protein structures

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

2014-03-01

We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

R package to estimate intracluster correlation coefficient with confidence interval for binary data.

PubMed

Chakraborty, Hrishikesh; Hossain, Akhtar

2018-03-01

The Intracluster Correlation Coefficient (ICC) is a major parameter of interest in cluster randomized trials that measures the degree to which responses within the same cluster are correlated. There are several types of ICC estimators and its confidence intervals (CI) suggested in the literature for binary data. Studies have compared relative weaknesses and advantages of ICC estimators as well as its CI for binary data and suggested situations where one is advantageous in practical research. The commonly used statistical computing systems currently facilitate estimation of only a very few variants of ICC and its CI. To address the limitations of current statistical packages, we developed an R package, ICCbin, to facilitate estimating ICC and its CI for binary responses using different methods. The ICCbin package is designed to provide estimates of ICC in 16 different ways including analysis of variance methods, moments based estimation, direct probabilistic methods, correlation based estimation, and resampling method. CI of ICC is estimated using 5 different methods. It also generates cluster binary data using exchangeable correlation structure. ICCbin package provides two functions for users. The function rcbin() generates cluster binary data and the function iccbin() estimates ICC and it's CI. The users can choose appropriate ICC and its CI estimate from the wide selection of estimates from the outputs. The R package ICCbin presents very flexible and easy to use ways to generate cluster binary data and to estimate ICC and it's CI for binary response using different methods. The package ICCbin is freely available for use with R from the CRAN repository (https://cran.r-project.org/package=ICCbin). We believe that this package can be a very useful tool for researchers to design cluster randomized trials with binary outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Corona graphs as a model of small-world networks

NASA Astrophysics Data System (ADS)

Lv, Qian; Yi, Yuhao; Zhang, Zhongzhi

2015-11-01

We introduce recursive corona graphs as a model of small-world networks. We investigate analytically the critical characteristics of the model, including order and size, degree distribution, average path length, clustering coefficient, and the number of spanning trees, as well as Kirchhoff index. Furthermore, we study the spectra for the adjacency matrix and the Laplacian matrix for the model. We obtain explicit results for all the quantities of the recursive corona graphs, which are similar to those observed in real-life networks.

Axelrod's Metanorm Games on Networks

PubMed Central

Galán, José M.; Łatek, Maciej M.; Rizi, Seyed M. Mussavi

2011-01-01

Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrod's analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation. PMID:21655211

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

PubMed

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

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial. © The Author(s) 2015.

Composing Music with Complex Networks

NASA Astrophysics Data System (ADS)

Liu, Xiaofan; Tse, Chi K.; Small, Michael

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

Graph analysis of cell clusters forming vascular networks

NASA Astrophysics Data System (ADS)

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.

Cluster analysis of novel isometric strength measures produces a valid and evidence-based classification structure for wheelchair track racing.

PubMed

Connick, Mark J; Beckman, Emma; Vanlandewijck, Yves; Malone, Laurie A; Blomqvist, Sven; Tweedy, Sean M

2017-11-25

The Para athletics wheelchair-racing classification system employs best practice to ensure that classes comprise athletes whose impairments cause a comparable degree of activity limitation. However, decision-making is largely subjective and scientific evidence which reduces this subjectivity is required. To evaluate whether isometric strength tests were valid for the purposes of classifying wheelchair racers and whether cluster analysis of the strength measures produced a valid classification structure. Thirty-two international level, male wheelchair racers from classes T51-54 completed six isometric strength tests evaluating elbow extensors, shoulder flexors, trunk flexors and forearm pronators and two wheelchair performance tests-Top-Speed (0-15 m) and Top-Speed (absolute). Strength tests significantly correlated with wheelchair performance were included in a cluster analysis and the validity of the resulting clusters was assessed. All six strength tests correlated with performance (r=0.54-0.88). Cluster analysis yielded four clusters with reasonable overall structure (mean silhouette coefficient=0.58) and large intercluster strength differences. Six athletes (19%) were allocated to clusters that did not align with their current class. While the mean wheelchair racing performance of the resulting clusters was unequivocally hierarchical, the mean performance of current classes was not, with no difference between current classes T53 and T54. Cluster analysis of isometric strength tests produced classes comprising athletes who experienced a similar degree of activity limitation. The strength tests reported can provide the basis for a new, more transparent, less subjective wheelchair racing classification system, pending replication of these findings in a larger, representative sample. This paper also provides guidance for development of evidence-based systems in other Para sports. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

On the topology of the world exchange arrangements web

NASA Astrophysics Data System (ADS)

Li, Xiang; Jin, Yu Ying; Chen, Guanrong

2004-11-01

Exchange arrangements among different countries over the world are foundations of the world economy, which generally stand behind the daily economic evolution. As the first study of the world exchange arrangements web (WEAW), we built a bipartite network with countries as one type of nodes and currencies as the other, and found it to have a prominent scale-free feature with a power-law degree distribution. In a further empirical study of the currency section of the WEAW, we calculated the clustering coefficients, average nearest-neighbors degree, and average shortest distance. As an essential economic network, the WEAW is found to be a correlated disassortative network with a hierarchical structure, possessing a more prominent scale-free feature than the world trade web (WTW).

Analysis of perceived similarity between pairs of microcalcification clusters in mammograms

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

Wang, Juan; Jing, Hao; Wernick, Miles N.

2014-05-15

Purpose: Content-based image retrieval aims to assist radiologists by presenting example images with known pathology that are visually similar to the case being evaluated. In this work, the authors investigate several fundamental issues underlying the similarity ratings between pairs of microcalcification (MC) lesions on mammograms as judged by radiologists: the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers’ similarity ratings. Methods: The authors conduct a reader study on a set of 1000 image pairs of MC lesions, in which amore » group of experienced breast radiologists rated the degree of similarity between each image pair. The image pairs are selected, from among possible pairings of 222 cases (110 malignant, 112 benign), based on quantitative image attributes (features) and the results of a preliminary reader study. Next, the authors apply analysis of variance (ANOVA) to quantify the level of variability in the readers’ similarity ratings, and study how the variability in individual reader ratings affects consistency between readers. The authors also measure the extent to which readers agree on images which are most similar to a given query, for which the Dice coefficient is used. To investigate how the similarity ratings potentially relate to the attributes underlying the cases, the authors study the fraction of perceptually similar images that also share the same benign or malignant pathology as the query image; moreover, the authors apply multidimensional scaling (MDS) to embed the cases according to their mutual perceptual similarity in a two-dimensional plot, which allows the authors to examine the manner in which similar lesions relate to one another in terms of benign or malignant pathology and clustered MCs. Results: The ANOVA results show that the coefficient of determination in the reader similarity ratings is 0.59. The variability level in the similarity ratings is proved to be a limiting factor, leading to only moderate correlation between the readers in their readings. The Dice coefficient, measuring agreement between readers in retrieval of similar images, can vary from 0.45 to 0.64 with different levels of similarity for individual readers, but is higher for average ratings from a group of readers (from 0.59 to 0.78). More importantly, the fraction of retrieved cases that match the benign or malignant pathology of the query image was found to increase with the degree of similarity among the retrieved images, reaching average value as high as 0.69 for the radiologists (p-value <10{sup −4} compared to random guessing). Moreover, MDS embedding of all the cases shows that cases having the same pathology tend to cluster together, and that neighboring cases in the plot tend to be similar in their clustered MCs. Conclusions: While individual readers exhibit substantial variability in their similarity ratings, similarity ratings averaged from a group of readers can achieve a high level of intergroup consistency and agreement in retrieval of similar images. More importantly, perceptually similar cases are also likely to be similar in their underlying benign or malignant pathology and image features of clustered MCs, which could be of diagnostic value in computer-aided diagnosis for lesions with clustered MCs.« less

Patterns in the English language: phonological networks, percolation and assembly models

NASA Astrophysics Data System (ADS)

Stella, Massimo; Brede, Markus

2015-05-01

In this paper we provide a quantitative framework for the study of phonological networks (PNs) for the English language by carrying out principled comparisons to null models, either based on site percolation, randomization techniques, or network growth models. In contrast to previous work, we mainly focus on null models that reproduce lower order characteristics of the empirical data. We find that artificial networks matching connectivity properties of the English PN are exceedingly rare: this leads to the hypothesis that the word repertoire might have been assembled over time by preferentially introducing new words which are small modifications of old words. Our null models are able to explain the ‘power-law-like’ part of the degree distributions and generally retrieve qualitative features of the PN such as high clustering, high assortativity coefficient and small-world characteristics. However, the detailed comparison to expectations from null models also points out significant differences, suggesting the presence of additional constraints in word assembly. Key constraints we identify are the avoidance of large degrees, the avoidance of triadic closure and the avoidance of large non-percolating clusters.

Cascading failure in scale-free networks with tunable clustering

NASA Astrophysics Data System (ADS)

Zhang, Xue-Jun; Gu, Bo; Guan, Xiang-Min; Zhu, Yan-Bo; Lv, Ren-Li

2016-02-01

Cascading failure is ubiquitous in many networked infrastructure systems, such as power grids, Internet and air transportation systems. In this paper, we extend the cascading failure model to a scale-free network with tunable clustering and focus on the effect of clustering coefficient on system robustness. It is found that the network robustness undergoes a nonmonotonic transition with the increment of clustering coefficient: both highly and lowly clustered networks are fragile under the intentional attack, and the network with moderate clustering coefficient can better resist the spread of cascading. We then provide an extensive explanation for this constructive phenomenon via the microscopic point of view and quantitative analysis. Our work can be useful to the design and optimization of infrastructure systems.

Surname complex network for Brazil and Portugal

NASA Astrophysics Data System (ADS)

Ferreira, G. D.; Viswanathan, G. M.; da Silva, L. R.; Herrmann, H. J.

2018-06-01

We present a study of social networks based on the analysis of Brazilian and Portuguese family names (surnames). We construct networks whose nodes are names of families and whose edges represent parental relations between two families. From these networks we extract the connectivity distribution, clustering coefficient, shortest path and centrality. We find that the connectivity distribution follows an approximate power law. We associate the number of hubs, centrality and entropy to the degree of miscegenation in the societies in both countries. Our results show that Portuguese society has a higher miscegenation degree than Brazilian society. All networks analyzed lead to approximate inverse square power laws in the degree distribution. We conclude that the thermodynamic limit is reached for small networks (3 or 4 thousand nodes). The assortative mixing of all networks is negative, showing that the more connected vertices are connected to vertices with lower connectivity. Finally, the network of surnames presents some small world characteristics.

The web graph of a tourism system

NASA Astrophysics Data System (ADS)

Baggio, Rodolfo

2007-06-01

The website network of a tourism destination is examined. Network theoretic metrics are used to gauge the static and dynamic characteristics of the webspace. The topology of the network is found partly similar to the one exhibited by similar systems. However, some differences are found, mainly due to the relatively poor connectivity and clusterisation of the network. These results are interpreted by considering the formation mechanisms and the connotation of the linkages between websites. Clustering and assortativity coefficients are proposed as quantitative estimations of the degree of collaboration and cooperation among destination stakeholders.

The topology of a causal network for the Chinese financial system

NASA Astrophysics Data System (ADS)

Gao, Bo; Ren, Ruo-en

2013-07-01

The paper builds a causal network for the Chinese financial system based on the Granger causality of company risks, studies its different topologies in crisis and bull period, and applies the centrality to explain individual risk and prevent systemic risk. The results show that this causal network possesses both small-world phenomenon and scale-free property, and has a little different average distance, clustering coefficient, and degree distribution in different periods, and financial institutions with high centrality not only have large individual risk, but also are important for systemic risk immunization.

Understanding network concepts in modules

PubMed Central

2007-01-01

Background Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory. Results Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks. Conclusion Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks PMID:17547772

The Dichotomy in Degree Correlation of Biological Networks

PubMed Central

Hao, Dapeng; Li, Chuanxing

2011-01-01

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled. PMID:22164269

A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction

PubMed Central

Liu, Meng; Li, Xia; Fan, Rui; Liu, Xinhua; Wang, Ju

2015-01-01

Nicotine, as the major psychoactive component of tobacco, has broad physiological effects within the central nervous system, but our understanding of the molecular mechanism underlying its neuronal effects remains incomplete. In this study, we performed a systematic analysis on a set of nicotine addiction-related genes to explore their characteristics at network levels. We found that NAGenes tended to have a more moderate degree and weaker clustering coefficient and to be less central in the network compared to alcohol addiction-related genes or cancer genes. Further, clustering of these genes resulted in six clusters with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis, which provided an intuitional view on the major molecular functions of the genes. Moreover, functional enrichment analysis revealed that neurodevelopment, neurotransmission activity, and metabolism related biological processes were involved in nicotine addiction. In summary, by analyzing the overall characteristics of the nicotine addiction related genes, this study provided valuable information for understanding the molecular mechanisms underlying nicotine addiction. PMID:26097843

Scale-free Graphs for General Aviation Flight Schedules

NASA Technical Reports Server (NTRS)

Alexandov, Natalia M. (Technical Monitor); Kincaid, Rex K.

2003-01-01

In the late 1990s a number of researchers noticed that networks in biology, sociology, and telecommunications exhibited similar characteristics unlike standard random networks. In particular, they found that the cummulative degree distributions of these graphs followed a power law rather than a binomial distribution and that their clustering coefficients tended to a nonzero constant as the number of nodes, n, became large rather than O(1/n). Moreover, these networks shared an important property with traditional random graphs as n becomes large the average shortest path length scales with log n. This latter property has been coined the small-world property. When taken together these three properties small-world, power law, and constant clustering coefficient describe what are now most commonly referred to as scale-free networks. Since 1997 at least six books and over 400 articles have been written about scale-free networks. In this manuscript an overview of the salient characteristics of scale-free networks. Computational experience will be provided for two mechanisms that grow (dynamic) scale-free graphs. Additional computational experience will be given for constructing (static) scale-free graphs via a tabu search optimization approach. Finally, a discussion of potential applications to general aviation networks is given.

An analysis of herding behavior in security analysts’ networks

NASA Astrophysics Data System (ADS)

Zhao, Zheng; Zhang, YongJie; Feng, Xu; Zhang, Wei

2014-11-01

In this paper, we build undirected weighted networks to study herding behavior among analysts and to analyze the characteristics and the structure of these networks. We then construct a new indicator based on the average degree of nodes and the average weighted clustering coefficient to research the various types of herding behavior. Our findings suggest that every industry has, to a certain degree, herding behavior among analysts. While there is obvious uninformed herding behavior in real estate and certain other industries, industries such as mining and nonferrous metals have informed herding behavior caused by analysts’ similar reactions to public information. Furthermore, we relate the two types of herding behavior to stock price and find that uninformed herding behavior has a positive effect on market prices, whereas informed herding behavior has a negative effect.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Worldwide Marine Transportation Network: Efficiency and Container Throughput

NASA Astrophysics Data System (ADS)

Deng, Wei-Bing; Guo, Long; Li, Wei; Cai, Xu

2009-11-01

Through empirical analysis of the global structure of the Worldwide Marine Transportation Network (WMTN), we find that the WMTN, a small-world network, exhibits an exponential-like degree distribution. We hereby investigate the efficiency of the WMTN by employing a simple definition. Compared with many other transportation networks, the WMTN possesses relatively low efficiency. Furthermore, by exploring the relationship between the topological structure and the container throughput, we find that strong correlations exist among the container throughout the degree and the clustering coefficient. Also, considering the navigational process that a ship travels in a real shipping line, we obtain that the weight of a seaport is proportional to the total probability contributed by all the passing shipping lines.

Impact of observational incompleteness on the structural properties of protein interaction networks

NASA Astrophysics Data System (ADS)

Kuhnt, Mathias; Glauche, Ingmar; Greiner, Martin

2007-01-01

The observed structure of protein interaction networks is corrupted by many false positive/negative links. This observational incompleteness is abstracted as random link removal and a specific, experimentally motivated (spoke) link rearrangement. Their impact on the structural properties of gene-duplication-and-mutation network models is studied. For the degree distribution a curve collapse is found, showing no sensitive dependence on the link removal/rearrangement strengths and disallowing a quantitative extraction of model parameters. The spoke link rearrangement process moves other structural observables, like degree correlations, cluster coefficient and motif frequencies, closer to their counterparts extracted from the yeast data. This underlines the importance to take a precise modeling of the observational incompleteness into account when network structure models are to be quantitatively compared to data.

Simple, distance-dependent formulation of the Watts-Strogatz model for directed and undirected small-world networks.

PubMed

Song, H Francis; Wang, Xiao-Jing

2014-12-01

Small-world networks-complex networks characterized by a combination of high clustering and short path lengths-are widely studied using the paradigmatic model of Watts and Strogatz (WS). Although the WS model is already quite minimal and intuitive, we describe an alternative formulation of the WS model in terms of a distance-dependent probability of connection that further simplifies, both practically and theoretically, the generation of directed and undirected WS-type small-world networks. In addition to highlighting an essential feature of the WS model that has previously been overlooked, namely the equivalence to a simple distance-dependent model, this alternative formulation makes it possible to derive exact expressions for quantities such as the degree and motif distributions and global clustering coefficient for both directed and undirected networks in terms of model parameters.

Simple, distance-dependent formulation of the Watts-Strogatz model for directed and undirected small-world networks

NASA Astrophysics Data System (ADS)

Song, H. Francis; Wang, Xiao-Jing

2014-12-01

Small-world networks—complex networks characterized by a combination of high clustering and short path lengths—are widely studied using the paradigmatic model of Watts and Strogatz (WS). Although the WS model is already quite minimal and intuitive, we describe an alternative formulation of the WS model in terms of a distance-dependent probability of connection that further simplifies, both practically and theoretically, the generation of directed and undirected WS-type small-world networks. In addition to highlighting an essential feature of the WS model that has previously been overlooked, namely the equivalence to a simple distance-dependent model, this alternative formulation makes it possible to derive exact expressions for quantities such as the degree and motif distributions and global clustering coefficient for both directed and undirected networks in terms of model parameters.

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

PubMed Central

Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M.; Jack, Clifford R.; Weiner, Michael W.; Bernstein, Matthew; Thompson, Paul M.

2015-01-01

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the ‘rich-club’ – a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich-club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich-club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline. PMID:26037224

Information Filtering via Clustering Coefficients of User-Object Bipartite Networks

NASA Astrophysics Data System (ADS)

Guo, Qiang; Leng, Rui; Shi, Kerui; Liu, Jian-Guo

The clustering coefficient of user-object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user-object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user-object bipartite networks should be investigated to estimate users' tastes.

Identification and classification of hubs in brain networks.

PubMed

Sporns, Olaf; Honey, Christopher J; Kötter, Rolf

2007-10-17

Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identification and classification of hub regions, which are thought to play pivotal roles in the coordination of information flow. We identify hubs and characterize their network contributions by examining motif fingerprints and centrality indices for all regions within the cerebral cortices of both the cat and the macaque. Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network. Within both cat and macaque networks, we find that a combination of degree, motif participation, betweenness centrality and closeness centrality allows for reliable identification of hub regions, many of which have previously been functionally classified as polysensory or multimodal. We then classify hubs as either provincial (intra-cluster) hubs or connector (inter-cluster) hubs, and proceed to show that lesioning hubs of each type from the network produces opposite effects on the small-world index. Our study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

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

Geometric evolution of complex networks with degree correlations

NASA Astrophysics Data System (ADS)

Murphy, Charles; Allard, Antoine; Laurence, Edward; St-Onge, Guillaume; Dubé, Louis J.

2018-03-01

We present a general class of geometric network growth mechanisms by homogeneous attachment in which the links created at a given time t are distributed homogeneously between a new node and the existing nodes selected uniformly. This is achieved by creating links between nodes uniformly distributed in a homogeneous metric space according to a Fermi-Dirac connection probability with inverse temperature β and general time-dependent chemical potential μ (t ) . The chemical potential limits the spatial extent of newly created links. Using a hidden variable framework, we obtain an analytical expression for the degree sequence and show that μ (t ) can be fixed to yield any given degree distributions, including a scale-free degree distribution. Additionally, we find that depending on the order in which nodes appear in the network—its history—the degree-degree correlations can be tuned to be assortative or disassortative. The effect of the geometry on the structure is investigated through the average clustering coefficient 〈c 〉 . In the thermodynamic limit, we identify a phase transition between a random regime where 〈c 〉→0 when β <βc and a geometric regime where 〈c 〉>0 when β >βc .

Improved targeted immunization strategies based on two rounds of selection

NASA Astrophysics Data System (ADS)

Xia, Ling-Ling; Song, Yu-Rong; Li, Chan-Chan; Jiang, Guo-Ping

2018-04-01

In the case of high degree targeted immunization where the number of vaccine is limited, when more than one node associated with the same degree meets the requirement of high degree centrality, how can we choose a certain number of nodes from those nodes, so that the number of immunized nodes will not exceed the limit? In this paper, we introduce a new idea derived from the selection process of second-round exam to solve this problem and then propose three improved targeted immunization strategies. In these proposed strategies, the immunized nodes are selected through two rounds of selection, where we increase the quotas of first-round selection according the evaluation criterion of degree centrality and then consider another characteristic parameter of node, such as node's clustering coefficient, betweenness and closeness, to help choose targeted nodes in the second-round selection. To validate the effectiveness of the proposed strategies, we compare them with the degree immunizations including the high degree targeted and the high degree adaptive immunizations using two metrics: the size of the largest connected component of immunized network and the number of infected nodes. Simulation results demonstrate that the proposed strategies based on two rounds of sorting are effective for heterogeneous networks and their immunization effects are better than that of the degree immunizations.

Robustness and structure of complex networks

NASA Astrophysics Data System (ADS)

Shao, Shuai

This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks are much more vulnerable to localized attack compared with random attack. In the second part, we extend the tree-like generating function method to incorporating clustering structure in complex networks. We study the robustness of a complex network system, especially a network of networks (NON) with clustering structure in each network. We find that the system becomes less robust as we increase the clustering coefficient of each network. For a partially dependent network system, we also find that the influence of the clustering coefficient on network robustness decreases as we decrease the coupling strength, and the critical coupling strength qc, at which the first-order phase transition changes to second-order, increases as we increase the clustering coefficient.

Link prediction based on local community properties

NASA Astrophysics Data System (ADS)

Yang, Xu-Hua; Zhang, Hai-Feng; Ling, Fei; Cheng, Zhi; Weng, Guo-Qing; Huang, Yu-Jiao

2016-09-01

The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results.

Analysis of Social Network Measures with Respect to Structural Properties of Networks

DTIC Science & Technology

2012-03-01

there has been increased interest in degree based generators. The three generators that this thesis is interested in are the Erdos- Renyi (ER...these generators has their pros and cons. The ER graph generator was developed in 1960 by Erdos and Renyi in hopes of producing networks that...0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 P e rc e n ta ge Average Clustering Coefficient Erdös- Renyi BA (2 edge) BA (5 edge) BA (10 edge) PNDCG (α=2.35

Directed clustering coefficient as a measure of systemic risk in complex banking networks

NASA Astrophysics Data System (ADS)

Tabak, Benjamin M.; Takami, Marcelo; Rocha, Jadson M. C.; Cajueiro, Daniel O.; Souza, Sergio R. S.

2014-01-01

Recent literature has focused on the study of systemic risk in complex networks. It is clear now, after the crisis of 2008, that the aggregate behavior of the interaction among agents is not straightforward and it is very difficult to predict. Contributing to this debate, this paper shows that the directed clustering coefficient may be used as a measure of systemic risk in complex networks. Furthermore, using data from the Brazilian interbank network, we show that the directed clustering coefficient is negatively correlated with domestic interest rates.

Modeling online social signed networks

NASA Astrophysics Data System (ADS)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Identification of food spoilage in the smart home based on neural and fuzzy processing of odour sensor responses.

PubMed

Green, Geoffrey C; Chan, Adrian D C; Goubran, Rafik A

2009-01-01

Adopting the use of real-time odour monitoring in the smart home has the potential to alert the occupant of unsafe or unsanitary conditions. In this paper, we measured (with a commercial metal-oxide sensor-based electronic nose) the odours of five household foods that had been left out at room temperature for a week to spoil. A multilayer perceptron (MLP) neural network was trained to recognize the age of the samples (a quantity related to the degree of spoilage). For four of these foods, median correlation coefficients (between target values and MLP outputs) of R > 0.97 were observed. Fuzzy C-means clustering (FCM) was applied to the evolving odour patterns of spoiling milk, which had been sampled more frequently (4h intervals for 7 days). The FCM results showed that both the freshest and oldest milk samples had a high degree of membership in "fresh" and "spoiled" clusters, respectively. In the future, as advancements in electronic nose development remove the present barriers to acceptance, signal processing methods like those explored in this paper can be incorporated into odour monitoring systems used in the smart home.

Node property of weighted networks considering connectability to nodes within two degrees of separation.

PubMed

Amano, Sun-Ichi; Ogawa, Ken-Ichiro; Miyake, Yoshihiro

2018-05-31

Weighted networks have been extensively studied because they can represent various phenomena in which the diversity of edges is essential. To investigate the properties of weighted networks, various centrality measures have been proposed, such as strength, weighted clustering coefficients, and weighted betweenness centrality. In such measures, only direct connections or entire network connectivity from arbitrary nodes have been used to calculate the connectivity of each node. However, in weighted networks composed of autonomous elements such as humans, middle ranges from each node are also considered to be meaningful for characterizing each node's connectability. In this study, we define a new node property in weighted networks to consider connectability to nodes within a range of two degrees of separation, then apply this new centrality to face-to-face human communication networks in corporate organizations. Our results show that the proposed centrality distinguishes inherent communities corresponding to the job types in each organization with a high degree of accuracy. This indicates the possibility that connectability to nodes within two degrees of separation reveals potential trends of weighted networks that are not apparent from conventional measures.

Gene essentiality and the topology of protein interaction networks

PubMed Central

Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

2005-01-01

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

Topology Property and Dynamic Behavior of a Growing Spatial Network

NASA Astrophysics Data System (ADS)

Cao, Xian-Bin; Du, Wen-Bo; Hu, Mao-Bin; Rong, Zhi-Hai; Sun, Peng; Chen, Cai-Long

In this paper, we propose a growing spatial network (GSN) model and investigate its topology properties and dynamical behaviors. The model is generated by adding one node i with m links into a square lattice at each time step and the new node i is connected to the existing nodes with probabilities proportional to: ({kj})α /dij2, where kj is the degree of node j, α is the tunable parameter and dij is the Euclidean distance between i and j. It is found that both the degree heterogeneity and the clustering coefficient monotonously increase with the increment of α, while the average shortest path length monotonously decreases. Moreover, the evolutionary game dynamics and network traffic dynamics are investigated. Simulation results show that the value of α can also greatly influence the dynamic behaviors.

Reconfiguration and Search of Social Networks

PubMed Central

Zhang, Lianming; Peng, Aoyuan

2013-01-01

Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based on k-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency. PMID:24574861

Effect of nitrogen plasma afterglow on the surface charge effect resulted during XPS surface analysis of amorphous carbon nitride thin films

NASA Astrophysics Data System (ADS)

Kayed, Kamal

2018-06-01

The aim of this paper is to investigate the relationship between the micro structure and the surface charge effect resulted during XPS surface analysis of amorphous carbon nitride thin films prepared by laser ablation method. The study results show that the charge effect coefficient (E) is not just a correction factor. We found that the changes in this coefficient value due to incorporation of nitrogen atoms into the carbon network are related to the spatial configurations of the sp2 bonded carbon atoms, order degree and sp2 clusters size. In addition, results show that the curve E vs. C(sp3)-N is a characteristic curve of the micro structure. This means that using this curve makes it easy to sorting the samples according to the micro structure (hexagonal rings or chains).

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Estimating regression coefficients from clustered samples: Sampling errors and optimum sample allocation

NASA Technical Reports Server (NTRS)

Kalton, G.

1983-01-01

A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.

Characterization of known protein complexes using k-connectivity and other topological measures

PubMed Central

Gallagher, Suzanne R; Goldberg, Debra S

2015-01-01

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms. PMID:26913183

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism.

PubMed

Caeyenberghs, Karen; Taymans, Tom; Wilson, Peter H; Vanderstraeten, Guy; Hosseini, Hadi; van Waelvelde, Hilde

2016-07-01

Children with autism spectrum disorders (ASD) often exhibit motor clumsiness (Developmental Coordination Disorder, DCD), i.e. they struggle with everyday tasks that require motor coordination like dressing, self-care, and participating in sport and leisure activities. Previous studies in these neurodevelopmental disorders have demonstrated functional abnormalities and alterations of white matter microstructural integrity in specific brain regions. These findings suggest that the global organization of brain networks is affected in DCD and ASD and support the hypothesis of a 'dys-connectivity syndrome' from a network perspective. No studies have compared the structural covariance networks between ASD and DCD in order to look for the signature of DCD independent of comorbid autism. Here, we aimed to address the question of whether abnormal connectivity in DCD overlaps that seen in autism or comorbid DCD-autism. Using graph theoretical analysis, we investigated differences in global and regional topological properties of structural brain networks in 53 children: 8 ASD children with DCD (DCD+ASD), 15 ASD children without DCD (ASD), 11 with DCD only, and 19 typically developing (TD) children. We constructed separate structural correlation networks based on cortical thickness derived from Freesurfer. The children were assessed on the Movement-ABC and the Beery Test of Visual Motor Integration. Behavioral results demonstrated that the DCD group and DCD+ASD group scored on average poorer than the TD and ASD groups on various motor measures. Furthermore, although the brain networks of all groups exhibited small-world properties, the topological architecture of the networks was significantly altered in children with ASD compared with DCD and TD. ASD children showed increased normalized path length and higher values of clustering coefficient. Also, paralimbic regions exhibited nodal clustering coefficient alterations in singular disorders. These changes were disorder-specific, and included alterations in clustering coefficient in the isthmus of the right cingulate gyrus and the pars orbitalis of the right inferior frontal gyrus in ASD children, and DCD-related increases in the lateral orbitofrontal cortex. Children meeting criteria for both DCD and ASD exhibited topological changes that were more widespread from those seen in children with only DCD, i.e. children with DCD+ASD showed alterations of clustering coefficient in (para)limbic regions, primary areas, and association areas. The DCD+ASD group showed changes in clustering coefficient in the left association cortex relative to the ASD group. Finally, the DCD+ASD group shared ASD-specific abnormalities in the pars orbitalis of right inferior frontal gyrus, which was hypothesized to reflect atypical emotional-cognitive processing. Our results provide evidence that DCD and ASD are neurodevelopmental disorders with a low degree of overlap in abnormalities in connectivity. The co-occurrence of DCD+ASD was also associated with a distinct topological pattern, highlighting the unique neural signature of comorbid neurodevelopmental disorders. © 2016 John Wiley & Sons Ltd.

p-capture reaction cycles in rotating massive stars and their impact on elemental abundances in globular cluster stars: A case study of O, Na and Al

NASA Astrophysics Data System (ADS)

Mahanta, Upakul; Goswami, Aruna; Duorah, Hiralal; Duorah, Kalpana

2017-08-01

Elemental abundance patterns of globular cluster stars can provide important clues for understanding cluster formation and early chemical evolution. The origin of the abundance patterns, however, still remains poorly understood. We have studied the impact of p-capture reaction cycles on the abundances of oxygen, sodium and aluminium considering nuclear reaction cycles of carbon-nitrogen-oxygen-fluorine, neon-sodium and magnesium-aluminium in massive stars in stellar conditions of temperature range 2×107 to 10×107 K and typical density of 102 gm cc-1. We have estimated abundances of oxygen, sodium and aluminium with respect to Fe, which are then assumed to be ejected from those stars because of rotation reaching a critical limit. These ejected abundances of elements are then compared with their counterparts that have been observed in some metal-poor evolved stars, mainly giants and red giants, of globular clusters M3, M4, M13 and NGC 6752. We observe an excellent agreement with [O/Fe] between the estimated and observed abundance values for globular clusters M3 and M4 with a correlation coefficient above 0.9 and a strong linear correlation for the remaining two clusters with a correlation coefficient above 0.7. The estimated [Na/Fe] is found to have a correlation coefficient above 0.7, thus implying a strong correlation for all four globular clusters. As far as [Al/Fe] is concerned, it also shows a strong correlation between the estimated abundance and the observed abundance for globular clusters M13 and NGC 6752, since here also the correlation coefficient is above 0.7 whereas for globular cluster M4 there is a moderate correlation found with a correlation coefficient above 0.6. Possible sources of these discrepancies are discussed.

Characterizing core-periphery structure of complex network by h-core and fingerprint curve

NASA Astrophysics Data System (ADS)

Li, Simon S.; Ye, Adam Y.; Qi, Eric P.; Stanley, H. Eugene; Ye, Fred Y.

2018-02-01

It is proposed that the core-periphery structure of complex networks can be simulated by h-cores and fingerprint curves. While the features of core structure are characterized by h-core, the features of periphery structure are visualized by rose or spiral curve as the fingerprint curve linking to entire-network parameters. It is suggested that a complex network can be approached by h-core and rose curves as the first-order Fourier-approach, where the core-periphery structure is characterized by five parameters: network h-index, network radius, degree power, network density and average clustering coefficient. The simulation looks Fourier-like analysis.

The complex network of the Brazilian Popular Music

NASA Astrophysics Data System (ADS)

de Lima e Silva, D.; Medeiros Soares, M.; Henriques, M. V. C.; Schivani Alves, M. T.; de Aguiar, S. G.; de Carvalho, T. P.; Corso, G.; Lucena, L. S.

2004-02-01

We study the Brazilian Popular Music in a network perspective. We call the Brazilian Popular Music Network, BPMN, the graph where the vertices are the song writers and the links are determined by the existence of at least a common singer. The linking degree distribution of such graph shows power law and exponential regions. The exponent of the power law is compatible with the values obtained by the evolving network algorithms seen in the literature. The average path length of the BPMN is similar to the correspondent random graph, its clustering coefficient, however, is significantly larger. These results indicate that the BPMN forms a small-world network.

Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks.

PubMed

Zhang, Juping; Yang, Chan; Jin, Zhen; Li, Jia

2018-07-14

In this paper, the correlation coefficients between nodes in states are used as dynamic variables, and we construct SIR epidemic dynamic models with correlation coefficients by using the pair approximation method in static networks and dynamic networks, respectively. Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks, which are compared with the results of the mean field equations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Right-side-stretched multifractal spectra indicate small-worldness in networks

NASA Astrophysics Data System (ADS)

Oświȩcimka, Paweł; Livi, Lorenzo; Drożdż, Stanisław

2018-04-01

Complex network formalism allows to explain the behavior of systems composed by interacting units. Several prototypical network models have been proposed thus far. The small-world model has been introduced to mimic two important features observed in real-world systems: i) local clustering and ii) the possibility to move across a network by means of long-range links that significantly reduce the characteristic path length. A natural question would be whether there exist several ;types; of small-world architectures, giving rise to a continuum of models with properties (partially) shared with other models belonging to different network families. Here, we take advantage of the interplay between network theory and time series analysis and propose to investigate small-world signatures in complex networks by analyzing multifractal characteristics of time series generated from such networks. In particular, we suggest that the degree of right-sided asymmetry of multifractal spectra is linked with the degree of small-worldness present in networks. This claim is supported by numerical simulations performed on several parametric models, including prototypical small-world networks, scale-free, fractal and also real-world networks describing protein molecules. Our results also indicate that right-sided asymmetry emerges with the presence of the following topological properties: low edge density, low average shortest path, and high clustering coefficient.

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks.

PubMed

Subramaniyam, Narayan Puthanmadam; Hyttinen, Jari

2015-02-01

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R, and the bivariate recurrence network measure, the average cross-clustering coefficient C(cross), can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L, and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error N and nonlinear interdependence measure L used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient C(cross) is independent of the outcome of the randomness test based on the average clustering coefficient C. Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks

NASA Astrophysics Data System (ADS)

Subramaniyam, Narayan Puthanmadam; Hyttinen, Jari

2015-02-01

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R , and the bivariate recurrence network measure, the average cross-clustering coefficient Ccross, can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L , and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error N and nonlinear interdependence measure L used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient Ccross is independent of the outcome of the randomness test based on the average clustering coefficient C . Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

Recombination of electrons with NH4/+/-/NH3/n-series ions

NASA Technical Reports Server (NTRS)

Huang, C.-M.; Biondi, M. A.; Johnsen, R.

1976-01-01

The paper examines the recombination of electrons with ammonium-series cluster ions, NH4(+)-(NH3)n, for two reasons: (1) NH4(+) may be a significant ion in the lower atmospheres of the earth and the outer planets, and (2) to investigate the weak temperature dependence of the cluster ion's recombination coefficient. A microwave afterglow mass spectrometer was used to determine the recombination coefficients for the first five members of the ammonium series, (18+) through (86+), at temperatures between 200 and 410 K. The electron temperature dependence of the recombination coefficient was determined for (35+) and (52+), the n = 1 and 2 cluster ions, over the temperature range 300-3000 K.

The correlation of metrics in complex networks with applications in functional brain networks

NASA Astrophysics Data System (ADS)

Li, C.; Wang, H.; de Haan, W.; Stam, C. J.; Van Mieghem, P.

2011-11-01

An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi-Albert graphs, Erdös-Rényi random graphs and Watts-Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös-Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected.

The Influence of the Phonological Neighborhood Clustering Coefficient on Spoken Word Recognition

ERIC Educational Resources Information Center

Chan, Kit Ying; Vitevitch, Michael S.

2009-01-01

Clustering coefficient--a measure derived from the new science of networks--refers to the proportion of phonological neighbors of a target word that are also neighbors of each other. Consider the words "bat", "hat", and "can", all of which are neighbors of the word "cat"; the words "bat" and…

Complex network structure of musical compositions: Algorithmic generation of appealing music

NASA Astrophysics Data System (ADS)

Liu, Xiao Fan; Tse, Chi K.; Small, Michael

2010-01-01

In this paper we construct networks for music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurring connections. We analyze classical music from Bach, Mozart, Chopin, as well as other types of music such as Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. We conjecture that preserving the universal network properties is a necessary step in artificial composition of music. Power-law exponents of node degree, node strength and/or edge weight distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be composed artificially using a controlled random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. By generating a large number of compositions, we find that this algorithm generates music which has the necessary qualities to be subjectively judged as appealing.

Assessment of langatate material constants and temperature coefficients using SAW delay line measurements.

PubMed

Sturtevant, Blake T; Pereira da Cunha, Mauricio

2010-03-01

This paper reports on the assessment of langatate (LGT) acoustic material constants and temperature coefficients by surface acoustic wave (SAW) delay line measurements up to 130 degrees C. Based upon a full set of material constants recently reported by the authors, 7 orientations in the LGT plane with Euler angles (90 degrees, 23 degrees, Psi) were identified for testing. Each of the 7 selected orientations exhibited calculated coupling coefficients (K(2)) between 0.2% and 0.75% and also showed a large range of predicted temperature coefficient of delay (TCD) values around room temperature. Additionally, methods for estimating the uncertainty in predicted SAW propagation properties were developed and applied to SAW phase velocity and temperature coefficient of delay calculations. Starting from a purchased LGT boule, the SAW wafers used in this work were aligned, cut, ground, and polished at University of Maine facilities, followed by device fabrication and testing. Using repeated measurements of 2 devices on separate wafers for each of the 7 orientations, the room temperature SAW phase velocities were extracted with a precision of 0.1% and found to be in agreement with the predicted values. The normalized frequency change and the temperature coefficient of delay for all 7 orientations agreed with predictions within the uncertainty of the measurement and the predictions over the entire 120 degrees C temperature range measured. Two orientations, with Euler angles (90 degrees, 23 degrees, 123 degrees) and (90 degrees, 23 degrees, 119 degrees), were found to have high predicted coupling for LGT (K(2) > 0.5%) and were shown experimentally to exhibit temperature compensation in the vicinity of room temperature, with turnover temperatures at 50 and 60 degrees C, respectively.

Iron catalyst chemistry in modeling a high-pressure carbon monoxide nanotube reactor

NASA Technical Reports Server (NTRS)

Scott, Carl D.; Povitsky, Alexander; Dateo, Christopher; Gokcen, Tahir; Willis, Peter A.; Smalley, Richard E.

2003-01-01

The high-pressure carbon monoxide (HiPco) technique for producing single-wall carbon nanotubes (SWNTs) is analyzed with the use of a chemical reaction model coupled with flow properties calculated along streamlines, calculated by the FLUENT code for pure carbon monoxide. Cold iron pentacarbonyl, diluted in CO at about 30 atmospheres, is injected into a conical mixing zone, where hot CO is also introduced via three jets at 30 degrees with respect to the axis. Hot CO decomposes the Fe(CO)5 to release atomic Fe. Then iron nucleates and forms clusters that catalyze the formation of SWNTs by a disproportionation reaction (Boudouard) of CO on Fe-containing clusters. Alternative nucleation rates are estimated from the theory of hard sphere collision dynamics with an activation energy barrier. The rate coefficient for carbon nanotube growth is estimated from activation energies in the literature. The calculated growth was found be about an order of magnitude greater than measured, regardless of the nucleation rate. A study of cluster formation in an incubation zone prior to injection into the reactor shows that direct dimer formation from Fe atoms is not as important as formation via an exchange reaction of Fe with CO in FeCO.

Iron catalyst chemistry in modeling a high-pressure carbon monoxide nanotube reactor.

PubMed

Scott, Carl D; Povitsky, Alexander; Dateo, Christopher; Gökçen, Tahir; Willis, Peter A; Smalley, Richard E

2003-01-01

The high-pressure carbon monoxide (HiPco) technique for producing single-wall carbon nanotubes (SWNTs) is analyzed with the use of a chemical reaction model coupled with flow properties calculated along streamlines, calculated by the FLUENT code for pure carbon monoxide. Cold iron pentacarbonyl, diluted in CO at about 30 atmospheres, is injected into a conical mixing zone, where hot CO is also introduced via three jets at 30 degrees with respect to the axis. Hot CO decomposes the Fe(CO)5 to release atomic Fe. Then iron nucleates and forms clusters that catalyze the formation of SWNTs by a disproportionation reaction (Boudouard) of CO on Fe-containing clusters. Alternative nucleation rates are estimated from the theory of hard sphere collision dynamics with an activation energy barrier. The rate coefficient for carbon nanotube growth is estimated from activation energies in the literature. The calculated growth was found be about an order of magnitude greater than measured, regardless of the nucleation rate. A study of cluster formation in an incubation zone prior to injection into the reactor shows that direct dimer formation from Fe atoms is not as important as formation via an exchange reaction of Fe with CO in FeCO.

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

PubMed

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

2016-06-23

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

Study on Spatial Spillover Effects of Logistics Industry Development for Economic Growth in the Yangtze River Delta City Cluster Based on Spatial Durbin Model

PubMed Central

Xu, Xinxing

2017-01-01

The overall entropy method is used to evaluate the development level of the logistics industry in the city based on a mechanism analysis of the spillover effect of the development of the logistics industry on economic growth, according to the panel data of 26 cities in the Yangtze River delta. On this basis, the paper uses the spatial durbin model to study the direct impact of the development of the logistics industry on economic growth and the spatial spillover effect. The results show that the direct impact coefficient of the development of the logistics industry in the Yangtze River Delta urban agglomeration on local economic growth is 0.092, and the significant spatial spillover effect on the economic growth in the surrounding area is 0.197. Compared with the labor force input, capital investment and the degree of opening to the world, and government functions, the logistics industry’s direct impact coefficient is the largest, other than capital investment; the coefficient of the spillover effect is higher than other control variables, making it a “strong engine” of the Yangtze River Delta urban agglomeration economic growth. PMID:29207555

Study on Spatial Spillover Effects of Logistics Industry Development for Economic Growth in the Yangtze River Delta City Cluster Based on Spatial Durbin Model.

PubMed

Xu, Xinxing; Wang, Yuhong

2017-12-04

The overall entropy method is used to evaluate the development level of the logistics industry in the city based on a mechanism analysis of the spillover effect of the development of the logistics industry on economic growth, according to the panel data of 26 cities in the Yangtze River delta. On this basis, the paper uses the spatial durbin model to study the direct impact of the development of the logistics industry on economic growth and the spatial spillover effect. The results show that the direct impact coefficient of the development of the logistics industry in the Yangtze River Delta urban agglomeration on local economic growth is 0.092, and the significant spatial spillover effect on the economic growth in the surrounding area is 0.197. Compared with the labor force input, capital investment and the degree of opening to the world, and government functions, the logistics industry's direct impact coefficient is the largest, other than capital investment; the coefficient of the spillover effect is higher than other control variables, making it a "strong engine" of the Yangtze River Delta urban agglomeration economic growth.

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

Molecular profiles of Venezuelan isolates of Trypanosoma sp. by random amplified polymorphic DNA method.

PubMed

Perrone, T M; Gonzatti, M I; Villamizar, G; Escalante, A; Aso, P M

2009-05-12

Nine Trypanosoma sp. Venezuelan isolates, initially presumed to be T. evansi, were collected from three different hosts, capybara (Apure state), horse (Apure state) and donkey (Guarico state) and compared by the random amplification polymorphic DNA technique (RAPD). Thirty-one to 46 reproducible fragments were obtained with 12 of the 40 primers that were used. Most of the primers detected molecular profiles with few polymorphisms between the seven horse, capybara and donkey isolates. Quantitative analyses of the RAPD profiles of these isolates revealed a high degree of genetic conservation with similarity coefficients between 85.7% and 98.5%. Ten of the primers generated polymorphic RAPD profiles with two of the three Trypanosoma sp. horse isolates, namely TeAp-N/D1 and TeGu-N/D1. The similarity coefficient between these two isolates and the rest, ranged from 57.9% to 68.4% and the corresponding dendrogram clustered TeAp-N/D1 and Te Gu-N/D1 in a genetically distinct group.

A new method for comparing rankings through complex networks: Model and analysis of competitiveness of major European soccer leagues

NASA Astrophysics Data System (ADS)

Criado, Regino; García, Esther; Pedroche, Francisco; Romance, Miguel

2013-12-01

In this paper, we show a new technique to analyze families of rankings. In particular, we focus on sports rankings and, more precisely, on soccer leagues. We consider that two teams compete when they change their relative positions in consecutive rankings. This allows to define a graph by linking teams that compete. We show how to use some structural properties of this competitivity graph to measure to what extend the teams in a league compete. These structural properties are the mean degree, the mean strength, and the clustering coefficient. We give a generalization of the Kendall's correlation coefficient to more than two rankings. We also show how to make a dynamic analysis of a league and how to compare different leagues. We apply this technique to analyze the four major European soccer leagues: Bundesliga, Italian Lega, Spanish Liga, and Premier League. We compare our results with the classical analysis of sport ranking based on measures of competitive balance.

Characterizing air quality data from complex network perspective.

PubMed

Fan, Xinghua; Wang, Li; Xu, Huihui; Li, Shasha; Tian, Lixin

2016-02-01

Air quality depends mainly on changes in emission of pollutants and their precursors. Understanding its characteristics is the key to predicting and controlling air quality. In this study, complex networks were built to analyze topological characteristics of air quality data by correlation coefficient method. Firstly, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) indexes of eight monitoring sites in Beijing were selected as samples from January 2013 to December 2014. Secondly, the C-C method was applied to determine the structure of phase space. Points in the reconstructed phase space were considered to be nodes of the network mapped. Then, edges were determined by nodes having the correlation greater than a critical threshold. Three properties of the constructed networks, degree distribution, clustering coefficient, and modularity, were used to determine the optimal value of the critical threshold. Finally, by analyzing and comparing topological properties, we pointed out that similarities and difference in the constructed complex networks revealed influence factors and their different roles on real air quality system.

Preparation and characterization of chemically defined oligomers of rabbit immunoglobulin G molecules for the complement binding studies.

PubMed Central

Wright, J K; Tschopp, J; Jaton, J C

1980-01-01

Pure dimers, trimers, tetramers and pentamers of rabbit non-immune IgG (immunoglobulin G) or antibody IgG were prepared by polymerization in the presence of the bifunctional cross-linking reagent dithiobis (succinimidylpropionate). Oligomerization was performed either in the presence of polysaccharide antigen and specific monomeric antibody (method A) or by random cross-linking of non-immune rabbit IgG in the absence of antigen (method B). By repeated gel-filtration chromatography, samples prepared by both methods exhibited a single band in analytical sodium dodecyl sulphate/polyacrylamide-gel electrophoresis. The electrophoretic mobilities of samples prepared by method A were slightly greater than those for the corresponding samples prepared by method B. This might suggest a role played by antigen in the orientation of IgG molecules within the clusters, which may be more compact than those formed by random cross-linking. The average numbers of cross-linker molecules per oligomer varied between 3 and 6 for clusters made by method A and between 1 and 3 for clusters made by method B. Ultracentrifugal analyses of the oligomers yielded sedimentation coefficients (S20,w) of 9.6S for the dimer, 11.2S for the trimer, 13.6S for the tetramer and 16.1S for the pentamer. Comparison of the observed sedimentation coefficients with those predicted by various hydrodynamic models suggested these oligomers possessed open and linear structures. Reduction of the cross-linking molecules converted oligomers into monomeric species of IgG. C.d. spectra of some oligomers studied in the range 200-250 nm were essentially the same as that of monomeric IgG molecules, thus strongly suggesting no major conformation changes in IgG molecules within clusters. These oligomers were found to be stable for up to 2 months when stored at -70 degrees C. Images Fig. 1. Fig. 4. PMID:7188424

Network of listed companies based on common shareholders and the prediction of market volatility

NASA Astrophysics Data System (ADS)

Li, Jie; Ren, Da; Feng, Xu; Zhang, Yongjie

2016-11-01

In this paper, we build a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013. We analyze the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. Additionally, we consider the economic implications of topological characteristic changes on market volatility and use them to make future predictions. Our study finds that the network diameter significantly predicts volatility. After adding control variables used in traditional financial studies (volume, turnover and previous volatility), network topology still significantly influences volatility and improves the predictive ability of the model.

Thermal requirements of Dermanyssus gallinae (De Geer, 1778) (Acari: Dermanyssidae).

PubMed

Tucci, Edna Clara; do Prado, Angelo P; de Araújo, Raquel Pires

2008-01-01

The thermal requirements for development of Dermanyssus gallinae were studied under laboratory conditions at 15, 20, 25, 30 and 35 degrees C, a 12h photoperiod and 60-85% RH. The thermal requirements for D. gallinae were as follows. Preoviposition: base temperature 3.4 degrees C, thermal constant (k) 562.85 degree-hours, determination coefficient (R(2)) 0.59, regression equation: Y= -0.006035 + 0.001777x. Egg: base temperature 10.60 degrees C, thermal constant (k) 689.65 degree-hours, determination coefficient (R(2)) 0.94, regression equation: Y= -0.015367 + 0.001450x. Larva: base temperature 9.82 degrees C, thermal constant (k) 464.91 degree-hours, determination coefficient (R(2)) 0.87, regression equation: Y= -0.021123 + 0.002151x. Protonymph: base temperature 10.17 degrees C, thermal constant (k) 504.49 degree-hours, determination coefficient (R(2)) 0.90, regression equation: Y= -0.020152 + 0.001982x. Deutonymph: base temperature 11.80 degrees C, thermal constant (k) 501.11 degree-hours, determination coefficient (R(2)) 0.99, regression equation: Y= -0.023555 + 0.001996x. The results obtained showed that 15 to 42 generations of Dermanyssus gallinae may occur during the year in the State of São Paulo, as estimated based on isotherm charts. Dermanyssus gallinae may develop continually in the State of São Paulo, with a population decrease in the winter. There were differences between the developmental stages of D. gallinae in relation to thermal requirements.

Longer-Term Impact of High and Low Temperature on Mortality: An International Study to Clarify Length of Mortality Displacement

PubMed Central

Bell, Michelle L.; de Sousa Zanotti Stagliorio Coelho, Micheline; Leon Guo, Yue-Liang; Guo, Yuming; Goodman, Patrick; Hashizume, Masahiro; Honda, Yasushi; Kim, Ho; Lavigne, Eric; Michelozzi, Paola; Hilario Nascimento Saldiva, Paulo; Schwartz, Joel; Scortichini, Matteo; Sera, Francesco; Tobias, Aurelio; Tong, Shilu; Wu, Chang-fu; Zanobetti, Antonella; Zeka, Ariana; Gasparrini, Antonio

2017-01-01

Background: In many places, daily mortality has been shown to increase after days with particularly high or low temperatures, but such daily time-series studies cannot identify whether such increases reflect substantial life shortening or short-term displacement of deaths (harvesting). Objectives: To clarify this issue, we estimated the association between annual mortality and annual summaries of heat and cold in 278 locations from 12 countries. Methods: Indices of annual heat and cold were used as predictors in regressions of annual mortality in each location, allowing for trends over time and clustering of annual count anomalies by country and pooling estimates using meta-regression. We used two indices of annual heat and cold based on preliminary standard daily analyses: a) mean annual degrees above/below minimum mortality temperature (MMT), and b) estimated fractions of deaths attributed to heat and cold. The first index was simpler and matched previous related research; the second was added because it allowed the interpretation that coefficients equal to 0 and 1 are consistent with none (0) or all (1) of the deaths attributable in daily analyses being displaced by at least 1 y. Results: On average, regression coefficients of annual mortality on heat and cold mean degrees were 1.7% [95% confidence interval (CI): 0.3, 3.1] and 1.1% (95% CI: 0.6, 1.6) per degree, respectively, and daily attributable fractions were 0.8 (95% CI: 0.2, 1.3) and 1.1 (95% CI: 0.9, 1.4). The proximity of the latter coefficients to 1.0 provides evidence that most deaths found attributable to heat and cold in daily analyses were brought forward by at least 1 y. Estimates were broadly robust to alternative model assumptions. Conclusions: These results provide strong evidence that most deaths associated in daily analyses with heat and cold are displaced by at least 1 y. https://doi.org/10.1289/EHP1756 PMID:29084393

[Genetic diversity and genetic structure of endangered wild Sinopodophyllum emodi by start codon targeted polymorphism].

PubMed

Chen, Da-Xia; Zhao, Ji-Feng; Liu, Xiang; Wang, Chang-Hua; Zhang, Zhi-Wei; Qin, Song-Yun; Zhong, Guo-Yue

2013-01-01

Revealed the genetic diversity level and genetic structure characteristics in Sinopodophyllum emodi, a rare and endangered species in China. We detected the genetic polymorphism within and among six wild populations (45 individuals) by the approach of Start Codon Targeted (SCoT) Polymorphism. The associated genetic parameters were calculated by POP-GENE1.31 and the relationship was constructed based on UPGMA method. A total of 350 bands were scored by 27 primers and 284 bands of them were polymorphic. The average polymorphic bands of each primer were 10.52. At species level, there was a high level of genetic diversity among six populations (PPB = 79.27%, N(e) = 1.332 7, H = 0.210 9 and H(sp) = 0.328 6). At population level, the genetic diversity level was low (PPB = 10.48% (4.00% -23.71%), N(e) = 1.048 7 (1.020 7-1.103 7), H = 0.029 7 (0.012 9-0.063 1), H(pop) = 0.046 2 (0.019 9-0.098 6). The Nei's coefficient of genetic differentiation was 0.841 1, which was consistent with the Shannon's coefficient of genetic differentiation (0.849 4). Two calculated methods all showed that most of the genetic variation existed among populations. The gene flow (N(m) = 0.094 4) was less among populations, indicating that the degree of genetic differentiation was higher. Genetic similarity coefficient were changed from 0.570 8 to 0.978 7. By clustering analysis, the tested populations were divided into two classes and had a tendency that the same geographical origin or material of similar habitats clustered into one group. The genetic diversity of samples of S. emodi is high,which laid a certain foundation for effective protection and improvement of germplasm resources.

Co-Authorship and Bibliographic Coupling Network Effects on Citations

PubMed Central

Biscaro, Claudio; Giupponi, Carlo

2014-01-01

This paper analyzes the effects of the co-authorship and bibliographic coupling networks on the citations received by scientific articles. It expands prior research that limited its focus on the position of co-authors and incorporates the effects of the use of knowledge sources within articles: references. By creating a network on the basis of shared references, we propose a way to understand whether an article bridges among extant strands of literature and infer the size of its research community and its embeddedness. Thus, we map onto the article – our unit of analysis – the metrics of authors' position in the co-authorship network and of the use of knowledge on which the scientific article is grounded. Specifically, we adopt centrality measures – degree, betweenneess, and closeness centrality – in the co-authorship network and degree, betweenness centrality and clustering coefficient in the bibliographic coupling and show their influence on the citations received in first two years after the year of publication. Findings show that authors' degree positively impacts citations. Also closeness centrality has a positive effect manifested only when the giant component is relevant. Author's betweenness centrality has instead a negative effect that persists until the giant component - largest component of the network in which all nodes can be linked by a path - is relevant. Moreover, articles that draw on fragmented strands of literature tend to be cited more, whereas the size of the scientific research community and the embeddedness of the article in a cohesive cluster of literature have no effect. PMID:24911416

A recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure.

PubMed

Liao, Fuyuan; Jan, Yih-Kuen

2012-06-01

This paper presents a recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure. Recurrence is a fundamental property of many dynamical systems, which can be explored in phase spaces constructed from observational time series. A visualization tool of recurrence analysis called recurrence plot (RP) has been proved to be highly effective to detect transitions in the dynamics of the system. However, it was found that delay embedding can produce spurious structures in RPs. Network-based concepts have been applied for the analysis of nonlinear time series recently. We demonstrate that time series with different types of dynamics exhibit distinct global clustering coefficients and distributions of local clustering coefficients and that the global clustering coefficient is robust to the embedding parameters. We applied the approach to study skin blood flow oscillations (BFO) response to loading pressure. The results showed that global clustering coefficients of BFO significantly decreased in response to loading pressure (p<0.01). Moreover, surrogate tests indicated that such a decrease was associated with a loss of nonlinearity of BFO. Our results suggest that the recurrence network approach can practically quantify the nonlinear dynamics of BFO.

Atomistic modeling of dropwise condensation

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

Sikarwar, B. S., E-mail: bssikarwar@amity.edu; Singh, P. L.; Muralidhar, K.

The basic aim of the atomistic modeling of condensation of water is to determine the size of the stable cluster and connect phenomena occurring at atomic scale to the macroscale. In this paper, a population balance model is described in terms of the rate equations to obtain the number density distribution of the resulting clusters. The residence time is taken to be large enough so that sufficient time is available for all the adatoms existing in vapor-phase to loose their latent heat and get condensed. The simulation assumes clusters of a given size to be formed from clusters of smallermore » sizes, but not by the disintegration of the larger clusters. The largest stable cluster size in the number density distribution is taken to be representative of the minimum drop radius formed in a dropwise condensation process. A numerical confirmation of this result against predictions based on a thermodynamic model has been obtained. Results show that the number density distribution is sensitive to the surface diffusion coefficient and the rate of vapor flux impinging on the substrate. The minimum drop radius increases with the diffusion coefficient and the impinging vapor flux; however, the dependence is weak. The minimum drop radius predicted from thermodynamic considerations matches the prediction of the cluster model, though the former does not take into account the effect of the surface properties on the nucleation phenomena. For a chemically passive surface, the diffusion coefficient and the residence time are dependent on the surface texture via the coefficient of friction. Thus, physical texturing provides a means of changing, within limits, the minimum drop radius. The study reveals that surface texturing at the scale of the minimum drop radius does not provide controllability of the macro-scale dropwise condensation at large timescales when a dynamic steady-state is reached.« less

The formation and analysis of a 5 deg equal area block terrestrial gravity field

NASA Technical Reports Server (NTRS)

Rapp, R. H.

1972-01-01

A set of 23,355 1 degree x 1 degree mean free air anomalies were used to predict a set of 5 degree equal area anomalies and their standard errors. Using the 1 degree data incorporating geophysically predicted values of ACIC, 1283 5 degree blocks were computed. Excluding the geophysically predicted anomalies 1249 blocks were computed. The 1 degree data were also used to compute covariance functions and the equatorial gravity and flattening implied by this data. The predicted anomalies were supplemented by model anomalies to form a complete 1654 global anomaly field. These data were used in a weighted least squares to determine potential coefficients to degree 15, and in a summation type formulation to determine potential coefficients to degree 25. These potential coefficients sets are compared to recent satellite determinations.

Competing contact processes in the Watts-Strogatz network

NASA Astrophysics Data System (ADS)

Rybak, Marcin; Malarz, Krzysztof; Kułakowski, Krzysztof

2016-06-01

We investigate two competing contact processes on a set of Watts-Strogatz networks with the clustering coefficient tuned by rewiring. The base for network construction is one-dimensional chain of N sites, where each site i is directly linked to nodes labelled as i ± 1 and i ± 2. So initially, each node has the same degree k i = 4. The periodic boundary conditions are assumed as well. For each node i the links to sites i + 1 and i + 2 are rewired to two randomly selected nodes so far not-connected to node i. An increase of the rewiring probability q influences the nodes degree distribution and the network clusterization coefficient 𝓒. For given values of rewiring probability q the set 𝓝(q)={𝓝1,𝓝2,...,𝓝 M } of M networks is generated. The network's nodes are decorated with spin-like variables s i ∈ { S,D }. During simulation each S node having a D-site in its neighbourhood converts this neighbour from D to S state. Conversely, a node in D state having at least one neighbour also in state D-state converts all nearest-neighbours of this pair into D-state. The latter is realized with probability p. We plot the dependence of the nodes S final density n S T on initial nodes S fraction n S 0. Then, we construct the surface of the unstable fixed points in (𝓒, p, n S 0) space. The system evolves more often toward n S T for (𝓒, p, n S 0) points situated above this surface while starting simulation with (𝓒, p, n S 0) parameters situated below this surface leads system to n S T =0. The points on this surface correspond to such value of initial fraction n S * of S nodes (for fixed values 𝓒 and p) for which their final density is n S T=1/2.

Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy. PMID:25608211

Spectral analysis of Chinese language: Co-occurrence networks from four literary genres

NASA Astrophysics Data System (ADS)

Liang, Wei; Chen, Guanrong

2016-05-01

The eigenvalues and eigenvectors of the adjacency matrix of a network contain essential information about its topology. For each of the Chinese language co-occurrence networks constructed from four literary genres, i.e., essay, popular science article, news report, and novel, it is found that the largest eigenvalue depends on the network size N, the number of edges, the average shortest path length, and the clustering coefficient. Moreover, it is found that their node-degree distributions all follow a power-law. The number of different eigenvalues, Nλ, is found numerically to increase in the manner of Nλ ∝ log N for novel and Nλ ∝ N for the other three literary genres. An ;M; shape or a triangle-like distribution appears in their spectral densities. The eigenvector corresponding to the largest eigenvalue is mostly localized to a node with the largest degree. For the above observed phenomena, mathematical analysis is provided with interpretation from a linguistic perspective.

Empirical study on human acupuncture point network

NASA Astrophysics Data System (ADS)

Li, Jian; Shen, Dan; Chang, Hui; He, Da-Ren

2007-03-01

Chinese medical theory is ancient and profound, however is confined by qualitative and faint understanding. The effect of Chinese acupuncture in clinical practice is unique and effective, and the human acupuncture points play a mysterious and special role, however there is no modern scientific understanding on human acupuncture points until today. For this reason, we attend to use complex network theory, one of the frontiers in the statistical physics, for describing the human acupuncture points and their connections. In the network nodes are defined as the acupuncture points, two nodes are connected by an edge when they are used for a medical treatment of a common disease. A disease is defined as an act. Some statistical properties have been obtained. The results certify that the degree distribution, act degree distribution, and the dependence of the clustering coefficient on both of them obey SPL distribution function, which show a function interpolating between a power law and an exponential decay. The results may be helpful for understanding Chinese medical theory.

Unimodular lattice triangulations as small-world and scale-free random graphs

NASA Astrophysics Data System (ADS)

Krüger, B.; Schmidt, E. M.; Mecke, K.

2015-02-01

Real-world networks, e.g., the social relations or world-wide-web graphs, exhibit both small-world and scale-free behaviour. We interpret lattice triangulations as planar graphs by identifying triangulation vertices with graph nodes and one-dimensional simplices with edges. Since these triangulations are ergodic with respect to a certain Pachner flip, applying different Monte Carlo simulations enables us to calculate average properties of random triangulations, as well as canonical ensemble averages, using an energy functional that is approximately the variance of the degree distribution. All considered triangulations have clustering coefficients comparable with real-world graphs; for the canonical ensemble there are inverse temperatures with small shortest path length independent of system size. Tuning the inverse temperature to a quasi-critical value leads to an indication of scale-free behaviour for degrees k≥slant 5. Using triangulations as a random graph model can improve the understanding of real-world networks, especially if the actual distance of the embedded nodes becomes important.

Evolution of Linux operating system network

NASA Astrophysics Data System (ADS)

Xiao, Guanping; Zheng, Zheng; Wang, Haoqin

2017-01-01

Linux operating system (LOS) is a sophisticated man-made system and one of the most ubiquitous operating systems. However, there is little research on the structure and functionality evolution of LOS from the prospective of networks. In this paper, we investigate the evolution of the LOS network. 62 major releases of LOS ranging from versions 1.0 to 4.1 are modeled as directed networks in which functions are denoted by nodes and function calls are denoted by edges. It is found that the size of the LOS network grows almost linearly, while clustering coefficient monotonically decays. The degree distributions are almost the same: the out-degree follows an exponential distribution while both in-degree and undirected degree follow power-law distributions. We further explore the functionality evolution of the LOS network. It is observed that the evolution of functional modules is shown as a sequence of seven events (changes) succeeding each other, including continuing, growth, contraction, birth, splitting, death and merging events. By means of a statistical analysis of these events in the top 4 largest components (i.e., arch, drivers, fs and net), it is shown that continuing, growth and contraction events occupy more than 95% events. Our work exemplifies a better understanding and describing of the dynamics of LOS evolution.

Empirical analysis of online social networks in the age of Web 2.0

NASA Astrophysics Data System (ADS)

Fu, Feng; Liu, Lianghuan; Wang, Long

2008-01-01

Today the World Wide Web is undergoing a subtle but profound shift to Web 2.0, to become more of a social web. The use of collaborative technologies such as blogs and social networking site (SNS) leads to instant online community in which people communicate rapidly and conveniently with each other. Moreover, there are growing interest and concern regarding the topological structure of these new online social networks. In this paper, we present empirical analysis of statistical properties of two important Chinese online social networks-a blogging network and an SNS open to college students. They are both emerging in the age of Web 2.0. We demonstrate that both networks possess small-world and scale-free features already observed in real-world and artificial networks. In addition, we investigate the distribution of topological distance. Furthermore, we study the correlations between degree (in/out) and degree (in/out), clustering coefficient and degree, popularity (in terms of number of page views) and in-degree (for the blogging network), respectively. We find that the blogging network shows disassortative mixing pattern, whereas the SNS network is an assortative one. Our research may help us to elucidate the self-organizing structural characteristics of these online social networks embedded in technical forms.

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters.

PubMed

Berenguer, Roberto; Pastor-Juan, María Del Rosario; Canales-Vázquez, Jesús; Castro-García, Miguel; Villas, María Victoria; Legorburo, Francisco Mansilla; Sabater, Sebastià

2018-04-24

Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy. © RSNA, 2018 Online supplemental material is available for this article.

Analysis of genetic diversity and genome relationships of four eggplant species (Solanum melongena L) using RAPD markers

NASA Astrophysics Data System (ADS)

Susilo; Setyaningsih, M.

2018-01-01

Solanum melongena (eggplant) is one of the diversity of the Solanum family which is grown and widely spread in Indonesia and widely used by the community. This research explored the genetic diversity of four local Indonesian eggplant species namely leuca, tekokak, gelatik and kopek by using RAPD (Random Amplified Polymorphic DNA). The samples were obtained from Agricultural Technology Assessment Institute (BPTP) Bogor, Indonesia. The result of data observation was in the form of Solanum melongena plant’s DNA profile analyzed descriptively and quantitatively. 30 DNA bands (28 polymorphic and 2 monomorphic) were successfully scored by using four primers (OPF-01, OPF-02, OPF-03, and OPF-04). The Primers were used able to amplify all of the four eggplant samples. The result of PCR-RAPD visualization produces bands of 300-1500 bp. The result of cluster analysis showed the existence of three clusters (A, B, and C). Cluster A (coefficient of equal to 49%) consisted of a gelatik, cluster B (coefficient of 65% equilibrium) consisted of TPU (Kopek) and TK (Tekokak), and cluster C (55% equilibrium coefficient) consisted of LC (Leunca). These results indicated that the closest proximity is found in samples of TK (Tekokak) and TPU (Kopek).

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

NASA Astrophysics Data System (ADS)

Nie, Chun-Xiao

2018-02-01

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

a New Dynamic Community Model for Social Networks

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Guo, Shi-Ze; Chen, Zhe; Song, Guang-Hua

2014-09-01

In this paper, based on the phenomenon that individuals join into and jump from the organizations in the society, we propose a dynamic community model to construct social networks. Two parameters are adopted in our model, one is the communication rate Pa that denotes the connection strength in the organization and the other is the turnover rate Pb, that stands for the frequency of jumping among the organizations. Based on simulations, we analyze not only the degree distribution, the clustering coefficient, the average distance and the network diameter but also the group distribution which is closely related to their community structure. Moreover, we discover that the networks generated by the proposed model possess the small-world property and can well reproduce the networks of social contacts.

Comprehensive risk assessment method of catastrophic accident based on complex network properties

NASA Astrophysics Data System (ADS)

Cui, Zhen; Pang, Jun; Shen, Xiaohong

2017-09-01

On the macro level, the structural properties of the network and the electrical characteristics of the micro components determine the risk of cascading failures. And the cascading failures, as a process with dynamic development, not only the direct risk but also potential risk should be considered. In this paper, comprehensively considered the direct risk and potential risk of failures based on uncertain risk analysis theory and connection number theory, quantified uncertain correlation by the node degree and node clustering coefficient, then established a comprehensive risk indicator of failure. The proposed method has been proved by simulation on the actual power grid. Modeling a network according to the actual power grid, and verified the rationality of the proposed method.

Approximate solution of coupled cluster equations: application to the coupled cluster doubles method and non-covalent interacting systems.

PubMed

Smiga, Szymon; Fabiano, Eduardo

2017-11-15

We have developed a simplified coupled cluster (SCC) methodology, using the basic idea of scaled MP2 methods. The scheme has been applied to the coupled cluster double equations and implemented in three different non-iterative variants. This new method (especially the SCCD[3] variant, which utilizes a spin-resolved formalism) has been found to be very efficient and to yield an accurate approximation of the reference CCD results for both total and interaction energies of different atoms and molecules. Furthermore, we demonstrate that the equations determining the scaling coefficients for the SCCD[3] approach can generate non-empirical SCS-MP2 scaling coefficients which are in good agreement with previous theoretical investigations.

Bivariate functional data clustering: grouping streams based on a varying coefficient model of the stream water and air temperature relationship

Treesearch

H. Li; X. Deng; Andy Dolloff; E. P. Smith

2015-01-01

A novel clustering method for bivariate functional data is proposed to group streams based on their water–air temperature relationship. A distance measure is developed for bivariate curves by using a time-varying coefficient model and a weighting scheme. This distance is also adjusted by spatial correlation of streams via the variogram. Therefore, the proposed...

Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis.

PubMed

Iyer, Parameswaran Mahadeva; Egan, Catriona; Pinto-Grau, Marta; Burke, Tom; Elamin, Marwa; Nasseroleslami, Bahman; Pender, Niall; Lalor, Edmund C; Hardiman, Orla

2015-01-01

Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS. 18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity. Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05). There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS.

Effects of amyloid and small vessel disease on white matter network disruption.

PubMed

Kim, Hee Jin; Im, Kiho; Kwon, Hunki; Lee, Jong Min; Ye, Byoung Seok; Kim, Yeo Jin; Cho, Hanna; Choe, Yearn Seong; Lee, Kyung Han; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Na, Duk L; Seo, Sang Won

2015-01-01

There is growing evidence that the human brain is a large scale complex network. The structural network is reported to be disrupted in cognitively impaired patients. However, there have been few studies evaluating the effects of amyloid and small vessel disease (SVD) markers, the common causes of cognitive impairment, on structural networks. Thus, we evaluated the association between amyloid and SVD burdens and structural networks using diffusion tensor imaging (DTI). Furthermore, we determined if network parameters predict cognitive impairments. Graph theoretical analysis was applied to DTI data from 232 cognitively impaired patients with varying degrees of amyloid and SVD burdens. All patients underwent Pittsburgh compound-B (PiB) PET to detect amyloid burden, MRI to detect markers of SVD, including the volume of white matter hyperintensities and the number of lacunes, and detailed neuropsychological testing. The whole-brain network was assessed by network parameters of integration (shortest path length, global efficiency) and segregation (clustering coefficient, transitivity, modularity). PiB retention ratio was not associated with any white matter network parameters. Greater white matter hyperintensity volumes or lacunae numbers were significantly associated with decreased network integration (increased shortest path length, decreased global efficiency) and increased network segregation (increased clustering coefficient, increased transitivity, increased modularity). Decreased network integration or increased network segregation were associated with poor performances in attention, language, visuospatial, memory, and frontal-executive functions. Our results suggest that SVD alters white matter network integration and segregation, which further predicts cognitive dysfunction.

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

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

Prediction of line failure fault based on weighted fuzzy dynamic clustering and improved relational analysis

NASA Astrophysics Data System (ADS)

Meng, Xiaocheng; Che, Renfei; Gao, Shi; He, Juntao

2018-04-01

With the advent of large data age, power system research has entered a new stage. At present, the main application of large data in the power system is the early warning analysis of the power equipment, that is, by collecting the relevant historical fault data information, the system security is improved by predicting the early warning and failure rate of different kinds of equipment under certain relational factors. In this paper, a method of line failure rate warning is proposed. Firstly, fuzzy dynamic clustering is carried out based on the collected historical information. Considering the imbalance between the attributes, the coefficient of variation is given to the corresponding weights. And then use the weighted fuzzy clustering to deal with the data more effectively. Then, by analyzing the basic idea and basic properties of the relational analysis model theory, the gray relational model is improved by combining the slope and the Deng model. And the incremental composition and composition of the two sequences are also considered to the gray relational model to obtain the gray relational degree between the various samples. The failure rate is predicted according to the principle of weighting. Finally, the concrete process is expounded by an example, and the validity and superiority of the proposed method are verified.

A Novel Energy-Aware Distributed Clustering Algorithm for Heterogeneous Wireless Sensor Networks in the Mobile Environment

PubMed Central

Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong

2015-01-01

In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network’s running and the degree of candidate nodes’ effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime. PMID:26690440

Time series of low-degree geopotential coefficients from SLR data: estimation of Earth's figure axis and LOD variations

NASA Astrophysics Data System (ADS)

Luceri, V.; Sciarretta, C.; Bianco, G.

2012-12-01

The redistribution of the mass within the earth system induces changes in the Earth's gravity field. In particular, the second-degree geopotential coefficients reflect the behaviour of the Earth's inertia tensor of order 2, describing the main mass variations of our planet impacting the EOPs. Thanks to the long record of accurate and continuous laser ranging observations to Lageos and other geodetic satellites, SLR is the only current space technique capable to monitor the long time variability of the Earth's gravity field with adequate accuracy. Time series of low-degree geopotential coefficients are estimated with our analysis of SLR data (spanning more than 25 years) from several geodetic satellites in order to detect trends and periodic variations related to tidal effects and atmospheric/oceanic mass variations. This study is focused on the variations of the second-degree Stokes coefficients related to the Earth's principal figure axis and oblateness: C21, S21 and C20. On the other hand, surface mass load variations induce excitations in the EOPs that are proportional to the same second-degree coefficients. The time series of direct estimates of low degree geopotential and those derived from the EOP excitation functions are compared and presented together with their time and frequency analysis.

Catchment classification by runoff behaviour with self-organizing maps (SOM)

NASA Astrophysics Data System (ADS)

Ley, R.; Casper, M. C.; Hellebrand, H.; Merz, R.

2011-09-01

Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

Catchment classification by runoff behaviour with self-organizing maps (SOM)

NASA Astrophysics Data System (ADS)

Ley, R.; Casper, M. C.; Hellebrand, H.; Merz, R.

2011-03-01

Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

Estimating Geocenter Motion and Changes in the Earth's Dynamic Oblateness from a Statistically Optimal Combination of GRACE Data and Geophysical Models

NASA Astrophysics Data System (ADS)

Sun, Y.; Ditmar, P.; Riva, R.

2016-12-01

Time-varying gravity field solutions of the GRACE satellite mission enable an observation of Earth's mass transport on a monthly basis since 2002. One of the remaining challenges is how to complement these solutions with sufficiently accurate estimates of very low-degree spherical harmonic coefficients, particularly degree-1 coefficients and C20. An absence or inaccurate estimation of these coefficients may result in strong biases in mass transports estimates. Variations in degree-1 coefficients reflect geocenter motion and variations in the C20coefficients describe changes in the Earth's dynamic oblateness (ΔJ2). In this study, we developed a novel methodology to estimate monthly variations in degree-1 and C20coefficients by combing GRACE data with oceanic mass anomalies (combination approach). Unlike the method by Swenson et al. (2008), the proposed approach exploits noise covariance information of both input datasets and thus produces stochastically optimal solutions. A numerical simulation study is carried out to verify the correctness and performance of the proposed approach. We demonstrate that solutions obtained with the proposed approach have a significantly higher quality, as compared to the method by Swenson et al. Finally, we apply the proposed approach to real monthly GRACE solutions. To evaluate the obtained results, we calculate mass transport time-series over selected regions where minimal mass anomalies are expected. A clear reduction in the RMS of the mass transport time-series (more than 50 %) is observed there when the degree-1 and C20 coefficients obtained with the proposed approach are used. In particular, the seasonal pattern in the mass transport time-series disappears almost entirely. The traditional approach (degree-1 coefficients based on Swenson et al. (2008) and C20 based on SLR data), in contrast, does not reduce that RMS or even makes it larger (e.g., over the Sahara desert). We further show that the degree-1 variations play a major role in the observed improvement. At the same time, the usage of the C20 solutions obtained with the combination approach yields a similar accuracy of mass anomaly estimates, as compared to the results based on SLR analysis. The computed degree-1 and C20 coefficients will be made publicly available.

Clustering stocks using partial correlation coefficients

NASA Astrophysics Data System (ADS)

Jung, Sean S.; Chang, Woojin

2016-11-01

A partial correlation analysis is performed on the Korean stock market (KOSPI). The difference between Pearson correlation and the partial correlation is analyzed and it is found that when conditioned on the market return, Pearson correlation coefficients are generally greater than those of the partial correlation, which implies that the market return tends to drive up the correlation between stock returns. A clustering analysis is then performed to study the market structure given by the partial correlation analysis and the members of the clusters are compared with the Global Industry Classification Standard (GICS). The initial hypothesis is that the firms in the same GICS sector are clustered together since they are in a similar business and environment. However, the result is inconsistent with the hypothesis and most clusters are a mix of multiple sectors suggesting that the traditional approach of using sectors to determine the proximity between stocks may not be sufficient enough to diversify a portfolio.

Analysis of ligand-protein exchange by Clustering of Ligand Diffusion Coefficient Pairs (CoLD-CoP)

NASA Astrophysics Data System (ADS)

Snyder, David A.; Chantova, Mihaela; Chaudhry, Saadia

2015-06-01

NMR spectroscopy is a powerful tool in describing protein structures and protein activity for pharmaceutical and biochemical development. This study describes a method to determine weak binding ligands in biological systems by using hierarchic diffusion coefficient clustering of multidimensional data obtained with a 400 MHz Bruker NMR. Comparison of DOSY spectrums of ligands of the chemical library in the presence and absence of target proteins show translational diffusion rates for small molecules upon interaction with macromolecules. For weak binders such as compounds found in fragment libraries, changes in diffusion rates upon macromolecular binding are on the order of the precision of DOSY diffusion measurements, and identifying such subtle shifts in diffusion requires careful statistical analysis. The "CoLD-CoP" (Clustering of Ligand Diffusion Coefficient Pairs) method presented here uses SAHN clustering to identify protein-binders in a chemical library or even a not fully characterized metabolite mixture. We will show how DOSY NMR and the "CoLD-CoP" method complement each other in identifying the most suitable candidates for lysozyme and wheat germ acid phosphatase.

Deterministic Joint Remote Preparation of an Arbitrary Sevenqubit Cluster-type State

NASA Astrophysics Data System (ADS)

Ding, MengXiao; Jiang, Min

2017-06-01

In this paper, we propose a scheme for joint remotely preparing an arbitrary seven-qubit cluster-type state by using several GHZ entangled states as the quantum channel. The coefficients of the prepared states can be not only real, but also complex. Firstly, Alice performs a three-qubit projective measurement according to the amplitude coefficients of the target state, and then Bob carries out another three-qubit projective measurement based on its phase coefficients. Next, one three-qubit state containing all information of the target state is prepared with suitable operation. Finally, the target seven-qubit cluster-type state can be prepared by introducing four auxiliary qubits and performing appropriate local unitary operations based on the prepared three-qubit state in a deterministic way. The receiver's all recovery operations are summarized into a concise formula. Furthermore, it's worth noting that our scheme is more novel and feasible with the present technologies than most other previous schemes.

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

PubMed

Kim, Jaehee

2011-01-01

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

Transmission Loss Calculation using A and B Loss Coefficients in Dynamic Economic Dispatch Problem

NASA Astrophysics Data System (ADS)

Jethmalani, C. H. Ram; Dumpa, Poornima; Simon, Sishaj P.; Sundareswaran, K.

2016-04-01

This paper analyzes the performance of A-loss coefficients while evaluating transmission losses in a Dynamic Economic Dispatch (DED) Problem. The performance analysis is carried out by comparing the losses computed using nominal A loss coefficients and nominal B loss coefficients in reference with load flow solution obtained by standard Newton-Raphson (NR) method. Density based clustering method based on connected regions with sufficiently high density (DBSCAN) is employed in identifying the best regions of A and B loss coefficients. Based on the results obtained through cluster analysis, a novel approach in improving the accuracy of network loss calculation is proposed. Here, based on the change in per unit load values between the load intervals, loss coefficients are updated for calculating the transmission losses. The proposed algorithm is tested and validated on IEEE 6 bus system, IEEE 14 bus, system IEEE 30 bus system and IEEE 118 bus system. All simulations are carried out using SCILAB 5.4 (www.scilab.org) which is an open source software.

Assessment of genetic diversity of Bermudagrass (Cynodon dactylon) using ISSR markers.

PubMed

Farsani, Tayebeh Mohammadi; Etemadi, Nematollah; Sayed-Tabatabaei, Badraldin Ebrahim; Talebi, Majid

2012-01-01

Bermudagrass (Cynodon spp.) is a major turfgrass for home lawns, public parks, golf courses and sport fields and is known to have originated in the Middle East. Morphological and physiological characteristics are not sufficient to differentiate some bermudagrass genotypes because the differences between them are often subtle and subjected to environmental influences. In this study, twenty seven bermudagrass accessions and introductions, mostly from different parts of Iran, were assayed by inter-simple sequence repeat (ISSR) markers to differentiate and explore their genetic relationships. Fourteen ISSR primers amplified 389 fragments of which 313 (80.5%) were polymorphic. The average polymorphism information content (PIC) was 0.328, which shows that the majority of primers are informative. Cluster analysis using the un-weighted paired group method with arithmetic average (UPGMA) method and Jaccard's similarity coefficient (r = 0.828) grouped the accessions into six main clusters according to some degree to geographical origin, their chromosome number and some morphological characteristics. It can be concluded that there exists a wide genetic base of bermudograss in Iran and that ISSR markers are effective in determining genetic diversity and relationships among them.

Assessment of Genetic Diversity of Bermudagrass (Cynodon dactylon) Using ISSR Markers

PubMed Central

Farsani, Tayebeh Mohammadi; Etemadi, Nematollah; Sayed-Tabatabaei, Badraldin Ebrahim; Talebi, Majid

2012-01-01

Bermudagrass (Cynodon spp.) is a major turfgrass for home lawns, public parks, golf courses and sport fields and is known to have originated in the Middle East. Morphological and physiological characteristics are not sufficient to differentiate some bermudagrass genotypes because the differences between them are often subtle and subjected to environmental influences. In this study, twenty seven bermudagrass accessions and introductions, mostly from different parts of Iran, were assayed by inter-simple sequence repeat (ISSR) markers to differentiate and explore their genetic relationships. Fourteen ISSR primers amplified 389 fragments of which 313 (80.5%) were polymorphic. The average polymorphism information content (PIC) was 0.328, which shows that the majority of primers are informative. Cluster analysis using the un-weighted paired group method with arithmetic average (UPGMA) method and Jaccard’s similarity coefficient (r = 0.828) grouped the accessions into six main clusters according to some degree to geographical origin, their chromosome number and some morphological characteristics. It can be concluded that there exists a wide genetic base of bermudograss in Iran and that ISSR markers are effective in determining genetic diversity and relationships among them. PMID:22312259

Estimation of the prevalence of adverse drug reactions from social media.

PubMed

Nguyen, Thin; Larsen, Mark E; O'Dea, Bridianne; Phung, Dinh; Venkatesh, Svetha; Christensen, Helen

2017-06-01

This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora. Agreement between these measures for a sample of ten popular psychiatric drugs was evaluated using the Pearson correlation coefficient, r, with values between 0.08 and 0.50. Word2vec, a novel neural learning framework, was utilized to improve the coverage of variants of ADR terms in the unstructured text by identifying syntactically or semantically similar terms. Improved correlation coefficients, between 0.29 and 0.59, demonstrates the capability of advanced techniques in machine learning to aid in the discovery of meaningful patterns from medical data, and social media data, at scale. Copyright © 2017 Elsevier B.V. All rights reserved.

["The severe degree of negligence" and its application in the settle of medical malpractice].

PubMed

Wang, You-Min; Zhang, Qin-Chu

2006-04-01

To found the quantifiable index of "The severe degree of negligence" in describing the general severity degree of medical malpractice or medical dispute. "The severe degree of negligence" can be calculated by the way of multiplying the coefficient of medical malpractice's grade by the coefficient of responsibility degree. There are 15 grades of "The severe degree of negligence" through calculation, from the severest degree of 1 to the lightest degree of 20. "The severe degree of negligence" can give an order of severe degree to different grade and different responsibility of medical malpractice. According to this order, the operation of medical malpractice and medical dispute settle will be easier and more rationality.

A Symmetric Time-Varying Cluster Rate of Descent Model

NASA Technical Reports Server (NTRS)

Ray, Eric S.

2015-01-01

A model of the time-varying rate of descent of the Orion vehicle was developed based on the observed correlation between canopy projected area and drag coefficient. This initial version of the model assumes cluster symmetry and only varies the vertical component of velocity. The cluster fly-out angle is modeled as a series of sine waves based on flight test data. The projected area of each canopy is synchronized with the primary fly-out angle mode. The sudden loss of projected area during canopy collisions is modeled at minimum fly-out angles, leading to brief increases in rate of descent. The cluster geometry is converted to drag coefficient using empirically derived constants. A more complete model is under development, which computes the aerodynamic response of each canopy to its local incidence angle.

Subspace Clustering via Learning an Adaptive Low-Rank Graph.

PubMed

Yin, Ming; Xie, Shengli; Wu, Zongze; Zhang, Yun; Gao, Junbin

2018-08-01

By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients are not the exact ones as the traditional definition is in a deterministic way. The two steps of representation and clustering are conducted in an independent manner, thus an overall optimal result cannot be guaranteed. Furthermore, it is unclear how the clustering performance will be affected by using this graph. For example, the graph parameters, i.e., the weights on edges, have to be artificially pre-specified while it is very difficult to choose the optimum. To this end, in this paper, a novel subspace clustering via learning an adaptive low-rank graph affinity matrix is proposed, where the affinity matrix and the representation coefficients are learned in a unified framework. As such, the pre-computed graph regularizer is effectively obviated and better performance can be achieved. Experimental results on several famous databases demonstrate that the proposed method performs better against the state-of-the-art approaches, in clustering.

Mobility of large clusters on a semiconductor surface: Kinetic Monte Carlo simulation results

NASA Astrophysics Data System (ADS)

M, Esen; A, T. Tüzemen; M, Ozdemir

2016-01-01

The mobility of clusters on a semiconductor surface for various values of cluster size is studied as a function of temperature by kinetic Monte Carlo method. The cluster resides on the surface of a square grid. Kinetic processes such as the diffusion of single particles on the surface, their attachment and detachment to/from clusters, diffusion of particles along cluster edges are considered. The clusters considered in this study consist of 150-6000 atoms per cluster on average. A statistical probability of motion to each direction is assigned to each particle where a particle with four nearest neighbors is assumed to be immobile. The mobility of a cluster is found from the root mean square displacement of the center of mass of the cluster as a function of time. It is found that the diffusion coefficient of clusters goes as D = A(T)Nα where N is the average number of particles in the cluster, A(T) is a temperature-dependent constant and α is a parameter with a value of about -0.64 < α < -0.75. The value of α is found to be independent of cluster sizes and temperature values (170-220 K) considered in this study. As the diffusion along the perimeter of the cluster becomes prohibitive, the exponent approaches a value of -0.5. The diffusion coefficient is found to change by one order of magnitude as a function of cluster size.

New approaches to model and study social networks

NASA Astrophysics Data System (ADS)

Lind, P. G.; Herrmann, H. J.

2007-07-01

We describe and develop three recent novelties in network research which are particularly useful for studying social systems. The first one concerns the discovery of some basic dynamical laws that enable the emergence of the fundamental features observed in social networks, namely the nontrivial clustering properties, the existence of positive degree correlations and the subdivision into communities. To reproduce all these features, we describe a simple model of mobile colliding agents, whose collisions define the connections between the agents which are the nodes in the underlying network, and develop some analytical considerations. The second point addresses the particular feature of clustering and its relationship with global network measures, namely with the distribution of the size of cycles in the network. Since in social bipartite networks it is not possible to measure the clustering from standard procedures, we propose an alternative clustering coefficient that can be used to extract an improved normalized cycle distribution in any network. Finally, the third point addresses dynamical processes occurring on networks, namely when studying the propagation of information in them. In particular, we focus on the particular features of gossip propagation which impose some restrictions in the propagation rules. To this end we introduce a quantity, the spread factor, which measures the average maximal fraction of nearest neighbours which get in contact with the gossip, and find the striking result that there is an optimal non-trivial number of friends for which the spread factor is minimized, decreasing the danger of being gossiped about.

The Nonlinear Jaynes-Cummings Model for the Multiphoton Transition

NASA Astrophysics Data System (ADS)

Liu, Xiao-Jing; Lu, Jing-Bin; Zhang, Si-Qi; Liu, Ji-Ping; Li, Hong; Liang, Yu; Ma, Ji; Weng, Yi-Jiao; Zhang, Qi-Rui; Liu, Han; Zhang, Xiao-Ru; Wu, Xiang-Yao

2018-01-01

With the nonlinear Jaynes-Cummings model, we have studied the atom and light field quantum entanglement of multiphoton transition in nonlinear medium, and researched the effect of the transition photon number N and the nonlinear coefficient χ on the quantum entanglement degrees. We have given the quantum entanglement degrees curves with time evolution, we find when the transition photon number N increases, the entanglement degrees oscillation get faster. When the nonlinear coefficient α > 0, the entanglement degrees oscillation get quickly, the nonlinear term is disadvantage of the atom and light field entanglement, and when the nonlinear coefficient α < 0, the entanglement degrees oscillation get slow, the nonlinear term is advantage of the atom and light field entanglement. These results will have been used in the quantum communication and quantum information.

Effective temperatures and the breakdown of the Stokes-Einstein relation for particle suspensions.

PubMed

Mendoza, Carlos I; Santamaría-Holek, I; Pérez-Madrid, A

2015-09-14

The short- and long-time breakdown of the classical Stokes-Einstein relation for colloidal suspensions at arbitrary volume fractions is explained here by examining the role that confinement and attractive interactions play in the intra- and inter-cage dynamics executed by the colloidal particles. We show that the measured short-time diffusion coefficient is larger than the one predicted by the classical Stokes-Einstein relation due to a non-equilibrated energy transfer between kinetic and configuration degrees of freedom. This transfer can be incorporated in an effective kinetic temperature that is higher than the temperature of the heat bath. We propose a Generalized Stokes-Einstein relation (GSER) in which the effective temperature replaces the temperature of the heat bath. This relation then allows to obtain the diffusion coefficient once the viscosity and the effective temperature are known. On the other hand, the temporary cluster formation induced by confinement and attractive interactions of hydrodynamic nature makes the long-time diffusion coefficient to be smaller than the corresponding one obtained from the classical Stokes-Einstein relation. Then, the use of the GSER allows to obtain an effective temperature that is smaller than the temperature of the heat bath. Additionally, we provide a simple expression based on a differential effective medium theory that allows to calculate the diffusion coefficient at short and long times. Comparison of our results with experiments and simulations for suspensions of hard and porous spheres shows an excellent agreement in all cases.

Improvement of the SEP protocol based on community structure of node degree

NASA Astrophysics Data System (ADS)

Li, Donglin; Wei, Suyuan

2017-05-01

Analyzing the Stable election protocol (SEP) in wireless sensor networks and aiming at the problem of inhomogeneous cluster-heads distribution and unreasonable cluster-heads selectivity and single hop transmission in the SEP, a SEP Protocol based on community structure of node degree (SEP-CSND) is proposed. In this algorithm, network node deployed by using grid deployment model, and the connection between nodes established by setting up the communication threshold. The community structure constructed by node degree, then cluster head is elected in the community structure. On the basis of SEP, the node's residual energy and node degree is added in cluster-heads election. The information is transmitted with mode of multiple hops between network nodes. The simulation experiments showed that compared to the classical LEACH and SEP, this algorithm balances the energy consumption of the entire network and significantly prolongs network lifetime.

Long-range correlations improve understanding of the influence of network structure on contact dynamics.

PubMed

Peyrard, N; Dieckmann, U; Franc, A

2008-05-01

Models of infectious diseases are characterized by a phase transition between extinction and persistence. A challenge in contemporary epidemiology is to understand how the geometry of a host's interaction network influences disease dynamics close to the critical point of such a transition. Here we address this challenge with the help of moment closures. Traditional moment closures, however, do not provide satisfactory predictions close to such critical points. We therefore introduce a new method for incorporating longer-range correlations into existing closures. Our method is technically simple, remains computationally tractable and significantly improves the approximation's performance. Our extended closures thus provide an innovative tool for quantifying the influence of interaction networks on spatially or socially structured disease dynamics. In particular, we examine the effects of a network's clustering coefficient, as well as of new geometrical measures, such as a network's square clustering coefficients. We compare the relative performance of different closures from the literature, with or without our long-range extension. In this way, we demonstrate that the normalized version of the Bethe approximation-extended to incorporate long-range correlations according to our method-is an especially good candidate for studying influences of network structure. Our numerical results highlight the importance of the clustering coefficient and the square clustering coefficient for predicting disease dynamics at low and intermediate values of transmission rate, and demonstrate the significance of path redundancy for disease persistence.

The optimal design of stepped wedge trials with equal allocation to sequences and a comparison to other trial designs.

PubMed

Thompson, Jennifer A; Fielding, Katherine; Hargreaves, James; Copas, Andrew

2017-12-01

Background/Aims We sought to optimise the design of stepped wedge trials with an equal allocation of clusters to sequences and explored sample size comparisons with alternative trial designs. Methods We developed a new expression for the design effect for a stepped wedge trial, assuming that observations are equally correlated within clusters and an equal number of observations in each period between sequences switching to the intervention. We minimised the design effect with respect to (1) the fraction of observations before the first and after the final sequence switches (the periods with all clusters in the control or intervention condition, respectively) and (2) the number of sequences. We compared the design effect of this optimised stepped wedge trial to the design effects of a parallel cluster-randomised trial, a cluster-randomised trial with baseline observations, and a hybrid trial design (a mixture of cluster-randomised trial and stepped wedge trial) with the same total cluster size for all designs. Results We found that a stepped wedge trial with an equal allocation to sequences is optimised by obtaining all observations after the first sequence switches and before the final sequence switches to the intervention; this means that the first sequence remains in the control condition and the last sequence remains in the intervention condition for the duration of the trial. With this design, the optimal number of sequences is [Formula: see text], where [Formula: see text] is the cluster-mean correlation, [Formula: see text] is the intracluster correlation coefficient, and m is the total cluster size. The optimal number of sequences is small when the intracluster correlation coefficient and cluster size are small and large when the intracluster correlation coefficient or cluster size is large. A cluster-randomised trial remains more efficient than the optimised stepped wedge trial when the intracluster correlation coefficient or cluster size is small. A cluster-randomised trial with baseline observations always requires a larger sample size than the optimised stepped wedge trial. The hybrid design can always give an equally or more efficient design, but will be at most 5% more efficient. We provide a strategy for selecting a design if the optimal number of sequences is unfeasible. For a non-optimal number of sequences, the sample size may be reduced by allowing a proportion of observations before the first or after the final sequence has switched. Conclusion The standard stepped wedge trial is inefficient. To reduce sample sizes when a hybrid design is unfeasible, stepped wedge trial designs should have no observations before the first sequence switches or after the final sequence switches.

Network-based study of Lagrangian transport and mixing

NASA Astrophysics Data System (ADS)

Padberg-Gehle, Kathrin; Schneide, Christiane

2017-10-01

Transport and mixing processes in fluid flows are crucially influenced by coherent structures and the characterization of these Lagrangian objects is a topic of intense current research. While established mathematical approaches such as variational methods or transfer-operator-based schemes require full knowledge of the flow field or at least high-resolution trajectory data, this information may not be available in applications. Recently, different computational methods have been proposed to identify coherent behavior in flows directly from Lagrangian trajectory data, that is, numerical or measured time series of particle positions in a fluid flow. In this context, spatio-temporal clustering algorithms have been proven to be very effective for the extraction of coherent sets from sparse and possibly incomplete trajectory data. Inspired by these recent approaches, we consider an unweighted, undirected network, where Lagrangian particle trajectories serve as network nodes. A link is established between two nodes if the respective trajectories come close to each other at least once in the course of time. Classical graph concepts are then employed to analyze the resulting network. In particular, local network measures such as the node degree, the average degree of neighboring nodes, and the clustering coefficient serve as indicators of highly mixing regions, whereas spectral graph partitioning schemes allow us to extract coherent sets. The proposed methodology is very fast to run and we demonstrate its applicability in two geophysical flows - the Bickley jet as well as the Antarctic stratospheric polar vortex.

Self-government of complex reading and writing brains informed by cingulo-opercular network for adaptive control and working memory components for language learning.

PubMed

Richards, Todd L; Abbott, Robert D; Yagle, Kevin; Peterson, Dan; Raskind, Wendy; Berninger, Virginia W

2017-01-01

To understand mental self-government of the developing reading and writing brain, correlations of clustering coefficients on fMRI reading or writing tasks with BASC 2 Adaptivity ratings (time 1 only) or working memory components (time 1 before and time 2 after instruction previously shown to improve achievement and change magnitude of fMRI connectivity) were investigated in 39 students in grades 4 to 9 who varied along a continuum of reading and writing skills. A Philips 3T scanner measured connectivity during six leveled fMRI reading tasks (subword-letters and sounds, word-word-specific spellings or affixed words, syntax comprehension-with and without homonym foils or with and without affix foils, and text comprehension) and three fMRI writing tasks-writing next letter in alphabet, adding missing letter in word spelling, and planning for composing. The Brain Connectivity Toolbox generated clustering coefficients based on the cingulo-opercular (CO) network; after controlling for multiple comparisons and movement, significant fMRI connectivity clustering coefficients for CO were identified in 8 brain regions bilaterally (cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, superior temporal gyrus, insula, cingulum-cingulate gyrus, and cingulum-hippocampus). BASC2 Parent Ratings for Adaptivity were correlated with CO clustering coefficients on three reading tasks (letter-sound, word affix judgments and sentence comprehension) and one writing task (writing next letter in alphabet). Before instruction, each behavioral working memory measure (phonology, orthography, morphology, and syntax coding, phonological and orthographic loops for integrating internal language and output codes, and supervisory focused and switching attention) correlated significantly with at least one CO clustering coefficient. After instruction, the patterning of correlations changed with new correlations emerging. Results show that the reading and writing brain's mental government, supported by both CO Adaptive Control and multiple working memory components, had changed in response to instruction during middle childhood/early adolescence.

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

Tai, Lin-Ru; Chou, Chang-Wei; Lee, I-Fang

In this study, we used a multiple copy (EGFP){sub 3} reporter system to establish a numeric nuclear index system to assess the degree of nuclear import. The system was first validated by a FRAP assay, and then was applied to evaluate the essential and multifaceted nature of basic amino acid clusters during the nuclear import of ribosomal protein L7. The results indicate that the sequence context of the basic cluster determines the degree of nuclear import, and that the number of basic residues in the cluster is irrelevant; rather the position of the pertinent basic residues is crucial. Moreover, itmore » also found that the type of carrier protein used by basic cluster has a great impact on the degree of nuclear import. In case of L7, importin β2 or importin β3 are preferentially used by clusters with a high import efficiency, notwithstanding that other importins are also used by clusters with a weaker level of nuclear import. Such a preferential usage of multiple basic clusters and importins to gain nuclear entry would seem to be a common practice among ribosomal proteins in order to ensure their full participation in high rate ribosome synthesis. - Highlights: ► We introduce a numeric index system that represents the degree of nuclear import. ► The rate of nuclear import is dictated by the sequence context of the basic cluster. ► Importin β2 and β3 were mainly responsible for the N4 mediated nuclear import.« less

A generalized approach to complex networks

NASA Astrophysics Data System (ADS)

Costa, L. Da F.; da Rocha, L. E. C.

2006-03-01

This work describes how the formalization of complex network concepts in terms of discrete mathematics, especially mathematical morphology, allows a series of generalizations and important results ranging from new measurements of the network topology to new network growth models. First, the concepts of node degree and clustering coefficient are extended in order to characterize not only specific nodes, but any generic subnetwork. Second, the consideration of distance transform and rings are used to further extend those concepts in order to obtain a signature, instead of a single scalar measurement, ranging from the single node to whole graph scales. The enhanced discriminative potential of such extended measurements is illustrated with respect to the identification of correspondence between nodes in two complex networks, namely a protein-protein interaction network and a perturbed version of it.

Application of fuzzy c-means clustering to PRTR chemicals uncovering their release and toxicity characteristics.

PubMed

Xue, Mianqiang; Zhou, Liang; Kojima, Naoya; Dos Muchangos, Leticia Sarmento; Machimura, Takashi; Tokai, Akihiro

2018-05-01

Increasing manufacture and usage of chemicals have not been matched by the increase in our understanding of their risks. Pollutant release and transfer register (PRTR) is becoming a popular measure for collecting chemical data and enhancing the public right to know. However, these data are usually in high dimensionality which restricts their wider use. The present study partitions Japanese PRTR chemicals into five fuzzy clusters by fuzzy c-mean clustering (FCM) to explore the implicit information. Each chemical with membership degrees belongs to each cluster. Cluster I features high releases from non-listed industries and the household sector and high environmental toxicity. Cluster II is characterized by high reported releases and transfers from 24 listed industries above the threshold, mutagenicity, and high environmental toxicity. Chemicals in cluster III have characteristics of high releases from non-listed industries and low toxicity. Cluster IV is characterized by high reported releases and transfers from 24 listed industries above the threshold and extremely high environmental toxicity. Cluster V is characterized by low releases yet mutagenicity and high carcinogenicity. Chemicals with the highest membership degree were identified as representatives for each cluster. For the highest membership degree, half of the chemicals have a value higher than 0.74. If we look at both the highest and the second highest membership degrees simultaneously, about 94% of the chemicals have a value higher than 0.5. FCM can serve as an approach to uncover the implicit information of highly complex chemical dataset, which subsequently supports the strategy development for efficient and effective chemical management. Copyright © 2017 Elsevier B.V. All rights reserved.

DENBRAN: A basic program for a significance test for multivariate normality of clusters from branching patterns in dendrograms

NASA Astrophysics Data System (ADS)

Sneath, P. H. A.

A BASIC program is presented for significance tests to determine whether a dendrogram is derived from clustering of points that belong to a single multivariate normal distribution. The significance tests are based on statistics of the Kolmogorov—Smirnov type, obtained by comparing the observed cumulative graph of branch levels with a graph for the hypothesis of multivariate normality. The program also permits testing whether the dendrogram could be from a cluster of lower dimensionality due to character correlations. The program makes provision for three similarity coefficients, (1) Euclidean distances, (2) squared Euclidean distances, and (3) Simple Matching Coefficients, and for five cluster methods (1) WPGMA, (2) UPGMA, (3) Single Linkage (or Minimum Spanning Trees), (4) Complete Linkage, and (5) Ward's Increase in Sums of Squares. The program is entitled DENBRAN.

Greedy bases in rank 2 quantum cluster algebras

PubMed Central

Lee, Kyungyong; Li, Li; Rupel, Dylan; Zelevinsky, Andrei

2014-01-01

We identify a quantum lift of the greedy basis for rank 2 coefficient-free cluster algebras. Our main result is that our construction does not depend on the choice of initial cluster, that it builds all cluster monomials, and that it produces bar-invariant elements. We also present several conjectures related to this quantum greedy basis and the triangular basis of Berenstein and Zelevinsky. PMID:24982182

New atlas of open star clusters

NASA Astrophysics Data System (ADS)

Seleznev, Anton F.; Avvakumova, Ekaterina; Kulesh, Maxim; Filina, Julia; Tsaregorodtseva, Polina; Kvashnina, Alvira

2017-11-01

Due to numerous new discoveries of open star clusters in the last two decades, astronomers need an easy-touse resource to get visual information on the relative position of clusters in the sky. Therefore we propose a new atlas of open star clusters. It is based on a table compiled from the largest modern cluster catalogues. The atlas shows the positions and sizes of 3291 clusters and associations, and consists of two parts. The first contains 108 maps of 12 by 12 degrees with an overlapping of 2 degrees in three strips along the Galactic equator. The second one is an online web application, which shows a square field of an arbitrary size, either in equatorial coordinates or in galactic coordinates by request. The atlas is proposed for the sampling of clusters and cluster stars for further investigation. Another use is the identification of clusters among overdensities in stellar density maps or among stellar groups in images of the sky.

Analysis of ligand-protein exchange by Clustering of Ligand Diffusion Coefficient Pairs (CoLD-CoP).

PubMed

Snyder, David A; Chantova, Mihaela; Chaudhry, Saadia

2015-06-01

NMR spectroscopy is a powerful tool in describing protein structures and protein activity for pharmaceutical and biochemical development. This study describes a method to determine weak binding ligands in biological systems by using hierarchic diffusion coefficient clustering of multidimensional data obtained with a 400 MHz Bruker NMR. Comparison of DOSY spectrums of ligands of the chemical library in the presence and absence of target proteins show translational diffusion rates for small molecules upon interaction with macromolecules. For weak binders such as compounds found in fragment libraries, changes in diffusion rates upon macromolecular binding are on the order of the precision of DOSY diffusion measurements, and identifying such subtle shifts in diffusion requires careful statistical analysis. The "CoLD-CoP" (Clustering of Ligand Diffusion Coefficient Pairs) method presented here uses SAHN clustering to identify protein-binders in a chemical library or even a not fully characterized metabolite mixture. We will show how DOSY NMR and the "CoLD-CoP" method complement each other in identifying the most suitable candidates for lysozyme and wheat germ acid phosphatase. Copyright © 2015 Elsevier Inc. All rights reserved.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Differences Between Ward's and UPGMA Methods of Cluster Analysis: Implications for School Psychology.

ERIC Educational Resources Information Center

Hale, Robert L.; Dougherty, Donna

1988-01-01

Compared the efficacy of two methods of cluster analysis, the unweighted pair-groups method using arithmetic averages (UPGMA) and Ward's method, for students grouped on intelligence, achievement, and social adjustment by both clustering methods. Found UPGMA more efficacious based on output, on cophenetic correlation coefficients generated by each…

Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis

PubMed Central

Iyer, Parameswaran Mahadeva; Egan, Catriona; Pinto-Grau, Marta; Burke, Tom; Elamin, Marwa; Nasseroleslami, Bahman; Pender, Niall; Lalor, Edmund C.; Hardiman, Orla

2015-01-01

Background Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS. Methods 18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity. Results Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05). Discussion There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS. PMID:26091258

Parameterization of Keeling's network generation algorithm.

PubMed

Badham, Jennifer; Abbass, Hussein; Stocker, Rob

2008-09-01

Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.

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

PubMed

MacGregor, James N

2015-10-01

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

Theory of scattering of electromagnetic waves of the microwave range in a turbid medium

NASA Astrophysics Data System (ADS)

Konstantinov, O. V.; Matveentsev, A. V.

2013-02-01

The coefficient of extinction of electromagnetic waves of the microwave range due to their scattering from clusters suspended in an amorphous medium and responsible for turbidity is calculated. Turbidity resembles the case when butter clusters transform water into milk. In the case under investigation, the clusters are conductors (metallic or semiconducting). The extinction coefficient is connected in a familiar way with the cross section of light scattering from an individual cluster. A new formula is derived for the light scattering cross section in the case when damping of oscillations of an electron is due only to spontaneous emission of light quanta. In this case, the resonant scattering cross section for light can be very large. It is shown that this can be observed only in a whisker nanocluster. In addition, the phonon energy on a whisker segment must be higher than the photon energy, which is close to the spacing between the electron energy levels in the cluster.

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study.

PubMed

Nguyen, Huyen T; Shah, Zarine K; Mortazavi, Amir; Pohar, Kamal S; Wei, Lai; Jia, Guang; Zynger, Debra L; Knopp, Michael V

2017-05-01

To quantify the heterogeneity of the tumour apparent diffusion coefficient (ADC) using voxel-based analysis to differentiate malignancy from benign wall thickening of the urinary bladder. Nineteen patients with histopathological findings of their cystectomy specimen were included. A data set of voxel-based ADC values was acquired for each patient's lesion. Histogram analysis was performed on each data set to calculate uniformity (U) and entropy (E). The k-means clustering of the voxel-wised ADC data set was implemented to measure mean intra-cluster distance (MICD) and largest inter-cluster distance (LICD). Subsequently, U, E, MICD, and LICD for malignant tumours were compared with those for benign lesions using a two-sample t-test. Eleven patients had pathological confirmation of malignancy and eight with benign wall thickening. Histogram analysis showed that malignant tumours had a significantly higher degree of ADC heterogeneity with lower U (P = 0.016) and higher E (P = 0.005) than benign lesions. In agreement with these findings, k-means clustering of voxel-wise ADC indicated that bladder malignancy presented with significantly higher MICD (P < 0.001) and higher LICD (P = 0.002) than benign wall thickening. The quantitative assessment of tumour diffusion heterogeneity using voxel-based ADC analysis has the potential to become a non-invasive tool to distinguish malignant from benign tissues of urinary bladder cancer. • Heterogeneity is an intrinsic characteristic of tumoral tissue. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information to improve cancer diagnosis accuracy. • Histogram analysis and k-means clustering can quantify tumour diffusion heterogeneity. • The quantification helps differentiate malignant from benign urinary bladder tissue.

Generalization of Clustering Coefficients to Signed Correlation Networks

PubMed Central

Costantini, Giulio; Perugini, Marco

2014-01-01

The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data. PMID:24586367

Sample size calculations for the design of cluster randomized trials: A summary of methodology.

PubMed

Gao, Fei; Earnest, Arul; Matchar, David B; Campbell, Michael J; Machin, David

2015-05-01

Cluster randomized trial designs are growing in popularity in, for example, cardiovascular medicine research and other clinical areas and parallel statistical developments concerned with the design and analysis of these trials have been stimulated. Nevertheless, reviews suggest that design issues associated with cluster randomized trials are often poorly appreciated and there remain inadequacies in, for example, describing how the trial size is determined and the associated results are presented. In this paper, our aim is to provide pragmatic guidance for researchers on the methods of calculating sample sizes. We focus attention on designs with the primary purpose of comparing two interventions with respect to continuous, binary, ordered categorical, incidence rate and time-to-event outcome variables. Issues of aggregate and non-aggregate cluster trials, adjustment for variation in cluster size and the effect size are detailed. The problem of establishing the anticipated magnitude of between- and within-cluster variation to enable planning values of the intra-cluster correlation coefficient and the coefficient of variation are also described. Illustrative examples of calculations of trial sizes for each endpoint type are included. Copyright © 2015 Elsevier Inc. All rights reserved.

Designing and evaluating health systems level hypertension control interventions for African-Americans: lessons from a pooled analysis of three cluster randomized trials.

PubMed

Pavlik, Valory N; Chan, Wenyaw; Hyman, David J; Feldman, Penny; Ogedegbe, Gbenga; Schwartz, Joseph E; McDonald, Margaret; Einhorn, Paula; Tobin, Jonathan N

2015-01-01

African-Americans (AAs) have a high prevalence of hypertension and their blood pressure (BP) control on treatment still lags behind other groups. In 2004, NHLBI funded five projects that aimed to evaluate clinically feasible interventions to effect changes in medical care delivery leading to an increased proportion of AA patients with controlled BP. Three of the groups performed a pooled analysis of trial results to determine: 1) the magnitude of the combined intervention effect; and 2) how the pooled results could inform the methodology for future health-system level BP interventions. Using a cluster randomized design, the trials enrolled AAs with uncontrolled hypertension to test interventions targeting a combination of patient and clinician behaviors. The 12-month Systolic BP (SBP) and Diastolic BP (DBP) effects of intervention or control cluster assignment were assessed using mixed effects longitudinal regression modeling. 2,015 patients representing 352 clusters participated across the three trials. Pooled BP slopes followed a quadratic pattern, with an initial decline, followed by a rise toward baseline, and did not differ significantly between intervention and control clusters: SBP linear coefficient = -2.60±0.21 mmHg per month, p<0.001; quadratic coefficient = 0.167± 0.02 mmHg/month, p<0.001; group by time interaction group by time group x linear time coefficient=0.145 ± 0.293, p=0.622; group x quadratic time coefficient= -0.017 ± 0.026, p=0.525). RESULTS were similar for DBP. The individual sites did not have significant intervention effects when analyzed separately. Investigators planning behavioral trials to improve BP control in health systems serving AAs should plan for small effect sizes and employ a "run-in" period in which BP can be expected to improve in both experimental and control clusters.

Analysis of ehealth search perspectives among female college students in the health professions using Q methodology.

PubMed

Stellefson, Michael; Hanik, Bruce; Chaney, J Don; Tennant, Bethany

2012-04-27

The current "Millennial Generation" of college students majoring in the health professions has unprecedented access to the Internet. Although some research has been initiated among medical professionals to investigate the cognitive basis for health information searches on the Internet, little is known about Internet search practices among health and medical professional students. To systematically identify health professional college student perspectives of personal eHealth search practices. Q methodology was used to examine subjective perspectives regarding personal eHealth search practices among allied health students majoring in a health education degree program. Thirteen (n = 13) undergraduate students were interviewed about their attitudes and experiences conducting eHealth searches. From the interviews, 36 statements were used in a structured ranking task to identify clusters and determine which specific perceptions of eHealth search practices discriminated students into different groups. Scores on an objective measure of eHealth literacy were used to help categorize participant perspectives. Q-technique factor analysis of the rankings identified 3 clusters of respondents with differing views on eHealth searches that generally coincided with participants' objective eHealth literacy scores. The proficient resourceful students (pattern/structure coefficient range 0.56-0.80) described themselves as using multiple resources to obtain eHealth information, as opposed to simply relying on Internet search engines. The intermediate reluctant students (pattern/structure coefficient range 0.75-0.90) reported engaging only Internet search engines to locate eHealth information, citing undeveloped evaluation skills when considering sources of information located on the Internet. Both groups of advanced students reported not knowing how to use Boolean operators to conduct Internet health searches. The basic hubristic students (pattern/structure coefficient range 0.54-0.76) described themselves as independent procrastinators when searching for eHealth information. Interestingly, basic hubristic students represented the only cluster of participants to describe themselves as (1) having received instruction on using the Internet to conduct eHealth searches, and (2) possessing relative confidence when completing a search task. Subjective perspectives of eHealth search practices differed among students possessing different levels of eHealth literacy. These multiple perspectives present both challenges and opportunities for empowering college students in the health professions to use the Internet to obtain and appraise evidence-based health information using the Internet.

Analysis of eHealth Search Perspectives Among Female College Students in the Health Professions Using Q Methodology

PubMed Central

Hanik, Bruce; Chaney, J. Don; Tennant, Bethany

2012-01-01

Background The current “Millennial Generation” of college students majoring in the health professions has unprecedented access to the Internet. Although some research has been initiated among medical professionals to investigate the cognitive basis for health information searches on the Internet, little is known about Internet search practices among health and medical professional students. Objective To systematically identify health professional college student perspectives of personal eHealth search practices. Methods Q methodology was used to examine subjective perspectives regarding personal eHealth search practices among allied health students majoring in a health education degree program. Thirteen (n = 13) undergraduate students were interviewed about their attitudes and experiences conducting eHealth searches. From the interviews, 36 statements were used in a structured ranking task to identify clusters and determine which specific perceptions of eHealth search practices discriminated students into different groups. Scores on an objective measure of eHealth literacy were used to help categorize participant perspectives. Results Q-technique factor analysis of the rankings identified 3 clusters of respondents with differing views on eHealth searches that generally coincided with participants’ objective eHealth literacy scores. The proficient resourceful students (pattern/structure coefficient range 0.56-0.80) described themselves as using multiple resources to obtain eHealth information, as opposed to simply relying on Internet search engines. The intermediate reluctant students (pattern/structure coefficient range 0.75-0.90) reported engaging only Internet search engines to locate eHealth information, citing undeveloped evaluation skills when considering sources of information located on the Internet. Both groups of advanced students reported not knowing how to use Boolean operators to conduct Internet health searches. The basic hubristic students (pattern/structure coefficient range 0.54-0.76) described themselves as independent procrastinators when searching for eHealth information. Interestingly, basic hubristic students represented the only cluster of participants to describe themselves as (1) having received instruction on using the Internet to conduct eHealth searches, and (2) possessing relative confidence when completing a search task. Conclusions Subjective perspectives of eHealth search practices differed among students possessing different levels of eHealth literacy. These multiple perspectives present both challenges and opportunities for empowering college students in the health professions to use the Internet to obtain and appraise evidence-based health information using the Internet. PMID:22543437

Network topology of an experimental futures exchange

NASA Astrophysics Data System (ADS)

Wang, S. C.; Tseng, J. J.; Tai, C. C.; Lai, K. H.; Wu, W. S.; Chen, S. H.; Li, S. P.

2008-03-01

Many systems of different nature exhibit scale free behaviors. Economic systems with power law distribution in the wealth are one of the examples. To better understand the working behind the complexity, we undertook an experiment recording the interactions between market participants. A Web server was setup to administer the exchange of futures contracts whose liquidation prices were coupled to event outcomes. After free registration, participants started trading to compete for the money prizes upon maturity of the futures contracts at the end of the experiment. The evolving `cash' flow network was reconstructed from the transactions between players. We show that the network topology is hierarchical, disassortative and small-world with a power law exponent of 1.02±0.09 in the degree distribution after an exponential decay correction. The small-world property emerged early in the experiment while the number of participants was still small. We also show power law-like distributions of the net incomes and inter-transaction time intervals. Big winners and losers are associated with high degree, high betweenness centrality, low clustering coefficient and low degree-correlation. We identify communities in the network as groups of the like-minded. The distribution of the community sizes is shown to be power-law distributed with an exponent of 1.19±0.16.

Clustering box office movie with Partition Around Medoids (PAM) Algorithm based on Text Mining of Indonesian subtitle

NASA Astrophysics Data System (ADS)

Alfarizy, A. D.; Indahwati; Sartono, B.

2017-03-01

Indonesia is the largest Hollywood movie industry target market in Southeast Asia in 2015. Hollywood movies distributed in Indonesia targeted people in all range of ages including children. Low awareness of guiding children while watching movies make them could watch any rated films even the unsuitable ones for their ages. Even after being translated into Bahasa and passed the censorship phase, words that uncomfortable for children to watch still exist. The purpose of this research is to cluster box office Hollywood movies based on Indonesian subtitle, revenue, IMDb user rating and genres as one of the reference for adults to choose right movies for their children to watch. Text mining is used to extract words from the subtitles and count the frequency for three group of words (bad words, sexual words and terror words), while Partition Around Medoids (PAM) Algorithm with Gower similarity coefficient as proximity matrix is used as clustering method. We clustered 624 movies from 2006 until first half of 2016 from IMDb. Cluster with highest silhouette coefficient value (0.36) is the one with 5 clusters. Animation, Adventure and Comedy movies with high revenue like in cluster 5 is recommended for children to watch, while Comedy movies with high revenue like in cluster 4 should be avoided to watch.

Pressure-Distribution Measurements of a Model of a Davis Wing Section with Fowler Flap Submitted by Consolidated Aircraft Corporation

NASA Technical Reports Server (NTRS)

Abbott, Ira H

1942-01-01

Wing pressure distribution diagrams for several angles of attack and flap deflections of 0 degrees, 20 degrees, and 40 degrees are presented. The normal force coefficients agree with lift coefficients obtained in previous test of the same model, except for the maximum lifts with flap deflection. Pressure distribution measurements were made at Reynolds Number of about 6,000,000.

Scientific authorship and collaboration network analysis on malaria research in Benin: papers indexed in the web of science (1996-2016).

PubMed

Azondekon, Roseric; Harper, Zachary James; Agossa, Fiacre Rodrigue; Welzig, Charles Michael; McRoy, Susan

2018-01-01

To sustain the critical progress made, prioritization and a multidisciplinary approach to malaria research remain important to the national malaria control program in Benin. To document the structure of the malaria collaborative research in Benin, we analyze authorship of the scientific documents published on malaria from Benin. We collected bibliographic data from the Web Of Science on malaria research in Benin from January 1996 to December 2016. From the collected data, a mulitigraph co-authorship network with authors representing vertices was generated. An edge was drawn between two authors when they co-author a paper. We computed vertex degree, betweenness, closeness, and eigenvectors among others to identify prolific authors. We further assess the weak points and how information flow in the network. Finally, we perform a hierarchical clustering analysis, and Monte-Carlo simulations. Overall, 427 publications were included in this study. The generated network contained 1792 authors and 116,388 parallel edges which converted in a weighted graph of 1792 vertices and 95,787 edges. Our results suggested that prolific authors with higher degrees tend to collaborate more. The hierarchical clustering revealed 23 clusters, seven of which form a giant component containing 94% of all the vertices in the network. This giant component has all the characteristics of a small-world network with a small shortest path distance between pairs of three, a diameter of 10 and a high clustering coefficient of 0.964. However, Monte-Carlo simulations suggested our observed network is an unusual type of small-world network. Sixteen vertices were identified as weak articulation points within the network. The malaria research collaboration network in Benin is a complex network that seems to display the characteristics of a small-world network. This research reveals the presence of closed research groups where collaborative research likely happens only between members. Interdisciplinary collaboration tends to occur at higher levels between prolific researchers. Continuously supporting, stabilizing the identified key brokers and most productive authors in the Malaria research collaborative network is an urgent need in Benin. It will foster the malaria research network and ensure the promotion of junior scientists in the field.

Genetic diversity of popcorn genotypes using molecular analysis.

PubMed

Resh, F S; Scapim, C A; Mangolin, C A; Machado, M F P S; do Amaral, A T; Ramos, H C C; Vivas, M

2015-08-19

In this study, we analyzed dominant molecular markers to estimate the genetic divergence of 26 popcorn genotypes and evaluate whether using various dissimilarity coefficients with these dominant markers influences the results of cluster analysis. Fifteen random amplification of polymorphic DNA primers produced 157 amplified fragments, of which 65 were monomorphic and 92 were polymorphic. To calculate the genetic distances among the 26 genotypes, the complements of the Jaccard, Dice, and Rogers and Tanimoto similarity coefficients were used. A matrix of Dij values (dissimilarity matrix) was constructed, from which the genetic distances among genotypes were represented in a more simplified manner as a dendrogram generated using the unweighted pair-group method with arithmetic average. Clusters determined by molecular analysis generally did not group material from the same parental origin together. The largest genetic distance was between varieties 17 (UNB-2) and 18 (PA-091). In the identification of genotypes with the smallest genetic distance, the 3 coefficients showed no agreement. The 3 dissimilarity coefficients showed no major differences among their grouping patterns because agreement in determining the genotypes with large, medium, and small genetic distances was high. The largest genetic distances were observed for the Rogers and Tanimoto dissimilarity coefficient (0.74), followed by the Jaccard coefficient (0.65) and the Dice coefficient (0.48). The 3 coefficients showed similar estimations for the cophenetic correlation coefficient. Correlations among the matrices generated using the 3 coefficients were positive and had high magnitudes, reflecting strong agreement among the results obtained using the 3 evaluated dissimilarity coefficients.

An Empirical Taxonomy of Youths' Fears: Cluster Analysis of the American Fear Survey Schedule

ERIC Educational Resources Information Center

Burnham, Joy J.; Schaefer, Barbara A.; Giesen, Judy

2006-01-01

Fears profiles among children and adolescents were explored using the Fear Survey Schedule for Children-American version (FSSC-AM; J.J. Burnham, 1995, 2005). Eight cluster profiles were identified via multistage Euclidean grouping and supported by homogeneity coefficients and replication. Four clusters reflected overall level of fears (i.e., very…

Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

ERIC Educational Resources Information Center

Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

2013-01-01

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

Theoretical modelling on thermal expansion of Al, Ag and Cu nanomaterials

NASA Astrophysics Data System (ADS)

Manu, Mehul; Dubey, Vikash

2018-05-01

A simple theoretical model is developed for the calculating the coefficient of volume thermal expansion (CTE) and volume thermal expansion (VTE) of Al, Ag and Cu nanomaterials by considering the cubo-octahedral structure with the change of temperature and the cluster size. At the room temperature, the coefficient of volume thermal expansion decreases sharply below 20-25 nm and the decrement of the coefficient of volume thermal expansion becomes slower above 20-25 nm. We also saw a variation in the volume thermal expansion with the variation of temperature and cluster size. At a fixed cluster size, the volume thermal expansion increases with an increase of temperature at below the melting temperature and show a linear relation of volume thermal expansion with the temperature. At a constant temperature, the volume thermal expansion decreases rapidly with an increase in cluster size below 20-25 nm and after 20-25 nm the decrement of volume thermal expansion becomes slower with the increase of the size of the cluster. Thermal expansion is due to the anharmonicity of the atom interaction. As the temperature rises the amplitude of crystal lattice vibration increases, but the equilibrium distance shifts as the atom spend more time at distance greater than the original spacing due as the repulsion at short distance greater than the corresponding attraction at farther distance. In considering the cubo- octahedral structure with the cluster order, the model prediction on the CTE and the VTE are in good agreement with the available experimental data which demonstrate the validity of our work.

Transport coefficients in high-temperature ionized air flows with electronic excitation

NASA Astrophysics Data System (ADS)

Istomin, V. A.; Oblapenko, G. P.

2018-01-01

Transport coefficients are studied in high-temperature ionized air mixtures using the modified Chapman-Enskog method. The 11-component mixture N2/N2+/N /N+/O2/O2+/O /O+/N O /N O+/e- , taking into account the rotational and vibrational degrees of freedom of molecules and electronic degrees of freedom of both atomic and molecular species, is considered. Using the PAINeT software package, developed by the authors of the paper, in wide temperature range calculations of the thermal conductivity, thermal diffusion, diffusion, and shear viscosity coefficients for an equilibrium ionized air mixture and non-equilibrium flow conditions for mixture compositions, characteristic of those in shock tube experiments and re-entry conditions, are performed. For the equilibrium air case, the computed transport coefficients are compared to those obtained using simplified kinetic theory algorithms. It is shown that neglecting electronic excitation leads to a significant underestimation of the thermal conductivity coefficient at temperatures higher than 25 000 K. For non-equilibrium test cases, it is shown that the thermal diffusion coefficients of neutral species and the self-diffusion coefficients of all species are strongly affected by the mixture composition, while the thermal conductivity coefficient is most strongly influenced by the degree of ionization of the flow. Neglecting electronic excitation causes noticeable underestimation of the thermal conductivity coefficient at temperatures higher than 20 000 K.

Cluster Tails for Critical Power-Law Inhomogeneous Random Graphs

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

On the connection coefficients and recurrence relations arising from expansions in series of Laguerre polynomials

NASA Astrophysics Data System (ADS)

Doha, E. H.

2003-05-01

A formula expressing the Laguerre coefficients of a general-order derivative of an infinitely differentiable function in terms of its original coefficients is proved, and a formula expressing explicitly the derivatives of Laguerre polynomials of any degree and for any order as a linear combination of suitable Laguerre polynomials is deduced. A formula for the Laguerre coefficients of the moments of one single Laguerre polynomial of certain degree is given. Formulae for the Laguerre coefficients of the moments of a general-order derivative of an infinitely differentiable function in terms of its Laguerre coefficients are also obtained. A simple approach in order to build and solve recursively for the connection coefficients between Jacobi-Laguerre and Hermite-Laguerre polynomials is described. An explicit formula for these coefficients between Jacobi and Laguerre polynomials is given, of which the ultra-spherical polynomials of the first and second kinds and Legendre polynomials are important special cases. An analytical formula for the connection coefficients between Hermite and Laguerre polynomials is also obtained.

Studying the evolutionary relationships and phylogenetic trees of 21 groups of tRNA sequences based on complex networks.

PubMed

Wei, Fangping; Chen, Bowen

2012-03-01

To find out the evolutionary relationships among different tRNA sequences of 21 amino acids, 22 networks are constructed. One is constructed from whole tRNAs, and the other 21 networks are constructed from the tRNAs which carry the same amino acids. A new method is proposed such that the alignment scores of any two amino acids groups are determined by the average degree and the average clustering coefficient of their networks. The anticodon feature of isolated tRNA and the phylogenetic trees of 21 group networks are discussed. We find that some isolated tRNA sequences in 21 networks still connect with other tRNAs outside their group, which reflects the fact that those tRNAs might evolve by intercrossing among these 21 groups. We also find that most anticodons among the same cluster are only one base different in the same sites when S ≥ 70, and they stay in the same rank in the ladder of evolutionary relationships. Those observations seem to agree on that some tRNAs might mutate from the same ancestor sequences based on point mutation mechanisms.

Tuning the nonlinear optical absorption of reduced graphene oxide by chemical reduction.

PubMed

Shi, Hongfei; Wang, Can; Sun, Zhipei; Zhou, Yueliang; Jin, Kuijuan; Redfern, Simon A T; Yang, Guozhen

2014-08-11

Reduced graphene oxides with varying degrees of reduction have been produced by hydrazine reduction of graphene oxide. The linear and nonlinear optical properties of both graphene oxide as well as the reduced graphene oxides have been measured by single beam Z-scan measurement in the picosecond region. The results reveal both saturable absorption and two-photon absorption, strongly dependent on the intensity of the pump pulse: saturable absorption occurs at lower pump pulse intensity (~1.5 GW/cm2 saturation intensity) whereas two-photon absorption dominates at higher intensities (≥5.7 GW/cm2). Intriguingly, we find that the two-photon absorption coefficient (from 1.5 cm/GW to 4.5cm/GW) and the saturation intensity (from 1 GW/cm2 to 2 GW/cm2) vary with chemical reduction, which is ascribed to the varying concentrations of sp2 domains and sp2 clusters in the reduced graphene oxides. Our results not only provide an insight into the evolution of the nonlinear optical coefficient in reduced graphene oxide, but also suggest that chemical engineering techniques may usefully be applied to tune the nonlinear optical properties of various nano-materials, including atomically thick graphene sheets.

Complex network study of Brazilian soccer players

NASA Astrophysics Data System (ADS)

Onody, Roberto N.; de Castro, Paulo A.

2004-09-01

Although being a very popular sport in many countries, soccer has not received much attention from the scientific community. In this paper, we study soccer from a complex network point of view. First, we consider a bipartite network with two kinds of vertices or nodes: the soccer players and the clubs. Real data were gathered from the 32 editions of the Brazilian soccer championship, in a total of 13411 soccer players and 127 clubs. We find a lot of interesting and perhaps unsuspected results. The probability that a Brazilian soccer player has worked at N clubs or played M games shows an exponential decay while the probability that he has scored G goals is power law. Now, if two soccer players who have worked at the same club at the same time are connected by an edge, then a new type of network arises (composed exclusively by soccer player nodes). Our analysis shows that for this network the degree distribution decays exponentially. We determine the exact values of the clustering coefficient, the assortativity coefficient and the average shortest path length and compare them with those of the Erdös-Rényi and configuration model. The time evolution of these quantities are calculated and the corresponding results discussed.

Iridium clusters in KLTL zeolite: Synthesis, structural characterization, and catalysis of toluene hydrogenation and n-hexane dehydrocyclization

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

Zhao, A.; Jentoft, R.E.; Gates, B.C.

Iridium clusters incorporating about six atoms each, on average, were prepared in KLTL zeolite by decarbonylation (in H{sub 2} at 400{degrees}C) of iridium carbonyl clusters formed by treatment of adsorbed [Ir(CO){sub 2}(acac)] in CO at 1 atm and 175{degrees}C. The supported species were characterized by infrared and extended X-ray absorption fine structure (EXAFS) spectroscopies. The iridium carbonyls formed from [Ir(CO){sub 2}(acac)] were predominantly [HIr{sub 4}(CO){sub 11}]{sup -} with a small amount of [Ir(CO){sub 4}]{sup -}. The synthesis chemistry of iridium carbonyls in the basic KLTL zeolite parallels that in basic solutions. Shifts of the {nu}{sub CO} bands of the iridiummore » carbonyl clusters relative to those of the same clusters in solution indicate strong interactions between the clusters and zeolite cations. The decarbonylated sample, approximated as Ir{sub 6}/KLTL zeolite, is catalytically active for toluene hydrogenation at 60-100{degrees}C, with the activity being approximately the same as those of Ir{sub 4} and Ir{sub 6} clusters supported on metal oxides, but an order of magnitude less than that of a conventional supported iridium catalyst consisting of aggregates of about 50 atoms each, on average. The catalyst is also active for conversion of n-hexane + H{sub 2} at 340-420{degrees}C, but the selectivity for aromatization is low and that for hydrogenolysis is high, consistent with earlier results for conventionally prepared (salt-derived) iridium clusters of about the same size supported in KLTL zeolite. The zeolite-supported iridium clusters are the first prepared from both salt and organometallic precursors; the results indicate that the organometallic and conventional preparation routes lead to supported iridium clusters having similar structures and catalytic properties. 59 refs., 6 figs., 7 tabs.« less

a Weighted Local-World Evolving Network Model Based on the Edge Weights Preferential Selection

NASA Astrophysics Data System (ADS)

Li, Ping; Zhao, Qingzhen; Wang, Haitang

2013-05-01

In this paper, we use the edge weights preferential attachment mechanism to build a new local-world evolutionary model for weighted networks. It is different from previous papers that the local-world of our model consists of edges instead of nodes. Each time step, we connect a new node to two existing nodes in the local-world through the edge weights preferential selection. Theoretical analysis and numerical simulations show that the scale of the local-world affect on the weight distribution, the strength distribution and the degree distribution. We give the simulations about the clustering coefficient and the dynamics of infectious diseases spreading. The weight dynamics of our network model can portray the structure of realistic networks such as neural network of the nematode C. elegans and Online Social Network.

A dynamic network model for interbank market

NASA Astrophysics Data System (ADS)

Xu, Tao; He, Jianmin; Li, Shouwei

2016-12-01

In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.

Performance evaluation of Space Shuttle SRB parachutes from air drop and scaled model wind tunnel tests. [Solid Rocket Booster recovery system

NASA Technical Reports Server (NTRS)

Moog, R. D.; Bacchus, D. L.; Utreja, L. R.

1979-01-01

The aerodynamic performance characteristics have been determined for the Space Shuttle Solid Rocket Booster drogue, main, and pilot parachutes. The performance evaluation on the 20-degree conical ribbon parachutes is based primarily on air drop tests of full scale prototype parachutes. In addition, parametric wind tunnel tests were performed and used in parachute configuration development and preliminary performance assessments. The wind tunnel test data are compared to the drop test results and both sets of data are used to determine the predicted performance of the Solid Rocket Booster flight parachutes. Data from other drop tests of large ribbon parachutes are also compared with the Solid Rocket Booster parachute performance characteristics. Parameters assessed include full open terminal drag coefficients, reefed drag area, opening characteristics, clustering effects, and forebody interference.

The predictive power of local properties of financial networks

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2017-01-01

The literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force. In this paper, I show that the local network properties have similar predictive power. I focus on key network measures like average path length, average degree or cluster coefficient, and also consider the diameter and the s-metric. Using Granger causality tests, I show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market. Average path length is most robust relative to the frequency of data used or specification (index or growth rate). Most measures are found to have predictive power only for monthly frequency. Further evidences that support this view are provided through a simple regression model.

Differential dynamic microscopy of weakly scattering and polydisperse protein-rich clusters

NASA Astrophysics Data System (ADS)

Safari, Mohammad S.; Vorontsova, Maria A.; Poling-Skutvik, Ryan; Vekilov, Peter G.; Conrad, Jacinta C.

2015-10-01

Nanoparticle dynamics impact a wide range of biological transport processes and applications in nanomedicine and natural resource engineering. Differential dynamic microscopy (DDM) was recently developed to quantify the dynamics of submicron particles in solutions from fluctuations of intensity in optical micrographs. Differential dynamic microscopy is well established for monodisperse particle populations, but has not been applied to solutions containing weakly scattering polydisperse biological nanoparticles. Here we use bright-field DDM (BDDM) to measure the dynamics of protein-rich liquid clusters, whose size ranges from tens to hundreds of nanometers and whose total volume fraction is less than 10-5. With solutions of two proteins, hemoglobin A and lysozyme, we evaluate the cluster diffusion coefficients from the dependence of the diffusive relaxation time on the scattering wave vector. We establish that for weakly scattering populations, an optimal thickness of the sample chamber exists at which the BDDM signal is maximized at the smallest sample volume. The average cluster diffusion coefficient measured using BDDM is consistently lower than that obtained from dynamic light scattering at a scattering angle of 90∘. This apparent discrepancy is due to Mie scattering from the polydisperse cluster population, in which larger clusters preferentially scatter more light in the forward direction.

M16

NASA Astrophysics Data System (ADS)

Murdin, P.

2000-11-01

M16 is an open cluster in the constellation Serpens, associated with the EAGLE NEBULA (1 degree north and 2.5 degrees west of γ Scuti). It was discovered by de Chéseaux in 1746 as a `cluster of stars' and sits on the next inner spiral arm of the galaxy away from us next to M17....

Naming Game with Multiple Hearers

NASA Astrophysics Data System (ADS)

Li, Bing; Chen, Guanrong; Chow, Tommy W. S.

2013-05-01

A new model called Naming Game with Multiple Hearers (NGMH) is proposed in this paper. A naming game over a population of individuals aims to reach consensus on the name of an object through pair-wise local interactions among all the individuals. The proposed NGMH model describes the learning process of a new word, in a population with one speaker and multiple hearers, at each interaction towards convergence. The characteristics of NGMH are examined on three types of network topologies, namely ER random-graph network, WS small-world network, and BA scale-free network. Comparative analysis on the convergence time is performed, revealing that the topology with a larger average (node) degree can reach consensus faster than the others over the same population. It is found that, for a homogeneous network, the average degree is the limiting value of the number of hearers, which reduces the individual ability of learning new words, consequently decreasing the convergence time; for a scale-free network, this limiting value is the deviation of the average degree. It is also found that a network with a larger clustering coefficient takes longer time to converge; especially a small-word network with smallest rewiring possibility takes longest time to reach convergence. As more new nodes are being added to scale-free networks with different degree distributions, their convergence time appears to be robust against the network-size variation. Most new findings reported in this paper are different from that of the single-speaker/single-hearer naming games documented in the literature.

[Sincerity of effort: isokinetic evaluation of knee extension].

PubMed

Colombo, R; Demaiti, G; Sartorio, F; Orlandini, D; Vercelli, S; Ferriero, G

2008-01-01

The aim of this study was to find a reliable method to evaluate the sincerity of the muscular maximal effort performed in a dynamometric isokinetic test of knee flexion-extension. The coefficient of variation of the peak torque (CV) and 3 new indices were analysed: (1) the average coefficient of variation calculated on the complete peak torque curve (CVM); (2) the slope of the regression line in an endurance test (PRR); (3) the correlation coefficient of the peak torques in the same endurance test (CCR). Twenty healthy subjects underwent assessment in two different trials, maximal (MX) and 50% submaximal (SMX), with 20 minutes of rest between trials. Each trial consisted of 4 tests, each of 3 repetitions, at angular speed of 30, 180, 30, and 180 degrees/s, respectively, and 1 test of 15 repetitions at 240 degrees/s. Our findings confirmed the ability of CV to detect a high percentage of sincere efforts: at 30 degrees/s Sensibility (Sns)=100% and Specificity (Spc)=70%; at 180 degrees/s Sns=75%, Spc=95%. The 3 new indices here proposed showed high characteristics of Sns and Spc, generally better than those of CV. CVM showed at 180 degrees/s Sns=90% and Spc=100%, while at 30 degrees/s Sns=90%, Spc=75%. PRR was the best index identifying all the efforts, except one (Sns=100%, Spc=95%). The CCR coefficient showed Sns and Spc values both of 90%.

[Correlation coefficient-based principle and method for the classification of jump degree in hydrological time series].

PubMed

Wu, Zi Yi; Xie, Ping; Sang, Yan Fang; Gu, Hai Ting

2018-04-01

The phenomenon of jump is one of the importantly external forms of hydrological variabi-lity under environmental changes, representing the adaption of hydrological nonlinear systems to the influence of external disturbances. Presently, the related studies mainly focus on the methods for identifying the jump positions and jump times in hydrological time series. In contrast, few studies have focused on the quantitative description and classification of jump degree in hydrological time series, which make it difficult to understand the environmental changes and evaluate its potential impacts. Here, we proposed a theatrically reliable and easy-to-apply method for the classification of jump degree in hydrological time series, using the correlation coefficient as a basic index. The statistical tests verified the accuracy, reasonability, and applicability of this method. The relationship between the correlation coefficient and the jump degree of series were described using mathematical equation by derivation. After that, several thresholds of correlation coefficients under different statistical significance levels were chosen, based on which the jump degree could be classified into five levels: no, weak, moderate, strong and very strong. Finally, our method was applied to five diffe-rent observed hydrological time series, with diverse geographic and hydrological conditions in China. The results of the classification of jump degrees in those series were closely accorded with their physically hydrological mechanisms, indicating the practicability of our method.

Aerodynamic Loads at Mach Numbers from 0.70 to 2.22 on an Airplane Model Having a Wing and Canard of Triangular Plan Form and Either Single or Twin Vertical Tails. Supplement 2; Tabulated Data for the Model with Twin Vertical Tails

NASA Technical Reports Server (NTRS)

Peterson, Victor L.; Menees, Gene P.

1961-01-01

Tabulated results of a wind-tunnel investigation of the aerodynamic loads on a canard airplane model with twin vertical tails are presented for Mach numbers from 0.70 to 2.22. The Reynolds number for the measurements was 2.9 x 10(exp 6) based on the wing mean aerodynamic chord. The results include local static-pressure coefficients measured on the wing, body, and one of the vertical tails for angles of attack from -4 degrees to 16 degree angles of sideslip of 0 degrees and 5.3 degrees, and nominal canard deflections of O degrees and 10 degrees. Also included are section force and moment coefficients obtained from integrations of the local pressures and model-component force and moment coefficients obtained from integrations of the section coefficients. Geometric details of the model are shown and the locations of the pressure orifices are shown. An index to the data contained herein is presented and definitions of nomenclature are given. Detailed descriptions of the model and experiments and a brief discussion of some of the results are given. Tabulated results of measurements of the aerodynamic loads on the same canard model but having a single vertical tail instead of twin vertical tails are presented.

A comparative study of DIGNET, average, complete, single hierarchical and k-means clustering algorithms in 2D face image recognition

NASA Astrophysics Data System (ADS)

Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

2014-06-01

The study in this paper belongs to a more general research of discovering facial sub-clusters in different ethnicity face databases. These new sub-clusters along with other metadata (such as race, sex, etc.) lead to a vector for each face in the database where each vector component represents the likelihood of participation of a given face to each cluster. This vector is then used as a feature vector in a human identification and tracking system based on face and other biometrics. The first stage in this system involves a clustering method which evaluates and compares the clustering results of five different clustering algorithms (average, complete, single hierarchical algorithm, k-means and DIGNET), and selects the best strategy for each data collection. In this paper we present the comparative performance of clustering results of DIGNET and four clustering algorithms (average, complete, single hierarchical and k-means) on fabricated 2D and 3D samples, and on actual face images from various databases, using four different standard metrics. These metrics are the silhouette figure, the mean silhouette coefficient, the Hubert test Γ coefficient, and the classification accuracy for each clustering result. The results showed that, in general, DIGNET gives more trustworthy results than the other algorithms when the metrics values are above a specific acceptance threshold. However when the evaluation results metrics have values lower than the acceptance threshold but not too low (too low corresponds to ambiguous results or false results), then it is necessary for the clustering results to be verified by the other algorithms.

Link prediction with node clustering coefficient

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Wang, Jing; Gregory, Steve

2016-06-01

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai-Alanis-Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors.

Low Temperature Kinetics of the First Steps of Water Cluster Formation

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

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

2016-03-01

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

Study on degenerate coefficient and degeneration evaluation of lithium-ion battery

NASA Astrophysics Data System (ADS)

Li, Bei; Li, Xiaopeng

2017-07-01

Some characteristic parameters were epurated in this paper by analyzing internal and external factors of the degradation degree of lithium-ion battery. These characteristic parameters include open circuit voltage (OCV), state of charge (SOC) and ambient temperature. The degradation degree was evaluated by discrete degree of the array, which is composed of the above parameters. The epurated parameters were verified through adaptive neuro-fuzzy inference system (ANFIS) model building. The expression of degradation coefficient was finally determined. The simulation results show that the expression is reasonable and precise to describe the degradation degree.

A Hybrid Approach for CpG Island Detection in the Human Genome.

PubMed

Yang, Cheng-Hong; Lin, Yu-Da; Chiang, Yi-Cheng; Chuang, Li-Yeh

2016-01-01

CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.

Investigation and Analysis of Genetic Diversity of Diospyros Germplasms Using SCoT Molecular Markers in Guangxi.

PubMed

Deng, Libao; Liang, Qingzhi; He, Xinhua; Luo, Cong; Chen, Hu; Qin, Zhenshi

2015-01-01

Knowledge about genetic diversity and relationships among germplasms could be an invaluable aid in diospyros improvement strategies. This study was designed to analyze the genetic diversity and relationship of local and natural varieties in Guangxi Zhuang Autonomous Region of China using start codon targeted polymorphism (SCoT) markers. The accessions of 95 diospyros germplasms belonging to four species Diospyros kaki Thunb, D. oleifera Cheng, D. kaki var. silverstris Mak, and D. lotus Linn were collected from different eco-climatic zones in Guangxi and were analyzed using SCoT markers. Results indicated that the accessions of 95 diospyros germplasms could be distinguished using SCoT markers, and were divided into three groups at similarity coefficient of 0.608; these germplasms that belong to the same species were clustered together; of these, the degree of genetic diversity of the natural D. kaki var. silverstris Mak population was richest among the four species; the geographical distance showed that the 12 natural populations of D. kaki var. silverstris Mak were divided into two groups at similarity coefficient of 0.19. Meanwhile, in order to further verify the stable and useful of SCoT markers in diospyros germplasms, SSR markers were also used in current research to analyze the genetic diversity and relationship in the same diospyros germplasms. Once again, majority of germplasms that belong to the same species were clustered together. Thus SCoT markers were stable and especially useful for analysis of the genetic diversity and relationship in diospyros germplasms. The molecular characterization and diversity assessment of diospyros were very important for conservation of diospyros germplasm resources, meanwhile for diospyros improvement.

Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy.

PubMed

Paldino, M J; Golriz, F; Chapieski, M L; Zhang, W; Chu, Z D

2017-02-01

The biologic basis for intelligence rests to a large degree on the capacity for efficient integration of information across the cerebral network. We aimed to measure the relationship between network architecture and intelligence in the pediatric, epileptic brain. Patients were retrospectively identified with the following: 1) focal epilepsy; 2) brain MR imaging at 3T, including resting-state functional MR imaging; and 3) full-scale intelligence quotient measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network "nodes." The strength of a connection between 2 nodes was defined by the correlation between their blood oxygen level-dependent time-series. We calculated the following topologic properties: clustering coefficient, transitivity, modularity, path length, and global efficiency. A machine learning algorithm was used to measure the independent contribution of each metric to the intelligence quotient after adjusting for all other metrics. Thirty patients met the criteria (4-18 years of age); 20 patients required anesthesia during MR imaging. After we accounted for age and sex, clustering coefficient and path length were independently associated with full-scale intelligence quotient. Neither motion parameters nor general anesthesia was an important variable with regard to accurate intelligence quotient prediction by the machine learning algorithm. A longer history of epilepsy was associated with shorter path lengths ( P = .008), consistent with reorganization of the network on the basis of seizures. Considering only patients receiving anesthesia during machine learning did not alter the patterns of network architecture contributing to global intelligence. These findings support the physiologic relevance of imaging-based metrics of network architecture in the pathologic, developing brain. © 2017 by American Journal of Neuroradiology.

Music and emotion: an EEG connectivity study in patients with disorders of consciousness.

PubMed

Varotto, G; Fazio, P; Rossi Sebastiano, D; Avanzini, G; Franceschetti, S; Panzica, F; CRC

2012-01-01

Human emotion perception is a topic of great interest for both cognitive and clinical neuroscience, but its electrophysiological correlates are still poorly understood. The present study is aimed at evaluating if measures of synchronization and indexes based on graph-theory are a tool suitable to study and quantify electrophysiological changes due to emotional stimuli perception. In particular, our study is aimed at evaluating if different EEG connectivity patterns can be induced by pleasant (consonant) or unpleasant (dissonant) music, in a population of healthy subjects, and in patients with severe disorders of consciousness (DOCs), namely vegetative state (VS) patients. In the control group, pleasant music induced an increase in network number of connections, compared with the resting condition, while no changes were caused by the unpleasant stimuli. However, clustering coefficient and path length, two indexes derived from graph theory, able to characterise segregation and integration properties of a network, were not affected by the stimuli, neither pleasant nor unpleasant. In the VS group, changes were found only in those patients with the less severe consciousness impairment, according to the clinical assessment. In these patients a stronger synchronization was found during the unpleasant condition; moreover we observed changes in the network topology, with decreased values of clustering coefficient and path length during both musical stimuli.Our results show that measures of synchronization can provide new insights into the study of the electro physiological correlates of emotion perception, indicating that these tools can be used to study patients with DOCs, in whom the issue of objective measures and quantification of the degree of impairment is still an open and unsolved question.

Investigation and Analysis of Genetic Diversity of Diospyros Germplasms Using SCoT Molecular Markers in Guangxi

PubMed Central

He, Xinhua; Luo, Cong; Chen, Hu; Qin, Zhenshi

2015-01-01

Background Knowledge about genetic diversity and relationships among germplasms could be an invaluable aid in diospyros improvement strategies. Methods This study was designed to analyze the genetic diversity and relationship of local and natural varieties in Guangxi Zhuang Autonomous Region of China using start codon targeted polymorphism (SCoT) markers. The accessions of 95 diospyros germplasms belonging to four species Diospyros kaki Thunb, D. oleifera Cheng, D. kaki var. silverstris Mak, and D. lotus Linn were collected from different eco-climatic zones in Guangxi and were analyzed using SCoT markers. Results Results indicated that the accessions of 95 diospyros germplasms could be distinguished using SCoT markers, and were divided into three groups at similarity coefficient of 0.608; these germplasms that belong to the same species were clustered together; of these, the degree of genetic diversity of the natural D. kaki var. silverstris Mak population was richest among the four species; the geographical distance showed that the 12 natural populations of D. kaki var. silverstris Mak were divided into two groups at similarity coefficient of 0.19. Meanwhile, in order to further verify the stable and useful of SCoT markers in diospyros germplasms, SSR markers were also used in current research to analyze the genetic diversity and relationship in the same diospyros germplasms. Once again, majority of germplasms that belong to the same species were clustered together. Thus SCoT markers were stable and especially useful for analysis of the genetic diversity and relationship in diospyros germplasms. Discussion The molecular characterization and diversity assessment of diospyros were very important for conservation of diospyros germplasm resources, meanwhile for diospyros improvement. PMID:26317414

Characterizing the degree of convective clustering using radar reflectivity and its application to evaluating model simulations

NASA Astrophysics Data System (ADS)

Cheng, W. Y.; Kim, D.; Rowe, A.; Park, S.

2017-12-01

Despite the impact of mesoscale convective organization on the properties of convection (e.g., mixing between updrafts and environment), parameterizing the degree of convective organization has only recently been attempted in cumulus parameterization schemes (e.g., Unified Convection Scheme UNICON). Additionally, challenges remain in determining the degree of convective organization from observations and in comparing directly with the organization metrics in model simulations. This study addresses the need to objectively quantify the degree of mesoscale convective organization using high quality S-PolKa radar data from the DYNAMO field campaign. One of the most noticeable aspects of mesoscale convective organization in radar data is the degree of convective clustering, which can be characterized by the number and size distribution of convective echoes and the distance between them. We propose a method of defining contiguous convective echoes (CCEs) using precipitating convective echoes identified by a rain type classification algorithm. Two classification algorithms, Steiner et al. (1995) and Powell et al. (2016), are tested and evaluated against high-resolution WRF simulations to determine which method better represents the degree of convective clustering. Our results suggest that the CCEs based on Powell et al.'s algorithm better represent the dynamical properties of the convective updrafts and thus provide the basis of a metric for convective organization. Furthermore, through a comparison with the observational data, the WRF simulations driven by the DYNAMO large-scale forcing, similarly applied to UNICON Single Column Model simulations, will allow us to evaluate the ability of both WRF and UNICON to simulate convective clustering. This evaluation is based on the physical processes that are explicitly represented in WRF and UNICON, including the mechanisms leading to convective clustering, and the feedback to the convective properties.

Ice friction of flared ice hockey skate blades.

PubMed

Federolf, Peter A; Mills, Robert; Nigg, Benno

2008-09-01

In ice hockey, skating performance depends on the skill and physical conditioning of the players and on the characteristics of their equipment. CT Edge have recently designed a new skate blade that angles outward near the bottom of the blade. The objective of this study was to compare the frictional characteristics of three CT Edge blades (with blade angles of 4 degrees, 60, and 8 degrees, respectively) with the frictional characteristics of a standard skate blade. The friction coefficients of the blades were determined by measuring the deceleration of an aluminium test sled equipped with three test blades. The measurements were conducted with an initial sled speed of 1.8 m s(-1) and with a load of 53 kg on each blade. The friction coefficient of the standard blades was 0.0071 (s = 0.0005). For the CT Edge blades with blade angles of 4 degrees, 6 degrees, and 8 degrees, friction coefficients were lower by about 13%, 21%, and 22%, respectively. Furthermore, the friction coefficients decreased with increasing load. The results of this study show that widely accepted paradigms such as "thinner blades cause less friction" need to be revisited. New blade designs might also be able to reduce friction in speed skating, figure skating, bobsledding, and luge.

Reduction of the uncertainty due to fissile clusters in radioactive waste characterization with the Differential Die-away Technique

NASA Astrophysics Data System (ADS)

Antoni, R.; Passard, C.; Perot, B.; Guillaumin, F.; Mazy, C.; Batifol, M.; Grassi, G.

2018-07-01

AREVA NC is preparing to process, characterize and compact old used fuel metallic waste stored at La Hague reprocessing plant in view of their future storage ("Haute Activité Oxyde" HAO project). For a large part of these historical wastes, the packaging is planned in CSD-C canisters ("Colis Standard de Déchets Compacté s") in the ACC hulls and nozzles compaction facility ("Atelier de Compactage des Coques et embouts"). . This paper presents a new method to take into account the possible presence of fissile material clusters, which may have a significant impact in the active neutron interrogation (Differential Die-away Technique) measurement of the CSD-C canisters, in the industrial neutron measurement station "P2-2". A matrix effect correction has already been investigated to predict the prompt fission neutron calibration coefficient (which provides the fissile mass) from an internal "drum flux monitor" signal provided during the active measurement by a boron-coated proportional counter located in the measurement cavity, and from a "drum transmission signal" recorded in passive mode by the detection blocks, in presence of an AmBe point source in the measurement cell. Up to now, the relationship between the calibration coefficient and these signals was obtained from a factorial design that did not consider the potential for occurrence of fissile material clusters. The interrogative neutron self-shielding in these clusters was treated separately and resulted in a penalty coefficient larger than 20% to prevent an underestimation of the fissile mass within the drum. In this work, we have shown that the incorporation of a new parameter in the factorial design, representing the fissile mass fraction in these clusters, provides an alternative to the penalty coefficient. This new approach finally does not degrade the uncertainty of the original prediction, which was calculated without taking into consideration the possible presence of clusters. Consequently, the accuracy of the fissile mass assessment is improved by this new method, and this last should be extended to similar DDT measurement stations of larger drums, also using an internal monitor for matrix effect correction.

Search for Directed Networks by Different Random Walk Strategies

NASA Astrophysics Data System (ADS)

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

2012-03-01

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

Optimized data fusion for K-means Laplacian clustering

PubMed Central

Yu, Shi; Liu, Xinhai; Tranchevent, Léon-Charles; Glänzel, Wolfgang; Suykens, Johan A. K.; De Moor, Bart; Moreau, Yves

2011-01-01

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20980271

Oxygen diffusion in alpha-Al2O3. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Cawley, J. D.; Halloran, J. W.; Cooper, A. R.

1984-01-01

Oxygen self diffusion coefficients were determined in single crystal alpha-Al2O3 using the gas exchange technique. The samples were semi-infinite slabs cut from five different boules with varying background impurities. The diffusion direction was parallel to the c-axis. The tracer profiles were determined by two techniques, single spectrum proton activation and secondary ion mass spectrometry. The SIMS proved to be a more useful tool. The determined diffusion coefficients, which were insensitive to impurity levels and oxygen partial pressure, could be described by D = .00151 exp (-572kJ/RT) sq m/s. The insensitivities are discussed in terms of point defect clustering. Two independent models are consistent with the findings, the first considers the clusters as immobile point defect traps which buffer changes in the defect chemistry. The second considers clusters to be mobile and oxygen diffusion to be intrinsic behavior, the mechanism for oxygen transport involving neutral clusters of Schottky quintuplets.

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

ERIC Educational Resources Information Center

MacGregor, James N.

2013-01-01

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

Wind-tunnel and Flight Investigations of the Use of Leading-Edge Area Suction for the Purpose of Increasing the Maximum Lift Coefficient of a 35 Degree Swept-Wing Airplane

NASA Technical Reports Server (NTRS)

Holzhauser, Curt A; Bray, Richard S

1956-01-01

An investigation was undertaken to determine the increase in maximum lift coefficient that could be obtained by applying area suction near the leading edge of a wing. This investigation was performed first with a 35 degree swept-wing model in the wind tunnel, and then with an operational 35 degree swept-wing airplane which was modified in accord with the wind-tunnel results. The wind-tunnel and flight tests indicated that the maximum lift coefficient was increased more than 50 percent by the use of area suction. Good agreement was obtained in the comparison of the wind-tunnel results with those measured in flight.

Searching for galaxy clusters in the Kilo-Degree Survey

NASA Astrophysics Data System (ADS)

Radovich, M.; Puddu, E.; Bellagamba, F.; Roncarelli, M.; Moscardini, L.; Bardelli, S.; Grado, A.; Getman, F.; Maturi, M.; Huang, Z.; Napolitano, N.; McFarland, J.; Valentijn, E.; Bilicki, M.

2017-02-01

Aims: In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. Methods: The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. Results: We obtained 1858 candidate clusters with redshift 0

Recurrences and explicit formulae for the expansion and connection coefficients in series of Bessel polynomials

NASA Astrophysics Data System (ADS)

Doha, E. H.; Ahmed, H. M.

2004-08-01

A formula expressing explicitly the derivatives of Bessel polynomials of any degree and for any order in terms of the Bessel polynomials themselves is proved. Another explicit formula, which expresses the Bessel expansion coefficients of a general-order derivative of an infinitely differentiable function in terms of its original Bessel coefficients, is also given. A formula for the Bessel coefficients of the moments of one single Bessel polynomial of certain degree is proved. A formula for the Bessel coefficients of the moments of a general-order derivative of an infinitely differentiable function in terms of its Bessel coefficients is also obtained. Application of these formulae for solving ordinary differential equations with varying coefficients, by reducing them to recurrence relations in the expansion coefficients of the solution, is explained. An algebraic symbolic approach (using Mathematica) in order to build and solve recursively for the connection coefficients between Bessel-Bessel polynomials is described. An explicit formula for these coefficients between Jacobi and Bessel polynomials is given, of which the ultraspherical polynomial and its consequences are important special cases. Two analytical formulae for the connection coefficients between Laguerre-Bessel and Hermite-Bessel are also developed.

Using neighborhood cohesiveness to infer interactions between protein domains.

PubMed

Segura, Joan; Sorzano, C O S; Cuenca-Alba, Jesus; Aloy, Patrick; Carazo, J M

2015-08-01

In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps, or interactomes, for a number of relevant organisms, including human. However, current interactomes provide a somehow limited picture of the molecular details involving protein interactions, mostly because essential experimental information, especially structural data, is lacking. Indeed, the gap between structural and interactomics information is enlarging and thus, for most interactions, key experimental information is missing. We elaborate on the observation that many interactions between proteins involve a pair of their constituent domains and, thus, the knowledge of how protein domains interact adds very significant information to any interactomic analysis. In this work, we describe a novel use of the neighborhood cohesiveness property to infer interactions between protein domains given a protein interaction network. We have shown that some clustering coefficients can be extended to measure a degree of cohesiveness between two sets of nodes within a network. Specifically, we used the meet/min coefficient to measure the proportion of interacting nodes between two sets of nodes and the fraction of common neighbors. This approach extends previous works where homolog coefficients were first defined around network nodes and later around edges. The proposed approach substantially increases both the number of predicted domain-domain interactions as well as its accuracy as compared with current methods. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Efficient calculation of atomic rate coefficients in dense plasmas

NASA Astrophysics Data System (ADS)

Aslanyan, Valentin; Tallents, Greg J.

2017-03-01

Modelling electron statistics in a cold, dense plasma by the Fermi-Dirac distribution leads to complications in the calculations of atomic rate coefficients. The Pauli exclusion principle slows down the rate of collisions as electrons must find unoccupied quantum states and adds a further computational cost. Methods to calculate these coefficients by direct numerical integration with a high degree of parallelism are presented. This degree of optimization allows the effects of degeneracy to be incorporated into a time-dependent collisional-radiative model. Example results from such a model are presented.

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

Two different approaches to the affective profiles model: median splits (variable-oriented) and cluster analysis (person-oriented).

PubMed

Garcia, Danilo; MacDonald, Shane; Archer, Trevor

2015-01-01

Background. The notion of the affective system as being composed of two dimensions led Archer and colleagues to the development of the affective profiles model. The model consists of four different profiles based on combinations of individuals' experience of high/low positive and negative affect: self-fulfilling, low affective, high affective, and self-destructive. During the past 10 years, an increasing number of studies have used this person-centered model as the backdrop for the investigation of between and within individual differences in ill-being and well-being. The most common approach to this profiling is by dividing individuals' scores of self-reported affect using the median of the population as reference for high/low splits. However, scores just-above and just-below the median might become high and low by arbitrariness, not by reality. Thus, it is plausible to criticize the validity of this variable-oriented approach. Our aim was to compare the median splits approach with a person-oriented approach, namely, cluster analysis. Method. The participants (N = 2, 225) were recruited through Amazons' Mechanical Turk and asked to self-report affect using the Positive Affect Negative Affect Schedule. We compared the profiles' homogeneity and Silhouette coefficients to discern differences in homogeneity and heterogeneity between approaches. We also conducted exact cell-wise analyses matching the profiles from both approaches and matching profiles and gender to investigate profiling agreement with respect to affectivity levels and affectivity and gender. All analyses were conducted using the ROPstat software. Results. The cluster approach (weighted average of cluster homogeneity coefficients = 0.62, Silhouette coefficients = 0.68) generated profiles with greater homogeneity and more distinctive from each other compared to the median splits approach (weighted average of cluster homogeneity coefficients = 0.75, Silhouette coefficients = 0.59). Most of the participants (n = 1,736, 78.0%) were allocated to the same profile (Rand Index = .83), however, 489 (21.98%) were allocated to different profiles depending on the approach. Both approaches allocated females and males similarly in three of the four profiles. Only the cluster analysis approach classified men significantly more often than chance to a self-fulfilling profile (type) and females less often than chance to this very same profile (antitype). Conclusions. Although the question whether one approach is more appropriate than the other is still without answer, the cluster method allocated individuals to profiles that are more in accordance with the conceptual basis of the model and also to expected gender differences. More importantly, regardless of the approach, our findings suggest that the model mirrors a complex and dynamic adaptive system.

What can graph theory tell us about word learning and lexical retrieval?

PubMed

Vitevitch, Michael S

2008-04-01

Graph theory and the new science of networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might influence the acquisition and retrieval of phonological word-forms. Pajek, a program for large network analysis and visualization (V. Batagelj & A. Mvrar, 1998), was used to examine several characteristics of a network derived from a computerized database of the adult lexicon. Nodes in the network represented words, and a link connected two nodes if the words were phonological neighbors. The average path length and clustering coefficient suggest that the phonological network exhibits small-world characteristics. The degree distribution was fit better by an exponential rather than a power-law function. Finally, the network exhibited assortative mixing by degree. Some of these structural characteristics were also found in graphs that were formed by 2 simple stochastic processes suggesting that similar processes might influence the development of the lexicon. The graph theoretic perspective may provide novel insights about the mental lexicon and lead to future studies that help us better understand language development and processing.

Toward cost-efficient sampling methods

NASA Astrophysics Data System (ADS)

Luo, Peng; Li, Yongli; Wu, Chong; Zhang, Guijie

2015-09-01

The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper proposes two new sampling methods based on the idea that a small part of vertices with high node degree could possess the most structure information of a complex network. The two proposed sampling methods are efficient in sampling high degree nodes so that they would be useful even if the sampling rate is low, which means cost-efficient. The first new sampling method is developed on the basis of the widely used stratified random sampling (SRS) method and the second one improves the famous snowball sampling (SBS) method. In order to demonstrate the validity and accuracy of two new sampling methods, we compare them with the existing sampling methods in three commonly used simulation networks that are scale-free network, random network, small-world network, and also in two real networks. The experimental results illustrate that the two proposed sampling methods perform much better than the existing sampling methods in terms of achieving the true network structure characteristics reflected by clustering coefficient, Bonacich centrality and average path length, especially when the sampling rate is low.

Thermal expansion coefficient determination of polylactic acid using digital image correlation

NASA Astrophysics Data System (ADS)

Botean, Adrian-Ioan

2018-02-01

This paper aims determining the linear thermal expansion coefficient (CTE) of polylactic acid (PLA) using an optical method for measuring deformations called digital image correlation method (DIC). Because PLA is often used in making many pieces with 3D printing technology, it is opportune to know this coefficient to obtain a higher degree of precision in the construction of parts and to monitor deformations when these parts are subjected to a thermal gradient. Are used two PLA discs with 20 and 40% degree of filling. In parallel with this approach was determined the linear thermal expansion coefficient (CTE) for the copper cylinder on the surface of which are placed the two discs of PLA.

Theoretical investigation about secondary deposition of thin-film formation by molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Chen, Huawei; Hagiwara, Ichiro; Kiet Tieu, A.; Kishimoto, Kikuo; Liu, Qiang

2007-05-01

The thin-film growth has been confirmed to be assembled by an enormous number of clusters in experiments of CVD. Sequence of clusters' depositions proceeds to form the thin-film in short time as gas fluids through surface of substrate. Such growth mechanism has been mainly investigated on the basis of experiment. Due to immense cost of the experimental equipment and low level of current measurement technology, the comprehension about authentic effect of formation condition on properties of nanomaterial is limited in qualitative manner. Three quantitative items: flatness of primary deposition, adhesion between cluster and substrate, and degree of epitaxial growth were proposed to evaluate the property of thin-film. In this simulation, three different cluster sizes of 203, 653, and 1563 atoms with different velocities (0, 10, 100, 1000, and 3000 m/s) were deposited on a Cu(0 0 1) substrate whose temperatures were set between 300 and 1000 K. Four clusters and one cluster were used in primary deposition and secondary deposition, respectively. To increase initial velocity not only enhanced the speed of epitaxial growth, adhesion between clusters and substrate, but also increased the degree of epitaxy for primary deposition and secondary deposition. Exfoliation pattern of thin-film was profoundly dependent on initial velocity through comparison between adhesion of primary and secondary deposition. Moreover, the epitaxial growth became well as the temperature of substrate was raised, and the degree of epitaxy of small cluster was larger than that of larger cluster, no matter of primary and secondary deposition.

Scattering properties of alumina particle clusters with different radius of monomers in aerocraft plume

NASA Astrophysics Data System (ADS)

Li, Jingying; Bai, Lu; Wu, Zhensen; Guo, Lixin; Gong, Yanjun

2017-11-01

In this paper, diffusion limited aggregation (DLA) algorithm is improved to generate the alumina particle cluster with different radius of monomers in the plume. Scattering properties of these alumina clusters are solved by the multiple sphere T matrix method (MSTM). The effect of the number and radius of monomers on the scattering properties of clusters of alumina particles is discussed. The scattering properties of two types of alumina particle clusters are compared, one has different radius of monomers that follows lognormal probability distribution, another has the same radius of monomers that equals the mean of lognormal probability distribution. The result show that the scattering phase functions and linear polarization degrees of these two types of alumina particle clusters are of great differences. For the alumina clusters with different radius of monomers, the forward scatterings are bigger and the linear polarization degree has multiple peaks. Moreover, the vary of their scattering properties do not have strong correlative with the change of number of monomers. For larger booster motors, 25-38% of the plume being condensed alumina. The alumina can scatter radiation from other sources present in the plume and effect on radiation transfer characteristics of plume. In addition, the shape, size distribution and refractive index of the particles in the plume are estimated by linear polarization degree. Therefore, accurate scattering properties calculation is very important to decrease the deviation in the related research.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

Analysis of the Chinese air route network as a complex network

NASA Astrophysics Data System (ADS)

Cai, Kai-Quan; Zhang, Jun; Du, Wen-Bo; Cao, Xian-Bin

2012-02-01

The air route network, which supports all the flight activities of the civil aviation, is the most fundamental infrastructure of air traffic management system. In this paper, we study the Chinese air route network (CARN) within the framework of complex networks. We find that CARN is a geographical network possessing exponential degree distribution, low clustering coefficient, large shortest path length and exponential spatial distance distribution that is obviously different from that of the Chinese airport network (CAN). Besides, via investigating the flight data from 2002 to 2010, we demonstrate that the topology structure of CARN is homogeneous, howbeit the distribution of flight flow on CARN is rather heterogeneous. In addition, the traffic on CARN keeps growing in an exponential form and the increasing speed of west China is remarkably larger than that of east China. Our work will be helpful to better understand Chinese air traffic systems.

Cross over of recurrence networks to random graphs and random geometric graphs

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2017-02-01

Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability density variations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measures and show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to the time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.

Two-population dynamics in a growing network model

NASA Astrophysics Data System (ADS)

Ivanova, Kristinka; Iordanov, Ivan

2012-02-01

We introduce a growing network evolution model with nodal attributes. The model describes the interactions between potentially violent V and non-violent N agents who have different affinities in establishing connections within their own population versus between the populations. The model is able to generate all stable triads observed in real social systems. In the framework of rate equations theory, we employ the mean-field approximation to derive analytical expressions of the degree distribution and the local clustering coefficient for each type of nodes. Analytical derivations agree well with numerical simulation results. The assortativity of the potentially violent network qualitatively resembles the connectivity pattern in terrorist networks that was recently reported. The assortativity of the network driven by aggression shows clearly different behavior than the assortativity of the networks with connections of non-aggressive nature in agreement with recent empirical results of an online social system.

Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric.

PubMed

Lee, Hyekyoung; Chung, Moo K; Kang, Hyejin; Kim, Boong-Nyun; Lee, Dong Soo

2011-01-01

The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the Gromov-Hausdorff (GH) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single linkage matrix. The single linkage matrix is subsequently used in measuring network differences using the GH distance. As an illustration, we apply the proposed framework to compare the FDG-PET based functional brain networks out of 24 attention deficit hyperactivity disorder (ADHD) children, 26 autism spectrum disorder (ASD) children and 11 pediatric control subjects.

Weak signal transmission in complex networks and its application in detecting connectivity.

PubMed

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

Distinguishing fiction from non-fiction with complex networks

NASA Astrophysics Data System (ADS)

Larue, David M.; Carr, Lincoln D.; Jones, Linnea K.; Stevanak, Joe T.

2014-03-01

Complex Network Measures are applied to networks constructed from texts in English to demonstrate an initial viability in textual analysis. Texts from novels and short stories obtained from Project Gutenberg and news stories obtained from NPR are selected. Unique word stems in a text are used as nodes in an associated unweighted undirected network, with edges connecting words occurring within a certain number of words somewhere in the text. Various combinations of complex network measures are computed for each text's network. Fisher's Linear Discriminant analysis is used to build a parameter optimizing the ability to separate the texts according to their genre. Success rates in the 70% range for correctly distinguishing fiction from non-fiction were obtained using edges defined as within four words, using 400 word samples from 400 texts from each of the two genres with some combinations of measures such as the power-law exponents of degree distributions and clustering coefficients.

Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis.

PubMed

Kim, Hyunsoo; Park, Haesun

2007-06-15

Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space. In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms. The software is available as supplementary material.

Modelling students' knowledge organisation: Genealogical conceptual networks

NASA Astrophysics Data System (ADS)

Koponen, Ismo T.; Nousiainen, Maija

2018-04-01

Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

A Direct Comparison of Two Densely Sampled HIV Epidemics: The UK and Switzerland

NASA Astrophysics Data System (ADS)

Ragonnet-Cronin, Manon L.; Shilaih, Mohaned; Günthard, Huldrych F.; Hodcroft, Emma B.; Böni, Jürg; Fearnhill, Esther; Dunn, David; Yerly, Sabine; Klimkait, Thomas; Aubert, Vincent; Yang, Wan-Lin; Brown, Alison E.; Lycett, Samantha J.; Kouyos, Roger; Brown, Andrew J. Leigh

2016-09-01

Phylogenetic clustering approaches can elucidate HIV transmission dynamics. Comparisons across countries are essential for evaluating public health policies. Here, we used a standardised approach to compare the UK HIV Drug Resistance Database and the Swiss HIV Cohort Study while maintaining data-protection requirements. Clusters were identified in subtype A1, B and C pol phylogenies. We generated degree distributions for each risk group and compared distributions between countries using Kolmogorov-Smirnov (KS) tests, Degree Distribution Quantification and Comparison (DDQC) and bootstrapping. We used logistic regression to predict cluster membership based on country, sampling date, risk group, ethnicity and sex. We analysed >8,000 Swiss and >30,000 UK subtype B sequences. At 4.5% genetic distance, the UK was more clustered and MSM and heterosexual degree distributions differed significantly by the KS test. The KS test is sensitive to variation in network scale, and jackknifing the UK MSM dataset to the size of the Swiss dataset removed the difference. Only heterosexuals varied based on the DDQC, due to UK male heterosexuals who clustered exclusively with MSM. Their removal eliminated this difference. In conclusion, the UK and Swiss HIV epidemics have similar underlying dynamics and observed differences in clustering are mainly due to different population sizes.

Formalized classification of moss litters in swampy spruce forests of intermontane depressions of Kuznetsk Alatau

NASA Astrophysics Data System (ADS)

Efremova, T. T.; Avrova, A. F.; Efremov, S. P.

2016-09-01

The approaches of multivariate statistics have been used for the numerical classification of morphogenetic types of moss litters in swampy spruce forests according to their physicochemical properties (the ash content, decomposition degree, bulk density, pH, mass, and thickness). Three clusters of moss litters— peat, peaty, and high-ash peaty—have been specified. The functions of classification for identification of new objects have been calculated and evaluated. The degree of decomposition and the ash content are the main classification parameters of litters, though all other characteristics are also statistically significant. The final prediction accuracy of the assignment of a litter to a particular cluster is 86%. Two leading factors participating in the clustering of litters have been determined. The first factor—the degree of transformation of plant remains (quality)—specifies 49% of the total variance, and the second factor—the accumulation rate (quantity)— specifies 26% of the total variance. The morphogenetic structure and physicochemical properties of the clusters of moss litters are characterized.

Ferromagnetism and spin glass ordering in transition metal alloys (invited)

NASA Astrophysics Data System (ADS)

Crane, S.; Carnegie, D. W., Jr.; Claus, H.

1982-03-01

Magnetic properties of transition metal alloys near the percolation threshold are often complicated by metallurgical effects. Alloys like AuFe, VFe, CuNi, RhNi, and PdNi are in general not random solid solutions but have various degrees of atomic clustering or short-range order (SRO), depending on the heat treatment. First, it is shown how the magnetic ordering temperature of these alloys varies with the degree of clustering or SRO. Second, by systematically changing this degree of clustering or SRO, important information can be obtained about the magnetic phase diagram. In all these alloys below the percolation limit, the onset of ferromagnetic order is probably preceded by a spin glass-type ordering. However, details of the magnetic phase diagram near the critical point can be quite different alloy systems.

Unusual clustering of coefficients of variation in published articles from a medical biochemistry department in India.

PubMed

Hudes, Mark L; McCann, Joyce C; Ames, Bruce N

2009-03-01

A simple statistical method is described to test whether data are consistent with minimum statistical variability expected in a biological experiment. The method is applied to data presented in data tables in a subset of 84 articles among more than 200 published by 3 investigators in a small medical biochemistry department at a major university in India and to 29 "control" articles selected by key word PubMed searches. Major conclusions include: 1) unusual clustering of coefficients of variation (CVs) was observed for data from the majority of articles analyzed that were published by the 3 investigators from 2000-2007; unusual clustering was not observed for data from any of their articles examined that were published between 1992 and 1999; and 2) among a group of 29 control articles retrieved by PubMed key word, title, or title/abstract searches, unusually clustered CVs were observed in 3 articles. Two of these articles were coauthored by 1 of the 3 investigators, and 1 was from the same university but a different department. We are unable to offer a statistical or biological explanation for the unusual clustering observed.

Effect of degree correlations above the first shell on the percolation transition

NASA Astrophysics Data System (ADS)

Valdez, L. D.; Buono, C.; Braunstein, L. A.; Macri, P. A.

2011-11-01

The use of degree-degree correlations to model realistic networks which are characterized by their Pearson's coefficient, has become widespread. However the effect on how different correlation algorithms produce different results on processes on top of them, has not yet been discussed. In this letter, using different correlation algorithms to generate assortative networks, we show that for very assortative networks the behavior of the main observables in percolation processes depends on the algorithm used to build the network. The different alghoritms used here introduce different inner structures that are missed in Pearson's coefficient. We explain the different behaviors through a generalization of Pearson's coefficient that allows to study the correlations at chemical distances l from a root node. We apply our findings to real networks.

Structure, Function, and Propagation of Information across Living Two, Four, and Eight Node Degree Topologies.

PubMed

Alagapan, Sankaraleengam; Franca, Eric; Pan, Liangbin; Leondopulos, Stathis; Wheeler, Bruce C; DeMarse, Thomas B

2016-01-01

In this study, we created four network topologies composed of living cortical neurons and compared resultant structural-functional dynamics including the nature and quality of information transmission. Each living network was composed of living cortical neurons and were created using microstamping of adhesion promoting molecules and each was "designed" with different levels of convergence embedded within each structure. Networks were cultured over a grid of electrodes that permitted detailed measurements of neural activity at each node in the network. Of the topologies we tested, the "Random" networks in which neurons connect based on their own intrinsic properties transmitted information embedded within their spike trains with higher fidelity relative to any other topology we tested. Within our patterned topologies in which we explicitly manipulated structure, the effect of convergence on fidelity was dependent on both topology and time-scale (rate vs. temporal coding). A more detailed examination using tools from network analysis revealed that these changes in fidelity were also associated with a number of other structural properties including a node's degree, degree-degree correlations, path length, and clustering coefficients. Whereas information transmission was apparent among nodes with few connections, the greatest transmission fidelity was achieved among the few nodes possessing the highest number of connections (high degree nodes or putative hubs). These results provide a unique view into the relationship between structure and its affect on transmission fidelity, at least within these small neural populations with defined network topology. They also highlight the potential role of tools such as microstamp printing and microelectrode array recordings to construct and record from arbitrary network topologies to provide a new direction in which to advance the study of structure-function relationships.

The balance between keystone clustering and bed roughness in experimental step-pool stabilization

NASA Astrophysics Data System (ADS)

Johnson, J. P.

2016-12-01

Predicting how mountain channels will respond to environmental perturbations such as floods requires an improved quantitative understanding of morphodynamic feedbacks among bed topography, surface grain size and sediment sorting. In boulder-rich gravel streams, transport and sorting often lead to the development of step pool morphologies, which are expressed both in bed topography and coarse grain clustering. Bed stability is difficult to measure, and is sometimes inferred from the presence of step pools. I use scaled flume experiments to explore feedbacks among surface grain sizes, coarse grain clustering, bed roughness and hydraulic roughness during progressive bed stabilization and over a range of sediment transport rates. While grain clusters are sometimes identified by subjective interpretation, I quantify the degree of coarse surface grain clustering using spatial statistics, including a novel normalization of Ripley's K function. This approach is objective and provides information on the strength of clustering over a range of length scales. Flume experiments start with an initial bed surface with a broad grain size distribution and spatially random positions. Flow causes the bed surface to progressively stabilize in response to erosion, surface coarsening, roughening and grain reorganization. At 95% confidence, many but not all beds stabilized with coarse grains becoming more clustered than complete spatial randomness (CSR). I observe a tradeoff between topographic roughness and clustering. Beds that stabilized with higher degrees of coarse-grain clustering were topographically smoother, and vice-versa. Initial conditions influenced the degree of clustering at stability: Beds that happened to have fewer initial coarse grains had more coarse grain reorganization during stabilization, leading to more clustering. Finally, regressions demonstrate that clustering statistics actually predict hydraulic roughness significantly better than does D84 (the size at which 84% of grains are smaller). In the experimental data, the spatial organization of surface grains is a stronger control on flow characteristics than the size of surface grains.

Investigation of global and local network properties of music perception with culturally different styles of music.

PubMed

Li, Yan; Rui, Xue; Li, Shuyu; Pu, Fang

2014-11-01

Graph theoretical analysis has recently become a popular research tool in neuroscience, however, there have been very few studies on brain responses to music perception, especially when culturally different styles of music are involved. Electroencephalograms were recorded from ten subjects listening to Chinese traditional music, light music and western classical music. For event-related potentials, phase coherence was calculated in the alpha band and then constructed into correlation matrices. Clustering coefficients and characteristic path lengths were evaluated for global properties, while clustering coefficients and efficiency were assessed for local network properties. Perception of light music and western classical music manifested small-world network properties, especially with a relatively low proportion of weights of correlation matrices. For local analysis, efficiency was more discernible than clustering coefficient. Nevertheless, there was no significant discrimination between Chinese traditional and western classical music perception. Perception of different styles of music introduces different network properties, both globally and locally. Research into both global and local network properties has been carried out in other areas; however, this is a preliminary investigation aimed at suggesting a possible new approach to brain network properties in music perception. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effect of sharp maximum in ion diffusivity for liquid xenon

NASA Astrophysics Data System (ADS)

Lankin, A. V.; Orekhov, M. A.

2016-11-01

Ion diffusion in a liquid usually could be treated as a movement of an ion cluster in a viscous media. For small ions this leads to a special feature: diffusion coefficient is either independent of the ion size or increases with it. We find a different behavior for small ions in liquid xenon. Calculation of the dependence of an ion diffusion coefficient in liquid xenon on the ion size is carried out. Classical molecular dynamics method is applied. Calculated dependence of the ion diffusion coefficient on its radius has sharp maximums at the ion radiuses 1.75 and 2 Å. Every maximum is placed between two regions with different stable ion cluster configurations. This leads to the instability of these configurations in a small region between them. Consequently ion with radius near 1.75 or 2 Å could jump from one configuration to another. This increases the speed of the diffusion. A simple qualitative model for this effect is suggested. The decomposition of the ion movement into continuous and jump diffusion shows that continuous part of the diffusion is the same as for the ion cluster in the stable region.

Evolution of Cooperation Patterns in Psoriasis Research: Co-Authorship Network Analysis of Papers in Medline (1942–2013)

PubMed Central

González-Alcaide, Gregorio; Park, Jinseo; Huamaní, Charles; Belinchón, Isabel; Ramos, José M.

2015-01-01

Background Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth century, new knowledge has been generated from within an ever-growing network of investigators, working cooperatively in research groups across countries and institutions. Cooperation is a crucial determinant of academic success. Objective The aim of the present paper is to analyze the evolution of scientific collaboration at the micro level, with regard to the scientific production generated on psoriasis research. Methods A bibliographic search in the Medline database containing the MeSH terms “psoriasis” or “psoriatic arthritis” was carried out. The search results were limited to articles, reviews and letters. After identifying the co-authorships of documents on psoriasis indexed in the Medline database (1942–2013), various bibliometric indicators were obtained, including the average number of authors per document and degree of multi-authorship over time. In addition, we performed a network analysis to study the evolution of certain features of the co-authorship network as a whole: average degree, size of the largest component, clustering coefficient, density and average distance. We also analyzed the evolution of the giant component to characterize the changing research patterns in the field, and we calculated social network indicators for the nodes, namely betweenness and closeness. Results The main active research clusters in the area were identified, along with their authors of reference. Our analysis of 28,670 documents sheds light on different aspects related to the evolution of scientific collaboration in the field, including the progressive increase in the mean number of co-authors (which stood at 5.17 in the 2004–2013 decade), and the rise in multi-authored papers signed by many different authors (in the same decade, 25.77% of the documents had between 6 and 9 co-authors, and 10.28% had 10 or more). With regard to the network indicators, the average degree gradually increased up to 10.97 in the study period. The percentage of authors pertaining to the largest component also rose to 73.02% of the authors. The clustering coefficient, on the other hand, remained stable throughout the entire 70-year period, with values hovering around 0.9. Finally, the average distance peaked in the decades 1974–1983 (8.29) and 1984–2003 (8.12) then fell over the next two decades, down to 5.25 in 2004–2013. The construction of the co-authorship network (threshold of collaboration ≥ 10 co-authored works) revealed a giant component of 161 researchers, containing 6 highly cohesive sub-components. Conclusions Our study reveals the existence of a growing research community in which collaboration is increasingly important. We can highlight an essential feature associated with scientific collaboration: multi-authored papers, with growing numbers of collaborators contributing to them, are becoming more and more common, therefore the formation of research groups of increasing depth (specialization) and breadth (multidisciplinarity) is now a cornerstone of research success. PMID:26658481

Evolution of Cooperation Patterns in Psoriasis Research: Co-Authorship Network Analysis of Papers in Medline (1942-2013).

PubMed

González-Alcaide, Gregorio; Park, Jinseo; Huamaní, Charles; Belinchón, Isabel; Ramos, José M

2015-01-01

Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth century, new knowledge has been generated from within an ever-growing network of investigators, working cooperatively in research groups across countries and institutions. Cooperation is a crucial determinant of academic success. The aim of the present paper is to analyze the evolution of scientific collaboration at the micro level, with regard to the scientific production generated on psoriasis research. A bibliographic search in the Medline database containing the MeSH terms "psoriasis" or "psoriatic arthritis" was carried out. The search results were limited to articles, reviews and letters. After identifying the co-authorships of documents on psoriasis indexed in the Medline database (1942-2013), various bibliometric indicators were obtained, including the average number of authors per document and degree of multi-authorship over time. In addition, we performed a network analysis to study the evolution of certain features of the co-authorship network as a whole: average degree, size of the largest component, clustering coefficient, density and average distance. We also analyzed the evolution of the giant component to characterize the changing research patterns in the field, and we calculated social network indicators for the nodes, namely betweenness and closeness. The main active research clusters in the area were identified, along with their authors of reference. Our analysis of 28,670 documents sheds light on different aspects related to the evolution of scientific collaboration in the field, including the progressive increase in the mean number of co-authors (which stood at 5.17 in the 2004-2013 decade), and the rise in multi-authored papers signed by many different authors (in the same decade, 25.77% of the documents had between 6 and 9 co-authors, and 10.28% had 10 or more). With regard to the network indicators, the average degree gradually increased up to 10.97 in the study period. The percentage of authors pertaining to the largest component also rose to 73.02% of the authors. The clustering coefficient, on the other hand, remained stable throughout the entire 70-year period, with values hovering around 0.9. Finally, the average distance peaked in the decades 1974-1983 (8.29) and 1984-2003 (8.12) then fell over the next two decades, down to 5.25 in 2004-2013. The construction of the co-authorship network (threshold of collaboration ≥ 10 co-authored works) revealed a giant component of 161 researchers, containing 6 highly cohesive sub-components. Our study reveals the existence of a growing research community in which collaboration is increasingly important. We can highlight an essential feature associated with scientific collaboration: multi-authored papers, with growing numbers of collaborators contributing to them, are becoming more and more common, therefore the formation of research groups of increasing depth (specialization) and breadth (multidisciplinarity) is now a cornerstone of research success.

[Investigation of the distribution of water clusters in vegetables, fruits, and natural waters by flicker noise spectroscopy].

PubMed

Zubov, A V; Zubov, K V; Zubov, V A

2007-01-01

The distribution of water clusters in fresh rain water and in rain water that was aged for 30 days (North Germany, 53 degrees 33' N, 12 degrees 47' E, 293 K, rain on 25.06.06) as well as in fresh vegetables and fruits was studied by flicker noise spectroscopy. In addition, the development of water clusters in apples and potatoes during ripening in 2006 was investigated. A different distribution of water clusters in irrigation water (river and rain) and in the biomatrix of vegetables (potatoes, onions, tomatoes, red beets) and fruits (apples, bananas) was observed. It was concluded that the cluster structure of irrigation water differs from that of water of the biomatrix of vegetables and fruits and depends on drought and the biomatrix nature. Water clusters in plants are more stable and reproducible than water clusters in natural water. The main characteristics of cluster formation in materials studied were given. The oscillation frequencies of water clusters in plants (biofield) are given at which they interact with water clusters of the Earth hydrosphere. A model of series of clusters 16(H2O)100 <--> 4(H2O)402 <--> 2(H2O)903 <--> (H2O)1889 in the biomatrix of vegetables and fruits was discussed.

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

PubMed Central

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

2014-01-01

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

In Situ Effective Diffusion Coefficient Profiles in Live Biofilms Using Pulsed-Field Gradient Nuclear Magnetic Resonance

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

Renslow, Ryan S.; Majors, Paul D.; McLean, Jeffrey S.

2010-08-15

Diffusive mass transfer in biofilms is characterized by the effective diffusion coefficient. It is well-documented that the effective diffusion coefficient can vary by location in a biofilm. The current literature is dominated by effective diffusion coefficient measurements for distinct cell clusters and stratified biofilms showing this spatial variation. Regardless of whether distinct cell clusters or surface-averaging methods are used, position-dependent measurements of the effective diffusion coefficient are currently: 1) invasive to the biofilm, 2) performed under unnatural conditions, 3) lethal to cells, and/or 4) spatially restricted to only certain regions of the biofilm. Invasive measurements can lead to inaccurate resultsmore » and prohibit further (time dependent) measurements which are important for the mathematical modeling of biofilms. In this study our goals were to: 1) measure the effective diffusion coefficient for water in live biofilms, 2) monitor how the effective diffusion coefficient changes over time under growth conditions, and 3) correlate the effective diffusion coefficient with depth in the biofilm. We measured in situ two-dimensional effective diffusion coefficient maps within Shewanella oneidensis MR-1biofilms using pulsed-field gradient nuclear magnetic resonance methods, and used them to calculate surface-averaged relative effective diffusion coefficient (Drs) profiles. We found that 1) Drs decreased from the top of the biofilm to the bottom, 2) Drs profiles differed for biofilms of different ages, 3) Drs profiles changed over time and generally decreased with time, 4) all the biofilms showed very similar Drs profiles near the top of the biofilm, and 5) the Drs profile near the bottom of the biofilm was different for each biofilm. Practically, our results demonstrate that advanced biofilm models should use a variable effective diffusivity which changes with time and location in the biofilm.« less

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Geography of cretaceous extinctions: Data base development

NASA Technical Reports Server (NTRS)

Raup, D. M.

1991-01-01

Data bases built from the source literature are plagued by problems of data quality. Unless the data acquisition is done by experts, working slowly, the data base may contain so much garbage that true signals and patterns cannot be detected. On the other hand, high quality data bases develop so slowly that satisfactory statistical analysis may never be possible due to the small sample sizes. Results of a test are presented of the opposite strategy: rapid data acquisition by non-experts with minimal control on data quality. A published list of 186 species and genera of fossil invertibrates of the latest Cretaceous Age (Maestrichtian) were located through a random search of the paleobiological and geological literature. The geographic location for each faunal list was then transformed electronically to Maestrichtian latitude and longitude and the lists were further digested to identify the genera occurring in each ten-degree, latitude-longitude block. The geographical lists were clustered using the Otsuka similarity coefficient and a standard unweight-pair-group method. The resulting clusters are remarkably consistent geographically, indicating that a strong biogeographic signal is visible despite low-quality data. A further test evaluated the geographic pattern of end-Cretaceaous extinctions. All genera in the data base were compared with Sepkoski's compendium of time ranges of genera to determine which of the reported genera survived the Cretaceous mass extinction. In turn, extinction rates for the ten-degree, latitude-longitude blocks were mapped. The resulting distribution is readily interpretable as a robust pattern of the geography of the mass extinction. The study demonstrates that a low-quality data base, built rapidly, can provide a basis for meaningful analysis of past biotic events.

The second virial coefficient of system ((nitrogen-water))

NASA Astrophysics Data System (ADS)

Podmurnaya, O. A.

2004-01-01

The virial coefficient data of various components of atmosphere are interesting because permit to evaluate a deviation from ideal gas model. These data may be useful while investigating the clusters generation and determination their contribution in absorption. The second cross virial coefficient Baw for system ((nitrogen water)) has been calculated form +9°C to +50°C using the last experimental data about water vapor mole fraction. The reliability of this coefficient has been tested by analysing of errors sources and by comparing the results with other available experimental data.

Regional brain network organization distinguishes the combined and inattentive subtypes of Attention Deficit Hyperactivity Disorder.

PubMed

Saad, Jacqueline F; Griffiths, Kristi R; Kohn, Michael R; Clarke, Simon; Williams, Leanne M; Korgaonkar, Mayuresh S

2017-01-01

Attention Deficit Hyperactivity Disorder (ADHD) is characterized clinically by hyperactive/impulsive and/or inattentive symptoms which determine diagnostic subtypes as Predominantly Hyperactive-Impulsive (ADHD-HI), Predominantly Inattentive (ADHD-I), and Combined (ADHD-C). Neuroanatomically though we do not yet know if these clinical subtypes reflect distinct aberrations in underlying brain organization. We imaged 34 ADHD participants defined using DSM-IV criteria as ADHD-I ( n  = 16) or as ADHD-C ( n  = 18) and 28 matched typically developing controls, aged 8-17 years, using high-resolution T1 MRI. To quantify neuroanatomical organization we used graph theoretical analysis to assess properties of structural covariance between ADHD subtypes and controls (global network measures: path length, clustering coefficient, and regional network measures: nodal degree). As a context for interpreting network organization differences, we also quantified gray matter volume using voxel-based morphometry. Each ADHD subtype was distinguished by a different organizational profile of the degree to which specific regions were anatomically connected with other regions (i.e., in "nodal degree"). For ADHD-I (compared to both ADHD-C and controls) the nodal degree was higher in the hippocampus. ADHD-I also had a higher nodal degree in the supramarginal gyrus, calcarine sulcus, and superior occipital cortex compared to ADHD-C and in the amygdala compared to controls. By contrast, the nodal degree was higher in the cerebellum for ADHD-C compared to ADHD-I and in the anterior cingulate, middle frontal gyrus and putamen compared to controls. ADHD-C also had reduced nodal degree in the rolandic operculum and middle temporal pole compared to controls. These regional profiles were observed in the context of no differences in gray matter volume or global network organization. Our results suggest that the clinical distinction between the Inattentive and Combined subtypes of ADHD may also be reflected in distinct aberrations in underlying brain organization.

Calculation of thermal expansion coefficient of glasses based on topological constraint theory

NASA Astrophysics Data System (ADS)

Zeng, Huidan; Ye, Feng; Li, Xiang; Wang, Ling; Yang, Bin; Chen, Jianding; Zhang, Xianghua; Sun, Luyi

2016-10-01

In this work, the thermal expansion behavior and the structure configuration evolution of glasses were studied. Degree of freedom based on the topological constraint theory is correlated with configuration evolution; considering the chemical composition and the configuration change, the analytical equation for calculating the thermal expansion coefficient of glasses from degree of freedom was derived. The thermal expansion of typical silicate and chalcogenide glasses was examined by calculating their thermal expansion coefficients (TEC) using the approach stated above. The results showed that this approach was energetically favorable for glass materials and revealed the corresponding underlying essence from viewpoint of configuration entropy. This work establishes a configuration-based methodology to calculate the thermal expansion coefficient of glasses that, lack periodic order.

Listing triangles in expected linear time on a class of power law graphs.

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

Nordman, Daniel J.; Wilson, Alyson G.; Phillips, Cynthia Ann

Enumerating triangles (3-cycles) in graphs is a kernel operation for social network analysis. For example, many community detection methods depend upon finding common neighbors of two related entities. We consider Cohen's simple and elegant solution for listing triangles: give each node a 'bucket.' Place each edge into the bucket of its endpoint of lowest degree, breaking ties consistently. Each node then checks each pair of edges in its bucket, testing for the adjacency that would complete that triangle. Cohen presents an informal argument that his algorithm should run well on real graphs. We formalize this argument by providing an analysismore » for the expected running time on a class of random graphs, including power law graphs. We consider a rigorously defined method for generating a random simple graph, the erased configuration model (ECM). In the ECM each node draws a degree independently from a marginal degree distribution, endpoints pair randomly, and we erase self loops and multiedges. If the marginal degree distribution has a finite second moment, it follows immediately that Cohen's algorithm runs in expected linear time. Furthermore, it can still run in expected linear time even when the degree distribution has such a heavy tail that the second moment is not finite. We prove that Cohen's algorithm runs in expected linear time when the marginal degree distribution has finite 4/3 moment and no vertex has degree larger than {radical}n. In fact we give the precise asymptotic value of the expected number of edge pairs per bucket. A finite 4/3 moment is required; if it is unbounded, then so is the number of pairs. The marginal degree distribution of a power law graph has bounded 4/3 moment when its exponent {alpha} is more than 7/3. Thus for this class of power law graphs, with degree at most {radical}n, Cohen's algorithm runs in expected linear time. This is precisely the value of {alpha} for which the clustering coefficient tends to zero asymptotically, and it is in the range that is relevant for the degree distribution of the World-Wide Web.« less

Grey matter networks in people at increased familial risk for schizophrenia.

PubMed

Tijms, Betty M; Sprooten, Emma; Job, Dominic; Johnstone, Eve C; Owens, David G C; Willshaw, David; Seriès, Peggy; Lawrie, Stephen M

2015-10-01

Grey matter brain networks are disrupted in schizophrenia, but it is still unclear at which point during the development of the illness these disruptions arise and whether these can be associated with behavioural predictors of schizophrenia. We investigated if single-subject grey matter networks were disrupted in a sample of people at familial risk of schizophrenia. Single-subject grey matter networks were extracted from structural MRI scans of 144 high risk subjects, 32 recent-onset patients and 36 healthy controls. The following network properties were calculated: size, connectivity density, degree, path length, clustering coefficient, betweenness centrality and small world properties. People at risk of schizophrenia showed decreased path length and clustering in mostly prefrontal and temporal areas. Within the high risk sample, the path length of the posterior cingulate cortex and the betweenness centrality of the left inferior frontal operculum explained 81% of the variance in schizotypal cognitions, which was previously shown to be the strongest behavioural predictor of schizophrenia in the study. In contrast, local grey matter volume measurements explained 48% of variance in schizotypy. The present results suggest that single-subject grey matter networks can quantify behaviourally relevant biological alterations in people at increased risk for schizophrenia before disease onset. Copyright © 2015 Elsevier B.V. All rights reserved.

Heterogeneity of interactions of microbial communities in regions of Taihu Lake with different nutrient loadings: A network analysis.

PubMed

Cao, Xinyi; Zhao, Dayong; Xu, Huimin; Huang, Rui; Zeng, Jin; Yu, Zhongbo

2018-06-11

To investigate the differences in the interactions of microbial communities in two regions in Taihu Lake with different nutrient loadings [Meiliang Bay (MLB) and Xukou Bay (XKB)], water samples were collected and both intra- and inter-kingdom microbial community interactions were examined with network analysis. It is demonstrated that all of the bacterioplankton, microeukaryotes and inter-kingdom communities networks in Taihu Lake were non-random. For the networks of bacterioplankton and inter-kingdom community in XKB, higher clustering coefficient and average degree but lower average path length indexes were observed, indicating the nodes in XKB were more clustered and closely connected with plenty edges than those of MLB. The bacterioplankton and inter-kingdom networks were considerably larger and more complex with more module hubs and connectors in XKB compared with those of MLB, whereas the microeukaryotes networks were comparable and had no module hubs or connectors in the two lake zones. The phyla of Acidobacteria, Cyanobacteria and Planctomycetes maintained greater cooperation with other phyla in XKB, rather than competition. The relationships between microbial communities and environmental factors in MLB were weaker. Compared with the microbial community networks of XKB, less modules in networks of MLB were significantly correlated with total phosphorous and total nitrogen.

Low degree Earth's gravity coefficients determined from different space geodetic observations and climate models

NASA Astrophysics Data System (ADS)

Wińska, Małgorzata; Nastula, Jolanta

2017-04-01

Large scale mass redistribution and its transport within the Earth system causes changes in the Earth's rotation in space, gravity field and Earth's ellipsoid shape. These changes are observed in the ΔC21, ΔS21, and ΔC20 spherical harmonics gravity coefficients, which are proportional to the mass load-induced Earth rotational excitations. In this study, linear trend, decadal, inter-annual, and seasonal variations of low degree spherical harmonics coefficients of Earth's gravity field, determined from different space geodetic techniques, Gravity Recovery and Climate Experiment (GRACE), satellite laser ranging (SLR), Global Navigation Satellite System (GNSS), Earth rotation, and climate models, are examined. In this way, the contribution of each measurement technique to interpreting the low degree surface mass density of the Earth is shown. Especially, we evaluate an usefulness of several climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) to determine the low degree Earth's gravity coefficients using GRACE satellite observations. To do that, Terrestrial Water Storage (TWS) changes from several CMIP5 climate models are determined and then these simulated data are compared with the GRACE observations. Spherical harmonics ΔC21, ΔS21, and ΔC20 changes are calculated as the sum of atmosphere and ocean mass effect (GAC values) taken from GRACE and a land surface hydrological estimate from the selected CMIP5 climate models. Low degree Stokes coefficients of the surface mass density determined from GRACE, SLR, GNSS, Earth rotation measurements and climate models are compared to each other in order to assess their consistency. The comparison is done by using different types of statistical and signal processing methods.

Sample Size Estimation in Cluster Randomized Educational Trials: An Empirical Bayes Approach

ERIC Educational Resources Information Center

Rotondi, Michael A.; Donner, Allan

2009-01-01

The educational field has now accumulated an extensive literature reporting on values of the intraclass correlation coefficient, a parameter essential to determining the required size of a planned cluster randomized trial. We propose here a simple simulation-based approach including all relevant information that can facilitate this task. An…

Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2011-01-01

Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

Sparse subspace clustering for data with missing entries and high-rank matrix completion.

PubMed

Fan, Jicong; Chow, Tommy W S

2017-09-01

Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Cluster analysis in biomedical researches].

PubMed

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.

Response of selected binomial coefficients to varying degrees of matrix sparseness and to matrices with known data interrelationships

USGS Publications Warehouse

Archer, A.W.; Maples, C.G.

1989-01-01

Numerous departures from ideal relationships are revealed by Monte Carlo simulations of widely accepted binomial coefficients. For example, simulations incorporating varying levels of matrix sparseness (presence of zeros indicating lack of data) and computation of expected values reveal that not only are all common coefficients influenced by zero data, but also that some coefficients do not discriminate between sparse or dense matrices (few zero data). Such coefficients computationally merge mutually shared and mutually absent information and do not exploit all the information incorporated within the standard 2 ?? 2 contingency table; therefore, the commonly used formulae for such coefficients are more complicated than the actual range of values produced. Other coefficients do differentiate between mutual presences and absences; however, a number of these coefficients do not demonstrate a linear relationship to matrix sparseness. Finally, simulations using nonrandom matrices with known degrees of row-by-row similarities signify that several coefficients either do not display a reasonable range of values or are nonlinear with respect to known relationships within the data. Analyses with nonrandom matrices yield clues as to the utility of certain coefficients for specific applications. For example, coefficients such as Jaccard, Dice, and Baroni-Urbani and Buser are useful if correction of sparseness is desired, whereas the Russell-Rao coefficient is useful when sparseness correction is not desired. ?? 1989 International Association for Mathematical Geology.

Second-degree Stokes coefficients from multi-satellite SLR

NASA Astrophysics Data System (ADS)

Bloßfeld, Mathis; Müller, Horst; Gerstl, Michael; Štefka, Vojtěch; Bouman, Johannes; Göttl, Franziska; Horwath, Martin

2015-09-01

The long wavelength part of the Earth's gravity field can be determined, with varying accuracy, from satellite laser ranging (SLR). In this study, we investigate the combination of up to ten geodetic SLR satellites using iterative variance component estimation. SLR observations to different satellites are combined in order to identify the impact of each satellite on the estimated Stokes coefficients. The combination of satellite-specific weekly or monthly arcs allows to reduce parameter correlations of the single-satellite solutions and leads to alternative estimates of the second-degree Stokes coefficients. This alternative time series might be helpful for assessing the uncertainty in the impact of the low-degree Stokes coefficients on geophysical investigations. In order to validate the obtained time series of second-degree Stokes coefficients, a comparison with the SLR RL05 time series of the Center of Space Research (CSR) is done. This investigation shows that all time series are comparable to the CSR time series. The precision of the weekly/monthly and coefficients is analyzed by comparing mass-related equatorial excitation functions with geophysical model results and reduced geodetic excitation functions. In case of , the annual amplitude and phase of the DGFI solution agrees better with three of four geophysical model combinations than other time series. In case of , all time series agree very well to each other. The impact of on the ice mass trend estimates for Antarctica are compared based on CSR GRACE RL05 solutions, in which different monthly time series are used for replacing. We found differences in the long-term Antarctic ice loss of Gt/year between the GRACE solutions induced by the different SLR time series of CSR and DGFI, which is about 13 % of the total ice loss of Antarctica. This result shows that Antarctic ice mass loss quantifications must be carefully interpreted.

Computational Fluid Dynamics (CFD) Simulations of a Humvee Airdropped from Aircraft

NASA Astrophysics Data System (ADS)

Reyes, Phillip M.

Military airdrop is a means of transporting and delivering cargo to inaccessible locales faster and more efficiently. The Humvee, an all-terrain truck, is one such payload that the U.S. Army drops routinely. Here, interesting physics occurs both structurally and aerodynamically. From a fluid dynamics and trajectory standpoint, determining the aerodynamic forces and moments acting on the parachute and payload is crucial particularly for trajectory prediction. This study primarily used Computational Fluid Dynamics (CFD) to simulate the aerodynamics of an airdrop Humvee model in two regimes of fall, namely, right after clearing the aircraft ramp, and during descent under parachute. This study was performed at a Reynolds number of 3.07x10. 6 and at an airspeedof 9.144m/s (30ft/s). The first humvee part of the study analyzed the aerodynamic coefficients drag, lift, and pitching moment over a 360 degree range of pitch angles for the Humvee configured for extraction. The second set of humvee simulations focused on the aerodynamic coefficients at pitch angles of -40 degrees to +40 degrees with the platform and vehicle configured for descent under parachute. The Humvee after ramp tip-off has a parachute pack on its hood, but lacks one during the descent phase. The numerical data was compared with the results of geometries from previous studies. These geometries include: the flat plate, Type-V LVADS and 10K-JPADS containers, and a cargo-carrying platform outfitted with a bumper. Our results clearly show the effects of the many angular features that characterize the shape of a Humvee in comparison to those of a simple cuboid, particularly with regards to the loss of lift in a sub-range of pitch angle (-45 degrees to -180 degrees). First, the aerodynamic coefficients were calculated over one full-revolution of the humvee (-180 degrees to +180 degrees static pitch angles with respect to the humvee's platform) best matched in lift, drag, and moment those of the type V LVADS payload analyzed in a previous study. Here, three important findings emerge: (1) Lift is not symmetric with positive to negative angles and more so, lift is negligible at pitch angles less than -45 degrees (2) the humvee-platofrm may be considered stable when oriented perpendicular to the flow (both 90 degrees and -90 degrees); (3) there is a range of pitch angle (52 degrees to 117 degrees) where the lift coefficient is linearly dependent on angle of attack. This is the orientation at which the oncoming flow meets the platform first (i.e. before moving past the humvee's body), thereby producing a forward-projected area similar to that of a flat-plate. The second part of the study (descent under parachute) also shows a similar result. Negative pitch angles show a continual increase in lift and moment coefficients, whereas for positive pitch angles at 30 degrees and 40 degrees the negative lift values do not decrease as fast as earlier positive pitch angles. This difference is explained with pressure coefficient curves. Validation of our CFD modeling is also discussed, with the presentation of numerical results generated on benchmark cases such as the flows about flat plates held at various pitch angles.

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

Mammen, Nisha; Spanu, Leonardo; Tyo, Eric C.

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 alsomore » 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.« less

Focus-based filtering + clustering technique for power-law networks with small world phenomenon

NASA Astrophysics Data System (ADS)

Boutin, François; Thièvre, Jérôme; Hascoët, Mountaz

2006-01-01

Realistic interaction networks usually present two main properties: a power-law degree distribution and a small world behavior. Few nodes are linked to many nodes and adjacent nodes are likely to share common neighbors. Moreover, graph structure usually presents a dense core that is difficult to explore with classical filtering and clustering techniques. In this paper, we propose a new filtering technique accounting for a user-focus. This technique extracts a tree-like graph with also power-law degree distribution and small world behavior. Resulting structure is easily drawn with classical force-directed drawing algorithms. It is also quickly clustered and displayed into a multi-level silhouette tree (MuSi-Tree) from any user-focus. We built a new graph filtering + clustering + drawing API and report a case study.

Efficient simultaneous dense coding and teleportation with two-photon four-qubit cluster states

NASA Astrophysics Data System (ADS)

Zhang, Cai; Situ, Haozhen; Li, Qin; He, Guang Ping

2016-08-01

We firstly propose a simultaneous dense coding protocol with two-photon four-qubit cluster states in which two receivers can simultaneously get their respective classical information sent by a sender. Because each photon has two degrees of freedom, the protocol will achieve a high transmittance. The security of the simultaneous dense coding protocol has also been analyzed. Secondly, we investigate how to simultaneously teleport two different quantum states with polarization and path degree of freedom using cluster states to two receivers, respectively, and discuss its security. The preparation and transmission of two-photon four-qubit cluster states is less difficult than that of four-photon entangled states, and it has been experimentally generated with nearly perfect fidelity and high generation rate. Thus, our protocols are feasible with current quantum techniques.

Electron-temperature dependence of dissociative recombination of electrons with CO/+/./CO/n-series ions

NASA Technical Reports Server (NTRS)

Whitaker, M.; Biondi, M. A.; Johnsen, R.

1981-01-01

The dependence on electron temperature of the coefficients for electron recombination with molecular cluster ions of the carbon monoxide series, CO(+).(CO)n, is determined. A microwave discharge lasting approximately 0.1 msec was applied in 5-20 Torr neon containing a few tenths percent CO in an afterglow mass spectrometer apparatus, and the time histories of the various afterglow ions were measured. Expressions for the dependence of the recombination coefficients of the dimer and trimer ions CO(+).CO and CO(+).(CO)2 are obtained which are found to be significantly different from those previously obtained for hydronium and ammonium series polar cluster ions, but similar to those of simple diatomic ions.

Inviscid Flow Computations of the Shuttle Orbiter for Mach 10 and 15 and Angle of Attack 40 to 60 Degrees

NASA Technical Reports Server (NTRS)

Prabhu, Ramadas K.; Sutton, Kenneth (Technical Monitor)

2001-01-01

This report documents the results of a computational study done to compute the inviscid longitudinal aerodynamic characteristics of the Space Shuttle Orbiter for Mach numbers 10 and 15 at angles of attack of 40, 50, 55, and 60 degrees. These computations were done to provide limited aerodynamic data in support of the Orbiter contingency abort task. The Orbiter had all the control surfaces in the undeflected position. The unstructured grid software FELISA was used for these computations with the equilibrium air option. Normal and axial force coefficients and pitching moment coefficients were computed. The hinge moment coefficients of the body flap and the inboard and outboard elevons were also computed. These results were compared with Orbiter Air Data Book (OADB) data and those computed using GASP. The comparison with the GASP results showed very good agreement in Cm and Ca at all the points. The computed axial force coefficients were smaller than those computed by GASP. There were noticeable differences between the present results and those in the OADB at angles of attack greater than 50 degrees.

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

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

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

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

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

NASA Astrophysics Data System (ADS)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-06-01

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

Numerical taxonomy of Vibrio cholerae and related species isolated from areas that are endemic and nonendemic for cholera.

PubMed Central

McNicol, L A; De, S P; Kaper, J B; West, P A; Colwell, R R

1983-01-01

A total of 165 strains of vibrios isolated from clinical and environmental sources in the United States, India, and Bangladesh, 11 reference cultures, and 4 duplicated cultures were compared in a numerical taxonomic study using 83 unit characters. Similarity between strains was computed by using the simple matching coefficient and the Jaccard coefficient. Strains were clustered by unweighted average linkage and single linkage algorithms. All methods gave similar cluster compositions. The estimated probability of error in the study was obtained from a comparison of the results of duplicated strains and was within acceptable limits. A total of 174 of the 180 organisms studied were divided into eight major clusters. Two clusters were identified as Vibrio cholerae, one as Vibrio mimicus, one as Vibrio parahaemolyticus, three as Vibrio species, and one as Aeromonas hydrophila. The V. mimicus cluster could be further divided into two subclusters, and the major V. cholerae group could be split into seven minor subclusters. Phenotypic traits routinely used to identify clinical isolates of V. cholerae can be used to identify environmental V. cholerae isolates. No distinction was found between strains of V. cholerae isolated from regions endemic for cholera and strains from nonendemic regions. PMID:6874901

A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation

NASA Astrophysics Data System (ADS)

Błaszczuk, Artur; Krzywański, Jarosław

2017-03-01

The interrelation between fuzzy logic and cluster renewal approaches for heat transfer modeling in a circulating fluidized bed (CFB) has been established based on a local furnace data. The furnace data have been measured in a 1296 t/h CFB boiler with low level of flue gas recirculation. In the present study, the bed temperature and suspension density were treated as experimental variables along the furnace height. The measured bed temperature and suspension density were varied in the range of 1131-1156 K and 1.93-6.32 kg/m3, respectively. Using the heat transfer coefficient for commercial CFB combustor, two empirical heat transfer correlation were developed in terms of important operating parameters including bed temperature and also suspension density. The fuzzy logic results were found to be in good agreement with the corresponding experimental heat transfer data obtained based on cluster renewal approach. The predicted bed-to-wall heat transfer coefficient covered a range of 109-241 W/(m2K) and 111-240 W/(m2K), for fuzzy logic and cluster renewal approach respectively. The divergence in calculated heat flux recovery along the furnace height between fuzzy logic and cluster renewal approach did not exceeded ±2%.

Packing the silica colloidal crystal beads: a facile route to superhydrophobic surfaces.

PubMed

Sun, Cheng; Gu, Zhong-Ze; Xu, Hua

2009-11-03

To mimic the structure of the lotus leaf, we present a facile route to prepare superhydrophobic surfaces by depositing nanoparticle clusters onto a solid surface. These clusters were fabricated via solidification of an emulsion droplet containing a nanoparticle in silicone oil. Thus, the microsized clusters and nanoparticles form dual-scale roughness structures. The surface is modified by fluoroalkylsilane and exhibits superhydrophobicity, with a contact angle greater than 165 degrees as well as a sliding angle less than 1 degrees . On the basis of size tuning of the nano/microstructures, various contact angles and sliding angles were investigated. Furthermore, the influence of micro/nanostructures on superhydrophobicity is discussed.

Investigation of installation effects of single-engine convergent-divergent nozzles

NASA Technical Reports Server (NTRS)

Burley, J. R., II; Berrier, B. L.

1982-01-01

An investigation was conducted in the Langley 16-Foot Transonic Tunnel to determine installation effects on single-engine convergent-divergent nozzles applicable to reduced-power supersonic cruise aircraft. Tests were conducted at Mach numbers from 0.50 to 1.20, at angles of attack from -3 degrees to 9 degrees, and at nozzle pressure ratios from 1.0 (jet off) to 8.0. The effects of empennage arrangement, nozzle length, a cusp fairing, and afterbody closure on total aft-end drag coefficient and component drag coefficients were investigated. Basic lift- and drag-coefficient data and external static-pressure distributions on the nozzle and afterbody are presented and discussed.

Quantization of collagen organization in the stroma with a new order coefficient

PubMed Central

Germann, James A.; Martinez-Enriquez, Eduardo; Marcos, Susana

2017-01-01

Many optical and biomechanical properties of the cornea, specifically the transparency of the stroma and its stiffness, can be traced to the degree of order and direction of the constituent collagen fibers. To measure the degree of order inside the cornea, a new metric, the order coefficient, was introduced to quantify the organization of the collagen fibers from images of the stroma produced with a custom-developed second harmonic generation microscope. The order coefficient method gave a quantitative assessment of the differences in stromal collagen arrangement across the cornea depths and between untreated stroma and cross-linked stroma. PMID:29359095

Dealing with Dependence (Part I): Understanding the Effects of Clustered Data

ERIC Educational Resources Information Center

McCoach, D. Betsy; Adelson, Jill L.

2010-01-01

This article provides a conceptual introduction to the issues surrounding the analysis of clustered (nested) data. We define the intraclass correlation coefficient (ICC) and the design effect, and we explain their effect on the standard error. When the ICC is greater than 0, then the design effect is greater than 1. In such a scenario, the…

Degree-based statistic and center persistency for brain connectivity analysis.

PubMed

Yoo, Kwangsun; Lee, Peter; Chung, Moo K; Sohn, William S; Chung, Sun Ju; Na, Duk L; Ju, Daheen; Jeong, Yong

2017-01-01

Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Teleportation of Three-Qubit State via Six-qubit Cluster State

NASA Astrophysics Data System (ADS)

Yu, Li-zhi; Sun, Shao-xin

2015-05-01

A scheme of probabilistic teleportation was proposed. In this scheme, we took a six-qubit nonmaximally cluster state as the quantum channel to teleport an unknown three-qubit entangled state. Based on Bob's three times Bell state measurement (BSM) results, the receiver Bob can by introducing an auxiliary particle and the appropriate transformation to reconstruct the initial state with a certain probability. We found that, the successful transmission probability depend on the absolute value of coefficients of two of six particle cluster state minimum.

A Systematic Study of Kelvin-Helmholtz Instability in Galaxy Clusters

NASA Astrophysics Data System (ADS)

Su, Yuanyuan

2017-09-01

Kelvin-Helmholtz instabilities (KHI) were observed at cold fronts in a handful of clusters. KHI are predicted at all cold fronts in hydro simulation of intracluster medium (ICM). Their presence and absence provides a unique probe of transport processes in the hot plasma, which are essential to the dissipation and redistribution of the energy in the ICM. We propose the first systematic study of the prevalence of KHI in galaxy clusters by analyzing the archived Chandra observations of a sample of 50 nearby galaxy clusters. We will associate the occurrence and properties of KHI rolls with various cluster parameters such as their gas temperature and density, and put constraints on effective transport coefficients in the ICM

An Electroencephalography Network and Connectivity Analysis for Deception in Instructed Lying Tasks

PubMed Central

Wang, Yue; Ng, Wu Chun; Ng, Khoon Siong; Yu, Ke; Wu, Tiecheng; Li, Xiaoping

2015-01-01

Deception is an impactful social event that has been the focus of an abundance of researches over recent decades. In this paper, an electroencephalography (EEG) study is presented regarding the cognitive processes of an instructed liar/truth-teller during the time window of stimulus (question) delivery period (SDP) prior to their deceptive/truthful responses towards questions related to authentic (WE: with prior experience) and fictional experience (NE: no prior experience). To investigate deception in non-experienced events, the subjects were given stimuli in a mock interview scenario that induced them to fabricate lies. To analyze the data, frequency domain network and connectivity analysis was performed in the source space in order to provide a more systematic level understanding of deception during SDP. This study reveals several groups of neuronal generators underlying both the instructed lying (IL) and the instructed truth-telling (IT) conditions for both tasks during the SDP. Despite the similarities existed in these group components, significant differences were found in the intra- and inter-group connectivity between the IL and IT conditions in either task. Additionally, the response time was found to be positively correlated with the clustering coefficient of the inferior frontal gyrus (44R) in the WE-IL condition and positively correlated with the clustering coefficient of the precuneus (7L) and the angular gyrus (39R) in the WE-IT condition. However, the response time was found to be marginally negatively correlated with the clustering coefficient of the secondary auditory cortex (42L) in the NE-IL condition and negatively correlated with the clustering coefficient of the somatosensory association cortex (5L, R) in the NE-IT condition. Therefore, these results provide complementary and intuitive evidence for the differences between the IL and IT conditions in SDP for two types of deception tasks, thus elucidating the electrophysiological mechanisms underlying SDP of deception from regional, inter-regional, network, and inter-network scale analyses. PMID:25679784

Luminosity segregation in galaxy clusters as an indication of dynamical evolution

NASA Technical Reports Server (NTRS)

Baier, F. W.; Schmidt, K.-H.

1993-01-01

Theoretical models describing the dynamical evolution of self-gravitating systems predict a spatial mass segregation for more evolved systems, with the more massive objects concentrated toward the center of the configuration. From the observational point of view, however, the existence of mass segregation in galaxy clusters seems to be a matter of controversy. A special problem in this connection is the formation of cD galaxies in the centers of galaxy clusters. The most promising scenarios of their formation are galaxy cannibalism (merger scenario) and growing by cooling flows. It seems to be plausible to consider the swallowing of smaller systems by a dominant galaxy as an important process in the evolution of a cD galaxy. The stage of the evolution of the dominant galaxy should be reflected by the surrounding galaxy population, especially by possible mass segregation effects. Assuming that mass segregation is tantamount to luminosity segregation we analyzed luminosity segregation in roughly 40 cD galaxy clusters. Obviously there are three different groups of clusters: (1) clusters with luminosity segregation, (2) clusters without luminosity segregation, and (3) such objects exhibiting a phenomenon which we call antisegregation in luminosity, i.e. a deficiency of bright galaxies in the central regions of clusters. This result is interpreted in the sense of different degrees of mass segregation and as an indication for different evolution stages of these clusters. The clusters are arranged in the three segregation classes 2, 1, and 0 (S2 = strong mass segregation, S1 = moderate mass segregation, S0 = weak or absent mass segregation). We assume that a galaxy cluster starts its dynamical evolution after virialization without any radial mass segregation. Energy exchange during encounters of cluster members as well as merger processes between cluster galaxies lead to an increasing radial mass segregation in the cluster (S1). If a certain degree of segregation (S2) has been established, an essential number of slow-moving and relative massive cluster members in the center will be cannibalized by the initial brightest cluster galaxy. This process should lead to the growing of the predominate galaxy, which is accompanied by a diminution of the mass segregation (transition to S1 and S0, respectively) in the neighborhood of the central very massive galaxy. An increase of the areal density of brighter galaxies towards the outer cluster regions (antisegregation of luminosity), i.e. an extreme low degree of mass segregation was estimated for a substantial percentage of cD clusters. This result favors the cannibalism scenario for the formation of cD galaxies.

Estimating the intra-cluster correlation coefficient for evaluating an educational intervention program to improve rabies awareness and dog bite prevention among children in Sikkim, India: A pilot study.

PubMed

Auplish, Aashima; Clarke, Alison S; Van Zanten, Trent; Abel, Kate; Tham, Charmaine; Bhutia, Thinlay N; Wilks, Colin R; Stevenson, Mark A; Firestone, Simon M

2017-05-01

Educational initiatives targeting at-risk populations have long been recognized as a mainstay of ongoing rabies control efforts. Cluster-based studies are often utilized to assess levels of knowledge, attitudes and practices of a population in response to education campaigns. The design of cluster-based studies requires estimates of intra-cluster correlation coefficients obtained from previous studies. This study estimates the school-level intra-cluster correlation coefficient (ICC) for rabies knowledge change following an educational intervention program. A cross-sectional survey was conducted with 226 students from 7 schools in Sikkim, India, using cluster sampling. In order to assess knowledge uptake, rabies education sessions with pre- and post-session questionnaires were administered. Paired differences of proportions were estimated for questions answered correctly. A mixed effects logistic regression model was developed to estimate school-level and student-level ICCs and to test for associations between gender, age, school location and educational level. The school- and student-level ICCs for rabies knowledge and awareness were 0.04 (95% CI: 0.01, 0.19) and 0.05 (95% CI: 0.2, 0.09), respectively. These ICCs suggest design effect multipliers of 5.45 schools and 1.05 students per school, will be required when estimating sample sizes and designing future cluster randomized trials. There was a good baseline level of rabies knowledge (mean pre-session score 71%), however, key knowledge gaps were identified in understanding appropriate behavior around scared dogs, potential sources of rabies and how to correctly order post rabies exposure precaution steps. After adjusting for the effect of gender, age, school location and education level, school and individual post-session test scores improved by 19%, with similar performance amongst boys and girls attending schools in urban and rural regions. The proportion of participants that were able to correctly order post-exposure precautionary steps following educational intervention increased by 87%. The ICC estimates presented in this study will aid in designing cluster-based studies evaluating educational interventions as part of disease control programs. This study demonstrates the likely benefits of educational intervention incorporating bite prevention and rabies education. Copyright © 2017 Elsevier B.V. All rights reserved.

Current State of an Intelligent System to Aid in Tephra Layer Correlation

NASA Astrophysics Data System (ADS)

Hanson-Hedgecock, S.; Bursik, M.; Rogova, G.

2007-12-01

We are developing a computer based intelligent system to correlate tephra layers by using the lithologic, mineralogic, and geochemical characteristics of field samples, to aid geologists in interpreting eruption patterns of volcanic chains and fields. The intelligent system is used to define groups of tephra source vents by utilizing geochemical data, and to correlate tephra layers based on lithostratigraphic characteristics. Understanding the eruption history of a volcano from stratigraphic studies is important for forecasting future eruptive behavior and hazards. In volcanic chains and fields with a complex eruptive history and no central vent, determining the spatio- temporal eruption patterns is difficult. Sedimentologic and chemical variability, and sparse sampling often result in relatively large variances and imprecision in the dataset. Lithostratigraphic and geochemical interpretation also depends on ones' level of expertise and can be subjective. The processing of lithostratigraphic features is conducted by a hybrid classifier, composed of supervised artificial neural networks (ANNs) combined within the framework of the Dempster-Shafer theory of evidence. Since lithostratigraphic features vary with distance from source, hypothetical vent locations are determined by using expert domain knowledge and geostatistical methods. Geochemical data are processed by a suit of fuzzy k- means classifiers. Each fuzzy k-means classifier assigns observations to multiple clusters with various degrees, called membership coefficients. The assignment minimizes a function of the total distance between the centers of clusters and the individual geochemical data patterns weighed by the membership coefficients. Improved clustering results of geochemical data are achieved by the fusion of individual clustering results with an evidential combination method. Lithostratigraphic data from individual tephra beds of the North Mono eruption sequence are used to test the effectiveness of the intelligent system for tephra layer correlation. Geochemical data from tephra bedsets of the Mono and Inyo Craters, CA, are used to test the effectiveness of the intelligent system for eruption sequence correlation. The intelligent system aids correlation by showing matches and disparities between data patterns from different outcrops that may have been overlooked in initial interpretations. Initial results show that the lithostratigraphic classifier is able to accurately differentiate known layers 76% of the time. Output from the lithostratigraphic classifier can furthermore be plotted directly as isopleth maps that can aid in rapid recognition of tephra layers as well as determination of eruption characteristics, e.g. eruption volume, plume height, etc. The intelligent system produces a useful recognition result, while dealing with the uncertainty from sparse data and the imprecise description of layer characteristics.

What Can Graph Theory Tell Us About Word Learning and Lexical Retrieval?

PubMed Central

Vitevitch, Michael S.

2008-01-01

Purpose Graph theory and the new science of networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might influence the acquisition and retrieval of phonological word-forms. Method Pajek, a program for large network analysis and visualization (V. Batagelj & A. Mvrar, 1998), was used to examine several characteristics of a network derived from a computerized database of the adult lexicon. Nodes in the network represented words, and a link connected two nodes if the words were phonological neighbors. Results The average path length and clustering coefficient suggest that the phonological network exhibits small-world characteristics. The degree distribution was fit better by an exponential rather than a power-law function. Finally, the network exhibited assortative mixing by degree. Some of these structural characteristics were also found in graphs that were formed by 2 simple stochastic processes suggesting that similar processes might influence the development of the lexicon. Conclusions The graph theoretic perspective may provide novel insights about the mental lexicon and lead to future studies that help us better understand language development and processing. PMID:18367686

Network reconstruction via graph blending

NASA Astrophysics Data System (ADS)

Estrada, Rolando

2016-05-01

Graphs estimated from empirical data are often noisy and incomplete due to the difficulty of faithfully observing all the components (nodes and edges) of the true graph. This problem is particularly acute for large networks where the number of components may far exceed available surveillance capabilities. Errors in the observed graph can render subsequent analyses invalid, so it is vital to develop robust methods that can minimize these observational errors. Errors in the observed graph may include missing and spurious components, as well fused (multiple nodes are merged into one) and split (a single node is misinterpreted as many) nodes. Traditional graph reconstruction methods are only able to identify missing or spurious components (primarily edges, and to a lesser degree nodes), so we developed a novel graph blending framework that allows us to cast the full estimation problem as a simple edge addition/deletion problem. Armed with this framework, we systematically investigate the viability of various topological graph features, such as the degree distribution or the clustering coefficients, and existing graph reconstruction methods for tackling the full estimation problem. Our experimental results suggest that incorporating any topological feature as a source of information actually hinders reconstruction accuracy. We provide a theoretical analysis of this phenomenon and suggest several avenues for improving this estimation problem.

Knockouts of high-ranking males have limited impact on baboon social networks.

PubMed

Franz, Mathias; Altmann, Jeanne; Alberts, Susan C

Social network structures can crucially impact complex social processes such as collective behaviour or the transmission of information and diseases. However, currently it is poorly understood how social networks change over time. Previous studies on primates suggest that `knockouts' (due to death or dispersal) of high-ranking individuals might be important drivers for structural changes in animal social networks. Here we test this hypothesis using long-term data on a natural population of baboons, examining the effects of 29 natural knockouts of alpha or beta males on adult female social networks. We investigated whether and how knockouts affected (1) changes in grooming and association rates among adult females, and (2) changes in mean degree and global clustering coefficient in these networks. The only significant effect that we found was a decrease in mean degree in grooming networks in the first month after knockouts, but this decrease was rather small, and grooming networks rebounded to baseline levels by the second month after knockouts. Taken together our results indicate that the removal of high-ranking males has only limited or no lasting effects on social networks of adult female baboons. This finding calls into question the hypothesis that the removal of high-ranking individuals has a destabilizing effect on social network structures in social animals.

Diffusion and mobility of atomic particles in a liquid

NASA Astrophysics Data System (ADS)

Smirnov, B. M.; Son, E. E.; Tereshonok, D. V.

2017-11-01

The diffusion coefficient of a test atom or molecule in a liquid is determined for the mechanism where the displacement of the test molecule results from the vibrations and motion of liquid molecules surrounding the test molecule and of the test particle itself. This leads to a random change in the coordinate of the test molecule, which eventually results in the diffusion motion of the test particle in space. Two models parameters of interaction of a particle and a liquid are used to find the activation energy of the diffusion process under consideration: the gas-kinetic cross section for scattering of test molecules in the parent gas and the Wigner-Seitz radius for test molecules. In the context of this approach, we have calculated the diffusion coefficient of atoms and molecules in water, where based on experimental data, we have constructed the dependence of the activation energy for the diffusion of test molecules in water on the interaction parameter and the temperature dependence for diffusion coefficient of atoms or molecules in water within the models considered. The statistically averaged difference of the activation energies for the diffusion coefficients of different test molecules in water that we have calculated based on each of the presented models does not exceed 10% of the diffusion coefficient itself. We have considered the diffusion of clusters in water and present the dependence of the diffusion coefficient on the cluster size. The accuracy of the presented formulas for the diffusion coefficient of atomic particles in water is estimated to be 50%.

Oceanic tide maps and spherical harmonic coefficients from Geosat altimetry

NASA Technical Reports Server (NTRS)

Cartwright, D. E.; Ray, R. D.; Sanchez, B. V.

1991-01-01

Maps and tables for the global ocean tides, 69 degree N to 68 degree S, derived from two years of Geosat altimetry are presented. Global maps of local and Greenwich admittance of the (altimetric) ocean tide, and maps of amplitude and Greenwich phase lag of the ocean tide are shown for M(sub 2), S(sub 2), N(sub 2), O(sub 1), and K(sub 1). Larger scale maps of amplitude and phases are also shown for regional areas of special interest. Spherical harmonic coefficients of the ocean tide through degree and order 8 are tabulated for the six major constituents.

Molecular Dynamics Study of the Solution Structure, Clustering, and Diffusion of Four Aqueous Alkanolamines.

PubMed

Melnikov, Sergey M; Stein, Matthias

2018-03-15

CO 2 sequestration from anthropogenic resources is a challenge to the design of environmental processes at a large scale. Reversible chemical absorption by amine-based solvents is one of the most efficient methods of CO 2 removal. Molecular simulation techniques are very useful tools to investigate CO 2 binding by aqueous alkanolamine molecules for further technological application. In the present work, we have performed detailed atomistic molecular dynamics simulations of aqueous solutions of three prototype amines: monoethanolamine (MEA) as a standard, 3-aminopropanol (MPA), 2-methylaminoethanol (MMEA), and 4-diethylamino-2-butanol (DEAB) as potential novel CO 2 absorptive solvents. Solvent densities, radial distribution functions, cluster size distributions, hydrogen-bonding statistics, and diffusion coefficients for a full range of mixture compositions have been obtained. The solvent densities and diffusion coefficients from simulations are in good agreement with those in the experiment. In aqueous solution, MEA, MPA, and MMEA molecules prefer to be fully solvated by water molecules, whereas DEAB molecules tend to self-aggregate. In a range from 30/70-50/50 (w/w) alkanolamine/water mixtures, they form a bicontinuous phase (both alkanolamine and water are organized in two mutually percolating clusters). Among the studied aqueous alkanolamine solutions, the diffusion coefficients decrease in the following order MEA > MPA = MMEA > DEAB. With an increase of water content, the diffusion coefficients increase for all studied alkanolamines. The presented results are a first step for process-scale simulation and provide important qualitative and quantitative information for the design and engineering of efficient new CO 2 removal processes.

Factors That Attenuate the Correlation Coefficient and Its Analogs.

ERIC Educational Resources Information Center

Dolenz, Beverly

The correlation coefficient is an integral part of many other statistical techniques (analysis of variance, t-tests, etc.), since all analytic methods are actually correlational (G. V. Glass and K. D. Hopkins, 1984). The correlation coefficient is a statistical summary that represents the degree and direction of relationship between two variables.…

Attenuation of the Squared Canonical Correlation Coefficient under Varying Estimates of Score Reliability

ERIC Educational Resources Information Center

Wilson, Celia M.

2010-01-01

Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability.…

Hoberman-sphere-inspired lattice metamaterials with tunable negative thermal expansion

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

Li, Yangbo; Chen, Yanyu; Li, Tiantian

Materials with engineered thermal expansion coefficients, capable of avoiding failure or irreversible destruction of structures and devices, are important for aerospace, civil, biomedical, optics, and semiconductor applications. In natural materials, thermal expansion usually cannot be adjusted easily and a negative thermal expansion coefficient is still uncommon. Here we propose a novel architected lattice bi-material system, inspired by the Hoberman sphere, showing a wide range of tunable thermal expansion coefficient from negative to positive, -1.04 x 10 -3 degrees C-1 to 1.0 x 10 -5 degrees C-1. Numerical simulations and analytical formulations are implemented to quantify the evolution of the thermalmore » expansion coefficients and reveal the underlying mechanisms responsible for this unusual behavior. We show that the thermal expansion coefficient of the proposed metamaterials depends on the thermal expansion coefficient ratio and the axial stiffness ratio of the constituent materials, as well as the bending stiffness and the topological arrangement of the constitutive elements. The finding reported here provides a new routine to design architected metamaterial systems with tunable negative thermal expansion for a wide range of potential applications.« less

Hoberman-sphere-inspired lattice metamaterials with tunable negative thermal expansion

DOE PAGES

Li, Yangbo; Chen, Yanyu; Li, Tiantian; ...

2018-02-02

Materials with engineered thermal expansion coefficients, capable of avoiding failure or irreversible destruction of structures and devices, are important for aerospace, civil, biomedical, optics, and semiconductor applications. In natural materials, thermal expansion usually cannot be adjusted easily and a negative thermal expansion coefficient is still uncommon. Here we propose a novel architected lattice bi-material system, inspired by the Hoberman sphere, showing a wide range of tunable thermal expansion coefficient from negative to positive, -1.04 x 10 -3 degrees C-1 to 1.0 x 10 -5 degrees C-1. Numerical simulations and analytical formulations are implemented to quantify the evolution of the thermalmore » expansion coefficients and reveal the underlying mechanisms responsible for this unusual behavior. We show that the thermal expansion coefficient of the proposed metamaterials depends on the thermal expansion coefficient ratio and the axial stiffness ratio of the constituent materials, as well as the bending stiffness and the topological arrangement of the constitutive elements. The finding reported here provides a new routine to design architected metamaterial systems with tunable negative thermal expansion for a wide range of potential applications.« less

[Range of Hip Joint Motion and Weight of Lower Limb Function under 3D Dynamic Marker].

PubMed

Xia, Q; Zhang, M; Gao, D; Xia, W T

2017-12-01

To explore the range of reasonable weight coefficient of hip joint in lower limb function. When the hip joints of healthy volunteers under normal conditions or fixed at three different positions including functional, flexed and extension positions, the movements of lower limbs were recorded by LUKOtronic motion capture and analysis system. The degree of lower limb function loss was calculated using Fugl-Meyer lower limb function assessment form when the hip joints were fixed at the aforementioned positions. One-way analysis of variance and Tamhane's T2 method were used to proceed statistics analysis and calculate the range of reasonable weight coefficient of hip joint. There were significant differences between the degree of lower limb function loss when the hip joints fixed at flexed and extension positions and at functional position. While the differences between the degree of lower limb function loss when the hip joints fixed at flexed position and extension position had no statistical significance. In 95% confidence interval, the reasonable weight coefficient of hip joint in lower limb function was between 61.05% and 73.34%. Expect confirming the reasonable weight coefficient, the effects of functional and non-functional positions on the degree of lower limb function loss should also be considered for the assessment of hip joint function loss. Copyright© by the Editorial Department of Journal of Forensic Medicine

Online writer identification using alphabetic information clustering

NASA Astrophysics Data System (ADS)

Tan, Guo Xian; Viard-Gaudin, Christian; Kot, Alex C.

2009-01-01

Writer identification is a topic of much renewed interest today because of its importance in applications such as writer adaptation, routing of documents and forensic document analysis. Various algorithms have been proposed to handle such tasks. Of particular interests are the approaches that use allographic features [1-3] to perform a comparison of the documents in question. The allographic features are used to define prototypes that model the unique handwriting styles of the individual writers. This paper investigates a novel perspective that takes alphabetic information into consideration when the allographic features are clustered into prototypes at the character level. We hypothesize that alphabetic information provides additional clues which help in the clustering of allographic prototypes. An alphabet information coefficient (AIC) has been introduced in our study and the effect of this coefficient is presented. Our experiments showed an increase of writer identification accuracy from 66.0% to 87.0% when alphabetic information was used in conjunction with allographic features on a database of 200 reference writers.

A New Cluster Analysis-Marker-Controlled Watershed Method for Separating Particles of Granular Soils.

PubMed

Alam, Md Ferdous; Haque, Asadul

2017-10-18

An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM), has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis.

Intraclass Correlation Coefficients for Obesity Indicators and Energy Balance-Related Behaviors among New York City Public Elementary Schools

ERIC Educational Resources Information Center

Gray, Heewon Lee; Burgermaster, Marissa; Tipton, Elizabeth; Contento, Isobel R.; Koch, Pamela A.; Di Noia, Jennifer

2016-01-01

Objective: Sample size and statistical power calculation should consider clustering effects when schools are the unit of randomization in intervention studies. The objective of the current study was to investigate how student outcomes are clustered within schools in an obesity prevention trial. Method: Baseline data from the Food, Health &…

Modelling clustering of vertically aligned carbon nanotube arrays.

PubMed

Schaber, Clemens F; Filippov, Alexander E; Heinlein, Thorsten; Schneider, Jörg J; Gorb, Stanislav N

2015-08-06

Previous research demonstrated that arrays of vertically aligned carbon nanotubes (VACNTs) exhibit strong frictional properties. Experiments indicated a strong decrease of the friction coefficient from the first to the second sliding cycle in repetitive measurements on the same VACNT spot, but stable values in consecutive cycles. VACNTs form clusters under shear applied during friction tests, and self-organization stabilizes the mechanical properties of the arrays. With increasing load in the range between 300 µN and 4 mN applied normally to the array surface during friction tests the size of the clusters increases, while the coefficient of friction decreases. To better understand the experimentally obtained results, we formulated and numerically studied a minimalistic model, which reproduces the main features of the system with a minimum of adjustable parameters. We calculate the van der Waals forces between the spherical friction probe and bunches of the arrays using the well-known Morse potential function to predict the number of clusters, their size, instantaneous and mean friction forces and the behaviour of the VACNTs during consecutive sliding cycles and at different normal loads. The data obtained by the model calculations coincide very well with the experimental data and can help in adapting VACNT arrays for biomimetic applications.

Identification of bacteria in pasteurized zucchini purées stored at different temperatures and comparison with those found in other pasteurized vegetable purées.

PubMed

Guinebretiere, M H; Berge, O; Normand, P; Morris, C; Carlin, F; Nguyen-The, C

2001-10-01

One hundred nineteen isolates from a commercial zucchini purée stored at 4, 10, and 20 to 25 degrees C were fingerprinted using repetitive sequence-based PCR (REP-PCR) and classified into 35 REP types. One representative isolate of each REP type was subsequently identified by API50CHB/20E profile and partial rrs gene sequence analysis. Nine REP types were misidentified by the API system. Strains were misidentified as being in the Bacillus circulans (group 2) API taxon or in taxa with a low number of positive API characters such as Brevibacillus brevis. A phylogenetic analysis pointed to one new species of Bacillus and three new species of Paenibacillus among the misidentified REP types. Bacterial components in zucchini purée were compared phenotypically with those obtained in previous work on broccoli, carrot, leek, potato, and split pea purées, based on simple matching coefficient and unweighted pair group method with averages cluster analysis. Out of 254 strains, 69 strains previously identified as B. circulans (group 2) or B. circulans/B. macerans/B. polymyxa were assigned to a new Paenibacillus taxon phylogenetically related to P. azotofixans. Storage conditions at 4 degrees C favored the development of "B. macroides/B. maroccanus" and Paenibacillus spp. in zucchini purées and Paenibacillus spp. in other purées. Storage conditions at 20 to 25 degrees C favored the development of B. subtilis group (B. licheniformis and B. subtilis) and B. cereus group strains. At 10 degrees C, Paenibacillus spp. were always present at high frequencies, whereas the occurrence of B. macroides/B. maroccanus (in zucchini purées), B. cereus, and B. pumilus varied with the experiment.

Low-degree gravity change from GPS data of COSMIC and GRACE satellite missions

NASA Astrophysics Data System (ADS)

Lin, Tingjung; Hwang, Cheinway; Tseng, Tzu-Pang; Chao, B. F.

2012-01-01

This paper demonstrates estimation of time-varying gravity harmonic coefficients from GPS data of COSMIC and GRACE satellite missions. The kinematic orbits of COSMIC and GRACE are determined to the cm-level accuracy. The NASA Goddard's GEODYN II software is used to model the orbit dynamics of COSMIC and GRACE, including the effect of a static gravity field. The surface forces are estimated per one orbital period. Residual orbits generated from kinematic and reference orbits serve as observables to determine the harmonic coefficients in the weighted-constraint least-squares. The monthly COSMIC and GRACE GPS data from September 2006 to December 2007 (16 months) are processed to estimate harmonic coefficients to degree 5. The geoid variations from the GPS and CSR RL04 (GRACE) solutions show consistent patterns over space and time, especially in regions of active hydrological changes. The monthly GPS-derived second zonal coefficient closely resembles the SLR-derived and CSR RL04 values, and third and fourth zonal coefficients resemble the CSR RL04 values.

Selection of intracellular calcium patterns in a model with clustered Ca2+ release channels

NASA Astrophysics Data System (ADS)

Shuai, J. W.; Jung, P.

2003-03-01

A two-dimensional model is proposed for intracellular Ca2+ waves, which incorporates both the discrete nature of Ca2+ release sites in the endoplasmic reticulum membrane and the stochastic dynamics of the clustered inositol 1,4,5-triphosphate (IP3) receptors. Depending on the Ca2+ diffusion coefficient and concentration of IP3, various spontaneous Ca2+ patterns, such as calcium puffs, local waves, abortive waves, global oscillation, and tide waves, can be observed. We further investigate the speed of the global waves as a function of the IP3 concentration and the Ca2+ diffusion coefficient and under what conditions the spatially averaged Ca2+ response can be described by a simple set of ordinary differential equations.

Deterministic Joint Remote Preparation of Arbitrary Four-Qubit Cluster-Type State Using EPR Pairs

NASA Astrophysics Data System (ADS)

Li, Wenqian; Chen, Hanwu; Liu, Zhihao

2017-02-01

Using four Einstein-Podolsky-Rosen (EPR) pairs as the pre-shared quantum channel, an economic and feasible scheme for deterministic joint remote preparation of the four-particle cluster-type state is presented. In the scheme, one of the senders performs a four-qubit projective measurement based on a set of ingeniously constructed vectors with real coefficients, while the other performs the bipartite projective measurements in terms of the imaginary coefficients. Followed with some appropriate unitary operations and controlled-NOT operations, the receiver can reconstruct the desired state. Compared with other analogous JRSP schemes, our scheme can not only reconstruct the original state (to be prepared remotely) with unit successful probability, but also ensure greater efficiency.

Clustering stock market companies via chaotic map synchronization

NASA Astrophysics Data System (ADS)

Basalto, N.; Bellotti, R.; De Carlo, F.; Facchi, P.; Pascazio, S.

2005-01-01

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series to the coupling strengths between maps. The simulation of a chaotic map dynamics gives rise to a natural partition of the data, as companies belonging to the same industrial branch are often grouped together. The identification of clusters of companies of a given stock market index can be exploited in the portfolio optimization strategies.

Stochastic theory of log-periodic patterns

NASA Astrophysics Data System (ADS)

Canessa, Enrique

2000-12-01

We introduce an analytical model based on birth-death clustering processes to help in understanding the empirical log-periodic corrections to power law scaling and the finite-time singularity as reported in several domains including rupture, earthquakes, world population and financial systems. In our stochastic theory log-periodicities are a consequence of transient clusters induced by an entropy-like term that may reflect the amount of co-operative information carried by the state of a large system of different species. The clustering completion rates for the system are assumed to be given by a simple linear death process. The singularity at t0 is derived in terms of birth-death clustering coefficients.

Ab initio calculation of one-nucleon halo states

NASA Astrophysics Data System (ADS)

Rodkin, D. M.; Tchuvil'sky, Yu M.

2018-02-01

We develop an approach to microscopic and ab initio description of clustered systems, states with halo nucleon and one-nucleon resonances. For these purposes a basis combining ordinary shell-model components and cluster-channel terms is built up. The transformation of clustered wave functions to the uniform Slater-determinant type is performed using the concept of cluster coefficients. The resulting basis of orthonormalized wave functions is used for calculating the eigenvalues and the eigenvectors of Hamiltonians built in the framework of ab initio approaches. Calculations of resonance and halo states of 5He, 9Be and 9B nuclei demonstrate that the approach is workable and labor-saving.

Complex regulation of the aflatoxin biosynthesis gene cluster of Aspergillus flavus in relation to various combinations of water activity and temperature.

PubMed

Schmidt-Heydt, Markus; Abdel-Hadi, Ahmed; Magan, Naresh; Geisen, Rolf

2009-11-15

A microarray analysis was performed to study the effect of varying combinations of water activity and temperature on the activation of aflatoxin biosynthesis genes in Aspergillusflavus grown on YES medium. Generally A. flavus showed expression of the aflatoxin biosynthetic genes at all parameter combinations tested. Certain combinations of a(w) and temperature, especially combinations which imposed stress on the fungus resulted in a significant reduction of the growth rate. At these conditions induction of the whole aflatoxin biosynthesis gene cluster occurred, however the produced aflatoxin B(1) was low. At all other combinations (25 degrees C/0.95 and 0.99; 30 degrees C/0.95 and 0.99; 35 degrees C/0.95 and 0.99) a reduced basal level of cluster gene expression occurred. At these combinations a high growth rate was obtained as well as high aflatoxin production. When single genes were compared, two groups with different expression profiles in relation to water activity/temperature combinations occurred. These two groups were co-ordinately localized within the aflatoxin gene cluster. The ratio of aflR/aflJ expression was correlated with increased aflatoxin biosynthesis.

Suppression of vacancy cluster growth in concentrated solid solution alloys

DOE PAGES

Zhao, Shijun; Velisa, Gihan; Xue, Haizhou; ...

2016-12-13

Large vacancy clusters, such as stacking-fault tetrahedra, are detrimental vacancy-type defects in ion-irradiated structural alloys. Suppression of vacancy cluster formation and growth is highly desirable to improve the irradiation tolerance of these materials. In this paper, we demonstrate that vacancy cluster growth can be inhibited in concentrated solid solution alloys by modifying cluster migration pathways and diffusion kinetics. The alloying effects of Fe and Cr on the migration of vacancy clusters in Ni concentrated alloys are investigated by molecular dynamics simulations and ion irradiation experiment. While the diffusion coefficients of small vacancy clusters in Ni-based binary and ternary solid solutionmore » alloys are higher than in pure Ni, they become lower for large clusters. This observation suggests that large clusters can easily migrate and grow to very large sizes in pure Ni. In contrast, cluster growth is suppressed in solid solution alloys owing to the limited mobility of large vacancy clusters. Finally, the differences in cluster sizes and mobilities in Ni and in solid solution alloys are consistent with the results from ion irradiation experiments.« less

Subtyping of Children with Developmental Dyslexia via Bootstrap Aggregated Clustering and the Gap Statistic: Comparison with the Double-Deficit Hypothesis

ERIC Educational Resources Information Center

King, Wayne M.; Giess, Sally A.; Lombardino, Linda J.

2007-01-01

Background: The marked degree of heterogeneity in persons with developmental dyslexia has motivated the investigation of possible subtypes. Attempts have proceeded both from theoretical models of reading and the application of unsupervised learning (clustering) methods. Previous cluster analyses of data obtained from persons with reading…

Computed Temperature Distribution and Cooling of Solid Gas-Turbine Blades

NASA Technical Reports Server (NTRS)

Reuter, J. George; Gazley, Carl, Jr.

1947-01-01

Computations were made to determine the temperature distribution and cooling of solid gas-turbine blades.A range of temperatures was used from 1500 degrees to 2500 degrees F, blade-root temperatures from 100 degrees to 1000 degrees F, blade thermal conductivity from 8 to 220 BTU/(hr)(sq ft)(degrees F/ft), and net gas to metal heat transfer coefficients from 75 to 250 BTU/(hr)(sq ft)(degrees F).

Steven's orbital reduction factor in ionic clusters

NASA Astrophysics Data System (ADS)

Gajek, Z.; Mulak, J.

1985-11-01

General expressions for reduction coefficients of matrix elements of angular momentum operator in ionic clusters or molecular systems have been derived. The reduction in this approach results from overlap and covalency effects and plays an important role in the reconciling of magnetic and spectroscopic experimental data. The formulated expressions make possible a phenomenological description of the effect with two independent parameters for typical equidistant clusters. Some detailed calculations also suggest the possibility of a one-parameter description. The results of these calculations for some ionic uranium compounds are presented as an example.

Algorithms of maximum likelihood data clustering with applications

NASA Astrophysics Data System (ADS)

Giada, Lorenzo; Marsili, Matteo

2002-12-01

We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

Correlation among extinction efficiency and other parameters in an aggregate dust model

NASA Astrophysics Data System (ADS)

Dhar, Tanuj Kumar; Sekhar Das, Himadri

2017-10-01

We study the extinction properties of highly porous Ballistic Cluster-Cluster Aggregate dust aggregates in a wide range of complex refractive indices (1.4≤ n≤ 2.0, 0.001≤ k≤ 1.0) and wavelengths (0.11 {{μ }}{{m}}≤ {{λ }}≤ 3.4 {{μ }} m). An attempt has been made for the first time to investigate the correlation among extinction efficiency ({Q}{ext}), composition of dust aggregates (n,k), wavelength of radiation (λ) and size parameter of the monomers (x). If k is fixed at any value between 0.001 and 1.0, {Q}{ext} increases with increase of n from 1.4 to 2.0. {Q}{ext} and n are correlated via linear regression when the cluster size is small, whereas the correlation is quadratic at moderate and higher sizes of the cluster. This feature is observed at all wavelengths (ultraviolet to optical to infrared). We also find that the variation of {Q}{ext} with n is very small when λ is high. When n is fixed at any value between 1.4 and 2.0, it is observed that {Q}{ext} and k are correlated via a polynomial regression equation (of degree 1, 2, 3 or 4), where the degree of the equation depends on the cluster size, n and λ. The correlation is linear for small size and quadratic/cubic/quartic for moderate and higher sizes. We have also found that {Q}{ext} and x are correlated via a polynomial regression (of degree 3, 4 or 5) for all values of n. The degree of regression is found to be n and k-dependent. The set of relations obtained from our work can be used to model interstellar extinction for dust aggregates in a wide range of wavelengths and complex refractive indices.

The relation between degree-2160 spectral models of Earth's gravitational and topographic potential: a guide on global correlation measures and their dependency on approximation effects

NASA Astrophysics Data System (ADS)

Hirt, Christian; Rexer, Moritz; Claessens, Sten; Rummel, Reiner

2017-10-01

Comparisons between high-degree models of the Earth's topographic and gravitational potential may give insight into the quality and resolution of the source data sets, provide feedback on the modelling techniques and help to better understand the gravity field composition. Degree correlations (cross-correlation coefficients) or reduction rates (quantifying the amount of topographic signal contained in the gravitational potential) are indicators used in a number of contemporary studies. However, depending on the modelling techniques and underlying levels of approximation, the correlation at high degrees may vary significantly, as do the conclusions drawn. The present paper addresses this problem by attempting to provide a guide on global correlation measures with particular emphasis on approximation effects and variants of topographic potential modelling. We investigate and discuss the impact of different effects (e.g., truncation of series expansions of the topographic potential, mass compression, ellipsoidal versus spherical approximation, ellipsoidal harmonic coefficient versus spherical harmonic coefficient (SHC) representation) on correlation measures. Our study demonstrates that the correlation coefficients are realistic only when the model's harmonic coefficients of a given degree are largely independent of the coefficients of other degrees, permitting degree-wise evaluations. This is the case, e.g., when both models are represented in terms of SHCs and spherical approximation (i.e. spherical arrangement of field-generating masses). Alternatively, a representation in ellipsoidal harmonics can be combined with ellipsoidal approximation. The usual ellipsoidal approximation level (i.e. ellipsoidal mass arrangement) is shown to bias correlation coefficients when SHCs are used. Importantly, gravity models from the International Centre for Global Earth Models (ICGEM) are inherently based on this approximation level. A transformation is presented that enables a transformation of ICGEM geopotential models from ellipsoidal to spherical approximation. The transformation is applied to generate a spherical transform of EGM2008 (sphEGM2008) that can meaningfully be correlated degree-wise with the topographic potential. We exploit this new technique and compare a number of models of topographic potential constituents (e.g., potential implied by land topography, ocean water masses) based on the Earth2014 global relief model and a mass-layer forward modelling technique with sphEGM2008. Different to previous findings, our results show very significant short-scale correlation between Earth's gravitational potential and the potential generated by Earth's land topography (correlation +0.92, and 60% of EGM2008 signals are delivered through the forward modelling). Our tests reveal that the potential generated by Earth's oceans water masses is largely unrelated to the geopotential at short scales, suggesting that altimetry-derived gravity and/or bathymetric data sets are significantly underpowered at 5 arc-min scales. We further decompose the topographic potential into the Bouguer shell and terrain correction and show that they are responsible for about 20 and 25% of EGM2008 short-scale signals, respectively. As a general conclusion, the paper shows the importance of using compatible models in topographic/gravitational potential comparisons and recommends the use of SHCs together with spherical approximation or EHCs with ellipsoidal approximation in order to avoid biases in the correlation measures.

Sloan Digital Sky Survey III photometric quasar clustering: Probing the initial conditions of the Universe

DOE PAGES

Ho, Shirley; Agarwal, Nishant; Myers, Adam D.; ...

2015-05-22

Here, the Sloan Digital Sky Survey has surveyed 14,555 square degrees of the sky, and delivered over a trillion pixels of imaging data. We present the large-scale clustering of 1.6 million quasars between z=0.5 and z=2.5 that have been classified from this imaging, representing the highest density of quasars ever studied for clustering measurements. This data set spans 0~ 11,00 square degrees and probes a volume of 80 h –3 Gpc 3. In principle, such a large volume and medium density of tracers should facilitate high-precision cosmological constraints. We measure the angular clustering of photometrically classified quasars using an optimalmore » quadratic estimator in four redshift slices with an accuracy of ~ 25% over a bin width of δ l ~ 10–15 on scales corresponding to matter-radiation equality and larger (0ℓ ~ 2–3).« less

MIXOR: a computer program for mixed-effects ordinal regression analysis.

PubMed

Hedeker, D; Gibbons, R D

1996-03-01

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

Possibilistic clustering for shape recognition

NASA Technical Reports Server (NTRS)

Keller, James M.; Krishnapuram, Raghu

1993-01-01

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

Possibilistic clustering for shape recognition

NASA Technical Reports Server (NTRS)

Keller, James M.; Krishnapuram, Raghu

1992-01-01

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Optimizing Dynamical Network Structure for Pinning Control

NASA Astrophysics Data System (ADS)

Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

2016-04-01

Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

Network Compression as a Quality Measure for Protein Interaction Networks

PubMed Central

Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

2012-01-01

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

Study on Incompatibility of Traditional Chinese Medicine: Evidence from Formula Network, Chemical Space, and Metabolism Room

PubMed Central

Zhang, Xiao-Dong; Wu, Hong-Ying; Jin, Jin; Yu, Guang-Yun; He, Xin; Wang, Hao; Shen, Xiu; Zhou, Ze-Wei; Liu, Pei-Xun; Fan, Sai-Jun

2013-01-01

A traditional Chinese medicine (TCM) formula network including 362 TCM formulas was built by using complex network methodologies. The properties of this network were analyzed including network diameter, average distance, clustering coefficient, and average degree. Meanwhile, we built a TCM chemical space and a TCM metabolism room under the theory of chemical space. The properties of chemical space and metabolism room were calculated and analyzed. The properties of the medicine pairs in “eighteen antagonisms and nineteen mutual inhibitors,” an ancient rule for TCM incompatibility, were studied based on the TCM formula network, chemical space, and metabolism room. The results showed that the properties of these incompatible medicine pairs are different from those of the other TCM based on the analysis of the TCM formula network, chemical space, and metabolism room. The lines of evidence derived from our work demonstrated that the ancient rule of TCM incompatibility, “eighteen antagonisms and nineteen mutual inhibitors,” is probably scientifically based. PMID:24369478

Benford’s Distribution in Complex Networks

PubMed Central

Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K.

2016-01-01

Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford’s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford’s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford’s law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford’s distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford’s law for scalefree networks but only for very narrow range of their parameters. PMID:27748398

A network analysis of indirect carbon emission flows among different industries in China.

PubMed

Du, Qiang; Xu, Yadan; Wu, Min; Sun, Qiang; Bai, Libiao; Yu, Ming

2018-06-17

Indirect carbon emissions account for a large ratio of the total carbon emissions in processes to make the final products, and this implies indirect carbon emission flow across industries. Understanding these flows is crucial for allocating a carbon allowance for each industry. By combining input-output analysis and complex network theory, this study establishes an indirect carbon emission flow network (ICEFN) for 41 industries from 2005 to 2014 to investigate the interrelationships among different industries. The results show that the ICEFN was consistent with a small-world nature based on an analysis of the average path lengths and the clustering coefficients. Moreover, key industries in the ICEFN were identified using complex network theory on the basis of degree centrality and betweenness centrality. Furthermore, the 41 industries of the ICEFN were divided into four industrial subgroups that are related closely to one another. Finally, possible policy implications were provided based on the knowledge of the structure of the ICEFN and its trend.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Site Distribution and Aliasing Effects in the Inversion for Load Coefficients and Geocenter Motion from GPS Data

NASA Technical Reports Server (NTRS)

Wu, Xiaoping; Argus, Donald F.; Heflin, Michael B.; Ivins, Erik R.; Webb, Frank H.

2002-01-01

Precise GPS measurements of elastic relative site displacements due to surface mass loading offer important constraints on global surface mass transport. We investigate effects of site distribution and aliasing by higher-degree (n greater than or equal 2) loading terms on inversion of GPS data for n = 1 load coefficients and geocenter motion. Covariance and simulation analyses are conducted to assess the sensitivity of the inversion to aliasing and mismodeling errors and possible uncertainties in the n = 1 load coefficient determination. We found that the use of center-of-figure approximation in the inverse formulation could cause 10- 15% errors in the inverted load coefficients. n = 1 load estimates may be contaminated significantly by unknown higher-degree terms, depending on the load scenario and the GPS site distribution. The uncertainty in n = 1 zonal load estimate is at the level of 80 - 95% for two load scenarios.

Sub-250nm room temperature optical gain from AlGaN materials with strong compositional fluctuations

NASA Astrophysics Data System (ADS)

Pecora, Emanuele; Zhang, Wei; Sun, Haiding; Nikiforov, A.; Yin, Jian; Paiella, Roberto; Moustakas, Theodore; Dal Negro, Luca

2013-03-01

Compact and portable deep-UV LEDs and laser sources are needed for a number of engineering applications including optical communications, gas sensing, biochemical agent detection, disinfection, biotechnology and medical diagnostics. We investigate the deep-UV optical emission and gain properties of AlxGa1-xN/AlyGa1-yN multiple quantum wells structure. These structures were grown by molecular-beam epitaxy on 6H-SiC substrates resulting in either homogeneous wells or various degrees of band-structure compositional fluctuations in the form of cluster-like features within the wells. We measured the TE-polarized amplified spontaneous emission in the sample with cluster-like features and quantified the optical absorption/gain coefficients and gain spectra by the Variable Stripe Length (VSL) technique under ultrafast optical pumping. We report blue-shift and narrowing of the emission, VSL traces, gain spectra, polarization studies, and the validity of the Schalow-Townes relation to demonstrate a maximum net modal gain of 120 cm-1 at 250 nm in the sample with strong compositional fluctuations. Moreover, we measure a very low gain threshold (15 μJ/cm2) . On the other hand, we found that samples with homogeneous quantum wells lead to absorption only. In addition, we report gain measurements in graded-index-separate-confined heterostructure (GRINSCH) designed to increase the device optical confinement factor.

Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking.

PubMed

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2016-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992-1998), global regime formation through the FCTC negotiations (1999-2005), and philanthropic funding through the Bloomberg Initiative (2006-2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999-2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network.

What can we learn from the network approach in finance?

NASA Astrophysics Data System (ADS)

Janos, Kertesz

2005-03-01

Correlations between variations of stock prices reveal information about relationships between companies. Different methods of analysis have been applied to such data in order to uncover the taxonomy of the market. We use Mantegna's miminum spanning tree (MST) method for daily data in a dynamic way: By introducing a moving window we study the temporal changes in the structure of the network defined by this ``asset tree.'' The MST is scale free with a significantly changing exponent of the degree distribution for crash periods, which demonstrates the restructuring of the network due to the enhancement of correlations. This approach is compared to that based on what we call ``asset graphs:'' We start from an empty graph with no edges where the vertices correspond to stocks and then, one by one, we insert edges between the vertices according to the rank of their correlation strength. We study the properties of the creatred (weighted) networks, such as topologically different growth types, number and size of clusters and clustering coefficient. Furthermore, we define new tools like subgraph intensity and coherence to describe the role of the weights. We also investigate the time shifted cross correlation functions for high frequency data and find a characteristic time delay in many cases representing that some stocks lead the price changes while others follow them. These data can be used to construct a directed network of influence.

Definition and quantification of acute inflammatory white matter injury in the immature brain by MRI/MRS at high magnetic field.

PubMed

Lodygensky, Gregory A; Kunz, Nicolas; Perroud, Elodie; Somm, Emmanuel; Mlynarik, Vladimir; Hüppi, Petra S; Gruetter, Rolf; Sizonenko, Stéphane V

2014-03-01

Lipopolysaccharide (LPS) injection in the corpus callosum (CC) of rat pups results in diffuse white matter injury similar to the main neuropathology of preterm infants. The aim of this study was to characterize the structural and metabolic markers of acute inflammatory injury by high-field magnetic resonance imaging (MRI) magnetic resonance spectroscopy (MRS) in vivo. Twenty-four hours after a 1-mg/kg injection of LPS in postnatal day 3 rat pups, diffusion tensor imaging and proton nuclear magnetic spectroscopy ((1)H NMR) were analyzed in conjunction to determine markers of cell death and inflammation using immunohistochemistry and gene expression. MRI and MRS in the CC revealed an increase in lactate and free lipids and a decrease of the apparent diffusion coefficient. Detailed evaluation of the CC showed a marked apoptotic response assessed by fractin expression. Interestingly, the degree of reduction in the apparent diffusion coefficient correlated strongly with the natural logarithm of fractin expression, in the same region of interest. LPS injection further resulted in increased activated microglia clustered in the cingulum, widespread astrogliosis, and increased expression of genes for interleukin (IL)-1, IL-6, and tumor necrosis factor. This model was able to reproduce the typical MRI hallmarks of acute diffuse white matter injury seen in preterm infants and allowed the evaluation of in vivo biomarkers of acute neuropathology after inflammatory challenge.

On p-mode oscillations in stars from 1 solar mass to 2 solar masses

NASA Astrophysics Data System (ADS)

Audard, N.; Provost, J.

1994-06-01

The structure of stars more massive than about 1.2 solar masses is characterized by a convective core. We have studied the evolution with age and mass of acoustic frequencies of high radical order n and low degree l for models of stars of 1, 1.5 and 2 solar masses. Using a polynomial approximation for the frequency, the p-mode spectrum can be characterized by derived global asteroseismic coefficients, i.e. the mean separation nu0 is approximately equal to nun, l - nun - 1, l and the small frequency separation Delta nu0, 2 is approximately equal to nun, l = 0 - nun - 1, l = 2. The diagram (nu0, delta nu0, 2/nu0) plotted along the evolutionary tracks would help to separate the effects of age and mass. We study the sensitivity of these coefficients and other observable quantities, like the radius and luminosity, to stellar parameters in the vicinity of 1 solar mass and 2 solar masses; this sensitivity substantially depends on the stellar mass and must be taken into account for asteroseismic calibration of stellar clusters. Considering finally some rapid variations of the internal structure, we show that the second frequency difference delta2 nu = nu(subn, l) - 2 nun - 1, l + nun - 2, l exponent gamma in the He II ionization zone.

An association between neighbourhood wealth inequality and HIV prevalence in sub-Saharan Africa.

PubMed

Brodish, Paul Henry

2015-05-01

This paper investigates whether community-level wealth inequality predicts HIV serostatus using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding 5%. The analysis relates the binary dependent variable HIV-positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each statistical enumeration area, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behaviour mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behaviour variables attenuates the effects of both inequality measures. Reporting eleven plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behaviour differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioural mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention.

An association between neighborhood wealth inequality and HIV prevalence in sub-Saharan Africa

PubMed Central

Brodish, Paul Henry

2016-01-01

Summary This paper investigates whether community-level wealth inequality predicts HIV serostatus, using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding five percent. The analysis relates the binary dependent variable HIV positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each SEA, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behavior mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behavior variables attenuates the effects of both inequality measures. Reporting 11 plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behavior differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioral mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention PMID:24406021

International scientific collaboration in HIV and HPV: a network analysis.

PubMed

Vanni, Tazio; Mesa-Frias, Marco; Sanchez-Garcia, Ruben; Roesler, Rafael; Schwartsmann, Gilberto; Goldani, Marcelo Z; Foss, Anna M

2014-01-01

Research endeavours require the collaborative effort of an increasing number of individuals. International scientific collaborations are particularly important for HIV and HPV co-infection studies, since the burden of disease is rising in developing countries, but most experts and research funds are found in developed countries, where the prevalence of HIV is low. The objective of our study was to investigate patterns of international scientific collaboration in HIV and HPV research using social network analysis. Through a systematic review of the literature, we obtained epidemiological data, as well as data on countries and authors involved in co-infection studies. The collaboration network was analysed in respect to the following: centrality, density, modularity, connected components, distance, clustering and spectral clustering. We observed that for many low- and middle-income countries there were no epidemiological estimates of HPV infection of the cervix among HIV-infected individuals. Most studies found only involved researchers from the same country (64%). Studies derived from international collaborations including high-income countries and either low- or middle-income countries had on average three times larger sample sizes than those including only high-income countries or low-income countries. The high global clustering coefficient (0.9) coupled with a short average distance between researchers (4.34) suggests a "small-world phenomenon." Researchers from high-income countries seem to have higher degree centrality and tend to cluster together in densely connected communities. We found a large well-connected community, which encompasses 70% of researchers, and 49 other small isolated communities. Our findings suggest that in the field of HIV and HPV, there seems to be both room and incentives for researchers to engage in collaborations between countries of different income-level. Through international collaboration resources available to researchers in high-income countries can be efficiently used to enroll more participants in low- and middle-income countries.

Long-term changes in the heat-mortality relationship according to heterogeneous regional climate: a time-series study in South Korea.

PubMed

Heo, Seulkee; Lee, Eunil; Kwon, Bo Yeon; Lee, Suji; Jo, Kyung Hee; Kim, Jinsun

2016-08-03

Several studies identified a heterogeneous impact of heat on mortality in hot and cool regions during a fixed period, whereas less evidence is available for changes in risk over time due to climate change in these regions. We compared changes in risk during periods without (1996-2000) and with (2008-2012) heatwave warning forecasts in regions of South Korea with different climates. Study areas were categorised into 3 clusters based on the spatial clustering of cooling degree days in the period 1993-2012: hottest cluster (cluster H), moderate cluster (cluster M) and cool cluster (cluster C). The risk was estimated according to increases in the daily all-cause, cardiovascular and respiratory mortality per 1°C change in daily temperature above the threshold, using a generalised additive model. The risk of all types of mortality increased in cluster H in 2008-2012, compared with 1996-2000, whereas the risks in all-combined regions and cooler clusters decreased. Temporal increases in mortality risk were larger for some vulnerable subgroups, including younger adults (<75 years), those with a lower education and blue-collar workers, in cluster H as well as all-combined regions. Different patterns of risk change among clusters might be attributable to large increases in heatwave frequency or duration during study periods and the degree of urbanisation in cluster H. People living in hotter regions or with a lower socioeconomic status are at higher risk following an increasing trend of heat-related mortality risks. Continuous efforts are needed to understand factors which affect changes in heat-related mortality risks. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

[Task redistribution in Dutch dental care in relation to dental hygienists' job satisfaction].

PubMed

Jerkovic, K; van Offenbeek, M A G; van der Schans, C P

2010-05-01

In research into a professional cross-section of dental hygienists, we studied the extent to which task redistribution has an influence on job satisfaction. The research among randomly chosen dental hygienists consisted of questions about organizational and personal characteristics, the set of assigned tasks, task characteristics and job satisfaction. The respondents were divided into 3 clusters which differed in the breadth of their sets of tasks. Although prevention and periodontology services remain the core tasks in dental hygienists' jobs, the degree of task redistribution differed strongly from cluster to cluster. Respondents with a considerable degree of task redistribution experienced the most task variation, but scored significantly lower on the task characteristics autonomy, feedback, task identity and task importance. This explains why redistribution does not directly correspond with a greater degree of job satisfaction. Moreover, it is precisely the dental hygienists with a broad set of tasks who are significantly less satisfied with their salary than those with a traditional set of tasks.

On Learning Cluster Coefficient of Private Networks

PubMed Central

Wang, Yue; Wu, Xintao; Zhu, Jun; Xiang, Yang

2013-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we treat a graph statistics as a function f and develop a divide and conquer approach to enforce differential privacy. The basic procedure of this approach is to first decompose the target computation f into several less complex unit computations f1, …, fm connected by basic mathematical operations (e.g., addition, subtraction, multiplication, division), then perturb the output of each fi with Laplace noise derived from its own sensitivity value and the distributed privacy threshold εi, and finally combine those perturbed fi as the perturbed output of computation f. We examine how various operations affect the accuracy of complex computations. When unit computations have large global sensitivity values, we enforce the differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We illustrate our approach by using clustering coefficient, which is a popular statistics used in social network analysis. Empirical evaluations on five real social networks and various synthetic graphs generated from three random graph models show the developed divide and conquer approach outperforms the direct approach. PMID:24429843

Angle-Resolved Photoemission of Solvated Electrons in Sodium-Doped Clusters.

PubMed

West, Adam H C; Yoder, Bruce L; Luckhaus, David; Saak, Clara-Magdalena; Doppelbauer, Maximilian; Signorell, Ruth

2015-04-16

Angle-resolved photoelectron spectroscopy of the unpaired electron in sodium-doped water, methanol, ammonia, and dimethyl ether clusters is presented. The experimental observations and the complementary calculations are consistent with surface electrons for the cluster size range studied. Evidence against internally solvated electrons is provided by the photoelectron angular distribution. The trends in the ionization energies seem to be mainly determined by the degree of hydrogen bonding in the solvent and the solvation of the ion core. The onset ionization energies of water and methanol clusters do not level off at small cluster sizes but decrease slightly with increasing cluster size.

Effect of plasma actuator and splitter plate on drag coefficient of a circular cylinder

NASA Astrophysics Data System (ADS)

Akbıyık, Hürrem; Erkan Akansu, Yahya; Yavuz, Hakan; Ertuğrul Bay, Ahmet

2016-03-01

In this paper, an experimental study on flow control around a circular cylinder with splitter plate and plasma actuator is investigated. The study is performed in wind tunnel for Reynolds numbers at 4000 and 8000. The wake region of circular cylinder with a splitter plate is analyzed at different angles between 0 and 180 degrees. In this the study, not only plasma actuators are activated but also splitter plate is placed behind the cylinder. A couple electrodes are mounted on circular cylinder at ±90 degrees. Also, flow visualization is achieved by using smoke wire method. Drag coefficient of the circular cylinder with splitter plate and the plasma actuator are obtained for different angles and compared with the plain circular cylinder. While attack angle is 0 degree, drag coefficient is decreased about 20% by using the splitter plate behind the circular cylinder. However, when the plasma actuators are activated, the improvement of the drag reduction is measured to be 50%.

Enhanced magnetostriction derived from magnetic single domain structures in cluster-assembled SmCo films

NASA Astrophysics Data System (ADS)

Bai, Yulong; Yang, Bo; Guo, Fei; Lu, Qingshan; Zhao, Shifeng

2017-11-01

Cluster-assembled SmCo alloy films were prepared by low energy cluster beam deposition. The structure, magnetic domain, magnetization, and magnetostriction of the films were characterized. It is shown that the as-prepared films are assembled in compact and uniformly distributed spherical cluster nanoparticles, most of which, after vacuum in situ annealing at 700 K, aggregated to form cluster islands. These cluster islands result in transformations from superparamagnetic states to magnetic single domain (MSD) states in the films. Such MSD structures contribute to the enhanced magnetostrictive behaviors with a saturation magnetostrictive coefficient of 160 × 10-6 in comparison to 105 × 10-6 for the as-prepared films. This work demonstrates candidate materials that could be applied in nano-electro-mechanical systems, low power information storage, and weak magnetic detecting devices.

Discrete Haar transform and protein structure.

PubMed

Morosetti, S

1997-12-01

The discrete Haar transform of the sequence of the backbone dihedral angles (phi and psi) was performed over a set of X-ray protein structures of high resolution from the Brookhaven Protein Data Bank. Afterwards, the new dihedral angles were calculated by the inverse transform, using a growing number of Haar functions, from the lower to the higher degree. New structures were obtained using these dihedral angles, with standard values for bond lengths and angles, and with omega = 0 degree. The reconstructed structures were compared with the experimental ones, and analyzed by visual inspection and statistical analysis. When half of the Haar coefficients were used, all the reconstructed structures were not yet collapsed to a tertiary folding, but they showed yet realized most of the secondary motifs. These results indicate a substantial separation of structural information in the space of Haar transform, with the secondary structural information mainly present in the Haar coefficients of lower degrees, and the tertiary one present in the higher degree coefficients. Because of this separation, the representation of the folded structures in the space of Haar transform seems a promising candidate to encompass the problem of premature convergence in genetic algorithms.

Mathematical Geology

ERIC Educational Resources Information Center

Merriam, Daniel F.

1978-01-01

Geomathematics is a developing field that is being used in practical applications. Classification is an important element and the dynamic-cluster method (DCM), a nonhierarchial procedure, was introduced this past year. A method for testing the degree of cluster distinctness was developed also. (MA)

Sequencing rare marine actinomycete genomes reveals high density of unique natural product biosynthetic gene clusters.

PubMed

Schorn, Michelle A; Alanjary, Mohammad M; Aguinaldo, Kristen; Korobeynikov, Anton; Podell, Sheila; Patin, Nastassia; Lincecum, Tommie; Jensen, Paul R; Ziemert, Nadine; Moore, Bradley S

2016-12-01

Traditional natural product discovery methods have nearly exhausted the accessible diversity of microbial chemicals, making new sources and techniques paramount in the search for new molecules. Marine actinomycete bacteria have recently come into the spotlight as fruitful producers of structurally diverse secondary metabolites, and remain relatively untapped. In this study, we sequenced 21 marine-derived actinomycete strains, rarely studied for their secondary metabolite potential and under-represented in current genomic databases. We found that genome size and phylogeny were good predictors of biosynthetic gene cluster diversity, with larger genomes rivalling the well-known marine producers in the Streptomyces and Salinispora genera. Genomes in the Micrococcineae suborder, however, had consistently the lowest number of biosynthetic gene clusters. By networking individual gene clusters into gene cluster families, we were able to computationally estimate the degree of novelty each genus contributed to the current sequence databases. Based on the similarity measures between all actinobacteria in the Joint Genome Institute's Atlas of Biosynthetic gene Clusters database, rare marine genera show a high degree of novelty and diversity, with Corynebacterium, Gordonia, Nocardiopsis, Saccharomonospora and Pseudonocardia genera representing the highest gene cluster diversity. This research validates that rare marine actinomycetes are important candidates for exploration, as they are relatively unstudied, and their relatives are historically rich in secondary metabolites.

Sequencing rare marine actinomycete genomes reveals high density of unique natural product biosynthetic gene clusters

PubMed Central

Schorn, Michelle A.; Alanjary, Mohammad M.; Aguinaldo, Kristen; Korobeynikov, Anton; Podell, Sheila; Patin, Nastassia; Lincecum, Tommie; Jensen, Paul R.; Ziemert, Nadine

2016-01-01

Traditional natural product discovery methods have nearly exhausted the accessible diversity of microbial chemicals, making new sources and techniques paramount in the search for new molecules. Marine actinomycete bacteria have recently come into the spotlight as fruitful producers of structurally diverse secondary metabolites, and remain relatively untapped. In this study, we sequenced 21 marine-derived actinomycete strains, rarely studied for their secondary metabolite potential and under-represented in current genomic databases. We found that genome size and phylogeny were good predictors of biosynthetic gene cluster diversity, with larger genomes rivalling the well-known marine producers in the Streptomyces and Salinispora genera. Genomes in the Micrococcineae suborder, however, had consistently the lowest number of biosynthetic gene clusters. By networking individual gene clusters into gene cluster families, we were able to computationally estimate the degree of novelty each genus contributed to the current sequence databases. Based on the similarity measures between all actinobacteria in the Joint Genome Institute's Atlas of Biosynthetic gene Clusters database, rare marine genera show a high degree of novelty and diversity, with Corynebacterium, Gordonia, Nocardiopsis, Saccharomonospora and Pseudonocardia genera representing the highest gene cluster diversity. This research validates that rare marine actinomycetes are important candidates for exploration, as they are relatively unstudied, and their relatives are historically rich in secondary metabolites. PMID:27902408

Percolation and epidemics in random clustered networks

NASA Astrophysics Data System (ADS)

Miller, Joel C.

2009-08-01

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

Cold collisions of SH- with He: Potential energy surface and rate coefficients

NASA Astrophysics Data System (ADS)

Bop, C. T.; Trabelsi, T.; Hammami, K.; Mogren Al Mogren, M.; Lique, F.; Hochlaf, M.

2017-09-01

Collisional energy transfer under cold conditions is of great importance from the fundamental and applicative point of view. Here, we investigate low temperature collisions of the SH- anion with He. We have generated a three-dimensional potential energy surface (PES) for the SH-(X1Σ+)-He(1S) van der Waals complex. The ab initio multi-dimensional interaction PES was computed using the explicitly correlated coupled cluster approach with simple, double, and perturbative triple excitation in conjunction with the augmented-correlation consistent-polarized valence triple zeta Gaussian basis set. The PES presents two minima located at linear geometries. Then, the PES was averaged over the ground vibrational wave function of the SH- molecule and the resulting two-dimensional PES was incorporated into exact quantum mechanical close coupling calculations to study the collisional excitation of SH- by He. We have computed inelastic cross sections among the 11 first rotational levels of SH- for energies up to 2500 cm-1. (De-)excitation rate coefficients were deduced for temperatures ranging from 1 to 300 K by thermally averaging the cross sections. We also performed calculations using the new PES for a fixed internuclear SH- distance. Both sets of results were found to be in reasonable agreement despite differences existing at low temperatures confirming that accurate predictions require the consideration of all internal degrees of freedom in the case of molecular hydrides. The rate coefficients presented here may be useful in interpreting future experimental work on the SH- negative ion colliding with He as those recently done for the OH--He collisional system as well as for possible astrophysical applications in case SH- would be detected in the interstellar medium.

Mapping the neuropsychological profile of temporal lobe epilepsy using cognitive network topology and graph theory.

PubMed

Kellermann, Tanja S; Bonilha, Leonardo; Eskandari, Ramin; Garcia-Ramos, Camille; Lin, Jack J; Hermann, Bruce P

2016-10-01

Normal cognitive function is defined by harmonious interaction among multiple neuropsychological domains. Epilepsy has a disruptive effect on cognition, but how diverse cognitive abilities differentially interact with one another compared with healthy controls (HC) is unclear. This study used graph theory to analyze the community structure of cognitive networks in adults with temporal lobe epilepsy (TLE) compared with that in HC. Neuropsychological assessment was performed in 100 patients with TLE and 82 HC. For each group, an adjacency matrix was constructed representing pair-wise correlation coefficients between raw scores obtained in each possible test combination. For each cognitive network, each node corresponded to a cognitive test; each link corresponded to the correlation coefficient between tests. Global network structure, community structure, and node-wise graph theory properties were qualitatively assessed. The community structure in patients with TLE was composed of fewer, larger, more mixed modules, characterizing three main modules representing close relationships between the following: 1) aspects of executive function (EF), verbal and visual memory, 2) speed and fluency, and 3) speed, EF, perception, language, intelligence, and nonverbal memory. Conversely, controls exhibited a relative division between cognitive functions, segregating into more numerous, smaller modules consisting of the following: 1) verbal memory, 2) language, perception, and intelligence, 3) speed and fluency, and 4) visual memory and EF. Overall node-wise clustering coefficient and efficiency were increased in TLE. Adults with TLE demonstrate a less clear and poorly structured segregation between multiple cognitive domains. This panorama suggests a higher degree of interdependency across multiple cognitive domains in TLE, possibly indicating compensatory mechanisms to overcome functional impairments. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatial and kinematic distributions of transition populations in intermediate redshift galaxy clusters

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

Crawford, Steven M.; Wirth, Gregory D.; Bershady, Matthew A., E-mail: crawford@saao.ac.za, E-mail: wirth@keck.hawaii.edu, E-mail: mab@astro.wisc.edu

2014-05-01

We analyze the spatial and velocity distributions of confirmed members in five massive clusters of galaxies at intermediate redshift (0.5 < z < 0.9) to investigate the physical processes driving galaxy evolution. Based on spectral classifications derived from broad- and narrow-band photometry, we define four distinct galaxy populations representing different evolutionary stages: red sequence (RS) galaxies, blue cloud (BC) galaxies, green valley (GV) galaxies, and luminous compact blue galaxies (LCBGs). For each galaxy class, we derive the projected spatial and velocity distribution and characterize the degree of subclustering. We find that RS, BC, and GV galaxies in these clusters havemore » similar velocity distributions, but that BC and GV galaxies tend to avoid the core of the two z ≈ 0.55 clusters. GV galaxies exhibit subclustering properties similar to RS galaxies, but their radial velocity distribution is significantly platykurtic compared to the RS galaxies. The absence of GV galaxies in the cluster cores may explain their somewhat prolonged star-formation history. The LCBGs appear to have recently fallen into the cluster based on their larger velocity dispersion, absence from the cores of the clusters, and different radial velocity distribution than the RS galaxies. Both LCBG and BC galaxies show a high degree of subclustering on the smallest scales, leading us to conclude that star formation is likely triggered by galaxy-galaxy interactions during infall into the cluster.« less

Effects of temperature and fertilizer on activity and community structure of soil ammonia oxidizers.

PubMed

Avrahami, Sharon; Liesack, Werner; Conrad, Ralf

2003-08-01

We investigated the effect of temperature on the activity of soil ammonia oxidizers caused by changes in the availability of ammonium and in the microbial community structure. Both short (5 days) and long (6.5, 16 and 20 weeks) incubation of an agricultural soil resulted in a decrease in ammonium concentration that was more pronounced at temperatures between 10 and 25 degrees C than at either 4 degrees C or 30-37 degrees C. Consistently, potential nitrification was higher between 10 and 25 degrees C than at either 4 degrees C or 37 degrees C. However, as long as ammonium was not limiting, release rates of N2O increased monotonously between 4 and 37 degrees C after short-term temperature adaptation, with nitrification accounting for about 35-50% of the N2O production between 4 and 25 degrees C. In order to see whether temperature may also affect the community structure of ammonia oxidizers, we studied moist soil during long incubation at low and high concentrations of commercial fertilizer. The soil was also incubated in buffered (pH 7) slurry amended with urea. Communities of ammonia oxidizers were assayed by denaturant gradient gel electrophoresis (DGGE) of the amoA gene coding for the alpha subunit of ammonia monooxygenase. We found that a polymerase chain reaction (PCR) system using a non-degenerated reverse primer (amoAR1) gave the best results. Community shifts occurred in all soil treatments after 16 weeks of incubation. The community shifts were obviously influenced by the different fertilizer treatments, indicating that ammonium was a selective factor for different ammonia oxidizer populations. Temperature was also a selective factor, in particular as community shifts were also observed in the soil slurries, in which ammonium concentrations and pH were better controlled. Cloning and sequencing of selected DGGE bands indicated that amoA sequences belonging to Nitrosospira cluster 1 were dominant at low temperatures (4-10 degrees C), but were absent after long incubation at low fertilizer treatment. Sequences of Nitrosospira cluster 9 could only be detected at low ammonium concentrations, whereas those of Nitrosospira cluster 3 were found at most ammonium concentrations and temperatures, although individual clones of this cluster exhibited trends with temperature. Obviously, ammonia oxidizers are able to adapt to soil conditions by changes in the community structure if sufficient time (several weeks) is available.

A Note on the Estimator of the Alpha Coefficient for Standardized Variables Under Normality

ERIC Educational Resources Information Center

Hayashi, Kentaro; Kamata, Akihito

2005-01-01

The asymptotic standard deviation (SD) of the alpha coefficient with standardized variables is derived under normality. The research shows that the SD of the standardized alpha coefficient becomes smaller as the number of examinees and/or items increase. Furthermore, this research shows that the degree of the dependence of the SD on the number of…

Deterministic Joint Remote Preparation of a Four-Qubit Cluster-Type State via GHZ States

NASA Astrophysics Data System (ADS)

Wang, Hai-bin; Zhou, Xiao-Yan; An, Xing-xing; Cui, Meng-Meng; Fu, De-sheng

2016-08-01

A scheme for the deterministic joint remote preparation of a four-qubit cluster-type state using only two Greenberger-Horne-Zeilinger (GHZ) states as quantum channels is presented. In this scheme, the first sender performs a two-qubit projective measurement according to the real coefficient of the desired state. Then, the other sender utilizes the measurement result and the complex coefficient to perform another projective measurement. To obtain the desired state, the receiver applies appropriate unitary operations to his/her own two qubits and two CNOT operations to the two ancillary ones. Most interestingly, our scheme can achieve unit success probability, i.e., P s u c =1. Furthermore, comparison reveals that the efficiency is higher than that of most other analogous schemes.

Pressure-induced positive electrical resistivity coefficient in Ni-Nb-Zr-H glassy alloy

NASA Astrophysics Data System (ADS)

Fukuhara, M.; Gangli, C.; Matsubayashi, K.; Uwatoko, Y.

2012-06-01

Measurements under hydrostatic pressure of the electrical resistivity of (Ni0.36Nb0.24Zr0.40)100-xHx (x = 9.8, 11.5, and 14) glassy alloys have been made in the range of 0-8 GPa and 0.5-300 K. The resistivity of the (Ni0.36Nb0.24Zr0.40)86H14 alloy changed its sign from negative to positive under application of 2-8 GPa in the temperature range of 300-22 K, coming from electron-phonon interaction in the cluster structure under pressure, accompanied by deformation of the clusters. In temperature region below 22 K, the resistivity showed negative thermal coefficient resistance by Debye-Waller factor contribution, and superconductivity was observed at 1.5 K.

Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

NASA Astrophysics Data System (ADS)

Wang, Na; Li, Dong; Wang, Qiwen

2012-12-01

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development.

Diffusion coefficients in systems with inclusion compounds. 1. alpha. -Cyclodextrin-L-phenylalanine-water at 25 degree C

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

Paduano, L.; Sartorio, R.; Vitagliano, V.

Diffusion coefficients in the ternary system {alpha}-cyclodextrin (at one concentration)-L-phenylalanine (at four concentrations)-water have been measured by using the Gouy interferometric technique. The effect of the inclusion equilibrium on the cross-term diffusion coefficients was observed. The measured diffusion coefficients in the ternary systems were used to calculate values of the binding constants. These values are in good agreement with the value obtained from calorimetric studies.

[Influence of acupuncture of Zusanli (ST 36) on connectivity of brain functional network in healthy subjects].

PubMed

Li, Nuo; Wang, Pang; Deng, Bin; Wei, Xi-le; Che, Yan-qiu; Jia, Chen-hui; Guo, Yi; Chao, Wang

2011-08-01

To observe the effect of acupuncture of Zusanli (ST 36) on electroencephalogram (EEG) so as to probe into its law in regulating the interconnectivity of brain functional network. A total of 9 healthy young volunteer students (6 male, 3 female) participated in the present study. They were asked to take a dorsal position on a test-bed. EEG signals were acquired from 22 surface scalp electrodes (Fp1, Fp2, F7, F3, F2, F4, F8, A1, T3, C3, C2, C4, T4, A2, T5, P3, P2, P4, T6, O2, O1 and O2) fixed on the subject's head. Acupuncture stimulation was applied to the right Zusanli (ST 36) by manipulating the filiform needle with uniform reducing-reinforcing method and at a frequency of about 50 cycles/min for 2 min. Then the stimulation was stopped for 10 min, and repeated once again (needle-twirling frequency: 150 and 200 cycles/min), 3 times altogether. The acquired EEG data were analyzed by using coherence estimation method, average path length, average clustering coefficient, and the average degree of the articulation points (nodes) for analyzing the synchronization of EEG signals before, during and after acupuncture. In comparison with pre-acupuncture, the coherence amplitude values of EEG-delta (1-4 Hz) and y (31-47 Hz) waves were increased significantly after acupuncture of ST 36. No significant changes were found in the amplitude values of EEG-theta (5-8 Hz), -alpha (9-13 Hz) and-beta (14-30 Hz) waves after acupuncture stimulation. During and after acupuncture, the synchronism values of EEG-delta waves of different leads and numbers of interconnectivity between every two brain functional regions in majority of the 9 volunteers were increased clearly. In all volunteers, the degree values of all nodes except A1 and A2, the average clustering coefficients along with the increase of the threshold (r), and the average path lengths of the brain functional network of EEG-delta waves during and after acupuncture were also increased evidently (the latter two items, P < 0.05), suggesting an increase of the information exchange and functional connectivity of different brain regions. Acupuncture of Zusanli (ST 36) can increase the amplitude and synchronization of EEG-delta waves of different leads, and potentiate the functional interconnectivity of brain functional network.

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

PubMed Central

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

2016-01-01

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

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods.

PubMed

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H

2010-07-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

Minimally processed foods are more satiating and less hyperglycemic than ultra-processed foods: a preliminary study with 98 ready-to-eat foods.

PubMed

Fardet, Anthony

2016-05-18

Beyond nutritional composition, food structure is increasingly recognized to play a role in food health potential, notably in satiety and glycemic responses. Food structure is also highly dependent on processing conditions. The hypothesis for this study is, based on a data set of 98 ready-to-eat foods, that the degree of food processing would correlate with the satiety index (SI) and glycemic response. Glycemic response was evaluated according to two indices: the glycemic index (GI) and a newly designed index, the glycemic glucose equivalent (GGE). The GGE indicates how a quantity of a certain food affects blood glucose levels by identifying the amount of food glucose that would have an effect equivalent to that of the food. Then, foods were clustered within three processing groups based on the international NOVA classification: (1) raw and minimally processed foods; (2) processed foods; and (3) ultra-processed foods. Ultra-processed foods are industrial formulations of substances extracted or derived from food and additives, typically with five or more and usually many (cheap) ingredients. The data were correlated by nonparametric Spearman's rank correlation coefficient on quantitative data. The main results show strong correlations between GGE, SI and the degree of food processing, while GI is not correlated with the degree of processing. Thus, the more food is processed, the higher the glycemic response and the lower its satiety potential. The study suggests that complex, natural, minimally and/or processed foods should be encouraged for consumption rather than highly unstructured and ultra-processed foods when choosing weakly hyperglycemic and satiating foods.

Research on the co-movement between high-end talent and economic growth: A complex network approach

NASA Astrophysics Data System (ADS)

Zhang, Zhen; Wang, Minggang; Xu, Hua; Zhang, Wenbin; Tian, Lixin

2018-02-01

The major goal of this paper is to focus on the co-movement between high-end talent and economic growth by a complex network approach. Firstly, the national high-end talent development efficiency from 1990 to 2015 is taken as the quantitative index to measure the development of high-end talent. The added values of the primary industry, secondary industry, tertiary industry are selected as economic growth indexes, and all the selected sample data are standardized by the mean value processing method. Secondly, let seven months as the length of the sliding window, and one month as the sliding step, then the grey correlation degrees between systems are measured using the slope correlation degrees, and the grey correlation degree sequence is mapped into the symbol series composed by three symbols { Y , O , N } based on the coarse graining method. Let three characters as a mode, the nodes are obtained by the modes according to the time sequence. Let the transformation between the modal be the edge, and the times of the transformation be weight, then the co-movement networks between national high-end talent development efficiency and the added values of the primary industry, secondary industry, tertiary industry are built respectively. Finally, the dynamic characteristics of the networks are analysed by the node strength, strength distribution, weighted clustering coefficient, conversion cycle of the modes and the transition between the co-movement modes. The results indicate that there are mutual influence and promotion relations between the national high-end talent development efficiency and the added values of the primary, secondary and tertiary industry.

Network community structure and loop coefficient method

NASA Astrophysics Data System (ADS)

Vragović, I.; Louis, E.

2006-07-01

A modular structure, in which groups of tightly connected nodes could be resolved as separate entities, is a property that can be found in many complex networks. In this paper, we propose a algorithm for identifying communities in networks. It is based on a local measure, so-called loop coefficient that is a generalization of the clustering coefficient. Nodes with a large loop coefficient tend to be core inner community nodes, while other vertices are usually peripheral sites at the borders of communities. Our method gives satisfactory results for both artificial and real-world graphs, if they have a relatively pronounced modular structure. This type of algorithm could open a way of interpreting the role of nodes in communities in terms of the local loop coefficient, and could be used as a complement to other methods.

Inter-individual variability and pattern recognition of surface electromyography in front crawl swimming.

PubMed

Martens, Jonas; Daly, Daniel; Deschamps, Kevin; Staes, Filip; Fernandes, Ricardo J

2016-12-01

Variability of electromyographic (EMG) recordings is a complex phenomenon rarely examined in swimming. Our purposes were to investigate inter-individual variability in muscle activation patterns during front crawl swimming and assess if there were clusters of sub patterns present. Bilateral muscle activity of rectus abdominis (RA) and deltoideus medialis (DM) was recorded using wireless surface EMG in 15 adult male competitive swimmers. The amplitude of the median EMG trial of six upper arm movement cycles was used for the inter-individual variability assessment, quantified with the coefficient of variation, coefficient of quartile variation, the variance ratio and mean deviation. Key features were selected based on qualitative and quantitative classification strategies to enter in a k-means cluster analysis to examine the presence of strong sub patterns. Such strong sub patterns were found when clustering in two, three and four clusters. Inter-individual variability in a group of highly skilled swimmers was higher compared to other cyclic movements which is in contrast to what has been reported in the previous 50years of EMG research in swimming. This leads to the conclusion that coaches should be careful in using overall reference EMG information to enhance the individual swimming technique of their athletes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Anatomical relationships between serotonin 5-HT2A and dopamine D2 receptors in living human brain.

PubMed

Ishii, Tatsuya; Kimura, Yasuyuki; Ichise, Masanori; Takahata, Keisuke; Kitamura, Soichiro; Moriguchi, Sho; Kubota, Manabu; Zhang, Ming-Rong; Yamada, Makiko; Higuchi, Makoto; Okubo, Yoshinori; Suhara, Tetsuya

2017-01-01

Seven healthy volunteers underwent PET scans with [18F]altanserin and [11C]FLB 457 for 5-HT2A and D2 receptors, respectively. As a measure of receptor density, a binding potential (BP) was calculated from PET data for 76 cerebral cortical regions. A correlation matrix was calculated between the binding potentials of [18F]altanserin and [11C]FLB 457 for those regions. The regional relationships were investigated using a bicluster analysis of the correlation matrix with an iterative signature algorithm. We identified two clusters of regions. The first cluster identified a distinct profile of correlation coefficients between 5-HT2A and D2 receptors, with the former in regions related to sensorimotor integration (supplementary motor area, superior parietal gyrus, and paracentral lobule) and the latter in most cortical regions. The second cluster identified another distinct profile of correlation coefficients between 5-HT2A receptors in the bilateral hippocampi and D2 receptors in most cortical regions. The observation of two distinct clusters in the correlation matrix suggests regional interactions between 5-HT2A and D2 receptors in sensorimotor integration and hippocampal function. A bicluster analysis of the correlation matrix of these neuroreceptors may be beneficial in understanding molecular networks in the human brain.

Characteristics of a Sealed Internally Balanced Aileron from Tests of a 1/4-Scale Partial-Span Model of the Republic XF-12 Airplane in the Langley 19-Foot Pressure Tunnel

NASA Technical Reports Server (NTRS)

Graham, Robert R.; Martina, Albert P.; Salmi, Reino J.

1946-01-01

This paper presents the results of the aileron investigation and includes rolling-moment, yawing-moment, and aileron hinge-moment coefficients and pressure coefficients across the aileron-balance seal through a range of angle of attack, tab deflection, and aileron deflection with flaps neutral and deflected 20 degrees and 55 degrees. Some of the effects of wing roughness and balance seal leakage on the aileron and tab characteristics are also presented.

Spread of information and infection on finite random networks

NASA Astrophysics Data System (ADS)

Isham, Valerie; Kaczmarska, Joanna; Nekovee, Maziar

2011-04-01

The modeling of epidemic-like processes on random networks has received considerable attention in recent years. While these processes are inherently stochastic, most previous work has been focused on deterministic models that ignore important fluctuations that may persist even in the infinite network size limit. In a previous paper, for a class of epidemic and rumor processes, we derived approximate models for the full probability distribution of the final size of the epidemic, as opposed to only mean values. In this paper we examine via direct simulations the adequacy of the approximate model to describe stochastic epidemics and rumors on several random network topologies: homogeneous networks, Erdös-Rényi (ER) random graphs, Barabasi-Albert scale-free networks, and random geometric graphs. We find that the approximate model is reasonably accurate in predicting the probability of spread. However, the position of the threshold and the conditional mean of the final size for processes near the threshold are not well described by the approximate model even in the case of homogeneous networks. We attribute this failure to the presence of other structural properties beyond degree-degree correlations, and in particular clustering, which are present in any finite network but are not incorporated in the approximate model. In order to test this “hypothesis” we perform additional simulations on a set of ER random graphs where degree-degree correlations and clustering are separately and independently introduced using recently proposed algorithms from the literature. Our results show that even strong degree-degree correlations have only weak effects on the position of the threshold and the conditional mean of the final size. On the other hand, the introduction of clustering greatly affects both the position of the threshold and the conditional mean. Similar analysis for the Barabasi-Albert scale-free network confirms the significance of clustering on the dynamics of rumor spread. For this network, though, with its highly skewed degree distribution, the addition of positive correlation had a much stronger effect on the final size distribution than was found for the simple random graph.

Aroma volatility from aqueous sucrose solutions at low and subzero temperatures.

PubMed

Covarrubias-Cervantes, Marco; Champion, Dominique; Debeaufort, Frédéric; Voilley, Andrée

2004-11-17

The gas-liquid partition coefficients of ethyl acetate and ethyl hexanoate have been measured in water and aqueous sucrose solutions from 25 to -10 degrees C by dynamic headspace. Experiments were carried out on sucrose solutions at temperatures where no ice formation was possible. Results showed that when sucrose concentration increased, aroma volatility increased except for ethyl hexanoate and in the highest sucrose concentration solution (57.5%). A quasi-linear temperature decrease on aroma volatility was observed in sucrose solutions from 25 to around 4 and 0 degrees C. Then, from 0 to -10 degrees C, aroma volatility did not decrease: ethyl acetate volatility remained constant but that of ethyl hexanoate increased. Enthalpy of vaporization and activity coefficients of the aroma compounds were calculated.

Tracing Gas Motions in the Centaurus Cluster

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

Graham, James; Fabian, A.C.; Sanders, J.S.

2006-03-01

We apply the stochastic model of iron transport developed by Rebusco et al. (2005) to the Centaurus cluster. Using this model, we find that an effective diffusion coefficient D in the range 2 x 10{sup 28} - 4 x 10{sup 28} cm{sup 2}s{sup -1} can approximately reproduce the observed abundance distribution. Reproducing the flat central profile and sharp drop around 30-70 kpc, however, requires a diffusion coefficient that drops rapidly with radius so that D > 4 x 10{sup 28} cm{sup 2}s{sup -1} only inside about 25 kpc. Assuming that all transport is due to fully-developed turbulence, which is alsomore » responsible for offsetting cooling in the cluster core, we calculate the length and velocity scales of energy injection. These length scales are found to be up to a factor of {approx} 10 larger than expected if the turbulence is due to the inflation and rising of a bubble. We also calculate the turbulent thermal conductivity and find it is unlikely to be significant in preventing cooling.« less

A New Cluster Analysis-Marker-Controlled Watershed Method for Separating Particles of Granular Soils

PubMed Central

Alam, Md Ferdous

2017-01-01

An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM), has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis. PMID:29057823

Film cooling performance of a row of dual-fanned holes at various injection angles

NASA Astrophysics Data System (ADS)

Li, Guangchao; Wang, Haofeng; Zhang, Wei; Kou, Zhihai; Xu, Rangshu

2017-10-01

Film cooling performance about a row of dual-fanned holes with injection angles of 30°, 60 ° and 90° were experimentally investigated at blowing ratios of 1.0 and 2.0. Dual-fanned hole is a novel shaped hole which has both inlet expansion and outlet expansion. A transient thermochromic liquid crystal technique was used to reveal the local values of film cooling effectiveness and heat transfer coefficient. The results show that injection angles have strong influence on the two dimensional distributions of film cooling effectiveness and heat transfer coefficient. For the small injection angle of 30 degree and small blowing ratio of 1.0, there is only a narrow spanwise region covered with film. The increase of injection angle and blowing ratio both leads to the enhanced spanwise film diffusion, but reduced local cooling ability far away from the hole. Injection angles have comprehensive influence on the averaged film cooling effectiveness for various x/d locations. As injection angles are 30 and 60 degree, two bands of high heat transfer coefficients are found in mixing region of the gas and coolant. As injection angle increases to 90 degree, the mixing leads to the enhanced heat transfer region near the film hole. The averaged heat transfer coefficient increases with the increase of injection angle.

Torus Approach in Gravity Field Determination from Simulated GOCE Gravity Gradients

NASA Astrophysics Data System (ADS)

Liu, Huanling; Wen, Hanjiang; Xu, Xinyu; Zhu, Guangbin

2016-08-01

In Torus approach, observations are projected to the nominal orbits with constant radius and inclination, lumped coefficients provides a linear relationship between observations and spherical harmonic coefficients. Based on the relationship, two-dimensional FFT and block-diagonal least-squares adjustment are used to recover Earth's gravity field model. The Earth's gravity field model complete to degree and order 200 is recovered using simulated satellite gravity gradients on a torus grid, and the degree median error is smaller than 10-18, which shows the effectiveness of Torus approach. EGM2008 is employed as a reference model and the gravity field model is resolved using the simulated observations without noise given on GOCE orbits of 61 days. The error from reduction and interpolation can be mitigated by iterations. Due to polar gap, the precision of low-order coefficients is lower. Without considering these coefficients the maximum geoid degree error and cumulative error are 0.022mm and 0.099mm, respectively. The Earth's gravity field model is also recovered from simulated observations with white noise 5mE/Hz1/2, which is compared to that from direct method. In conclusion, it is demonstrated that Torus approach is a valid method for processing massive amount of GOCE gravity gradients.

Two worlds collide: Image analysis methods for quantifying structural variation in cluster molecular dynamics

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

Steenbergen, K. G., E-mail: kgsteen@gmail.com; Gaston, N.

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement formore » a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.« less

Ab Initio Molecular Dynamics Studies of Pb m Sb n ( m + n ≤ 9) Alloy Clusters

NASA Astrophysics Data System (ADS)

Song, Bingyi; Xu, Baoqiang; Yang, Bin; Jiang, Wenlong; Chen, Xiumin; Xu, Na; Liu, Dachun; Dai, Yongnian

2017-10-01

Structure, stability, and dynamics of Pb m Sb n ( m + n ≤ 9) clusters were investigated using ab initio molecular dynamics. Size dependence of binding energies, the second-order energy difference of clusters, dissociation energy, HOMO-LUMO gaps, Mayer bond order, and the diffusion coefficient of Pb m Sb n clusters were discussed. Results suggest that Pb3Sb2, Pb4Sb2, and Pb5Sb4 ( n = 2 or 4) clusters have higher stability than other clusters, which is consistent with previous findings. In case of Pb-Sb alloy, the dynamics results show that Pb4Sb2 (Pb-22.71 wt pct Sb) can exist in gas phase at 1073 K (800 °C), which reasonably explains the azeotropic phenomenon, and the calculated values are in agreement with the experimental results (Pb-22 wt pct Sb).

Two worlds collide: image analysis methods for quantifying structural variation in cluster molecular dynamics.

PubMed

Steenbergen, K G; Gaston, N

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.

On the coefficients of integrated expansions of Bessel polynomials

NASA Astrophysics Data System (ADS)

Doha, E. H.; Ahmed, H. M.

2006-03-01

A new formula expressing explicitly the integrals of Bessel polynomials of any degree and for any order in terms of the Bessel polynomials themselves is proved. Another new explicit formula relating the Bessel coefficients of an expansion for infinitely differentiable function that has been integrated an arbitrary number of times in terms of the coefficients of the original expansion of the function is also established. An application of these formulae for solving ordinary differential equations with varying coefficients is discussed.

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

PubMed

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

2011-01-19

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

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

Geopotential coefficient determination and the gravimetric boundary value problem: A new approach

NASA Technical Reports Server (NTRS)

Sjoeberg, Lars E.

1989-01-01

New integral formulas to determine geopotential coefficients from terrestrial gravity and satellite altimetry data are given. The formulas are based on the integration of data over the non-spherical surface of the Earth. The effect of the topography to low degrees and orders of coefficients is estimated numerically. Formulas for the solution of the gravimetric boundary value problem are derived.

The geometry of chaotic dynamics — a complex network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Heitzig, J.; Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J.

2011-12-01

Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ɛ-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ɛ-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ɛ-recurrence networks exhibit an important link between dynamical systems and graph theory.

Mass calibration of galaxy clusters at redshift 0.1–1.0 using weak lensing in the Sloan Digital Sky Survey Stripe 82 co-add

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

Wiesner, Matthew P.; Lin, Huan; Soares-Santos, Marcelle

We present galaxy cluster mass–richness relations found in the Sloan Digital Sky Survey Stripe 82 co-add using clusters found using a Voronoi tessellation cluster finder. These relations were found using stacked weak lensing shear observed in a large sample of galaxy clusters. These mass–richness relations are presented for four redshift bins, 0.1 < z ≤ 0.4, 0.4 < z ≤ 0.7, 0.7 < z ≤ 1.0 and 0.1 < z ≤ 1.0. We describe the sample of galaxy clusters and explain how these clusters were found using a Voronoi tessellation cluster finder. We fit a Navarro-Frenk-White profile to the stackedmore » weak lensing shear signal in redshift and richness bins in order to measure virial mass (M 200). We describe several effects that can bias weak lensing measurements, including photometric redshift bias, the effect of the central BCG, halo miscentering, photometric redshift uncertainty and foreground galaxy contamination. We present mass–richness relations using richness measure N VT with each of these effects considered separately as well as considered altogether. We also examine redshift evolution of the mass–richness relation. As a result, we present measurements of the mass coefficient (M 200|20) and the power-law slope (α) for power-law fits to the mass and richness values in each of the redshift bins. We find values of the mass coefficient of 8.49 ± 0.526, 14.1 ± 1.78, 30.2 ± 8.74 and 9.23 ± 0.525 × 10 13 h –1 M ⊙ for each of the four redshift bins, respectively. As a result, we find values of the power-law slope of 0.905 ± 0.0585, 0.948 ± 0.100, 1.33 ± 0.260 and 0.883 ± 0.0500, respectively.« less

Mass calibration of galaxy clusters at redshift 0.1–1.0 using weak lensing in the Sloan Digital Sky Survey Stripe 82 co-add

DOE PAGES

Wiesner, Matthew P.; Lin, Huan; Soares-Santos, Marcelle

2015-07-08

We present galaxy cluster mass–richness relations found in the Sloan Digital Sky Survey Stripe 82 co-add using clusters found using a Voronoi tessellation cluster finder. These relations were found using stacked weak lensing shear observed in a large sample of galaxy clusters. These mass–richness relations are presented for four redshift bins, 0.1 < z ≤ 0.4, 0.4 < z ≤ 0.7, 0.7 < z ≤ 1.0 and 0.1 < z ≤ 1.0. We describe the sample of galaxy clusters and explain how these clusters were found using a Voronoi tessellation cluster finder. We fit a Navarro-Frenk-White profile to the stackedmore » weak lensing shear signal in redshift and richness bins in order to measure virial mass (M 200). We describe several effects that can bias weak lensing measurements, including photometric redshift bias, the effect of the central BCG, halo miscentering, photometric redshift uncertainty and foreground galaxy contamination. We present mass–richness relations using richness measure N VT with each of these effects considered separately as well as considered altogether. We also examine redshift evolution of the mass–richness relation. As a result, we present measurements of the mass coefficient (M 200|20) and the power-law slope (α) for power-law fits to the mass and richness values in each of the redshift bins. We find values of the mass coefficient of 8.49 ± 0.526, 14.1 ± 1.78, 30.2 ± 8.74 and 9.23 ± 0.525 × 10 13 h –1 M ⊙ for each of the four redshift bins, respectively. As a result, we find values of the power-law slope of 0.905 ± 0.0585, 0.948 ± 0.100, 1.33 ± 0.260 and 0.883 ± 0.0500, respectively.« less

A novel look at energy equipartition in globular clusters

NASA Astrophysics Data System (ADS)

Bianchini, P.; van de Ven, G.; Norris, M. A.; Schinnerer, E.; Varri, A. L.

2016-06-01

Two-body interactions play a major role in shaping the structural and dynamical properties of globular clusters (GCs) over their long-term evolution. In particular, GCs evolve towards a state of partial energy equipartition that induces a mass dependence in their kinematics. By using a set of Monte Carlo cluster simulations evolved in quasi-isolation, we show that the stellar mass dependence of the velocity dispersion σ(m) can be described by an exponential function σ2 ∝ exp (-m/meq), with the parameter meq quantifying the degree of partial energy equipartition of the systems. This simple parametrization successfully captures the behaviour of the velocity dispersion at lower as well as higher stellar masses, that is, the regime where the system is expected to approach full equipartition. We find a tight correlation between the degree of equipartition reached by a GC and its dynamical state, indicating that clusters that are more than about 20 core relaxation times old, have reached a maximum degree of equipartition. This equipartition-dynamical state relation can be used as a tool to characterize the relaxation condition of a cluster with a kinematic measure of the meq parameter. Vice versa, the mass dependence of the kinematics can be predicted knowing the relaxation time solely on the basis of photometric measurements. Moreover, any deviations from this tight relation could be used as a probe of a peculiar dynamical history of a cluster. Finally, our novel approach is important for the interpretation of state-of-the-art Hubble Space Telescope proper motion data, for which the mass dependence of kinematics can now be measured, and for the application of modelling techniques which take into consideration multimass components and mass segregation.

Clustering of Multivariate Geostatistical Data

NASA Astrophysics Data System (ADS)

Fouedjio, Francky

2017-04-01

Multivariate data indexed by geographical coordinates have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations belonging to the same cluster have a certain degree of homogeneity while data locations in the different clusters have to be as different as possible. However, groups of data locations created through classical clustering techniques turn out to show poor spatial contiguity, a feature obviously inconvenient for many geoscience applications. In this work, we develop a clustering method that overcomes this problem by accounting the spatial dependence structure of data; thus reinforcing the spatial contiguity of resulting cluster. The capability of the proposed clustering method to provide spatially contiguous and meaningful clusters of data locations is assessed using both synthetic and real datasets. Keywords: clustering, geostatistics, spatial contiguity, spatial dependence.

Lumping of degree-based mean-field and pair-approximation equations for multistate contact processes

NASA Astrophysics Data System (ADS)

Kyriakopoulos, Charalampos; Grossmann, Gerrit; Wolf, Verena; Bortolussi, Luca

2018-01-01

Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information-spreading networks. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appearing in real applications. One strategy to reduce the complexity of their analysis is to rely on approximations, often in terms of a set of differential equations capturing the evolution of a random node, distinguishing nodes with different topological contexts (i.e., different degrees of different neighborhoods), such as degree-based mean-field (DBMF), approximate-master-equation (AME), or pair-approximation (PA) approaches. The number of differential equations so obtained is typically proportional to the maximum degree kmax of the network, which is much smaller than the size of the master equation of the underlying stochastic model, yet numerically solving these equations can still be problematic for large kmax. In this paper, we consider AME and PA, extended to cope with multiple local states, and we provide an aggregation procedure that clusters together nodes having similar degrees, treating those in the same cluster as indistinguishable, thus reducing the number of equations while preserving an accurate description of global observables of interest. We also provide an automatic way to build such equations and to identify a small number of degree clusters that give accurate results. The method is tested on several case studies, where it shows a high level of compression and a reduction of computational time of several orders of magnitude for large networks, with minimal loss in accuracy.

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

PubMed

van Schie, S; Moerbeek, M

2014-08-30

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 level and individual level variance should be known before the study starts, but this is often not the case. We suggest using an internal pilot study design to address this problem of unknown variances. A pilot can be useful to re-estimate the variances and re-calculate the sample size during the trial. Using simulated data, it is shown that an initially low or high power can be adjusted using an internal pilot with the type I error rate remaining within an acceptable range. The intracluster correlation coefficient can be re-estimated with more precision, which has a positive effect on the sample size. We conclude that an internal pilot study design may be used if active recruitment is feasible within a limited number of clusters. Copyright © 2014 John Wiley & Sons, Ltd.

Transformation between surface spherical harmonic expansion of arbitrary high degree and order and double Fourier series on sphere

NASA Astrophysics Data System (ADS)

Fukushima, Toshio

2018-02-01

In order to accelerate the spherical harmonic synthesis and/or analysis of arbitrary function on the unit sphere, we developed a pair of procedures to transform between a truncated spherical harmonic expansion and the corresponding two-dimensional Fourier series. First, we obtained an analytic expression of the sine/cosine series coefficient of the 4 π fully normalized associated Legendre function in terms of the rectangle values of the Wigner d function. Then, we elaborated the existing method to transform the coefficients of the surface spherical harmonic expansion to those of the double Fourier series so as to be capable with arbitrary high degree and order. Next, we created a new method to transform inversely a given double Fourier series to the corresponding surface spherical harmonic expansion. The key of the new method is a couple of new recurrence formulas to compute the inverse transformation coefficients: a decreasing-order, fixed-degree, and fixed-wavenumber three-term formula for general terms, and an increasing-degree-and-order and fixed-wavenumber two-term formula for diagonal terms. Meanwhile, the two seed values are analytically prepared. Both of the forward and inverse transformation procedures are confirmed to be sufficiently accurate and applicable to an extremely high degree/order/wavenumber as 2^{30} {≈ } 10^9. The developed procedures will be useful not only in the synthesis and analysis of the spherical harmonic expansion of arbitrary high degree and order, but also in the evaluation of the derivatives and integrals of the spherical harmonic expansion.

Glove thermal insulation: local heat transfer measures and relevance.

PubMed

Sari, Hayet; Gartner, Maurice; Hoeft, Alain; Candas, Victor

2004-09-01

When exposed to cold, the hands need to be protected against heat loss not only in order to reduce thermal discomfort, but also to keep their efficiency. Although gloves are usually the most common protection, their thermal insulation is generally unknown. The aim of this study was to measure the heat losses from a gloved hand with a special interest in local variations. Using a calorimetric hand placed in a cold box, several types of gloves were tested. The results indicated that depending on the glove and on the area covered the heat loss reduction may vary from almost 60% to 90%. When the least efficient pair of gloves was excluded, heat exchange coefficients varied from 1.8 to 4.8 W/m2 per degrees C for the palm and from 4.2 to 6.2 W/m2 per degrees C for the back of the hand. The three medium fingers seemed to be equally treated, with a heat exchange coefficient variation of 6.3-9.0 W/m2 per degrees C. The thumb and the little finger, which require better insulation, exhibited higher local heat transfer coefficients of 8.3-12.7 W/m2 per degrees C. Some practical aspects are evoked.

Iontophoretic transport of associates based on porous Keplerate-type cluster polyoxometalate Mo72Fe30 and containing biologically active substances

NASA Astrophysics Data System (ADS)

Ostroushko, A. A.; Gagarin, I. D.; Tonkushina, M. O.; Grzhegorzhevskii, K. V.; Danilova, I. G.; Gette, I. F.; Kim, G. A.

2017-09-01

The possibility of iontophoretic transport through the native membranes of biologically active substances (vitamin B1 and insulin) associated with porous clusters Mo72Fe30 polyoxometalate of the Keplerate type is demonstrated for the first time in an experimental setup. The diffusion coefficient is estimated. The possibility of transferring Keplerate ions with a protective coating of biocompatible polymer polyvinylpyrrolidone is also shown.

Folksonomies and clustering in the collaborative system CiteULike

NASA Astrophysics Data System (ADS)

Capocci, Andrea; Caldarelli, Guido

2008-06-01

We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tri-partite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient can be used to analyze the semantical patterns among tags.

Exact low-temperature series expansion for the partition function of the zero-field Ising model on the infinite square lattice.

PubMed

Siudem, Grzegorz; Fronczak, Agata; Fronczak, Piotr

2016-10-10

In this paper, we provide the exact expression for the coefficients in the low-temperature series expansion of the partition function of the two-dimensional Ising model on the infinite square lattice. This is equivalent to exact determination of the number of spin configurations at a given energy. With these coefficients, we show that the ferromagnetic-to-paramagnetic phase transition in the square lattice Ising model can be explained through equivalence between the model and the perfect gas of energy clusters model, in which the passage through the critical point is related to the complete change in the thermodynamic preferences on the size of clusters. The combinatorial approach reported in this article is very general and can be easily applied to other lattice models.

Wedge sampling for computing clustering coefficients and triangle counts on large graphs

DOE PAGES

Seshadhri, C.; Pinar, Ali; Kolda, Tamara G.

2014-05-08

Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of such graphs. Some of the most useful graph metrics are based on triangles, such as those measuring social cohesion. Despite the importance of these triadic measures, algorithms to compute them can be extremely expensive. We discuss the method of wedge sampling. This versatile technique allows for the fast and accurate approximation of various types of clustering coefficients and triangle counts. Furthermore, these techniques are extensible to counting directed triangles in digraphs. Our methods come with provable andmore » practical time-approximation tradeoffs for all computations. We provide extensive results that show our methods are orders of magnitude faster than the state of the art, while providing nearly the accuracy of full enumeration.« less

Exact low-temperature series expansion for the partition function of the zero-field Ising model on the infinite square lattice

PubMed Central

Siudem, Grzegorz; Fronczak, Agata; Fronczak, Piotr

2016-01-01

In this paper, we provide the exact expression for the coefficients in the low-temperature series expansion of the partition function of the two-dimensional Ising model on the infinite square lattice. This is equivalent to exact determination of the number of spin configurations at a given energy. With these coefficients, we show that the ferromagnetic–to–paramagnetic phase transition in the square lattice Ising model can be explained through equivalence between the model and the perfect gas of energy clusters model, in which the passage through the critical point is related to the complete change in the thermodynamic preferences on the size of clusters. The combinatorial approach reported in this article is very general and can be easily applied to other lattice models. PMID:27721435

Report on simulation of fission gas and fission product diffusion in UO 2

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

Andersson, Anders David; Perriot, Romain Thibault; Pastore, Giovanni

2016-07-22

In UO 2 nuclear fuel, the retention and release of fission gas atoms such as xenon (Xe) are important for nuclear fuel performance by, for example, reducing the fuel thermal conductivity, causing fuel swelling that leads to mechanical interaction with the clad, increasing the plenum pressure and reducing the fuel–clad gap thermal conductivity. We use multi-­scale simulations to determine fission gas diffusion mechanisms as well as the corresponding rates in UO 2 under both intrinsic and irradiation conditions. In addition to Xe and Kr, the fission products Zr, Ru, Ce, Y, La, Sr and Ba have been investigated. Density functionalmore » theory (DFT) calculations are used to study formation, binding and migration energies of small clusters of Xe atoms and vacancies. Empirical potential calculations enable us to determine the corresponding entropies and attempt frequencies for migration as well as investigate the properties of large clusters or small fission gas bubbles. A continuum reaction-­diffusion model is developed for Xe and point defects based on the mechanisms and rates obtained from atomistic simulations. Effective fission gas diffusivities are then obtained by solving this set of equations for different chemical and irradiation conditions using the MARMOT phase field code. The predictions are compared to available experimental data. The importance of the large Xe U3O cluster (a Xe atom in a uranium + oxygen vacancy trap site with two bound uranium vacancies) is emphasized, which is a consequence of its high mobility and high binding energy. We find that the Xe U3O cluster gives Xe diffusion coefficients that are higher for intrinsic conditions than under irradiation over a wide range of temperatures. Under irradiation the fast-­moving Xe U3O cluster recombines quickly with irradiation-induced interstitial U ions, while this mechanism is less important for intrinsic conditions. The net result is higher concentration of the Xe U3O cluster for intrinsic conditions than under irradiation. We speculate that differences in the irradiation conditions and their impact on the Xe U3O cluster can explain the wide range of diffusivities reported in experimental studies. However, all vacancy-­mediated mechanisms underestimate the Xe diffusivity compared to the empirical radiation-­enhanced rate used in most fission gas release models. We investigate the possibility that diffusion of small fission gas bubbles or extended Xe-­vacancy clusters may give rise to the observed radiation-­enhanced diffusion coefficient. These studies highlight the importance of U divacancies and an octahedron coordination of uranium vacancies encompassing a Xe fission gas atom. The latter cluster can migrate via a multistep mechanism with a rather low effective barrier, which together with irradiation-induced clusters of uranium vacancies, gives rise to the irradiation-enhanced diffusion coefficient observed in experiments.« less

Hyperfine excitation of C2H in collisions with ortho- and para-H2

NASA Astrophysics Data System (ADS)

Dagdigian, Paul J.

2018-06-01

Accurate estimation of the abundance of the ethynyl (C2H) radical requires accurate radiative and collisional rate coefficients. Hyperfine-resolved rate coefficients for (de-)excitation of C2H in collisions with ortho- and para-H2 are presented in this work. These rate coefficients were computed in time-independent close-coupling quantum scattering calculations that employed a potential energy surface recently computed at the coupled-clusters level of theory that describes the interaction of C2H with H2. Rate coefficients for temperatures from 10 to 300 K were computed for all transitions among the first 40 hyperfine energy levels of C2H in collisions with ortho- and para-H2. These rate coefficients were employed in simple radiative transfer calculations to simulate the excitation of C2H in typical molecular clouds.

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

PubMed

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

2015-11-30

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

The effect of core configuration on temperature coefficient of reactivity in IRR-1

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

Bettan, M.; Silverman, I.; Shapira, M.

1997-08-01

Experiments designed to measure the effect of coolant moderator temperature on core reactivity in an HEU swimming pool type reactor were performed. The moderator temperature coefficient of reactivity ({alpha}{sub {omega}}) was obtained and found to be different in two core loadings. The measured {alpha}{sub {omega}} of one core loading was {minus}13 pcm/{degrees}C at the temperature range of 23-30{degrees}C. This value of {alpha}{sub {omega}} is comparable to the data published by the IAEA. The {alpha}{sub {omega}} measured in the second core loading was found to be {minus}8 pcm/{degrees}C at the same temperature range. Another phenomenon considered in this study is coremore » behavior during reactivity insertion transient. The results were compared to a core simulation using the Dynamic Simulator for Nuclear Power Plants. It was found that in the second core loading factors other than the moderator temperature influence the core reactivity more than expected. These effects proved to be extremely dependent on core configuration and may in certain core loadings render the reactor`s reactivity coefficient undesirable.« less

Amelioration de l'implementation des volets dans un modele de dynamique et controle de vol de l'avion L1011-500

NASA Astrophysics Data System (ADS)

Saafi, Kais

The aerodynamic model of the aircraft L1011-500 was designed and simulated in Matlab and Simulink by Bombardier to serve the Esterline-CMC Electronics Company in its goals to improve the Flight Management System FMS. In this model implemented in FLSIM by CMC-Electronics Esterline, a longitudinal instability appears during the approach phase and when flaps have a higher or equal angle to 4 degrees. The global project at LARCASE consisted in the improvement of the L1011-500 aerodynamic model stability under Matlab / Simulink and mainly for flaps angles situated between 4 degrees and 22 degrees. The L1011-500 global model was finalized in order to visualize and analyze its dynamic behavior. When the global model of the aircraft L1011-500 was generated, corrections were added to the lift coefficient (CL), the drag coefficient (CD) and the pitching moment coefficient (CM) to ensure the trim of the aircraft. The obtained results are compared with the flight tests data delivered by CMC Electronics-Esterline to validate our numerical studies.

Earth's isostatic gravity anomaly field: Contributions to National Geodetic Satellite Program

NASA Technical Reports Server (NTRS)

Khan, M. A.

1973-01-01

On the assumption that the compensation for the topographic load is achieved in the manner of Airy-Heiskenenan hypothesis at a compensation depth of 30 kilometers, the spherical harmonic coefficients of the isostatic reduction potential U are computed. The degree power spectra of these coefficients are compared with the power spectra of the isostatic reduction coefficients given by Uotila. Results are presented in tabular form.

Comparison Analysis among Large Amount of SNS Sites

NASA Astrophysics Data System (ADS)

Toriumi, Fujio; Yamamoto, Hitoshi; Suwa, Hirohiko; Okada, Isamu; Izumi, Kiyoshi; Hashimoto, Yasuhiro

In recent years, application of Social Networking Services (SNS) and Blogs are growing as new communication tools on the Internet. Several large-scale SNS sites are prospering; meanwhile, many sites with relatively small scale are offering services. Such small-scale SNSs realize small-group isolated type of communication while neither mixi nor MySpace can do that. However, the studies on SNS are almost about particular large-scale SNSs and cannot analyze whether their results apply for general features or for special characteristics on the SNSs. From the point of view of comparison analysis on SNS, comparison with just several types of those cannot reach a statistically significant level. We analyze many SNS sites with the aim of classifying them by using some approaches. Our paper classifies 50,000 sites for small-scale SNSs and gives their features from the points of network structure, patterns of communication, and growth rate of SNS. The result of analysis for network structure shows that many SNS sites have small-world attribute with short path lengths and high coefficients of their cluster. Distribution of degrees of the SNS sites is close to power law. This result indicates the small-scale SNS sites raise the percentage of users with many friends than mixi. According to the analysis of their coefficients of assortativity, those SNS sites have negative values of assortativity, and that means users with high degree tend to connect users with small degree. Next, we analyze the patterns of user communication. A friend network of SNS is explicit while users' communication behaviors are defined as an implicit network. What kind of relationships do these networks have? To address this question, we obtain some characteristics of users' communication structure and activation patterns of users on the SNS sites. By using new indexes, friend aggregation rate and friend coverage rate, we show that SNS sites with high value of friend coverage rate activate diary postings and their comments. Besides, they become activated when hub users with high degree do not behave actively on the sites with high value of friend aggregation rate and high value of friend coverage rate. On the other hand, activation emerges when hub users behave actively on the sites with low value of friend aggregation rate and high value of friend coverage rate. Finally, we observe SNS sites which are increasing the number of users considerably, from the viewpoint of network structure, and extract characteristics of high growth SNS sites. As a result of discrimination on the basis of the decision tree analysis, we can recognize the high growth SNS sites with a high degree of accuracy. Besides, this approach suggests mixi and the other small-scale SNS sites have different character trait.

An Investigation of the Coefficient of Discharge of Liquids Through Small Round Orifices

NASA Technical Reports Server (NTRS)

Joachim, W F

1926-01-01

The work covered by this report was undertaken in connection with a general investigation of fuel injection engine principles as applied to engines for aircraft propulsion, the specific purpose being to obtain information on the coefficient of discharge of small round orifices suitable for use as fuel injection nozzles. Values for the coefficient were determined for the more important conditions of engine service such as discharge under pressures up to 8,000 pounds per square inch, at temperatures between 80 degrees and 180 degrees F. And into air compressed to pressures up to 1,000 pounds per square inch. The results show that the coefficient ranges between 0.62 and 0.88 for the different test conditions between 1,000 and 8,000 pounds per square inch hydraulic pressure. At lower pressures the coefficient increases materially. It is concluded that within the range of these tests and for hydraulic pressures above 1,000 pound per square inch the coefficient does not change materially with pressure or temperature; that it depends considerably upon the liquid, decreases with increase in orifice size, and increases in the case of discharge into compressed air until the compressed-air pressure equals approximately three-tenths of the hydraulic pressure, beyond which pressure ratio it remains practically constant.

Evaluating the Impact of Guessing and Its Interactions With Other Test Characteristics on Confidence Interval Procedures for Coefficient Alpha

PubMed Central

Paek, Insu

2015-01-01

The effect of guessing on the point estimate of coefficient alpha has been studied in the literature, but the impact of guessing and its interactions with other test characteristics on the interval estimators for coefficient alpha has not been fully investigated. This study examined the impact of guessing and its interactions with other test characteristics on four confidence interval (CI) procedures for coefficient alpha in terms of coverage rate (CR), length, and the degree of asymmetry of CI estimates. In addition, interval estimates of coefficient alpha when data follow the essentially tau-equivalent condition were investigated as a supplement to the case of dichotomous data with examinee guessing. For dichotomous data with guessing, the results did not reveal salient negative effects of guessing and its interactions with other test characteristics (sample size, test length, coefficient alpha levels) on CR and the degree of asymmetry, but the effect of guessing was salient as a main effect and an interaction effect with sample size on the length of the CI estimates, making longer CI estimates as guessing increases, especially when combined with a small sample size. Other important effects (e.g., CI procedures on CR) are also discussed. PMID:29795863

The effect of a patient centred care bundle intervention on pressure ulcer incidence (INTACT): A cluster randomised trial.

PubMed

Chaboyer, Wendy; Bucknall, Tracey; Webster, Joan; McInnes, Elizabeth; Gillespie, Brigid M; Banks, Merrilyn; Whitty, Jennifer A; Thalib, Lukman; Roberts, Shelley; Tallott, Mandy; Cullum, Nicky; Wallis, Marianne

2016-12-01

Hospital-acquired pressure ulcers are a serious patient safety concern, associated with poor patient outcomes and high healthcare costs. They are also viewed as an indicator of nursing care quality. To evaluate the effectiveness of a pressure ulcer prevention care bundle in preventing hospital-acquired pressure ulcers among at risk patients. Pragmatic cluster randomised trial. Eight tertiary referral hospitals with >200 beds each in three Australian states. 1600 patients (200/hospital) were recruited. Patients were eligible if they were: ≥18 years old; at risk of pressure ulcer because of limited mobility; expected to stay in hospital ≥48h and able to read English. Hospitals (clusters) were stratified in two groups by recent pressure ulcer rates and randomised within strata to either a pressure ulcer prevention care bundle or standard care. The care bundle was theoretically and empirically based on patient participation and clinical practice guidelines. It was multi-component, with three messages for patients' participation in pressure ulcer prevention care: keep moving; look after your skin; and eat a healthy diet. Training aids for patients included a DVD, brochure and poster. Nurses in intervention hospitals were trained in partnering with patients in their pressure ulcer prevention care. The statistician, recruiters, and outcome assessors were blinded to group allocation and interventionists blinded to the study hypotheses, tested at both the cluster and patient level. The primary outcome, incidence of hospital-acquired pressure ulcers, which applied to both the cluster and individual participant level, was measured by daily skin inspection. Four clusters were randomised to each group and 799 patients per group analysed. The intraclass correlation coefficient was 0.035. After adjusting for clustering and pre-specified covariates (age, pressure ulcer present at baseline, body mass index, reason for admission, residence and number of comorbidities on admission), the hazard ratio for new pressure ulcers developed (pressure ulcer prevention care bundle relative to standard care) was 0.58 (95% CI: 0.25, 1.33; p=0.198). No adverse events or harms were reported. Although the pressure ulcer prevention care bundle was associated with a large reduction in the hazard of ulceration, there was a high degree of uncertainty around this estimate and the difference was not statistically significant. Possible explanations for this non-significant finding include that the pressure ulcer prevention care bundle was effective but the sample size too small to detect this. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Development of Ag-Pd-Au-Cu alloy for multiple dental applications. Part 1. Effects of Pd and Cu contents, and addition of Ga or Sn on physical properties and bond with ultra-low fusing ceramic.

PubMed

Goto, S; Miyagawa, Y; Ogura, H

2000-09-01

Ag-Pd-Au-Cu quaternary alloys consisting of 30-50% Ag, 20-40% Pd, 10-20% Cu and 20% Au (mother alloys) were prepared. Then 5% Sn or 5% Ga was added to the mother alloy compositions, and another two alloy systems (Sn-added alloys and Ga-added alloys) were also prepared. The bond between the prepared alloys and an ultra-low fusing ceramic as well as their physical properties such as the solidus point, liquidus point and the coefficient of thermal expansion were evaluated. The solidus point and liquidus point of the prepared alloys ranged from 802 degrees C to 1142 degrees C and from 931 degrees C to 1223 degrees C, respectively. The coefficient of thermal expansion ranged from 14.6 to 17.1 x 10(-6)/degrees C for the Sn- and Ga-added alloys. In most cases, the Pd and Cu contents significantly influenced the solidus point, liquidus point and coefficient of thermal expansion. All Sn- and Ga-added alloys showed high area fractions of retained ceramic (92.1-100%), while the mother alloy showed relatively low area fractions (82.3%) with a high standard deviation (20.5%). Based on the evaluated properties, six Sn-added alloys and four Ga-added alloys among the prepared alloys were suitable for the application of the tested ultra-low fusing ceramic.

Melting of isolated tin nanoparticles

PubMed

Bachels; Guntherodt; Schafer

2000-08-07

The melting of isolated neutral tin cluster distributions with mean sizes of about 500 atoms has been investigated in a molecular beam experiment by calorimetrically measuring the clusters' formation energies as a function of their internal temperature. For this purpose the possibility to adjust the temperature of the clusters' internal degrees of freedom by means of the temperature of the cluster source's nozzle was exploited. The melting point of the investigated tin clusters was found to be lowered by 125 K and the latent heat of fusion per atom is reduced by 35% compared to bulk tin. The melting behavior of the isolated tin clusters is discussed with respect to the occurrence of surface premelting.

Epidemic Threshold in Structured Scale-Free Networks

NASA Astrophysics Data System (ADS)

EguíLuz, VíCtor M.; Klemm, Konstantin

2002-08-01

We analyze the spreading of viruses in scale-free networks with high clustering and degree correlations, as found in the Internet graph. For the susceptible-infected-susceptible model of epidemics the prevalence undergoes a phase transition at a finite threshold of the transmission probability. Comparing with the absence of a finite threshold in networks with purely random wiring, our result suggests that high clustering (modularity) and degree correlations protect scale-free networks against the spreading of viruses. We introduce and verify a quantitative description of the epidemic threshold based on the connectivity of the neighborhoods of the hubs.

A phase cell cluster expansion for Euclidean field theories

NASA Astrophysics Data System (ADS)

Battle, Guy A., III; Federbush, Paul

1982-08-01

We adapt the cluster expansion first used to treat infrared problems for lattice models (a mass zero cluster expansion) to the usual field theory situation. The field is expanded in terms of special block spin functions and the cluster expansion given in terms of the expansion coefficients (phase cell variables); the cluster expansion expresses correlation functions in terms of contributions from finite coupled subsets of these variables. Most of the present work is carried through in d space time dimensions (for φ24 the details of the cluster expansion are pursued and convergence is proven). Thus most of the results in the present work will apply to a treatment of φ34 to which we hope to return in a succeeding paper. Of particular interest in this paper is a substitute for the stability of the vacuum bound appropriate to this cluster expansion (for d = 2 and d = 3), and a new method for performing estimates with tree graphs. The phase cell cluster expansions have the renormalization group incorporated intimately into their structure. We hope they will be useful ultimately in treating four dimensional field theories.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

PubMed

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

2018-03-13

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

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

PubMed Central

Lee, Yii-Ching; Huang, Shian-Chang; Huang, Chih-Hsuan; Wu, Hsin-Hung

2016-01-01

This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups. PMID:27895218

New trend- trigonometric model for interpolation and prediction of the geomagnetic field utilizing the new DGRF models.

USGS Publications Warehouse

Alldredge, L.R.

1988-01-01

At the IUGG Assembly at Vancouver during August 1987 new definitive geomagnetic reference field (DGRF) models to degree 10 for 1945, 1950, 1955, and 1960 were adopted by IAGA. Before these new DGRF models were accepted, the author developed a trend and trigonometric model (old trig model) based on the models IGRF 1945, IGRF 1950, IGRF 1955, IGRF 1960, DGRF 1965, DGRF 1970, DGRF 1975, DGRF 1980, and IGRF 1985, which were all approved by IAGA in Prague in August 1985. The old trig model consists of 720 trend and trigonometric coefficients for the calculation of spherical harmonic coefficients (SHC) only to degree eight because the early IGRF models were truncated there. These trend and Fourier sine coefficients can replace the equal number of SHC contained in the 9 DGRF-IGRF models. -from Authors

Partitioning coefficients of polycyclic aromatic hydrocarbons in stack gas from a municipal incinerator

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

Lee, W.M.G.; Chen, J.C.

1995-12-31

In this study, solid-gas partitioning coefficients of PAHs on fly ash in stack gas from a municipal incinerator were determined according to elution analysis with gas-solid chromatography. The fly ash from the electrostatic precipitator was sieved and packed into a 1/4 inch (6.3 mm) pyrex column. Elution analysis with gas-solid chromatography was conducted for three PAEs, Napthalene, Anthracene, and Pyrene. The temperature for elution analysis was in the range of 100{degrees}C to 300{degrees}C. Vg, specific retention volume obtained from elution analysis, and S, specific surface area of fly ash measured by a surface area measurement instrument were used to estimatemore » the solid-gas partitioning coefficient KR. In addition, the relationships between KR and temperature and KR and PAH concentrations were investigated.« less

The contact heat transfer between the heating plate and granular materials in rotary heat exchanger under overloaded condition

NASA Astrophysics Data System (ADS)

Duan, Luanfang; Qi, Chonggang; Ling, Xiang; Peng, Hao

2018-03-01

In the present work, the contact heat transfer between the granular materials and heating plates inside plate rotary heat exchanger (PRHE) was investigated. The heat transfer coefficient is dominated by the contact heat transfer coefficient at hot wall surface of the heating plates and the heat penetration inside the solid bed. A plot scale PRHE with a diameter of Do = 273 mm and a length of L = 1000 mm has been established. Quartz sand with dp = 2 mm was employed as the experimental material. The operational parameters were in the range of ω = 1 - 8 rpm, and F = 15, 20, 25, 30%, and the effect of these parameters on the time-average contact heat transfer coefficient was analyzed. The time-average contact heat transfer coefficient increases with the increase of rotary speed, but decreases with the increase of the filling degree. The measured data of time-average heat transfer coefficients were compared with theoretical calculations from Schlünder's model, a good agreement between the measurements and the model could be achieved, especially at a lower rotary speed and filling degree level. The maximum deviation between the calculated data and the experimental data is approximate 10%.

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking

PubMed Central

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2017-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992–1998), global regime formation through the FCTC negotiations (1999–2005), and philanthropic funding through the Bloomberg Initiative (2006–2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999–2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network. PMID:28596813

Potential use of random amplified polymorphic DNA (RAPD) technique to study the genetic diversity in Indian mustard (Brassica juncea) and its relationship to heterosis.

PubMed

Jain, A; Bhatia, S; Banga, S S; Prakash, S; Lakshmikumaran, M

1994-04-01

RAPD assays were performed, using 34 arbitrary decamer oligonucleotide primers and six combinations of two primers, to detect inherent variations and genetic relationships among 12 Indian and 11 exotic B. juncea genotypes. Of 595 amplification products identified, 500 of them were polymorphic across all genotypes. A low level of genetic variability was detected among the Indian genotypes, while considerable polymorphism was present among the exotic ones. Based on the pair-wise comparisons of amplification products the genetic similarity was calculated using Jaccard's similarity coefficients and a dendrogram was constructed using an unweighted pair group method was arithmetical averages (UPGMA). On the basis of this analysis the genotypes were clustered into two groups, A and B. Group A comprised only exotic genotypes, whereas all the Indian genotypes and four of the exotic genotypes were clustered in group B. Almost similar genotypic rankings could also be established by computing as few as 200 amplification products. In general, a high per cent of heterosis was recorded in crosses involving Indian x exotic genotypes. On the other hand, when crosses were made amongst Indian or exotic genotypes, about 80% of them exhibited negative heterosis. Results from this study indicate that, despite the lack of direct correlation between the genetic distance and the degree of heterosis, genetic diversity forms a very useful guide not only for investigating the relationships among Brassica genotypes but also in the selection of parents for heterotic hybrid combinations.

Gray matter network disruptions and amyloid beta in cognitively normal adults.

PubMed

Tijms, Betty M; Kate, Mara Ten; Wink, Alle Meije; Visser, Pieter Jelle; Ecay, Mirian; Clerigue, Montserrat; Estanga, Ainara; Garcia Sebastian, Maite; Izagirre, Andrea; Villanua, Jorge; Martinez Lage, Pablo; van der Flier, Wiesje M; Scheltens, Philip; Sanz Arigita, Ernesto; Barkhof, Frederik

2016-01-01

Gray matter networks are disrupted in Alzheimer's disease (AD). It is unclear when these disruptions start during the development of AD. Amyloid beta 1-42 (Aβ42) is among the earliest changes in AD. We studied, in cognitively healthy adults, the relationship between Aβ42 levels in cerebrospinal fluid (CSF) and single-subject cortical gray matter network measures. Single-subject gray matter networks were extracted from structural magnetic resonance imaging scans in a sample of cognitively healthy adults (N = 185; age range 39-79, mini-mental state examination >25, N = 12 showed abnormal Aβ42 < 550 pg/mL). Degree, clustering coefficient, and path length were computed at whole brain level and for 90 anatomical areas. Associations between continuous Aβ42 CSF levels and single-subject cortical gray matter network measures were tested. Smoothing splines were used to determine whether a linear or nonlinear relationship gave a better fit to the data. Lower Aβ42 CSF levels were linearly associated at whole brain level with lower connectivity density, and nonlinearly with lower clustering values and higher path length values, which is indicative of a less-efficient network organization. These relationships were specific to medial temporal areas, precuneus, and the middle frontal gyrus (all p < 0.05). These results suggest that mostly within the normal spectrum of amyloid, lower Aβ42 levels can be related to gray matter networks disruptions. Copyright © 2016 Elsevier Inc. All rights reserved.

VizieR Online Data Catalog: Globular cluster candidates in NGC253 (Cantiello+, 2018)

NASA Astrophysics Data System (ADS)

Cantiello, M.; Grado, A.; Rejkuba, M.; Arnaboldi, M.; Capaccioli, M.; Greggio, L.; Iodice, E.; Limatola, L.

2017-11-01

Photometric catalogs for globular cluster (GC) candidates over the 1 sq. degree area around NGC253. The catalogues are based on ugri-band photometry from the VST data, and JKs photometry from VISTA. Aperture magnitudes, corrected for aperture correction are reported. (1 data file).

Molecular characterization of three common olive (Olea europaea L.) cultivars in Palestine, using simple sequence repeat (SSR) markers.

PubMed

Obaid, Ramiz; Abu-Qaoud, Hassan; Arafeh, Rami

2014-09-03

Eight accessions of olive trees from three common varieties in Palestine, Nabali Baladi, Nabali Mohassan and Surri, were genetically evaluated using five simple sequence repeat (SSR) markers. A total of 17 alleles from 5 loci were observed in which 15 (88.2%) were polymorphic and 2 (11.8%) were monomorphic. An average of 3.4 alleles per locus was found ranging from 2.0 alleles with the primers GAPU-103 and DCA-9 to 5.0 alleles with U9932 and DCA-16. The smallest amplicon size observed was 50 bp with the primer DCA-16, whereas the largest one (450 bp) with the primer U9932. Cluster analysis with the unweighted pair group method with arithmetic average (UPGMA) showed three clusters: a cluster with four accessions from the 'Nabali Baladi' cultivar, another cluster with three accessions that represents the 'Nabali Mohassen' cultivar and finally the 'Surri' cultivar. The similarity coefficient for the eight olive tree samples ranged from a maximum of 100% between two accessions from Nabali Baladi and also in two other samples from Nabali Mohassan, to a minimum similarity coefficient (0.315) between the Surri and two Nabali Baladi accessions. The results in this investigation clearly highlight the genetic dissimilarity between the three main olive cultivars that have been misidentified and mixed up in the past, based on conventional morphological characters.

An improved global dynamic routing strategy for scale-free network with tunable clustering

NASA Astrophysics Data System (ADS)

Sun, Lina; Huang, Ning; Zhang, Yue; Bai, Yannan

2016-08-01

An efficient routing strategy can deliver packets quickly to improve the network capacity. Node congestion and transmission path length are inevitable real-time factors for a good routing strategy. Existing dynamic global routing strategies only consider the congestion of neighbor nodes and the shortest path, which ignores other key nodes’ congestion on the path. With the development of detection methods and techniques, global traffic information is readily available and important for the routing choice. Reasonable use of this information can effectively improve the network routing. So, an improved global dynamic routing strategy is proposed, which considers the congestion of all nodes on the shortest path and incorporates the waiting time of the most congested node into the path. We investigate the effectiveness of the proposed routing for scale-free network with different clustering coefficients. The shortest path routing strategy and the traffic awareness routing strategy only considering the waiting time of neighbor node are analyzed comparatively. Simulation results show that network capacity is greatly enhanced compared with the shortest path; congestion state increase is relatively slow compared with the traffic awareness routing strategy. Clustering coefficient increase will not only reduce the network throughput, but also result in transmission average path length increase for scale-free network with tunable clustering. The proposed routing is favorable to ease network congestion and network routing strategy design.

Laguerre-Freud Equations for the Recurrence Coefficients of Some Discrete Semi-Classical Orthogonal Polynomials of Class Two

NASA Astrophysics Data System (ADS)

Hounga, C.; Hounkonnou, M. N.; Ronveaux, A.

2006-10-01

In this paper, we give Laguerre-Freud equations for the recurrence coefficients of discrete semi-classical orthogonal polynomials of class two, when the polynomials in the Pearson equation are of the same degree. The case of generalized Charlier polynomials is also presented.

Estimates of the Sampling Distribution of Scalability Coefficient H

ERIC Educational Resources Information Center

Van Onna, Marieke J. H.

2004-01-01

Coefficient "H" is used as an index of scalability in nonparametric item response theory (NIRT). It indicates the degree to which a set of items rank orders examinees. Theoretical sampling distributions, however, have only been derived asymptotically and only under restrictive conditions. Bootstrap methods offer an alternative possibility to…

Clustering in complex directed networks

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio

2007-08-01

Many empirical networks display an inherent tendency to cluster, i.e., to form circles of connected nodes. This feature is typically measured by the clustering coefficient (CC). The CC, originally introduced for binary, undirected graphs, has been recently generalized to weighted, undirected networks. Here we extend the CC to the case of (binary and weighted) directed networks and we compute its expected value for random graphs. We distinguish between CCs that count all directed triangles in the graph (independently of the direction of their edges) and CCs that only consider particular types of directed triangles (e.g., cycles). The main concepts are illustrated by employing empirical data on world-trade flows.

Conductive heat exchange with a gel-coated circulating water mattress.

PubMed

Bräuer, Anselm; Pacholik, Larissa; Perl, Thorsten; English, Michael John Murray; Weyland, Wolfgang; Braun, Ulrich

2004-12-01

The use of forced-air warming is associated with costs for the disposable blankets. As an alternative method, we studied heat transfer with a reusable gel-coated circulating water mattress placed under the back in eight healthy volunteers. Heat flux was measured with six calibrated heat flux transducers. Additionally, mattress temperature, skin temperature, and core temperature were measured. Water temperature was set to 25 degrees C, 30 degrees C, 35 degrees C, and 41 degrees C. Heat transfer was calculated by multiplying heat flux by contact area. Mattress temperature, skin temperature, and heat flux were used to determine the heat exchange coefficient for conduction. Heat flux and water temperature were related by the following equation: heat flux = 10.3 x water temperature - 374 (r(2) = 0.98). The heat exchange coefficient for conduction was 121 W . m(-2) . degrees C(-1). The maximal heat transfer with the gel-coated circulating water mattress was 18.4 +/- 3.3 W. Because of the small effect on the heat balance of the body, a gel-coated circulating water mattress placed only on the back cannot replace a forced-air warming system.

[Analysis of the population structure of the Black Forest Draught Horse].

PubMed

Aberle, Kerstin; Wrede, Jörn; Distl, Ottmar

2003-01-01

Gene contributions of foreign populations as well as coefficients of inbreeding and relationship were evaluated in 699 Black Forest Draught horses of Baden-Württemberg actually registered in the year 2002. Based on nearly complete 5-generation-pedigrees and after taking into account the remaining incompleteness, the mean coefficient of inbreeding for the total population was 6.5%. The recently by incrossing with different breeds newly established lines of stallions showed significantly lower mean coefficients of inbreeding. High rates of inbreeding of about 1.6% in the last five generations could also be faced by incrossing stallions of foreign coldblooded populations what resulted in a decrease of inbreeding in the last generation. In the total population the mean degree of relationship was 16%. The mean degree of relationships within lines of stallions was between 18.3 and 26.8%. The coefficients of relationships between lines of stallions varied between 5.1 and 16.6%. Especially, the newly established lines of stallions showed a lower mean degree of relationships to the other different lines of stallions. The proportion of purebred Black Forest Draught horses in the total population was nearly 70%. Assuming that most animals of unknown origin were purebred, the proportion of purebred Black Forest Draught horses reached about 90%. Austrian Noric, Swiss Freiberg and South German Coldblood stallions were the most important contributors to the Black Forest Draught horse population.

Cosmic Pressure Fronts Mapped by Chandra

NASA Astrophysics Data System (ADS)

2000-03-01

A colossal cosmic "weather system" produced by the collision of two giant clusters of galaxies has been imaged by NASA's Chandra X-ray Observatory. For the first time, the pressure fronts in the system can be traced in detail, and they show a bright, but relatively cool 50 million degree Celsius central region embedded in large elongated cloud of 70 million degree Celsius gas, all of which is roiling in a faint "atmosphere"of 100 million degree Celsius gas. "We can compare this to an intergalactic cold front," said Maxim Markevitch of the Harvard-Smithsonian Center for Astrophysics, Cambridge, Mass. and leader of the international team involved in the analysis of the observations. "A major difference is that in this case, cold means 70 million degree Celsius." The gas clouds are in the core of a galaxy cluster known as Abell 2142. The cluster is six million light years across and contains hundreds of galaxies and enough gas to make a thousand more. It is one of the most massive objects in the universe. Galaxy clusters grow to vast sizes as smaller clusters are pulled inward under the influence of gravity. They collide and merge over the course of billions of years, releasing tremendous amounts of energy that heats the cluster gas to 100 million degrees Celsius. The Chandra data provides the first detailed look at the late stages of this merger process. Previously, scientists had used the German-US Roentgensatellite to produce a broad brush picture of the cluster. The elongated shape of the bright cloud suggested that two clouds were in the process of coalescing into one, but the details remained unclear. Chandra is able to measure variations of temperature, density, and pressure with unprecedented resolution. "Now we can begin to understand the physics of these mergers, which are among the most energetic events in the universe," said Markevitch. "The pressure and density maps of the cluster show a sharp boundary that can only exist in the moving environment of a merger." With this information scientists can make a comparison with computer simulations of cosmic mergers. This comparison, which is in the early stages, shows that this merger has progressed to an advanced stage. Strong shock waves predicted by the theory for the initial collision of clusters are not observed. It appears likely that these sub-clusters have collided two or three times in a billion years or more, and have nearly completed their merger. The observations were made on August 20, 1999 using the Advanced CCD Imaging Spectrometer (ACIS). The team involved scientists from Harvard-Smithsonian; the Massachusetts Institute of Technology, Cambridge; NASA's Marshall Space Flight Center, Huntsville, Ala.; the University of Hawaii, Honolulu; the University of Birmingham, U.K.; the University of Wollongong, Australia; the Space Research Organization Netherlands; the University of Rome, Italy; and the Russian Academy of Sciences. The results will be published in an upcoming issue of the Astrophysical Journal. The ACIS instrument was built for NASA by the Massachusetts Institute of Technology, Cambridge, and Pennsylvania State University, University Park. NASA's Marshall Space Flight Center in Huntsville, Ala., manages the Chandra program. TRW, Inc., Redondo Beach, Calif., is the prime contractor for the spacecraft. The Smithsonian's Chandra X-ray Center controls science and flight operations from Cambridge, Mass. For images connected to this release, and to follow Chandra's progress, visit the Chandra site at: http://chandra.harvard.edu/photo/2000/a2142/index.html AND http://chandra.nasa.gov High resolution digital versions of the X-ray image (JPG, 300 dpi TIFF) are available at the Internet sites listed above. This image will be available on NASA Video File which airs at noon, 3:00 p.m., 6:00 p.m., 9:00 p.m. and midnight Eastern Time. NASA Television is available on GE-2, transponder 9C at 85 degrees West longitude, with vertical polarization. Frequency is on 3880.0 megahertz, with audio on 6.8 megahertz.

Lumping of degree-based mean-field and pair-approximation equations for multistate contact processes.

PubMed

Kyriakopoulos, Charalampos; Grossmann, Gerrit; Wolf, Verena; Bortolussi, Luca

2018-01-01

Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information-spreading networks. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appearing in real applications. One strategy to reduce the complexity of their analysis is to rely on approximations, often in terms of a set of differential equations capturing the evolution of a random node, distinguishing nodes with different topological contexts (i.e., different degrees of different neighborhoods), such as degree-based mean-field (DBMF), approximate-master-equation (AME), or pair-approximation (PA) approaches. The number of differential equations so obtained is typically proportional to the maximum degree k_{max} of the network, which is much smaller than the size of the master equation of the underlying stochastic model, yet numerically solving these equations can still be problematic for large k_{max}. In this paper, we consider AME and PA, extended to cope with multiple local states, and we provide an aggregation procedure that clusters together nodes having similar degrees, treating those in the same cluster as indistinguishable, thus reducing the number of equations while preserving an accurate description of global observables of interest. We also provide an automatic way to build such equations and to identify a small number of degree clusters that give accurate results. The method is tested on several case studies, where it shows a high level of compression and a reduction of computational time of several orders of magnitude for large networks, with minimal loss in accuracy.

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks.

PubMed

Sendiña-Nadal, I; Danziger, M M; Wang, Z; Havlin, S; Boccaletti, S

2016-02-18

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph's hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks

NASA Astrophysics Data System (ADS)

Sendiña-Nadal, I.; Danziger, M. M.; Wang, Z.; Havlin, S.; Boccaletti, S.

2016-02-01

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph’s hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

Ion mobility and clustering of sodium hydroxybenzoates in aqueous solutions: a molecular dynamics simulation study.

PubMed

Gujt, Jure; Podlipnik, Črtomir; Bešter-Rogač, Marija; Spohr, Eckhard

2014-09-28

The relative position of the hydroxylic and the carboxylic group in the isomeric hydroxybenzoate (HB) anions is known to have a large impact on transport properties of this species. It also influences crucially the self-organisation of cationic surfactants. In this article a systematic investigation of aqueous solutions of the ortho, meta, and para isomers of the HB anion is presented. Molecular dynamics simulations of all three HB isomers were conducted for two different concentrations at 298.15 K and using two separate water models. From the resulting trajectories we calculated the self-diffusion coefficient of each isomer. According to the calculated self-diffusion coefficients, isomers were ranked in the order o-HB > m-HB > p-HB at both concentrations for both the used SPC and SPC/E water models, which agrees very well with the experiment. The structural analysis revealed that at lower concentration, where the tendency for dimerisation or cluster formation is low, hydrogen bonding with water determines the mobility of the HB anion. o-HB forms the least hydrogen bonds and is therefore the most mobile, and p-HB, which forms the most hydrogen bonds with water, is the least mobile isomer. At higher concentration the formation of clusters also needs to be considered. The ortho isomer predominantly forms dimers with 2 hydrogen bonds per dimer between one OH and one carboxylate group of each anion. m-HB mostly forms clusters of sizes around 5 and p-HB forms clusters of sizes even larger than 10, which can be either rings or chains.

Robustness of networks formed from interdependent correlated networks under intentional attacks

NASA Astrophysics Data System (ADS)

Liu, Long; Meng, Ke; Dong, Zhaoyang

2018-02-01

We study the problem of intentional attacks targeting to interdependent networks generated with known degree distribution (in-degree oriented model) or distribution of interlinks (out-degree oriented model). In both models, each node's degree is correlated with the number of its links that connect to the other network. For both models, varying the correlation coefficient has a significant effect on the robustness of a system undergoing random attacks or attacks targeting nodes with low degree. For a system with an assortative relationship between in-degree and out-degree, reducing the broadness of networks' degree distributions can increase the resistance of systems against intentional attacks.

Variability in the performance of preventive services and in the degree of control of identified health problems: A primary care study protocol

PubMed Central

Bolíbar, Bonaventura; Pareja, Clara; Astier-Peña, M Pilar; Morán, Julio; Rodríguez-Blanco, Teresa; Rosell-Murphy, Magdalena; Iglesias, Manuel; Juncosa, Sebastián; Mascort, Juanjo; Violan, Concepció; Magallón, Rosa; Apezteguia, Javier

2008-01-01

Background Preventive activities carried out in primary care have important variability that makes necessary to know which factors have an impact in order to establish future strategies for improvement. The present study has three objectives: 1) To describe the variability in the implementation of 7 preventive services (screening for smoking status, alcohol abuse, hypertension, hypercholesterolemia, obesity, influenza and tetanus immunization) and to determine their related factors; 2) To describe the degree of control of 5 identified health problems (smoking, alcohol abuse, hypertension, hypercholesterolemia and obesity); 3) To calculate intraclass correlation coefficients. Design Multi-centered cross-sectional study of a randomised sample of primary health care teams from 3 regions of Spain designed to analyse variability and related factors of 7 selected preventive services in years 2006 and 2007. At the end of 2008, we will perform a cross-sectional study of a cohort of patients attended in 2006 or 2007 to asses the degree of control of 5 identified health problems. All subjects older than16 years assigned to a randomised sample of 22 computerized primary health care teams and attended during the study period are included in each region providing a sample with more than 850.000 subjects. The main outcome measures will be implementation of 7 preventive services and control of 5 identified health problems. Furthermore, there will be 3 levels of data collection: 1) Patient level (age, gender, morbidity, preventive services, attendance); 2) Health-care professional level (professional characteristics, years working at the team, workload); 3) Team level (characteristics, electronic clinical record system). Data will be transferred from electronic clinical records to a central database with prior encryption and dissociation of subject, professional and team identity. Global and regional analysis will be performed including standard analysis for primary health care teams and health-care professional level. Linear and logistic regression multilevel analysis adjusted for individual and cluster variables will also be performed. Variability in the number of preventive services implemented will be calculated with Poisson multilevel models. Team and health-care professional will be considered random effects. Intraclass correlation coefficients, standard error and variance components for the different outcome measures will be calculated. PMID:18691407

Cross-scale analysis of cluster correspondence using different operational neighborhoods

NASA Astrophysics Data System (ADS)

Lu, Yongmei; Thill, Jean-Claude

2008-09-01

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.

SCUD: fast structure clustering of decoys using reference state to remove overall rotation.

PubMed

Li, Hongzhi; Zhou, Yaoqi

2005-08-01

We developed a method for fast decoy clustering by using reference root-mean-squared distance (rRMSD) rather than commonly used pairwise RMSD (pRMSD) values. For 41 proteins with 2000 decoys each, the computing efficiency increases nine times without a significant change in the accuracy of near-native selections. Tests on additional protein decoys based on different reference conformations confirmed this result. Further analysis indicates that the pRMSD and rRMSD values are highly correlated (with an average correlation coefficient of 0.82) and the clusters obtained from pRMSD and rRMSD values are highly similar (the representative structures of the top five largest clusters from the two methods are 74% identical). SCUD (Structure ClUstering of Decoys) with an automatic cutoff value is available at http://theory.med.buffalo.edu. (c) 2005 Wiley Periodicals, Inc.

Numerical taxonomy and ecology of petroleum-degrading bacteria.

PubMed Central

Austin, B; Calomiris, J J; Walker, J D; Colwell, R R

1977-01-01

A total of 99 strains of petroleum-degrading bacteria isolated from Chesapeake Bay water and sediment were identified by using numerical taxonomy procedures. The isolates, together with 33 reference cultures, were examined for 48 biochemical, cultural, morphological, and physiological characters. The data were analyzed by computer, using both the simple matching and the Jaccard coefficients. Clustering was achieved by the unweighted average linkage method. From the sorted similarity matrix and dendrogram, 14 phenetic groups, comprising 85 of the petroleum-degrading bacteria, were defined at the 80 to 85% similarity level. These groups were identified as actinomycetes (mycelial forms, four clusters), coryneforms, Enterobacteriaceae, Klebsiella aerogenes, Micrococcus spp. (two clusters), Nocardia species (two clusters), Pseudomonas spp. (two clusters), and Sphaerotilus natans. It is concluded that the degradation of petroleum is accomplished by a diverse range of bacterial taxa, some of which were isolated only at given sampling stations and, more specifically, from sediment collected at a given station. PMID:889329

Development of a Three-Dimensional PSE Code for Compressible Flows: Stability of Three-Dimensional Compressible Boundary Layers

NASA Technical Reports Server (NTRS)

Balakumar, P.; Jeyasingham, Samarasingham

1999-01-01

A program is developed to investigate the linear stability of three-dimensional compressible boundary layer flows over bodies of revolutions. The problem is formulated as a two dimensional (2D) eigenvalue problem incorporating the meanflow variations in the normal and azimuthal directions. Normal mode solutions are sought in the whole plane rather than in a line normal to the wall as is done in the classical one dimensional (1D) stability theory. The stability characteristics of a supersonic boundary layer over a sharp cone with 50 half-angle at 2 degrees angle of attack is investigated. The 1D eigenvalue computations showed that the most amplified disturbances occur around x(sub 2) = 90 degrees and the azimuthal mode number for the most amplified disturbances range between m = -30 to -40. The frequencies of the most amplified waves are smaller in the middle region where the crossflow dominates the instability than the most amplified frequencies near the windward and leeward planes. The 2D eigenvalue computations showed that due to the variations in the azimuthal direction, the eigenmodes are clustered into isolated confined regions. For some eigenvalues, the eigenfunctions are clustered in two regions. Due to the nonparallel effect in the azimuthal direction, the eigenmodes are clustered into isolated confined regions. For some eigenvalues, the eigenfunctions are clustered in two regions. Due to the nonparallel effect in the azimuthal direction, the most amplified disturbances are shifted to 120 degrees compared to 90 degrees for the parallel theory. It is also observed that the nonparallel amplification rates are smaller than that is obtained from the parallel theory.

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

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

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

2015-03-31

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

Cosmological constraints from Galaxy Clusters in 2500 square-degree SPT-SZ survey

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

Haan, T. de; Benson, B. A.; Bleem, L. E.

We present cosmological parameter constraints obtained from galaxy clusters identified by their SunyaevZel'dovich effect signature in the 2500 square-degree South Pole Telescope Sunyaev Zel'dovich (SPT-SZ) survey. We consider the 377 cluster candidates identified at z > 0.25 with a detection significance greater than five, corresponding to the 95% purity threshold for the survey. We compute constraints on cosmological models using the measured cluster abundance as a function of mass and redshift. We include additional constraints from multi-wavelength observations, including Chandra X-ray data for 82 clusters and a weak lensing-based prior on the normalization of the mass-observable scaling relations. Assuming amore » spatially flat Lambda CDM cosmology, we combine the cluster data with a prior on H-0 and find sigma(8)= 0.784. +/- 0.039 and Omega(m) = 0.289. +/- 0.042, with the parameter combination sigma(8) (Omega(m)/0.27)(0.3) = 0.797 +/- 0.031. These results are in good agreement with constraints from the cosmic microwave background (CMB) from SPT, WMAP, and Planck, as well as with constraints from other cluster data sets. We also consider several extensions to Lambda CDM, including models in which the equation of state of dark energy w, the species-summed neutrino mass, and/or the effective number of relativistic species (N-eff) are free parameters. When combined with constraints from the Planck CMB, H-0, baryon acoustic oscillation, and SNe, adding the SPT cluster data improves the w constraint by 14%, to w = -1.023 +/- 0.042.« less

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

DOE PAGES

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

2015-03-31

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

Reactions and properties of clusters

NASA Astrophysics Data System (ADS)

Castleman, A. W., Jr.

1992-09-01

The elucidation from a molecular point of view of the differences and similarities in the properties and reactivity of matter in the gaseous compared to the condensed state is a subject of considerable current interest. One of the promising approaches to this problem is to utilize mass spectrometry in conjunction with laser spectroscopy and fast-flow reaction devices to investigate the changing properties, structure and reactivity of clusters as a function of the degree of solvation under well-controlled conditions. In this regard, an investigation of molecular cluster ions has provided considerable new insight into the basic mechanisms of ion reactions within a cluster, and this paper reviews some of the recent advances in cluster production, the origin of magic numbers and relationship to cluster ion stabilities, and solvation effects on reactions. There have been some notable advances in the production of large cluster ions under thermal reaction conditions, enabling a systematic study of the influence of solvation on reactions to be carried out. These and other new studies of magic numbers have traced their origin to the thermochemical stability of cluster ions. There are several classes of reaction where solvation has a notable influence on reactivity. A particularly interesting example comes from recent studies of the reactions of the hydroxyl anion with CO2 and SO2, studied as a function of the degree of hydration of OH-. Both reactions are highly exothermic, yet the differences in reactivity are dramatic. In the case of SO2, the reaction occurs at near the collision rate. By contrast, CO2 reactivity plummets dramatically for clusters having more than four water molecules. The slow rate is in accord with observations in the liquid phase.

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

PubMed

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

2018-04-27

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

On the link between energy equipartition and radial variation in the stellar mass function of star clusters

NASA Astrophysics Data System (ADS)

Webb, Jeremy J.; Vesperini, Enrico

2017-01-01

We make use of N-body simulations to determine the relationship between two observable parameters that are used to quantify mass segregation and energy equipartition in star clusters. Mass segregation can be quantified by measuring how the slope of a cluster's stellar mass function α changes with clustercentric distance r, and then calculating δ _α = d α (r)/d ln(r/r_m), where rm is the cluster's half-mass radius. The degree of energy equipartition in a cluster is quantified by η, which is a measure of how stellar velocity dispersion σ depends on stellar mass m via σ(m) ∝ m-η. Through a suite of N-body star cluster simulations with a range of initial sizes, binary fractions, orbits, black hole retention fractions, and initial mass functions, we present the co-evolution of δα and η. We find that measurements of the global η are strongly affected by the radial dependence of σ and mean stellar mass and the relationship between η and δα depends mainly on the cluster's initial conditions and the tidal field. Within rm, where these effects are minimized, we find that η and δα initially share a linear relationship. However, once the degree of mass segregation increases such that the radial dependence of σ and mean stellar mass become a factor within rm, or the cluster undergoes core collapse, the relationship breaks down. We propose a method for determining η within rm from an observational measurement of δα. In cases where η and δα can be measured independently, this new method offers a way of measuring the cluster's dynamical state.

Extinction coefficients of CC and CC bands in ethyne and ethene molecules interacting with Cu+ and Ag+ in zeolites--IR studies and quantumchemical DFT calculations.

PubMed

Kozyra, Paweł; Góra-Marek, Kinga; Datka, Jerzy

2015-02-05

The values of extinction coefficients of CC and CC IR bands of ethyne and ethene interacting with Cu+ and Ag+ in zeolites were determined in quantitative IR experiments and also by quantumchemical DFT calculations with QM/MM method. Both experimental and calculated values were in very good agreement validating the reliability of calculations. The values of extinction coefficients of ethyne and ethene interacting with bare cations and cations embedded in zeolite-like clusters were calculated. The interaction of organic molecules with Cu+ and Ag+ in zeolites ZSM-5 and especially charge transfers between molecule, cation and zeolite framework was also discussed in relation to the values of extinction coefficients. Copyright © 2014 Elsevier B.V. All rights reserved.

Solubility and durability of cardanol derived plasticizers for soft PVC

NASA Astrophysics Data System (ADS)

Greco, Antonio; Ferrari, Francesca; Velardi, Rosario; Frigione, Mariaenrica; Maffezzoli, Alfonso

2015-12-01

This work is aimed to study the suitability of cardanol derivatives as primary plasticizer for PVC. The innovative plasticizer is obtained by chemical modification of cardanol, a natural, renewable resource, obtained as a by-product of the cashew nut shell industry. Cardanol derived plasticizers (CDP) were prepared by following various procedures, that allow obtaining different degrees of conversion of cardanol. Rheological and ageing tests were made on soft PVC produced by the addition of CDP;results obtained were compared to soft PVC attained by the use of di-ethyl-hexyl-phthalate (DEHP) and other natural derived plasticizers already used in PVC industry (epoxidated soybean oil, ESBO, and acetic acid ester, AAE).A high dependence on the degree of conversion was found: CDP with a good degree of conversion have similar gelation temperature and diffusion coefficient compared to DEHP based plastisols. Otherwise,CDP with a low degree of conversionshow a higher diffusion coefficient, index of a fast migration of the plasticizer from soft PVC.

A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression

PubMed Central

Nguyen, Nha; Vo, An; Choi, Inchan

2015-01-01

Abstract Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation. PMID:25383910

Genetic diversity of red-grained rice landraces in Hani's terraced fields based on phenotypic characteristics

NASA Astrophysics Data System (ADS)

Zhou, Xiaomei; Zheng, Yun; Zhang, Tingting; Zhang, Xiaoqian; Ma, Mengli; Meng, Hengling; Wang, Tiantao; Lu, Bingyue

2018-06-01

In order to provide useful information for protection and utilization of red-grained rice landraces from Hani's terraced fields, the phenotypic diversity of 61 red-grained rice landraces were assessed based 20 quantitative traits. The results indicated that the phenotypic diversity was abundant in red-grained rice landraces. Coefficients of variation (CV) ranged from 4.878% to 72.878%, and the largest of CV was the panicle neck length, while grain width was smallest. Shannon-Weaver diversity index (H') of 20 traits ranged from 1.464 to 2.165, the largest and the smallest H' values were observed in filled grain number and chalkiness, respectively. Cluster analysis based on unweighted pair group method showed 61 red-grain rice landraces grouped into eight clusters at a cut-off value of 6.2631. The first cluster included 11 landraces, the main cluster II involved 42 landraces, and the cluster IV included 3 landraces. Laopinzhonghongmi, Chena2, Laojingnuo, Bianhao6 and Baimi were separated from the main clusters.

Togetherness among Plasmodium falciparum gametocytes: interpretation through simulation and consequences for malaria transmission.

PubMed

Gaillard, F O; Boudin, C; Chau, N P; Robert, V; Pichon, G

2003-11-01

Previous experimental gametocyte infections of Anopheles arabiensis on 3 volunteers naturally infected with Plasmodium falciparum were conducted in Senegal. They showed that gametocyte counts in the mosquitoes are, like macroparasite intakes, heterogeneous (overdispersed). They followed a negative binomial distribution, the overdispersion coefficient seeming constant (k = 3.1). To try to explain this heterogeneity, we used an individual-based model (IBM), simulating the behaviour of gametocytes in the human blood circulation and their ingestion by mosquitoes. The hypothesis was that there exists a clustering of the gametocytes in the capillaries. From a series of simulations, in the case of clustering the following results were obtained: (i) the distribution of the gametocytes ingested by the mosquitoes followed a negative binomial, (ii) the k coefficient significantly increased with the density of circulating gametocytes. To validate this model result, 2 more experiments were conducted in Cameroon. Pooled experiments showed a distinct density dependency of the k-values. The simulation results and the experimental results were thus in agreement and suggested that an aggregation process at the microscopic level might produce the density-dependent overdispersion at the macroscopic level. Simulations also suggested that the clustering of gametocytes might facilitate fertilization of gametes.

Homogeneity Pursuit

PubMed Central

Ke, Tracy; Fan, Jianqing; Wu, Yichao

2014-01-01

This paper explores the homogeneity of coefficients in high-dimensional regression, which extends the sparsity concept and is more general and suitable for many applications. Homogeneity arises when regression coefficients corresponding to neighboring geographical regions or a similar cluster of covariates are expected to be approximately the same. Sparsity corresponds to a special case of homogeneity with a large cluster of known atom zero. In this article, we propose a new method called clustering algorithm in regression via data-driven segmentation (CARDS) to explore homogeneity. New mathematics are provided on the gain that can be achieved by exploring homogeneity. Statistical properties of two versions of CARDS are analyzed. In particular, the asymptotic normality of our proposed CARDS estimator is established, which reveals better estimation accuracy for homogeneous parameters than that without homogeneity exploration. When our methods are combined with sparsity exploration, further efficiency can be achieved beyond the exploration of sparsity alone. This provides additional insights into the power of exploring low-dimensional structures in high-dimensional regression: homogeneity and sparsity. Our results also shed lights on the properties of the fussed Lasso. The newly developed method is further illustrated by simulation studies and applications to real data. Supplementary materials for this article are available online. PMID:26085701

Evaluation of genetic diversity amongst Descurainia sophia L. genotypes by inter-simple sequence repeat (ISSR) marker.

PubMed

Saki, Sahar; Bagheri, Hedayat; Deljou, Ali; Zeinalabedini, Mehrshad

2016-01-01

Descurainia sophia is a valuable medicinal plant in family of Brassicaceae. To determine the range of diversity amongst D. sophia in Iran, 32 naturally distributed plants belonging to six natural populations of the Iranian plateau were investigated by inter-simple sequence repeat (ISSR) markers. The average percentage of polymorphism produced by 12 ISSR primers was 86 %. The PIC values for primers ranged from 0.22 to 0.40 and Rp values ranged between 6.5 and 19.9. The relative genetic diversity of the populations was not high (Gst =0.32). However, the value of gene flow revealed by the ISSR marker was high (Nm = 1.03). UPGMA clustering method based on Jaccard similarity coefficient grouped the genotypes into two major clusters. Graph results from Neighbor-Net Network generated after a 1000 bootstrap test using Jaccard coefficient, and STRUCTURE analysis confirmed the UPGMA clustering. The first three PCAs represented 57.31 % of the total variation. The high levels of genetic diversity were observed within populations, which is useful in breeding and conservation programs. ISSR is found to be an eligible marker to study genetic diversity of D. sophia.

A cytochemical note on nucleoli of granulocytic precursors and granulocytes in patients suffering from the refractory anemia with excess blasts (RAEB) of the myelodysplastic syndrome (MDS).

PubMed

Smetana, K; Jirásková, I; Malasková, V; Cermák, J

2002-01-01

Nucleoli were studied in the proliferation as well as maturation granulopoietic compartment in patients suffering from refractory anemia with excess blasts (RAEB) of the myelodysplastic syndrome (MDS) by means of simple cytochemical procedures for the demonstration of nucleolar RNA and silver stained proteins of nucleolus organizer regions. Regardless of the procedure used for the nucleolar visualization, early stages of the granulopoietic compartment and particularly myeloblasts of RAEB patients were characterized by reduction of the nucleolar number expressed by the nucleolar coefficient the values of which resembled those described previously in acute myeloid leukemias. The reduced values of the nucleolar coefficient of these cells in silver stained specimens of RAEB patients were accompanied by a decreased number of clusters of silver stained particles representing interphasic silver stained nucleolus organizer regions (AgNORs). The reduction of these clusters was also described previously in leukemic cells. In addition, the differences in the values of the nucleolar coefficient of granulocytic precursors between specimens stained for RNA and those stained with the silver reaction might reflect changing composition and proportions of nucleolar components in the course of the granulocytic development.

Hierarchical coefficient of a multifractal based network

NASA Astrophysics Data System (ADS)

Moreira, Darlan A.; Lucena, Liacir dos Santos; Corso, Gilberto

2014-02-01

The hierarchical property for a general class of networks stands for a power-law relation between clustering coefficient, CC and connectivity k: CC∝kβ. This relation is empirically verified in several biologic and social networks, as well as in random and deterministic network models, in special for hierarchical networks. In this work we show that the hierarchical property is also present in a Lucena network. To create a Lucena network we use the dual of a multifractal lattice ML, the vertices are the sites of the ML and links are established between neighbouring lattices, therefore this network is space filling and planar. Besides a Lucena network shows a scale-free distribution of connectivity. We deduce a relation for the maximal local clustering coefficient CCimax of a vertex i in a planar graph. This condition expresses that the number of links among neighbour, N△, of a vertex i is equal to its connectivity ki, that means: N△=ki. The Lucena network fulfils the condition N△≃ki independent of ki and the anisotropy of ML. In addition, CCmax implies the threshold β=1 for the hierarchical property for any scale-free planar network.

Classification of different degrees of adiposity in sedentary rats.

PubMed

Leopoldo, A S; Lima-Leopoldo, A P; Nascimento, A F; Luvizotto, R A M; Sugizaki, M M; Campos, D H S; da Silva, D C T; Padovani, C R; Cicogna, A C

2016-01-01

In experimental studies, several parameters, such as body weight, body mass index, adiposity index, and dual-energy X-ray absorptiometry, have commonly been used to demonstrate increased adiposity and investigate the mechanisms underlying obesity and sedentary lifestyles. However, these investigations have not classified the degree of adiposity nor defined adiposity categories for rats, such as normal, overweight, and obese. The aim of the study was to characterize the degree of adiposity in rats fed a high-fat diet using cluster analysis and to create adiposity intervals in an experimental model of obesity. Thirty-day-old male Wistar rats were fed a normal (n=41) or a high-fat (n=43) diet for 15 weeks. Obesity was defined based on the adiposity index; and the degree of adiposity was evaluated using cluster analysis. Cluster analysis allowed the rats to be classified into two groups (overweight and obese). The obese group displayed significantly higher total body fat and a higher adiposity index compared with those of the overweight group. No differences in systolic blood pressure or nonesterified fatty acid, glucose, total cholesterol, or triglyceride levels were observed between the obese and overweight groups. The adiposity index of the obese group was positively correlated with final body weight, total body fat, and leptin levels. Despite the classification of sedentary rats into overweight and obese groups, it was not possible to identify differences in the comorbidities between the two groups.

The influence of phylogeny, social style, and sociodemographic factors on macaque social network structure.

PubMed

Balasubramaniam, Krishna N; Beisner, Brianne A; Berman, Carol M; De Marco, Arianna; Duboscq, Julie; Koirala, Sabina; Majolo, Bonaventura; MacIntosh, Andrew J; McFarland, Richard; Molesti, Sandra; Ogawa, Hideshi; Petit, Odile; Schino, Gabriele; Sosa, Sebastian; Sueur, Cédric; Thierry, Bernard; de Waal, Frans B M; McCowan, Brenda

2018-01-01

Among nonhuman primates, the evolutionary underpinnings of variation in social structure remain debated, with both ancestral relationships and adaptation to current conditions hypothesized to play determining roles. Here we assess whether interspecific variation in higher-order aspects of female macaque (genus: Macaca) dominance and grooming social structure show phylogenetic signals, that is, greater similarity among more closely-related species. We use a social network approach to describe higher-order characteristics of social structure, based on both direct interactions and secondary pathways that connect group members. We also ask whether network traits covary with each other, with species-typical social style grades, and/or with sociodemographic characteristics, specifically group size, sex-ratio, and current living condition (captive vs. free-living). We assembled 34-38 datasets of female-female dyadic aggression and allogrooming among captive and free-living macaques representing 10 species. We calculated dominance (transitivity, certainty), and grooming (centrality coefficient, Newman's modularity, clustering coefficient) network traits as aspects of social structure. Computations of K statistics and randomization tests on multiple phylogenies revealed moderate-strong phylogenetic signals in dominance traits, but moderate-weak signals in grooming traits. GLMMs showed that grooming traits did not covary with dominance traits and/or social style grade. Rather, modularity and clustering coefficient, but not centrality coefficient, were strongly predicted by group size and current living condition. Specifically, larger groups showed more modular networks with sparsely-connected clusters than smaller groups. Further, this effect was independent of variation in living condition, and/or sampling effort. In summary, our results reveal that female dominance networks were more phylogenetically conserved across macaque species than grooming networks, which were more labile to sociodemographic factors. Such findings narrow down the processes that influence interspecific variation in two core aspects of macaque social structure. Future directions should include using phylogeographic approaches, and addressing challenges in examining the effects of socioecological factors on primate social structure. © 2017 Wiley Periodicals, Inc.

Colorimetric Sensor Array for White Wine Tasting.

PubMed

Chung, Soo; Park, Tu San; Park, Soo Hyun; Kim, Joon Yong; Park, Seongmin; Son, Daesik; Bae, Young Min; Cho, Seong In

2015-07-24

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

Theory of competitive solvation of polymers by two solvents and entropy-enthalpy compensation in the solvation free energy upon dilution with the second solvent.

PubMed

Dudowicz, Jacek; Freed, Karl F; Douglas, Jack F

2015-06-07

We develop a statistical mechanical lattice theory for polymer solvation by a pair of relatively low molar mass solvents that compete for binding to the polymer backbone. A theory for the equilibrium mixture of solvated polymer clusters {AiBCj} and free unassociated molecules A, B, and C is formulated in the spirit of Flory-Huggins mean-field approximation. This theoretical framework enables us to derive expressions for the boundaries for phase stability (spinodals) and other basic properties of these polymer solutions: the internal energy U, entropy S, specific heat CV, extent of solvation Φsolv, average degree of solvation 〈Nsolv〉, and second osmotic virial coefficient B2 as functions of temperature and the composition of the mixture. Our theory predicts many new phenomena, but the current paper applies the theory to describe the entropy-enthalpy compensation in the free energy of polymer solvation, a phenomenon observed for many years without theoretical explanation and with significant relevance to liquid chromatography and other polymer separation methods.

Analysis of inter-country input-output table based on bibliographic coupling network: How industrial sectors on the GVC compete for production resources

NASA Astrophysics Data System (ADS)

Guan, Jun; Xu, Xiaoyu; Xing, Lizhi

2018-03-01

The input-output table is comprehensive and detailed in describing national economic systems with abundance of economic relationships depicting information of supply and demand among industrial sectors. This paper focuses on how to quantify the degree of competition on the global value chain (GVC) from the perspective of econophysics. Global Industrial Strongest Relevant Network models are established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output (ICIO) tables and then have them transformed into Global Industrial Resource Competition Network models to analyze the competitive relationships based on bibliographic coupling approach. Three indicators well suited for the weighted and undirected networks with self-loops are introduced here, including unit weight for competitive power, disparity in the weight for competitive amplitude and weighted clustering coefficient for competitive intensity. Finally, these models and indicators were further applied empirically to analyze the function of industrial sectors on the basis of the latest World Input-Output Database (WIOD) in order to reveal inter-sector competitive status during the economic globalization.