Empirical Reference Distributions for Networks of Different Size

PubMed Central

Smith, Anna; Calder, Catherine A.; Browning, Christopher R.

2016-01-01

Network analysis has become an increasingly prevalent research tool across a vast range of scientific fields. Here, we focus on the particular issue of comparing network statistics, i.e. graph-level measures of network structural features, across multiple networks that differ in size. Although “normalized” versions of some network statistics exist, we demonstrate via simulation why direct comparison is often inappropriate. We consider normalizing network statistics relative to a simple fully parameterized reference distribution and demonstrate via simulation how this is an improvement over direct comparison, but still sometimes problematic. We propose a new adjustment method based on a reference distribution constructed as a mixture model of random graphs which reflect the dependence structure exhibited in the observed networks. We show that using simple Bernoulli models as mixture components in this reference distribution can provide adjusted network statistics that are relatively comparable across different network sizes but still describe interesting features of networks, and that this can be accomplished at relatively low computational expense. Finally, we apply this methodology to a collection of ecological networks derived from the Los Angeles Family and Neighborhood Survey activity location data. PMID:27721556

Tropical forest fragmentation affects floral visitors but not the structure of individual-based palm-pollinator networks.

PubMed

Dáttilo, Wesley; Aguirre, Armando; Quesada, Mauricio; Dirzo, Rodolfo

2015-01-01

Despite increasing knowledge about the effects of habitat loss on pollinators in natural landscapes, information is very limited regarding the underlying mechanisms of forest fragmentation affecting plant-pollinator interactions in such landscapes. Here, we used a network approach to describe the effects of forest fragmentation on the patterns of interactions involving the understory dominant palm Astrocaryum mexicanum (Arecaceae) and its floral visitors (including both effective and non-effective pollinators) at the individual level in a Mexican tropical rainforest landscape. Specifically, we asked: (i) Does fragment size affect the structure of individual-based plant-pollinator networks? (ii) Does the core of highly interacting visitor species change along the fragmentation size gradient? (iii) Does forest fragment size influence the abundance of effective pollinators of A. mexicanum? We found that fragment size did not affect the topological structure of the individual-based palm-pollinator network. Furthermore, while the composition of peripheral non-effective pollinators changed depending on fragment size, effective core generalist species of pollinators remained stable. We also observed that both abundance and variance of effective pollinators of male and female flowers of A. mexicanum increased with forest fragment size. These findings indicate that the presence of effective pollinators in the core of all forest fragments could keep the network structure stable along the gradient of forest fragmentation. In addition, pollination of A. mexicanum could be more effective in larger fragments, since the greater abundance of pollinators in these fragments may increase the amount of pollen and diversity of pollen donors between flowers of individual plants. Given the prevalence of fragmentation in tropical ecosystems, our results indicate that the current patterns of land use will have consequences on the underlying mechanisms of pollination in remnant forests.

Social networks of patients with psychosis: a systematic review.

PubMed

Palumbo, Claudia; Volpe, Umberto; Matanov, Aleksandra; Priebe, Stefan; Giacco, Domenico

2015-10-12

Social networks are important for mental health outcomes as they can mobilise resources and help individuals to cope with social stressors. Individuals with psychosis may have specific difficulties in establishing and maintaining social relationships which impacts on their well-being and quality of life. There has been a growing interest in developing social network interventions for patients with psychotic disorders. A systematic literature review was conducted to investigate the size of social networks of patients with psychotic disorders, as well as their friendship networks. A systematic electronic search was carried out in MEDLINE, EMBASE and PsychINFO databases using a combination of search terms relating to 'social network', 'friendship' and 'psychotic disorder'. The search identified 23 relevant papers. Out of them, 20 reported patient social network size. Four papers reported the mean number of friends in addition to whole network size, while three further papers focused exclusively on the number of friends. Findings varied substantially across the studies, with a weighted mean size of 11.7 individuals for whole social networks and 3.4 individuals for friendship networks. On average, 43.1 % of the whole social network was composed of family members, while friends accounted for 26.5 %. Studies assessing whole social network size and friendship networks of people with psychosis are difficult to compare as different concepts and methods of assessment were applied. The extent of the overlap between different social roles assessed in the networks was not always clear. Greater conceptual and methodological clarity is needed in order to help the development of effective strategies to increase social resources of patients with psychosis.

Effects of maximum node degree on computer virus spreading in scale-free networks

NASA Astrophysics Data System (ADS)

Bamaarouf, O.; Ould Baba, A.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.

2017-10-01

The increase of the use of the Internet networks favors the spread of viruses. In this paper, we studied the spread of viruses in the scale-free network with different topologies based on the Susceptible-Infected-External (SIE) model. It is found that the network structure influences the virus spreading. We have shown also that the nodes of high degree are more susceptible to infection than others. Furthermore, we have determined a critical maximum value of node degree (Kc), below which the network is more resistible and the computer virus cannot expand into the whole network. The influence of network size is also studied. We found that the network with low size is more effective to reduce the proportion of infected nodes.

Emergence of hysteresis loop in social contagions on complex networks.

PubMed

Su, Zhen; Wang, Wei; Li, Lixiang; Xiao, Jinghua; Stanley, H Eugene

2017-07-21

Understanding the spreading mechanisms of social contagions in complex network systems has attracted much attention in the physics community. Here we propose a generalized threshold model to describe social contagions. Using extensive numerical simulations and theoretical analyses, we find that a hysteresis loop emerges in the system. Specifically, the steady state of the system is sensitive to the initial conditions of the dynamics of the system. In the steady state, the adoption size increases discontinuously with the transmission probability of information about social contagions, and trial size exhibits a non-monotonic pattern, i.e., it first increases discontinuously then decreases continuously. Finally we study social contagions on heterogeneous networks and find that network topology does not qualitatively affect our results.

Thin Watts-Strogatz networks.

PubMed

de Moura, Alessandro P S

2006-01-01

A modified version of the Watts-Strogatz (WS) network model is proposed, in which the number of shortcuts scales with the network size N as Nalpha, with alpha < 1. In these networks, the ratio of the number of shortcuts to the network size approaches zero as N --> infinity, whereas in the original WS model, this ratio is constant. We call such networks "thin Watts-Strogatz networks." We show that even though the fraction of shortcuts becomes vanishingly small for large networks, they still cause a kind of small-world effect, in the sense that the length L of the network increases sublinearly with the size. We develop a mean-field theory for these networks, which predicts that the length scales as N1-alpha ln N for large N. We also study how a search using only local information works in thin WS networks. We find that the search performance is enhanced compared to the regular network, and we predict that the search time tau scales as N1-alpha/2. These theoretical results are tested using numerical simulations. We comment on the possible relevance of thin WS networks for the design of high-performance low-cost communication networks.

Self-avoiding walks on scale-free networks

NASA Astrophysics Data System (ADS)

Herrero, Carlos P.

2005-01-01

Several kinds of walks on complex networks are currently used to analyze search and navigation in different systems. Many analytical and computational results are known for random walks on such networks. Self-avoiding walks (SAW’s) are expected to be more suitable than unrestricted random walks to explore various kinds of real-life networks. Here we study long-range properties of random SAW’s on scale-free networks, characterized by a degree distribution P (k) ˜ k-γ . In the limit of large networks (system size N→∞ ), the average number sn of SAW’s starting from a generic site increases as μn , with μ= < k2 > / -1 . For finite N , sn is reduced due to the presence of loops in the network, which causes the emergence of attrition of the paths. For kinetic growth walks, the average maximum length increases as a power of the system size: ˜ Nα , with an exponent α increasing as the parameter γ is raised. We discuss the dependence of α on the minimum allowed degree in the network. A similar power-law dependence is found for the mean self-intersection length of nonreversal random walks. Simulation results support our approximate analytical calculations.

Social network effects on post-traumatic stress disorder (PTSD) in female North Korean immigrants.

PubMed

Lee, Byungkyu; Youm, Yoosik

2011-09-01

The goal of this paper is to examine the social network effects on post-traumatic sdress disorder (PTSD) in female North Korean immigrants who entered South Korea in 2007. Specifically, it attempts to verify if the density and composition of networks make a difference after controlling for the network size. A multivariate logistic regression is used to probe the effects of social networks using the North Korean Immigrant Panel data set. Because the data set had only completed its initial survey when this paper was written, the analysis was cross-sectional. The size of the support networks was systematically related to PTSD. Female North Korean immigrants with more supporting ties were less likely to develop PTSD, even after controlling for other risk factors (odds-ratio for one more tie was 0.8). However, once we control for the size of the network, neither the density nor the composition of the networks remains statistically significant. The prevalence of the PTSD among female North Korean immigrants is alarmingly high, and regardless of the characteristics of supporting network members, the size of the supporting networks provides substantial protection. This implies that a simple strategy that focuses on increasing the number of supporting ties will be effective among North Korean immigrants who entered South Korea in recent years.

A virus spreading model for cognitive radio networks

NASA Astrophysics Data System (ADS)

Hou, L.; Yeung, K. H.; Wong, K. Y.

2012-12-01

Since cognitive radio (CR) networks could solve the spectrum scarcity problem, they have drawn much research in recent years. Artificial intelligence(AI) is introduced into CRs to learn from and adapt to their environment. Nonetheless, AI brings in a new kind of attacks specific to CR networks. The most powerful one is a self-propagating AI virus. And no spreading properties specific to this virus have been reported in the literature. To fill this research gap, we propose a virus spreading model of an AI virus by considering the characteristics of CR networks and the behavior of CR users. Several important observations are made from the simulation results based on the model. Firstly, the time taken to infect the whole network increases exponentially with the network size. Based on this result, CR network designers could calculate the optimal network size to slow down AI virus propagation rate. Secondly, the anti-virus performance of static networks to an AI virus is better than dynamic networks. Thirdly, if the CR devices with the highest degree are initially infected, the AI virus propagation rate will be increased substantially. Finally, it is also found that in the area with abundant spectrum resource, the AI virus propagation speed increases notably but the variability of the spectrum does not affect the propagation speed much.

An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs.

PubMed

Gharaei, Niayesh; Abu Bakar, Kamalrulnizam; Mohd Hashim, Siti Zaiton; Hosseingholi Pourasl, Ali; Siraj, Mohammad; Darwish, Tasneem

2017-08-11

Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network's lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network's lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.

Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food-web structure.

PubMed

Binzer, Amrei; Guill, Christian; Rall, Björn C; Brose, Ulrich

2016-01-01

Warming and eutrophication are two of the most important global change stressors for natural ecosystems, but their interaction is poorly understood. We used a dynamic model of complex, size-structured food webs to assess interactive effects on diversity and network structure. We found antagonistic impacts: Warming increases diversity in eutrophic systems and decreases it in oligotrophic systems. These effects interact with the community size structure: Communities of similarly sized species such as parasitoid-host systems are stabilized by warming and destabilized by eutrophication, whereas the diversity of size-structured predator-prey networks decreases strongly with warming, but decreases only weakly with eutrophication. Nonrandom extinction risks for generalists and specialists lead to higher connectance in networks without size structure and lower connectance in size-structured communities. Overall, our results unravel interactive impacts of warming and eutrophication and suggest that size structure may serve as an important proxy for predicting the community sensitivity to these global change stressors. © 2015 John Wiley & Sons Ltd.

Use of social network sites and instant messaging does not lead to increased offline social network size, or to emotionally closer relationships with offline network members.

PubMed

Pollet, Thomas V; Roberts, Sam G B; Dunbar, Robin I M

2011-04-01

The effect of Internet use on social relationships is still a matter of intense debate. This study examined the relationships between use of social media (instant messaging and social network sites), network size, and emotional closeness in a sample of 117 individuals aged 18 to 63 years old. Time spent using social media was associated with a larger number of online social network "friends." However, time spent using social media was not associated with larger offline networks, or feeling emotionally closer to offline network members. Further, those that used social media, as compared to non-users of social media, did not have larger offline networks, and were not emotionally closer to offline network members. These results highlight the importance of considering potential time and cognitive constraints on offline social networks when examining the impact of social media use on social relationships.

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.

Social Network Effects on Post-Traumatic Stress Disorder (PTSD) in Female North Korean Immigrants

PubMed Central

Lee, Byungkyu

2011-01-01

Objectives The goal of this paper is to examine the social network effects on post-traumatic sdress disorder (PTSD) in female North Korean immigrants who entered South Korea in 2007. Specifically, it attempts to verify if the density and composition of networks make a difference after controlling for the network size. Methods A multivariate logistic regression is used to probe the effects of social networks using the North Korean Immigrant Panel data set. Because the data set had only completed its initial survey when this paper was written, the analysis was cross-sectional. Results The size of the support networks was systematically related to PTSD. Female North Korean immigrants with more supporting ties were less likely to develop PTSD, even after controlling for other risk factors (odds-ratio for one more tie was 0.8). However, once we control for the size of the network, neither the density nor the composition of the networks remains statistically significant. Conclusions The prevalence of the PTSD among female North Korean immigrants is alarmingly high, and regardless of the characteristics of supporting network members, the size of the supporting networks provides substantial protection. This implies that a simple strategy that focuses on increasing the number of supporting ties will be effective among North Korean immigrants who entered South Korea in recent years. PMID:22020184

Mitigating Handoff Call Dropping in Wireless Cellular Networks: A Call Admission Control Technique

NASA Astrophysics Data System (ADS)

Ekpenyong, Moses Effiong; Udoh, Victoria Idia; Bassey, Udoma James

2016-06-01

Handoff management has been an important but challenging issue in the field of wireless communication. It seeks to maintain seamless connectivity of mobile users changing their points of attachment from one base station to another. This paper derives a call admission control model and establishes an optimal step-size coefficient (k) that regulates the admission probability of handoff calls. An operational CDMA network carrier was investigated through the analysis of empirical data collected over a period of 1 month, to verify the performance of the network. Our findings revealed that approximately 23 % of calls in the existing system were lost, while 40 % of the calls (on the average) were successfully admitted. A simulation of the proposed model was then carried out under ideal network conditions to study the relationship between the various network parameters and validate our claim. Simulation results showed that increasing the step-size coefficient degrades the network performance. Even at optimum step-size (k), the network could still be compromised in the presence of severe network crises, but our model was able to recover from these problems and still functions normally.

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.

Selective Narrowing of Social Networks across Adulthood is Associated with Improved Emotional Experience in Daily Life

ERIC Educational Resources Information Center

English, Tammy; Carstensen, Laura L.

2014-01-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory…

Selective Narrowing of Social Networks Across Adulthood is Associated With Improved Emotional Experience in Daily Life.

PubMed

English, Tammy; Carstensen, Laura L

2014-03-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory function that supports an age-related increase in the priority that people place on emotional well-being. The present study employed a longitudinal design with a sample that spanned the full adult age range to examine whether there is evidence of within-individual (developmental) change in social networks and whether the characteristics of relationships predict emotional experiences in daily life. Using growth curve analyses, social networks were found to increase in size in young adulthood and then decline steadily throughout later life. As postulated by socioemotional selectivity theory, reductions were observed primarily in the number of peripheral partners; the number of close partners was relatively stable over time. In addition, cross-sectional analyses revealed that older adults reported that social network members elicited less negative emotion and more positive emotion. The emotional tone of social networks, particularly when negative emotions were associated with network members, also predicted experienced emotion of participants. Overall, findings were robust after taking into account demographic variables and physical health. The implications of these findings are discussed in the context of socioemotional selectivity theory and related theoretical models.

Network characteristics, perceived social support, and psychological adjustment in mothers of children with autism spectrum disorder.

PubMed

Benson, Paul R

2012-12-01

This study examined the characteristics of the support networks of 106 mothers of children with ASD and their relationship to perceived social support, depressed mood, and subjective well-being. Using structural equation modeling, two competing sets of hypotheses were assessed: (1) that network characteristics would impact psychological adjustment directly, and (2) that network effects on adjustment would be indirect, mediated by perceived social support. Results primarily lent support to the latter hypotheses, with measures of network structure (network size) and function (proportion of network members providing emotional support) predicting increased levels of perceived social support which, in turn, predicted decreased depressed mood and increased well-being. Results also indicated that increased interpersonal strain in the maternal network was directly and indirectly associated with increased maternal depression, while being indirectly linked to reduced well-being. Study limitations and implications are discussed.

SEIR Model of Rumor Spreading in Online Social Network with Varying Total Population Size

NASA Astrophysics Data System (ADS)

Dong, Suyalatu; Deng, Yan-Bin; Huang, Yong-Chang

2017-10-01

Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network. Supported by National Natural Science Foundation of China under Grant Nos. 11275017 and 11173028

Capacity to adapt to environmental change: evidence from a network of organizations concerned with increasing wildfire risk

Treesearch

A. Paige Fischer; Lorien Jasny

2017-01-01

Because wildfire size and frequency are expected to increase in many forested areas in the United States, organizations involved in forest and wildfire management could arguably benefit from working together and sharing information to develop strategies for how to adapt to this increasing risk. Social capital theory suggests that actors in cohesive networks are...

Protein complex prediction for large protein protein interaction networks with the Core&Peel method.

PubMed

Pellegrini, Marco; Baglioni, Miriam; Geraci, Filippo

2016-11-08

Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed. We present a new fast algorithm for clustering large sparse networks: Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G (which is roughly proportional to the maximum average degree of any induced subgraph in G). We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms. Our algorithm Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks.

Abruptness of Cascade Failures in Power Grids

NASA Astrophysics Data System (ADS)

Pahwa, Sakshi; Scoglio, Caterina; Scala, Antonio

2014-01-01

Electric power-systems are one of the most important critical infrastructures. In recent years, they have been exposed to extreme stress due to the increasing demand, the introduction of distributed renewable energy sources, and the development of extensive interconnections. We investigate the phenomenon of abrupt breakdown of an electric power-system under two scenarios: load growth (mimicking the ever-increasing customer demand) and power fluctuations (mimicking the effects of renewable sources). Our results on real, realistic and synthetic networks indicate that increasing the system size causes breakdowns to become more abrupt; in fact, mapping the system to a solvable statistical-physics model indicates the occurrence of a first order transition in the large size limit. Such an enhancement for the systemic risk failures (black-outs) with increasing network size is an effect that should be considered in the current projects aiming to integrate national power-grids into ``super-grids''.

Abruptness of cascade failures in power grids.

PubMed

Pahwa, Sakshi; Scoglio, Caterina; Scala, Antonio

2014-01-15

Electric power-systems are one of the most important critical infrastructures. In recent years, they have been exposed to extreme stress due to the increasing demand, the introduction of distributed renewable energy sources, and the development of extensive interconnections. We investigate the phenomenon of abrupt breakdown of an electric power-system under two scenarios: load growth (mimicking the ever-increasing customer demand) and power fluctuations (mimicking the effects of renewable sources). Our results on real, realistic and synthetic networks indicate that increasing the system size causes breakdowns to become more abrupt; in fact, mapping the system to a solvable statistical-physics model indicates the occurrence of a first order transition in the large size limit. Such an enhancement for the systemic risk failures (black-outs) with increasing network size is an effect that should be considered in the current projects aiming to integrate national power-grids into "super-grids".

Abruptness of Cascade Failures in Power Grids

PubMed Central

Pahwa, Sakshi; Scoglio, Caterina; Scala, Antonio

2014-01-01

Electric power-systems are one of the most important critical infrastructures. In recent years, they have been exposed to extreme stress due to the increasing demand, the introduction of distributed renewable energy sources, and the development of extensive interconnections. We investigate the phenomenon of abrupt breakdown of an electric power-system under two scenarios: load growth (mimicking the ever-increasing customer demand) and power fluctuations (mimicking the effects of renewable sources). Our results on real, realistic and synthetic networks indicate that increasing the system size causes breakdowns to become more abrupt; in fact, mapping the system to a solvable statistical-physics model indicates the occurrence of a first order transition in the large size limit. Such an enhancement for the systemic risk failures (black-outs) with increasing network size is an effect that should be considered in the current projects aiming to integrate national power-grids into “super-grids”. PMID:24424239

Grain-size considerations for optoelectronic multistage interconnection networks.

PubMed

Krishnamoorthy, A V; Marchand, P J; Kiamilev, F E; Esener, S C

1992-09-10

This paper investigates, at the system level, the performance-cost trade-off between optical and electronic interconnects in an optoelectronic interconnection network. The specific system considered is a packet-switched, free-space optoelectronic shuffle-exchange multistage interconnection network (MIN). System bandwidth is used as the performance measure, while system area, system power, and system volume constitute the cost measures. A detailed design and analysis of a two-dimensional (2-D) optoelectronic shuffle-exchange routing network with variable grain size K is presented. The architecture permits the conventional 2 x 2 switches or grains to be generalized to larger K x K grain sizes by replacing optical interconnects with electronic wires without affecting the functionality of the system. Thus the system consists of log(k) N optoelectronic stages interconnected with free-space K-shuffles. When K = N, the MIN consists of a single electronic stage with optical input-output. The system design use an effi ient 2-D VLSI layout and a single diffractive optical element between stages to provide the 2-D K-shuffle interconnection. Results indicate that there is an optimum range of grain sizes that provides the best performance per cost. For the specific VLSI/GaAs multiple quantum well technology and system architecture considered, grain sizes larger than 256 x 256 result in a reduced performance, while grain sizes smaller than 16 x 16 have a high cost. For a network with 4096 channels, the useful range of grain sizes corresponds to approximately 250-400 electronic transistors per optical input-output channel. The effect of varying certain technology parameters such as the number of hologram phase levels, the modulator driving voltage, the minimum detectable power, and VLSI minimum feature size on the optimum grain-size system is studied. For instance, results show that using four phase levels for the interconnection hologram is a good compromise for the cost functions mentioned above. As VLSI minimum feature sizes decrease, the optimum grain size increases, whereas, if optical interconnect performance in terms of the detector power or modulator driving voltage requirements improves, the optimum grain size may be reduced. Finally, several architectural modifications to the system, such as K x K contention-free switches and sorting networks, are investigated and optimized for grain size. Results indicate that system bandwidth can be increased, but at the price of reduced performance/cost. The optoelectronic MIN architectures considered thus provide a broad range of performance/cost alternatives and offer a superior performance over purely electronic MIN's.

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

The scaling of human interactions with city size

PubMed Central

Schläpfer, Markus; Bettencourt, Luís M. A.; Grauwin, Sébastian; Raschke, Mathias; Claxton, Rob; Smoreda, Zbigniew; West, Geoffrey B.; Ratti, Carlo

2014-01-01

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases. PMID:24990287

Regulation of Cerebral Cortical Size and Neuron Number by Fibroblast Growth Factors: Implications for Autism

ERIC Educational Resources Information Center

Vaccarino, Flora M.; Grigorenko, Elena L.; Smith, Karen Muller; Stevens, Hanna E.

2009-01-01

Increased brain size is common in children with autism spectrum disorders. Here we propose that an increased number of cortical excitatory neurons may underlie the increased brain volume, minicolumn pathology and excessive network excitability, leading to sensory hyper-reactivity and seizures, which are often found in autism. We suggest that…

Correlation between Academic and Skills-Based Tests in Computer Networks

ERIC Educational Resources Information Center

Buchanan, William

2006-01-01

Computing-related programmes and modules have many problems, especially related to large class sizes, large-scale plagiarism, module franchising, and an increased requirement from students for increased amounts of hands-on, practical work. This paper presents a practical computer networks module which uses a mixture of online examinations and a…

A Minimax Network Flow Model for Characterizing the Impact of Slot Restrictions

NASA Technical Reports Server (NTRS)

Lee, Douglas W.; Patek, Stephen D.; Alexandrov, Natalia; Bass, Ellen J.; Kincaid, Rex K.

2010-01-01

This paper proposes a model for evaluating long-term measures to reduce congestion at airports in the National Airspace System (NAS). This model is constructed with the goal of assessing the global impacts of congestion management strategies, specifically slot restrictions. We develop the Minimax Node Throughput Problem (MINNTHRU), a multicommodity network flow model that provides insight into air traffic patterns when one minimizes the worst-case operation across all airports in a given network. MINNTHRU is thus formulated as a model where congestion arises from network topology. It reflects not market-driven airline objectives, but those of a regulatory authority seeking a distribution of air traffic beneficial to all airports, in response to congestion management measures. After discussing an algorithm for solving MINNTHRU for moderate-sized (30 nodes) and larger networks, we use this model to study the impacts of slot restrictions on the operation of an entire hub-spoke airport network. For both a small example network and a medium-sized network based on 30 airports in the NAS, we use MINNTHRU to demonstrate that increasing the severity of slot restrictions increases the traffic around unconstrained hub airports as well as the worst-case level of operation over all airports.

The role of heterogeneity in contact timing and duration in network models of influenza spread in schools

PubMed Central

Toth, Damon J. A.; Leecaster, Molly; Pettey, Warren B. P.; Gundlapalli, Adi V.; Gao, Hongjiang; Rainey, Jeanette J.; Uzicanin, Amra; Samore, Matthew H.

2015-01-01

Influenza poses a significant health threat to children, and schools may play a critical role in community outbreaks. Mathematical outbreak models require assumptions about contact rates and patterns among students, but the level of temporal granularity required to produce reliable results is unclear. We collected objective contact data from students aged 5–14 at an elementary school and middle school in the state of Utah, USA, and paired those data with a novel, data-based model of influenza transmission in schools. Our simulations produced within-school transmission averages consistent with published estimates. We compared simulated outbreaks over the full resolution dynamic network with simulations on networks with averaged representations of contact timing and duration. For both schools, averaging the timing of contacts over one or two school days caused average outbreak sizes to increase by 1–8%. Averaging both contact timing and pairwise contact durations caused average outbreak sizes to increase by 10% at the middle school and 72% at the elementary school. Averaging contact durations separately across within-class and between-class contacts reduced the increase for the elementary school to 5%. Thus, the effect of ignoring details about contact timing and duration in school contact networks on outbreak size modelling can vary across different schools. PMID:26063821

Evaluation of the impact of deep learning architectural components selection and dataset size on a medical imaging task

NASA Astrophysics Data System (ADS)

Dutta, Sandeep; Gros, Eric

2018-03-01

Deep Learning (DL) has been successfully applied in numerous fields fueled by increasing computational power and access to data. However, for medical imaging tasks, limited training set size is a common challenge when applying DL. This paper explores the applicability of DL to the task of classifying a single axial slice from a CT exam into one of six anatomy regions. A total of 29000 images selected from 223 CT exams were manually labeled for ground truth. An additional 54 exams were labeled and used as an independent test set. The network architecture developed for this application is composed of 6 convolutional layers and 2 fully connected layers with RELU non-linear activations between each layer. Max-pooling was used after every second convolutional layer, and a softmax layer was used at the end. Given this base architecture, the effect of inclusion of network architecture components such as Dropout and Batch Normalization on network performance and training is explored. The network performance as a function of training and validation set size is characterized by training each network architecture variation using 5,10,20,40,50 and 100% of the available training data. The performance comparison of the various network architectures was done for anatomy classification as well as two computer vision datasets. The anatomy classifier accuracy varied from 74.1% to 92.3% in this study depending on the training size and network layout used. Dropout layers improved the model accuracy for all training sizes.

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.

Network Reliability: The effect of local network structure on diffusive processes

PubMed Central

Youssef, Mina; Khorramzadeh, Yasamin; Eubank, Stephen

2014-01-01

This paper re-introduces the network reliability polynomial – introduced by Moore and Shannon in 1956 – for studying the effect of network structure on the spread of diseases. We exhibit a representation of the polynomial that is well-suited for estimation by distributed simulation. We describe a collection of graphs derived from Erdős-Rényi and scale-free-like random graphs in which we have manipulated assortativity-by-degree and the number of triangles. We evaluate the network reliability for all these graphs under a reliability rule that is related to the expected size of a connected component. Through these extensive simulations, we show that for positively or neutrally assortative graphs, swapping edges to increase the number of triangles does not increase the network reliability. Also, positively assortative graphs are more reliable than neutral or disassortative graphs with the same number of edges. Moreover, we show the combined effect of both assortativity-by-degree and the presence of triangles on the critical point and the size of the smallest subgraph that is reliable. PMID:24329321

A simple model of global cascades on random networks

NASA Astrophysics Data System (ADS)

Watts, Duncan J.

2002-04-01

The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascadesherein called global cascadesthat occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.

Gossip in Random Networks

NASA Astrophysics Data System (ADS)

Malarz, K.; Szvetelszky, Z.; Szekf, B.; Kulakowski, K.

2006-11-01

We consider the average probability X of being informed on a gossip in a given social network. The network is modeled within the random graph theory of Erd{õ}s and Rényi. In this theory, a network is characterized by two parameters: the size N and the link probability p. Our experimental data suggest three levels of social inclusion of friendship. The critical value pc, for which half of agents are informed, scales with the system size as N-gamma with gamma approx 0.68. Computer simulations show that the probability X varies with p as a sigmoidal curve. Influence of the correlations between neighbors is also evaluated: with increasing clustering coefficient C, X decreases.

Social support network characteristics of incarcerated women with co-occurring major depressive and substance use disorders.

PubMed

Nargiso, Jessica E; Kuo, Caroline C; Zlotnick, Caron; Johnson, Jennifer E

2014-01-01

The nature of social support available to incarcerated women is not well-understood, particularly among women at high risk of negative outcomes, including women dually diagnosed with Major Depressive Disorder and a Substance Use Disorder (MDD-SUD). Descriptive statistics and paired-tests were conducted on 60 incarcerated MDD-SUD women receiving in-prison substance use and depression treatments to characterize the women's social networks, including the strength of support, network characteristics, and types of support provided as well as to determine what aspects of social support may be amenable to change during incarceration and post-release. Study results showed that, on average, women perceived they had moderately supportive individuals in their lives, although more than a quarter of the sample could not identify any regular supporters in their network at baseline. During incarceration, women's social networks significantly increased in general supportiveness, and decreased in network size and percentage of substance users in their networks. Participants maintained positive social support gains post-release in most areas while also significantly increasing the size of their support network post-release. Findings suggest that there are aspects of incarcerated MDD-SUD women's social networks that are amenable to change during incarceration and post-release and provide insight into treatment targets for this vulnerable population.

Social support network characteristics of incarcerated women with co-occurring major depressive and substance use disorders

PubMed Central

Nargiso, Jessica E.; Kuo, Caroline C.; Zlotnick, Caron; Johnson, Jennifer E.

2014-01-01

The nature of social support available to incarcerated women is not well understood, particularly among women at high risk of negative outcomes, including women dually-diagnosed with Major Depressive Disorder and a Substance Use Disorder (MDD-SUD). Descriptive statistics and paired-tests were conducted on 60 incarcerated MDD-SUD women receiving in-prison substance use and depression treatments to characterize the women’s social networks, including the strength of support, network characteristics, and types of support provided as well as to determine what aspects of social support may be amenable to change during incarceration and post-release. Study results showed that on average women perceived they had moderately supportive individuals in their lives, although more than a quarter of the sample could not identify any regular supporters in their network at baseline. During incarceration, women’s social networks significantly increased in general supportiveness, and decreased in network size and percentage of substance users in their networks. Participants maintained positive social support gains post-release in most areas while also significantly increasing the size of their support network post-release. Findings suggest that there are aspects of incarcerated MDD-SUD women’s social networks that are amenable to change during incarceration and post-release and provide insight into treatment targets for this vulnerable population. PMID:25052785

Selective Narrowing of Social Networks Across Adulthood is Associated With Improved Emotional Experience in Daily Life

PubMed Central

English, Tammy; Carstensen, Laura L.

2014-01-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory function that supports an age-related increase in the priority that people place on emotional well-being. The present study employed a longitudinal design with a sample that spanned the full adult age range to examine whether there is evidence of within-individual (developmental) change in social networks and whether the characteristics of relationships predict emotional experiences in daily life. Using growth curve analyses, social networks were found to increase in size in young adulthood and then decline steadily throughout later life. As postulated by socioemotional selectivity theory, reductions were observed primarily in the number of peripheral partners; the number of close partners was relatively stable over time. In addition, cross-sectional analyses revealed that older adults reported that social network members elicited less negative emotion and more positive emotion. The emotional tone of social networks, particularly when negative emotions were associated with network members, also predicted experienced emotion of participants. Overall, findings were robust after taking into account demographic variables and physical health. The implications of these findings are discussed in the context of socioemotional selectivity theory and related theoretical models. PMID:24910483

Network confinement and heterogeneity slows nanoparticle diffusion in polymer gels

NASA Astrophysics Data System (ADS)

Parrish, Emmabeth; Caporizzo, Matthew A.; Composto, Russell J.

2017-05-01

Nanoparticle (NP) diffusion was measured in polyacrylamide gels (PAGs) with a mesh size comparable to the NP size, 21 nm. The confinement ratio (CR), NP diameter/mesh size, increased from 0.4 to 3.8 by increasing crosslinker density and from 0.4 to 2.1 by adding acetone, which collapsed the PAGs. In all gels, NPs either became localized, moving less than 200 nm, diffused microns, or exhibited a combination of these behaviors, as measured by single particle tracking. Mean squared displacements (MSDs) of mobile NPs decreased as CR increased. In collapsed gels, the localized NP population increased and MSD of mobile NPs decreased compared to crosslinked PAGs. For all CRs, van Hove distributions exhibited non-Gaussian displacements, consistent with intermittent localization of NPs. The non-Gaussian parameter increased from a maximum of 1.5 for crosslinked PAG to 5 for collapsed PAG, consistent with greater network heterogeneity in these gels. Diffusion coefficients decreased exponentially as CR increased for crosslinked gels; however, in collapsed gels, the diffusion coefficients decreased more strongly, which was attributed to network heterogeneity. Collapsing the gel resulted in an increasingly tortuous pathway for NPs, slowing diffusion at a given CR. Understanding how gel structure affects NP mobility will allow the design and enhanced performance of gels that separate and release molecules in membranes and drug delivery platforms.

Networks In ACA Marketplaces Are Narrower For Mental Health Care Than For Primary Care.

PubMed

Zhu, Jane M; Zhang, Yuehan; Polsky, Daniel

2017-09-01

There is increasing concern about the extent to which narrow-network plans, generally defined as those including fewer than 25 percent of providers in a given health insurance market, affect consumers' choice of and access to specialty providers-particularly in mental health care. Using data for 2016 from 531 unique provider networks in the Affordable Care Act Marketplaces, we evaluated how network size and the percentage of providers who participate in any network differ between mental health care providers and a control group of primary care providers. Compared to primary care networks, participation in mental health networks was low, with only 42.7 percent of psychiatrists and 19.3 percent of nonphysician mental health care providers participating in any network. On average, plan networks included 24.3 percent of all primary care providers and 11.3 percent of all mental health care providers practicing in a given state-level market. These findings raise important questions about provider-side barriers to meeting the goal of mental health parity regulations: that insurers cover mental health services on a par with general medical and surgical services. Concerted efforts to increase network participation by mental health care providers, along with greater regulatory attention to network size and composition, could improve consumer choice and complement efforts to achieve mental health parity. Project HOPE—The People-to-People Health Foundation, Inc.

Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks.

PubMed

Bardoscia, Marco; Marsili, Matteo; Samal, Areejit

2015-07-01

System-level properties of metabolic networks may be the direct product of natural selection or arise as a by-product of selection on other properties. Here we study the effect of direct selective pressure for growth or viability in particular environments on two properties of metabolic networks: latent versatility to function in additional environments and carbon usage efficiency. Using a Markov chain Monte Carlo (MCMC) sampling based on flux balance analysis (FBA), we sample from a known biochemical universe random viable metabolic networks that differ in the number of directly constrained environments. We find that the latent versatility of sampled metabolic networks increases with the number of directly constrained environments and with the size of the networks. We then show that the average carbon wastage of sampled metabolic networks across the constrained environments decreases with the number of directly constrained environments and with the size of the networks. Our work expands the growing body of evidence about nonadaptive origins of key functional properties of biological networks.

Five-year trajectories of social networks and social support in older adults with major depression.

PubMed

Voils, Corrine I; Allaire, Jason C; Olsen, Maren K; Steffens, David C; Hoyle, Rick H; Bosworth, Hayden B

2007-12-01

Research with nondepressed adults suggests that social networks and social support are stable over the life course until very late age. This may not hold true for older adults with depression. We examined baseline status and trajectories of social networks and social support at the group and individual levels over five years. The sample consisted of 339 initially depressed adults aged 59 or older (M = 69 years) enrolled in a naturalistic study of depression. Measures of social ties, including social network size, frequency of interaction, instrumental support, and subjective support, were administered at baseline and yearly for five years. Latent growth curve models were estimated for each aspect of social ties. On average, social network size and frequency of interaction were low at baseline and remained stable over time, whereas subjective and instrumental support were high at baseline yet increased over time. There was significant variation in the direction and rate of change over time, which was not predicted by demographic or clinical factors. Because increasing social networks may be ineffective and may not be possible for a portion of people who already receive maximal support, interventions to increase social support may only work for a portion of older depressed adults.

The impact of an unfavorable depression course on network size and loneliness in older people: a longitudinal study in the community.

PubMed

Houtjes, Wim; van Meijel, Berno; van de Ven, Peter M; Deeg, Dorly; van Tilburg, Theo; Beekman, Aartjan

2014-10-01

This work aims to gain insight into the long-term impact of depression course on social network size and perceived loneliness in older people living in the community. Within a large representative sample of older people in the community (Longitudinal Aging Study Amsterdam (LASA)), participants with clinically relevant levels of depressive symptoms (scores >16 on the Center for Epidemiological Studies Depression Scale) were followed up over a period of 13 years of the LASA study (five waves). General estimating equations were used to estimate the impact of depression course on network size and loneliness and the interaction with gender and age. An unfavorable course of depression was found to be associated with smaller network sizes and higher levels of loneliness over time, especially in men and older participants. The findings of this study stress the importance of clinical attention to the negative consequences of chronicity in depressed older people. Clinicians should assess possible erosion of the social network over time and be aware of increased feelings of loneliness in this patient group. Copyright © 2014 John Wiley & Sons, Ltd.

Synchronization in scale-free networks: The role of finite-size effects

NASA Astrophysics Data System (ADS)

Torres, D.; Di Muro, M. A.; La Rocca, C. E.; Braunstein, L. A.

2015-06-01

Synchronization problems in complex networks are very often studied by researchers due to their many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, scale-free networks with degree distribution P(k)∼ k-λ , are widely used in research since they are ubiquitous in Nature and other real systems. In this paper we focus on the surface relaxation growth model in scale-free networks with 2.5< λ <3 , and study the scaling behavior of the fluctuations, in the steady state, with the system size N. We find a novel behavior of the fluctuations characterized by a crossover between two regimes at a value of N=N* that depends on λ: a logarithmic regime, found in previous research, and a constant regime. We propose a function that describes this crossover, which is in very good agreement with the simulations. We also find that, for a system size above N* , the fluctuations decrease with λ, which means that the synchronization of the system improves as λ increases. We explain this crossover analyzing the role of the network's heterogeneity produced by the system size N and the exponent of the degree distribution.

Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows.

PubMed

Moussawi, A; Derzsy, N; Lin, X; Szymanski, B K; Korniss, G

2017-09-15

Cascading failures are a critical vulnerability of complex information or infrastructure networks. Here we investigate the properties of load-based cascading failures in real and synthetic spatially-embedded network structures, and propose mitigation strategies to reduce the severity of damages caused by such failures. We introduce a stochastic method for optimal heterogeneous distribution of resources (node capacities) subject to a fixed total cost. Additionally, we design and compare the performance of networks with N-stable and (N-1)-stable network-capacity allocations by triggering cascades using various real-world node-attack and node-failure scenarios. We show that failure mitigation through increased node protection can be effectively achieved against single-node failures. However, mitigating against multiple node failures is much more difficult due to the combinatorial increase in possible sets of initially failing nodes. We analyze the robustness of the system with increasing protection, and find that a critical tolerance exists at which the system undergoes a phase transition, and above which the network almost completely survives an attack. Moreover, we show that cascade-size distributions measured in this region exhibit a power-law decay. Finally, we find a strong correlation between cascade sizes induced by individual nodes and sets of nodes. We also show that network topology alone is a weak predictor in determining the progression of cascading failures.

Assessing the efficiency of different CSO positions based on network graph characteristics.

PubMed

Sitzenfrei, R; Urich, C; Möderl, M; Rauch, W

2013-01-01

The technical design of urban drainage systems comprises two major aspects: first, the spatial layout of the sewer system and second, the pipe-sizing process. Usually, engineers determine the spatial layout of the sewer network manually, taking into account physical features and future planning scenarios. Before the pipe-sizing process starts, it is important to determine locations of possible weirs and combined sewer overflows (CSOs) based on, e.g. distance to receiving water bodies or to a wastewater treatment plant and available space for storage units. However, positions of CSOs are also determined by topological characteristics of the sewer networks. In order to better understand the impact of placement choices for CSOs and storage units in new systems, this work aims to determine case unspecific, general rules. Therefore, based on numerous, stochastically generated virtual alpine sewer systems of different sizes it is investigated how choices for placement of CSOs and storage units have an impact on the pipe-sizing process (hence, also on investment costs) and on technical performance (CSO efficiency and flooding). To describe the impact of the topological positions of these elements in the sewer networks, graph characteristics are used. With an evaluation of 2,000 different alpine combined sewer systems, it was found that, as expected, with CSOs at more downstream positions in the network, greater construction costs and better performance regarding CSO efficiency result. At a specific point (i.e. topological network position), no significant difference (further increase) in construction costs can be identified. Contrarily, the flooding efficiency increases with more upstream positions of the CSOs. Therefore, CSO and flooding efficiency are in a trade-off conflict and a compromise is required.

Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin; Cheng, Runwei

Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

Source and Size of Emotional and Financial-Related Social Support Network on Physical Activity Behavior Among Older Adults.

PubMed

Loprinzi, Paul D; Joyner, Chelsea

2016-07-01

To examine the association of source of emotional- and financial-related social support and size of social support network on physical activity behavior among older adults. Data from the 1999-2006 NHANES were used (N = 5616; 60 to 85 yrs). Physical activity and emotional- and financial-related social support were assessed via self-report. Older adults with perceived having emotional social support had a 41% increased odds of meeting physical activity guidelines (OR = 1.41; 95% CI: 1.01-1.97). The only specific sources of social support that were associated with meeting physical activity guidelines was friend emotional support (OR = 1.19; 95% CI: 1.01-1.41) and financial support (OR = 1.28; 95% CI: 1.09-1.49). With regard to size of social support network, a dose-response relationship was observed. Compared with those with 0 close friends, those with 1 to 2, 3 to 4, 5, and 6+ close friends, respectively, had a 1.70-, 2.38-, 2.57-, and 2.71-fold increased odds of meeting physical activity guidelines. There was some evidence of gender- and age-specific associations between social support and physical activity. Emotional- and financial-related social support and size of social support network are associated with higher odds of meeting physical activity guidelines among older adults.

Identifying changes in the support networks of end-of-life carers using social network analysis

PubMed Central

Leonard, Rosemary; Horsfall, Debbie; Noonan, Kerrie

2015-01-01

End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital. PMID:24644162

The scaling of human interactions with city size.

PubMed

Schläpfer, Markus; Bettencourt, Luís M A; Grauwin, Sébastian; Raschke, Mathias; Claxton, Rob; Smoreda, Zbigniew; West, Geoffrey B; Ratti, Carlo

2014-09-06

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.

PubMed

Tuckwell, Henry C

2006-01-01

The circuitry of cortical networks involves interacting populations of excitatory (E) and inhibitory (I) neurons whose relationships are now known to a large extent. Inputs to E- and I-cells may have their origins in remote or local cortical areas. We consider a rudimentary model involving E- and I-cells. One of our goals is to test an analytic approach to finding firing rates in neural networks without using a diffusion approximation and to this end we consider in detail networks of excitatory neurons with leaky integrate-and-fire (LIF) dynamics. A simple measure of synchronization, denoted by S(q), where q is between 0 and 100 is introduced. Fully connected E-networks have a large tendency to become dominated by synchronously firing groups of cells, except when inputs are relatively weak. We observed random or asynchronous firing in such networks with diverse sets of parameter values. When such firing patterns were found, the analytical approach was often able to accurately predict average neuronal firing rates. We also considered several properties of E-E networks, distinguishing several kinds of firing pattern. Included were those with silences before or after periods of intense activity or with periodic synchronization. We investigated the occurrence of synchronized firing with respect to changes in the internal excitatory postsynaptic potential (EPSP) magnitude in a network of 100 neurons with fixed values of the remaining parameters. When the internal EPSP size was less than a certain value, synchronization was absent. The amount of synchronization then increased slowly as the EPSP amplitude increased until at a particular EPSP size the amount of synchronization abruptly increased, with S(5) attaining the maximum value of 100%. We also found network frequency transfer characteristics for various network sizes and found a linear dependence of firing frequency over wide ranges of the external afferent frequency, with non-linear effects at lower input frequencies. The theory may also be applied to sparsely connected networks, whose firing behaviour was found to change abruptly as the probability of a connection passed through a critical value. The analytical method was also found to be useful for a feed-forward excitatory network and a network of excitatory and inhibitory neurons.

Cascades on a class of clustered random networks

NASA Astrophysics Data System (ADS)

Hackett, Adam; Melnik, Sergey; Gleeson, James P.

2011-05-01

We present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of random networks with arbitrary degree distribution and nonzero clustering introduced previously in [M. E. J. Newman, Phys. Rev. Lett. PRLTAO0031-900710.1103/PhysRevLett.103.058701103, 058701 (2009)]. A condition for the existence of global cascades is derived as well as a general criterion that determines whether increasing the level of clustering will increase, or decrease, the expected cascade size. Applications, examples of which are provided, include site percolation, bond percolation, and Watts’ threshold model; in all cases analytical results give excellent agreement with numerical simulations.

The Complex Nature of Family Support across the Lifespan: Implications for Psychological Well-being

PubMed Central

Fuller-Iglesias, Heather R.; Webster, Noah J.; Antonucci, Toni C.

2015-01-01

This study examines the complex role of family networks in shaping adult psychological well-being over time. We examine the unique and interactive longitudinal influences of family structure (i.e., composition and size) and negative family relationship quality on psychological well-being among young (aged 18-34), middle-aged (aged 35-49), and older adults (aged 50+). A sample of 881 adults (72% White; 26% Black) was drawn from the longitudinal Social Relations, Age and Health Study. Structural equation modeling indicated that among young and middle-aged adults, increasing family negativity was associated with increases in depressive symptoms over time. In contrast, among older adults, lowered proportion of family in network and an increasing number of family members in the network (i.e. family size) were associated with decreases in depressive symptoms. These findings were moderated by family negativity. Among older adults with low family negativity, having a lower proportion family and larger family size were associated with decreasing depressive symptoms, but there was no effect among those reporting high family negativity. Overall, these results contribute to an increased understanding of the complex, developmental nature of how family support influences well-being across the lifespan and highlights unique age differences. PMID:25602936

Aquatic Nitrate Retention at River Network Scales Across Flow Conditions Determined Using Nested In Situ Sensors

NASA Astrophysics Data System (ADS)

Wollheim, W. M.; Mulukutla, G. K.; Cook, C.; Carey, R. O.

2017-11-01

Nonpoint pollution sources are strongly influenced by hydrology and are therefore sensitive to climate variability. Some pollutants entering aquatic ecosystems, e.g., nitrate, can be mitigated by in-stream processes during transport through river networks. Whole river network nitrate retention is difficult to quantify with observations. High frequency, in situ nitrate sensors, deployed in nested locations within a single watershed, can improve estimates of both nonpoint inputs and aquatic retention at river network scales. We deployed a nested sensor network and associated sampling in the urbanizing Oyster River watershed in coastal New Hampshire, USA, to quantify storm event-scale loading and retention at network scales. An end member analysis used the relative behavior of reactive nitrate and conservative chloride to infer river network fate of nitrate. In the headwater catchments, nitrate and chloride concentrations are both increasingly diluted with increasing storm size. At the mouth of the watershed, chloride is also diluted, but nitrate tended to increase. The end member analysis suggests that this pattern is the result of high retention during small storms (51-78%) that declines to zero during large storms. Although high frequency nitrate sensors did not alter estimates of fluxes over seasonal time periods compared to less frequent grab sampling, they provide the ability to estimate nitrate flux versus storm size at event scales that is critical for such analyses. Nested sensor networks can improve understanding of the controls of both loading and network scale retention, and therefore also improve management of nonpoint source pollution.

Bribery games on interdependent complex networks.

PubMed

Verma, Prateek; Nandi, Anjan K; Sengupta, Supratim

2018-08-07

Bribe demands present a social conflict scenario where decisions have wide-ranging economic and ethical consequences. Nevertheless, such incidents occur daily in many countries across the globe. Harassment bribery constitute a significant sub-set of such bribery incidents where a government official demands a bribe for providing a service to a citizen legally entitled to it. We employ an evolutionary game-theoretic framework to analyse the evolution of corrupt and honest strategies in structured populations characterized by an interdependent complex network. The effects of changing network topology, average number of links and asymmetry in size of the citizen and officer population on the proliferation of incidents of bribery are explored. A complex network topology is found to be beneficial for the dominance of corrupt strategies over a larger region of phase space when compared with the outcome for a regular network, for equal citizen and officer population sizes. However, the extent of the advantage depends critically on the network degree and topology. A different trend is observed when there is a difference between the citizen and officer population sizes. Under those circumstances, increasing randomness of the underlying citizen network can be beneficial to the fixation of honest officers up to a certain value of the network degree. Our analysis reveals how the interplay between network topology, connectivity and strategy update rules can affect population level outcomes in such asymmetric games. Copyright © 2018 Elsevier Ltd. All rights reserved.

Distributed sensor networks: a cellular nonlinear network perspective.

PubMed

Haenggi, Martin

2003-12-01

Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.

Disentangling reward anticipation with simultaneous pupillometry / fMRI.

PubMed

Schneider, Max; Leuchs, Laura; Czisch, Michael; Sämann, Philipp G; Spoormaker, Victor I

2018-05-05

The reward system may provide an interesting intermediate phenotype for anhedonia in affective disorders. Reward anticipation is characterized by an increase in arousal, and previous studies have linked the anterior cingulate cortex (ACC) to arousal responses such as dilation of the pupil. Here, we examined pupil dynamics during a reward anticipation task in forty-six healthy human subjects and evaluated its neural correlates using functional magnetic resonance imaging (fMRI). Pupil size showed a strong increase during monetary reward anticipation, a moderate increase during verbal reward anticipation and a decrease during control trials. For fMRI analyses, average pupil size and pupil change were computed in 1-s time bins during the anticipation phase. Activity in the ventral striatum was inversely related to the pupil size time course, indicating an early onset of activation and a role in reward prediction processing. Pupil dilations were linked to increased activity in the salience network (dorsal ACC and bilateral insula), which likely triggers an increase in arousal to enhance task performance. Finally, increased pupil size preceding the required motor response was associated with activity in the ventral attention network. In sum, pupillometry provides an effective tool for disentangling different phases of reward anticipation, with relevance for affective symptomatology. Copyright © 2018 Elsevier Inc. All rights reserved.

Exploring productivity and collaboration in Australian Indigenous health research, 1995-2008.

PubMed

Rumbold, Alice R; Cunningham, Joan; Purbrick, Brydie; Lewis, Jenny M

2013-11-08

Building research capacity in Indigenous health has been recognised as integral in efforts to reduce the significant health disparities between Indigenous and other Australian populations. The past few decades have seen substantial changes in funding policy for Australian Indigenous health research, including increases in overall expenditure and a greater focus on collaborative and priority-driven research. However, whether these policy shifts have resulted in any change to the structure of the research workforce in this field is unclear. We examine research publications in Australian Indigenous health from 1995-2008 to explore trends in publication output, key themes investigated, and research collaborations. A comprehensive literature search was undertaken to identify research publications about Australian Indigenous health from 1995-2008. Abstracts of all publications identified were reviewed by two investigators for relevance. Eligible publications were classified according to key themes. Social network analyses of co-authorship patterns were used to examine collaboration in the periods 1995-1999, 2000-2004 and 2005-2008. Nine hundred and fifty three publications were identified. Over time, the number of publications per year increased, particularly after 2005, and there was a substantial increase in assessment of health service-related issues. Network analyses revealed a highly collaborative core group of authors responsible for the majority of outputs, in addition to a series of smaller separate groups. In the first two periods there was a small increase in the overall network size (from n = 583 to n = 642 authors) due to growth in collaborations around the core. In the last period, the network size increased considerably (n = 1,083), largely due to an increase in the number and size of separate groups. The general size of collaborations also increased in this period. In the past few decades there has been substantial development of the research workforce in Indigenous health, characterised by an increase in authors and outputs, a greater focus on some identified priority areas and sustained growth in collaborations. This has occurred in conjunction with significant changes to funding policy for Indigenous health research, suggesting that both productivity and collaboration may be sensitive to reform, including the provision of dedicated funding.

Modeling a Million-Node Slim Fly Network Using Parallel Discrete-Event Simulation

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

Wolfe, Noah; Carothers, Christopher; Mubarak, Misbah

As supercomputers close in on exascale performance, the increased number of processors and processing power translates to an increased demand on the underlying network interconnect. The Slim Fly network topology, a new lowdiameter and low-latency interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this paper, we present a high-fidelity Slim Fly it-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate our Slim Fly model with the Kathareios et al. Slim Fly model results provided at moderately sized network scales. We further scale the modelmore » size up to n unprecedented 1 million compute nodes; and through visualization of network simulation metrics such as link bandwidth, packet latency, and port occupancy, we get an insight into the network behavior at the million-node scale. We also show linear strong scaling of the Slim Fly model on an Intel cluster achieving a peak event rate of 36 million events per second using 128 MPI tasks to process 7 billion events. Detailed analysis of the underlying discrete-event simulation performance shows that a million-node Slim Fly model simulation can execute in 198 seconds on the Intel cluster.« less

Satisfaction with support versus size of network: differential effects of social support on psychological distress in parents of pediatric cancer patients.

PubMed

Harper, Felicity W K; Peterson, Amy M; Albrecht, Terrance L; Taub, Jeffrey W; Phipps, Sean; Penner, Louis A

2016-05-01

This study examined the direct and buffering effects of social support on longer-term global psychological distress among parents coping with pediatric cancer. In both sets of analyses, we examined whether these effects depended on the dimension of social support provided (i.e., satisfaction with support versus size of support network). Participants were 102 parents of pediatric cancer patients. At study entry, parents reported their trait anxiety, depression, and two dimensions of their social support network (satisfaction with support and size of support network). Parents subsequently reported their psychological distress in 3- and 9-month follow-up assessments. Parents' satisfaction with support had a direct effect on longer-term psychological distress; satisfaction was negatively associated with distress at both follow-ups. In contrast, size of support network buffered (moderated) the impact of trait anxiety and depression on later distress. Parents with smaller support networks and higher levels of trait anxiety and depression at baseline had higher levels of psychological distress at both follow-ups; for parents with larger support networks, there was no relationship. Social support can attenuate psychological distress in parents coping with pediatric cancer; however, the nature of the effect depends on the dimension of support. Whereas interventions that focus on increasing satisfaction with social support may benefit all parents, at-risk parents will likely benefit from interventions that ensure they have an adequate number of support resources. Copyright © 2015 John Wiley & Sons, Ltd.

Satisfaction with support versus size of network: Differential effects of social support on psychological distress in parents of pediatric cancer patients

PubMed Central

Harper, Felicity W. K.; Peterson, Amy M.; Albrecht, Terrance L.; Taub, Jeffrey W.; Phipps, Sean; Penner, Louis A.

2016-01-01

Objective This study examined the direct and the buffering effects of social support on longer-term global psychological distress among parents coping with pediatric cancer. In both sets of analyses we examined whether these effects depended on the dimension of social support provided (i.e., satisfaction with support versus size of support network). Method Participants were 102 parents of pediatric cancer patients. At study entry, parents reported their trait anxiety, depression, and two dimensions of their social support network (satisfaction with support and size of support network). Parents subsequently reported their psychological distress in 3- and 9-month follow-up assessments. Results Parents’ satisfaction with support had a direct effect on longer-term psychological distress; satisfaction was negatively associated with distress at both follow-ups. In contrast, size of support network buffered (moderated) the impact of trait anxiety and depression on later distress. Parents with smaller support networks and higher levels of trait anxiety and depression at baseline had higher levels of psychological distress at both follow-ups; for parents with larger support networks, there was no relationship. Conclusion Social support can attenuate psychological distress in parents coping with pediatric cancer; however, the nature of the effect depends on the dimension of support. Whereas, interventions that focus on increasing satisfaction with social support may benefit all parents, at-risk parents will likely benefit from interventions that ensure they have an adequate number of support resources. PMID:27092714

Structural complexity, movement bias, and metapopulation extinction risk in dendritic ecological networks

USGS Publications Warehouse

Campbell Grant, Evan H.

2011-01-01

Spatial complexity in metacommunities can be separated into 3 main components: size (i.e., number of habitat patches), spatial arrangement of habitat patches (network topology), and diversity of habitat patch types. Much attention has been paid to lattice-type networks, such as patch-based metapopulations, but interest in understanding ecological networks of alternative geometries is building. Dendritic ecological networks (DENs) include some increasingly threatened ecological systems, such as caves and streams. The restrictive architecture of dendritic ecological networks might have overriding implications for species persistence. I used a modeling approach to investigate how number and spatial arrangement of habitat patches influence metapopulation extinction risk in 2 DENs of different size and topology. Metapopulation persistence was higher in larger networks, but this relationship was mediated by network topology and the dispersal pathways used to navigate the network. Larger networks, especially those with greater topological complexity, generally had lower extinction risk than smaller and less-complex networks, but dispersal bias and magnitude affected the shape of this relationship. Applying these general results to real systems will require empirical data on the movement behavior of organisms and will improve our understanding of the implications of network complexity on population and community patterns and processes.

Experimental fault characterization of a neural network

NASA Technical Reports Server (NTRS)

Tan, Chang-Huong

1990-01-01

The effects of a variety of faults on a neural network is quantified via simulation. The neural network consists of a single-layered clustering network and a three-layered classification network. The percentage of vectors mistagged by the clustering network, the percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are all measured. The results show that both transient and permanent faults have a significant impact on the performance of the measured network. The corresponding mistag and misclassification percentages are typically within 5 to 10 percent of each other. The average mistag percentage and the average misclassification percentage are both about 25 percent. After relearning, the percentage of misclassifications is reduced to 9 percent. In addition, transient faults are found to cause the network to be increasingly unstable as the duration of a transient is increased. The impact of link faults is relatively insignificant in comparison with node faults (1 versus 19 percent misclassified after relearning). There is a linear increase in the mistag and misclassification percentages with decreasing hardware redundancy. In addition, the mistag and misclassification percentages linearly decrease with increasing network size.

Noise influence on spike activation in a Hindmarsh-Rose small-world neural network

NASA Astrophysics Data System (ADS)

Zhe, Sun; Micheletto, Ruggero

2016-07-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.

Rumor Spreading Model with Trust Mechanism in Complex Social Networks

NASA Astrophysics Data System (ADS)

Wang, Ya-Qi; Yang, Xiao-Yuan; Han, Yi-Liang; Wang, Xu-An

2013-04-01

In this paper, to study rumor spreading, we propose a novel susceptible-infected-removed (SIR) model by introducing the trust mechanism. We derive mean-field equations that describe the dynamics of the SIR model on homogeneous networks and inhomogeneous networks. Then a steady-state analysis is conducted to investigate the critical threshold and the final size of the rumor spreading. We show that the introduction of trust mechanism reduces the final rumor size and the velocity of rumor spreading, but increases the critical thresholds on both networks. Moreover, the trust mechanism not only greatly reduces the maximum rumor influence, but also postpones the rumor terminal time, which provides us with more time to take measures to control the rumor spreading. The theoretical results are confirmed by sufficient numerical simulations.

Settlement scaling and increasing returns in an ancient society

PubMed Central

Ortman, Scott G.; Cabaniss, Andrew H. F.; Sturm, Jennie O.; Bettencourt, Luís M. A.

2015-01-01

A key property of modern cities is increasing returns to scale—the finding that many socioeconomic outputs increase more rapidly than their population size. Recent theoretical work proposes that this phenomenon is the result of general network effects typical of human social networks embedded in space and, thus, is not necessarily limited to modern settlements. We examine the extent to which increasing returns are apparent in archaeological settlement data from the pre-Hispanic Basin of Mexico. We review previous work on the quantitative relationship between population size and average settled area in this society and then present a general analysis of their patterns of monument construction and house sizes. Estimated scaling parameter values and residual statistics support the hypothesis that increasing returns to scale characterized various forms of socioeconomic production available in the archaeological record and are found to be consistent with key expectations from settlement scaling theory. As a consequence, these results provide evidence that the essential processes that lead to increasing returns in contemporary cities may have characterized human settlements throughout history, and demonstrate that increasing returns do not require modern forms of political or economic organization. PMID:26601129

Determining average path length and average trapping time on generalized dual dendrimer

NASA Astrophysics Data System (ADS)

Li, Ling; Guan, Jihong

2015-03-01

Dendrimer has wide number of important applications in various fields. In some cases during transport or diffusion process, it transforms into its dual structure named Husimi cactus. In this paper, we study the structure properties and trapping problem on a family of generalized dual dendrimer with arbitrary coordination numbers. We first calculate exactly the average path length (APL) of the networks. The APL increases logarithmically with the network size, indicating that the networks exhibit a small-world effect. Then we determine the average trapping time (ATT) of the trapping process in two cases, i.e., the trap placed on a central node and the trap is uniformly distributed in all the nodes of the network. In both case, we obtain explicit solutions of ATT and show how they vary with the networks size. Besides, we also discuss the influence of the coordination number on trapping efficiency.

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

Chen, Dong; Heidelberger, Philip; Sugawara, Yutaka

An apparatus and method for extending the scalability and improving the partitionability of networks that contain all-to-all links for transporting packet traffic from a source endpoint to a destination endpoint with low per-endpoint (per-server) cost and a small number of hops. An all-to-all wiring in the baseline topology is decomposed into smaller all-to-all components in which each smaller all-to-all connection is replaced with star topology by using global switches. Stacking multiple copies of the star topology baseline network creates a multi-planed switching topology for transporting packet traffic. Point-to-point unified stacking method using global switch wiring methods connects multiple planes ofmore » a baseline topology by using the global switches to create a large network size with a low number of hops, i.e., low network latency. Grouped unified stacking method increases the scalability (network size) of a stacked topology.« less

Genetic attack on neural cryptography.

PubMed

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

2006-03-01

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

Neural Classifiers for Learning Higher-Order Correlations

NASA Astrophysics Data System (ADS)

Güler, Marifi

1999-01-01

Studies by various authors suggest that higher-order networks can be more powerful and are biologically more plausible with respect to the more traditional multilayer networks. These architectures make explicit use of nonlinear interactions between input variables in the form of higher-order units or product units. If it is known a priori that the problem to be implemented possesses a given set of invariances like in the translation, rotation, and scale invariant pattern recognition problems, those invariances can be encoded, thus eliminating all higher-order terms which are incompatible with the invariances. In general, however, it is a serious set-back that the complexity of learning increases exponentially with the size of inputs. This paper reviews higher-order networks and introduces an implicit representation in which learning complexity is mainly decided by the number of higher-order terms to be learned and increases only linearly with the input size.

Genetic attack on neural cryptography

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

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka

2006-03-15

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold formore » the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.« less

Genetic attack on neural cryptography

NASA Astrophysics Data System (ADS)

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

2006-03-01

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

Microstructure and Properties of (TiB2 + NiTi)/Ti Composite Coating Fabricated by Laser Cladding

NASA Astrophysics Data System (ADS)

Lin, Yinghua; Lei, Yongping; Fu, Hanguang; Lin, Jian

2015-10-01

Agglomerated TiB2 particle and network-like structure-reinforced titanium matrix composite coatings were prepared by laser cladding of the Ni + TiB2 + Ti preplaced powders on Ti-6Al-4V alloy. The network-like structure mainly consisted of NiTi and Ni3Ti. Through the experiment, it was found that the size of agglomerated particle gradually decreased with the increase of Ti content, but the number of the network-like structure first increased and then disappeared. In-situ reaction competition mechanism and the formation of network-like structure were discussed. The average micro-hardness gradually decreased with the increase of Ti content, but the average fracture toughness gradually increased. Meanwhile, the wear resistance of the coatings is higher than that of the substrate, but the wear loss of the coatings is gradually increased with the increase of Ti content.

The effect of nanowire length and diameter on the properties of transparent, conducting nanowire films

NASA Astrophysics Data System (ADS)

Bergin, Stephen M.; Chen, Yu-Hui; Rathmell, Aaron R.; Charbonneau, Patrick; Li, Zhi-Yuan; Wiley, Benjamin J.

2012-03-01

This article describes how the dimensions of nanowires affect the transmittance and sheet resistance of a random nanowire network. Silver nanowires with independently controlled lengths and diameters were synthesized with a gram-scale polyol synthesis by controlling the reaction temperature and time. Characterization of films composed of nanowires of different lengths but the same diameter enabled the quantification of the effect of length on the conductance and transmittance of silver nanowire films. Finite-difference time-domain calculations were used to determine the effect of nanowire diameter, overlap, and hole size on the transmittance of a nanowire network. For individual nanowires with diameters greater than 50 nm, increasing diameter increases the electrical conductance to optical extinction ratio, but the opposite is true for nanowires with diameters less than this size. Calculations and experimental data show that for a random network of nanowires, decreasing nanowire diameter increases the number density of nanowires at a given transmittance, leading to improved connectivity and conductivity at high transmittance (>90%). This information will facilitate the design of transparent, conducting nanowire films for flexible displays, organic light emitting diodes and thin-film solar cells.This article describes how the dimensions of nanowires affect the transmittance and sheet resistance of a random nanowire network. Silver nanowires with independently controlled lengths and diameters were synthesized with a gram-scale polyol synthesis by controlling the reaction temperature and time. Characterization of films composed of nanowires of different lengths but the same diameter enabled the quantification of the effect of length on the conductance and transmittance of silver nanowire films. Finite-difference time-domain calculations were used to determine the effect of nanowire diameter, overlap, and hole size on the transmittance of a nanowire network. For individual nanowires with diameters greater than 50 nm, increasing diameter increases the electrical conductance to optical extinction ratio, but the opposite is true for nanowires with diameters less than this size. Calculations and experimental data show that for a random network of nanowires, decreasing nanowire diameter increases the number density of nanowires at a given transmittance, leading to improved connectivity and conductivity at high transmittance (>90%). This information will facilitate the design of transparent, conducting nanowire films for flexible displays, organic light emitting diodes and thin-film solar cells. Electronic supplementary information (ESI) available: Includes methods and transmission spectra of nanowire films. See DOI: 10.1039/c2nr30126a

Social network analysis of children with autism spectrum disorder: Predictors of fragmentation and connectivity in elementary school classrooms

PubMed Central

Anderson, Ariana; Locke, Jill; Kretzmann, Mark; Kasari, Connie

2016-01-01

Although children with autism spectrum disorder are frequently included in mainstream classrooms, it is not known how their social networks change compared to typically developing children and whether the factors predictive of this change may be unique. This study identified and compared predictors of social connectivity of children with and without autism spectrum disorder using a social network analysis. Participants included 182 children with autism spectrum disorder and 152 children without autism spectrum disorder, aged 5–12 years in 152 general education K-5 classrooms. General linear models were used to compare how age, classroom size, gender, baseline connectivity, diagnosis, and intelligence quotient predicted changes in social connectivity (closeness). Gender and classroom size had a unique interaction in predicting final social connectivity and the change in connectivity for children with autism spectrum disorder; boys who were placed in larger classrooms showed increased social network fragmentation. This increased fragmentation for boys when placed in larger classrooms was not seen in typically developing boys. These results have implications regarding placement, intervention objectives, and ongoing school support that aimed to increase the social success of children with autism spectrum disorder in public schools. PMID:26567264

Network Interventions on Physical Activity in an Afterschool Program: An Agent-Based Social Network Study

PubMed Central

Zhang, Jun; Shoham, David A.; Tesdahl, Eric

2015-01-01

Objectives. We studied simulated interventions that leveraged social networks to increase physical activity in children. Methods. We studied a real-world social network of 81 children (average age = 7.96 years) who lived in low socioeconomic status neighborhoods, and attended public schools and 1 of 2 structured afterschool programs. The sample was ethnically diverse, and 44% were overweight or obese. We used social network analysis and agent-based modeling simulations to test whether implementing a network intervention would increase children’s physical activity. We tested 3 intervention strategies. Results. The intervention that targeted opinion leaders was effective in increasing the average level of physical activity across the entire network. However, the intervention that targeted the most sedentary children was the best at increasing their physical activity levels. Conclusions. Which network intervention to implement depends on whether the goal is to shift the entire distribution of physical activity or to influence those most adversely affected by low physical activity. Agent-based modeling could be an important complement to traditional project planning tools, analogous to sample size and power analyses, to help researchers design more effective interventions for increasing children’s physical activity. PMID:25689202

Mandala Networks: ultra-small-world and highly sparse graphs

PubMed Central

Sampaio Filho, Cesar I. N.; Moreira, André A.; Andrade, Roberto F. S.; Herrmann, Hans J.; Andrade, José S.

2015-01-01

The increasing demands in security and reliability of infrastructures call for the optimal design of their embedded complex networks topologies. The following question then arises: what is the optimal layout to fulfill best all the demands? Here we present a general solution for this problem with scale-free networks, like the Internet and airline networks. Precisely, we disclose a way to systematically construct networks which are robust against random failures. Furthermore, as the size of the network increases, its shortest path becomes asymptotically invariant and the density of links goes to zero, making it ultra-small world and highly sparse, respectively. The first property is ideal for communication and navigation purposes, while the second is interesting economically. Finally, we show that some simple changes on the original network formulation can lead to an improved topology against malicious attacks. PMID:25765450

Stream network geomorphology mediates predicted vulnerability of anadromous fish habitat to hydrologic change in southeast Alaska

Treesearch

Matthew R. Sloat; Gordon H. Reeves; Kelly R. Christiansen

2016-01-01

In rivers supporting Pacific salmon in southeast Alaska, USA, regional trends toward a warmer, wetter climate are predicted to increase mid- and late-21st-century mean annual flood size by 17% and 28%, respectively. Increased flood size could alter stream habitats used by Pacific salmon for reproduction, with negative consequences for the substantial economic, cultural...

Optimization of Turbine Blade Design for Reusable Launch Vehicles

NASA Technical Reports Server (NTRS)

Shyy, Wei

1998-01-01

To facilitate design optimization of turbine blade shape for reusable launching vehicles, appropriate techniques need to be developed to process and estimate the characteristics of the design variables and the response of the output with respect to the variations of the design variables. The purpose of this report is to offer insight into developing appropriate techniques for supporting such design and optimization needs. Neural network and polynomial-based techniques are applied to process aerodynamic data obtained from computational simulations for flows around a two-dimensional airfoil and a generic three- dimensional wing/blade. For the two-dimensional airfoil, a two-layered radial-basis network is designed and trained. The performances of two different design functions for radial-basis networks, one based on the accuracy requirement, whereas the other one based on the limit on the network size. While the number of neurons needed to satisfactorily reproduce the information depends on the size of the data, the neural network technique is shown to be more accurate for large data set (up to 765 simulations have been used) than the polynomial-based response surface method. For the three-dimensional wing/blade case, smaller aerodynamic data sets (between 9 to 25 simulations) are considered, and both the neural network and the polynomial-based response surface techniques improve their performance as the data size increases. It is found while the relative performance of two different network types, a radial-basis network and a back-propagation network, depends on the number of input data, the number of iterations required for radial-basis network is less than that for the back-propagation network.

Fat fractal scaling of drainage networks from a random spatial network model

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1992-01-01

An alternative quantification of the scaling properties of river channel networks is explored using a spatial network model. Whereas scaling descriptions of drainage networks previously have been presented using a fractal analysis primarily of the channel lengths, we illustrate the scaling of the surface area of the channels defining the network pattern with an exponent which is independent of the fractal dimension but not of the fractal nature of the network. The methodology presented is a fat fractal analysis in which the drainage basin minus the channel area is considered the fat fractal. Random channel networks within a fixed basin area are generated on grids of different scales. The sample channel networks generated by the model have a common outlet of fixed width and a rule of upstream channel narrowing specified by a diameter branching exponent using hydraulic and geomorphologic principles. Scaling exponents are computed for each sample network on a given grid size and are regressed against network magnitude. Results indicate that the size of the exponents are related to magnitude of the networks and generally decrease as network magnitude increases. Cases showing differences in scaling exponents with like magnitudes suggest a direction of future work regarding other topologic basin characteristics as potential explanatory variables.

Dynamic vs. static social networks in models of parasite transmission: predicting Cryptosporidium spread in wild lemurs.

PubMed

Springer, Andrea; Kappeler, Peter M; Nunn, Charles L

2017-05-01

Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real-world populations. Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (Propithecus verreauxi), and simulated networks to investigate pathogen spread in dynamic vs. static social networks. First, we constructed a susceptible-exposed-infected-recovered model of Cryptosporidium spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks. We found that average outbreak size of Cryptosporidium infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery. Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between-group contact after an infectious agent has been introduced. © 2016 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Dendritic nonlinearities reduce network size requirements and mediate ON and OFF states of persistent activity in a PFC microcircuit model.

PubMed

Papoutsi, Athanasia; Sidiropoulou, Kyriaki; Poirazi, Panayiota

2014-07-01

Technological advances have unraveled the existence of small clusters of co-active neurons in the neocortex. The functional implications of these microcircuits are in large part unexplored. Using a heavily constrained biophysical model of a L5 PFC microcircuit, we recently showed that these structures act as tunable modules of persistent activity, the cellular correlate of working memory. Here, we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes. We show that (a) NMDA-mediated dendritic spikes gate the induction of persistent firing in the microcircuit. (b) The minimum network size required for persistent activity induction is inversely proportional to the synaptic drive of each excitatory neuron. (c) Relaxation of connectivity and synaptic delay constraints eliminates the gating effect of NMDA spikes, albeit at a cost of much larger networks. (d) Persistent activity termination by increased inhibition depends on the strength of the synaptic input and is negatively modulated by dADP. (e) Slow synaptic mechanisms and network activity contain predictive information regarding the ability of a given stimulus to turn ON and/or OFF persistent firing in the microcircuit model. Overall, this study zooms out from dendrites to cell assemblies and suggests a tight interaction between dendritic non-linearities and network properties (size/connectivity) that may facilitate the short-memory function of the PFC.

Infrared spectroscopy of phenol-(H2O)(n>10): structural strains in hydrogen bond networks of neutral water clusters.

PubMed

Mizuse, Kenta; Hamashima, Toru; Fujii, Asuka

2009-11-05

To investigate hydrogen bond network structures of tens of water molecules, we report infrared spectra of moderately size (n)-selected phenol-(H2O)n (approximately 10 < or = n < or = approximately 50), which have essentially the same network structures as (H2O)(n+1). The phenyl group in phenol-(H2O)(n) allows us to apply photoionization-based size selection and infrared-ultraviolet double resonance spectroscopy. The spectra show a clear low-frequency shift of the free OH stretching band with increasing n. Detailed analyses with density functional theory calculations indicate that this shift is accounted for by the hydrogen bond network development from highly strained ones in the small (n < approximately 10) clusters to more relaxed ones in the larger clusters, in addition to the cooperativity of hydrogen bonds.

Micro- and nano-X-ray computed-tomography: A step forward in the characterization of the pore network of a leached cement paste

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

Bossa, Nathan, E-mail: bossanathan@gmail.com; INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte; iCEINT, CNRS, Duke Univ. International Consortium for the Environmental Implications of Nanotechnology, Aix-en-Provence

2015-01-15

Pore structure of leached cement pastes (w/c = 0.5) was studied for the first time from micro-scale down to the nano-scale by combining micro- and nano-X-ray computed tomography (micro- and nano-CT). This allowed assessing the 3D heterogeneity of the pore network along the cement profile (from the core to the altered layer) of almost the entire range of cement pore size, i.e. from capillary to gel pores. We successfully quantified an increase of porosity in the altered layer at both resolutions. Porosity is increasing from 1.8 to 6.1% and from 18 to 58% at the micro-(voxel = 1.81 μm) andmore » nano-scale (voxel = 63.5 nm) respectively. The combination of both CT allowed to circumvent weaknesses inherent of both investigation scales. In addition the connectivity and the channel size of the pore network were also evaluated to obtain a complete 3D pore network characterization at both scales.« less

Distribution of genotype network sizes in sequence-to-structure genotype-phenotype maps.

PubMed

Manrubia, Susanna; Cuesta, José A

2017-04-01

An essential quantity to ensure evolvability of populations is the navigability of the genotype space. Navigability, understood as the ease with which alternative phenotypes are reached, relies on the existence of sufficiently large and mutually attainable genotype networks. The size of genotype networks (e.g. the number of RNA sequences folding into a particular secondary structure or the number of DNA sequences coding for the same protein structure) is astronomically large in all functional molecules investigated: an exhaustive experimental or computational study of all RNA folds or all protein structures becomes impossible even for moderately long sequences. Here, we analytically derive the distribution of genotype network sizes for a hierarchy of models which successively incorporate features of increasingly realistic sequence-to-structure genotype-phenotype maps. The main feature of these models relies on the characterization of each phenotype through a prototypical sequence whose sites admit a variable fraction of letters of the alphabet. Our models interpolate between two limit distributions: a power-law distribution, when the ordering of sites in the prototypical sequence is strongly constrained, and a lognormal distribution, as suggested for RNA, when different orderings of the same set of sites yield different phenotypes. Our main result is the qualitative and quantitative identification of those features of sequence-to-structure maps that lead to different distributions of genotype network sizes. © 2017 The Author(s).

Hydrophobic-Interaction-Induced Stiffening of α -Synuclein Fibril Networks

NASA Astrophysics Data System (ADS)

Semerdzhiev, Slav A.; Lindhoud, Saskia; Stefanovic, Anja; Subramaniam, Vinod; van der Schoot, Paul; Claessens, Mireille M. A. E.

2018-05-01

In water, networks of semiflexible fibrils of the protein α -synuclein stiffen significantly with increasing temperature. We make plausible that this reversible stiffening is a result of hydrophobic contacts between the fibrils that become more prominent with increasing temperature. The good agreement of our experimentally observed temperature dependence of the storage modulus of the network with a scaling theory linking network elasticity with reversible cross-linking enables us to quantify the endothermic binding enthalpy and estimate the effective size of hydrophobic patches on the fibril surface. Our findings may not only shed light on the role of amyloid deposits in disease conditions, but can also inspire new approaches for the design of thermoresponsive materials.

Hydrophobic-Interaction-Induced Stiffening of α-Synuclein Fibril Networks.

PubMed

Semerdzhiev, Slav A; Lindhoud, Saskia; Stefanovic, Anja; Subramaniam, Vinod; van der Schoot, Paul; Claessens, Mireille M A E

2018-05-18

In water, networks of semiflexible fibrils of the protein α-synuclein stiffen significantly with increasing temperature. We make plausible that this reversible stiffening is a result of hydrophobic contacts between the fibrils that become more prominent with increasing temperature. The good agreement of our experimentally observed temperature dependence of the storage modulus of the network with a scaling theory linking network elasticity with reversible cross-linking enables us to quantify the endothermic binding enthalpy and estimate the effective size of hydrophobic patches on the fibril surface. Our findings may not only shed light on the role of amyloid deposits in disease conditions, but can also inspire new approaches for the design of thermoresponsive materials.

Network bandwidth utilization forecast model on high bandwidth networks

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

Yoo, Wuchert; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

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

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Naming game with biased assimilation over adaptive networks

NASA Astrophysics Data System (ADS)

Fu, Guiyuan; Zhang, Weidong

2018-01-01

The dynamics of two-word naming game incorporating the influence of biased assimilation over adaptive network is investigated in this paper. Firstly an extended naming game with biased assimilation (NGBA) is proposed. The hearer in NGBA accepts the received information in a biased manner, where he may refuse to accept the conveyed word from the speaker with a predefined probability, if the conveyed word is different from his current memory. Secondly, the adaptive network is formulated by rewiring the links. Theoretical analysis is developed to show that the population in NGBA will eventually reach global consensus on either A or B. Numerical simulation results show that the larger strength of biased assimilation on both words, the slower convergence speed, while larger strength of biased assimilation on only one word can slightly accelerate the convergence; larger population size can make the rate of convergence slower to a large extent when it increases from a relatively small size, while such effect becomes minor when the population size is large; the behavior of adaptively reconnecting the existing links can greatly accelerate the rate of convergence especially on the sparse connected network.

Theoretical study of network design methodologies for the aerial relay system. [energy consumption and air traffic control

NASA Technical Reports Server (NTRS)

Rivera, J. M.; Simpson, R. W.

1980-01-01

The aerial relay system network design problem is discussed. A generalized branch and bound based algorithm is developed which can consider a variety of optimization criteria, such as minimum passenger travel time and minimum liner and feeder operating costs. The algorithm, although efficient, is basically useful for small size networks, due to its nature of exponentially increasing computation time with the number of variables.

Power Conservation through Energy Efficient Routing in Wireless Sensor Networks.

PubMed

Kandris, Dionisis; Tsioumas, Panagiotis; Tzes, Anthony; Nikolakopoulos, George; Vergados, Dimitrios D

2009-01-01

The power awareness issue is the primary concern within the domain of Wireless Sensor Networks (WSNs). Most power dissipation ocurrs during communication, thus routing protocols in WSNs mainly aim at power conservation. Moreover, a routing protocol should be scalable, so that its effectiveness does not degrade as the network size increases. In response to these issues, this work describes the development of an efficient routing protocol, named SHPER (Scaling Hierarchical Power Efficient Routing).

Duplicate retention in signalling proteins and constraints from network dynamics.

PubMed

Soyer, O S; Creevey, C J

2010-11-01

Duplications are a major driving force behind evolution. Most duplicates are believed to fix through genetic drift, but it is not clear whether this process affects all duplications equally or whether there are certain gene families that are expected to show neutral expansions under certain circumstances. Here, we analyse the neutrality of duplications in different functional classes of signalling proteins based on their effects on response dynamics. We find that duplications involving intermediary proteins in a signalling network are neutral more often than those involving receptors. Although the fraction of neutral duplications in all functional classes increase with decreasing population size and selective pressure on dynamics, this effect is most pronounced for receptors, indicating a possible expansion of receptors in species with small population size. In line with such an expectation, we found a statistically significant increase in the number of receptors as a fraction of genome size in eukaryotes compared with prokaryotes. Although not confirmative, these results indicate that neutral processes can be a significant factor in shaping signalling networks and affect proteins from different functional classes differently. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.

Optimal Sizing and Placement of Battery Energy Storage in Distribution System Based on Solar Size for Voltage Regulation

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

Nazaripouya, Hamidreza; Wang, Yubo; Chu, Peter

2016-07-26

This paper proposes a new strategy to achieve voltage regulation in distributed power systems in the presence of solar energy sources and battery storage systems. The goal is to find the minimum size of battery storage and its corresponding location in the network based on the size and place of the integrated solar generation. The proposed method formulates the problem by employing the network impedance matrix to obtain an analytical solution instead of using a recursive algorithm such as power flow. The required modifications for modeling the slack and PV buses (generator buses) are utilized to increase the accuracy ofmore » the approach. The use of reactive power control to regulate the voltage regulation is not always an optimal solution as in distribution systems R/X is large. In this paper the minimum size and the best place of battery storage is achieved by optimizing the amount of both active and reactive power exchanged by battery storage and its gridtie inverter (GTI) based on the network topology and R/X ratios in the distribution system. Simulation results for the IEEE 14-bus system verify the effectiveness of the proposed approach.« less

Impact of the intermixed phase and the channel network on the carrier mobility of nanostructured solar cells

NASA Astrophysics Data System (ADS)

Woellner, Cristiano F.; Freire, José A.

2016-02-01

We analyzed the impact of the complex channel network of donor and acceptor domains in nanostructured solar cells on the mobility of the charge carriers moving by thermally activated hopping. Particular attention was given to the so called intermixed phase, or interface roughness, that has recently been shown to promote an increase in the cell efficiency. The domains were obtained from a Monte Carlo simulation of a two-species lattice gas. We generated domain morphologies with controllable channel size and interface roughness. The field and density dependence of the carrier hopping mobility in different morphologies was obtained by solving a master equation. Our results show that the mobility decreases with roughness and increases with typical channel sizes. The deleterious effect of the roughness on the mobility is quite dramatic at low carrier densities and high fields. The complex channel network is shown to be directly responsible for two potentially harmful effects to the cell performance: a remarkable decrease of the mobility with increasing field and the accumulation of charge at the domains interface, which leads to recombination losses.

Left-right analysis of mammary gland development in retinoid X receptor-α+/- mice.

PubMed

Robichaux, Jacqulyne P; Fuseler, John W; Patel, Shrusti S; Kubalak, Steven W; Hartstone-Rose, Adam; Ramsdell, Ann F

2016-12-19

Left-right (L-R) differences in mammographic parenchymal patterns are an early predictor of breast cancer risk; however, the basis for this asymmetry is unknown. Here, we use retinoid X receptor alpha heterozygous null (RXRα +/- ) mice to propose a developmental origin: perturbation of coordinated anterior-posterior (A-P) and L-R axial body patterning. We hypothesized that by analogy to somitogenesis-in which retinoic acid (RA) attenuation causes anterior somite pairs to develop L-R asynchronously-that RA pathway perturbation would likewise result in asymmetric mammary development. To test this, mammary glands of RXRα +/- mice were quantitatively assessed to compare left- versus right-side ductal epithelial networks. Unlike wild-type controls, half of the RXRα +/- thoracic mammary gland (TMG) pairs exhibited significant L-R asymmetry, with left-side reduction in network size. In RXRα +/- TMGs in which symmetry was maintained, networks had bilaterally increased size, with left networks showing greater variability in area and pattern. Reminiscent of posterior somites, whose bilateral symmetry is refractory to RA attenuation, inguinal mammary glands (IMGs) also had bilaterally increased network size, but no loss of symmetry. Together, these results demonstrate that mammary glands exhibit differential A-P sensitivity to RXRα heterozygosity, with ductal network symmetry markedly compromised in anterior but not posterior glands. As TMGs more closely model human breast development than IMGs, these findings raise the possibility that for some women, breast cancer risk may initiate with subtle axial patterning defects that result in L-R asymmetric growth and pattern of the mammary ductal epithelium.This article is part of the themed issue 'Provocative questions in left-right asymmetry'. © 2016 The Author(s).

Design of a MIMD neural network processor

NASA Astrophysics Data System (ADS)

Saeks, Richard E.; Priddy, Kevin L.; Pap, Robert M.; Stowell, S.

1994-03-01

The Accurate Automation Corporation (AAC) neural network processor (NNP) module is a fully programmable multiple instruction multiple data (MIMD) parallel processor optimized for the implementation of neural networks. The AAC NNP design fully exploits the intrinsic sparseness of neural network topologies. Moreover, by using a MIMD parallel processing architecture one can update multiple neurons in parallel with efficiency approaching 100 percent as the size of the network increases. Each AAC NNP module has 8 K neurons and 32 K interconnections and is capable of 140,000,000 connections per second with an eight processor array capable of over one billion connections per second.

Solar Coronal Heating and the Magnetic Flux Content of the Network

NASA Technical Reports Server (NTRS)

Falconer, D. A.; Moore, R. L.; Porter, J. G.; Hathaway, D. H.

2003-01-01

We investigate the heating of the quiet corona by measuring the increase of coronal luminosity with the amount of magnetic flux in the underlying network at solar minimum when there were no active regions on the face of the Sun. The coronal luminosity is measured from Fe IX/X-Fe XII pairs of coronal images from SOHO/EIT. The network magnetic flux content is measured from SOHO/MDI magnetograms. We find that the luminosity of the corona in our quiet regions increases roughly in proportion to the square root of the magnetic flux content of the network and roughly in proportion to the length of the perimeter of the network magnetic flux clumps. From (1) this result, (2) other observations of many fine-scale explosive events at the edges of network flux clumps, and (3) a demonstration that it is energetically feasible for the heating of the corona in quiet regions to be driven by explosions of granule-sized sheared-core magnetic bipoles embedded in the edges of network flux clumps, we infer that in quiet regions that are not influenced by active regions the corona is mainly heated by such magnetic activity in the edges of the network flux clumps. Our observational results together with our feasibility analysis allow us to predict that (1) at the edges of the network flux clumps there are many transient sheared-core bipoles of the size and lifetime of granules and having transverse field strengths > approx. 100 G, (2) approx. 30 of these bipoles are present per supergranule, and (3) most spicules are produced by explosions of these bipoles.

Solar Coronal Heating and the Magnetic Flux Content of the Network

NASA Technical Reports Server (NTRS)

Moore, R. L.; Falconer, D. A.; Porter, J. G.; Hathaway, D. H.

2003-01-01

We investigate the heating of the quiet corona by measuring the increase of coronal luminosity with the amount of magnetic flux in the underlying network at solar minimum when there were no active regions on the face of the Sun. The coronal luminosity is measured from Fe IX/X-Fe XII pairs of coronal images from SOHO/EIT. The network magnetic flux content is measured from SOHO/MDI magnetograms. We find that the luminosity of the corona in our quiet regions increases roughly in proportion to the square root of the magnetic flux content of the network and roughly in proportion to the length of the perimeter of the network magnetic flux clumps. From (1) this result, (2) other observations of many fine-scale explosive events at the edges of network flux clumps, and (3) a demonstration that it is energetically feasible for the heating of the corona in quiet regions to be driven by explosions of granule-sized sheared-core magnetic bipoles embedded in the edges of network flux clumps, we infer that in quiet regions that are not influenced by active regions the corona is mainly heated by such magnetic activity in the edges of the network flux clumps. Our observational results together with our feasibility analysis allow us to predict that (1) at the edges of the network flux clumps there are many transient sheared-core bipoles of the size and lifetime of granules and having transverse field strengths greater than approximately - 100 G, (2) approximately 30 of these bipoles are present per supergranule, and (3) most spicules are produced by explosions of these bipoles.

Breakdown of interdependent directed networks.

PubMed

Liu, Xueming; Stanley, H Eugene; Gao, Jianxi

2016-02-02

Increasing evidence shows that real-world systems interact with one another via dependency connectivities. Failing connectivities are the mechanism behind the breakdown of interacting complex systems, e.g., blackouts caused by the interdependence of power grids and communication networks. Previous research analyzing the robustness of interdependent networks has been limited to undirected networks. However, most real-world networks are directed, their in-degrees and out-degrees may be correlated, and they are often coupled to one another as interdependent directed networks. To understand the breakdown and robustness of interdependent directed networks, we develop a theoretical framework based on generating functions and percolation theory. We find that for interdependent Erdős-Rényi networks the directionality within each network increases their vulnerability and exhibits hybrid phase transitions. We also find that the percolation behavior of interdependent directed scale-free networks with and without degree correlations is so complex that two criteria are needed to quantify and compare their robustness: the percolation threshold and the integrated size of the giant component during an entire attack process. Interestingly, we find that the in-degree and out-degree correlations in each network layer increase the robustness of interdependent degree heterogeneous networks that most real networks are, but decrease the robustness of interdependent networks with homogeneous degree distribution and with strong coupling strengths. Moreover, by applying our theoretical analysis to real interdependent international trade networks, we find that the robustness of these real-world systems increases with the in-degree and out-degree correlations, confirming our theoretical analysis.

Effect of Cross-Linking on Free Volume Properties of PEG Based Thiol-Ene Networks

NASA Astrophysics Data System (ADS)

Ramakrishnan, Ramesh; Vasagar, Vivek; Nazarenko, Sergei

According to the Fox and Loshaek theory, in elastomeric networks, free volume decreases linearly with the cross-link density increase. The aim of this study is to show whether the poly(ethylene glycol) (PEG) based multicomponent thiol-ene elastomeric networks demonstrate this model behavior? Networks with a broad cross-link density range were prepared by changing the ratio of the trithiol crosslinker to PEG dithiol and then UV cured with PEG diene while maintaining 1:1 thiol:ene stoichiometry. Pressure-volume-temperature (PVT) data of the networks was generated from the high pressure dilatometry experiments which was fit using the Simha-Somcynsky Equation-of-State analysis to obtain the fractional free volume of the networks. Using Positron Annihilation Lifetime Spectroscopy (PALS) analysis, the average free volume hole size of the networks was also quantified. The fractional free volume and the average free volume hole size showed a linear change with the cross-link density confirming that the Fox and Loshaek theory can be applied to this multicomponent system. Gas diffusivities of the networks showed a good correlation with free volume. A free volume based model was developed to describe the gas diffusivity trends as a function of cross-link density.

Facebook, stress, and incidence of upper respiratory infection in undergraduate college students.

PubMed

Campisi, Jay; Bynog, Pamela; McGehee, Hope; Oakland, Joshua C; Quirk, Shannon; Taga, Carlee; Taylor, Morgan

2012-12-01

Having a large social network is generally beneficial to health. However, it is unclear how Internet-based social networks might influence health. Chronic stress can have negative health consequences, and some data suggest that Facebook could be a new source of psychological stress. Thus, we examined undergraduate college student perceptions of Facebook use and incidence of upper respiratory infections (URIs). We hypothesized that subjects with more diverse networks (i.e., more friends on Facebook) would have fewer URIs than their less diverse counterparts; that subjects reporting Facebook-induced stress would be more susceptible to URIs; and that subjects with more diverse networks who report Facebook-induced stress would be less susceptible to URIs than subjects with less diverse social networks who reported Facebook-induced stress. In this prospective study, healthy college students completed online questionnaires that assessed use and perceptions of Facebook and technology, and then were interviewed weekly for 10 weeks to track incidence of URI. URI episodes were defined by a symptom-based criterion. The social network size was significantly related to the rate of URI, such that, the larger the social network, the greater the incidence rate of URI. Most (85.7 percent) respondents experienced some degree of Facebook-induced stress. The effects of Facebook-induced stress on incidence of URI varied across the social network size, such that, the impact of stress on the URI incidence rate increased with the size of the social network. These results are largely in contrast to our hypotheses, but clearly suggest an association between Facebook use, psychological stress, and health.

Supporting large scale applications on networks of workstations

NASA Technical Reports Server (NTRS)

Cooper, Robert; Birman, Kenneth P.

1989-01-01

Distributed applications on networks of workstations are an increasingly common way to satisfy computing needs. However, existing mechanisms for distributed programming exhibit poor performance and reliability as application size increases. Extension of the ISIS distributed programming system to support large scale distributed applications by providing hierarchical process groups is discussed. Incorporation of hierarchy in the program structure and exploitation of this to limit the communication and storage required in any one component of the distributed system is examined.

Damage spreading in spatial and small-world random Boolean networks

NASA Astrophysics Data System (ADS)

Lu, Qiming; Teuscher, Christof

2014-02-01

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities (K¯≪1) and that the critical connectivity of stability Ks changes compared to random networks. At higher K¯, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size N increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

Stable and pH-responsive core-shell nanoparticles based on HEC and PMAA networks via template copolymerization

NASA Astrophysics Data System (ADS)

Zhang, Y.; Jin, Q.; Chen, Y.; Zhao, J.

2011-10-01

Taking advantage of the specific hydrogen bonding interactions, stable and pH-responsive core-shell nanoparticles based on hydroxyethyl cellulose (HEC) and polymethacrylic acid (PMAA) networks, with a < D h > size ranging from 190 to 250 nm, can be efficiently prepared via facile one-step co-polymerization of methacrylic acid (MAA) and N, N'-methylenebisacrylamide (MBA) on HEC template in water. Using dynamic light scattering, electrophoretic light scattering, fluorescence spectrometry, thermo-gravimetric analysis, TEM, and AFM observations, the influence of crosslinker MBA as well as the reaction parameters were studied. The results show that after the introduction of crosslinker MBA, the nanoparticles became less compact; their size exhibited a smaller pH sensitivity, and their stability against pH value was improved greatly. Furthermore, the size, structure, and pH response of the nanoparticles can be adjusted via varying the reaction parameters: nanoparticles of smaller size, more compact structure, and higher swelling capacity were produced as pH value of the reaction medium increased or the HEC/MAA ratio decreased; while nanoparticles of smaller size, less compact structure and smaller swelling capacity were produced as the total feeding concentration increased.

Size, Loading Efficiency, and Cytotoxicity of Albumin-Loaded Chitosan Nanoparticles: An Artificial Neural Networks Study.

PubMed

Baharifar, Hadi; Amani, Amir

2017-01-01

When designing nanoparticles for drug delivery, many variables such as size, loading efficiency, and cytotoxicity should be considered. Usually, smaller particles are preferred in drug delivery because of longer blood circulation time and their ability to escape from immune system, whereas smaller nanoparticles often show increased toxicity. Determination of parameters which affect size of particles and factors such as loading efficiency and cytotoxicity could be very helpful in designing drug delivery systems. In this work, albumin (as a protein drug model)-loaded chitosan nanoparticles were prepared by polyelectrolyte complexation method. Simultaneously, effects of 4 independent variables including chitosan and albumin concentrations, pH, and reaction time were determined on 3 dependent variables (i.e., size, loading efficiency, and cytotoxicity) by artificial neural networks. Results showed that concentrations of initial materials are the most important factors which may affect the dependent variables. A drop in the concentrations decreases the size directly, but they simultaneously decrease loading efficiency and increase cytotoxicity. Therefore, an optimization of the independent variables is required to obtain the most useful preparation. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Urban ecological stewardship: understanding the structure, function and network of community-based urban land management

Treesearch

Erika s. Svendsen; Lindsay K. Campbell

2008-01-01

Urban environmental stewardship activities are on the rise in cities throughout the Northeast. Groups participating in stewardship activities range in age, size, and geography and represent an increasingly complex and dynamic arrangement of civil society, government and business sectors. To better understand the structure, function and network of these community-based...

Energy-saving scheme based on downstream packet scheduling in ethernet passive optical networks

NASA Astrophysics Data System (ADS)

Zhang, Lincong; Liu, Yejun; Guo, Lei; Gong, Xiaoxue

2013-03-01

With increasing network sizes, the energy consumption of Passive Optical Networks (PONs) has grown significantly. Therefore, it is important to design effective energy-saving schemes in PONs. Generally, energy-saving schemes have focused on sleeping the low-loaded Optical Network Units (ONUs), which tends to bring large packet delays. Further, the traditional ONU sleep modes are not capable of sleeping the transmitter and receiver independently, though they are not required to transmit or receive packets. Clearly, this approach contributes to wasted energy. Thus, in this paper, we propose an Energy-Saving scheme that is based on downstream Packet Scheduling (ESPS) in Ethernet PON (EPON). First, we design both an algorithm and a rule for downstream packet scheduling at the inter- and intra-ONU levels, respectively, to reduce the downstream packet delay. After that, we propose a hybrid sleep mode that contains not only ONU deep sleep mode but also independent sleep modes for the transmitter and the receiver. This ensures that the energy consumed by the ONUs is minimal. To realize the hybrid sleep mode, a modified GATE control message is designed that involves 10 time points for sleep processes. In ESPS, the 10 time points are calculated according to the allocated bandwidths in both the upstream and the downstream. The simulation results show that ESPS outperforms traditional Upstream Centric Scheduling (UCS) scheme in terms of energy consumption and the average delay for both real-time and non-real-time packets downstream. The simulation results also show that the average energy consumption of each ONU in larger-sized networks is less than that in smaller-sized networks; hence, our ESPS is better suited for larger-sized networks.

Localization of diffusion sources in complex networks with sparse observations

NASA Astrophysics Data System (ADS)

Hu, Zhao-Long; Shen, Zhesi; Tang, Chang-Bing; Xie, Bin-Bin; Lu, Jian-Feng

2018-04-01

Locating sources in a large network is of paramount importance to reduce the spreading of disruptive behavior. Based on the backward diffusion-based method and integer programming, we propose an efficient approach to locate sources in complex networks with limited observers. The results on model networks and empirical networks demonstrate that, for a certain fraction of observers, the accuracy of our method for source localization will improve as the increase of network size. Besides, compared with the previous method (the maximum-minimum method), the performance of our method is much better with a small fraction of observers, especially in heterogeneous networks. Furthermore, our method is more robust against noise environments and strategies of choosing observers.

Classification of urine sediment based on convolution neural network

NASA Astrophysics Data System (ADS)

Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

2018-04-01

By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

Biphasic response of cell invasion to matrix stiffness in 3-dimensional biopolymer networks

PubMed Central

Lang, Nadine R.; Skodzek, Kai; Hurst, Sebastian; Mainka, Astrid; Steinwachs, Julian; Schneider, Julia; Aifantis, Katerina E.; Fabry, Ben

2015-01-01

When cells come in contact with an adhesive matrix, they begin to spread and migrate with a speed that depends on the stiffness of the extracellular matrix. On a flat surface, migration speed decreases with matrix stiffness mainly due to an increased stability of focal adhesions. In a 3-dimensional (3D) environment, cell migration is thought to be additionally impaired by the steric hindrance imposed by the surrounding matrix. For porous 3D biopolymer networks such as collagen gels, however, the effect of matrix stiffness on cell migration is difficult to separate from effects of matrix pore size and adhesive ligand density, and is therefore unknown. Here we used glutaraldehyde as a crosslinker to increase the stiffness of self-assembled collagen biopolymer networks independently of collagen concentration or pore size. Breast carcinoma cells were seeded onto the surface of 3D collagen gels, and the invasion depth was measured after 3 days of culture. Cell invasion in gels with pore sizes larger than 5 μm increased with higher gel stiffness, whereas invasion in gels with smaller pores decreased with higher gel stiffness. These data show that 3D cell invasion is enhanced by higher matrix stiffness, opposite to cell behavior in 2D, as long as the pore size does not fall below a critical value where it causes excessive steric hindrance. These findings may be important for optimizing the recellularization of soft tissue implants or for the design of 3D invasion models in cancer research. PMID:25462839

Network marketing on a small-world network

NASA Astrophysics Data System (ADS)

Kim, Beom Jun; Jun, Tackseung; Kim, Jeong-Yoo; Choi, M. Y.

2006-02-01

We investigate a dynamic model of network marketing in a small-world network structure artificially constructed similarly to the Watts-Strogatz network model. Different from the traditional marketing, consumers can also play the role of the manufacturer's selling agents in network marketing, which is stimulated by the referral fee the manufacturer offers. As the wiring probability α is increased from zero to unity, the network changes from the one-dimensional regular directed network to the star network where all but one player are connected to one consumer. The price p of the product and the referral fee r are used as free parameters to maximize the profit of the manufacturer. It is observed that at α=0 the maximized profit is constant independent of the network size N while at α≠0, it increases linearly with N. This is in parallel to the small-world transition. It is also revealed that while the optimal value of p stays at an almost constant level in a broad range of α, that of r is sensitive to a change in the network structure. The consumer surplus is also studied and discussed.

Can mental health interventions change social networks? A systematic review.

PubMed

Anderson, Kimberley; Laxhman, Neelam; Priebe, Stefan

2015-11-21

Social networks of patients with psychosis can provide social support, and improve health and social outcomes, including quality of life. However, patients with psychosis often live rather isolated with very limited social networks. Evidence for interventions targeting symptoms or social skills, are largely unsuccessful at improving social networks indirectly. As an alternative, interventions may directly focus on expanding networks. In this systematic review, we assessed what interventions have previously been tested for this and to what extent they have been effective. A systematic review was conducted of randomised controlled trials, testing psychosocial interventions designed to directly increase the social networks of patients with psychosis. Searches of five online databases (PsycINFO, CINAHL, Cochrane Database, MEDLINE, Embase), hand searching of grey literature, and both forward and backward snowballing of key papers were conducted and completed on 12 December 2014. Trial reports were included if they were written in English, the social network size was the primary outcome, participants were ≥ 18 years old and diagnosed with a psychotic disorder. Five studies (n = 631 patients) met the complete inclusion criteria. Studies were from different countries and published since 2008. Four trials had significant positive results, i.e. an observable increase in patients' social network size at the end of the intervention. The interventions included: guided peer support, a volunteer partner scheme, supported engagement in social activity, dog-assisted integrative psychological therapy and psychosocial skills training. Other important elements featured were the presence of a professional, and a focus on friendships and peers outside of services and the immediate family. Despite the small number and heterogeneity of included studies, the results suggest that interventions directly targeting social isolation can be effective and achieve a meaningful increase in patients' networks. Thus, although limited, the existing evidence is encouraging, and the range of interventions used in the reported trials leave various options for future research and further improvements. Future research is needed to test the findings in different settings, identify which components are particularly effective, and determine to what extent the increased networks, over time, impact on patients' symptoms and quality of life.

Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

NASA Astrophysics Data System (ADS)

Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

Receiver-Assisted Congestion Control to Achieve High Throughput in Lossy Wireless Networks

NASA Astrophysics Data System (ADS)

Shi, Kai; Shu, Yantai; Yang, Oliver; Luo, Jiarong

2010-04-01

Many applications would require fast data transfer in high-speed wireless networks nowadays. However, due to its conservative congestion control algorithm, Transmission Control Protocol (TCP) cannot effectively utilize the network capacity in lossy wireless networks. In this paper, we propose a receiver-assisted congestion control mechanism (RACC) in which the sender performs loss-based control, while the receiver is performing delay-based control. The receiver measures the network bandwidth based on the packet interarrival interval and uses it to compute a congestion window size deemed appropriate for the sender. After receiving the advertised value feedback from the receiver, the sender then uses the additive increase and multiplicative decrease (AIMD) mechanism to compute the correct congestion window size to be used. By integrating the loss-based and the delay-based congestion controls, our mechanism can mitigate the effect of wireless losses, alleviate the timeout effect, and therefore make better use of network bandwidth. Simulation and experiment results in various scenarios show that our mechanism can outperform conventional TCP in high-speed and lossy wireless environments.

Emergence of synchronization and regularity in firing patterns in time-varying neural hypernetworks

NASA Astrophysics Data System (ADS)

Rakshit, Sarbendu; Bera, Bidesh K.; Ghosh, Dibakar; Sinha, Sudeshna

2018-05-01

We study synchronization of dynamical systems coupled in time-varying network architectures, composed of two or more network topologies, corresponding to different interaction schemes. As a representative example of this class of time-varying hypernetworks, we consider coupled Hindmarsh-Rose neurons, involving two distinct types of networks, mimicking interactions that occur through the electrical gap junctions and the chemical synapses. Specifically, we consider the connections corresponding to the electrical gap junctions to form a small-world network, while the chemical synaptic interactions form a unidirectional random network. Further, all the connections in the hypernetwork are allowed to change in time, modeling a more realistic neurobiological scenario. We model this time variation by rewiring the links stochastically with a characteristic rewiring frequency f . We find that the coupling strength necessary to achieve complete neuronal synchrony is lower when the links are switched rapidly. Further, the average time required to reach the synchronized state decreases as synaptic coupling strength and/or rewiring frequency increases. To quantify the local stability of complete synchronous state we use the Master Stability Function approach, and for global stability we employ the concept of basin stability. The analytically derived necessary condition for synchrony is in excellent agreement with numerical results. Further we investigate the resilience of the synchronous states with respect to increasing network size, and we find that synchrony can be maintained up to larger network sizes by increasing either synaptic strength or rewiring frequency. Last, we find that time-varying links not only promote complete synchronization, but also have the capacity to change the local dynamics of each single neuron. Specifically, in a window of rewiring frequency and synaptic coupling strength, we observe that the spiking behavior becomes more regular.

Monitoring and assessing global impacts of roads and off-road vehicle traffic

USDA-ARS?s Scientific Manuscript database

Rapid increases in the number of vehicles, urban sprawl, exurban development and infrastructure development for energy and water have led to dramatic increases in both the size and extent of the global road network. Anecdotal evidence suggests that off-road vehicle traffic has also increased in many...

The phenotype of cancer cell invasion controlled by fibril diameter and pore size of 3D collagen networks.

PubMed

Sapudom, Jiranuwat; Rubner, Stefan; Martin, Steve; Kurth, Tony; Riedel, Stefanie; Mierke, Claudia T; Pompe, Tilo

2015-06-01

The behavior of cancer cells is strongly influenced by the properties of extracellular microenvironments, including topology, mechanics and composition. As topological and mechanical properties of the extracellular matrix are hard to access and control for in-depth studies of underlying mechanisms in vivo, defined biomimetic in vitro models are needed. Herein we show, how pore size and fibril diameter of collagen I networks distinctively regulate cancer cell morphology and invasion. Three-dimensional collagen I matrices with a tight control of pore size, fibril diameter and stiffness were reconstituted by adjustment of concentration and pH value during matrix reconstitution. At first, a detailed analysis of topology and mechanics of matrices using confocal laser scanning microscopy, image analysis tools and force spectroscopy indicate pore size and not fibril diameter as the major determinant of matrix elasticity. Secondly, by using two different breast cancer cell lines (MDA-MB-231 and MCF-7), we demonstrate collagen fibril diameter--and not pore size--to primarily regulate cell morphology, cluster formation and invasion. Invasiveness increased and clustering decreased with increasing fibril diameter for both, the highly invasive MDA-MB-231 cells with mesenchymal migratory phenotype and the MCF-7 cells with amoeboid migratory phenotype. As this behavior was independent of overall pore size, matrix elasticity is shown to be not the major determinant of the cell characteristics. Our work emphasizes the complex relationship between structural-mechanical properties of the extracellular matrix and invasive behavior of cancer cells. It suggests a correlation of migratory and invasive phenotype of cancer cells in dependence on topological and mechanical features of the length scale of single fibrils and not on coarse-grained network properties. Copyright © 2015 Elsevier Ltd. All rights reserved.

Oral insulin delivery using P(MAA-g-EG) hydrogels: effects of network morphology on insulin delivery characteristics.

PubMed

Nakamura, Koji; Murray, Robert J; Joseph, Jeffrey I; Peppas, Nicholas A; Morishita, Mariko; Lowman, Anthony M

2004-03-24

Hydrogels of poly(methacrylic acid-g-ethylene glycol) were prepared using different reaction water contents in order to vary the network mesh size, swelling behavior and insulin loading/release kinetics. Gels prepared with greater reaction solvent contents swelled to a greater degree and had a larger network mesh size. All of the hydrogels were able to incorporate insulin and protected it from release in acidic media. At higher pH (7.4), the release rates increased with reaction solvent content. Using a closed loop animal model, all of the insulin loaded formulations produced significant insulin absorption in the upper small intestine combined with hypoglycemic effects. In these studies, bioavailabilities ranged from 4.6% to 7.2% and were dependent on reaction solvent content.

Effect of emulsifier type and concentration, aqueous phase volume and wax ratio on physical, material and mechanical properties of water in oil lipsticks.

PubMed

Beri, A; Norton, J E; Norton, I T

2013-12-01

Water-in-oil emulsions in lipsticks could have the potential to improve moisturizing properties and deliver hydrophilic molecules to the lips. The aims of this work were (i) to investigate the effect of emulsifier type (polymer vs. monomer, and saturated vs. unsaturated chain) and concentration on droplet size and (ii) to investigate the effect of wax ratio (carnauba wax, microcrystalline wax, paraffin wax and performalene) and aqueous phase volume on material properties (Young's modulus, point of fracture, elastic modulus and viscous modulus). Emulsion formation was achieved using a high shear mixer. Results showed that the saturated nature of the emulsifier had very little effect on droplet size, neither did the use of an emulsifier with a larger head group (droplet size ~18-25 μm). Polyglycerol polyricinoleate (PGPR) resulted in emulsions with the smallest droplets (~3-5 μm), as expected from previous studies that show that it produces a thick elastic interface. The results also showed that both Young's modulus and point of fracture increase with increasing percentage of carnauba wax (following a power law dependency of 3), but decrease with increasing percentage of microcrystalline wax, suggesting that the carnauba wax is included in the overall wax network formed by the saturated components, whereas the microcrystalline wax forms irregular crystals that disrupt the overall wax crystal network. Young's modulus, elastic modulus and viscous modulus all decrease with increasing aqueous phase volume in the emulsions, although the slope of the decrease in elastic and viscous moduli is dependent on the addition of solid wax, as a result of strengthening the network. This work suggests the potential use for emulsions in lipstick applications, particularly when PGPR is used as an emulsifier, and with the addition of solid wax, as it increases network strength. © 2013 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Planning of Green Space Ecological Network in Urban Areas: An Example of Nanchang, China

PubMed Central

Li, Haifeng; Chen, Wenbo; He, Wei

2015-01-01

Green space plays an important role in sustainable urban development and ecology by virtue of multiple environmental, recreational, and economic benefits. Constructing an effective and harmonious urban ecological network and maintaining a sustainable living environment in response to rapid urbanization are the key issues required to be resolved by landscape planners. In this paper, Nanchang City, China was selected as a study area. Based on a series of landscape metrics, the landscape pattern analysis of the current (in 2005) and planned (in 2020) green space system were, respectively, conducted by using FRAGSTATS 3.3 software. Considering the actual situation of the Nanchang urban area, a “one river and two banks, north and south twin cities” ecological network was constructed by using network analysis. Moreover, the ecological network was assessed by using corridor structure analysis, and the improvement of an ecological network on the urban landscape was quantitatively assessed through a comparison between the ecological network and green space system planning. The results indicated that: (1) compared to the green space system in 2005, the planned green space system in 2020 of the Nanchang urban area will decline in both districts (Changnan and Changbei districts). Meanwhile, an increase in patch density and a decrease in mean patch size of green space patches at the landscape level implies the fragmentation of the urban green space landscape. In other words, the planned green space system does not necessarily improve the present green space system; (2) the ecological network of two districts has high corridor density, while Changnan’s ecological network has higher connectivity, but Changbei’s ecological network is more viable from an economic point of view, since it has relatively higher cost efficiency; (3) decrease in patch density, Euclidean nearest neighbor distance, and an increase in mean patch size and connectivity implied that the ecological network could improve landscape connectivity greatly, as compared with the planned green space system. That is to say, the planned ecological network would reduce landscape fragmentation, and increase the shape complexity of green space patches and landscape connectivity. As a result, the quality of the urban ecological environment would be improved. PMID:26501298

Planning of Green Space Ecological Network in Urban Areas: An Example of Nanchang, China.

PubMed

Li, Haifeng; Chen, Wenbo; He, Wei

2015-10-15

Green space plays an important role in sustainable urban development and ecology by virtue of multiple environmental, recreational, and economic benefits. Constructing an effective and harmonious urban ecological network and maintaining a sustainable living environment in response to rapid urbanization are the key issues required to be resolved by landscape planners. In this paper, Nanchang City, China was selected as a study area. Based on a series of landscape metrics, the landscape pattern analysis of the current (in 2005) and planned (in 2020) green space system were, respectively, conducted by using FRAGSTATS 3.3 software. Considering the actual situation of the Nanchang urban area, a "one river and two banks, north and south twin cities" ecological network was constructed by using network analysis. Moreover, the ecological network was assessed by using corridor structure analysis, and the improvement of an ecological network on the urban landscape was quantitatively assessed through a comparison between the ecological network and green space system planning. The results indicated that: (1) compared to the green space system in 2005, the planned green space system in 2020 of the Nanchang urban area will decline in both districts (Changnan and Changbei districts). Meanwhile, an increase in patch density and a decrease in mean patch size of green space patches at the landscape level implies the fragmentation of the urban green space landscape. In other words, the planned green space system does not necessarily improve the present green space system; (2) the ecological network of two districts has high corridor density, while Changnan's ecological network has higher connectivity, but Changbei's ecological network is more viable from an economic point of view, since it has relatively higher cost efficiency; (3) decrease in patch density, Euclidean nearest neighbor distance, and an increase in mean patch size and connectivity implied that the ecological network could improve landscape connectivity greatly, as compared with the planned green space system. That is to say, the planned ecological network would reduce landscape fragmentation, and increase the shape complexity of green space patches and landscape connectivity. As a result, the quality of the urban ecological environment would be improved.

Is Social Network a Protective Factor for Cognitive Impairment in US Chinese Older Adults? Findings from the PINE Study.

PubMed

Li, Mengting; Dong, Xinqi

2018-01-01

Social network has been identified as a protective factor for cognitive impairment. However, the relationship between social network and global and subdomains of cognitive function remains unclear. This study aims to provide an analytic framework to examine quantity, composition, and quality of social network and investigate the association between social network, global cognition, and cognitive domains among US Chinese older adults. Data were derived from the Population Study of Chinese Elderly (PINE), a community-engaged, population-based epidemiological study of US Chinese older adults aged 60 and above in the greater Chicago area, with a sample size of 3,157. Social network was assessed by network size, volume of contact, proportion kin, proportion female, proportion co-resident, and emotional closeness. Cognitive function was evaluated by global cognition, episodic memory, executive function, working memory, and Chinese Mini-Mental State Examination (C-MMSE). Linear regression and quantile regression were performed. Every 1-point increase in network size (b = 0.048, p < 0.001) and volume of contact (b = 0.049, p < 0.01) and every 1-point decrease in proportion kin (b = -0.240, p < 0.01) and proportion co-resident (b = -0.099, p < 0.05) were associated with higher level of global cognition. Similar trends were observed in specific cognitive domains, including episodic memory, working memory, executive function, and C-MMSE. However, emotional closeness was only significantly associated with C-MMSE (b = 0.076, p < 0.01). Social network has differential effects on female versus male older adults. This study found that social network dimensions have different relationships with global and domains of cognitive function. Quantitative and structural aspects of social network were essential to maintain an optimal level of cognitive function. Qualitative aspects of social network were protective factors for C-MMSE. It is necessary for public health practitioners to consider interventions that enhance different aspects of older adults' social network. © 2017 S. Karger AG, Basel.

Sexual Networks and HIV Risk among Black Men Who Have Sex with Men in 6 U.S. Cities.

PubMed

Tieu, Hong-Van; Liu, Ting-Yuan; Hussen, Sophia; Connor, Matthew; Wang, Lei; Buchbinder, Susan; Wilton, Leo; Gorbach, Pamina; Mayer, Kenneth; Griffith, Sam; Kelly, Corey; Elharrar, Vanessa; Phillips, Gregory; Cummings, Vanessa; Koblin, Beryl; Latkin, Carl

2015-01-01

Sexual networks may place U.S. Black men who have sex with men (MSM) at increased HIV risk. Self-reported egocentric sexual network data from the prior six months were collected from 1,349 community-recruited Black MSM in HPTN 061, a multi-component HIV prevention intervention feasibility study. Sexual network composition, size, and density (extent to which members are having sex with one another) were compared by self-reported HIV serostatus and age of the men. GEE models assessed network and other factors associated with having a Black sex partner, having a partner with at least two age category difference (age difference between participant and partner of at least two age group categories), and having serodiscordant/serostatus unknown unprotected anal/vaginal intercourse (SDUI) in the last six months. Over half had exclusively Black partners in the last six months, 46% had a partner of at least two age category difference, 87% had ≤5 partners. Nearly 90% had sex partners who were also part of their social networks. Among HIV-negative men, not having anonymous/exchange/ trade partners and lower density were associated with having a Black partner; larger sexual network size and having non-primary partners were associated with having a partner with at least two age category difference; and having anonymous/exchange/ trade partners was associated with SDUI. Among HIV-positive men, not having non-primary partners was associated with having a Black partner; no sexual network characteristics were associated with having a partner with at least two age category difference and SDUI. Black MSM sexual networks were relatively small and often overlapped with the social networks. Sexual risk was associated with having non-primary partners and larger network size. Network interventions that engage the social networks of Black MSM, such as interventions utilizing peer influence, should be developed to address stable partnerships, number of partners, and serostatus disclosure.

Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach.

PubMed

Noel, Jean-Paul; Blanke, Olaf; Magosso, Elisa; Serino, Andrea

2018-06-01

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons' RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant's peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons' RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli. NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic properties are unexplained. We demonstrate with a psychophysical approach that PPS enlarges as incoming stimulus velocity increases, efficiently preventing contacts with faster approaching objects. We present a neurocomputational model of multisensory PPS implementing neural adaptation to persistent stimulation to propose a neurophysiological mechanism underlying this effect.

Do online social media cut through the constraints that limit the size of offline social networks?

PubMed Central

Dunbar, R. I. M.

2016-01-01

The social brain hypothesis has suggested that natural social network sizes may have a characteristic size in humans. This is determined in part by cognitive constraints and in part by the time costs of servicing relationships. Online social networking offers the potential to break through the glass ceiling imposed by at least the second of these, potentially enabling us to maintain much larger social networks. This is tested using two separate UK surveys, each randomly stratified by age, gender and regional population size. The data show that the size and range of online egocentric social networks, indexed as the number of Facebook friends, is similar to that of offline face-to-face networks. For one sample, respondents also specified the number of individuals in the inner layers of their network (formally identified as support clique and sympathy group), and these were also similar in size to those observed in offline networks. This suggests that, as originally proposed by the social brain hypothesis, there is a cognitive constraint on the size of social networks that even the communication advantages of online media are unable to overcome. In practical terms, it may reflect the fact that real (as opposed to casual) relationships require at least occasional face-to-face interaction to maintain them. PMID:26909163

Do online social media cut through the constraints that limit the size of offline social networks?

PubMed

Dunbar, R I M

2016-01-01

The social brain hypothesis has suggested that natural social network sizes may have a characteristic size in humans. This is determined in part by cognitive constraints and in part by the time costs of servicing relationships. Online social networking offers the potential to break through the glass ceiling imposed by at least the second of these, potentially enabling us to maintain much larger social networks. This is tested using two separate UK surveys, each randomly stratified by age, gender and regional population size. The data show that the size and range of online egocentric social networks, indexed as the number of Facebook friends, is similar to that of offline face-to-face networks. For one sample, respondents also specified the number of individuals in the inner layers of their network (formally identified as support clique and sympathy group), and these were also similar in size to those observed in offline networks. This suggests that, as originally proposed by the social brain hypothesis, there is a cognitive constraint on the size of social networks that even the communication advantages of online media are unable to overcome. In practical terms, it may reflect the fact that real (as opposed to casual) relationships require at least occasional face-to-face interaction to maintain them.

Size-dependent survival of brook trout Salvelinus fontinalis in summer: effects of water temperature and stream flow.

PubMed

Xu, C L; Letcher, B H; Nislow, K H

2010-06-01

A 5 year individual-based data set was used to estimate size-specific survival rates in a wild brook trout Salvelinus fontinalis population in a stream network encompassing a mainstem and three tributaries (1.5-6 m wetted width), western Massachusetts, U.S.A. The relationships between survival in summer and temperature and flow metrics derived from continuous monitoring data were then tested. Increased summer temperatures significantly reduced summer survival rates for S. fontinalis in almost all size classes in all four sites throughout the network. In contrast, extreme low summer flows reduced survival of large fish, but only in small tributaries, and had no significant effects on fish in smaller size classes in any location. These results provide direct evidence of a link between season-specific survival and environmental factors likely to be affected by climate change and have important consequences for the management of both habitats and populations.

Spacecraft Will Communicate "on the Fly"

NASA Technical Reports Server (NTRS)

Laufenberg, Lawrence

2003-01-01

As NASA probes deeper into space, the distance between sensor and scientist increases, as does the time delay. NASA needs to close that gap, while integrating more spacecraft types and missions-from near-Earth orbit to deep space. To speed and integrate communications from space missions to scientists on Earth and back again. NASA needs a comprehensive, high-performance communications network. To this end, the CICT Programs Space Communications (SC) Project is providing technologies for building the Space Internet which will consist of large backbone network, mid-size access networks linked to the backbones, and smaller, ad-hoc network linked to the access network. A key component will be mobile, wireless networks for spacecraft flying in different configurations.

Nested neural networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1988-01-01

Nested neural networks, consisting of small interconnected subnetworks, allow for the storage and retrieval of neural state patterns of different sizes. The subnetworks are naturally categorized by layers of corresponding to spatial frequencies in the pattern field. The storage capacity and the error correction capability of the subnetworks generally increase with the degree of connectivity between layers (the nesting degree). Storage of only few subpatterns in each subnetworks results in a vast storage capacity of patterns and subpatterns in the nested network, maintaining high stability and error correction capability.

TiO2 nanoparticles disrupt cell adhesion and the architecture of cytoskeletal networks of human osteoblast-like cells in a size dependent manner.

PubMed

Ibrahim, Mohamed; Schoelermann, Julia; Mustafa, Kamal; Cimpan, Mihaela R

2018-04-30

Human exposure to titanium dioxide nanoparticles (nano-TiO 2 ) is increasing. An internal source of nano-TiO 2 is represented by titanium-based orthopedic and dental implants can release nanoparticles (NPs) upon abrasion. Little is known about how the size of NPs influences their interaction with cytoskeletal protein networks and the functional/homeostatic consequences that might follow at the implant-bone interface with regard to osteoblasts. We investigated the effects of size of anatase nano-TiO 2 on SaOS-2 human osteoblast-like cells exposed to clinically relevant concentrations (0.05, 0.5, 5 mg/L) of 5 and 40 nm spherical nano-TiO 2 . Cell viability and proliferation, adhesion, spread and migration were assessed, as well as the orientation of actin and microtubule cytoskeletal networks. The phosphorylation of focal adhesion kinase (p-FAK Y397 ) and the expression of vinculin in response to nano-TiO 2 were also assessed. Treatment with nano-TiO 2 disrupted the actin and microtubule cytoskeletal networks leading to morphological modifications of SaOS-2 cells. The phosphorylation of p-FAK Y397 and the expression of vinculin were also modified depending on the particle size, which affected cell adhesion. Consequently, the cell migration was significantly impaired in the 5 nm-exposed cells compared to unexposed cells. The present work shows that the orientation of cytoskeletal networks and the focal adhesion proteins and subsequently the adhesion, spread and migration of SaOS-2 cells were affected by the selected nano-TiO 2 in a size dependent manner. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Optimal input sizes for neural network de-interlacing

NASA Astrophysics Data System (ADS)

Choi, Hyunsoo; Seo, Guiwon; Lee, Chulhee

2009-02-01

Neural network de-interlacing has shown promising results among various de-interlacing methods. In this paper, we investigate the effects of input size for neural networks for various video formats when the neural networks are used for de-interlacing. In particular, we investigate optimal input sizes for CIF, VGA and HD video formats.

Extending Stability Through Hierarchical Clusters in Echo State Networks

PubMed Central

Jarvis, Sarah; Rotter, Stefan; Egert, Ulrich

2009-01-01

Echo State Networks (ESN) are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantitatively determine the influence of the most relevant network parameters, we analyzed the impact of reservoir substructures on stability in hierarchically clustered ESNs, as they allow a smooth transition from highly structured to increasingly homogeneous reservoirs. Previous studies used the largest eigenvalue of the reservoir connectivity matrix (spectral radius) as a predictor for stable network dynamics. Here, we evaluate the impact of clusters, hierarchy and intercluster connectivity on the predictive power of the spectral radius for stability. Both hierarchy and low relative cluster sizes extend the range of spectral radius values, leading to stable networks, while increasing intercluster connectivity decreased maximal spectral radius. PMID:20725523

Dissolved Nutrient Retention Dynamics in River Networks: A Modeling Investigation of Transient Flow and Scale Effects

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

Ye, Sheng; Covino, Timothy P.; Sivapalan, Murugesu

In this paper, we use a dynamic network flow model, coupled with a transient storage zone biogeochemical model, to simulate dissolved nutrient removal processes at the channel network scale. We have explored several scenarios in respect of the combination of rainfall variability, and the biological and geomorphic characteristics of the catchment, to understand the dominant controls on removal and delivery of dissolved nutrients (e.g., nitrate). These model-based theoretical analyses suggested that while nutrient removal efficiency is lower during flood events compared to during baseflow periods, flood events contribute significantly to bulk nutrient removal, whereas bulk removal during baseflow periods ismore » less. This is due to the fact that nutrient supply is larger during flood events; this trend is even stronger in large rivers. However, the efficiency of removal during both periods decreases in larger rivers, however, due to (i) increasing flow velocities and thus decreasing residence time, and (ii) increasing flow depth, and thus decreasing nutrient uptake rates. Besides nutrient removal processes can be divided into two parts: in the main channel and in the hyporheic transient storage zone. When assessing their relative contributions the size of the transient storage zone is a dominant control, followed by uptake rates in the main channel and in the transient storage zone. Increasing size of the transient storage zone with downstream distance affects the relative contributions to nutrient removal of the water column and the transient storage zone, which also impacts the way nutrient removal rates scale with increasing size of rivers. Intra-annual hydrologic variability has a significant impact on removal rates at all scales: the more variable the streamflow is, compared to mean discharge, the less nutrient is removed in the channel network. A scale-independent first order uptake coefficient, ke, estimated from model simulations, is highly dependent on the relative size of the transient storage zone and how it changes in the downstream direction, as well as the nature of hydrologic variability.« less

Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

PubMed

Kim, Seongjung; Kim, Jongman; Ahn, Soonjae; Kim, Youngho

2018-04-18

Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Criticality in finite dynamical networks

NASA Astrophysics Data System (ADS)

Rohlf, Thimo; Gulbahce, Natali; Teuscher, Christof

2007-03-01

It has been shown analytically and experimentally that both random boolean and random threshold networks show a transition from ordered to chaotic dynamics at a critical average connectivity Kc in the thermodynamical limit [1]. By looking at the statistical distributions of damage spreading (damage sizes), we go beyond this extensively studied mean-field approximation. We study the scaling properties of damage size distributions as a function of system size N and initial perturbation size d(t=0). We present numerical evidence that another characteristic point, Kd exists for finite system sizes, where the expectation value of damage spreading in the network is independent of the system size N. Further, the probability to obtain critical networks is investigated for a given system size and average connectivity k. Our results suggest that, for finite size dynamical networks, phase space structure is very complex and may not exhibit a sharp order-disorder transition. Finally, we discuss the implications of our findings for evolutionary processes and learning applied to networks which solve specific computational tasks. [1] Derrida, B. and Pomeau, Y. (1986), Europhys. Lett., 1, 45-49

Fast-response and scattering-free polymer network liquid crystals for infrared light modulators

NASA Astrophysics Data System (ADS)

Fan, Yun-Hsing; Lin, Yi-Hsin; Ren, Hongwen; Gauza, Sebastian; Wu, Shin-Tson

2004-02-01

A fast-response and scattering-free homogeneously aligned polymer network liquid crystal (PNLC) light modulator is demonstrated at λ=1.55 μm wavelength. Light scattering in the near-infrared region is suppressed by optimizing the polymer concentration such that the network domain sizes are smaller than the wavelength. The strong polymer network anchoring assists LC to relax back quickly as the electric field is removed. As a result, the PNLC response time is ˜250× faster than that of the E44 LC mixture except that the threshold voltage is increased by ˜25×.

Dynamics Behaviors of Scale-Free Networks with Elastic Demand

NASA Astrophysics Data System (ADS)

Li, Yan-Lai; Sun, Hui-Jun; Wu, Jian-Jun

Many real-world networks, such as transportation networks and Internet, have the scale-free properties. It is important to study the bearing capacity of such networks. Considering the elastic demand condition, we analyze load distributions and bearing capacities with different parameters through artificially created scale-free networks. The simulation results show that the load distribution follows a power-law form, which means some ordered pairs, playing the dominant role in the transportation network, have higher demand than other pairs. We found that, with the decrease of perceptual error, the total and average ordered pair demand will decrease and then stay in a steady state. However, with the increase of the network size, the average demand of each ordered pair will decrease, which is particularly interesting for the network design problem.

Parrondo's games based on complex networks and the paradoxical effect.

PubMed

Ye, Ye; Wang, Lu; Xie, Nenggang

2013-01-01

Parrondo's games were first constructed using a simple tossing scenario, which demonstrates the following paradoxical situation: in sequences of games, a winning expectation may be obtained by playing the games in a random order, although each game (game A or game B) in the sequence may result in losing when played individually. The available Parrondo's games based on the spatial niche (the neighboring environment) are applied in the regular networks. The neighbors of each node are the same in the regular graphs, whereas they are different in the complex networks. Here, Parrondo's model based on complex networks is proposed, and a structure of game B applied in arbitrary topologies is constructed. The results confirm that Parrondo's paradox occurs. Moreover, the size of the region of the parameter space that elicits Parrondo's paradox depends on the heterogeneity of the degree distributions of the networks. The higher heterogeneity yields a larger region of the parameter space where the strong paradox occurs. In addition, we use scale-free networks to show that the network size has no significant influence on the region of the parameter space where the strong or weak Parrondo's paradox occurs. The region of the parameter space where the strong Parrondo's paradox occurs reduces slightly when the average degree of the network increases.

Subjective well-being associated with size of social network and social support of elderly.

PubMed

Wang, Xingmin

2016-06-01

The current study examined the impact of size of social network on subjective well-being of elderly, mainly focused on confirmation of the mediator role of perceived social support. The results revealed that both size of social network and perceived social support were significantly correlated with subjective well-being. Structural equation modeling indicated that perceived social support partially mediated size of social network to subjective well-being. The final model also revealed significant both paths from size of social network to subjective well-being through perceived social support. The findings extended prior researches and provided valuable evidence on how to promote mental health of the elderly. © The Author(s) 2014.

Illustration of distributed generation effects on protection system coordination

NASA Astrophysics Data System (ADS)

Alawami, Hussain Adnan

Environmental concerns, market forces, and emergence of new technologies have recently resulted in restructuring electric utility from vertically integrated networks to competitive deregulated entities. Distributed generation (DG) is playing a major role in such deregulated markets. When they are installed in small amounts and small sizes, their impacts on the system may be negligible. When their penetration levels increase as well as their sizes, however, they may start affecting the system performance from more than one aspect. Power system protection needs to be re-assessed after the emergence of DG. This thesis attempts to illustrate the impact of DG on the power system protection coordination. It will study the operation of the impedance relays, fuses, reclosers and overcurrent relays when a DG is added to the distribution network. Different DG sizes, distances from the network and locations within the distribution system will be considered. Power system protection coordination is very sensitive to the DG size where it is not for the DG distance. DG location has direct impact on the operation of the protective devices especially when it is inserted in the middle point of the distribution system. Key Words, Distributed Generation, Impedance relay, fuses, reclosers, overcurrent relays, power system protection coordination.

Social Support and Survival in Young Women with Breast Carcinoma

PubMed Central

Chou, Ann F.; Stewart, Susan L.; Wild, Robert C.; Bloom, Joan R.

2010-01-01

Purpose While previous evidence has shown increased likelihood for survival in cancer patients who have social support, little is known about changes in social support during illness and their impact on survival. This study examines the relationship between social support and survival among women diagnosed with breast carcinoma, specifically assessing the effect of network size and changes in social contact post-diagnosis. Methods A population-based sample of 584 women was followed for up to 12.5 years (median follow-up =10.3 years). The mean age at diagnosis was 44 years, 81% were married, and 29% were racial/ethnic minorities. Cox regression analysis was used to estimate survival as a function of social support (changes in social contact and the size of social support), disease severity, treatment, health status, and socio-demographic factors. Results Fifty-four-percent of the women had local and 44% had regional stage disease. About 53% underwent mastectomy, 68% received chemotherapy, and 55% had radiation. Regression results showed that disease stage, estrogen receptor status, and mastectomy were associated with greater risk of dying. Although network size was not related to survival, increased contact with friends/family post-diagnosis was associated with lower risk of death, with a hazard ratio of 0.31 (95% CI, 0.17-0.57). Conclusion Findings from this study have identified an important aspect of a woman’s social network that impacts survival. An increase in the amount of social contact, representing greater social support, may increase the likelihood of the women’s survival by enhancing their coping skills, providing emotional support, and expanding opportunities for information-sharing. PMID:20967848

Gas Bubble Migration and Trapping in Porous Media: Pore-Scale Simulation

NASA Astrophysics Data System (ADS)

Mahabadi, Nariman; Zheng, Xianglei; Yun, Tae Sup; van Paassen, Leon; Jang, Jaewon

2018-02-01

Gas bubbles can be naturally generated or intentionally introduced in sediments. Gas bubble migration and trapping affect the rate of gas emission into the atmosphere or modify the sediment properties such as hydraulic and mechanical properties. In this study, the migration and trapping of gas bubbles are simulated using the pore-network model extracted from the 3D X-ray image of in situ sediment. Two types of bubble size distribution (mono-sized and distributed-sized cases) are used in the simulation. The spatial and statistical bubble size distribution, residual gas saturation, and hydraulic conductivity reduction due to the bubble trapping are investigated. The results show that the bubble size distribution becomes wider during the gas bubble migration due to bubble coalescence for both mono-sized and distributed-sized cases. And the trapped bubble fraction and the residual gas saturation increase as the bubble size increases. The hydraulic conductivity is reduced as a result of the gas bubble trapping. The reduction in hydraulic conductivity is apparently observed as bubble size and the number of nucleation points increase.

Revealing catastrophic failure of leaf networks under stress

PubMed Central

Brodribb, Timothy J.; Bienaimé, Diane; Marmottant, Philippe

2016-01-01

The intricate patterns of veins that adorn the leaves of land plants are among the most important networks in biology. Water flows in these leaf irrigation networks under tension and is vulnerable to embolism-forming cavitations, which cut off water supply, ultimately causing leaf death. Understanding the ways in which plants structure their vein supply network to protect against embolism-induced failure has enormous ecological and evolutionary implications, but until now there has been no way of observing dynamic failure in natural leaf networks. Here we use a new optical method that allows the initiation and spread of embolism bubbles in the leaf network to be visualized. Examining embolism-induced failure of architecturally diverse leaf networks, we found that conservative rules described the progression of hydraulic failure within veins. The most fundamental rule was that within an individual venation network, susceptibility to embolism always increased proportionally with the size of veins, and initial nucleation always occurred in the largest vein. Beyond this general framework, considerable diversity in the pattern of network failure was found between species, related to differences in vein network topology. The highest-risk network was found in a fern species, where single events caused massive disruption to leaf water supply, whereas safer networks in angiosperm leaves contained veins with composite properties, allowing a staged failure of water supply. These results reveal how the size structure of leaf venation is a critical determinant of the spread of embolism damage to leaves during drought. PMID:27071104

Spreading in online social networks: the role of social reinforcement.

PubMed

Zheng, Muhua; Lü, Linyuan; Zhao, Ming

2013-07-01

Some epidemic spreading models are usually applied to analyze the propagation of opinions or news. However, the dynamics of epidemic spreading and information or behavior spreading are essentially different in many aspects. Centola's experiments [Science 329, 1194 (2010)] on behavior spreading in online social networks showed that the spreading is faster and broader in regular networks than in random networks. This result contradicts with the former understanding that random networks are preferable for spreading than regular networks. To describe the spreading in online social networks, a unknown-known-approved-exhausted four-status model was proposed, which emphasizes the effect of social reinforcement and assumes that the redundant signals can improve the probability of approval (i.e., the spreading rate). Performing the model on regular and random networks, it is found that our model can well explain the results of Centola's experiments on behavior spreading and some former studies on information spreading in different parameter space. The effects of average degree and network size on behavior spreading process are further analyzed. The results again show the importance of social reinforcement and are accordant with Centola's anticipation that increasing the network size or decreasing the average degree will enlarge the difference of the density of final approved nodes between regular and random networks. Our work complements the former studies on spreading dynamics, especially the spreading in online social networks where the information usually requires individuals' confirmations before being transmitted to others.

Revealing catastrophic failure of leaf networks under stress.

PubMed

Brodribb, Timothy J; Bienaimé, Diane; Marmottant, Philippe

2016-04-26

The intricate patterns of veins that adorn the leaves of land plants are among the most important networks in biology. Water flows in these leaf irrigation networks under tension and is vulnerable to embolism-forming cavitations, which cut off water supply, ultimately causing leaf death. Understanding the ways in which plants structure their vein supply network to protect against embolism-induced failure has enormous ecological and evolutionary implications, but until now there has been no way of observing dynamic failure in natural leaf networks. Here we use a new optical method that allows the initiation and spread of embolism bubbles in the leaf network to be visualized. Examining embolism-induced failure of architecturally diverse leaf networks, we found that conservative rules described the progression of hydraulic failure within veins. The most fundamental rule was that within an individual venation network, susceptibility to embolism always increased proportionally with the size of veins, and initial nucleation always occurred in the largest vein. Beyond this general framework, considerable diversity in the pattern of network failure was found between species, related to differences in vein network topology. The highest-risk network was found in a fern species, where single events caused massive disruption to leaf water supply, whereas safer networks in angiosperm leaves contained veins with composite properties, allowing a staged failure of water supply. These results reveal how the size structure of leaf venation is a critical determinant of the spread of embolism damage to leaves during drought.

Neutral evolution of proteins: The superfunnel in sequence space and its relation to mutational robustness

NASA Astrophysics Data System (ADS)

Noirel, Josselin; Simonson, Thomas

2008-11-01

Following Kimura's neutral theory of molecular evolution [M. Kimura, The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983) (reprinted in 1986)], it has become common to assume that the vast majority of viable mutations of a gene confer little or no functional advantage. Yet, in silico models of protein evolution have shown that mutational robustness of sequences could be selected for, even in the context of neutral evolution. The evolution of a biological population can be seen as a diffusion on the network of viable sequences. This network is called a "neutral network." Depending on the mutation rate μ and the population size N, the biological population can evolve purely randomly (μN ≪1) or it can evolve in such a way as to select for sequences of higher mutational robustness (μN ≫1). The stringency of the selection depends not only on the product μN but also on the exact topology of the neutral network, the special arrangement of which was named "superfunnel." Even though the relation between mutation rate, population size, and selection was thoroughly investigated, a study of the salient topological features of the superfunnel that could affect the strength of the selection was wanting. This question is addressed in this study. We use two different models of proteins: on lattice and off lattice. We compare neutral networks computed using these models to random networks. From this, we identify two important factors of the topology that determine the stringency of the selection for mutationally robust sequences. First, the presence of highly connected nodes ("hubs") in the network increases the selection for mutationally robust sequences. Second, the stringency of the selection increases when the correlation between a sequence's mutational robustness and its neighbors' increases. The latter finding relates a global characteristic of the neutral network to a local one, which is attainable through experiments or molecular modeling.

Neutral evolution of proteins: The superfunnel in sequence space and its relation to mutational robustness.

PubMed

Noirel, Josselin; Simonson, Thomas

2008-11-14

Following Kimura's neutral theory of molecular evolution [M. Kimura, The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983) (reprinted in 1986)], it has become common to assume that the vast majority of viable mutations of a gene confer little or no functional advantage. Yet, in silico models of protein evolution have shown that mutational robustness of sequences could be selected for, even in the context of neutral evolution. The evolution of a biological population can be seen as a diffusion on the network of viable sequences. This network is called a "neutral network." Depending on the mutation rate mu and the population size N, the biological population can evolve purely randomly (muN<1) or it can evolve in such a way as to select for sequences of higher mutational robustness (muN>1). The stringency of the selection depends not only on the product muN but also on the exact topology of the neutral network, the special arrangement of which was named "superfunnel." Even though the relation between mutation rate, population size, and selection was thoroughly investigated, a study of the salient topological features of the superfunnel that could affect the strength of the selection was wanting. This question is addressed in this study. We use two different models of proteins: on lattice and off lattice. We compare neutral networks computed using these models to random networks. From this, we identify two important factors of the topology that determine the stringency of the selection for mutationally robust sequences. First, the presence of highly connected nodes ("hubs") in the network increases the selection for mutationally robust sequences. Second, the stringency of the selection increases when the correlation between a sequence's mutational robustness and its neighbors' increases. The latter finding relates a global characteristic of the neutral network to a local one, which is attainable through experiments or molecular modeling.

Energy-aware virtual network embedding in flexi-grid networks.

PubMed

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng

2017-11-27

Network virtualization technology has been proposed to allow multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network, which increases the utilization of the substrate network. Efficiently mapping VNs on the substrate network is a major challenge on account of the VN embedding (VNE) problem. Meanwhile, energy efficiency has been widely considered in the network design in terms of operation expenses and the ecological awareness. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the electricity cost of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low electricity cost. Numerical results show that the heuristic algorithm performs closely to the ILP for a small size network, and we also demonstrate its applicability to larger networks.

Is Social Network Diversity Associated with Tooth Loss among Older Japanese Adults?

PubMed Central

Kondo, Katsunori; Yamamoto, Tatsuo; Saito, Masashige; Ito, Kanade; Suzuki, Kayo; Osaka, Ken; Kawachi, Ichiro

2016-01-01

Background We sought to examine social network diversity as a potential determinant of oral health, considering size and contact frequency of the social network and oral health behaviors. Methods Our cross-sectional study was based on data from the 2010 Japan Gerontological Evaluation Study. Data from 19,756 community-dwelling individuals aged 65 years or older were analyzed. We inquired about diversity of friendships based on seven types of friends. Ordered logistic regression models were developed to determine the association between the diversity of social networks and number of teeth (categorized as ≥20, 10–19, 1–9, and 0). Results Of the participants, 54.1% were women (mean age, 73.9 years; standard deviation, 6.2). The proportion of respondents with ≥20 teeth was 34.1%. After adjusting for age, sex, socioeconomic status (income, education, and occupation), marital status, health status (diabetes and mental health), and size and contact frequency of the social network, an increase in the diversity of social networks was significantly associated with having more teeth (odds ratio = 1.08; 95% confidence interval, 1.04–1.11). Even adjusted for oral health behaviors (smoking, curative/preventive dental care access, use of dental floss/fluoride toothpaste), significant association was still observed (odds ratio = 1.05 (95% confidence interval, 1.02–1.08)). Conclusion Social connectedness among people from diverse backgrounds may increase information channels and promote the diffusion of oral health behaviors and prevent tooth loss. PMID:27459102

Is Social Network Diversity Associated with Tooth Loss among Older Japanese Adults?

PubMed

Aida, Jun; Kondo, Katsunori; Yamamoto, Tatsuo; Saito, Masashige; Ito, Kanade; Suzuki, Kayo; Osaka, Ken; Kawachi, Ichiro

2016-01-01

We sought to examine social network diversity as a potential determinant of oral health, considering size and contact frequency of the social network and oral health behaviors. Our cross-sectional study was based on data from the 2010 Japan Gerontological Evaluation Study. Data from 19,756 community-dwelling individuals aged 65 years or older were analyzed. We inquired about diversity of friendships based on seven types of friends. Ordered logistic regression models were developed to determine the association between the diversity of social networks and number of teeth (categorized as ≥20, 10-19, 1-9, and 0). Of the participants, 54.1% were women (mean age, 73.9 years; standard deviation, 6.2). The proportion of respondents with ≥20 teeth was 34.1%. After adjusting for age, sex, socioeconomic status (income, education, and occupation), marital status, health status (diabetes and mental health), and size and contact frequency of the social network, an increase in the diversity of social networks was significantly associated with having more teeth (odds ratio = 1.08; 95% confidence interval, 1.04-1.11). Even adjusted for oral health behaviors (smoking, curative/preventive dental care access, use of dental floss/fluoride toothpaste), significant association was still observed (odds ratio = 1.05 (95% confidence interval, 1.02-1.08)). Social connectedness among people from diverse backgrounds may increase information channels and promote the diffusion of oral health behaviors and prevent tooth loss.

Neural Network Emulation of Reionization Simulations

NASA Astrophysics Data System (ADS)

Schmit, Claude J.; Pritchard, Jonathan R.

2018-05-01

Next generation radio experiments such as LOFAR, HERA and SKA are expected to probe the Epoch of Reionization and claim a first direct detection of the cosmic 21cm signal within the next decade. One of the major challenges for these experiments will be dealing with enormous incoming data volumes. Machine learning is key to increasing our data analysis efficiency. We consider the use of an artificial neural network to emulate 21cmFAST simulations and use it in a Bayesian parameter inference study. We then compare the network predictions to a direct evaluation of the EoR simulations and analyse the dependence of the results on the training set size. We find that the use of a training set of size 100 samples can recover the error contours of a full scale MCMC analysis which evaluates the model at each step.

Evolution on neutral networks accelerates the ticking rate of the molecular clock.

PubMed

Manrubia, Susanna; Cuesta, José A

2015-01-06

Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive 'phenotypic entrapment' entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Evolution on neutral networks accelerates the ticking rate of the molecular clock

PubMed Central

Manrubia, Susanna; Cuesta, José A.

2015-01-01

Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive ‘phenotypic entrapment’ entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives. PMID:25392402

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.

Cascading failures in interdependent networks with finite functional components

NASA Astrophysics Data System (ADS)

Di Muro, M. A.; Buldyrev, S. V.; Stanley, H. E.; Braunstein, L. A.

2016-10-01

We present a cascading failure model of two interdependent networks in which functional nodes belong to components of size greater than or equal to s . We find theoretically and via simulation that in complex networks with random dependency links the transition is first order for s ≥3 and continuous for s =2 . We also study interdependent lattices with a distance constraint r in the dependency links and find that increasing r moves the system from a regime without a phase transition to one with a second-order transition. As r continues to increase, the system collapses in a first-order transition. Each regime is associated with a different structure of domain formation of functional nodes.

Dependence of physical and mechanical properties on polymer architecture for model polymer networks

NASA Astrophysics Data System (ADS)

Guo, Ruilan

Effect of architecture at nanoscale on the macroscopic properties of polymer materials has long been a field of major interest, as evidenced by inhomogeneities in networks, multimodal network topologies, etc. The primary purpose of this research is to establish the architecture-property relationship of polymer networks by studying the physical and mechanical responses of a series of topologically different PTHF networks. Monodispersed allyl-tenninated PTHF precursors were synthesized through "living" cationic polymerization and functional end-capping. Model networks of various crosslink densities and inhomogeneities levels (unimodal, bimodal and clustered) were prepared by endlinking precursors via thiol-ene reaction. Thermal characteristics, i.e., glass transition, melting point, and heat of fusion, of model PTHF networks were investigated as functions of crosslink density and inhomogeneities, which showed different dependence on these two architectural parameters. Study of freezing point depression (FPD) of solvent confined in swollen networks indicated that the size of solvent microcrystals is comparable to the mesh size formed by intercrosslink chains depending on crosslink density and inhomogeneities. Relationship between crystal size and FPD provided a good reflection of the existing architecture facts in the networks. Mechanical responses of elastic chains to uniaxial strains were studied through SANS. Spatial inhomogeneities in bimodal and clustered networks gave rise to "abnormal butterfly patterns", which became more pronounced as elongation ratio increases. Radii of gyration of chains were analyzed at directions parallel and perpendicular to stretching axis. Dependence of Rg on lambda was compared to three rubber elasticity models and the molecular deformation mechanisms for unimodal, bimodal and clustered networks were explored. The thesis focused its last part on the investigation of evolution of free volume distribution of linear polymer (PE) subjected to uniaxial strain at various temperatures using a combination of MD, hard sphere probe method and Voronoi tessellation. Combined effects of temperature and strain on free volume were studied and mechanism of formation of large and ellipsoidal free volume voids was explored.

Effect of crystals and fibrous network polymer additives on cellular morphology of microcellular foams

NASA Astrophysics Data System (ADS)

Miyamoto, Ryoma; Utano, Tatsumi; Yasuhara, Shunya; Ishihara, Shota; Ohshima, Masahiro

2015-05-01

In this study, the core-back foam injection molding was used for preparing microcelluar polypropylene (PP) foam with either a 1,3:2,4 bis-O-(4-methylbenzylidene)-D-sorbitol gelling agent (Gel-all MD) or a fibros network polymer additive (Metablen 3000). Both agent and addiive could effectively control the celluar morphology in foams but somehow different ways. In course of cooling the polymer with Gel-all MD in the mold caity, the agent enhanced the crystal nucleation and resulted in the large number of small crystals. The crystals acted as effective bubble nucleation agent in foaming process. Thus, the agent reduced the cell size and increased the cell density, drastically. Furthermore, the small crystals provided an inhomogenuity to the expanding cell wall and produced the high open cell content with nano-scale fibril structure. Gell-all as well as Metablene 3000 formed a gel-like fibrous network in melt. The network increased the elongational viscosity and tended to prevent the cell wall from breaking up. The foaming temperature window was widened by the presence of the network. Especially, the temperature window where the macro-fibrous structure was formed was expanded to the higher temperature. The effects of crystal nucleating agent and PTFE on crystals' size and number, viscoelsticity, rheological propreties of PP and cellular morphology were compared and thorougly investigated.

Convergence and approximate calculation of average degree under different network sizes for decreasing random birth-and-death networks

NASA Astrophysics Data System (ADS)

Long, Yin; Zhang, Xiao-Jun; Wang, Kui

2018-05-01

In this paper, convergence and approximate calculation of average degree under different network sizes for decreasing random birth-and-death networks (RBDNs) are studied. First, we find and demonstrate that the average degree is convergent in the form of power law. Meanwhile, we discover that the ratios of the back items to front items of convergent reminder are independent of network link number for large network size, and we theoretically prove that the limit of the ratio is a constant. Moreover, since it is difficult to calculate the analytical solution of the average degree for large network sizes, we adopt numerical method to obtain approximate expression of the average degree to approximate its analytical solution. Finally, simulations are presented to verify our theoretical results.

Cache-enabled small cell networks: modeling and tradeoffs.

PubMed

Baştuǧ, Ejder; Bennis, Mehdi; Kountouris, Marios; Debbah, Mérouane

We consider a network model where small base stations (SBSs) have caching capabilities as a means to alleviate the backhaul load and satisfy users' demand. The SBSs are stochastically distributed over the plane according to a Poisson point process (PPP) and serve their users either (i) by bringing the content from the Internet through a finite rate backhaul or (ii) by serving them from the local caches. We derive closed-form expressions for the outage probability and the average delivery rate as a function of the signal-to-interference-plus-noise ratio (SINR), SBS density, target file bitrate, storage size, file length, and file popularity. We then analyze the impact of key operating parameters on the system performance. It is shown that a certain outage probability can be achieved either by increasing the number of base stations or the total storage size. Our results and analysis provide key insights into the deployment of cache-enabled small cell networks (SCNs), which are seen as a promising solution for future heterogeneous cellular networks.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

PubMed

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

Designing marine reserve networks for both conservation and fisheries management.

PubMed

Gaines, Steven D; White, Crow; Carr, Mark H; Palumbi, Stephen R

2010-10-26

Marine protected areas (MPAs) that exclude fishing have been shown repeatedly to enhance the abundance, size, and diversity of species. These benefits, however, mean little to most marine species, because individual protected areas typically are small. To meet the larger-scale conservation challenges facing ocean ecosystems, several nations are expanding the benefits of individual protected areas by building networks of protected areas. Doing so successfully requires a detailed understanding of the ecological and physical characteristics of ocean ecosystems and the responses of humans to spatial closures. There has been enormous scientific interest in these topics, and frameworks for the design of MPA networks for meeting conservation and fishery management goals are emerging. Persistent in the literature is the perception of an inherent tradeoff between achieving conservation and fishery goals. Through a synthetic analysis across these conservation and bioeconomic studies, we construct guidelines for MPA network design that reduce or eliminate this tradeoff. We present size, spacing, location, and configuration guidelines for designing networks that simultaneously can enhance biological conservation and reduce fishery costs or even increase fishery yields and profits. Indeed, in some settings, a well-designed MPA network is critical to the optimal harvest strategy. When reserves benefit fisheries, the optimal area in reserves is moderately large (mode ≈30%). Assessing network design principals is limited currently by the absence of empirical data from large-scale networks. Emerging networks will soon rectify this constraint.

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

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.

Performance evaluation of NASA/KSC CAD/CAE graphics local area network

NASA Technical Reports Server (NTRS)

Zobrist, George

1988-01-01

This study had as an objective the performance evaluation of the existing CAD/CAE graphics network at NASA/KSC. This evaluation will also aid in projecting planned expansions, such as the Space Station project on the existing CAD/CAE network. The objectives were achieved by collecting packet traffic on the various integrated sub-networks. This included items, such as total number of packets on the various subnetworks, source/destination of packets, percent utilization of network capacity, peak traffic rates, and packet size distribution. The NASA/KSC LAN was stressed to determine the useable bandwidth of the Ethernet network and an average design station workload was used to project the increased traffic on the existing network and the planned T1 link. This performance evaluation of the network will aid the NASA/KSC network managers in planning for the integration of future workload requirements into the existing network.

Population size estimation of men who have sex with men through the network scale-up method in Japan.

PubMed

Ezoe, Satoshi; Morooka, Takeo; Noda, Tatsuya; Sabin, Miriam Lewis; Koike, Soichi

2012-01-01

Men who have sex with men (MSM) are one of the groups most at risk for HIV infection in Japan. However, size estimates of MSM populations have not been conducted with sufficient frequency and rigor because of the difficulty, high cost and stigma associated with reaching such populations. This study examined an innovative and simple method for estimating the size of the MSM population in Japan. We combined an internet survey with the network scale-up method, a social network method for estimating the size of hard-to-reach populations, for the first time in Japan. An internet survey was conducted among 1,500 internet users who registered with a nationwide internet-research agency. The survey participants were asked how many members of particular groups with known population sizes (firepersons, police officers, and military personnel) they knew as acquaintances. The participants were also asked to identify the number of their acquaintances whom they understood to be MSM. Using these survey results with the network scale-up method, the personal network size and MSM population size were estimated. The personal network size was estimated to be 363.5 regardless of the sex of the acquaintances and 174.0 for only male acquaintances. The estimated MSM prevalence among the total male population in Japan was 0.0402% without adjustment, and 2.87% after adjusting for the transmission error of MSM. The estimated personal network size and MSM prevalence seen in this study were comparable to those from previous survey results based on the direct-estimation method. Estimating population sizes through combining an internet survey with the network scale-up method appeared to be an effective method from the perspectives of rapidity, simplicity, and low cost as compared with more-conventional methods.

Inferring global network properties from egocentric data with applications to epidemics.

PubMed

Britton, Tom; Trapman, Pieter

2015-03-01

Social networks are often only partly observed, and it is sometimes desirable to infer global properties of the network from 'egocentric' data. In the current paper, we study different types of egocentric data, and show which global network properties are consistent with data. Two global network properties are considered: the size of the largest connected component (the giant) and the size of an epidemic outbreak taking place on the network. The main conclusion is that, in most cases, egocentric data allow for a large range of possible sizes of the giant and the outbreak, implying that egocentric data carry very little information about these global properties. The asymptotic size of the giant and the outbreak is also characterized, assuming the network is selected uniformly among networks with prescribed egocentric data. © The Authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Are Nested Networks More Robust to Disturbance? A Test Using Epiphyte-Tree, Comensalistic Networks

PubMed Central

Piazzon, Martín; Larrinaga, Asier R.; Santamaría, Luis

2011-01-01

Recent research on ecological networks suggests that mutualistic networks are more nested than antagonistic ones and, as a result, they are more robust against chains of extinctions caused by disturbances. We evaluate whether mutualistic networks are more nested than comensalistic and antagonistic networks, and whether highly nested, host-epiphyte comensalistic networks fit the prediction of high robustness against disturbance. A review of 59 networks including mutualistic, antagonistic and comensalistic relationships showed that comensalistic networks are significantly more nested than antagonistic and mutualistic networks, which did not differ between themselves. Epiphyte-host networks from old-growth forests differed from those from disturbed forest in several topological parameters based on both qualitative and quantitative matrices. Network robustness increased with network size, but the slope of this relationship varied with nestedness and connectance. Our results indicate that interaction networks show complex responses to disturbances, which influence their topology and indirectly affect their robustness against species extinctions. PMID:21589931

Social brain volume is associated with in-degree social network size among older adults

PubMed Central

2018-01-01

The social brain hypothesis proposes that large neocortex size evolved to support cognitively demanding social interactions. Accordingly, previous studies have observed that larger orbitofrontal and amygdala structures predict the size of an individual's social network. However, it remains uncertain how an individual's social connectedness reported by other people is associated with the social brain volume. In this study, we found that a greater in-degree network size, a measure of social ties identified by a subject's social connections rather than by the subject, significantly correlated with a larger regional volume of the orbitofrontal cortex, dorsomedial prefrontal cortex and lingual gyrus. By contrast, out-degree size, which is based on an individual's self-perceived connectedness, showed no associations. Meta-analytic reverse inference further revealed that regional volume pattern of in-degree size was specifically involved in social inference ability. These findings were possible because our dataset contained the social networks of an entire village, i.e. a global network. The results suggest that the in-degree aspect of social network size not only confirms the previously reported brain correlates of the social network but also shows an association in brain regions involved in the ability to infer other people's minds. This study provides insight into understanding how the social brain is uniquely associated with sociocentric measures derived from a global network. PMID:29367402

The Effects of Block Size on the Performance of Coherent Caches in Shared-Memory Multiprocessors

DTIC Science & Technology

1993-05-01

increase with the bandwidth and latency. For those applications with poor spatial locality, the best choice of cache line size is determined by the...observation was used in the design of two schemes: LimitLESS di- rectories and Tag caches. LimitLESS directories [15] were designed for the ALEWIFE...small packets may be used to avoid network congestion. The most important factor influencing the choice of cache line size for a multipro- cessor is the

Effect of the granule size in porous silicon on the photosensitization efficiency of molecular oxygen on the surface of silicon nanocrystals

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

Demin, V. A.; Konstantinova, E. A., E-mail: liza35@mail.ru; Gongal'skii, M. B.

2009-03-15

Photoluminescence is used to study the effect of the granule size in porous silicon on the generation efficiency of the excited state of molecular oxygen ({sup 1}O{sub 2}) on the surface of silicon nanocrystals. The generation efficiency is found to increase as the granule size becomes smaller than 100 nm, which can be explained by a change in the conditions of exciton diffusion along a network of silicon nanocrystals.

Factors impacting sense of community among adults with brain injury.

PubMed

Ditchman, Nicole; Chan, Fong; Haak, Christopher; Easton, Amanda B

2017-05-01

Despite increasing interest in examining community outcomes following disability, sense of community (SOC) has received relatively no attention in the rehabilitation literature. SOC refers to feelings of belonging and attachment one has for a community and is of particular relevance for people with brain injury who are at increased risk of social isolation. The aim of this study was to investigate factors contributing to SOC for individuals with brain injury. Members from 2 brain injury associations (n = 98) participated in this survey-based study. Hierarchical regression analysis was used to explore demographic, disability-related, community and social participation variables' impact on SOC with regard to one's town or city. Follow-up mediation analyses were conducted to explore relationships among social self-efficacy, support network, neighboring behavior, and SOC. Findings indicated that disability-related and community variables accounted for over 40% of the variance in SOC. Size of social support network was the only significant independent contributor to SOC variance. Follow-up analyses provided support for (a) the partial mediating effect of social support network size on the relationship between social self-efficacy and SOC, and (b) the mediating effect of neighboring behavior on the relationship between social self-efficacy and social support network size. Findings from this study highlight the particular importance of self-efficacy, social support, and neighboring behaviors in promoting SOC for people with brain injury. Recommendations are provided to advance research efforts and inform intervention approaches to improve the felt experience of community among people with brain injury. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Molnets: An Artificial Chemistry Based on Neural Networks

NASA Technical Reports Server (NTRS)

Colombano, Silvano; Luk, Johnny; Segovia-Juarez, Jose L.; Lohn, Jason; Clancy, Daniel (Technical Monitor)

2002-01-01

The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

Effects of Vertex Activity and Self-organized Criticality Behavior on a Weighted Evolving Network

NASA Astrophysics Data System (ADS)

Zhang, Gui-Qing; Yang, Qiu-Ying; Chen, Tian-Lun

2008-08-01

Effects of vertex activity have been analyzed on a weighted evolving network. The network is characterized by the probability distribution of vertex strength, each edge weight and evolution of the strength of vertices with different vertex activities. The model exhibits self-organized criticality behavior. The probability distribution of avalanche size for different network sizes is also shown. In addition, there is a power law relation between the size and the duration of an avalanche and the average of avalanche size has been studied for different vertex activities.

Analysis of the characteristics of the synchronous clusters in the adaptive Kuramoto network and neural network of the epileptic brain

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Kharchenko, Alexander A.; Makarov, Vladimir V.; Khramova, Marina V.; Koronovskii, Alexey A.; Pavlov, Alexey N.; Dana, Syamal K.

2016-04-01

In the paper we study the mechanisms of phase synchronization in the adaptive model network of Kuramoto oscillators and the neural network of brain by consideration of the integral characteristics of the observed networks signals. As the integral characteristics of the model network we consider the summary signal produced by the oscillators. Similar to the model situation we study the ECoG signal as the integral characteristic of neural network of the brain. We show that the establishment of the phase synchronization results in the increase of the peak, corresponding to synchronized oscillators, on the wavelet energy spectrum of the integral signals. The observed correlation between the phase relations of the elements and the integral characteristics of the whole network open the way to detect the size of synchronous clusters in the neural networks of the epileptic brain before and during seizure.

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling

PubMed Central

Shin, Junha; Lee, Insuk

2015-01-01

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life—Archaea, Bacteria, and Eukaryota—suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes. PMID:26394049

Understanding the role of hydrogen bonding in the aggregation of fumed silica particles in triglyceride solvents.

PubMed

Whitby, Catherine P; Krebsz, Melinda; Booty, Samuel J

2018-10-01

Fumed silica particles are thought to thicken organic solvents into gels by aggregating to form networks. Hydrogen bonding between silanol groups on different particle surfaces causes the aggregation. The gel structure and hence flow behaviour is altered by varying the proportion of silanol groups on the particle surfaces. However, characterising the gel using rheology measurements alone is not sufficient to optimise the aggregation. We have used confocal microscopy to characterise the changes in the network microstructure caused by altering the particle surface chemistry. Organogels were formed by dispersing fumed silica nanoparticles in a triglyceride solvent. The particle surface chemistry was systematically varied from oleophobic to oleophilic by functionalisation with hydrocarbons. We directly visualised the particle networks using confocal scanning laser microscopy and investigated the correlations between the network structure and the shear response of the organogels. Our key finding is that the sizes of the pore spaces in the networks depend on the fraction of silanol groups available to form hydrogen bonds. The reduction in the network elasticity of gels formed by methylated particles can be accounted for by the increasing pore size and tenuous nature of the networks. This is the first report that characterises the changes in the microstructure of fumed silica particle networks in non-polar solvents caused by manipulating the particle surface chemistry. Copyright © 2018 Elsevier Inc. All rights reserved.

Impact of Network Activity on the Spread of Infectious Diseases through the German Pig Trade Network.

PubMed

Lebl, Karin; Lentz, Hartmut H K; Pinior, Beate; Selhorst, Thomas

2016-01-01

The trade of livestock is an important and growing economic sector, but it is also a major factor in the spread of diseases. The spreading of diseases in a trade network is likely to be influenced by how often existing trade connections are active. The activity α is defined as the mean frequency of occurrences of existing trade links, thus 0 < α ≤ 1. The observed German pig trade network had an activity of α = 0.11, thus each existing trade connection between two farms was, on average, active at about 10% of the time during the observation period 2008-2009. The aim of this study is to analyze how changes in the activity level of the German pig trade network influence the probability of disease outbreaks, size, and duration of epidemics for different disease transmission probabilities. Thus, we want to investigate the question, whether it makes a difference for a hypothetical spread of an animal disease to transport many animals at the same time or few animals at many times. A SIR model was used to simulate the spread of a disease within the German pig trade network. Our results show that for transmission probabilities <1, the outbreak probability increases in the case of a decreased frequency of animal transports, peaking range of α from 0.05 to 0.1. However, for the final outbreak size, we find that a threshold exists such that finite outbreaks occur only above a critical value of α, which is ~0.1, and therefore in proximity of the observed activity level. Thus, although the outbreak probability increased when decreasing α, these outbreaks affect only a small number of farms. The duration of the epidemic peaks at an activity level in the range of α = 0.2-0.3. Additionally, the results of our simulations show that even small changes in the activity level of the German pig trade network would have dramatic effects on outbreak probability, outbreak size, and epidemic duration. Thus, we can conclude and recommend that the network activity is an important aspect, which should be taken into account when modeling the spread of diseases within trade networks.

Phloem networks in leaves.

PubMed

Carvalho, Mónica R; Losada, Juan M; Niklas, Karl J

2018-06-01

The survival of all vascular plants depends on phloem and xylem, which comprise a hydraulically coupled tissue system that transports photosynthates, water, and a variety of other molecules and ions. Although xylem hydraulics has been extensively studied, until recently, comparatively little is known quantitatively about the phloem hydraulic network and how it is functionally coupled to the xylem network, particularly in photosynthetic leaves. Here, we summarize recent advances in quantifying phloem hydraulics in fully expanded mature leaves with different vascular architectures and show that (1) the size of phloem conducting cells across phylogenetically different taxa scales isometrically with respect to xylem conducting cell size, (2) cell transport areas and lengths increase along phloem transport pathways in a manner that can be used to model Münch's pressure-flow hypothesis, and (3) report observations that invalidate da Vinci's and Murray's hydraulic models as plausible constructs for understanding photosynthate transport in the leaf lamina. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

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

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Global value chains: Building blocks and network dynamics

NASA Astrophysics Data System (ADS)

Tsekeris, Theodore

2017-12-01

The paper employs measures and tools from complex network analysis to enhance the understanding and interpretation of structural characteristics pertaining to the Global Value Chains (GVCs) during the period 1995-2011. The analysis involves the country, sector and country-sector value chain networks to identify main drivers of structural change. The results indicate significant intertemporal changes, mirroring the increased globalization in terms of network size, strength and connectivity. They also demonstrate higher clustering and increased concentration of the most influential countries and country-sectors relative to all others in the GVC network, with the geographical dimension to prevail over the sectoral dimension in the formation of value chains. The regionalization and less hierarchical organization drive country-sector production sharing, while the sectoral value chain network has become more integrated and more competitive over time. The findings suggest that the impact of country-sector policies and/or shocks may vary with the own-group and network-wide influence of each country, take place in multiple geographical scales, as GVCs have a block structure, and involve time dynamics.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE PAGES

Yoo, Wucherl; Sim, Alex

2016-06-24

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Rapid social network assessment for predicting HIV and STI risk among men attending bars and clubs in San Diego, California.

PubMed

Drumright, Lydia N; Frost, Simon D W

2010-12-01

To test the use of a rapid assessment tool to determine social network size, and to test whether social networks with a high density of HIV/sexually transmitted infection (STI) or substance using persons were independent predictors of HIV and STI status among men who have sex with men (MSM) using a rapid tool for collecting network information. We interviewed 609 MSM from 14 bars in San Diego, California, USA, using an enhanced version of the Priorities for Local AIDS Control Efforts (PLACE) methodology. Social network size was assessed using a series of 19 questions of the form 'How many people do you know that have the name X?', where X included specific male and female names (eg, Keith), use illicit substances, and have HIV. Generalised linear models were used to estimate average and group-specific network sizes, and their association with HIV status, STI history and methamphetamine use. Despite possible errors in ascertaining network size, average reported network sizes were larger for larger groups. Those who reported having HIV infection or having past STI reported significantly more HIV infected and methamphetamine or popper using individuals in their social network. There was a dose-dependent effect of social network size of HIV infected individuals on self-reported HIV status, past STI and use of methamphetamine in the last 12 months, after controlling for age, ethnicity and numbers of sexual partners in the last year. Relatively simple measures of social networks are associated with HIV/STI risk, and may provide a useful tool for targeting HIV/STI surveillance and prevention.

Dynamic properties of epidemic spreading on finite size complex networks

NASA Astrophysics Data System (ADS)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Self-assembled Ag nanoparticle network passivated by a nano-sized ZnO layer for transparent and flexible film heaters

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

Seo, Ki-Won; Kim, Han-Ki, E-mail: imdlhkkim@khu.ac.kr; Kim, Min-Yi

2015-12-15

We investigated a self-assembled Ag nanoparticle network electrode passivated by a nano-sized ZnO layer for use in high-performance transparent and flexible film heaters (TFFHs). The low temperature atomic layer deposition of a nano-sized ZnO layer effectively filled the uncovered area of Ag network and improved the current spreading in the self-assembled Ag network without a change in the sheet resistance and optical transmittance as well as mechanical flexibility. The time-temperature profiles and heat distribution analysis demonstrate that the performance of the TFTH with the ZnO/Ag network is superior to that of a TFFH with Ag nanowire electrodes. In addition, themore » TFTHs with ZnO/Ag network exhibited better stability than the TFFH with a bare Ag network due to the effective current spreading through the nano-sized ZnO layer.« less

Social Networks of Lesbian, Gay, Bisexual, and Transgender Older Adults

PubMed Central

Erosheva, Elena A.; Kim, Hyun-Jun; Emlet, Charles; Fredriksen-Goldsen, Karen I.

2015-01-01

Purpose This study examines global social networks—including friendship, support, and acquaintance networks—of lesbian, gay, bisexual, and transgender (LGBT) older adults. Design and Methods Utilizing data from a large community-based study, we employ multiple regression analyses to examine correlates of social network size and diversity. Results Controlling for background characteristics, network size was positively associated with being female, transgender identity, employment, higher income, having a partner or a child, identity disclosure to a neighbor, engagement in religious activities, and service use. Controlling in addition for network size, network diversity was positively associated with younger age, being female, transgender identity, identity disclosure to a friend, religious activity, and service use. Implications According to social capital theory, social networks provide a vehicle for social resources that can be beneficial for successful aging and well-being. This study is a first step at understanding the correlates of social network size and diversity among LGBT older adults. PMID:25882129

Optical network scaling: roles of spectral and spatial aggregation.

PubMed

Arık, Sercan Ö; Ho, Keang-Po; Kahn, Joseph M

2014-12-01

As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral and spatial traffic aggregation become essential for optical network scaling. These aggregation techniques reduce network routing complexity by increasing spectral efficiency to decrease the number of fibers, and by increasing switching granularity to decrease the number of switching components. Spectral aggregation yields a modest decrease in the number of fibers but a substantial decrease in the number of switching components. Spatial aggregation yields a substantial decrease in both the number of fibers and the number of switching components. To quantify routing complexity reduction, we analyze the number of multi-cast and wavelength-selective switches required in a colorless, directionless and contentionless reconfigurable optical add-drop multiplexer architecture. Traffic aggregation has two potential drawbacks: reduced routing power and increased switching component size.

How the Size of Our Social Network Influences Our Semantic Skills

ERIC Educational Resources Information Center

Lev-Ari, Shiri

2016-01-01

People differ in the size of their social network, and thus in the properties of the linguistic input they receive. This article examines whether differences in social network size influence individuals' linguistic skills in their native language, focusing on global comprehension of evaluative language. Study 1 exploits the natural variation in…

Class Size, School Size and the Size of the School Network

ERIC Educational Resources Information Center

Coupé, Tom; Olefir, Anna; Alonso, Juan Diego

2016-01-01

In many transition countries, including Ukraine, decreases in population and fertility have led to substantial falls in the number of school-aged children. As a consequence, these countries now have school networks that consist of many small schools, leading many countries to consider reorganizing their networks by closing smaller schools and…

Limits to high-speed simulations of spiking neural networks using general-purpose computers.

PubMed

Zenke, Friedemann; Gerstner, Wulfram

2014-01-01

To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

Effective Network Size Predicted From Simulations of Pathogen Outbreaks Through Social Networks Provides a Novel Measure of Structure-Standardized Group Size.

PubMed

McCabe, Collin M; Nunn, Charles L

2018-01-01

The transmission of infectious disease through a population is often modeled assuming that interactions occur randomly in groups, with all individuals potentially interacting with all other individuals at an equal rate. However, it is well known that pairs of individuals vary in their degree of contact. Here, we propose a measure to account for such heterogeneity: effective network size (ENS), which refers to the size of a maximally complete network (i.e., unstructured, where all individuals interact with all others equally) that corresponds to the outbreak characteristics of a given heterogeneous, structured network. We simulated susceptible-infected (SI) and susceptible-infected-recovered (SIR) models on maximally complete networks to produce idealized outbreak duration distributions for a disease on a network of a given size. We also simulated the transmission of these same diseases on random structured networks and then used the resulting outbreak duration distributions to predict the ENS for the group or population. We provide the methods to reproduce these analyses in a public R package, "enss." Outbreak durations of simulations on randomly structured networks were more variable than those on complete networks, but tended to have similar mean durations of disease spread. We then applied our novel metric to empirical primate networks taken from the literature and compared the information represented by our ENSs to that by other established social network metrics. In AICc model comparison frameworks, group size and mean distance proved to be the metrics most consistently associated with ENS for SI simulations, while group size, centralization, and modularity were most consistently associated with ENS for SIR simulations. In all cases, ENS was shown to be associated with at least two other independent metrics, supporting its use as a novel metric. Overall, our study provides a proof of concept for simulation-based approaches toward constructing metrics of ENS, while also revealing the conditions under which this approach is most promising.

Network analysis of translocated Takahe populations to identify disease surveillance targets.

PubMed

Grange, Zoë L; VAN Andel, Mary; French, Nigel P; Gartrell, Brett D

2014-04-01

Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i(total)) and the number of ties (t(total)) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k(i)(in)), out degree (k(i)(out)), and betweenness (B(i)). Key players were assigned according to the highest node measure of k(i)(in), k(i)(out), and B(i) per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final network in 2011. Likewise, the wild Murchison Mountains population was consistently the sink of the network. Other nodes, such as the offshore islands and the wildlife hospital, varied in importance over time. Common network descriptors and measures of centrality identified key locations for targeting disease surveillance. The visual representation of movements of animals in a population that this technique provides can aid decision makers when they evaluate translocation proposals or attempt to control a disease outbreak. © 2014 Society for Conservation Biology.

Energy challenges in optical access and aggregation networks.

PubMed

Kilper, Daniel C; Rastegarfar, Houman

2016-03-06

Scalability is a critical issue for access and aggregation networks as they must support the growth in both the size of data capacity demands and the multiplicity of access points. The number of connected devices, the Internet of Things, is growing to the tens of billions. Prevailing communication paradigms are reaching physical limitations that make continued growth problematic. Challenges are emerging in electronic and optical systems and energy increasingly plays a central role. With the spectral efficiency of optical systems approaching the Shannon limit, increasing parallelism is required to support higher capacities. For electronic systems, as the density and speed increases, the total system energy, thermal density and energy per bit are moving into regimes that become impractical to support-for example requiring single-chip processor powers above the 100 W limit common today. We examine communication network scaling and energy use from the Internet core down to the computer processor core and consider implications for optical networks. Optical switching in data centres is identified as a potential model from which scalable access and aggregation networks for the future Internet, with the application of integrated photonic devices and intelligent hybrid networking, will emerge. © 2016 The Author(s).

Finite Memory Walk and Its Application to Small-World Network

NASA Astrophysics Data System (ADS)

Oshima, Hiraku; Odagaki, Takashi

2012-07-01

In order to investigate the effects of cycles on the dynamical process on both regular lattices and complex networks, we introduce a finite memory walk (FMW) as an extension of the simple random walk (SRW), in which a walker is prohibited from moving to sites visited during m steps just before the current position. This walk interpolates the simple random walk (SRW), which has no memory (m = 0), and the self-avoiding walk (SAW), which has an infinite memory (m = ∞). We investigate the FMW on regular lattices and clarify the fundamental characteristics of the walk. We find that (1) the mean-square displacement (MSD) of the FMW shows a crossover from the SAW at a short time step to the SRW at a long time step, and the crossover time is approximately equivalent to the number of steps remembered, and that the MSD can be rescaled in terms of the time step and the size of memory; (2) the mean first-return time (MFRT) of the FMW changes significantly at the number of remembered steps that corresponds to the size of the smallest cycle in the regular lattice, where ``smallest'' indicates that the size of the cycle is the smallest in the network; (3) the relaxation time of the first-return time distribution (FRTD) decreases as the number of cycles increases. We also investigate the FMW on the Watts--Strogatz networks that can generate small-world networks, and show that the clustering coefficient of the Watts--Strogatz network is strongly related to the MFRT of the FMW that can remember two steps.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

PubMed

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

PubMed Central

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

Predictors of Change in Self-Reported Social Networks among Homeless Young People

ERIC Educational Resources Information Center

Falci, Christina D.; Whitbeck, Les B.; Hoyt, Dan R.; Rose, Trina

2011-01-01

This research investigates changes in social network size and composition of 351 homeless adolescents over 3 years. Findings show that network size decreases over time. Homeless youth with a conduct disorder begin street life with small networks that remain small over time. Caregiver abuse is associated with smaller emotional networks due to fewer…

On Channel-Discontinuity-Constraint Routing in Wireless Networks☆

PubMed Central

Sankararaman, Swaminathan; Efrat, Alon; Ramasubramanian, Srinivasan; Agarwal, Pankaj K.

2011-01-01

Multi-channel wireless networks are increasingly deployed as infrastructure networks, e.g. in metro areas. Network nodes frequently employ directional antennas to improve spatial throughput. In such networks, between two nodes, it is of interest to compute a path with a channel assignment for the links such that the path and link bandwidths are the same. This is achieved when any two consecutive links are assigned different channels, termed as “Channel-Discontinuity-Constraint” (CDC). CDC-paths are also useful in TDMA systems, where, preferably, consecutive links are assigned different time-slots. In the first part of this paper, we develop a t-spanner for CDC-paths using spatial properties; a sub-network containing O(n/θ) links, for any θ > 0, such that CDC-paths increase in cost by at most a factor t = (1−2 sin (θ/2))−2. We propose a novel distributed algorithm to compute the spanner using an expected number of O(n log n) fixed-size messages. In the second part, we present a distributed algorithm to find minimum-cost CDC-paths between two nodes using O(n2) fixed-size messages, by developing an extension of Edmonds’ algorithm for minimum-cost perfect matching. In a centralized implementation, our algorithm runs in O(n2) time improving the previous best algorithm which requires O(n3) running time. Moreover, this running time improves to O(n/θ) when used in conjunction with the spanner developed. PMID:24443646

Computational analysis of complex systems: Applications to population dynamics and networks

NASA Astrophysics Data System (ADS)

Molnar, Ferenc

In most complex evolving systems, we can often find a critical subset of the constituents that can initiate a global change in the entire system. For example, in complex networks, a critical subset of nodes can efficiently spread information, influence, or control dynamical processes over the entire network. Similarly, in nonlinear dynamics, we can locate key variables, or find the necessary parameters, to reach the attraction basin of a desired global state. In both cases, a fundamental goal is finding the ability to efficiently control these systems. We study two distinct complex systems in this dissertation, exploring these topics. First, we analyze a population dynamics model describing interactions of sex-structured population groups. Specifically, we analyze how a sex-linked genetic trait's ecological consequence (population survival or extinction) can be influenced by the presence of sex-specific cultural mortality traits, motivated by the desire to expand the theoretical understanding of the role of biased sex ratios in organisms. We analyze dynamics within a single population group, as well as between competing groups. We find that there is a finite range of sex ratio bias that can be maintained in stable equilibrium by sex-specific mortalities. We also find that the outcome of an invasion and the ensuing between-group competition depends not on larger equilibrium group densities, but on the higher allocation of sex-ratio genes. When we extend the model with diffusive dispersal, we find that a critical patch size for achieving positive growth only exists if the population expands into an empty environment. If a resident population is already present that can be exploited by the invading group, then any small seed of invader can advance from rarity, in the mean-field approximation, as long as the local competition dynamics favors the invader's survival. Most spatial models assume initial populations with a uniform distribution inside a finite patch; a simple, but not a cost-efficient approach. We show, using a novel application of simulated annealing, that a specific, non-trivial shape of spatial distribution can minimize the total cost of successful invasion, i.e., the cost of ecological restoration. Further, our approach can be generalized to essentially any reaction-diffusion model with diffusive spreading. In the second part of the dissertation we conduct an extensive study of minimum dominating sets (MDS) in complex networks; particularly, in scale-free networks. MDS is the smallest subset of nodes in a network that can reach every other node as nearest neighbors, thus it provides a key subset of nodes that play critical role in controllability and observability of social, biological, and technological networks. Continued interest in network control, monitoring and influencing of complex networks motivates our research of understanding the properties and practical application-related issues of the MDS. Our study of the scaling behavior reveals that the size of MDS always scales linearly with network size, as long as the power-law degree exponent gamma of the degree distribution is larger than 2. However, when gamma<2, a domination transition occurs, allowing the MDS size to become O(1), leading to easily dominated networks, under certain structural conditions. Motivated by practical applicability in large networks, we develop a new dominating set selection method, derived from probabilistic node selection techniques, which can select small dominating sets without complete network topology information. We also show that the effectiveness of our method, as well as the effectiveness of other heuristics of dominating set selection, strongly depends on the assortativity of networks. Finally, we conduct a numerical study to analyze the fraction of nodes that remain dominated, after the network is damaged, and some nodes are removed. We find that dominating sets optimized for small size are particularly vulnerable to damage; a significant amount of "domination coverage" may be lost if key dominator nodes are deleted. However, we also find that increasing the redundancy of dominating sets by adding a few well-picked nodes can successfully increase the post-damage dominated fraction of the network. Based on this idea, we develop two algorithms to build dominating sets with flexible balance between size and damage resilience.

Social network analysis of Iranian researchers in the field of violence.

PubMed

Salamati, Payman; Soheili, Faramarz

2016-10-01

The social network analysis (SNA) is a paradigm for analyzing structural patterns in social re- lations, testing knowledge sharing process and identifying bottlenecks of information flow. The purpose of this study was to determine the status of research in the fleld of violence in Iran using SNA. Research population included all the papers with at least one Iranian affiliation published in violence fleld indexed in SCIE, PubMed and Scopus databases. The co-word maps, co-authorship network and structural holes were drawn using related software. In the next step, the active authors and some measures of our network including degree centrality (DC), closeness, eigenvector, betweeness, density, diameter, compactness and size of the main component were assessed. Likewise, the trend of the published articles was evaluated based on the number of documents and their citations from 1972 to 2014. Five hundred and seventy one records were obtained. The five main clusters and hot spots were mental health, violence, war, psychiatric disorders and suicide. The co-authorship network was complex, tangled and scale free. The top nine authors with cut point role and top ten active authors were identified. The mean (standard deviation) of normalized DC, closeness, eigenvector and betweeness were 0.449 (0.805), 0.609 (0.214), 2.373 (7.353) and 0.338 (1.122), respectively. The density, diameter and mean compactness of our co-authorship network were 0.0494, 3.955 and 0.125, respectively. The main component consisted of 216 nodes that formed 17% of total size of the network. Both the number of the documents and their citations has increased in the field of violence in the recent years. Although the number of the documents has recently increased in the field of violence, the information flow is slow and there are not many relations among the authors in the network. However, the active authors have ability to influence the flow of knowledge within the network.

Population Size Estimation of Men Who Have Sex with Men through the Network Scale-Up Method in Japan

PubMed Central

Ezoe, Satoshi; Morooka, Takeo; Noda, Tatsuya; Sabin, Miriam Lewis; Koike, Soichi

2012-01-01

Background Men who have sex with men (MSM) are one of the groups most at risk for HIV infection in Japan. However, size estimates of MSM populations have not been conducted with sufficient frequency and rigor because of the difficulty, high cost and stigma associated with reaching such populations. This study examined an innovative and simple method for estimating the size of the MSM population in Japan. We combined an internet survey with the network scale-up method, a social network method for estimating the size of hard-to-reach populations, for the first time in Japan. Methods and Findings An internet survey was conducted among 1,500 internet users who registered with a nationwide internet-research agency. The survey participants were asked how many members of particular groups with known population sizes (firepersons, police officers, and military personnel) they knew as acquaintances. The participants were also asked to identify the number of their acquaintances whom they understood to be MSM. Using these survey results with the network scale-up method, the personal network size and MSM population size were estimated. The personal network size was estimated to be 363.5 regardless of the sex of the acquaintances and 174.0 for only male acquaintances. The estimated MSM prevalence among the total male population in Japan was 0.0402% without adjustment, and 2.87% after adjusting for the transmission error of MSM. Conclusions The estimated personal network size and MSM prevalence seen in this study were comparable to those from previous survey results based on the direct-estimation method. Estimating population sizes through combining an internet survey with the network scale-up method appeared to be an effective method from the perspectives of rapidity, simplicity, and low cost as compared with more-conventional methods. PMID:22563366

The Dynamics of Coalition Formation on Complex Networks

NASA Astrophysics Data System (ADS)

Auer, S.; Heitzig, J.; Kornek, U.; Schöll, E.; Kurths, J.

2015-08-01

Complex networks describe the structure of many socio-economic systems. However, in studies of decision-making processes the evolution of the underlying social relations are disregarded. In this report, we aim to understand the formation of self-organizing domains of cooperation (“coalitions”) on an acquaintance network. We include both the network’s influence on the formation of coalitions and vice versa how the network adapts to the current coalition structure, thus forming a social feedback loop. We increase complexity from simple opinion adaptation processes studied in earlier research to more complex decision-making determined by costs and benefits, and from bilateral to multilateral cooperation. We show how phase transitions emerge from such coevolutionary dynamics, which can be interpreted as processes of great transformations. If the network adaptation rate is high, the social dynamics prevent the formation of a grand coalition and therefore full cooperation. We find some empirical support for our main results: Our model develops a bimodal coalition size distribution over time similar to those found in social structures. Our detection and distinguishing of phase transitions may be exemplary for other models of socio-economic systems with low agent numbers and therefore strong finite-size effects.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J. D.; Aldrich, G.; Viswanathan, H.; Makedonska, N.; Karra, S.

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J.; Aldrich, G. A.; Viswanathan, H. S.; Makedonska, N.; Karra, S.

2016-12-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semi-correlation, and non-correlation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same.We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Limited-path-length entanglement percolation in quantum complex networks

NASA Astrophysics Data System (ADS)

Cuquet, Martí; Calsamiglia, John

2011-03-01

We study entanglement distribution in quantum complex networks where nodes are connected by bipartite entangled states. These networks are characterized by a complex structure, which dramatically affects how information is transmitted through them. For pure quantum state links, quantum networks exhibit a remarkable feature absent in classical networks: it is possible to effectively rewire the network by performing local operations on the nodes. We propose a family of such quantum operations that decrease the entanglement percolation threshold of the network and increase the size of the giant connected component. We provide analytic results for complex networks with an arbitrary (uncorrelated) degree distribution. These results are in good agreement with numerical simulations, which also show enhancement in correlated and real-world networks. The proposed quantum preprocessing strategies are not robust in the presence of noise. However, even when the links consist of (noisy) mixed-state links, one can send quantum information through a connecting path with a fidelity that decreases with the path length. In this noisy scenario, complex networks offer a clear advantage over regular lattices, namely, the fact that two arbitrary nodes can be connected through a relatively small number of steps, known as the small-world effect. We calculate the probability that two arbitrary nodes in the network can successfully communicate with a fidelity above a given threshold. This amounts to working out the classical problem of percolation with a limited path length. We find that this probability can be significant even for paths limited to few connections and that the results for standard (unlimited) percolation are soon recovered if the path length exceeds by a finite amount the average path length, which in complex networks generally scales logarithmically with the size of the network.

Design and implementation of a UNIX based distributed computing system

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

Love, J.S.; Michael, M.W.

1994-12-31

We have designed, implemented, and are running a corporate-wide distributed processing batch queue on a large number of networked workstations using the UNIX{reg_sign} operating system. Atlas Wireline researchers and scientists have used the system for over a year. The large increase in available computer power has greatly reduced the time required for nuclear and electromagnetic tool modeling. Use of remote distributed computing has simultaneously reduced computation costs and increased usable computer time. The system integrates equipment from different manufacturers, using various CPU architectures, distinct operating system revisions, and even multiple processors per machine. Various differences between the machines have tomore » be accounted for in the master scheduler. These differences include shells, command sets, swap spaces, memory sizes, CPU sizes, and OS revision levels. Remote processing across a network must be performed in a manner that is seamless from the users` perspective. The system currently uses IBM RISC System/6000{reg_sign}, SPARCstation{sup TM}, HP9000s700, HP9000s800, and DEC Alpha AXP{sup TM} machines. Each CPU in the network has its own speed rating, allowed working hours, and workload parameters. The system if designed so that all of the computers in the network can be optimally scheduled without adversely impacting the primary users of the machines. The increase in the total usable computational capacity by means of distributed batch computing can change corporate computing strategy. The integration of disparate computer platforms eliminates the need to buy one type of computer for computations, another for graphics, and yet another for day-to-day operations. It might be possible, for example, to meet all research and engineering computing needs with existing networked computers.« less

Sampling for Global Epidemic Models and the Topology of an International Airport Network

PubMed Central

Bobashev, Georgiy; Morris, Robert J.; Goedecke, D. Michael

2008-01-01

Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation. PMID:18776932

Cooperation in N-person evolutionary snowdrift game in scale-free Barabási Albert networks

NASA Astrophysics Data System (ADS)

Lee, K. H.; Chan, Chun-Him; Hui, P. M.; Zheng, Da-Fang

2008-09-01

Cooperation in the N-person evolutionary snowdrift game (NESG) is studied in scale-free Barabási-Albert (BA) networks. Due to the inhomogeneity of the network, two versions of NESG are proposed and studied. In a model where the size of the competing group varies from agent to agent, the fraction of cooperators drops as a function of the payoff parameter. The networking effect is studied via the fraction of cooperative agents for nodes with a particular degree. For small payoff parameters, it is found that the small- k agents are dominantly cooperators, while large- k agents are of non-cooperators. Studying the spatial correlation reveals that cooperative agents will avoid to be nearest neighbors and the correlation disappears beyond the next-nearest neighbors. The behavior can be explained in terms of the networking effect and payoffs. In another model with a fixed size of competing groups, the fraction of cooperators could show a non-monotonic behavior in the regime of small payoff parameters. This non-trivial behavior is found to be a combined effect of the many agents with the smallest degree in the BA network and the increasing fraction of cooperators among these agents with the payoff for small payoffs.

Social Network Decay as Potential Recovery from Homelessness: A Mixed Methods Study in Housing First Programming

PubMed Central

Golembiewski, Elizabeth; Watson, Dennis P.; Robison, Lisa; Coberg, John W.

2017-01-01

The positive relationship between social support and mental health has been well documented, but individuals experiencing chronic homelessness face serious disruptions to their social networks. Housing First (HF) programming has been shown to improve health and stability of formerly chronically homeless individuals. However, researchers are only just starting to understand the impact HF has on residents’ individual social integration. The purpose of the current study was to describe and understand changes in social networks of residents living in a HF program. Researchers employed a longitudinal, convergent parallel mixed method design, collecting quantitative social network data through structured interviews (n = 13) and qualitative data through semi-structured interviews (n = 20). Quantitative results demonstrated a reduction in network size over the course of one year. However, increases in both network density and frequency of contact with network members increased. Qualitative interviews demonstrated a strengthening in the quality of relationships with family and housing providers and a shedding of burdensome and abusive relationships. These results suggest network decay is a possible indicator of participants’ recovery process as they discontinued negative relationships and strengthened positive ones. PMID:28890807

Practical approximation method for firing-rate models of coupled neural networks with correlated inputs

NASA Astrophysics Data System (ADS)

Barreiro, Andrea K.; Ly, Cheng

2017-08-01

Rapid experimental advances now enable simultaneous electrophysiological recording of neural activity at single-cell resolution across large regions of the nervous system. Models of this neural network activity will necessarily increase in size and complexity, thus increasing the computational cost of simulating them and the challenge of analyzing them. Here we present a method to approximate the activity and firing statistics of a general firing rate network model (of the Wilson-Cowan type) subject to noisy correlated background inputs. The method requires solving a system of transcendental equations and is fast compared to Monte Carlo simulations of coupled stochastic differential equations. We implement the method with several examples of coupled neural networks and show that the results are quantitatively accurate even with moderate coupling strengths and an appreciable amount of heterogeneity in many parameters. This work should be useful for investigating how various neural attributes qualitatively affect the spiking statistics of coupled neural networks.

Modelling of information diffusion on social networks with applications to WeChat

NASA Astrophysics Data System (ADS)

Liu, Liang; Qu, Bo; Chen, Bin; Hanjalic, Alan; Wang, Huijuan

2018-04-01

Traces of user activities recorded in online social networks open new possibilities to systematically understand the information diffusion process on social networks. From the online social network WeChat, we collected a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbours. In this work, we propose two heterogeneous non-linear models, one for the topologies of the information cascade trees and the other for the stochastic process of information diffusion on a social network. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees. Specifically, we apply the Random Recursive Tree (RRT) to model the growth of cascade trees. The RRT model could capture key features, i.e. the average path length and degree variance of a cascade tree in relation to the number of nodes (size) of the tree. Its single identified parameter quantifies the relative depth or broadness of the cascade trees and indicates that information propagates via a star-like broadcasting or viral-like hop by hop spreading. The RRT model explains the appearance of hubs, thus a possibly smaller average path length as the cascade size increases, as observed in WeChat. We further propose the stochastic Susceptible View Forward Removed (SVFR) model to depict the dynamic user behaviour including creating, viewing, forwarding and ignoring a message on a given social network. Beside the average path length and degree variance of the cascade trees in relation to their sizes, the SVFR model could further explain the power-law cascade size distribution in WeChat and unravel that a user with a large number of friends may actually have a smaller probability to read a message (s)he receives due to limited attention.

Can Social Network Analysis Help Address the High Rates of Bacterial Sexually Transmitted Infections in Saskatchewan?

PubMed

Trecker, Molly A; Dillon, Jo-Anne R; Lloyd, Kathy; Hennink, Maurice; Jolly, Ann; Waldner, Cheryl

2017-06-01

Saskatchewan has one of the highest rates of gonorrhea among the Canadian provinces-more than double the national rate. In light of these high rates, and the growing threat of untreatable infections, improved understanding of gonorrhea transmission dynamics in the province and evaluation of the current system and tools for disease control are important. We extracted data from a cross-sectional sample of laboratory-confirmed gonorrhea cases between 2003 and 2012 from the notifiable disease files of the Regina Qu'Appelle Health Region. The database was stratified by calendar year, and social network analysis combined with statistical modeling was used to identify associations between measures of connection within the network and the odds of repeat gonorrhea and risk of coinfection with chlamydia at the time of diagnosis. Networks were highly fragmented. Younger age and component size were positively associated with being coinfected with chlamydia. Being coinfected, reporting sex trade involvement, and component size were all positively associated with repeat infection. This is the first study to apply social network analysis to gonorrhea transmission in Saskatchewan and contributes important information about the relationship of network connections to gonorrhea/chlamydia coinfection and repeat gonorrhea. This study also suggests several areas for change of systems-related factors that could greatly increase understanding of social networks and enhance the potential for bacterial sexually transmitted infection control in Saskatchewan.

Predicting community responses to perturbations in the face of imperfect knowledge and network complexity

USGS Publications Warehouse

Novak, Mark; Wootton, J. Timothy; Doak, Daniel F.; Emmerson, Mark; Estes, James A.; Tinker, M. Timothy

2011-01-01

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (∼25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities.

Influence of vascular network design on gas transfer in lung assist device technology.

PubMed

Bassett, Erik K; Hoganson, David M; Lo, Justin H; Penson, Elliot J N; Vacanti, Joseph P

2011-01-01

Blood oxygenators are vital for the critically ill, but their use is limited to the hospital setting. A portable blood oxygenator or a lung assist device for ambulatory or long-term use would greatly benefit patients with chronic lung disease. In this work, a biomimetic blood oxygenator system was developed which consisted of a microfluidic vascular network covered by a gas permeable silicone membrane. This system was used to determine the influence of key microfluidic parameters-channel size, oxygen exposure length, and blood shear rate-on blood oxygenation and carbon dioxide removal. Total gas transfer increased linearly with flow rate, independent of channel size and oxygen exposure length. On average, CO(2) transfer was 4.3 times higher than oxygen transfer. Blood oxygen saturation was also found to depend on the flow rate per channel but in an inverse manner; oxygenation decreased and approached an asymptote as the flow rate per channel increased. These relationships can be used to optimize future biomimetic vascular networks for specific lung applications: gas transfer for carbon dioxide removal in patients with chronic obstructive pulmonary disease or oxygenation for premature infants requiring complete lung replacement therapy.

Pollinators visit related plant species across 29 plant–pollinator networks

PubMed Central

Vamosi, Jana C; Moray, Clea M; Garcha, Navdeep K; Chamberlain, Scott A; Mooers, Arne Ø

2014-01-01

Understanding the evolution of specialization in host plant use by pollinators is often complicated by variability in the ecological context of specialization. Flowering communities offer their pollinators varying numbers and proportions of floral resources, and the uniformity observed in these floral resources is, to some degree, due to shared ancestry. Here, we find that pollinators visit related plant species more so than expected by chance throughout 29 plant–pollinator networks of varying sizes, with “clade specialization” increasing with community size. As predicted, less versatile pollinators showed more clade specialization overall. We then asked whether this clade specialization varied with the ratio of pollinator species to plant species such that pollinators were changing their behavior when there was increased competition (and presumably a forced narrowing of the realized niche) by examining pollinators that were present in at least three of the networks. Surprisingly, we found little evidence that variation in clade specialization is caused by pollinator species changing their behavior in different community contexts, suggesting that clade specialization is observed when pollinators are either restricted in their floral choices due to morphological constraints or innate preferences. The resulting pollinator sharing between closely related plant species could result in selection for greater pollinator specialization. PMID:25360269

[Stochastic model of infectious diseases transmission].

PubMed

Ruiz-Ramírez, Juan; Hernández-Rodríguez, Gabriela Eréndira

2009-01-01

Propose a mathematic model that shows how population structure affects the size of infectious disease epidemics. This study was conducted during 2004 at the University of Colima. It used generalized small-world network topology to represent contacts that occurred within and between families. To that end, two programs in MATLAB were conducted to calculate the efficiency of the network. The development of a program in the C programming language was also required, that represents the stochastic susceptible-infectious-removed model, and simultaneous results were obtained for the number of infected people. An increased number of families connected by meeting sites impacted the size of the infectious diseases by roughly 400%. Population structure influences the rapid spread of infectious diseases, reaching epidemic effects.

Evolving bipartite authentication graph partitions

DOE PAGES

Pope, Aaron Scott; Tauritz, Daniel Remy; Kent, Alexander D.

2017-01-16

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users’ access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users’ access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of largemore » connected components while minimizing overall restrictions on network users. Lastly, results are presented on a real world data set that demonstrate the effectiveness of the introduced method compared to previous naive methods.« less

Evolving bipartite authentication graph partitions

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

Pope, Aaron Scott; Tauritz, Daniel Remy; Kent, Alexander D.

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users’ access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users’ access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of largemore » connected components while minimizing overall restrictions on network users. Lastly, results are presented on a real world data set that demonstrate the effectiveness of the introduced method compared to previous naive methods.« less

Simplifications for hydronic system models in modelica

DOE PAGES

Jorissen, F.; Wetter, M.; Helsen, L.

2018-01-12

Building systems and their heating, ventilation and air conditioning flow networks, are becoming increasingly complex. Some building energy simulation tools simulate these flow networks using pressure drop equations. These flow network models typically generate coupled algebraic nonlinear systems of equations, which become increasingly more difficult to solve as their sizes increase. This leads to longer computation times and can cause the solver to fail. These problems also arise when using the equation-based modelling language Modelica and Annex 60-based libraries. This may limit the applicability of the library to relatively small problems unless problems are restructured. This paper discusses two algebraicmore » loop types and presents an approach that decouples algebraic loops into smaller parts, or removes them completely. The approach is applied to a case study model where an algebraic loop of 86 iteration variables is decoupled into smaller parts with a maximum of five iteration variables.« less

Mobility particle size spectrometers: harmonization of technical standards and data structure to facilitate high quality long-term observations of atmospheric particle number size distributions

NASA Astrophysics Data System (ADS)

Wiedensohler, A.; Birmili, W.; Nowak, A.; Sonntag, A.; Weinhold, K.; Merkel, M.; Wehner, B.; Tuch, T.; Pfeifer, S.; Fiebig, M.; Fjäraa, A. M.; Asmi, E.; Sellegri, K.; Depuy, R.; Venzac, H.; Villani, P.; Laj, P.; Aalto, P.; Ogren, J. A.; Swietlicki, E.; Williams, P.; Roldin, P.; Quincey, P.; Hüglin, C.; Fierz-Schmidhauser, R.; Gysel, M.; Weingartner, E.; Riccobono, F.; Santos, S.; Grüning, C.; Faloon, K.; Beddows, D.; Harrison, R.; Monahan, C.; Jennings, S. G.; O'Dowd, C. D.; Marinoni, A.; Horn, H.-G.; Keck, L.; Jiang, J.; Scheckman, J.; McMurry, P. H.; Deng, Z.; Zhao, C. S.; Moerman, M.; Henzing, B.; de Leeuw, G.; Löschau, G.; Bastian, S.

2012-03-01

Mobility particle size spectrometers often referred to as DMPS (Differential Mobility Particle Sizers) or SMPS (Scanning Mobility Particle Sizers) have found a wide range of applications in atmospheric aerosol research. However, comparability of measurements conducted world-wide is hampered by lack of generally accepted technical standards and guidelines with respect to the instrumental set-up, measurement mode, data evaluation as well as quality control. Technical standards were developed for a minimum requirement of mobility size spectrometry to perform long-term atmospheric aerosol measurements. Technical recommendations include continuous monitoring of flow rates, temperature, pressure, and relative humidity for the sheath and sample air in the differential mobility analyzer. We compared commercial and custom-made inversion routines to calculate the particle number size distributions from the measured electrical mobility distribution. All inversion routines are comparable within few per cent uncertainty for a given set of raw data. Furthermore, this work summarizes the results from several instrument intercomparison workshops conducted within the European infrastructure project EUSAAR (European Supersites for Atmospheric Aerosol Research) and ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) to determine present uncertainties especially of custom-built mobility particle size spectrometers. Under controlled laboratory conditions, the particle number size distributions from 20 to 200 nm determined by mobility particle size spectrometers of different design are within an uncertainty range of around ±10% after correcting internal particle losses, while below and above this size range the discrepancies increased. For particles larger than 200 nm, the uncertainty range increased to 30%, which could not be explained. The network reference mobility spectrometers with identical design agreed within ±4% in the peak particle number concentration when all settings were done carefully. The consistency of these reference instruments to the total particle number concentration was demonstrated to be less than 5%. Additionally, a new data structure for particle number size distributions was introduced to store and disseminate the data at EMEP (European Monitoring and Evaluation Program). This structure contains three levels: raw data, processed data, and final particle size distributions. Importantly, we recommend reporting raw measurements including all relevant instrument parameters as well as a complete documentation on all data transformation and correction steps. These technical and data structure standards aim to enhance the quality of long-term size distribution measurements, their comparability between different networks and sites, and their transparency and traceability back to raw data.

Hopping Diffusion of Nanoparticles in Polymer Matrices

PubMed Central

2016-01-01

We propose a hopping mechanism for diffusion of large nonsticky nanoparticles subjected to topological constraints in both unentangled and entangled polymer solids (networks and gels) and entangled polymer liquids (melts and solutions). Probe particles with size larger than the mesh size ax of unentangled polymer networks or tube diameter ae of entangled polymer liquids are trapped by the network or entanglement cells. At long time scales, however, these particles can diffuse by overcoming free energy barrier between neighboring confinement cells. The terminal particle diffusion coefficient dominated by this hopping diffusion is appreciable for particles with size moderately larger than the network mesh size ax or tube diameter ae. Much larger particles in polymer solids will be permanently trapped by local network cells, whereas they can still move in polymer liquids by waiting for entanglement cells to rearrange on the relaxation time scales of these liquids. Hopping diffusion in entangled polymer liquids and networks has a weaker dependence on particle size than that in unentangled networks as entanglements can slide along chains under polymer deformation. The proposed novel hopping model enables understanding the motion of large nanoparticles in polymeric nanocomposites and the transport of nano drug carriers in complex biological gels such as mucus. PMID:25691803

Bidirectional influence: A longitudinal analysis of size of drug network and depression among inner-city residents in Baltimore, Maryland

PubMed Central

Yang, Jingyan; Latkin, Carl A.; Davey-Rothwell, Melissa

2015-01-01

BACKGROUND The prevalence of depression among drug users is high. It has been recognized that drug use behaviors can be influenced and spread through social networks. OBJECTIVES We investigated the directional relationship between social network factors and depressive symptoms among a sample of inner-city residents in Baltimore, MD. METHODS We performed a longitudinal study of four-wave data collected from a network-based HIV/STI prevention intervention for women and network members, consisting of both men and women. Our primary outcome and exposure were depression using CESD scale and social network characteristics, respectively. Linear mixed model with clustering adjustment was used to account for both repeated measurement and network design. RESULTS Of the 746 participants, those who had high levels of depression tended to be female, less educated, homeless, smokers, and did not have a main partner. In the univariate longitudinal model, larger size of drug network was significantly associated with depression (OR=1.38, p<0.001). This relationship held after controlling for age, gender, homeless in the past six months, college education, having a main partner, cigarette smoking, perceived health, and social support network (aOR=1.19, p=0.001). In the univariate mixed model using depression to predict size of drug network, the data suggested that depression was associated with larger size of drug network (coef.=1.23, p<0.001) and the same relation held in multivariate model (adjusted coef.=1.08, p=0.001). CONCLUSIONS The results suggest that larger size of drug network is a risk factor for depression, and vice versa. Further intervention strategies to reduce depression should address social networks factors. PMID:26584046

Drug Network Characteristics and HIV Risk Among Injection Drug Users in Russia: The roles of Trust, Size, and Stability

PubMed Central

Odinokova, Veronika A.; Heimer, Robert; Grau, Lauretta E.; Lyubimova, Alexandra; Safiullina, Liliya; Levina, Olga S.; Niccolai, Linda M.

2011-01-01

We investigated the influence of drug network characteristics including trust, size, and stability on HIV risk behaviors and HIV testing among injection drug users (IDUs) in St. Petersburg, Russia. Overall, male and female IDUs who reported having high levels of trust in their drug networks were significantly more likely to share syringes than those with lower levels of trust (OR [95% CI]) 2.87 [1.06, 7.81] and 4.89 [1.05, 21.94], respectively). Male and female IDUs in larger drug networks were more likely to share syringes than those in smaller networks (4.21 [1.54, 11.51] and 4.80 [1.20, 19.94], respectively). Characteristics that were significantly associated with not having been HIV tested included drug network instability among men and larger network size among women. High trust, large size, and instability were positively and significantly associated with syringe sharing and not having been HIV tested. Effectiveness of interventions in Russia to reduce the risk of HIV infection may be enhanced if network characteristics are addressed. PMID:20872063

FPGA implementation of advanced FEC schemes for intelligent aggregation networks

NASA Astrophysics Data System (ADS)

Zou, Ding; Djordjevic, Ivan B.

2016-02-01

In state-of-the-art fiber-optics communication systems the fixed forward error correction (FEC) and constellation size are employed. While it is important to closely approach the Shannon limit by using turbo product codes (TPC) and low-density parity-check (LDPC) codes with soft-decision decoding (SDD) algorithm; rate-adaptive techniques, which enable increased information rates over short links and reliable transmission over long links, are likely to become more important with ever-increasing network traffic demands. In this invited paper, we describe a rate adaptive non-binary LDPC coding technique, and demonstrate its flexibility and good performance exhibiting no error floor at BER down to 10-15 in entire code rate range, by FPGA-based emulation, making it a viable solution in the next-generation high-speed intelligent aggregation networks.

Network Confinement and Heterogeneity Slows Nanoparticle Diffusion in Polymer Gels

NASA Astrophysics Data System (ADS)

Parrish, Emmabeth; Caporizzo, Matthew; Composto, Russell

Nanoparticle (NP) diffusion was measured in polyacrylamide gels (PAG) with a mesh size comparable to NP size, 20nm. The confinement ratio (CR), NP diameter/mesh, increased from 0.4 to 3.8 by increasing crosslinker density and 0.4 to 2 by adding acetone, which collapsed PAG. In all gels, NPs either became localized (<200nm) or diffused microns, as measured by single particle tracking. Mean squared displacements (MSD) of mobile NPs decreased as CR increased. In collapsed gels, the localized NP population increased and MSD of mobile NPs decreased compared to crosslinked PAG. For all CRs, van Hove distributions exhibited non-Gaussian displacements consistent with intermittent localization of NPs. The non-Gaussian parameter increased from a maximum of 1.5 for crosslinked PAG to 5 for collapsed PAG, consistent with greater network heterogeneity. Diffusion coefficients, D, decreased exponentially as CR increased for crosslinked gels, but in collapsed gels D decreased more strongly, suggesting CR alone was insufficient to capture diffusion. Collapsing the gel resulted in an increasingly tortuous pathway for NPs, slowing diffusion at a given CR. Understanding how gel structure affects NP mobility will allow the design of gels with improved ability to separate and release molecules. ACS/PRF 54028-ND7, NSF/MWN DMR-1210379.

Metabolic Networks and Metabolites Underlie Associations Between Maternal Glucose During Pregnancy and Newborn Size at Birth

PubMed Central

Bain, James R.; Reisetter, Anna C.; Muehlbauer, Michael J.; Nodzenski, Michael; Stevens, Robert D.; Ilkayeva, Olga; Lowe, Lynn P.; Metzger, Boyd E.; Newgard, Christopher B.; Lowe, William L.

2016-01-01

Maternal metabolites and metabolic networks underlying associations between maternal glucose during pregnancy and newborn birth weight and adiposity demand fuller characterization. We performed targeted and nontargeted gas chromatography/mass spectrometry metabolomics on maternal serum collected at fasting and 1 h following glucose beverage consumption during an oral glucose tolerance test (OGTT) for 400 northern European mothers at ∼28 weeks' gestation in the Hyperglycemia and Adverse Pregnancy Outcome Study. Amino acids, fatty acids, acylcarnitines, and products of lipid metabolism decreased and triglycerides increased during the OGTT. Analyses of individual metabolites indicated limited maternal glucose associations at fasting, but broader associations, including amino acids, fatty acids, carbohydrates, and lipids, were found at 1 h. Network analyses modeling metabolite correlations provided context for individual metabolite associations and elucidated collective associations of multiple classes of metabolic fuels with newborn size and adiposity, including acylcarnitines, fatty acids, carbohydrates, and organic acids. Random forest analyses indicated an improved ability to predict newborn size outcomes by using maternal metabolomics data beyond traditional risk factors, including maternal glucose. Broad-scale association of fuel metabolites with maternal glucose is evident during pregnancy, with unique maternal metabolites potentially contributing specifically to newborn birth weight and adiposity. PMID:27207545

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

DOE PAGES

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; ...

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

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

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.

2014-09-01

According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.

The equal load-sharing model of cascade failures in power grids

NASA Astrophysics Data System (ADS)

Scala, Antonio; De Sanctis Lucentini, Pier Giorgio

2016-11-01

Electric power-systems are one of the most important critical infrastructures. In recent years, they have been exposed to extreme stress due to the increasing power demand, the introduction of distributed renewable energy sources, and the development of extensive interconnections. We investigate the phenomenon of abrupt breakdown of an electric power-system under two scenarios: load growth (mimicking the ever-increasing customer demand) and power fluctuations (mimicking the effects of renewable sources). Our results indicate that increasing the system size causes breakdowns to become more abrupt; in fact, mapping the system to a solvable statistical-physics model indicates the occurrence of a first order transition in the large size limit. Such an enhancement for the systemic risk failures (black-outs) with increasing network size is an effect that should be considered in the current projects aiming to integrate national power-grids into ;super-grids;.

Secure Data Aggregation in Wireless Sensor Network-Fujisaki Okamoto(FO) Authentication Scheme against Sybil Attack.

PubMed

Nirmal Raja, K; Maraline Beno, M

2017-07-01

In the wireless sensor network(WSN) security is a major issue. There are several network security schemes proposed in research. In the network, malicious nodes obstruct the performance of the network. The network can be vulnerable by Sybil attack. When a node illicitly assertions multiple identities or claims fake IDs, the WSN grieves from an attack named Sybil attack. This attack threatens wireless sensor network in data aggregation, synchronizing system, routing, fair resource allocation and misbehavior detection. Henceforth, the research is carried out to prevent the Sybil attack and increase the performance of the network. This paper presents the novel security mechanism and Fujisaki Okamoto algorithm and also application of the work. The Fujisaki-Okamoto (FO) algorithm is ID based cryptographic scheme and gives strong authentication against Sybil attack. By using Network simulator2 (NS2) the scheme is simulated. In this proposed scheme broadcasting key, time taken for different key sizes, energy consumption, Packet delivery ratio, Throughput were analyzed.

Neural connections foster social connections: a diffusion-weighted imaging study of social networks

PubMed Central

Hampton, William H.; Unger, Ashley; Von Der Heide, Rebecca J.

2016-01-01

Although we know the transition from childhood to adulthood is marked by important social and neural development, little is known about how social network size might affect neurocognitive development or vice versa. Neuroimaging research has identified several brain regions, such as the amygdala, as key to this affiliative behavior. However, white matter connectivity among these regions, and its behavioral correlates, remain unclear. Here we tested two hypotheses: that an amygdalocentric structural white matter network governs social affiliative behavior and that this network changes during adolescence and young adulthood. We measured social network size behaviorally, and white matter microstructure using probabilistic diffusion tensor imaging in a sample of neurologically normal adolescents and young adults. Our results suggest amygdala white matter microstructure is key to understanding individual differences in social network size, with connectivity to other social brain regions such as the orbitofrontal cortex and anterior temporal lobe predicting much variation. In addition, participant age correlated with both network size and white matter variation in this network. These findings suggest the transition to adulthood may constitute a critical period for the optimization of structural brain networks underlying affiliative behavior. PMID:26755769

Utilizing Maximal Independent Sets as Dominating Sets in Scale-Free Networks

NASA Astrophysics Data System (ADS)

Derzsy, N.; Molnar, F., Jr.; Szymanski, B. K.; Korniss, G.

Dominating sets provide key solution to various critical problems in networked systems, such as detecting, monitoring, or controlling the behavior of nodes. Motivated by graph theory literature [Erdos, Israel J. Math. 4, 233 (1966)], we studied maximal independent sets (MIS) as dominating sets in scale-free networks. We investigated the scaling behavior of the size of MIS in artificial scale-free networks with respect to multiple topological properties (size, average degree, power-law exponent, assortativity), evaluated its resilience to network damage resulting from random failure or targeted attack [Molnar et al., Sci. Rep. 5, 8321 (2015)], and compared its efficiency to previously proposed dominating set selection strategies. We showed that, despite its small set size, MIS provides very high resilience against network damage. Using extensive numerical analysis on both synthetic and real-world (social, biological, technological) network samples, we demonstrate that our method effectively satisfies four essential requirements of dominating sets for their practical applicability on large-scale real-world systems: 1.) small set size, 2.) minimal network information required for their construction scheme, 3.) fast and easy computational implementation, and 4.) resiliency to network damage. Supported by DARPA, DTRA, and NSF.

From field notes to data portal - An operational QA/QC framework for tower networks

NASA Astrophysics Data System (ADS)

Sturtevant, C.; Hackley, S.; Meehan, T.; Roberti, J. A.; Holling, G.; Bonarrigo, S.

2016-12-01

Quality assurance and control (QA/QC) is one of the most important yet challenging aspects of producing research-quality data. This is especially so for environmental sensor networks collecting numerous high-frequency measurement streams at distributed sites. Here, the quality issues are multi-faceted, including sensor malfunctions, unmet theoretical assumptions, and measurement interference from the natural environment. To complicate matters, there are often multiple personnel managing different sites or different steps in the data flow. For large, centrally managed sensor networks such as NEON, the separation of field and processing duties is in the extreme. Tower networks such as Ameriflux, ICOS, and NEON continue to grow in size and sophistication, yet tools for robust, efficient, scalable QA/QC have lagged. Quality control remains a largely manual process relying on visual inspection of the data. In addition, notes of observed measurement interference or visible problems are often recorded on paper without an explicit pathway to data flagging during processing. As such, an increase in network size requires a near-proportional increase in personnel devoted to QA/QC, quickly stressing the human resources available. There is a need for a scalable, operational QA/QC framework that combines the efficiency and standardization of automated tests with the power and flexibility of visual checks, and includes an efficient communication pathway from field personnel to data processors to end users. Here we propose such a framework and an accompanying set of tools in development, including a mobile application template for recording tower maintenance and an R/shiny application for efficiently monitoring and synthesizing data quality issues. This framework seeks to incorporate lessons learned from the Ameriflux community and provide tools to aid continued network advancements.

The complexity and robustness of metro networks

NASA Astrophysics Data System (ADS)

Derrible, Sybil; Kennedy, Christopher

2010-09-01

Transportation systems, being real-life examples of networks, are particularly interesting to analyze from the viewpoint of the new and rapidly emerging field of network science. Two particular concepts seem to be particularly relevant: scale-free patterns and small-worlds. By looking at 33 metro systems in the world, this paper adapts network science methodologies to the transportation literature, and offers one application to the robustness of metros; here, metro refers to urban rail transit with exclusive right-of-way, whether it is underground, at grade or elevated. We find that most metros are indeed scale-free (with scaling factors ranging from 2.10 to 5.52) and small-worlds; they show atypical behaviors, however, with increasing size. In particular, the presence of transfer-hubs (stations hosting more than three lines) results in relatively large scaling factors. The analysis provides insights/recommendations for increasing the robustness of metro networks. Smaller networks should focus on creating transfer stations, thus generating cycles to offer alternative routes. For larger networks, few stations seem to detain a certain monopole on transferring, it is therefore important to create additional transfers, possibly at the periphery of city centers; the Tokyo system seems to remarkably incorporate these properties.

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D

PubMed Central

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron

2017-01-01

Abstract Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. PMID:28814063

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D.

PubMed

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron; Gümüs, Zeynep H

2017-08-01

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. © The Authors 2017. Published by Oxford University Press.

Performance Evaluation of Telemedicine System based on multicasting over Heterogeneous Network.

PubMed

Yun, H Y; Yoo, S K; Kim, D K; Rim Kim, Sung

2005-01-01

For appropriate diagnosis, medical image such as high quality image of patient's affected part and vital signal, patient information, and teleconferencing data for communication between specialists will be transmitted. After connecting patient and specialist the center, sender acquires patient data and transmits to the center through TCP/IP protocol. Data that is transmitted to center is retransmitted to each specialist side that accomplish connection after being copied according to listener's number from transmission buffer. At transmission of medical information data in network, transmission delay and loss occur by the change of buffer size, packet size, number of user and kind of networks. As there lies the biggest delay possibility in ADSL, buffer Size should be established by 1Mbytes first to minimize transmission regionalism and each packet's size must be set accordingly to MTU Size in order to improve network efficiency by maximum. Also, listener's number should be limited by less than 6 people. Data transmission consisted smoothly all in experiment result in common use network- ADSL, VDSL, WLAN, LAN-. But, possibility of delay appeared most greatly in ADSL that has the most confined bandwidth. To minimize the possibility of delay, some adjustment is needed such as buffer size, number of receiver, packet size.

Solute-specific scaling of inorganic nitrogen and phosphorus uptake in streams

NASA Astrophysics Data System (ADS)

Hall, R. O., Jr.; Baker, M. A.; Rosi-Marshall, E. J.; Tank, J. L.; Newbold, J. D.

2013-11-01

Stream ecosystem processes such as nutrient cycling may vary with stream position in the network. Using a scaling approach, we examined the relationship between stream size and nutrient uptake length, which represents the mean distance that a dissolved solute travels prior to removal from the water column. Ammonium (NH4+) uptake length increased proportionally with stream size measured as specific discharge (discharge/stream width) with a scaling exponent = 1.01. In contrast, uptake lengths for nitrate (NO3-) and soluble reactive phosphorus (SRP) increased more rapidly than increases in specific discharge (scaling exponents = 1.19 for NO3- and 1.35 for SRP). Additionally, the ratio of inorganic nitrogen (N) uptake length to SRP uptake length declined with stream size; there was relatively lower demand for SRP compared to N as stream size increased. Finally, we related the scaling of uptake length with specific discharge to that of stream length using Hack's law and downstream hydraulic geometry. Ammonium uptake length increased less than proportionally with distance from the headwaters, suggesting a strong role for larger streams and rivers in regulating nutrient transport.

Driven Boson Sampling.

PubMed

Barkhofen, Sonja; Bartley, Tim J; Sansoni, Linda; Kruse, Regina; Hamilton, Craig S; Jex, Igor; Silberhorn, Christine

2017-01-13

Sampling the distribution of bosons that have undergone a random unitary evolution is strongly believed to be a computationally hard problem. Key to outperforming classical simulations of this task is to increase both the number of input photons and the size of the network. We propose driven boson sampling, in which photons are input within the network itself, as a means to approach this goal. We show that the mean number of photons entering a boson sampling experiment can exceed one photon per input mode, while maintaining the required complexity, potentially leading to less stringent requirements on the input states for such experiments. When using heralded single-photon sources based on parametric down-conversion, this approach offers an ∼e-fold enhancement in the input state generation rate over scattershot boson sampling, reaching the scaling limit for such sources. This approach also offers a dramatic increase in the signal-to-noise ratio with respect to higher-order photon generation from such probabilistic sources, which removes the need for photon number resolution during the heralding process as the size of the system increases.

The Determination of Production and Distribution Policy in Push-Pull Production Chain with Supply Hub as the Junction Point

NASA Astrophysics Data System (ADS)

Sinaga, A. T.; Wangsaputra, R.

2018-03-01

The development of technology causes the needs of products and services become increasingly complex, diverse, and fluctuating. This causes the level of inter-company dependencies within a production chains increased. To be able to compete, efficiency improvements need to be done collaboratively in the production chain network. One of the efforts to increase efficiency is to harmonize production and distribution activities in the production chain network. This paper describes the harmonization of production and distribution activities by applying the use of push-pull system and supply hub in the production chain between two companies. The research methodology begins with conducting empirical and literature studies, formulating research questions, developing mathematical models, conducting trials and analyses, and taking conclusions. The relationship between the two companies is described in the MINLP mathematical model with the total cost of production chain as the objective function. Decisions generated by the mathematical models are the size of production lot, size of delivery lot, number of kanban, frequency of delivery, and the number of understock and overstock lot.

Complexity analysis on public transport networks of 97 large- and medium-sized cities in China

NASA Astrophysics Data System (ADS)

Tian, Zhanwei; Zhang, Zhuo; Wang, Hongfei; Ma, Li

2018-04-01

The traffic situation in Chinese urban areas is continuing to deteriorate. To make a better planning and designing of the public transport system, it is necessary to make profound research on the structure of urban public transport networks (PTNs). We investigate 97 large- and medium-sized cities’ PTNs in China, construct three types of network models — bus stop network, bus transit network and bus line network, then analyze the structural characteristics of them. It is revealed that bus stop network is small-world and scale-free, bus transit network and bus line network are both small-world. Betweenness centrality of each city’s PTN shows similar distribution pattern, although these networks’ size is various. When classifying cities according to the characteristics of PTNs or economic development level, the results are similar. It means that the development of cities’ economy and transport network has a strong correlation, PTN expands in a certain model with the development of economy.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes.

PubMed

Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku; Zhang, Wei; Kreisberg, Jason F; Tamayo, Pablo; Ideker, Trey

2018-04-25

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research. Copyright © 2018 Elsevier Inc. All rights reserved.

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

Characterizing mesh size distributions (MSDs) in thermosetting materials using a high-pressure system.

PubMed

Larché, J-F; Seynaeve, J-M; Voyard, G; Bussière, P-O; Gardette, J-L

2011-04-21

The thermoporosimetry method was adapted to determine the mesh size distribution of an acrylate thermoset clearcoat. This goal was achieved by increasing the solvent rate transfer by increasing the pressure and temperature. A comparison of the results obtained using this approach with those obtained by DMA (dynamic mechanical analysis) underlined the accuracy of thermoporosimetry in characterizing the macromolecular architecture of thermosets. The thermoporosimetry method was also used to analyze the effects of photoaging on cross-linking, which result from the photodegradation of the acrylate thermoset. It was found that the formation of a three-dimensional network followed by densification generates a modification of the average mesh size that leads to a dramatic decrease of the meshes of the polymer.

Importance of latrine communication in European rabbits shifts along a rural-to-urban gradient.

PubMed

Ziege, Madlen; Bierbach, David; Bischoff, Svenja; Brandt, Anna-Lena; Brix, Mareike; Greshake, Bastian; Merker, Stefan; Wenninger, Sandra; Wronski, Torsten; Plath, Martin

2016-06-14

Information transfer in mammalian communication networks is often based on the deposition of excreta in latrines. Depending on the intended receiver(s), latrines are either formed at territorial boundaries (between-group communication) or in core areas of home ranges (within-group communication). The relative importance of both types of marking behavior should depend, amongst other factors, on population densities and social group sizes, which tend to differ between urban and rural wildlife populations. Our study is the first to assess (direct and indirect) anthropogenic influences on mammalian latrine-based communication networks along a rural-to-urban gradient in European rabbits (Oryctolagus cuniculus) living in urban, suburban and rural areas in and around Frankfurt am Main (Germany). The proportion of latrines located in close proximity to the burrow was higher at rural study sites compared to urban and suburban ones. At rural sites, we found the largest latrines and highest latrine densities close to the burrow, suggesting that core marking prevailed. By contrast, latrine dimensions and densities increased with increasing distance from the burrow in urban and suburban populations, suggesting a higher importance of peripheral marking. Increased population densities, but smaller social group sizes in urban rabbit populations may lead to an increased importance of between-group communication and thus, favor peripheral over core marking. Our study provides novel insights into the manifold ways by which man-made habitat alterations along a rural-to-urban gradient directly and indirectly affect wildlife populations, including latrine-based communication networks.

Leverage Between the Buffering Effect and the Bystander Effect in Social Networking.

PubMed

Chiu, Yu-Ping; Chang, Shu-Chen

2015-08-01

This study examined encouraged and inhibited social feedback behaviors based on the theories of the buffering effect and the bystander effect. A system program was used to collect personal data and social feedback from a Facebook data set to test the research model. The results revealed that the buffering effect induced a positive relationship between social network size and feedback gained from friends when people's social network size was under a certain cognitive constraint. For people with a social network size that exceeds this cognitive constraint, the bystander effect may occur, in which having more friends may inhibit social feedback. In this study, two social psychological theories were applied to explain social feedback behavior on Facebook, and it was determined that social network size and social feedback exhibited no consistent linear relationship.

Effects of Social Support Network Size on Mortality Risk: Considerations by Diabetes Status.

PubMed

Loprinzi, Paul D; Ford, M Allison

2018-05-01

Previous work demonstrates that social support is inversely associated with mortality risk. Less research, however, has examined the effects of the size of the social support network on mortality risk among those with and without diabetes, which was the purpose of this study. Data from the 1999-2008 National Health and Nutrition Examination Survey were used, with participants followed through 2011. This study included 1,412 older adults (≥60 years of age) with diabetes and 5,872 older adults without diabetes. The size of the social support network was assessed via self-report and reported as the number of participants' close friends. Among those without diabetes, various levels of social support network size were inversely associated with mortality risk. However, among those with diabetes, only those with a high social support network size (i.e., at least six close friends) had a reduced risk of all-cause mortality. That is, compared to those with zero close friends, those with diabetes who had six or more close friends had a 49% reduced risk of all-cause mortality (hazard ratio 0.51, 95% CI 0.27-0.94). To mitigate mortality risk, a greater social support network size may be needed for those with diabetes.

Tactical Network Load Balancing in Multi-Gateway Wireless Sensor Networks

DTIC Science & Technology

2013-12-01

writeup scrsz = get( 0 ,’ScreenSize’); %Creation of the random Sensor Network fig = figure(1); set(fig, ’Position’,[1 scrsz( 4 )*.25 scrsz(3)*.7...thesis writeup scrsz = get( 0 ,’ScreenSize’); %Creation of the random Sensor Network fig = figure(1); set(fig, ’Position’,[1 scrsz( 4 )*.25 scrsz(3)*.7...TYPE AND DATES COVERED Master’s Thesis 4 . TITLE AND SUBTITLE TACTICAL NETWORK LOAD BALANCING IN MULTI-GATEWAY WIRELESS SENSOR NETWORKS 5

Second look at the spread of epidemics on networks

NASA Astrophysics Data System (ADS)

Kenah, Eben; Robins, James M.

2007-09-01

In an important paper, Newman [Phys. Rev. E66, 016128 (2002)] claimed that a general network-based stochastic Susceptible-Infectious-Removed (SIR) epidemic model is isomorphic to a bond percolation model, where the bonds are the edges of the contact network and the bond occupation probability is equal to the marginal probability of transmission from an infected node to a susceptible neighbor. In this paper, we show that this isomorphism is incorrect and define a semidirected random network we call the epidemic percolation network that is exactly isomorphic to the SIR epidemic model in any finite population. In the limit of a large population, (i) the distribution of (self-limited) outbreak sizes is identical to the size distribution of (small) out-components, (ii) the epidemic threshold corresponds to the phase transition where a giant strongly connected component appears, (iii) the probability of a large epidemic is equal to the probability that an initial infection occurs in the giant in-component, and (iv) the relative final size of an epidemic is equal to the proportion of the network contained in the giant out-component. For the SIR model considered by Newman, we show that the epidemic percolation network predicts the same mean outbreak size below the epidemic threshold, the same epidemic threshold, and the same final size of an epidemic as the bond percolation model. However, the bond percolation model fails to predict the correct outbreak size distribution and probability of an epidemic when there is a nondegenerate infectious period distribution. We confirm our findings by comparing predictions from percolation networks and bond percolation models to the results of simulations. In the Appendix, we show that an isomorphism to an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model.

Elastic collapse in disordered isostatic networks

NASA Astrophysics Data System (ADS)

Moukarzel, C. F.

2012-02-01

Isostatic networks are minimally rigid and therefore have, generically, nonzero elastic moduli. Regular isostatic networks have finite moduli in the limit of large sizes. However, numerical simulations show that all elastic moduli of geometrically disordered isostatic networks go to zero with system size. This holds true for positional as well as for topological disorder. In most cases, elastic moduli decrease as inverse power laws of system size. On directed isostatic networks, however, of which the square and cubic lattices are particular cases, the decrease of the moduli is exponential with size. For these, the observed elastic weakening can be quantitatively described in terms of the multiplicative growth of stresses with system size, giving rise to bulk and shear moduli of order e-bL. The case of sphere packings, which only accept compressive contact forces, is considered separately. It is argued that these have a finite bulk modulus because of specific correlations in contact disorder, introduced by the constraint of compressivity. We discuss why their shear modulus, nevertheless, is again zero for large sizes. A quantitative model is proposed that describes the numerically measured shear modulus, both as a function of the loading angle and system size. In all cases, if a density p>0 of overconstraints is present, as when a packing is deformed by compression or when a glass is outside its isostatic composition window, all asymptotic moduli become finite. For square networks with periodic boundary conditions, these are of order \\sqrt{p} . For directed networks, elastic moduli are of order e-c/p, indicating the existence of an "isostatic length scale" of order 1/p.

Smartcards in Libraries: A Brave New World.

ERIC Educational Resources Information Center

Myhill, Martin

1998-01-01

Describes the University of Exeter (UK), Mondex, and NatWest UK smartcard-based campus card system project. Smartcards, wallet-sized plastic cards with microprocessors, interface with network terminal devices and are programmable as data, identity, and finance cards. International standard multiple operating system (MULTOS) increases current…

Socialization Patterns and Their Association with Unprotected Anal Intercourse, HIV, and Syphilis Among High-Risk Men Who Have Sex with Men and Transgender Women in Peru

PubMed Central

Verre, MC; Peinado, J; Segura, ER; Clark, JC; Gonzales, P; Benites, C; Cabello, R; Sanchez, J; Lama, JR

2014-01-01

The association of socialization patterns with unprotected anal intercourse (UAI) and HIV/STI prevalence remains underexplored in men who have sex with men (MSM) and transgender women (TW) in developing country settings. We evaluated the correlation of UAI, HIV, and syphilis with MSM/TW venue attendance and social network size among high-risk MSM and TW in Peru according to self-reported sexual identity. Frequency of venue attendance and MSM/TW social network size were lowest among heterosexual MSM and highest among TW respondents. Attendance (frequent or occasional) at MSM/TW venues was associated with increased odds of insertive UAI among heterosexual participants. Frequent venue attendance was associated with increased odds of receptive UAI among gay/homosexual, bisexual, and TW participants. Further investigation of the differing socialization patterns and associations with HIV/STI transmission within subgroups of Peruvian MSM and TW will enable more effective prevention interventions for these populations. PMID:24788782

Socialization patterns and their associations with unprotected anal intercourse, HIV, and syphilis among high-risk men who have sex with men and transgender women in Peru.

PubMed

Verre, Michael C; Peinado, Jesus; Segura, Eddy R; Clark, Jesse; Gonzales, Pedro; Benites, Carlos; Cabello, Robinson; Sanchez, Jorge; Lama, Javier R

2014-10-01

The association of socialization patterns with unprotected anal intercourse (UAI) and HIV/STI prevalence remains underexplored in men who have sex with men (MSM) and transgender women (TW) in developing country settings. We evaluated the correlation of UAI, HIV, and syphilis with MSM/TW venue attendance and social network size among high-risk MSM and TW in Peru according to self-reported sexual identity. Frequency of venue attendance and MSM/TW social network size were lowest among heterosexual MSM and highest among TW respondents. Attendance (frequent or occasional) at MSM/TW venues was associated with increased odds of insertive UAI among heterosexual participants. Frequent venue attendance was associated with increased odds of receptive UAI among gay/homosexual, bisexual, and TW participants. Further investigation of the differing socialization patterns and associations with HIV/STI transmission within subgroups of Peruvian MSM and TW will enable more effective prevention interventions for these populations.

Growth and containment of a hierarchical criminal network

NASA Astrophysics Data System (ADS)

Marshak, Charles Z.; Rombach, M. Puck; Bertozzi, Andrea L.; D'Orsogna, Maria R.

2016-02-01

We model the hierarchical evolution of an organized criminal network via antagonistic recruitment and pursuit processes. Within the recruitment phase, a criminal kingpin enlists new members into the network, who in turn seek out other affiliates. New recruits are linked to established criminals according to a probability distribution that depends on the current network structure. At the same time, law enforcement agents attempt to dismantle the growing organization using pursuit strategies that initiate on the lower level nodes and that unfold as self-avoiding random walks. The global details of the organization are unknown to law enforcement, who must explore the hierarchy node by node. We halt the pursuit when certain local criteria of the network are uncovered, encoding if and when an arrest is made; the criminal network is assumed to be eradicated if the kingpin is arrested. We first analyze recruitment and study the large scale properties of the growing network; later we add pursuit and use numerical simulations to study the eradication probability in the case of three pursuit strategies, the time to first eradication, and related costs. Within the context of this model, we find that eradication becomes increasingly costly as the network increases in size and that the optimal way of arresting the kingpin is to intervene at the early stages of network formation. We discuss our results in the context of dark network disruption and their implications on possible law enforcement strategies.

Growth and containment of a hierarchical criminal network.

PubMed

Marshak, Charles Z; Rombach, M Puck; Bertozzi, Andrea L; D'Orsogna, Maria R

2016-02-01

We model the hierarchical evolution of an organized criminal network via antagonistic recruitment and pursuit processes. Within the recruitment phase, a criminal kingpin enlists new members into the network, who in turn seek out other affiliates. New recruits are linked to established criminals according to a probability distribution that depends on the current network structure. At the same time, law enforcement agents attempt to dismantle the growing organization using pursuit strategies that initiate on the lower level nodes and that unfold as self-avoiding random walks. The global details of the organization are unknown to law enforcement, who must explore the hierarchy node by node. We halt the pursuit when certain local criteria of the network are uncovered, encoding if and when an arrest is made; the criminal network is assumed to be eradicated if the kingpin is arrested. We first analyze recruitment and study the large scale properties of the growing network; later we add pursuit and use numerical simulations to study the eradication probability in the case of three pursuit strategies, the time to first eradication, and related costs. Within the context of this model, we find that eradication becomes increasingly costly as the network increases in size and that the optimal way of arresting the kingpin is to intervene at the early stages of network formation. We discuss our results in the context of dark network disruption and their implications on possible law enforcement strategies.

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

PubMed

Li, Xiumin; Small, Michael

2012-06-01

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

Learning and robustness to catch-and-release fishing in a shark social network

PubMed Central

Brown, Culum; Planes, Serge

2017-01-01

Individuals can play different roles in maintaining connectivity and social cohesion in animal populations and thereby influence population robustness to perturbations. We performed a social network analysis in a reef shark population to assess the vulnerability of the global network to node removal under different scenarios. We found that the network was generally robust to the removal of nodes with high centrality. The network appeared also highly robust to experimental fishing. Individual shark catchability decreased as a function of experience, as revealed by comparing capture frequency and site presence. Altogether, these features suggest that individuals learnt to avoid capture, which ultimately increased network robustness to experimental catch-and-release. Our results also suggest that some caution must be taken when using capture–recapture models often used to assess population size as assumptions (such as equal probabilities of capture and recapture) may be violated by individual learning to escape recapture. PMID:28298593

Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation.

PubMed

Fung, Wai-keung; Liu, Yun-hui

2003-12-01

Adaptive Resonance Theory (ART) networks are employed in robot behavior learning. Two of the difficulties in online robot behavior learning, namely, (1) exponential memory increases with time, (2) difficulty for operators to specify learning tasks accuracy and control learning attention before learning. In order to remedy the aforementioned difficulties, an adaptive categorization mechanism is introduced in ART networks for perceptual and action patterns categorization in this paper. A game-theoretic formulation of adaptive categorization for ART networks is proposed for vigilance parameter adaptation for category size control on the categories formed. The proposed vigilance parameter update rule can help improving categorization performance in the aspect of category number stability and solve the problem of selecting initial vigilance parameter prior to pattern categorization in traditional ART networks. Behavior learning using physical robot is conducted to demonstrate the effectiveness of the proposed adaptive categorization mechanism in ART networks.

Seeing Eye to Eye: Predicting Teacher-Student Agreement on Classroom Social Networks

PubMed Central

Neal, Jennifer Watling; Cappella, Elise; Wagner, Caroline; Atkins, Marc S.

2010-01-01

This study examines the association between classroom characteristics and teacher-student agreement in perceptions of students’ classroom peer networks. Social network, peer nomination, and observational data were collected from a sample of second through fourth grade teachers (N=33) and students (N=669) in 33 classrooms across five high poverty urban schools. Results demonstrate that variation in teacher-student agreement on the structure of students’ peer networks can be explained, in part, by developmental factors and classroom characteristics. Developmental increases in network density partially mediated the positive relationship between grade level and teacher-student agreement. Larger class sizes and higher levels of normative aggressive behavior resulted in lower levels of teacher-student agreement. Teachers’ levels of classroom organization had mixed influences, with behavior management negatively predicting agreement, and productivity positively predicting agreement. These results underscore the importance of the classroom context in shaping teacher and student perceptions of peer networks. PMID:21666768

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

PubMed Central

Jarnuczak, Andrew F.; Eyers, Claire E.; Schwartz, Jean‐Marc; Grant, Christopher M.

2015-01-01

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC‐based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs. PMID:25689132

Controlling the rheological behavior of ceramic slurries and consolidated bodies: Interpenetrating networks and ion size effects

NASA Astrophysics Data System (ADS)

Fisher, Matthew Lyle

Colloidal processing has been demonstrated as an effective technique for increasing the reliability of ceramic components by reducing the flaw populations in sintered bodies. The formation of long-range repulsive potentials produces a dispersed slurry which can be filtered to remove heterogeneities and truncate the flaw size distribution. When the pair potentials are changed from repulsive to weakly attractive, a short-range repulsive potential can be developed in the slurry state which prevents mass segregation, allows particles to consolidate to high volume fractions, and produces plastic consolidated bodies. Plastic behavior in saturated ceramic compacts would allow plastic shape forming technologies to be implemented on advanced ceramic powders. Two networks of different interparticle potential have been mixed to control the rheological properties of slurries and develop clay-like plasticity in consolidated bodies. The elastic modulus and yield stress of slurries were found to increase with volume fraction in a power law fashion. Consolidated bodies containing mixtures of alkylated and non-alkylated powder pack to high volume fraction and exhibit similar flow properties to clay. The mixing of aqueous networks of different pair potential can also be effective in tailoring the flow properties. The flow stress of saturated compacts has been adjusted by the addition of a second network of uncoated particles which is stabilized electrostatically. The influence of the addition of silica of various sizes on the viscosity and zeta potentials of alumina suspensions has been investigated. The adsorption of nano-silica to the surface of alumina shifts the iep. The amount of silica at which the maximum shift in zeta potential occurs is consistent with the silica required to produce the minimum viscosity. This level of silica on the surface is consistent with calculations of the amount necessary for dense random parking of silica spheres around alumina. The influence of counterion size on short range repulsive forces at high salt concentrations was investigated with alumina and silica slurries coagulated with the chlorides of Li+, Na+, K+, Cs+ and TMA+ (tetramethylammonium+). The results clearly show that the range of the repulsive forces correlated with the size of the unhydrated ion, namely stronger particle networks are achieved with smaller counterions. The findings are contradictory to the widely accepted hydration force model. Silica and alumina slurries were also studied at and below the iep where the indifferent electrolyte cations would not be expected to adsorb. It appears that a lyotropic sequence for excluded ions exists and is correlated to the hydration of ions and surfaces.

Wireless Sensor Network Security Enhancement Using Directional Antennas: State of the Art and Research Challenges.

PubMed

Curiac, Daniel-Ioan

2016-04-07

Being often deployed in remote or hostile environments, wireless sensor networks are vulnerable to various types of security attacks. A possible solution to reduce the security risks is to use directional antennas instead of omnidirectional ones or in conjunction with them. Due to their increased complexity, higher costs and larger sizes, directional antennas are not traditionally used in wireless sensor networks, but recent technology trends may support this method. This paper surveys existing state of the art approaches in the field, offering a broad perspective of the future use of directional antennas in mitigating security risks, together with new challenges and open research issues.

Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity

PubMed Central

Vasquez, Juan C.; Houweling, Arthur R.; Tiesinga, Paul

2013-01-01

Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that reported quantifiable behavioral responses in response to short spike trains elicited in single neurons. Hence, these networks are stable against internally generated fluctuations in firing rate but at the same time remain sensitive to similarly-sized externally induced perturbations. We investigated stability and sensitivity in a simple recurrent network of stochastic binary neurons and determined numerically the effects of correlation between the number of afferent (“in-degree”) and efferent (“out-degree”) connections in neurons. The key advance reported in this work is that anti-correlation between in-/out-degree distributions increased the stability of the network in comparison to networks with no correlation or positive correlations, while being able to achieve the same level of sensitivity. The experimental characterization of degree distributions is difficult because all pre-synaptic and post-synaptic neurons have to be identified and counted. We explored whether the statistics of network motifs, which requires the characterization of connections between small subsets of neurons, could be used to detect evidence for degree anti-correlations. We find that the sample frequency of the 3-neuron “ring” motif (1→2→3→1), can be used to detect degree anti-correlation for sub-networks of size 30 using about 50 samples, which is of significance because the necessary measurements are achievable experimentally in the near future. Taken together, we hypothesize that barrel cortex networks exhibit degree anti-correlations and specific network motif statistics. PMID:24223550

Micropore analysis of polymer networks by gas sorption and 129Xe NMR spectroscopy: toward a better understanding of intrinsic microporosity.

PubMed

Weber, Jens; Schmidt, Johannes; Thomas, Arne; Böhlmann, Winfried

2010-10-05

The microporosity of two microporous polymer networks is investigated in detail. Both networks are based on a central spirobifluorene motif but have different linker groups, namely, imide and thiophene units. The microporosity of the networks is based on the "polymers of intrinsic microporosity (PIM)" design strategy. Nitrogen, argon, and carbon dioxide were used as sorbates in order to analyze the microporosity in greater detail. The gas sorption data was analyzed with respect to important parameters such as specific surface area, pore volume, and pore size (distribution). It is shown that the results can be strongly model dependent and swelling effects have to be regarded. (129)Xe NMR was used as an independent technique for the estimation of the average pore size of the polymer networks. The results indicate that both networks are mainly ultramicroporous (pore sizes < 0.8 nm) in the dry state, which was not expected based on the molecular design. Phase separation and network defects might influence the overall network morphology strongly. Finally, the observed swelling indicates that this "soft" microporous matter might have a different micropore size in the solvent swollen/filled state that in the dry state.

Time-dependent breakdown of fiber networks: Uncertainty of lifetime

NASA Astrophysics Data System (ADS)

Mattsson, Amanda; Uesaka, Tetsu

2017-05-01

Materials often fail when subjected to stresses over a prolonged period. The time to failure, also called the lifetime, is known to exhibit large variability of many materials, particularly brittle and quasibrittle materials. For example, a coefficient of variation reaches 100% or even more. Its distribution shape is highly skewed toward zero lifetime, implying a large number of premature failures. This behavior contrasts with that of normal strength, which shows a variation of only 4%-10% and a nearly bell-shaped distribution. The fundamental cause of this large and unique variability of lifetime is not well understood because of the complex interplay between stochastic processes taking place on the molecular level and the hierarchical and disordered structure of the material. We have constructed fiber network models, both regular and random, as a paradigm for general material structures. With such networks, we have performed Monte Carlo simulations of creep failure to establish explicit relationships among fiber characteristics, network structures, system size, and lifetime distribution. We found that fiber characteristics have large, sometimes dominating, influences on the lifetime variability of a network. Among the factors investigated, geometrical disorders of the network were found to be essential to explain the large variability and highly skewed shape of the lifetime distribution. With increasing network size, the distribution asymptotically approaches a double-exponential form. The implication of this result is that, so-called "infant mortality," which is often predicted by the Weibull approximation of the lifetime distribution, may not exist for a large system.

Isolated in a technologically connected world?: Changes in the core professional ties of female researchers in Ghana, Kenya, and Kerala, India.

PubMed

Miller, B Paige; Shrum, Wesley

2012-01-01

Using panel data gathered across two waves (2001 and 2005) from researchers in Ghana, Kenya, and Kerala, India, we examine three questions: (1) To what extent do gender differences exist in the core professional networks of scientists in low-income areas? (2) How do gender differences shift over time? (3) Does use of information and communication technologies (ICTs) mediate the relationship between gender and core network composition? Our results indicate that over a period marked by dramatic increases in access to and use of various ICTs, the composition and size of female researchers core professional ties have either not changed significantly or have changed in an unexpected direction. Indeed, the size of women's ties are retracting over time rather than expanding.

Size matters: concurrency and the epidemic potential of HIV in small networks.

PubMed

Carnegie, Nicole Bohme; Morris, Martina

2012-01-01

Generalized heterosexual epidemics are responsible for the largest share of the global burden of HIV. These occur in populations that do not have high rates of partner acquisition, and research suggests that a pattern of fewer, but concurrent, partnerships may be the mechanism that provides the connectivity necessary for sustained transmission. We examine how network size affects the impact of concurrency on network connectivity. We use a stochastic network model to generate a sample of networks, varying the size of the network and the level of concurrency, and compare the largest components for each scenario to the asymptotic expected values. While the threshold for the growth of a giant component does not change, the transition is more gradual in the smaller networks. As a result, low levels of concurrency generate more connectivity in small networks. Generalized HIV epidemics are by definition those that spread to a larger fraction of the population, but the mechanism may rely in part on the dynamics of transmission in a set of linked small networks. Examples include rural populations in sub-Saharan Africa and segregated minority populations in the US, where the effective size of the sexual network may well be in the hundreds, rather than thousands. Connectivity emerges at lower levels of concurrency in smaller networks, but these networks can still be disconnected with small changes in behavior. Concurrency remains a strategic target for HIV combination prevention programs in this context.

Minimal perceptrons for memorizing complex patterns

NASA Astrophysics Data System (ADS)

Pastor, Marissa; Song, Juyong; Hoang, Danh-Tai; Jo, Junghyo

2016-11-01

Feedforward neural networks have been investigated to understand learning and memory, as well as applied to numerous practical problems in pattern classification. It is a rule of thumb that more complex tasks require larger networks. However, the design of optimal network architectures for specific tasks is still an unsolved fundamental problem. In this study, we consider three-layered neural networks for memorizing binary patterns. We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity. We formulated the minimal network size for regular, random, and complex patterns. In particular, the minimal size for complex patterns, which are neither ordered nor disordered, was predicted by measuring their Hamming distances from known ordered patterns. Our predictions agree with simulations based on the back-propagation algorithm.

Functional modular architecture underlying attentional control in aging.

PubMed

Monge, Zachary A; Geib, Benjamin R; Siciliano, Rachel E; Packard, Lauren E; Tallman, Catherine W; Madden, David J

2017-07-15

Previous research suggests that age-related differences in attention reflect the interaction of top-down and bottom-up processes, but the cognitive and neural mechanisms underlying this interaction remain an active area of research. Here, within a sample of community-dwelling adults 19-78 years of age, we used diffusion reaction time (RT) modeling and multivariate functional connectivity to investigate the behavioral components and whole-brain functional networks, respectively, underlying bottom-up and top-down attentional processes during conjunction visual search. During functional MRI scanning, participants completed a conjunction visual search task in which each display contained one item that was larger than the other items (i.e., a size singleton) but was not informative regarding target identity. This design allowed us to examine in the RT components and functional network measures the influence of (a) additional bottom-up guidance when the target served as the size singleton, relative to when the distractor served as the size singleton (i.e., size singleton effect) and (b) top-down processes during target detection (i.e., target detection effect; target present vs. absent trials). We found that the size singleton effect (i.e., increased bottom-up guidance) was associated with RT components related to decision and nondecision processes, but these effects did not vary with age. Also, a modularity analysis revealed that frontoparietal module connectivity was important for both the size singleton and target detection effects, but this module became central to the networks through different mechanisms for each effect. Lastly, participants 42 years of age and older, in service of the target detection effect, relied more on between-frontoparietal module connections. Our results further elucidate mechanisms through which frontoparietal regions support attentional control and how these mechanisms vary in relation to adult age. Copyright © 2017 Elsevier Inc. All rights reserved.

Multicast backup reprovisioning problem for Hamiltonian cycle-based protection on WDM networks

NASA Astrophysics Data System (ADS)

Din, Der-Rong; Huang, Jen-Shen

2014-03-01

As networks grow in size and complexity, the chance and the impact of failures increase dramatically. The pre-allocated backup resources cannot provide 100% protection guarantee when continuous failures occur in a network. In this paper, the multicast backup re-provisioning problem (MBRP) for Hamiltonian cycle (HC)-based protection on WDM networks for the link-failure case is studied. We focus on how to recover the protecting capabilities of Hamiltonian cycle against the subsequent link-failures on WDM networks for multicast transmissions, after recovering the multicast trees affected by the previous link-failure. Since this problem is a hard problem, an algorithm, which consists of several heuristics and a genetic algorithm (GA), is proposed to solve it. The simulation results of the proposed method are also given. Experimental results indicate that the proposed algorithm can solve this problem efficiently.

Achieving quality of service in IP networks

NASA Astrophysics Data System (ADS)

Hays, Tim

2001-07-01

The Internet Protocol (IP) has served global networks well, providing a standardized method to transmit data among many disparate systems. But IP is designed for simplicity, and only enables a `best effort' service that can be subject to delays and loss of data. For data networks, this is an acceptable trade-off. In the emerging world of convergence, driven by new applications such as video streaming and IP telephony, minimizing latency and packet loss as well as jitter can be critical. Simply increasing the size of the IP network `pipe' to meet those demands is not always sufficient. In this environment, vendors and standards bodies are endeavoring to create technologies and techniques to enable IP to improve the quality of service it can provide, while retaining the characteristics that has enabled it to become the dominant networking protocol.

SIR rumor spreading model considering the effect of difference in nodes’ identification capabilities

NASA Astrophysics Data System (ADS)

Wang, Ya-Qi; Wang, Jing

In this paper, we study the effect of difference in network nodes’ identification capabilities on rumor propagation. A novel susceptible-infected-removed (SIR) model is proposed, based on the mean-field theory, to investigate the dynamical behaviors of such model on homogeneous networks and inhomogeneous networks, respectively. Theoretical analysis and simulation results demonstrate that when we consider the influence of difference in nodes’ identification capabilities, the critical thresholds obviously increase, but the final rumor sizes are apparently reduced. We also find that the difference in nodes’ identification capabilities prolongs the time of rumor propagation reaching a steady state, and decreases the number of nodes that finally accept rumors. Additionally, under the influence of difference of nodes’ identification capabilities, compared with the homogeneous networks, the rumor transmission rate on the inhomogeneous networks is relatively large.

The strength-of-weak-ties perspective on creativity: a comprehensive examination and extension.

PubMed

Baer, Markus

2010-05-01

Disentangling the effects of weak ties on creativity, the present study separated, both theoretically and empirically, the effects of the size and strength of actors' idea networks and examined their joint impact while simultaneously considering the separate, moderating role of network diversity. I hypothesized that idea networks of optimal size and weak strength were more likely to boost creativity when they afforded actors access to a wide range of different social circles. In addition, I examined whether the joint effects of network size, strength, and diversity on creativity were further qualified by the openness to experience personality dimension. As expected, results indicated that actors were most creative when they maintained idea networks of optimal size, weak strength, and high diversity and when they scored high on the openness dimension. The implications of these results are discussed. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Animal social networks as substrate for cultural behavioural diversity.

PubMed

Whitehead, Hal; Lusseau, David

2012-02-07

We used individual-based stochastic models to examine how social structure influences the diversity of socially learned behaviour within a non-human population. For continuous behavioural variables we modelled three forms of dyadic social learning, averaging the behavioural value of the two individuals, random transfer of information from one individual to the other, and directional transfer from the individual with highest behavioural value to the other. Learning had potential error. We also examined the transfer of categorical behaviour between individuals with random directionality and two forms of error, the adoption of a randomly chosen existing behavioural category or the innovation of a new type of behaviour. In populations without social structuring the diversity of culturally transmitted behaviour increased with learning error and population size. When the populations were structured socially either by making individuals members of permanent social units or by giving them overlapping ranges, behavioural diversity increased with network modularity under all scenarios, although the proportional increase varied considerably between continuous and categorical behaviour, with transmission mechanism, and population size. Although functions of the form e(c)¹(m)⁻(c)² + (c)³(Log(N)) predicted the mean increase in diversity with modularity (m) and population size (N), behavioural diversity could be highly unpredictable both between simulations with the same set of parameters, and within runs. Errors in social learning and social structuring generally promote behavioural diversity. Consequently, social learning may be considered to produce culture in populations whose social structure is sufficiently modular. Copyright © 2011 Elsevier Ltd. All rights reserved.

Numbers And Gains Of Neurons In Winner-Take-All Networks

NASA Technical Reports Server (NTRS)

Brown, Timothy X.

1993-01-01

Report presents theoretical study of gains required in neurons to implement winner-take-all electronic neural network of given size and related question of maximum size of winner-take-all network in which neurons have specified sigmoid transfer or response function with specified gain.

A Proposal of TLS Implementation for Cross Certification Model

NASA Astrophysics Data System (ADS)

Kaji, Tadashi; Fujishiro, Takahiro; Tezuka, Satoru

Today, TLS is widely used for achieving a secure communication system. And TLS is used PKI for server authentication and/or client authentication. However, its PKI environment, which is called as “multiple trust anchors environment,” causes the problem that the verifier has to maintain huge number of CA certificates in the ubiquitous network because the increase of terminals connected to the network brings the increase of CAs. However, most of terminals in the ubiquitous network will not have enough memory to hold such huge number of CA certificates. Therefore, another PKI environment, “cross certification environment”, is useful for the ubiquitous network. But, because current TLS is designed for the multiple trust anchors model, TLS cannot work efficiently on the cross-certification model. This paper proposes a TLS implementation method to support the cross certification model efficiently. Our proposal reduces the size of exchanged messages between the TLS client and the TLS server during the handshake process. Therefore, our proposal is suitable for implementing TLS in the terminals that do not have enough computing power and memory in ubiquitous network.

Low Loss Polymer Nanoparticle Composites for RF Applications

DTIC Science & Technology

2014-09-17

size of nanoparticles below a critical dimension ( skin depth).6 It is possible to increase the skin depth of the hybrid material by decreasing the...filled with elastomers,[10-12] polymer-nanoparticle composites,[13, 14] liquid metal filled microfluidic channels,[4, 15] conductive networks on pre

Disease invasion risk in a growing population.

PubMed

Yuan, Sanling; van den Driessche, P; Willeboordse, Frederick H; Shuai, Zhisheng; Ma, Junling

2016-09-01

The spread of an infectious disease may depend on the population size. For simplicity, classic epidemic models assume homogeneous mixing, usually standard incidence or mass action. For standard incidence, the contact rate between any pair of individuals is inversely proportional to the population size, and so the basic reproduction number (and thus the initial exponential growth rate of the disease) is independent of the population size. For mass action, this contact rate remains constant, predicting that the basic reproduction number increases linearly with the population size, meaning that disease invasion is easiest when the population is largest. In this paper, we show that neither of these may be true on a slowly evolving contact network: the basic reproduction number of a short epidemic can reach its maximum while the population is still growing. The basic reproduction number is proportional to the spectral radius of a contact matrix, which is shown numerically to be well approximated by the average excess degree of the contact network. We base our analysis on modeling the dynamics of the average excess degree of a random contact network with constant population input, proportional deaths, and preferential attachment for contacts brought in by incoming individuals (i.e., individuals with more contacts attract more incoming contacts). In addition, we show that our result also holds for uniform attachment of incoming contacts (i.e., every individual has the same chance of attracting incoming contacts), and much more general population dynamics. Our results show that a disease spreading in a growing population may evade control if disease control planning is based on the basic reproduction number at maximum population size.

The role of storm scale, position and movement in controlling urban flood response

NASA Astrophysics Data System (ADS)

ten Veldhuis, Marie-claire; Zhou, Zhengzheng; Yang, Long; Liu, Shuguang; Smith, James

2018-01-01

The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semi)urbanized basins ranging from 7.0 to 111.1 km2 in size. Results showed that the largest peak flows in the event catalog were associated with storm core scales exceeding basin scale, for all except the largest basin. Spatial scale of flood-producing storm events in the smaller basins fell into two groups: storms of large spatial scales exceeding basin size or small, concentrated events, with storm core much smaller than basin size. For the majority of events, spatial rainfall variability was strongly smoothed by the flow-path network, increasingly so for larger basin size. Correlation analysis showed that position of the storm in relation to the flow-path network was significantly correlated with peak flow in the smallest and in the two more urbanized basins. Analysis of storm movement relative to the flow-path network showed that direction of storm movement, upstream or downstream relative to the flow-path network, had little influence on hydrological response. Slow-moving storms tend to be associated with higher peak flows and longer lag times. Unexpectedly, position of the storm relative to impervious cover within the basins had little effect on flow peaks. These findings show the importance of observation-based analysis in validating and improving our understanding of interactions between the spatial distribution of rainfall and catchment variability.

Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community.

PubMed

Liu, Chuchu; Lu, Xin

2018-01-05

Traditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community. By elaborately designing crawlers, we retrieved a complete dataset from the "HIV bar," the largest bar related to HIV on the Baidu Tieba platform, for all records from January 2005 to August 2016. Through natural language processing and social network analysis, we explored the psychology, behavior and demand of online HIV population and examined the network community structure. In HIV communities, the average topic similarity among members is positively correlated to network efficiency (r = 0.70, p < 0.001), indicating that the closer the social distance between members of the community, the more similar their topics. The proportion of negative users in each community is around 60%, weakly correlated with community size (r = 0.25, p = 0.002). It is found that users suspecting initial HIV infection or first in contact with high-risk behaviors tend to seek help and advice on the social networking platform, rather than immediately going to a hospital for blood tests. Online communities have generated copious amounts of data offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. It is recommended that support through online services for HIV/AIDS consultation and diagnosis be improved to avoid privacy concerns and social discrimination in China.

Stability of discrete memory states to stochastic fluctuations in neuronal systems

PubMed Central

Miller, Paul; Wang, Xiao-Jing

2014-01-01

Noise can degrade memories by causing transitions from one memory state to another. For any biological memory system to be useful, the time scale of such noise-induced transitions must be much longer than the required duration for memory retention. Using biophysically-realistic modeling, we consider two types of memory in the brain: short-term memories maintained by reverberating neuronal activity for a few seconds, and long-term memories maintained by a molecular switch for years. Both systems require persistence of (neuronal or molecular) activity self-sustained by an autocatalytic process and, we argue, that both have limited memory lifetimes because of significant fluctuations. We will first discuss a strongly recurrent cortical network model endowed with feedback loops, for short-term memory. Fluctuations are due to highly irregular spike firing, a salient characteristic of cortical neurons. Then, we will analyze a model for long-term memory, based on an autophosphorylation mechanism of calcium/calmodulin-dependent protein kinase II (CaMKII) molecules. There, fluctuations arise from the fact that there are only a small number of CaMKII molecules at each postsynaptic density (putative synaptic memory unit). Our results are twofold. First, we demonstrate analytically and computationally the exponential dependence of stability on the number of neurons in a self-excitatory network, and on the number of CaMKII proteins in a molecular switch. Second, for each of the two systems, we implement graded memory consisting of a group of bistable switches. For the neuronal network we report interesting ramping temporal dynamics as a result of sequentially switching an increasing number of discrete, bistable, units. The general observation of an exponential increase in memory stability with the system size leads to a trade-off between the robustness of memories (which increases with the size of each bistable unit) and the total amount of information storage (which decreases with increasing unit size), which may be optimized in the brain through biological evolution. PMID:16822041

A modular neural network scheme applied to fault diagnosis in electric power systems.

PubMed

Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

2014-01-01

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

PubMed Central

Flores, Agustín; Morant, Francisco

2014-01-01

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

Predicting community responses to perturbations in the face of imperfect knowledge and network complexity

USGS Publications Warehouse

Novak, M.; Wootton, J.T.; Doak, D.F.; Emmerson, M.; Estes, J.A.; Tinker, M.T.

2011-01-01

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (??25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities. ?? 2011 by the Ecological Society of America.

Complex and unexpected dynamics in simple genetic regulatory networks

NASA Astrophysics Data System (ADS)

Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

2014-03-01

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity.

PubMed

Khan, Bilal; Lee, Hsuan-Wei; Fellows, Ian; Dombrowski, Kirk

2018-01-01

Size estimation is particularly important for populations whose members experience disproportionate health issues or pose elevated health risks to the ambient social structures in which they are embedded. Efforts to derive size estimates are often frustrated when the population is hidden or hard-to-reach in ways that preclude conventional survey strategies, as is the case when social stigma is associated with group membership or when group members are involved in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, for use in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. We give provably sufficient conditions for the consistency of these estimators in large configuration networks. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which also perform well on a real-world location-based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable tradeoffs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population size estimates are derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. We discuss limitations and future work in the concluding section.

New solutions for climate network visualization

NASA Astrophysics Data System (ADS)

Nocke, Thomas; Buschmann, Stefan; Donges, Jonathan F.; Marwan, Norbert

2016-04-01

An increasing amount of climate and climate impact research methods deals with geo-referenced networks, including energy, trade, supply-chain, disease dissemination and climatic tele-connection networks. At the same time, the size and complexity of these networks increases, resulting in networks of more than hundred thousand or even millions of edges, which are often temporally evolving, have additional data at nodes and edges, and can consist of multiple layers even in real 3D. This gives challenges to both the static representation and the interactive exploration of these networks, first of all avoiding edge clutter ("edge spagetti") and allowing interactivity even for unfiltered networks. Within this presentation, we illustrate potential solutions to these challenges. Therefore, we give a glimpse on a questionnaire performed with climate and complex system scientists with respect to their network visualization requirements, and on a review of available state-of-the-art visualization techniques and tools for this purpose (see as well Nocke et al., 2015). In the main part, we present alternative visualization solutions for several use cases (global, regional, and multi-layered climate networks) including alternative geographic projections, edge bundling, and 3-D network support (based on CGV and GTX tools), and implementation details to reach interactive frame rates. References: Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H.-J. Schulz, and C. Tominski: Review: Visual analytics of climate networks, Nonlinear Processes in Geophysics, 22, 545-570, doi:10.5194/npg-22-545-2015, 2015

Network Centrality of Metro Systems

PubMed Central

Derrible, Sybil

2012-01-01

Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no “winner takes all”) unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities. PMID:22792373

A New Cross-By-Pass-Torus Architecture Based on CBP-Mesh and Torus Interconnection for On-Chip Communication.

PubMed

Gulzari, Usman Ali; Sajid, Muhammad; Anjum, Sheraz; Agha, Shahrukh; Torres, Frank Sill

2016-01-01

A Mesh topology is one of the most promising architecture due to its regular and simple structure for on-chip communication. Performance of mesh topology degraded greatly by increasing the network size due to small bisection width and large network diameter. In order to overcome this limitation, many researchers presented modified Mesh design by adding some extra links to improve its performance in terms of network latency and power consumption. The Cross-By-Pass-Mesh was presented by us as an improved version of Mesh topology by intelligent addition of extra links. This paper presents an efficient topology named Cross-By-Pass-Torus for further increase in the performance of the Cross-By-Pass-Mesh topology. The proposed design merges the best features of the Cross-By-Pass-Mesh and Torus, to reduce the network diameter, minimize the average number of hops between nodes, increase the bisection width and to enhance the overall performance of the network. In this paper, the architectural design of the topology is presented and analyzed against similar kind of 2D topologies in terms of average latency, throughput and power consumption. In order to certify the actual behavior of proposed topology, the synthetic traffic trace and five different real embedded application workloads are applied to the proposed as well as other competitor network topologies. The simulation results indicate that Cross-By-Pass-Torus is an efficient candidate among its predecessor's and competitor topologies due to its less average latency and increased throughput at a slight cost in network power and energy for on-chip communication.

The social network-network: size is predicted by brain structure and function in the amygdala and paralimbic regions

PubMed Central

Von Der Heide, Rebecca; Vyas, Govinda

2014-01-01

The social brain hypothesis proposes that the large size of the primate neocortex evolved to support complex and demanding social interactions. Accordingly, recent studies have reported correlations between the size of an individual’s social network and the density of gray matter (GM) in regions of the brain implicated in social cognition. However, the reported relationships between GM density and social group size are somewhat inconsistent with studies reporting correlations in different brain regions. One factor that might account for these discrepancies is the use of different measures of social network size (SNS). This study used several measures of SNS to assess the relationships SNS and GM density. The second goal of this study was to test the relationship between social network measures and functional brain activity. Participants performed a social closeness task using photos of their friends and unknown people. Across the VBM and functional magnetic resonance imaging analyses, individual differences in SNS were consistently related to structural and functional differences in three regions: the left amygdala, right amygdala and the right entorhinal/ventral anterior temporal cortex. PMID:24493846

A dual-scale metal nanowire network transparent conductor for highly efficient and flexible organic light emitting diodes.

PubMed

Lee, Jinhwan; An, Kunsik; Won, Phillip; Ka, Yoonseok; Hwang, Hyejin; Moon, Hyunjin; Kwon, Yongwon; Hong, Sukjoon; Kim, Changsoon; Lee, Changhee; Ko, Seung Hwan

2017-02-02

Although solution processed metal nanowire (NW) percolation networks are a strong candidate to replace commercial indium tin oxide, their performance is limited in thin film device applications due to reduced effective electrical areas arising from the dimple structure and percolative voids that single size metal NW percolation networks inevitably possess. Here, we present a transparent electrode based on a dual-scale silver nanowire (AgNW) percolation network embedded in a flexible substrate to demonstrate a significant enhancement in the effective electrical area by filling the large percolative voids present in a long/thick AgNW network with short/thin AgNWs. As a proof of concept, the performance enhancement of a flexible phosphorescent OLED is demonstrated with the dual-scale AgNW percolation network compared to the previous mono-scale AgNWs. Moreover, we report that mechanical and oxidative robustness, which are critical for flexible OLEDs, are greatly increased by embedding the dual-scale AgNW network in a resin layer.

The rational design of recognitive polymeric networks for sensing applications

NASA Astrophysics Data System (ADS)

Noss, Kimberly Ryanne Dial

Testosterone recognitive networks were synthesized with varying feed crosslinking percentages and length of the bi-functional crosslinking agent to analyze the effect of changing structural parameters on template binding properties such as affinity, selectivity, capacity, and diffusional transport. The crosslinking percentage of the crosslinking monomer ethylene glycol dimethacrylate was varied from 50% to 90% and associated networks experienced a 2 fold increase in capacity and a 4 fold increase in affinity with the equilibrium association constants, Ka, ranging from 0.32 +/- 0.02 x 10 4 M-1 to 1.3 +/- 0.1 x 104 M -1, respectively. The higher concentration of crosslinking monomer increased the crosslinking points available for inter-chain stabilization creating an increased number of stable cavities for template association. However, by increasing the length of the crosslinking agent and increasing the feed crosslinking percentage from 77% crosslinked poly(methacrylic acid- co-ethylene glycol dimethacrylate) (poly(MAA-co-EGDMA)) to 50% crosslinked poly(methacrylic acid-co-poly(ethylene glycol)200 dimethacrylate) (poly(MAA-co-PEG200DMA)), the mesh size of the network increased resulting in an increased template diffusion coefficient from (2.83 +/- 0.06) x 109 cm2/s to (4.3 +/- 0.06) x 109 cm2/s, respectively, which is approximately a 40% faster template diffussional transport. A 77% crosslinked poly (MAA-co-PEG200DMA) recognitive network had an association constant of (0.20 +/- 0.05) x 104 M -1 and bound (0.72 +/- 0.04) x 10-2 mmol testosterone/g dry polymer, which was less by 6 and 3 fold, respectively, compared to a similarly crosslinked poly(MAA-co-EGDMA) recognitive network. Structural manipulation of the macromolecular architecture illustrates the programmability of recognitive networks for specific template binding parameters and diffusional transport, which may lead to enhanced imprinted sensor materials and successful integration onto sensor platforms.

Estimating the size of hidden populations using respondent-driven sampling data: Case examples from Morocco

PubMed Central

Johnston, Lisa G; McLaughlin, Katherine R; Rhilani, Houssine El; Latifi, Amina; Toufik, Abdalla; Bennani, Aziza; Alami, Kamal; Elomari, Boutaina; Handcock, Mark S

2015-01-01

Background Respondent-driven sampling is used worldwide to estimate the population prevalence of characteristics such as HIV/AIDS and associated risk factors in hard-to-reach populations. Estimating the total size of these populations is of great interest to national and international organizations, however reliable measures of population size often do not exist. Methods Successive Sampling-Population Size Estimation (SS-PSE) along with network size imputation allows population size estimates to be made without relying on separate studies or additional data (as in network scale-up, multiplier and capture-recapture methods), which may be biased. Results Ten population size estimates were calculated for people who inject drugs, female sex workers, men who have sex with other men, and migrants from sub-Sahara Africa in six different cities in Morocco. SS-PSE estimates fell within or very close to the likely values provided by experts and the estimates from previous studies using other methods. Conclusions SS-PSE is an effective method for estimating the size of hard-to-reach populations that leverages important information within respondent-driven sampling studies. The addition of a network size imputation method helps to smooth network sizes allowing for more accurate results. However, caution should be used particularly when there is reason to believe that clustered subgroups may exist within the population of interest or when the sample size is small in relation to the population. PMID:26258908

A Network-Individual-Resource Model for HIV Prevention

PubMed Central

Johnson, Blair T.; Redding, Colleen A.; DiClemente, Ralph J.; Mustanski, Brian S.; Dodge, Brian M.; Sheeran, Paschal; Warren, Michelle R.; Zimmerman, Rick S.; Fisher, William A.; Conner, Mark T.; Carey, Michael P.; Fisher, Jeffrey D.; Stall, Ronald D.; Fishbein, Martin

2014-01-01

HIV is transmitted through dyadic exchanges of individuals linked in transitory or permanent networks of varying sizes. To optimize prevention efficacy, a complementary theoretical perspective that bridges key individual level elements with important network elements can be a foundation for developing and implementing HIV interventions with outcomes that are more sustainable over time and have greater dissemination potential. Toward that end, we introduce a Network-Individual-Resource (NIR) model for HIV prevention that recognizes how exchanges of resources between individuals and their networks underlies and sustains HIV-risk behaviors. Individual behavior change for HIV prevention, then, may be dependent on increasing the supportiveness of that individual's relevant networks for such change. Among other implications, an NIR model predicts that the success of prevention efforts depends on whether the prevention efforts (1) prompt behavior changes that can be sustained by the resources the individual or their networks possess; (2) meet individual and network needs and are consistent with the individual's current situation/developmental stage; (3) are trusted and valued; and (4) target high HIV-prevalence networks. PMID:20862606

Hack's relation and optimal channel networks: The elongation of river basins as a consequence of energy minimization

NASA Astrophysics Data System (ADS)

Ijjasz-Vasquez, Ede J.; Bras, Rafael L.; Rodriguez-Iturbe, Ignacio

1993-08-01

As pointed by Hack (1957), river basins tend to become longer and narrower as their size increases. This work shows that this property may be partially regarded as the consequence of competition and minimization of energy expenditure in river basins.

Embedded 100 Gbps Photonic Components

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

Kuznia, Charlie

This innovation to fiber optic component technology increases the performance, reduces the size and reduces the power consumption of optical communications within dense network systems, such as advanced distributed computing systems and data centers. VCSEL technology is enabling short-reach (< 100 m) and >100 Gbps optical interconnections over multi-mode fiber in commercial applications.

Understanding the value of social networks in life satisfaction of elderly people: a comparative study of 16 European countries using SHARE data.

PubMed

Tomini, Florian; Tomini, Sonila M; Groot, Wim

2016-12-01

Networks of family and friends are a source of support and are generally associated with higher life satisfaction values among older adults. On the other hand, older adults who are satisfied with their life may be more able to develop and maintain a wider social network. For this reason, the causal link between size and composition of the social networks and satisfaction with life is yet to be explored. This paper investigates the effect of the 'size', (number of family and friends, and network) and the 'composition' (the proportion of friends over total number of persons) of the social network on life satisfaction among older adults (50+). Moreover, we also investigate the patterns of this relation between different European countries. Data from the 4 th wave of Survey of Health, Ageing and Retirement in Europe and an instrumental variable approach are used to estimate the extent of the relation between life satisfaction and size and composition of social networks. Respondents in Western and Northern European (WNE) countries report larger networks than respondents in Eastern and Southern European (ESE) countries. However, the positive relationship between network size and life satisfaction is consistent across countries. On the other hand, the share of friends in the network appears to be generally negatively related to satisfaction with life, though results are not statistically significant for all countries. Apparently, a larger personal network is important for older adults (50+) to be more satisfied with life. Our results suggest that this relation is particularly positive if the network is comprised of family members.

Social Relations in Lebanon: Convoys Across the Life Course

PubMed Central

Antonucci, Toni C.; Ajrouch, Kristine J.; Abdulrahim, Sawsan

2015-01-01

Objectives: This study systematically analyzed convoys of social relations to investigate the ways in which gender and income shape patterns of social relations across the life course in Lebanon. Methods: Data were drawn from a representative sample of adults aged 18 and older in Greater Beirut, Lebanon (N = 500). Multiple linear regression and multilevel models were conducted to examine main and interactive effects of age, gender, and income on social relations. Results: Findings indicate main effects of age, income, and gender on network structure and relationship quality. Older age was associated with larger network size, greater proportion of kin in network, higher positive and lower negative relationship quality. Higher income was associated with larger network size and decreased contact frequency. Female gender was also associated with decreased contact frequency. Gender interacted with income to influence network size and network composition. Higher income was associated with a larger network size and higher proportion of kin for women. Discussion: Findings suggest diversity in the experience of social relations. Such nuance is particularly relevant to the Lebanese context where family is the main source of support in old age. Policy makers and program planners may need to refrain from viewing social relations simplistically. PMID:24501252

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

PubMed

Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

2014-11-30

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

Representing perturbed dynamics in biological network models

NASA Astrophysics Data System (ADS)

Stoll, Gautier; Rougemont, Jacques; Naef, Felix

2007-07-01

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy H and the perturbation size Δ , a quantity that reflects the distance between the set of fixed points of the perturbed network and that of the unperturbed network. Applying our approach to the yeast-cell-cycle network introduced by Li [Proc. Natl. Acad. Sci. U.S.A. 101, 4781 (2004)] provides a low-dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoint functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.

Social inclusion and relationship satisfaction of patients with a severe mental illness.

PubMed

Koenders, Jitske F; de Mooij, Liselotte D; Dekker, Jack M; Kikkert, Martijn

2017-12-01

Research suggests that patients with a severe mental illness (SMI) are among the most social excluded in society. However, comparisons of social network composition and relationship satisfaction between SMI patients and a control group are rare. Our aim was to compare differences in size, satisfaction and composition of the social network between patients with SMI and a control group. Potential sociodemographic and clinical risk factors in relation to social network size in SMI patients were explored. The sample consisted of a control group ( N = 949) and SMI patients ( N = 211) who were under treatment in Dutch mental health care institutions. In these groups, network size, relationship satisfaction, sociodemographic and clinical (patients only) characteristics were assessed. Social network size was 2.5 times lower in SMI patients, which was also reflected in a lower relationship satisfaction. The composition of the social network of SMI patients differs from that of controls: patients' network seems to consist of a smaller part of friends. Different risk factors were associated with the impoverishment of the social network of family, friends and acquaintances of patients with SMI. SMI patients have very small networks compared to controls. This may be a problem, given the ongoing emphasis on outpatient treatment of SMI patients and self-dependence. This outcome advocates for more attention to social isolation of SMI patients and involvement of family in the treatment and aftercare of SMI patients.

An analysis of respondent-driven sampling with injecting drug users in a high HIV prevalent state of India.

PubMed

Phukan, Sanjib Kumar; Medhi, Gajendra Kumar; Mahanta, Jagadish; Adhikary, Rajatashuvra; Thongamba, Gay; Paranjape, Ramesh S; Akoijam, Brogen S

2017-07-03

Personal networks are significant social spaces to spread of HIV or other blood-borne infections among hard-to-reach population, viz., injecting drug users, female sex workers, etc. Sharing of infected needles or syringes among drug users is one of the major routes of HIV transmission in Manipur, a high HIV prevalence state in India. This study was carried out to describe the network characteristics and recruitment patterns of injecting drug users and to assess the association of personal network with injecting risky behaviors in Manipur. A total of 821 injecting drug users were recruited into the study using respondent-driven sampling (RDS) from Bishnupur and Churachandpur districts of Manipur; data on demographic characteristics, HIV risk behaviors, and network size were collected from them. Transition probability matrices and homophily indices were used to describe the network characteristics, and recruitment patterns of injecting drug users. Univariate and multivariate binary logistic regression models were performed to analyze the association between the personal networks and sharing of needles or syringes. The average network size was similar in both the districts. Recruitment analysis indicates injecting drug users were mostly engaged in mixed age group setting for injecting practice. Ever married and new injectors showed lack of in-group ties. Younger injecting drug users had mainly recruited older injecting drug users from their personal network. In logistic regression analysis, higher personal network was found to be significantly associated with increased likelihood of injecting risky behaviors. Because of mixed personal network of new injectors and higher network density associated with HIV exposure, older injecting drug users may act as a link for HIV transmission or other blood-borne infections to new injectors and also to their sexual partners. The information from this study may be useful to understanding the network pattern of injecting drug users for enriching the HIV prevention in this region.

Augmenting groundwater monitoring networks near landfills with slurry cutoff walls.

PubMed

Hudak, Paul F

2004-01-01

This study investigated the use of slurry cutoff walls in conjunction with monitoring wells to detect contaminant releases from a solid waste landfill. The 50 m wide by 75 m long landfill was oriented oblique to regional groundwater flow in a shallow sand aquifer. Computer models calculated flow fields and the detection capability of six monitoring networks, four including a 1 m wide by 50 m long cutoff wall at various positions along the landfill's downgradient boundaries and upgradient of the landfill. Wells were positioned to take advantage of convergent flow induced downgradient of the cutoff walls. A five-well network with no cutoff wall detected 81% of contaminant plumes originating within the landfill's footprint before they reached a buffer zone boundary located 50 m from the landfill's downgradient corner. By comparison, detection efficiencies of networks augmented with cutoff walls ranged from 81 to 100%. The most efficient network detected 100% of contaminant releases with four wells, with a centrally located, downgradient cutoff wall. In general, cutoff walls increased detection efficiency by delaying transport of contaminant plumes to the buffer zone boundary, thereby allowing them to increase in size, and by inducing convergent flow at downgradient areas, thereby funneling contaminant plumes toward monitoring wells. However, increases in detection efficiency were too small to offset construction costs for cutoff walls. A 100% detection efficiency was also attained by an eight-well network with no cutoff wall, at approximately one-third the cost of the most efficient wall-augmented network.

Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex.

PubMed

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo; Vanni, Simo

2015-01-01

In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate (which has direct association to neural energy consumption), entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response.

[Characterization of access to normal childbirth care in Bahia State, Brazil, based on graph theory].

PubMed

Sousa, Ludmilla Monfort Oliveira; Araújo, Edna Maria de; Miranda, José Garcia Vivas

2017-12-18

Origin-destination flow is a phenomenon that can be modeled as a network. Graph theory is a mathematical tool to characterize a network and thus allows studying the topological properties and temporal and spatial development of a set of related elements. The article aims to estimate the topological evolution of an inter-municipal network of normal deliveries. We selected the admissions for normal deliveries in the Hospital Information System of the Brazilian Unified National Health System, from 2008 to 2014, for women residing in Bahia State, Brazil. The following indices were applied: entry degree (from how many municipalities the women came for childbirth), exit degree (to how many municipalities they left), entry flow (how many women came), exit flow (how many women left), and the mean size of the exit edge (distance traveled). Analyses between macro-regions used the following indicators: proportion of normal deliveries performed outside the municipality of residence and mean size of the exit edge. The results indicate an increase in deliveries performed outside the municipality of residence, in addition to the persistence of concentration of deliveries in the hub municipalities in the Health Regions, and an increase in the distance between the municipality of residence and the municipality where the delivery took place. The organization of networks for normal childbirth poses an on-going challenge. It is important to analyze the flow of women for childbirth care in order to support the establishment of inter-municipal references to guarantee safe labor and childbirth. In conclusion, it is necessary to develop a regionalized network to meet the demand by pregnant women in the territory with universal and equitable coverage.

Understanding the Scalability of Bayesian Network Inference Using Clique Tree Growth Curves

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.

2010-01-01

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a Bayesian network, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN s non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

Wireless Sensor Network Security Enhancement Using Directional Antennas: State of the Art and Research Challenges

PubMed Central

Curiac, Daniel-Ioan

2016-01-01

Being often deployed in remote or hostile environments, wireless sensor networks are vulnerable to various types of security attacks. A possible solution to reduce the security risks is to use directional antennas instead of omnidirectional ones or in conjunction with them. Due to their increased complexity, higher costs and larger sizes, directional antennas are not traditionally used in wireless sensor networks, but recent technology trends may support this method. This paper surveys existing state of the art approaches in the field, offering a broad perspective of the future use of directional antennas in mitigating security risks, together with new challenges and open research issues. PMID:27070601

Nickel ferrite aerogels with monodisperse nanoscale building blocks--the importance of processing temperature and atmosphere.

PubMed

Pettigrew, Katherine A; Long, Jeffrey W; Carpenter, Everett E; Baker, Colin C; Lytle, Justin C; Chervin, Christopher N; Logan, Michael S; Stroud, Rhonda M; Rolison, Debra R

2008-04-01

Using two-step (air/argon) thermal processing, sol-gel-derived nickel-iron oxide aerogels are transformed into monodisperse, networked nanocrystalline magnetic oxides of NiFe(2)O(4) with particle diameters that can be ripened with increasing temperature under argon to 4.6, 6.4, and 8.8 nm. Processing in air alone yields poorly crystalline materials; heating in argon alone leads to single phase, but diversiform, polydisperse NiFe(2)O(4), which hampers interpretation of the magnetic properties of the nanoarchitectures. The two-step method yields an improved model system to study magnetic effects as a function of size on the nanoscale while maintaining the particles within the size regime of single domain magnets, as networked building blocks, not agglomerates, and without stabilizing ligands capping the surface.

Software-defined optical network for metro-scale geographically distributed data centers.

PubMed

Samadi, Payman; Wen, Ke; Xu, Junjie; Bergman, Keren

2016-05-30

The emergence of cloud computing and big data has rapidly increased the deployment of small and mid-sized data centers. Enterprises and cloud providers require an agile network among these data centers to empower application reliability and flexible scalability. We present a software-defined inter data center network to enable on-demand scale out of data centers on a metro-scale optical network. The architecture consists of a combined space/wavelength switching platform and a Software-Defined Networking (SDN) control plane equipped with a wavelength and routing assignment module. It enables establishing transparent and bandwidth-selective connections from L2/L3 switches, on-demand. The architecture is evaluated in a testbed consisting of 3 data centers, 5-25 km apart. We successfully demonstrated end-to-end bulk data transfer and Virtual Machine (VM) migrations across data centers with less than 100 ms connection setup time and close to full link capacity utilization.

RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

NASA Astrophysics Data System (ADS)

Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

2016-02-01

Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

Nanoporous Metallic Networks: Fabrication, Optical Properties, and Applications.

PubMed

Ron, Racheli; Haleva, Emir; Salomon, Adi

2018-05-17

Nanoporous metallic networks are a group of porous materials made of solid metals with suboptical wavelength sizes of both particles and voids. They are characterized by unique optical properties, as well as high surface area and permeability of guest materials. As such, they attract a great focus as novel materials for photonics, catalysis, sensing, and renewable energy. Their properties together with the ability for scaling-up evoke an increased interest also in the industrial field. Here, fabrication techniques of large-scale metallic networks are discussed, and their interesting optical properties as well as their applications are considered. In particular, the focus is on disordered systems, which may facilitate the fabrication technique, yet, endow the three-dimensional (3D) network with distinct optical properties. These metallic networks bridge the nanoworld into the macroscopic world, and therefore pave the way to the fabrication of innovative materials with unique optoelectronic properties. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Network characteristics and patent value-Evidence from the Light-Emitting Diode industry.

PubMed

Huang, Way-Ren; Hsieh, Chia-Jen; Chang, Ke-Chiun; Kiang, Yen-Jo; Yuan, Chien-Chung; Chu, Woei-Chyn

2017-01-01

This study proposes a different angle to social network analysis that evaluates patent value and explores its influencing factors using the network centrality and network position. This study utilizes a logistic regression model to explore the relationships in the LED industry between patent value and network centrality as measured from out-degree centrality, in-degree centrality, in-closeness centrality, and network position, which is measured from effect size. The empirical result shows that out-degree centrality and in-degree centrality have significant positive effects on patent value and that effect size has a significant negative effect on patent value.

Pseudo-orthogonalization of memory patterns for associative memory.

PubMed

Oku, Makito; Makino, Takaki; Aihara, Kazuyuki

2013-11-01

A new method for improving the storage capacity of associative memory models on a neural network is proposed. The storage capacity of the network increases in proportion to the network size in the case of random patterns, but, in general, the capacity suffers from correlation among memory patterns. Numerous solutions to this problem have been proposed so far, but their high computational cost limits their scalability. In this paper, we propose a novel and simple solution that is locally computable without any iteration. Our method involves XNOR masking of the original memory patterns with random patterns, and the masked patterns and masks are concatenated. The resulting decorrelated patterns allow higher storage capacity at the cost of the pattern length. Furthermore, the increase in the pattern length can be reduced through blockwise masking, which results in a small amount of capacity loss. Movie replay and image recognition are presented as examples to demonstrate the scalability of the proposed method.

The European project Merlin on multi-gigabit, energy-efficient, ruggedized lightwave engines for advanced on-board digital processors

NASA Astrophysics Data System (ADS)

Stampoulidis, L.; Kehayas, E.; Karppinen, M.; Tanskanen, A.; Heikkinen, V.; Westbergh, P.; Gustavsson, J.; Larsson, A.; Grüner-Nielsen, L.; Sotom, M.; Venet, N.; Ko, M.; Micusik, D.; Kissinger, D.; Ulusoy, A. C.; King, R.; Safaisini, R.

2017-11-01

Modern broadband communication networks rely on satellites to complement the terrestrial telecommunication infrastructure. Satellites accommodate global reach and enable world-wide direct broadcasting by facilitating wide access to the backbone network from remote sites or areas where the installation of ground segment infrastructure is not economically viable. At the same time the new broadband applications increase the bandwidth demands in every part of the network - and satellites are no exception. Modern telecom satellites incorporate On-Board Processors (OBP) having analogue-to-digital (ADC) and digital-to-analogue converters (DAC) at their inputs/outputs and making use of digital processing to handle hundreds of signals; as the amount of information exchanged increases, so do the physical size, mass and power consumption of the interconnects required to transfer massive amounts of data through bulk electric wires.

The Relationship of Age to Personal Network Size, Relational Multiplexity, and Proximity to Alters in the Western United States

PubMed Central

Smith, Emily J.; Marcum, Christopher S.; Boessen, Adam; Almquist, Zack W.; Hipp, John R.; Nagle, Nicholas N.

2015-01-01

Objectives. This study examines the association of age and other sociodemographic variables with properties of personal networks; using samples of individuals residing in the rural western United States and the City of Los Angeles, we evaluate the degree to which these associations vary with geographical context. For both samples, we test the hypothesis that age is negatively associated with network size (i.e., degree) and positively associated with network multiplexity (the extent of overlap) on 6 different relations: core discussion members, social activity participants, emergency contacts, neighborhood safety contacts, job informants, and kin. We also examine the relationship between age and spatial proximity to alters. Method. Our data consist of a large-scale, spatially stratified egocentric network survey containing information about respondents and those to whom they are tied. We use Poisson regression to test our hypothesis regarding degree while adjusting for covariates, including education, gender, race, and self-reported sense of neighborhood belonging. We use multiple linear regression to test our hypotheses on multiplexity and distance to alters. Results. For both rural and urban populations, we find a nonmonotone association between age and numbers of core discussants and emergency contacts, with rural populations also showing nonmonotone associations for social activity partners and kin. These nonmonotone relationships show a peak in expected degree at midlife, followed by an eventual decline. We find a decline in degree among the elderly for all relations in both populations. Age is positively associated with distance to nonhousehold alters for the rural population, although residential tenure is associated with shorter ego-alter distances in both rural and urban settings. Additionally, age is negatively associated with network multiplexity for both populations. Discussion. Although personal network size ultimately declines with age, we find that increases for some relations extend well into late-midlife and most elders still maintain numerous contacts across diverse relations. The evidence we present suggests that older people tap into an wider variety of different network members for different types of relations than do younger people. This is true even for populations in rural settings, for whom immediate access to potential alters is more limited. PMID:25324292

A built-in sensor with carbon nanotubes coated by Ag clusters for deformation monitoring of glass fibre/epoxy composites

NASA Astrophysics Data System (ADS)

Slobodian, P.; Riha, P.; Matyas, J.; Olejnik, R.; Lloret Pertegás, S.; Schledjewski, R.; Kovar, M.

2018-03-01

A multiwalled carbon nanotube network embedded in a polyurethane membrane was integrated into a glass fibre reinforced epoxy composite by means of vacuum infusion to become a part of the composite and has been serving for a strain self-sensing functionality. Besides the pristine nanotubes also nanotubes with Ag nanoparticles attached to their surfaces were used to increase strain sensing. Moreover, the design of the carbon nanotube/polyurethane sensor allowed formation of network micro-sized cracks which increased its reversible electrical resistance resulted in an enhancement of strain sensing. The resistance sensitivity, quantified by a gauge factor, increased more than hundredfold in case of a pre-strained sensor with Ag decorated nanotubes in comparison with the sensor with pristine nanotubes.

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae.

PubMed

Jarnuczak, Andrew F; Eyers, Claire E; Schwartz, Jean-Marc; Grant, Christopher M; Hubbard, Simon J

2015-09-01

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC-based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs. © 2015 The Authors. PROTEOMICS published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spectra of English evolving word co-occurrence networks

NASA Astrophysics Data System (ADS)

Liang, Wei

2017-02-01

Spectral analysis is a powerful tool that provides global measures of the network properties. In this paper, 200 English articles are collected. A word co-occurrence network is constructed from each single article (denoted by single network). Furthermore, 5 large English word co-occurrence networks are constructed (denoted by large network). Spectra of their adjacency matrices are computed. The largest eigenvalue, λ1, depends on the network size N and the number of edges E as λ1 ∝N0.66 and λ1 ∝E0.54, respectively. The number of different eigenvalues, Nλ, increase in the manner of Nλ ∝N0.58 and Nλ ∝E0.47. The middle part of the spectral distribution can be fitted by a line with slope - 0.01 in each of the large networks, whereas two segments with the same slope - 0.03 for 0 ≪ N < 260 and - 0.02 for 260 < N < 2800 are needed for the single networks. An "M"-shape distribution appears in each of the spectral densities of the large networks. These and other results can provide useful insight into the structural properties of English linguistic networks.

Efficiency disparities among community hospitals in Tennessee: do size, location, ownership, and network matter?

PubMed

Roh, Chul-Young; Moon, M Jae; Jung, Kwangho

2013-11-01

This study examined the impact of ownership, size, location, and network on the relative technical efficiency of community hospitals in Tennessee for the 2002-2006 period, by applying data envelopment analysis (DEA) to measure technical efficiency (decomposed into scale efficiency and pure technical efficiency). Data envelopment analysis results indicate that medium-size hospitals (126-250 beds) are more efficient than their counterparts. Interestingly, public hospitals are significantly more efficient than private and nonprofit hospitals in Tennessee, and rural hospitals are more efficient than urban hospitals. This is the first study to investigate whether hospital networks with other health care providers affect hospital efficiency. Results indicate that community hospitals with networks are more efficient than non-network hospitals. From a management and policy perspective, this study suggests that public policies should induce hospitals to downsize or upsize into optional size, and private hospitals and nonprofit hospitals should change their organizational objectives from profit-driven to quality-driven.

Effects of the underlying topology on perturbation spreading in the Axelrod model for cultural dissemination

NASA Astrophysics Data System (ADS)

Kim, Yup; Cho, Minsoo; Yook, Soon-Hyung

2011-10-01

We study the effects of the underlying topologies on a single feature perturbation imposed to the Axelrod model of consensus formation. From the numerical simulations we show that there are successive updates which are similar to avalanches in many self-organized criticality systems when a perturbation is imposed. We find that the distribution of avalanche size satisfies the finite-size scaling (FSS) ansatz on two-dimensional lattices and random networks. However, on scale-free networks with the degree exponent γ≤3 we show that the avalanche size distribution does not satisfy the FSS ansatz. The results indicate that the disordered configurations on two-dimensional lattices or on random networks are still stable against the perturbation in the limit N (network size) →∞. However, on scale-free networks with γ≤3 the perturbation always drives the disordered phase into an ordered phase. The possible relationship between the properties of phase transition of the Axelrod model and the avalanche distribution is also discussed.

Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity

PubMed Central

Albantakis, Larissa; Hintze, Arend; Koch, Christof; Adami, Christoph; Tononi, Giulio

2014-01-01

Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment's causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ‘Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity. PMID:25521484

Low-Voltage High-Performance UV Photodetectors: An Interplay between Grain Boundaries and Debye Length.

PubMed

Bo, Renheng; Nasiri, Noushin; Chen, Hongjun; Caputo, Domenico; Fu, Lan; Tricoli, Antonio

2017-01-25

Accurate detection of UV light by wearable low-power devices has many important applications including environmental monitoring, space to space communication, and defense. Here, we report the structural engineering of ultraporous ZnO nanoparticle networks for fabrication of very low-voltage high-performance UV photodetectors. A record high photo- to dark-current ratio of 3.3 × 10 5 and detectivity of 3.2 × 10 12 Jones at an ultralow operation bias of 2 mV and low UV-light intensity of 86 μW·cm -2 are achieved by controlling the interplay between grain boundaries and surface depletion depth of ZnO nanoscale semiconductors. An optimal window of structural properties is determined by varying the particle size of ultraporous nanoparticle networks from 10 to 42 nm. We find that small electron-depleted nanoparticles (≤40 nm) are necessary to minimize the dark-current; however, the rise in photocurrent is tampered with decreasing particle size due to the increasing density of grain boundaries. These findings reveal that nanoparticles with a size close to twice their Debye length are required for high photo- to dark-current ratio and detectivity, while further decreasing their size decreases the photodetector performance.

Cytoskeletal actin dynamics shape a ramifying actin network underpinning immunological synapse formation

PubMed Central

Fritzsche, Marco; Fernandes, Ricardo A.; Chang, Veronica T.; Colin-York, Huw; Clausen, Mathias P.; Felce, James H.; Galiani, Silvia; Erlenkämper, Christoph; Santos, Ana M.; Heddleston, John M.; Pedroza-Pacheco, Isabela; Waithe, Dominic; de la Serna, Jorge Bernardino; Lagerholm, B. Christoffer; Liu, Tsung-li; Chew, Teng-Leong; Betzig, Eric; Davis, Simon J.; Eggeling, Christian

2017-01-01

T cell activation and especially trafficking of T cell receptor microclusters during immunological synapse formation are widely thought to rely on cytoskeletal remodeling. However, important details on the involvement of actin in the latter transport processes are missing. Using a suite of advanced optical microscopes to analyze resting and activated T cells, we show that, following contact formation with activating surfaces, these cells sequentially rearrange their cortical actin across the entire cell, creating a previously unreported ramifying actin network above the immunological synapse. This network shows all the characteristics of an inward-growing transportation network and its dynamics correlating with T cell receptor rearrangements. This actin reorganization is accompanied by an increase in the nanoscale actin meshwork size and the dynamic adjustment of the turnover times and filament lengths of two differently sized filamentous actin populations, wherein formin-mediated long actin filaments support a very flat and stiff contact at the immunological synapse interface. The initiation of immunological synapse formation, as highlighted by calcium release, requires markedly little contact with activating surfaces and no cytoskeletal rearrangements. Our work suggests that incipient signaling in T cells initiates global cytoskeletal rearrangements across the whole cell, including a stiffening process for possibly mechanically supporting contact formation at the immunological synapse interface as well as a central ramified transportation network apparently directed at the consolidation of the contact and the delivery of effector functions. PMID:28691087

Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates

PubMed Central

Magrou, Loïc; Gămănuț, Bianca; Van Essen, David C.; Burkhalter, Andreas; Knoblauch, Kenneth; Toroczkai, Zoltán; Kennedy, Henry

2016-01-01

Mammals show a wide range of brain sizes, reflecting adaptation to diverse habitats. Comparing interareal cortical networks across brains of different sizes and mammalian orders provides robust information on evolutionarily preserved features and species-specific processing modalities. However, these networks are spatially embedded, directed, and weighted, making comparisons challenging. Using tract tracing data from macaque and mouse, we show the existence of a general organizational principle based on an exponential distance rule (EDR) and cortical geometry, enabling network comparisons within the same model framework. These comparisons reveal the existence of network invariants between mouse and macaque, exemplified in graph motif profiles and connection similarity indices, but also significant differences, such as fractionally smaller and much weaker long-distance connections in the macaque than in mouse. The latter lends credence to the prediction that long-distance cortico-cortical connections could be very weak in the much-expanded human cortex, implying an increased susceptibility to disconnection syndromes such as Alzheimer disease and schizophrenia. Finally, our data from tracer experiments involving only gray matter connections in the primary visual areas of both species show that an EDR holds at local scales as well (within 1.5 mm), supporting the hypothesis that it is a universally valid property across all scales and, possibly, across the mammalian class. PMID:27441598

Stability-to-instability transition in the structure of large-scale networks

NASA Astrophysics Data System (ADS)

Hu, Dandan; Ronhovde, Peter; Nussinov, Zohar

2012-12-01

We examine phase transitions between the “easy,” “hard,” and “unsolvable” phases when attempting to identify structure in large complex networks (“community detection”) in the presence of disorder induced by network “noise” (spurious links that obscure structure), heat bath temperature T, and system size N. The partition of a graph into q optimally disjoint subgraphs or “communities” inherently requires Potts-type variables. In earlier work [Philos. Mag.1478-643510.1080/14786435.2011.616547 92, 406 (2012)], when examining power law and other networks (and general associated Potts models), we illustrated that transitions in the computational complexity of the community detection problem typically correspond to spin-glass-type transitions (and transitions to chaotic dynamics in mechanical analogs) at both high and low temperatures and/or noise. The computationally “hard” phase exhibits spin-glass type behavior including memory effects. The region over which the hard phase extends in the noise and temperature phase diagram decreases as N increases while holding the average number of nodes per community fixed. This suggests that in the thermodynamic limit a direct sharp transition may occur between the easy and unsolvable phases. When present, transitions at low temperature or low noise correspond to entropy driven (or “order by disorder”) annealing effects, wherein stability may initially increase as temperature or noise is increased before becoming unsolvable at sufficiently high temperature or noise. Additional transitions between contending viable solutions (such as those at different natural scales) are also possible. Identifying community structure via a dynamical approach where “chaotic-type” transitions were found earlier. The correspondence between the spin-glass-type complexity transitions and transitions into chaos in dynamical analogs might extend to other hard computational problems. In this work, we examine large networks (with a power law distribution in cluster size) that have a large number of communities (q≫1). We infer that large systems at a constant ratio of q to the number of nodes N asymptotically tend towards insolvability in the limit of large N for any positive T. The asymptotic behavior of temperatures below which structure identification might be possible, T×=O[1/lnq], decreases slowly, so for practical system sizes, there remains an accessible, and generally easy, global solvable phase at low temperature. We further employ multivariate Tutte polynomials to show that increasing q emulates increasing T for a general Potts model, leading to a similar stability region at low T. Given the relation between Tutte and Jones polynomials, our results further suggest a link between the above complexity transitions and transitions associated with random knots.

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes.

PubMed

Vegué, Marina; Perin, Rodrigo; Roxin, Alex

2017-08-30

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering. Copyright © 2017 the authors 0270-6474/17/378498-13$15.00/0.

The role of discharge variation in scaling of drainage area and food chain length in rivers

USGS Publications Warehouse

Sabo, John L.; Finlay, Jacques C.; Kennedy, Theodore A.; Post, David M.

2010-01-01

Food chain length (FCL) is a fundamental component of food web structure. Studies in a variety of ecosystems suggest that FCL is determined by energy supply, environmental stability, and/or ecosystem size, but the nature of the relationship between environmental stability and FCL, and the mechanism linking ecosystem size to FCL, remain unclear. Here we show that FCL increases with drainage area and decreases with hydrologic variability and intermittency across 36 North American rivers. Our analysis further suggests that hydrologic variability is the mechanism underlying the correlation between ecosystem size and FCL in rivers. Ecosystem size lengthens river food chains by integrating and attenuating discharge variation through stream networks, thereby enhancing environmental stability in larger river systems.

The role of discharge variation in scaling of drainage area and food chain length in rivers.

PubMed

Sabo, John L; Finlay, Jacques C; Kennedy, Theodore; Post, David M

2010-11-12

Food chain length (FCL) is a fundamental component of food web structure. Studies in a variety of ecosystems suggest that FCL is determined by energy supply, environmental stability, and/or ecosystem size, but the nature of the relationship between environmental stability and FCL, and the mechanism linking ecosystem size to FCL, remain unclear. Here we show that FCL increases with drainage area and decreases with hydrologic variability and intermittency across 36 North American rivers. Our analysis further suggests that hydrologic variability is the mechanism underlying the correlation between ecosystem size and FCL in rivers. Ecosystem size lengthens river food chains by integrating and attenuating discharge variation through stream networks, thereby enhancing environmental stability in larger river systems.

Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1993-01-01

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

Tailoring the affinity of organosilica membranes by introducing polarizable ethenylene bridges and aqueous ozone modification.

PubMed

Xu, Rong; Kanezashi, Masakoto; Yoshioka, Tomohisa; Okuda, Tetsuji; Ohshita, Joji; Tsuru, Toshinori

2013-07-10

Bis(triethoxysilyl)ethylene (BTESEthy) was used as a novel precursor to develop a microporous organosilica membrane via the sol-gel technique. Water sorption measurements confirmed that ethenylene-bridged BTESEthy networks had a higher affinity for water than that of ethane-bridged organosilica materials. High permeance of CO2 with high CO2/N2 selectivity was explained relative to the strong CO2 adsorption on the network with π-bond electrons. The introduction of polarizable and rigid ethenylene bridges in the network structure led to improved water permeability and high NaCl rejection (>98.5%) in reverse osmosis (RO). Moreover, the aqueous ozone modification promoted significant improvement in the water permeability of the membrane. After 60 min of ozone exposure, the water permeability reached 1.1 × 10(-12) m(3)/(m(2) s Pa), which is close to that of a commercial seawater RO membrane. Meanwhile, molecular weight cutoff measurements indicated a gradual increase in the effective pore size with ozone modification, which may present new options for fine-tuning of membrane pore sizes.

Optimal sensor placement for leak location in water distribution networks using genetic algorithms.

PubMed

Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert

2013-11-04

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

PubMed Central

Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert

2013-01-01

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099

Predicting Slag Generation in Sub-Scale Test Motors Using a Neural Network

NASA Technical Reports Server (NTRS)

Wiesenberg, Brent

1999-01-01

Generation of slag (aluminum oxide) is an important issue for the Reusable Solid Rocket Motor (RSRM). Thiokol performed testing to quantify the relationship between raw material variations and slag generation in solid propellants by testing sub-scale motors cast with propellant containing various combinations of aluminum fuel and ammonium perchlorate (AP) oxidizer particle sizes. The test data were analyzed using statistical methods and an artificial neural network. This paper primarily addresses the neural network results with some comparisons to the statistical results. The neural network showed that the particle sizes of both the aluminum and unground AP have a measurable effect on slag generation. The neural network analysis showed that aluminum particle size is the dominant driver in slag generation, about 40% more influential than AP. The network predictions of the amount of slag produced during firing of sub-scale motors were 16% better than the predictions of a statistically derived empirical equation. Another neural network successfully characterized the slag generated during full-scale motor tests. The success is attributable to the ability of neural networks to characterize multiple complex factors including interactions that affect slag generation.

Revisiting node-based SIR models in complex networks with degree correlations

NASA Astrophysics Data System (ADS)

Wang, Yi; Cao, Jinde; Alofi, Abdulaziz; AL-Mazrooei, Abdullah; Elaiw, Ahmed

2015-11-01

In this paper, we consider two growing networks which will lead to the degree-degree correlations between two nearest neighbors in the network. When the network grows to some certain size, we introduce an SIR-like disease such as pandemic influenza H1N1/09 to the population. Due to its rapid spread, the population size changes slowly, and thus the disease spreads on correlated networks with approximately fixed size. To predict the disease evolution on correlated networks, we first review two node-based SIR models incorporating degree correlations and an edge-based SIR model without considering degree correlation, and then compare the predictions of these models with stochastic SIR simulations, respectively. We find that the edge-based model, even without considering degree correlations, agrees much better than the node-based models incorporating degree correlations with stochastic SIR simulations in many respects. Moreover, simulation results show that for networks with positive correlation, the edge-based model provides a better upper bound of the cumulative incidence than the node-based SIR models, whereas for networks with negative correlation, it provides a lower bound of the cumulative incidence.

Support vector regression to predict porosity and permeability: Effect of sample size

NASA Astrophysics Data System (ADS)

Al-Anazi, A. F.; Gates, I. D.

2012-02-01

Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function type and loss functions used.

Experimental evidence for the effect of habitat loss on the dynamics of migratory networks.

PubMed

Betini, Gustavo S; Fitzpatrick, Mark J; Norris, D Ryan

2015-06-01

Migratory animals present a unique challenge for understanding the consequences of habitat loss on population dynamics because individuals are typically distributed over a series of interconnected breeding and non-breeding sites (termed migratory network). Using replicated breeding and non-breeding populations of Drosophila melanogaster and a mathematical model, we investigated three hypotheses to explain how habitat loss influenced the dynamics of populations in networks with different degrees of connectivity between breeding and non-breeding seasons. We found that habitat loss increased the degree of connectivity in the network and influenced population size at sites that were not directly connected to the site where habitat loss occurred. However, connected networks only buffered global population declines at high levels of habitat loss. Our results demonstrate why knowledge of the patterns of connectivity across a species range is critical for predicting the effects of environmental change and provide empirical evidence for why connected migratory networks are commonly found in nature. © 2015 John Wiley & Sons Ltd/CNRS.

Narcissism and Social Networking Behavior: A Meta-Analysis.

PubMed

Gnambs, Timo; Appel, Markus

2018-04-01

The increasing popularity of social networking sites (SNS) such as Facebook and Twitter has given rise to speculations that the intensity of using these platforms is associated with narcissistic tendencies. However, recent research on this issue has been all but conclusive. We present a three-level, random effects meta-analysis including 289 effect sizes from 57 studies (total N = 25,631) on the association between trait narcissism and social networking behavior. The meta-analysis identified a small to moderate effect of ρ = .17 (τ = .11), 95% CI [.13, .21], for grandiose narcissism that replicated across different social networking platforms, respondent characteristics, and time. Moderator analyses revealed pronounced cultural differences, with stronger associations in power-distant cultures. Moreover, social networking behaviors geared toward self-presentation and the number of SNS friends exhibited stronger effects than usage durations. Overall, the study not only supported but also refined the notion of a relationship between engaging in social networking sites and narcissistic personality traits. © 2017 Wiley Periodicals, Inc.

Sex Differences in Virtual Network Characteristics and Sexual Risk Behavior among Emerging Adults

PubMed Central

Cook, Stephanie H.; Bauermeister, José A.; Zimmerman, Marc A.

2016-01-01

Emerging adults (EAs)ages 18 to 24 account for a large proportion of all sexually transmitted infections (STIs), HIV infections, and unintended pregnancies in the United States. Given the increased influence of online media on decision-making, we examined how EA online networks were associated with sexual risk behaviors. We used egocentric network data collected from EAs aged 18 to 24 years old across the United States (N=1,687) to examine how online norms (e.g., acceptance of HIV infections, other STIs, and pregnancy) and network characteristics (i.e., network size and density; ties' closeness, race, age, and sex similarities) were associated with participants' unprotected vaginal intercourse (UVI) in the last 30 days. Findings suggested that in male EAs, there was a strong association between sexual norms, structural characteristics, and sexual risk behavior compared to females. Researchers and practitioners may wish to address online peer norms and EAs' online network composition when developing online sexual risk prevention tools. PMID:28083447

Surface adsorption and hopping cause probe-size-dependent microrheology of actin networks

NASA Astrophysics Data System (ADS)

He, Jun; Tang, Jay X.

2011-04-01

A network of filaments formed primarily by the abundant cytoskeletal protein actin gives animal cells their shape and elasticity. The rheological properties of reconstituted actin networks have been studied by tracking micron-sized probe beads embedded within the networks. We investigate how microrheology depends on surface properties of probe particles by varying the stickiness of their surface. For this purpose, we chose carboxylate polystyrene (PS) beads, silica beads, bovine serum albumin (BSA) -coated PS beads, and polyethylene glycol (PEG) -grafted PS beads, which show descending stickiness to actin filaments, characterized by confocal imaging and microrheology. Probe size dependence of microrheology is observed for all four types of beads. For the slippery PEG beads, particle-tracking microrheology detects weaker networks using smaller beads, which tend to diffuse through the network by hopping from one confinement “cage” to another. This trend is reversed for the other three types of beads, for which microrheology measures stiffer networks for smaller beads due to physisorption of nearby filaments to the bead surface. We explain the probe size dependence with two simple models. We also evaluate depletion effect near nonadsorption bead surface using quantitative image analysis and discuss the possible impact of depletion on microrheology. Analysis of these effects is necessary in order to accurately define the actin network rheology both in vitro and in vivo.

Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

PubMed

Sperry, Megan M; Kartha, Sonia; Granquist, Eric J; Winkelstein, Beth A

2018-07-01

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p < 0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

Nonequilibrium transitions in complex networks: A model of social interaction

NASA Astrophysics Data System (ADS)

Klemm, Konstantin; Eguíluz, Víctor M.; Toral, Raúl; San Miguel, Maxi

2003-02-01

We analyze the nonequilibrium order-disorder transition of Axelrod’s model of social interaction in several complex networks. In a small-world network, we find a transition between an ordered homogeneous state and a disordered state. The transition point is shifted by the degree of spatial disorder of the underlying network, the network disorder favoring ordered configurations. In random scale-free networks the transition is only observed for finite size systems, showing system size scaling, while in the thermodynamic limit only ordered configurations are always obtained. Thus, in the thermodynamic limit the transition disappears. However, in structured scale-free networks, the phase transition between an ordered and a disordered phase is restored.

Robust scalable stabilisability conditions for large-scale heterogeneous multi-agent systems with uncertain nonlinear interactions: towards a distributed computing architecture

NASA Astrophysics Data System (ADS)

Manfredi, Sabato

2016-06-01

Large-scale dynamic systems are becoming highly pervasive in their occurrence with applications ranging from system biology, environment monitoring, sensor networks, and power systems. They are characterised by high dimensionality, complexity, and uncertainty in the node dynamic/interactions that require more and more computational demanding methods for their analysis and control design, as well as the network size and node system/interaction complexity increase. Therefore, it is a challenging problem to find scalable computational method for distributed control design of large-scale networks. In this paper, we investigate the robust distributed stabilisation problem of large-scale nonlinear multi-agent systems (briefly MASs) composed of non-identical (heterogeneous) linear dynamical systems coupled by uncertain nonlinear time-varying interconnections. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, new conditions are given for the distributed control design of large-scale MASs that can be easily solved by the toolbox of MATLAB. The stabilisability of each node dynamic is a sufficient assumption to design a global stabilising distributed control. The proposed approach improves some of the existing LMI-based results on MAS by both overcoming their computational limits and extending the applicative scenario to large-scale nonlinear heterogeneous MASs. Additionally, the proposed LMI conditions are further reduced in terms of computational requirement in the case of weakly heterogeneous MASs, which is a common scenario in real application where the network nodes and links are affected by parameter uncertainties. One of the main advantages of the proposed approach is to allow to move from a centralised towards a distributed computing architecture so that the expensive computation workload spent to solve LMIs may be shared among processors located at the networked nodes, thus increasing the scalability of the approach than the network size. Finally, a numerical example shows the applicability of the proposed method and its advantage in terms of computational complexity when compared with the existing approaches.

The Effect of Cognitive Therapy on Structural Social Capital: Results From a Randomized Controlled Trial Among Sexual Violence Survivors in the Democratic Republic of the Congo

PubMed Central

Bolton, Paul A.; Annan, Jeannie; Kaysen, Debra; Robinette, Katie; Cetinoglu, Talita; Wachter, Karin; Bass, Judith K.

2014-01-01

Objectives. We evaluated changes in social capital following group-based cognitive processing therapy (CPT) for female survivors of sexual violence. Methods. We compared CPT with individual support in a cluster-randomized trial in villages in South Kivu province, Democratic Republic of the Congo. Local psychosocial assistants delivered the interventions from April through July 2011. We evaluated differences between CPT and individual support conditions for structural social capital (i.e., time spent with nonkin social network, group membership and participation, and the size of financial and instrumental support networks) and emotional support seeking. We analyzed intervention effects with longitudinal random effects models. Results. We obtained small to medium effect size differences for 2 study outcomes. Women in the CPT villages increased group membership and participation at 6-month follow-up and emotional support seeking after the intervention compared with women in the individual support villages. Conclusions. Results support the efficacy of group CPT to increase dimensions of social capital among survivors of sexual violence in a low-income conflict-affected context. PMID:25033113

Effects of the size and content of protein aggregates on the rheological and structural properties of soy protein isolate emulsion gels induced by CaSO4.

PubMed

Wang, Xufeng; He, Zhiyong; Zeng, Maomao; Qin, Fang; Adhikari, Benu; Chen, Jie

2017-04-15

The effects of the size and content of soy protein isolate (SPI) aggregates on the rheological and textural properties of CaSO 4 -induced SPI emulsion gels were investigated. Considerable differences in the rheological, water-holding, and micro-structural properties were observed. The gels with larger and/or more SPI aggregates showed substantial increase in the elastic modulus and had lower gelation temperatures. Creep data suggested that the size of the SPI aggregates contributed more to the elastic modulus, whereas the increase of aggregate content enhanced the elastic modulus and viscous component of the gels. The water-holding capacity was markedly enhanced (p<0.05) with the increase in both the size and content of SPI aggregates. Confocal laser scanning microscopy and scanning electron microscopy showed that larger and/or more SPI aggregates resulted in more homogeneous networks with smaller oil droplets. These insights provide important information for the product development in relation to soy protein-stabilized emulsions and emulsion gels. Copyright © 2016. Published by Elsevier Ltd.

Shear strength and microstructure of polydisperse packings: The effect of size span and shape of particle size distribution.

PubMed

Azéma, Emilien; Linero, Sandra; Estrada, Nicolas; Lizcano, Arcesio

2017-08-01

By means of extensive contact dynamics simulations, we analyzed the effect of particle size distribution (PSD) on the strength and microstructure of sheared granular materials composed of frictional disks. The PSDs are built by means of a normalized β function, which allows the systematic investigation of the effects of both, the size span (from almost monodisperse to highly polydisperse) and the shape of the PSD (from linear to pronouncedly curved). We show that the shear strength is independent of the size span, which substantiates previous results obtained for uniform distributions by packing fraction. Notably, the shear strength is also independent of the shape of the PSD, as shown previously for systems composed of frictionless disks. In contrast, the packing fraction increases with the size span, but decreases with more pronounced PSD curvature. At the microscale, we analyzed the connectivity and anisotropies of the contacts and forces networks. We show that the invariance of the shear strength with the PSD is due to a compensation mechanism which involves both geometrical sources of anisotropy. In particular, contact orientation anisotropy decreases with the size span and increases with PSD curvature, while the branch length anisotropy behaves inversely.

Network characteristics and patent value—Evidence from the Light-Emitting Diode industry

PubMed Central

Huang, Way-Ren; Hsieh, Chia-Jen; Chang, Ke-Chiun; Kiang, Yen-Jo; Yuan, Chien-Chung; Chu, Woei-Chyn

2017-01-01

This study proposes a different angle to social network analysis that evaluates patent value and explores its influencing factors using the network centrality and network position. This study utilizes a logistic regression model to explore the relationships in the LED industry between patent value and network centrality as measured from out-degree centrality, in-degree centrality, in-closeness centrality, and network position, which is measured from effect size. The empirical result shows that out-degree centrality and in-degree centrality have significant positive effects on patent value and that effect size has a significant negative effect on patent value. PMID:28817587

Effects of biotic and abiotic factors on the temporal dynamic of bat-fruit interactions

NASA Astrophysics Data System (ADS)

Laurindo, Rafael de Souza; Gregorin, Renato; Tavares, Davi Castro

2017-08-01

Mutualistic interactions between animals and plants vary over time and space based on the abundance of fruits or animals and seasonality. Little is known about this temporal dynamic and the influence of biotic and abiotic factors on the structure of interaction networks. We evaluated changes in the structure of network interactions between bats and fruits in relation to variations in rainfall. Our results suggest that fruit abundance is the main variable responsible for temporal changes in network attributes, such as network size, connectance, and number of interactions. In the same way, temperature positively affected the abundance of fruits and bats. An increase in temperature and alterations in rainfall patterns, due to human induced climate change, can cause changes in phenological patterns and fruit production, with negative consequences to biodiversity maintenance, ecological interactions, and ecosystem functioning.

Estimating the Size of a Large Network and its Communities from a Random Sample

PubMed Central

Chen, Lin; Karbasi, Amin; Crawford, Forrest W.

2017-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V, E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K, and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios. PMID:28867924

Estimating the Size of a Large Network and its Communities from a Random Sample.

PubMed

Chen, Lin; Karbasi, Amin; Crawford, Forrest W

2016-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = ( V, E ) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G ( W ) be the induced subgraph in G of the vertices in W . In addition to G ( W ), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K , and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios.

Promoting Wired Links in Wireless Mesh Networks: An Efficient Engineering Solution

PubMed Central

Barekatain, Behrang; Raahemifar, Kaamran; Ariza Quintana, Alfonso; Triviño Cabrera, Alicia

2015-01-01

Wireless Mesh Networks (WMNs) cannot completely guarantee good performance of traffic sources such as video streaming. To improve the network performance, this study proposes an efficient engineering solution named Wireless-to-Ethernet-Mesh-Portal-Passageway (WEMPP) that allows effective use of wired communication in WMNs. WEMPP permits transmitting data through wired and stable paths even when the destination is in the same network as the source (Intra-traffic). Tested with four popular routing protocols (Optimized Link State Routing or OLSR as a proactive protocol, Dynamic MANET On-demand or DYMO as a reactive protocol, DYMO with spanning tree ability and HWMP), WEMPP considerably decreases the end-to-end delay, jitter, contentions and interferences on nodes, even when the network size or density varies. WEMPP is also cost-effective and increases the network throughput. Moreover, in contrast to solutions proposed by previous studies, WEMPP is easily implemented by modifying the firmware of the actual Ethernet hardware without altering the routing protocols and/or the functionality of the IP/MAC/Upper layers. In fact, there is no need for modifying the functionalities of other mesh components in order to work with WEMPPs. The results of this study show that WEMPP significantly increases the performance of all routing protocols, thus leading to better video quality on nodes. PMID:25793516

Scale dependence of the mechanics of active gels with increasing motor concentration.

PubMed

Sonn-Segev, Adar; Bernheim-Groswasser, Anne; Roichman, Yael

2017-10-18

Actin is a protein that plays an essential role in maintaining the mechanical integrity of cells. In response to strong external stresses, it can assemble into large bundles, but it grows into a fine branched network to induce cell motion. In some cases, the self-organization of actin fibers and networks involves the action of bipolar filaments of the molecular motor myosin. Such self-organization processes mediated by large myosin bipolar filaments have been studied extensively in vitro. Here we create active gels, composed of single actin filaments and small myosin bipolar filaments. The active steady state in these gels persists long enough to enable the characterization of their mechanical properties using one and two point microrheology. We study the effect of myosin concentration on the mechanical properties of this model system for active matter, for two different motor assembly sizes. In contrast to previous studies of networks with large motor assemblies, we find that the fluctuations of tracer particles embedded in the network decrease in amplitude as motor concentration increases. Nonetheless, we show that myosin motors stiffen the actin networks, in accordance with bulk rheology measurements of networks containing larger motor assemblies. This implies that such stiffening is of universal nature and may be relevant to a wider range of cytoskeleton-based structures.

Effects of the distance among multiple spreaders on the spreading

NASA Astrophysics Data System (ADS)

Hu, Z.-L.; Liu, J.-G.; Yang, G.-Y.; Ren, Z.-M.

2014-04-01

It is very important to investigate the multiple spreaders' effects since the spreading phenomenon is ubiquitous in many complex systems. In this letter, we investigate the effects of the distance among the initial multiple spreaders for regular networks and WS (Watts-Strogatz) small-world networks based on the SIR (Susceptible-Infected-Recovered) model. Assuming the epidemics can spread over the network, the theoretical and experimental results show that for regular networks the larger the distance between spreaders is, the more effective the spreading is. For WS networks, the spreading efficiency will decrease when the distance exceeds a certain value, and a larger connection probability and average degree will result in a smaller distance of the most effective spreading. A better spreading strategy using n spreaders is to choose either the highest k or ks nodes with the condition that there are not any pairs of the n spreaders linked directly (Kitsak M. et al., Nat. Phys., 6 (2010) 888). However, we find that the spreading will be more effective when the distances among the largest-degree spreaders increase. All these results are independent of the network size for the two initial spreaders case. This work may give new insights to explore more effective methods to inhibit the epidemic spreading or increase the information diffusion.

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.

SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations.

PubMed

Shannon, Casey P; Chen, Virginia; Takhar, Mandeep; Hollander, Zsuzsanna; Balshaw, Robert; McManus, Bruce M; Tebbutt, Scott J; Sin, Don D; Ng, Raymond T

2016-11-14

Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues.

Appropriate Domain Size for Groundwater Flow Modeling with a Discrete Fracture Network Model.

PubMed

Ji, Sung-Hoon; Koh, Yong-Kwon

2017-01-01

When a discrete fracture network (DFN) is constructed from statistical conceptualization, uncertainty in simulating the hydraulic characteristics of a fracture network can arise due to the domain size. In this study, the appropriate domain size, where less significant uncertainty in the stochastic DFN model is expected, was suggested for the Korea Atomic Energy Research Institute Underground Research Tunnel (KURT) site. The stochastic DFN model for the site was established, and the appropriate domain size was determined with the density of the percolating cluster and the percolation probability using the stochastically generated DFNs for various domain sizes. The applicability of the appropriate domain size to our study site was evaluated by comparing the statistical properties of stochastically generated fractures of varying domain sizes and estimating the uncertainty in the equivalent permeability of the generated DFNs. Our results show that the uncertainty of the stochastic DFN model is acceptable when the modeling domain is larger than the determined appropriate domain size, and the appropriate domain size concept is applicable to our study site. © 2016, National Ground Water Association.

Understanding the implementation of evidence-based care: a structural network approach.

PubMed

Parchman, Michael L; Scoglio, Caterina M; Schumm, Phillip

2011-02-24

Recent study of complex networks has yielded many new insights into phenomenon such as social networks, the internet, and sexually transmitted infections. The purpose of this analysis is to examine the properties of a network created by the 'co-care' of patients within one region of the Veterans Health Affairs. Data were obtained for all outpatient visits from 1 October 2006 to 30 September 2008 within one large Veterans Integrated Service Network. Types of physician within each clinic were nodes connected by shared patients, with a weighted link representing the number of shared patients between each connected pair. Network metrics calculated included edge weights, node degree, node strength, node coreness, and node betweenness. Log-log plots were used to examine the distribution of these metrics. Sizes of k-core networks were also computed under multiple conditions of node removal. There were 4,310,465 encounters by 266,710 shared patients between 722 provider types (nodes) across 41 stations or clinics resulting in 34,390 edges. The number of other nodes to which primary care provider nodes have a connection (172.7) is 42% greater than that of general surgeons and two and one-half times as high as cardiology. The log-log plot of the edge weight distribution appears to be linear in nature, revealing a 'scale-free' characteristic of the network, while the distributions of node degree and node strength are less so. The analysis of the k-core network sizes under increasing removal of primary care nodes shows that about 10 most connected primary care nodes play a critical role in keeping the k-core networks connected, because their removal disintegrates the highest k-core network. Delivery of healthcare in a large healthcare system such as that of the US Department of Veterans Affairs (VA) can be represented as a complex network. This network consists of highly connected provider nodes that serve as 'hubs' within the network, and demonstrates some 'scale-free' properties. By using currently available tools to explore its topology, we can explore how the underlying connectivity of such a system affects the behavior of providers, and perhaps leverage that understanding to improve quality and outcomes of care.

Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1994-01-01

A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

Performance Analysis of Different Backoff Algorithms for WBAN-Based Emerging Sensor Networks

PubMed Central

Khan, Pervez; Ullah, Niamat; Ali, Farman; Ullah, Sana; Hong, Youn-Sik; Lee, Ki-Young; Kim, Hoon

2017-01-01

The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) procedure of IEEE 802.15.6 Medium Access Control (MAC) protocols for the Wireless Body Area Network (WBAN) use an Alternative Binary Exponential Backoff (ABEB) procedure. The backoff algorithm plays an important role to avoid collision in wireless networks. The Binary Exponential Backoff (BEB) algorithm used in different standards does not obtain the optimum performance due to enormous Contention Window (CW) gaps induced from packet collisions. Therefore, The IEEE 802.15.6 CSMA/CA has developed the ABEB procedure to avoid the large CW gaps upon each collision. However, the ABEB algorithm may lead to a high collision rate (as the CW size is incremented on every alternative collision) and poor utilization of the channel due to the gap between the subsequent CW. To minimize the gap between subsequent CW sizes, we adopted the Prioritized Fibonacci Backoff (PFB) procedure. This procedure leads to a smooth and gradual increase in the CW size, after each collision, which eventually decreases the waiting time, and the contending node can access the channel promptly with little delay; while ABEB leads to irregular and fluctuated CW values, which eventually increase collision and waiting time before a re-transmission attempt. We analytically approach this problem by employing a Markov chain to design the PFB scheme for the CSMA/CA procedure of the IEEE 80.15.6 standard. The performance of the PFB algorithm is compared against the ABEB function of WBAN CSMA/CA. The results show that the PFB procedure adopted for IEEE 802.15.6 CSMA/CA outperforms the ABEB procedure. PMID:28257112

Increased Sparsity of Hippocampal CA1 Neuronal Ensembles in a Mouse Model of Down Syndrome Assayed by Arc Expression

PubMed Central

Smith-Hicks, Constance L.; Cai, Peiling; Savonenko, Alena V.; Reeves, Roger H.; Worley, Paul F.

2017-01-01

Down syndrome (DS) is the leading chromosomal cause of intellectual disability, yet the neural substrates of learning and memory deficits remain poorly understood. Here, we interrogate neural networks linked to learning and memory in a well-characterized model of DS, the Ts65Dn mouse. We report that Ts65Dn mice exhibit exploratory behavior that is not different from littermate wild-type (WT) controls yet behavioral activation of Arc mRNA transcription in pyramidal neurons of the CA1 region of the hippocampus is altered in Ts65Dn mice. In WT mice, a 5 min period of exploration of a novel environment resulted in Arc mRNA transcription in 39% of CA1 neurons. By contrast, the same period of exploration resulted in only ~20% of CA1 neurons transcribing Arc mRNA in Ts65Dn mice indicating increased sparsity of the behaviorally induced ensemble. Like WT mice the CA1 pyramidal neurons of Ts65Dn mice reactivated Arc transcription during a second exposure to the same environment 20 min after the first experience, but the size of the reactivated ensemble was only ~60% of that in WT mice. After repeated daily exposures there was a further decline in the size of the reactivated ensemble in Ts65Dn and a disruption of reactivation. Together these data demonstrate reduction in the size of the behaviorally induced network that expresses Arc in Ts65Dn mice and disruption of the long-term stability of the ensemble. We propose that these deficits in network formation and stability contribute to cognitive symptoms in DS. PMID:28217086

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.

A Coalitional Game for Distributed Inference in Sensor Networks With Dependent Observations

NASA Astrophysics Data System (ADS)

He, Hao; Varshney, Pramod K.

2016-04-01

We consider the problem of collaborative inference in a sensor network with heterogeneous and statistically dependent sensor observations. Each sensor aims to maximize its inference performance by forming a coalition with other sensors and sharing information within the coalition. It is proved that the inference performance is a nondecreasing function of the coalition size. However, in an energy constrained network, the energy consumption of inter-sensor communication also increases with increasing coalition size, which discourages the formation of the grand coalition (the set of all sensors). In this paper, the formation of non-overlapping coalitions with statistically dependent sensors is investigated under a specific communication constraint. We apply a game theoretical approach to fully explore and utilize the information contained in the spatial dependence among sensors to maximize individual sensor performance. Before formulating the distributed inference problem as a coalition formation game, we first quantify the gain and loss in forming a coalition by introducing the concepts of diversity gain and redundancy loss for both estimation and detection problems. These definitions, enabled by the statistical theory of copulas, allow us to characterize the influence of statistical dependence among sensor observations on inference performance. An iterative algorithm based on merge-and-split operations is proposed for the solution and the stability of the proposed algorithm is analyzed. Numerical results are provided to demonstrate the superiority of our proposed game theoretical approach.

Synthesis of pore-variable mesoporous CdS and evaluation of its photocatalytic activity in degrading methylene blue

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

Zhang, Wei-Min, E-mail: chm_zhangwm@ujn.edu.cn; Jiang, Yao-Quan; Cao, Xiao-Yan

2013-10-15

Graphical abstract: - Highlights: • Self-templated synthesis of tubular CdS. • Cadmium complexes of aliphatic acids sustain the network of mesoporous structures. • Aliphatic acids affect the phase composition and particle size. • Pore size and volume vary with aliphatic acids having different hydrocarbonyl. - Abstract: In this study, mesoporous CdS polycrystallites have been synthesized using aliphatic acids of hexanoic acid, octanoic acid, and oleic acid as coordinating and capping agents, respectively. The fibrous Cd–fatty acid salts act as a template to form the tubular CdS. The organic species are found to be necessary for maintaining the network of mesoporousmore » CdS. The characterization results indicate that the shorter carbon chain length in aliphatic acids favors the wurtzite phase and particle size growth the specific surface area, pore diameter and pore volume show a monotonic raise with increasing carbon chain. The photocatalytic activities of mesoporous CdS tubes exhibit much higher efficiency than those of nanosized CdS powders in decolorizing methylene blue under simulated visible light.« less

Using the D-Wave 2X Quantum Computer to Explore the Formation of Global Terrorist Networks

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

Ambrosiano, John Joseph; Roberts, Randy Mark; Sims, Benjamin Hayden

Social networks with signed edges (+/-) play an important role in an area of social network theory called structural balance. In these networks, edges represent relationships that are labeled as either friendly (+) or hostile (-). A signed social network is balanced only if all cycles of three or more nodes in the graph have an odd number of hostile edges. A fundamental property of a balanced network is that it can be cleanly divided into 2 factions, where all relationships within each faction are friendly, and all relationships between members of different factions are hostile. The more unbalanced amore » network is, the more edges will fail to adhere to this rule, making factions more ambiguous. Social theory suggests unbalanced networks should be unstable, a finding that has been supported by research on gangs, which shows that unbalanced relationships are associated with greater violence, possibly due to this increased ambiguity about factional allegiances (Nakamura et al). One way to estimate the imbalance in a network, if only edge relationships are known, is to assign nodes to factions that minimize the number of violations of the edge rule described above. This problem is known to be computationally NP-hard. However, Facchetti et al. have pointed out that it is equivalent to an Ising model with a Hamiltonian that effectively counts the number of edge rule violations. Therefore, finding the assignment of factions that minimizes energy of the equivalent Ising system yields an estimate of the imbalance in the network. Based on the Ising model equivalence of the signed-social network balance problem, we have used the D-Wave 2X quantum annealing computer to explore some aspects of signed social networks. Because connectivity in the D-Wave computer is limited to its particular native topology, arbitrary networks cannot be represented directly. Rather, they must be “embedded” using a technique in which multiple qubits are chained together with special weights to simulate a collection of nodes with the required connectivity. This limits the size of a fully connected network in the D-Wave to about 50 simulated nodes, using all of the approximately 1150 qubits in the machine. In order to keep within this limitation, while exploring a problem of potential social relevance, we constructed time series of historical network snapshots from Stanford’s Mapping Militants Project, where nodes represent militant organizations, and edges represent either alliances or rivalries between organizations. We constructed two series from different theaters – Iraq and Syria – spanning timelines from about 2000 to 2016, each with networks whose maximum size was in the 20-30 node range. Computationally, our experience suggests D-Wave technology is promising, providing fast, nearly constant scaling of computational effort in the main part of the calculation that relies on the quantum annealing cycle. However, the cost of embedding an arbitrary network of interest in the D-Wave native topology scales poorly. If the embedding cost can be amortized relative to the annealing cycle, it may be possible to gain a substantial advantage over classical computing methods, provided a large enough network can be accommodated by partitioning into subnetworks or some similar strategy. In terms of our application to networks of militant organizations, we found a rise in network imbalance in the Syrian theater that appears to correspond roughly with the entrance of the Islamic State into a milieu already populated with other groups, a phenomenon we plan to explore in more detail. In these very preliminary results, we also noticed that during at least one period where both the size and imbalance of the network increased substantially, the imbalance per edge seemed to remain fairly steady. This may suggest some adaptive behavior among the participating factions, which may also warrant further exploration.« less

On the Dynamics of the Spontaneous Activity in Neuronal Networks

PubMed Central

Bonifazi, Paolo; Ruaro, Maria Elisabetta; Torre, Vincent

2007-01-01

Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABAA receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics. PMID:17502919

Systemic risk in multiplex networks with asymmetric coupling and threshold feedback

NASA Astrophysics Data System (ADS)

Burkholz, Rebekka; Leduc, Matt V.; Garas, Antonios; Schweitzer, Frank

2016-06-01

We study cascades on a two-layer multiplex network, with asymmetric feedback that depends on the coupling strength between the layers. Based on an analytical branching process approximation, we calculate the systemic risk measured by the final fraction of failed nodes on a reference layer. The results are compared with the case of a single layer network that is an aggregated representation of the two layers. We find that systemic risk in the two-layer network is smaller than in the aggregated one only if the coupling strength between the two layers is small. Above a critical coupling strength, systemic risk is increased because of the mutual amplification of cascades in the two layers. We even observe sharp phase transitions in the cascade size that are less pronounced on the aggregated layer. Our insights can be applied to a scenario where firms decide whether they want to split their business into a less risky core business and a more risky subsidiary business. In most cases, this may lead to a drastic increase of systemic risk, which is underestimated in an aggregated approach.

Interacting epidemics and coinfection on contact networks.

PubMed

Newman, M E J; Ferrario, Carrie R

2013-01-01

The spread of certain diseases can be promoted, in some cases substantially, by prior infection with another disease. One example is that of HIV, whose immunosuppressant effects significantly increase the chances of infection with other pathogens. Such coinfection processes, when combined with nontrivial structure in the contact networks over which diseases spread, can lead to complex patterns of epidemiological behavior. Here we consider a mathematical model of two diseases spreading through a single population, where infection with one disease is dependent on prior infection with the other. We solve exactly for the sizes of the outbreaks of both diseases in the limit of large population size, along with the complete phase diagram of the system. Among other things, we use our model to demonstrate how diseases can be controlled not only by reducing the rate of their spread, but also by reducing the spread of other infections upon which they depend.

A tensor network approach to many-body localization

NASA Astrophysics Data System (ADS)

Yu, Xiongjie; Pekker, David; Clark, Bryan

Understanding the many-body localized phase requires access to eigenstates in the middle of the many-body spectrum. While exact-diagonalization is able to access these eigenstates, it is restricted to systems sizes of about 22 spins. To overcome this limitation, we develop tensor network algorithms which increase the accessible system size by an order of magnitude. We describe both our new algorithms as well as the additional physics about MBL we can extract from them. For example, we demonstrate the power of these methods by verifying the breakdown of the Eigenstate Thermalization Hypothesis (ETH) in the many-body localized phase of the random field Heisenberg model, and show the saturation of entanglement in the MBL phase and generate eigenstates that differ by local excitations. Work was supported by AFOSR FA9550-10-1-0524 and FA9550-12-1-0057, the Kaufmann foundation, and SciDAC FG02-12ER46875.

Research on a simulation-based ship production support system for middle-sized shipbuilding companies

NASA Astrophysics Data System (ADS)

Song, Young Joo; Woo, Jong Hun; Shin, Jong Gye

2009-12-01

Today, many middle-sized shipbuilding companies in Korea are experiencing strong competition from shipbuilding companies in other nations. This competition is particularly affecting small- and middle-sized shipyards, rather than the major shipyards that have their own support systems and development capabilities. The acquisition of techniques that would enable maximization of production efficiency and minimization of the gap between planning and execution would increase the competitiveness of small- and middle-sized Korean shipyards. In this paper, research on a simulation-based support system for ship production management, which can be applied to the shipbuilding processes of middle-sized shipbuilding companies, is presented. The simulation research includes layout optimization, load balancing, work stage operation planning, block logistics, and integrated material management. Each item is integrated into a network system with a value chain that includes all shipbuilding processes.

Correcting evaluation bias of relational classifiers with network cross validation

DOE PAGES

Neville, Jennifer; Gallagher, Brian; Eliassi-Rad, Tina; ...

2011-01-04

Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess themore » models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). Lastly, we propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1–Type II error).« less

Neural network for control of rearrangeable Clos networks.

PubMed

Park, Y K; Cherkassky, V

1994-09-01

Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

Enhanced storage capacity with errors in scale-free Hopfield neural networks: An analytical study.

PubMed

Kim, Do-Hyun; Park, Jinha; Kahng, Byungnam

2017-01-01

The Hopfield model is a pioneering neural network model with associative memory retrieval. The analytical solution of the model in mean field limit revealed that memories can be retrieved without any error up to a finite storage capacity of O(N), where N is the system size. Beyond the threshold, they are completely lost. Since the introduction of the Hopfield model, the theory of neural networks has been further developed toward realistic neural networks using analog neurons, spiking neurons, etc. Nevertheless, those advances are based on fully connected networks, which are inconsistent with recent experimental discovery that the number of connections of each neuron seems to be heterogeneous, following a heavy-tailed distribution. Motivated by this observation, we consider the Hopfield model on scale-free networks and obtain a different pattern of associative memory retrieval from that obtained on the fully connected network: the storage capacity becomes tremendously enhanced but with some error in the memory retrieval, which appears as the heterogeneity of the connections is increased. Moreover, the error rates are also obtained on several real neural networks and are indeed similar to that on scale-free model networks.

Characteristics of real futures trading networks

NASA Astrophysics Data System (ADS)

Wang, Junjie; Zhou, Shuigeng; Guan, Jihong

2011-01-01

Futures trading is the core of futures business, and it is considered as one of the typical complex systems. To investigate the complexity of futures trading, we employ the analytical method of complex networks. First, we use real trading records from the Shanghai Futures Exchange to construct futures trading networks, in which nodes are trading participants, and two nodes have a common edge if the two corresponding investors appear simultaneously in at least one trading record as a purchaser and a seller, respectively. Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks. Empirical results show that the futures trading networks exhibit features such as scale-free behavior with interesting odd-even-degree divergence in low-degree regions, small-world effect, hierarchical organization, power-law betweenness distribution, disassortative mixing, and shrinkage of both the average path length and the diameter as network size increases. To the best of our knowledge, this is the first work that uses real data to study futures trading networks, and we argue that the research results can shed light on the nature of real futures business.

Distributive routing and congestion control in wireless multihop ad hoc communication networks

NASA Astrophysics Data System (ADS)

Glauche, Ingmar; Krause, Wolfram; Sollacher, Rudolf; Greiner, Martin

2004-10-01

Due to their inherent complexity, engineered wireless multihop ad hoc communication networks represent a technological challenge. Having no mastering infrastructure the nodes have to selforganize themselves in such a way that for example network connectivity, good data traffic performance and robustness are guaranteed. In this contribution the focus is on routing and congestion control. First, random data traffic along shortest path routes is studied by simulations as well as theoretical modeling. Measures of congestion like end-to-end time delay and relaxation times are given. A scaling law of the average time delay with respect to network size is revealed and found to depend on the underlying network topology. In the second step, a distributive routing and congestion control is proposed. Each node locally propagates its routing cost estimates and information about its congestion state to its neighbors, which then update their respective cost estimates. This allows for a flexible adaptation of end-to-end routes to the overall congestion state of the network. Compared to shortest-path routing, the critical network load is significantly increased.

Generative model selection using a scalable and size-independent complex network classifier

NASA Astrophysics Data System (ADS)

Motallebi, Sadegh; Aliakbary, Sadegh; Habibi, Jafar

2013-12-01

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our proposed method, which is named "Generative Model Selection for Complex Networks," outperforms existing methods with respect to accuracy, scalability, and size-independence.

Internal structure of inertial granular flows.

PubMed

Azéma, Emilien; Radjaï, Farhang

2014-02-21

We analyze inertial granular flows and show that, for all values of the inertial number I, the effective friction coefficient μ arises from three different parameters pertaining to the contact network and force transmission: (1) contact anisotropy, (2) force chain anisotropy, and (3) friction mobilization. Our extensive 3D numerical simulations reveal that μ increases with I mainly due to an increasing contact anisotropy and partially by friction mobilization whereas the anisotropy of force chains declines as a result of the destabilizing effect of particle inertia. The contact network undergoes topological transitions, and beyond I≃0.1 the force chains break into clusters immersed in a background "soup" of floating particles. We show that this transition coincides with the divergence of the size of fluidized zones characterized from the local environments of floating particles and a slower increase of μ with I.

Internal Structure of Inertial Granular Flows

NASA Astrophysics Data System (ADS)

Azéma, Emilien; Radjaï, Farhang

2014-02-01

We analyze inertial granular flows and show that, for all values of the inertial number I, the effective friction coefficient μ arises from three different parameters pertaining to the contact network and force transmission: (1) contact anisotropy, (2) force chain anisotropy, and (3) friction mobilization. Our extensive 3D numerical simulations reveal that μ increases with I mainly due to an increasing contact anisotropy and partially by friction mobilization whereas the anisotropy of force chains declines as a result of the destabilizing effect of particle inertia. The contact network undergoes topological transitions, and beyond I≃0.1 the force chains break into clusters immersed in a background "soup" of floating particles. We show that this transition coincides with the divergence of the size of fluidized zones characterized from the local environments of floating particles and a slower increase of μ with I.

Convolutional neural network for road extraction

NASA Astrophysics Data System (ADS)

Li, Junping; Ding, Yazhou; Feng, Fajie; Xiong, Baoyu; Cui, Weihong

2017-11-01

In this paper, the convolution neural network with large block input and small block output was used to extract road. To reflect the complex road characteristics in the study area, a deep convolution neural network VGG19 was conducted for road extraction. Based on the analysis of the characteristics of different sizes of input block, output block and the extraction effect, the votes of deep convolutional neural networks was used as the final road prediction. The study image was from GF-2 panchromatic and multi-spectral fusion in Yinchuan. The precision of road extraction was 91%. The experiments showed that model averaging can improve the accuracy to some extent. At the same time, this paper gave some advice about the choice of input block size and output block size.

Predictors of Change in Self-Reported Social Networks among Homeless Young People

PubMed Central

Falci, Christina D.; Whitbeck, Les B.; Hoyt, Dan R.; Rose, Trina

2011-01-01

This research investigates changes in social network size and composition of 351 homeless adolescents over three years. Findings show that network size decreases over time. Homeless youth with a conduct disorder begin street life with small networks that remain small over time. Caregiver abuse is associated with smaller emotional networks due to fewer home ties, especially to parents, and a more rapid loss of emotional home ties over time. Homeless youth with major depression start out with small networks, but are more likely to maintain network ties. Youth with substance abuse problems are more likely to maintain instrumental home ties. Finally, homeless adolescents tend to reconnect with their parents for instrumental aid and form romantic relationship that provide emotional support. PMID:22121332

Dynamic and interacting complex networks

NASA Astrophysics Data System (ADS)

Dickison, Mark E.

This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible individuals protect themselves by disconnecting their links to infected neighbors with probability w and reconnecting them to other susceptible individuals chosen at random. Starting from a single infected individual, we show by an analytical approach and simulations that there is a phase transition at a critical rewiring (quarantine) threshold wc separating a phase (w < wc) where the disease reaches a large fraction of the population from a phase (w > wc) where the disease does not spread out. We find that in our model the topology of the network strongly affects the size of the propagation and that wc increases with the mean degree and heterogeneity of the network. We also find that wc is reduced if we perform a preferential rewiring, in which the rewiring probability is proportional to the degree of infected nodes. In the fourth chapter, we study epidemic processes on interconnected network systems, and find two distinct regimes. In strongly-coupled network systems, epidemics occur simultaneously across the entire system at a critical value betac. In contrast, in weakly-coupled network systems, a mixed phase exists below betac where an epidemic occurs in one network but does not spread to the coupled network. We derive an expression for the network and disease parameters that allow this mixed phase and verify it numerically. Public health implications of communities comprising these two classes of network systems are also mentioned.

Determining a bisection bandwidth for a multi-node data communications network

DOEpatents

Faraj, Ahmad A.

2010-01-26

Methods, systems, and products are disclosed for determining a bisection bandwidth for a multi-node data communications network that include: partitioning nodes in the network into a first sub-network and a second sub-network in dependence upon a topology of the network; sending, by each node in the first sub-network to a destination node in the second sub-network, a first message having a predetermined message size; receiving, by each node in the first sub-network from a source node in the second sub-network, a second message; measuring, by each node in the first sub-network, the elapsed communications time between the sending of the first message and the receiving of the second message; selecting the longest elapsed communications time; and calculating the bisection bandwidth for the network in dependence upon the number of the nodes in the first sub-network, the predetermined message size of the first test message, and the longest elapsed communications time.

Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks.

PubMed

Truong, Cong-Doan; Kwon, Yung-Keun

2017-12-21

Biological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks. In this paper, we investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. We first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edge-removal mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. We note that these results were consistently observed in randomly structure networks. Additionally, we identified two groups of genes which are incident to the highly-modularity-increasing and the highly-robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, we showed that the highly-robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of our analysis. Taken together, the analysis of changes of robustness and modularity against edge-removal mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.

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.

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

PubMed

Yin, Weiwei; Garimalla, Swetha; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

2015-08-28

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

Modeling Aggregation Processes of Lennard-Jones particles Via Stochastic Networks

NASA Astrophysics Data System (ADS)

Forman, Yakir; Cameron, Maria

2017-07-01

We model an isothermal aggregation process of particles/atoms interacting according to the Lennard-Jones pair potential by mapping the energy landscapes of each cluster size N onto stochastic networks, computing transition probabilities from the network for an N-particle cluster to the one for N+1, and connecting these networks into a single joint network. The attachment rate is a control parameter. The resulting network representing the aggregation of up to 14 particles contains 6427 vertices. It is not only time-irreversible but also reducible. To analyze its transient dynamics, we introduce the sequence of the expected initial and pre-attachment distributions and compute them for a wide range of attachment rates and three values of temperature. As a result, we find the configurations most likely to be observed in the process of aggregation for each cluster size. We examine the attachment process and conduct a structural analysis of the sets of local energy minima for every cluster size. We show that both processes taking place in the network, attachment and relaxation, lead to the dominance of icosahedral packing in small (up to 14 atom) clusters.

Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels

NASA Astrophysics Data System (ADS)

Yang, GuanYa; Wu, Jiang; Chen, ShuGuang; Zhou, WeiJun; Sun, Jian; Chen, GuanHua

2018-06-01

Neural network-based first-principles method for predicting heat of formation (HOF) was previously demonstrated to be able to achieve chemical accuracy in a broad spectrum of target molecules [L. H. Hu et al., J. Chem. Phys. 119, 11501 (2003)]. However, its accuracy deteriorates with the increase in molecular size. A closer inspection reveals a systematic correlation between the prediction error and the molecular size, which appears correctable by further statistical analysis, calling for a more sophisticated machine learning algorithm. Despite the apparent difference between simple and complex molecules, all the essential physical information is already present in a carefully selected set of small molecule representatives. A model that can capture the fundamental physics would be able to predict large and complex molecules from information extracted only from a small molecules database. To this end, a size-independent, multi-step multi-variable linear regression-neural network-B3LYP method is developed in this work, which successfully improves the overall prediction accuracy by training with smaller molecules only. And in particular, the calculation errors for larger molecules are drastically reduced to the same magnitudes as those of the smaller molecules. Specifically, the method is based on a 164-molecule database that consists of molecules made of hydrogen and carbon elements. 4 molecular descriptors were selected to encode molecule's characteristics, among which raw HOF calculated from B3LYP and the molecular size are also included. Upon the size-independent machine learning correction, the mean absolute deviation (MAD) of the B3LYP/6-311+G(3df,2p)-calculated HOF is reduced from 16.58 to 1.43 kcal/mol and from 17.33 to 1.69 kcal/mol for the training and testing sets (small molecules), respectively. Furthermore, the MAD of the testing set (large molecules) is reduced from 28.75 to 1.67 kcal/mol.

Critical size of ego communication networks

NASA Astrophysics Data System (ADS)

Wang, Qing; Gao, Jian; Zhou, Tao; Hu, Zheng; Tian, Hui

2016-06-01

With the help of information and communication technologies, studies on the overall social networks have been extensively reported recently. However, investigations on the directed Ego Communication Networks (ECNs) remain insufficient, where an ECN stands for a sub network composed of a centralized individual and his/her direct contacts. In this paper, the directed ECNs are built on the Call Detail Records (CDRs), which cover more than 7 million people of a provincial capital city in China for half a year. Results show that there is a critical size for ECN at about 150, above which the average emotional closeness between ego and alters drops, the balanced relationship between ego and network collapses, and the proportion of strong ties decreases. This paper not only demonstrate the significance of ECN size in affecting its properties, but also shows accordance with the “Dunbar's Number”. These results can be viewed as a cross-culture supportive evidence to the well-known Social Brain Hypothesis (SBH).

Organization and scaling in water supply networks

NASA Astrophysics Data System (ADS)

Cheng, Likwan; Karney, Bryan W.

2017-12-01

Public water supply is one of the society's most vital resources and most costly infrastructures. Traditional concepts of these networks capture their engineering identity as isolated, deterministic hydraulic units, but overlook their physics identity as related entities in a probabilistic, geographic ensemble, characterized by size organization and property scaling. Although discoveries of allometric scaling in natural supply networks (organisms and rivers) raised the prospect for similar findings in anthropogenic supplies, so far such a finding has not been reported in public water or related civic resource supplies. Examining an empirical ensemble of large number and wide size range, we show that water supply networks possess self-organized size abundance and theory-explained allometric scaling in spatial, infrastructural, and resource- and emission-flow properties. These discoveries establish scaling physics for water supply networks and may lead to novel applications in resource- and jurisdiction-scale water governance.

Continuous time limits of the utterance selection model

NASA Astrophysics Data System (ADS)

Michaud, Jérôme

2017-02-01

In this paper we derive alternative continuous time limits of the utterance selection model (USM) for language change [G. J. Baxter et al., Phys. Rev. E 73, 046118 (2006), 10.1103/PhysRevE.73.046118]. This is motivated by the fact that the Fokker-Planck continuous time limit derived in the original version of the USM is only valid for a small range of parameters. We investigate the consequences of relaxing these constraints on parameters. Using the normal approximation of the multinomial approximation, we derive a continuous time limit of the USM in the form of a weak-noise stochastic differential equation. We argue that this weak noise, not captured by the Kramers-Moyal expansion, cannot be neglected. We then propose a coarse-graining procedure, which takes the form of a stochastic version of the heterogeneous mean field approximation. This approximation groups the behavior of nodes of the same degree, reducing the complexity of the problem. With the help of this approximation, we study in detail two simple families of networks: the regular networks and the star-shaped networks. The analysis reveals and quantifies a finite-size effect of the dynamics. If we increase the size of the network by keeping all the other parameters constant, we transition from a state where conventions emerge to a state where no convention emerges. Furthermore, we show that the degree of a node acts as a time scale. For heterogeneous networks such as star-shaped networks, the time scale difference can become very large, leading to a noisier behavior of highly connected nodes.

Systematic network coding for two-hop lossy transmissions

NASA Astrophysics Data System (ADS)

Li, Ye; Blostein, Steven; Chan, Wai-Yip

2015-12-01

In this paper, we consider network transmissions over a single or multiple parallel two-hop lossy paths. These scenarios occur in applications such as sensor networks or WiFi offloading. Random linear network coding (RLNC), where previously received packets are re-encoded at intermediate nodes and forwarded, is known to be a capacity-achieving approach for these networks. However, a major drawback of RLNC is its high encoding and decoding complexity. In this work, a systematic network coding method is proposed. We show through both analysis and simulation that the proposed method achieves higher end-to-end rate as well as lower computational cost than RLNC for finite field sizes and finite-sized packet transmissions.

Sampling of temporal networks: Methods and biases

NASA Astrophysics Data System (ADS)

Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter

2017-11-01

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.

Social Relations in Lebanon: Convoys Across the Life Course.

PubMed

Antonucci, Toni C; Ajrouch, Kristine J; Abdulrahim, Sawsan

2015-10-01

This study systematically analyzed convoys of social relations to investigate the ways in which gender and income shape patterns of social relations across the life course in Lebanon. Data were drawn from a representative sample of adults aged 18 and older in Greater Beirut, Lebanon (N = 500). Multiple linear regression and multilevel models were conducted to examine main and interactive effects of age, gender, and income on social relations. Findings indicate main effects of age, income, and gender on network structure and relationship quality. Older age was associated with larger network size, greater proportion of kin in network, higher positive and lower negative relationship quality. Higher income was associated with larger network size and decreased contact frequency. Female gender was also associated with decreased contact frequency. Gender interacted with income to influence network size and network composition. Higher income was associated with a larger network size and higher proportion of kin for women. Findings suggest diversity in the experience of social relations. Such nuance is particularly relevant to the Lebanese context where family is the main source of support in old age. Policy makers and program planners may need to refrain from viewing social relations simplistically. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The Role of Small Impoundments on Flow Alteration Within River Networks

NASA Astrophysics Data System (ADS)

Brogan, C. O.; Keys, T.; Scott, D.; Burgholzer, R.; Kleiner, J.

2017-12-01

Numerous water quality and quantity models have been established to illustrate the ecologic and hydrologic effects of large reservoirs. Smaller, unregulated ponds are often assumed to have a negligible impact on watershed flow regimes even though they overwhelmingly outnumber larger waterbodies. Individually, these small impoundments impart merely a fraction of the flow alteration larger reservoirs do; however, a network of ponds may act cumulatively to alter the flow regime. Many models have attempted to study smaller impoundments but rely on selectively available rating curves or bathymetry surveys. This study created a generalized process to model impoundments of varying size across a 58 square mile watershed exclusively using satellite imagery and publicly available information as inputs. With information drawn from public Army Corps of Engineers databases and LiDAR surveys, it was found that impoundment surface and drainage area served as useful explanatory variables, capable of predicting both pond bathymetry and outlet structure area across the 37 waterbodies modeled within the study area. Working within a flow routing model with inputs from the Chesapeake Bay HSPF model and verified with USGS gauge data, flow simulations were conducted with increasing number of impoundments to quantify how small ponds affect the overall flow regime. As the total impounded volume increased, simulations showed a notable reduction in both low and peak flows. Medium-sized floods increased as the network of ponds and reservoirs stabilized the catchment's streamflow. The results of this study illustrate the importance of including ponded waters into river corridor models to improve downstream management of both water quantity and quality.

One Size Does Not Fit All! Gender In/Exclusion in a Rural Community-Based ICT Initiative

ERIC Educational Resources Information Center

Faulkner, Wendy; Kleif, Tine

2005-01-01

Community-based information and communication technologies (ICT) networks are seen as an important means of reducing social exclusion, and at the same time fostering community development. Increasing ICT capability locally is arguably crucial to furthering both of these broad aims. This paper presents evidence about the capability-building…

Built-in self-repair of VLSI memories employing neural nets

NASA Astrophysics Data System (ADS)

Mazumder, Pinaki

1998-10-01

The decades of the Eighties and the Nineties have witnessed the spectacular growth of VLSI technology, when the chip size has increased from a few hundred devices to a staggering multi-millon transistors. This trend is expected to continue as the CMOS feature size progresses towards the nanometric dimension of 100 nm and less. SIA roadmap projects that, where as the DRAM chips will integrate over 20 billion devices in the next millennium, the future microprocessors may incorporate over 100 million transistors on a single chip. As the VLSI chip size increase, the limited accessibility of circuit components poses great difficulty for external diagnosis and replacement in the presence of faulty components. For this reason, extensive work has been done in built-in self-test techniques, but little research is known concerning built-in self-repair. Moreover, the extra hardware introduced by conventional fault-tolerance techniques is also likely to become faulty, therefore causing the circuit to be useless. This research demonstrates the feasibility of implementing electronic neural networks as intelligent hardware for memory array repair. Most importantly, we show that the neural network control possesses a robust and degradable computing capability under various fault conditions. Overall, a yield analysis performed on 64K DRAM's shows that the yield can be improved from as low as 20 percent to near 99 percent due to the self-repair design, with overhead no more than 7 percent.

Coding for Parallel Links to Maximize the Expected Value of Decodable Messages

NASA Technical Reports Server (NTRS)

Klimesh, Matthew A.; Chang, Christopher S.

2011-01-01

When multiple parallel communication links are available, it is useful to consider link-utilization strategies that provide tradeoffs between reliability and throughput. Interesting cases arise when there are three or more available links. Under the model considered, the links have known probabilities of being in working order, and each link has a known capacity. The sender has a number of messages to send to the receiver. Each message has a size and a value (i.e., a worth or priority). Messages may be divided into pieces arbitrarily, and the value of each piece is proportional to its size. The goal is to choose combinations of messages to send on the links so that the expected value of the messages decodable by the receiver is maximized. There are three parts to the innovation: (1) Applying coding to parallel links under the model; (2) Linear programming formulation for finding the optimal combinations of messages to send on the links; and (3) Algorithms for assisting in finding feasible combinations of messages, as support for the linear programming formulation. There are similarities between this innovation and methods developed in the field of network coding. However, network coding has generally been concerned with either maximizing throughput in a fixed network, or robust communication of a fixed volume of data. In contrast, under this model, the throughput is expected to vary depending on the state of the network. Examples of error-correcting codes that are useful under this model but which are not needed under previous models have been found. This model can represent either a one-shot communication attempt, or a stream of communications. Under the one-shot model, message sizes and link capacities are quantities of information (e.g., measured in bits), while under the communications stream model, message sizes and link capacities are information rates (e.g., measured in bits/second). This work has the potential to increase the value of data returned from spacecraft under certain conditions.

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa

PubMed Central

Geisler, Daniel; Borchardt, Viola; Lord, Anton R.; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A.; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Background Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. Methods To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Results Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. Limitations The present results may be limited to the methods applied during preprocessing and network construction. Conclusion We demonstrated anorexia nervosa–related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger. PMID:26252451

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa.

PubMed

Geisler, Daniel; Borchardt, Viola; Lord, Anton R; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. The present results may be limited to the methods applied during preprocessing and network construction. We demonstrated anorexia nervosa-related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger.

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.

Network evolution induced by the dynamical rules of two populations

NASA Astrophysics Data System (ADS)

Platini, Thierry; Zia, R. K. P.

2010-10-01

We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (Na and Nb) and preferred degree (κa and \\kappa_b\\ll \\kappa_a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees langkbbrang and langkabrang presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal Na = Nb, the ratio of the restricted degree θ0 = langkabrang/langkbbrang appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t < t1 = κb) the total number of links presents a linear evolution, where the two populations are indistinguishable and where θ0 = 1. Interestingly, in the intermediate time regime (defined for t_1\\lt t\\lt t_2\\propto \\kappa_a and for which θ0 = 5), the system reaches a transient stationary state, where the number of contacts among introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ0 = 3.

Dimensionless, Scale Invariant, Edge Weight Metric for the Study of Complex Structural Networks

PubMed Central

Colon-Perez, Luis M.; Spindler, Caitlin; Goicochea, Shelby; Triplett, William; Parekh, Mansi; Montie, Eric; Carney, Paul R.; Price, Catherine; Mareci, Thomas H.

2015-01-01

High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime. PMID:26173147

Dimensionless, Scale Invariant, Edge Weight Metric for the Study of Complex Structural Networks.

PubMed

Colon-Perez, Luis M; Spindler, Caitlin; Goicochea, Shelby; Triplett, William; Parekh, Mansi; Montie, Eric; Carney, Paul R; Price, Catherine; Mareci, Thomas H

2015-01-01

High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime.

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

PubMed Central

Stover, Lori J.; Nair, Niketh S.; Faeder, James R.

2014-01-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. PMID:24699269

Exact hybrid particle/population simulation of rule-based models of biochemical systems.

PubMed

Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R

2014-04-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

Hopping Diffusion of Nanoparticles Subjected to Topological Constraints

NASA Astrophysics Data System (ADS)

Cai, Li-Heng; Panyukov, Sergey; Rubinstein, Michael

2013-03-01

We describe a novel hopping mechanism for diffusion of large non-sticky nanoparticles subjected to topological constraints in polymer solids (networks and gels) and entangled polymer liquids (melts and solutions). Probe particles with size larger than the mesh size of unentangled polymer networks (tube diameter of entangled polymer liquids) are trapped by the network (entanglement) cages at time scales longer than the relaxation time of the network (entanglement) strand. At long time scales, however, these particles can move further by hopping between neighboring confinement cages. This hopping is controlled by fluctuations of surrounding confinement cages, which could be large enough to allow particles to slip through. The terminal particle diffusion coefficient dominated by this hopping diffusion is appreciable for particles with size slightly larger than the network mesh size (tube diameter). Very large particles in polymer solids will be permanently trapped by local network cages, whereas they can still move in polymer liquids by waiting for entanglement cages to rearrange on the relaxation time scale of the liquids. We would like to acknowledge the financial support of NSF CHE-0911588, DMR-0907515, DMR-1121107, DMR-1122483, and CBET-0609087, NIH R01HL077546 and P50HL107168, and Cystic Fibrosis Foundation under grant RUBIN09XX0.

Generative model selection using a scalable and size-independent complex network classifier

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

Motallebi, Sadegh, E-mail: motallebi@ce.sharif.edu; Aliakbary, Sadegh, E-mail: aliakbary@ce.sharif.edu; Habibi, Jafar, E-mail: jhabibi@sharif.edu

2013-12-15

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree formore » model selection. Our proposed method, which is named “Generative Model Selection for Complex Networks,” outperforms existing methods with respect to accuracy, scalability, and size-independence.« less

Scaling theory for information networks.

PubMed

Moses, Melanie E; Forrest, Stephanie; Davis, Alan L; Lodder, Mike A; Brown, James H

2008-12-06

Networks distribute energy, materials and information to the components of a variety of natural and human-engineered systems, including organisms, brains, the Internet and microprocessors. Distribution networks enable the integrated and coordinated functioning of these systems, and they also constrain their design. The similar hierarchical branching networks observed in organisms and microprocessors are striking, given that the structure of organisms has evolved via natural selection, while microprocessors are designed by engineers. Metabolic scaling theory (MST) shows that the rate at which networks deliver energy to an organism is proportional to its mass raised to the 3/4 power. We show that computational systems are also characterized by nonlinear network scaling and use MST principles to characterize how information networks scale, focusing on how MST predicts properties of clock distribution networks in microprocessors. The MST equations are modified to account for variation in the size and density of transistors and terminal wires in microprocessors. Based on the scaling of the clock distribution network, we predict a set of trade-offs and performance properties that scale with chip size and the number of transistors. However, there are systematic deviations between power requirements on microprocessors and predictions derived directly from MST. These deviations are addressed by augmenting the model to account for decentralized flow in some microprocessor networks (e.g. in logic networks). More generally, we hypothesize a set of constraints between the size, power and performance of networked information systems including transistors on chips, hosts on the Internet and neurons in the brain.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

PubMed Central

2018-01-01

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems. PMID:29657814

A Markov model for the temporal dynamics of balanced random networks of finite size

PubMed Central

Lagzi, Fereshteh; Rotter, Stefan

2014-01-01

The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between neuronal populations also opens new doors to analyze the joint dynamics of multiple interacting networks. PMID:25520644

Video Conferences through the Internet: How to Survive in a Hostile Environment

PubMed Central

Fernández, Carlos; Fernández-Navajas, Julián; Sequeira, Luis; Casadesus, Luis

2014-01-01

This paper analyzes and compares two different video conference solutions, widely used in corporate and home environments, with a special focus on the mechanisms used for adapting the traffic to the network status. The results show how these mechanisms are able to provide a good quality in the hostile environment of the public Internet, a best effort network without delay or delivery guarantees. Both solutions are evaluated in a laboratory, where different network impairments (bandwidth limit, delay, and packet loss) are set, in both the uplink and the downlink, and the reaction of the applications is measured. The tests show how these solutions modify their packet size and interpacket time, in order to increase or reduce the sent data. One of the solutions also uses a scalable video codec, able to adapt the traffic to the network status and to the end devices. PMID:24605066

Uncovering the spatial structure of mobility networks

NASA Astrophysics Data System (ADS)

Louail, Thomas; Lenormand, Maxime; Picornell, Miguel; García Cantú, Oliva; Herranz, Ricardo; Frias-Martinez, Enrique; Ramasco, José J.; Barthelemy, Marc

2015-01-01

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.

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

Dorier, Matthieu; Mubarak, Misbah; Ross, Rob

Two-tiered direct network topologies such as Dragonflies have been proposed for future post-petascale and exascale machines, since they provide a high-radix, low-diameter, fast interconnection network. Such topologies call for redesigning MPI collective communication algorithms in order to attain the best performance. Yet as increasingly more applications share a machine, it is not clear how these topology-aware algorithms will react to interference with concurrent jobs accessing the same network. In this paper, we study three topology-aware broadcast algorithms, including one designed by ourselves. We evaluate their performance through event-driven simulation for small- and large-sized broadcasts (in terms of both data sizemore » and number of processes). We study the effect of different routing mechanisms on the topology-aware collective algorithms, as well as their sensitivity to network contention with other jobs. Our results show that while topology-aware algorithms dramatically reduce link utilization, their advantage in terms of latency is more limited.« less

Design a Wearable Device for Blood Oxygen Concentration and Temporal Heart Beat Rate

NASA Astrophysics Data System (ADS)

Myint, Cho Zin; Barsoum, Nader; Ing, Wong Kiing

2010-06-01

The wireless network technology is increasingly important in healthcare as a result of the aging population and the tendency to acquire chronic disease such as heart attack, high blood pressure amongst the elderly. A wireless sensor network system that has the capability to monitor physiological sign such as SpO2 (Saturation of Arterial Oxygen) and heart beat rate in real-time from the human's body is highlighted in this study. This research is to design a prototype sensor network hardware, which consists of microcontroller PIC18F series and transceiver unit. The sensor is corporate into a wearable body sensor network which is small in size and easy to use. The sensor allows a non invasive, real time method to provide information regarding the health of the body. This enables a more efficient and economical means for managing the health care of the population.

Formation of Molecular Networks: Tailored Quantum Boxes and Behavior of Adsorbed CO in Them

NASA Astrophysics Data System (ADS)

Wyrick, Jon; Sun, Dezheng; Kim, Dae-Ho; Cheng, Zhihai; Lu, Wenhao; Zhu, Yeming; Luo, Miaomiao; Kim, Yong Su; Rotenberg, Eli; Kim, Kwangmoo; Einstein, T. L.; Bartels, Ludwig

2011-03-01

We show that the behavior of CO adsorbed into the pores of large regular networks on Cu(111) is significantly affected by their nano-scale lateral confinement and that formation of the networks themselves is directed by the Shockley surface state. Saturation coverages of CO are found to exhibit persistent dislocation lines; at lower coverages their mobility increases. Individual CO within the pores titrate the surface state, providing crucial information for understanding formation of the network as a result of optimization of the number N of electrons bound within each pore. Determination of N is based on quinone-coverage-dependent UPS data and an analysis of states of particles in a pore-shaped box (verified by CO's titration); a wide range of possible pore shapes and sizes has been considered. Work at UCR supported by NSF CHE 07-49949; at UMD by NSF CHE 07-50334 & UMD NSF-MRSEC DMR 05-20471.

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

PubMed

Kusumoto, Dai; Lachmann, Mark; Kunihiro, Takeshi; Yuasa, Shinsuke; Kishino, Yoshikazu; Kimura, Mai; Katsuki, Toshiomi; Itoh, Shogo; Seki, Tomohisa; Fukuda, Keiichi

2018-06-05

Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

On Crowd-verification of Biological Networks

PubMed Central

Ansari, Sam; Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Hayes, William; Hoeng, Julia; Iskandar, Anita; Kleiman, Robin; Norel, Raquel; O’Neel, Bruce; Peitsch, Manuel C.; Poussin, Carine; Pratt, Dexter; Rhrissorrakrai, Kahn; Schlage, Walter K.; Stolovitzky, Gustavo; Talikka, Marja

2013-01-01

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community. PMID:24151423

Sol-Gel assembly of CdSe nanoparticles to form porous aerogel networks.

PubMed

Arachchige, Indika U; Brock, Stephanie L

2006-06-21

A detailed study of CdSe aerogels prepared by oxidative aggregation of primary nanoparticles (prepared at room temperature and high temperature conditions, >250 degrees C), followed by CO2 supercritical drying, is described. The resultant materials are mesoporous, with an interconnected network of colloidal nanoparticles, and exhibit BET surface areas up to 224 m2/g and BJH average pore diameters in the range of 16-32 nm. Powder X-ray diffraction studies indicate that these materials retain the crystal structure of the primary nanoparticles, with a slight increase in primary particle size upon gelation and aerogel formation. Optical band gap measurements and photoluminescence studies show that the as-prepared aerogels retain the quantum-confined optical properties of the nanoparticle building blocks despite being connected into a 3-D network. The specific optical characteristics of the aerogel can be further modified by surface ligand exchange at the wet-gel stage, without destroying the gel network.

Social networks among Indigenous peoples in Mexico.

PubMed

Skoufias, Emmanuel; Lunde, Trine; Patrinos, Harry Anthony

2010-01-01

We examine the extent to which social networks among indigenous peoples in Mexico have a significant effect on a variety of human capital investment and economic activities, such as school attendance and work among teenage boys and girls, and migration, welfare participation, employment status, occupation, and sector of employment among adult males and females. Using data from the 10 percent population sample of the 2000 Population and Housing Census of Mexico and the empirical strategy that Bertrand, Luttmer, and Mullainathan (2000) propose, which allows us to take into account the role of municipality and language group fixed effects, we confirm empirically that social network effects play an important role in the economic decisions of indigenous people, especially in rural areas. Our analysis also provides evidence that better access to basic services such as water and electricity increases the size and strength of network effects in rural areas.

Artificial neural network based particle size prediction of polymeric nanoparticles.

PubMed

Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

2017-10-01

Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

Combined effect of storm movement and drainage network configuration on flood peaks

NASA Astrophysics Data System (ADS)

Seo, Yongwon; Son, Kwang Ik; Choi, Hyun Il

2016-04-01

This presentation reports the combined effect of storm movement and drainage network layout on resulting hydrographs and its implication to flood process and also flood mitigation. First, we investigate, in general terms, the effects of storm movement on the resulting flood peaks, and the underlying process controls. For this purpose, we utilize a broad theoretical framework that uses characteristic time and space scales associated with stationary rainstorms as well as moving rainstorms. For a stationary rainstorm the characteristic timescales that govern the peak response include two intrinsic timescales of a catchment and one extrinsic timescale of a rainstorm. On the other hand, for a moving rainstorm, two additional extrinsic scales are required; the storm travel time and storm size. We show that the relationship between the peak response and the timescales appropriate for a stationary rainstorm can be extended in a straightforward manner to describe the peak response for a moving rainstorm. For moving rainstorms, we show that the augmentation of peak response arises from both effect of overlaying the responses from subcatchments (resonance condition) and effect of increased responses from subcatchments due to increased duration (interdependence), which results in maximum peak response when the moving rainstorm is slower than the channel flow velocity. Second, we show the relation between channel network configurations and hydrograph sensitivity to storm kinematics. For this purpose, Gibbs' model is used to evaluate the network characteristics. The results show that the storm kinematics that produces the maximum peak discharge depends on the network configuration because the resonance condition changes with the network configuration. We show that an "efficient" network layout is more sensitive and results in higher increase in peak response compared to "inefficient" one. These results imply different flood potential risks for river networks depending on network characteristics. In addition, they imply a possibility of an alternative drainage network layout as an effective measure for flood mitigation in urban environments.

Rescue of endemic states in interconnected networks with adaptive coupling

NASA Astrophysics Data System (ADS)

Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San

2016-07-01

We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.

Rescue of endemic states in interconnected networks with adaptive coupling

PubMed Central

Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San

2016-01-01

We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771

Infectious disease control using contact tracing in random and scale-free networks

PubMed Central

Kiss, Istvan Z; Green, Darren M; Kao, Rowland R

2005-01-01

Contact tracing aims to identify and isolate individuals that have been in contact with infectious individuals. The efficacy of contact tracing and the hierarchy of traced nodes—nodes with higher degree traced first—is investigated and compared on random and scale-free (SF) networks with the same number of nodes N and average connection K. For values of the transmission rate larger than a threshold, the final epidemic size on SF networks is smaller than that on corresponding random networks. While in random networks new infectious and traced nodes from all classes have similar average degrees, in SF networks the average degree of nodes that are in more advanced stages of the disease is higher at any given time. On SF networks tracing removes possible sources of infection with high average degree. However a higher tracing effort is required to control the epidemic than on corresponding random networks due to the high initial velocity of spread towards the highly connected nodes. An increased latency period fails to significantly improve contact tracing efficacy. Contact tracing has a limited effect if the removal rate of susceptible nodes is relatively high, due to the fast local depletion of susceptible nodes. PMID:16849217

Automatic Screening for Perturbations in Boolean Networks.

PubMed

Schwab, Julian D; Kestler, Hans A

2018-01-01

A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.

A consensus opinion model based on the evolutionary game

NASA Astrophysics Data System (ADS)

Yang, Han-Xin

2016-08-01

We propose a consensus opinion model based on the evolutionary game. In our model, both of the two connected agents receive a benefit if they have the same opinion, otherwise they both pay a cost. Agents update their opinions by comparing payoffs with neighbors. The opinion of an agent with higher payoff is more likely to be imitated. We apply this model in scale-free networks with tunable degree distribution. Interestingly, we find that there exists an optimal ratio of cost to benefit, leading to the shortest consensus time. Qualitative analysis is obtained by examining the evolution of the opinion clusters. Moreover, we find that the consensus time decreases as the average degree of the network increases, but increases with the noise introduced to permit irrational choices. The dependence of the consensus time on the network size is found to be a power-law form. For small or larger ratio of cost to benefit, the consensus time decreases as the degree exponent increases. However, for moderate ratio of cost to benefit, the consensus time increases with the degree exponent. Our results may provide new insights into opinion dynamics driven by the evolutionary game theory.

Percolation in three-dimensional fracture networks for arbitrary size and shape distributions

NASA Astrophysics Data System (ADS)

Thovert, J.-F.; Mourzenko, V. V.; Adler, P. M.

2017-04-01

The percolation threshold of fracture networks is investigated by extensive direct numerical simulations. The fractures are randomly located and oriented in three-dimensional space. A very wide range of regular, irregular, and random fracture shapes is considered, in monodisperse or polydisperse networks containing fractures with different shapes and/or sizes. The results are rationalized in terms of a dimensionless density. A simple model involving a new shape factor is proposed, which accounts very efficiently for the influence of the fracture shape. It applies with very good accuracy in monodisperse or moderately polydisperse networks, and provides a good first estimation in other situations. A polydispersity index is shown to control the need for a correction, and the corrective term is modelled for the investigated size distributions.

Properties of four real world collaboration--competition networks

NASA Astrophysics Data System (ADS)

Fu, Chun-Hua; Xu, Xiu-Lian; He, Da-Ren

2009-03-01

Our research group has empirically investigated 9 real world collaboration networks and 25 real world cooperation-competition networks. Among the 34 real world systems, all the 9 real world collaboration networks and 6 real world cooperation-competition networks show the unimodal act-size distribution and the shifted power law distribution of degree and act-degree. We have proposed a collaboration network evolution model for an explanation of the rules [1]. The other 14 real world cooperation-competition networks show that the act-size distributions are not unimodal; instead, they take qualitatively the same shifted power law forms as the degree and act-degree distributions. The properties of four systems (the main land movie film network, Beijing restaurant network, 2004 Olympic network, and Tao-Bao notebook computer sale network) are reported in detail as examples. Via a numerical simulation, we show that the new rule can still be explained by the above-mentioned model. [1] H. Chang, B. B. Su, et al. Phsica A, 2007, 383: 687-702.

Disease dynamics during wildlife translocations: disruptions to the host population and potential consequences for transmission in desert tortoise contact networks

USGS Publications Warehouse

Aiello, Christina M.; Nussear, Kenneth E.; Walde, Andrew D.; Esque, Todd C.; Emblidge, Patrick G.; Sah, Pratha; Bansal, S.; Hudson, Peter J.

2014-01-01

Wildlife managers consider animal translocation a means of increasing the viability of a local population. However, augmentation may disrupt existing resident disease dynamics and initiate an outbreak that would effectively offset any advantages the translocation may have achieved. This paper examines fundamental concepts of disease ecology and identifies the conditions that will increase the likelihood of a disease outbreak following translocation. We highlight the importance of susceptibility to infection, population size and population connectivity – a characteristic likely affected by translocation but not often considered in risk assessments – in estimating outbreak risk due to translocation. We then explore these features in a species of conservation concern often translocated in the presence of infectious disease, the Mojave Desert tortoise, and use data from experimental tortoise translocations to detect changes in population connectivity that may influence pathogen transmission. Preliminary analyses comparing contact networks inferred from spatial data at control and translocation plots and infection simulation results through these networks suggest increased outbreak risk following translocation due to dispersal-driven changes in contact frequency and network structure. We outline future research goals to test these concepts and aid managers in designing effective risk assessment and intervention strategies that will improve translocation success.

The feasibility of measuring social networks among older adults in assisted living and dementia special care units.

PubMed

Abbott, Katherine M; Bettger, Janet Prvu; Hampton, Keith N; Kohler, Hans-Peter

2015-03-01

Studies indicate that social integration has a significant influence on physical and mental health. Older adults experience an increased risk of social isolation as their social networks decline with fewer traditional opportunities to add new social relationships. Deaths of similar aged friends, cognitive and functional impairments, and relocating to a nursing home (NH) or assisted-living (AL) facility contribute to difficulties in maintaining one's social network. Due to the paucity of research examining the social networks of people residing in AL and NH, this study was designed to develop and test the feasibility of using a combination of methodological approaches to capture social network data among older adults living in AL and a dementia special care unit NH. Social network analysis of both egocentric and sociocentric networks was conducted to visualize the social networks of 15 residents of an AL neighborhood and 12 residents of a dementia special care unit NH and to calculate measures network size, centrality, and reciprocity. The combined egocentric and sociocentric method was feasible and provided a robust indicator of resident social networks highlighting individuals who were socially integrated as well as isolated. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Modeling stream network-scale variation in coho salmon overwinter survival and smolt size

EPA Science Inventory

We used multiple regression and hierarchical mixed-effects models to examine spatial patterns of overwinter survival and size at smolting in juvenile coho salmon Oncorhynchus kisutch in relation to habitat attributes across an extensive stream network in southwestern Oregon over ...

Four-state rock-paper-scissors games in constrained Newman-Watts networks.

PubMed

Zhang, Guo-Yong; Chen, Yong; Qi, Wei-Kai; Qing, Shao-Meng

2009-06-01

We study the cyclic dominance of three species in two-dimensional constrained Newman-Watts networks with a four-state variant of the rock-paper-scissors game. By limiting the maximal connection distance Rmax in Newman-Watts networks with the long-range connection probability p , we depict more realistically the stochastic interactions among species within ecosystems. When we fix mobility and vary the value of p or Rmax, the Monte Carlo simulations show that the spiral waves grow in size, and the system becomes unstable and biodiversity is lost with increasing p or Rmax. These results are similar to recent results of Reichenbach et al. [Nature (London) 448, 1046 (2007)], in which they increase the mobility only without including long-range interactions. We compared extinctions with or without long-range connections and computed spatial correlation functions and correlation length. We conclude that long-range connections could improve the mobility of species, drastically changing their crossover to extinction and making the system more unstable.

The effect of microscopic friction and size distributions on conditional probability distributions in soft particle packings

NASA Astrophysics Data System (ADS)

Saitoh, Kuniyasu; Magnanimo, Vanessa; Luding, Stefan

2017-10-01

Employing two-dimensional molecular dynamics (MD) simulations of soft particles, we study their non-affine responses to quasi-static isotropic compression where the effects of microscopic friction between the particles in contact and particle size distributions are examined. To quantify complicated restructuring of force-chain networks under isotropic compression, we introduce the conditional probability distributions (CPDs) of particle overlaps such that a master equation for distribution of overlaps in the soft particle packings can be constructed. From our MD simulations, we observe that the CPDs are well described by q-Gaussian distributions, where we find that the correlation for the evolution of particle overlaps is suppressed by microscopic friction, while it significantly increases with the increase of poly-dispersity.

Competitive cluster growth in complex networks.

PubMed

Moreira, André A; Paula, Demétrius R; Costa Filho, Raimundo N; Andrade, José S

2006-06-01

In this work we propose an idealized model for competitive cluster growth in complex networks. Each cluster can be thought of as a fraction of a community that shares some common opinion. Our results show that the cluster size distribution depends on the particular choice for the topology of the network of contacts among the agents. As an application, we show that the cluster size distributions obtained when the growth process is performed on hierarchical networks, e.g., the Apollonian network, have a scaling form similar to what has been observed for the distribution of a number of votes in an electoral process. We suggest that this similarity may be due to the fact that social networks involved in the electoral process may also possess an underlining hierarchical structure.

Think locally, act locally: detection of small, medium-sized, and large communities in large networks.

PubMed

Jeub, Lucas G S; Balachandran, Prakash; Porter, Mason A; Mucha, Peter J; Mahoney, Michael W

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

PubMed Central

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2016-01-01

It is common in the study of networks to investigate intermediate-sized (or “meso-scale”) features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify “communities,” which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that “communities” are associated with bottlenecks of locally-biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for “size-resolved community structure” that can arise in real (and realistic) networks: (i) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (ii) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (iii) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify “good” communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally-biased methods that focus on communities that are centered around a given seed node might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate subtler structural properties that are important to consider in the development of better benchmark networks to test methods for community detection. PMID:25679670

Think locally, act locally: Detection of small, medium-sized, and large communities in large networks

NASA Astrophysics Data System (ADS)

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.

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.

Leveraging social influence to address overweight and obesity using agent-based models: the role of adolescent social networks.

PubMed

Zhang, J; Tong, L; Lamberson, P J; Durazo-Arvizu, R A; Luke, A; Shoham, D A

2015-01-01

The prevalence of adolescent overweight and obesity (hereafter, simply "overweight") in the US has increased over the past several decades. Individually-targeted prevention and treatment strategies targeting individuals have been disappointing, leading some to propose leveraging social networks to improve interventions. We hypothesized that social network dynamics (social marginalization; homophily on body mass index, BMI) and the strength of peer influence would increase or decrease the proportion of network member (agents) becoming overweight over a simulated year, and that peer influence would operate differently in social networks with greater overweight. We built an agent-based model (ABM) using results from R-SIENA. ABMs allow for the exploration of potential interventions using simulated agents. Initial model specifications were drawn from Wave 1 of the National Longitudinal Study of Adolescent Health (Add Health). We focused on a single saturation school with complete network and BMI data over two waves (n = 624). The model was validated against empirical observations at Wave 2. We focused on overall overweight prevalence after a simulated year. Five experiments were conducted: (1) changing attractiveness of high-BMI agents; (2) changing homophily on BMI; (3) changing the strength of peer influence; (4) shifting the overall BMI distribution; and (5) targeting dietary interventions to highly connected individuals. Increasing peer influence showed a dramatic decrease in the prevalence of overweight; making peer influence negative (i.e., doing the opposite of friends) increased overweight. However, the effect of peer influence varied based on the underlying distribution of BMI; when BMI was increased overall, stronger peer influence increased proportion of overweight. Other interventions, including targeted dieting, had little impact. Peer influence may be a viable target in overweight interventions, but the distribution of body size in the population needs to be taken into account. In low-obesity populations, strengthening peer influence may be a useful strategy. Copyright © 2014 Elsevier Ltd. All rights reserved.

Leveraging social influence to address overweight and obesity using agent-based models: the role of adolescent social networks

PubMed Central

Zhang, J; Tong, L; Lamberson, PJ; Durazo, R; Luke, A; Shoham, DA

2014-01-01

The prevalence of adolescent overweight and obesity (hereafter, simply “overweight”) in the US has increased over the past several decades. Individually-targeted prevention and treatment strategies targeting individuals have been disappointing, leading some to propose leveraging social networks to improve interventions. We hypothesized that social network dynamics (social marginalization; homophily on body mass index, BMI) and the strength of peer influence would increase or decrease the proportion of network member (agents) becoming overweight over a simulated year, and that peer influence would operate differently in social networks with greater overweight. We built an agent-based model (ABM) using results from R-SIENA. ABMs allow for the exploration of potential interventions using simulated agents. Initial model specifications were drawn from Wave 1 of the National Longitudinal Study of Adolescent Health (Add Health). We focused on a single saturation school with complete network and BMI data over two waves (n=624). The model was validated against empirical observations at Wave 2. We focused on overall overweight prevalence after a simulated year. Five experiments were conducted: (1) changing attractiveness of high-BMI agents; (2) changing homophily on BMI; (3) changing the strength of peer influence; (4) shifting the overall BMI distribution; and (5) targeting dietary interventions to highly connected individuals. Increasing peer influence showed a dramatic decrease in the prevalence of overweight; making peer influence negative (ie, doing the opposite of friends) increased overweight. However, the effect of peer influence varied based on the underlying distribution of BMI; when BMI was increased overall, stronger peer influence increased proportion of overweight. Other interventions, including targeted dieting, had little impact. Peer influence may be a viable target in overweight interventions, but the distribution of body size in the population needs to be taken into account. In low-obesity populations, strengthening peer influence may be a useful strategy. PMID:24951404

Exit probability of the one-dimensional q-voter model: Analytical results and simulations for large networks

NASA Astrophysics Data System (ADS)

Timpanaro, André M.; Prado, Carmen P. C.

2014-05-01

We discuss the exit probability of the one-dimensional q-voter model and present tools to obtain estimates about this probability, both through simulations in large networks (around 107 sites) and analytically in the limit where the network is infinitely large. We argue that the result E(ρ )=ρq/ρq+(1-ρ)q, that was found in three previous works [F. Slanina, K. Sznajd-Weron, and P. Przybyła, Europhys. Lett. 82, 18006 (2008), 10.1209/0295-5075/82/18006; R. Lambiotte and S. Redner, Europhys. Lett. 82, 18007 (2008), 10.1209/0295-5075/82/18007, for the case q =2; and P. Przybyła, K. Sznajd-Weron, and M. Tabiszewski, Phys. Rev. E 84, 031117 (2011), 10.1103/PhysRevE.84.031117, for q >2] using small networks (around 103 sites), is a good approximation, but there are noticeable deviations that appear even for small systems and that do not disappear when the system size is increased (with the notable exception of the case q =2). We also show that, under some simple and intuitive hypotheses, the exit probability must obey the inequality ρq/ρq+(1-ρ)≤E(ρ)≤ρ/ρ +(1-ρ)q in the infinite size limit. We believe this settles in the negative the suggestion made [S. Galam and A. C. R. Martins, Europhys. Lett. 95, 48005 (2001), 10.1209/0295-5075/95/48005] that this result would be a finite size effect, with the exit probability actually being a step function. We also show how the result that the exit probability cannot be a step function can be reconciled with the Galam unified frame, which was also a source of controversy.

Lens ion homeostasis relies on the assembly and/or stability of large connexin 46 gap junction plaques on the broad sides of differentiating fiber cells

PubMed Central

Cheng, Catherine; Nowak, Roberta B.; Gao, Junyuan; Sun, Xiurong; Biswas, Sondip K.; Lo, Woo-Kuen; Mathias, Richard T.

2015-01-01

The eye lens consists of layers of tightly packed fiber cells, forming a transparent and avascular organ that is important for focusing light onto the retina. A microcirculation system, facilitated by a network of gap junction channels composed of connexins 46 and 50 (Cx46 and Cx50), is hypothesized to maintain and nourish lens fiber cells. We measured lens impedance in mice lacking tropomodulin 1 (Tmod1, an actin pointed-end capping protein), CP49 (a lens-specific intermediate filament protein), or both Tmod1 and CP49. We were surprised to find that simultaneous loss of Tmod1 and CP49, which disrupts cytoskeletal networks in lens fiber cells, results in increased gap junction coupling resistance, hydrostatic pressure, and sodium concentration. Protein levels of Cx46 and Cx50 in Tmod1−/−;CP49−/− double-knockout (DKO) lenses were unchanged, and electron microscopy revealed normal gap junctions. However, immunostaining and quantitative analysis of three-dimensional confocal images showed that Cx46 gap junction plaques are smaller and more dispersed in DKO differentiating fiber cells. The localization and sizes of Cx50 gap junction plaques in DKO fibers were unaffected, suggesting that Cx46 and Cx50 form homomeric channels. We also demonstrate that gap junction plaques rest in lacunae of the membrane-associated actin-spectrin network, suggesting that disruption of the actin-spectrin network in DKO fibers may interfere with gap junction plaque accretion into micrometer-sized domains or alter the stability of large plaques. This is the first work to reveal that normal gap junction plaque localization and size are associated with normal lens coupling conductance. PMID:25740157

Lens ion homeostasis relies on the assembly and/or stability of large connexin 46 gap junction plaques on the broad sides of differentiating fiber cells.

PubMed

Cheng, Catherine; Nowak, Roberta B; Gao, Junyuan; Sun, Xiurong; Biswas, Sondip K; Lo, Woo-Kuen; Mathias, Richard T; Fowler, Velia M

2015-05-15

The eye lens consists of layers of tightly packed fiber cells, forming a transparent and avascular organ that is important for focusing light onto the retina. A microcirculation system, facilitated by a network of gap junction channels composed of connexins 46 and 50 (Cx46 and Cx50), is hypothesized to maintain and nourish lens fiber cells. We measured lens impedance in mice lacking tropomodulin 1 (Tmod1, an actin pointed-end capping protein), CP49 (a lens-specific intermediate filament protein), or both Tmod1 and CP49. We were surprised to find that simultaneous loss of Tmod1 and CP49, which disrupts cytoskeletal networks in lens fiber cells, results in increased gap junction coupling resistance, hydrostatic pressure, and sodium concentration. Protein levels of Cx46 and Cx50 in Tmod1(-/-);CP49(-/-) double-knockout (DKO) lenses were unchanged, and electron microscopy revealed normal gap junctions. However, immunostaining and quantitative analysis of three-dimensional confocal images showed that Cx46 gap junction plaques are smaller and more dispersed in DKO differentiating fiber cells. The localization and sizes of Cx50 gap junction plaques in DKO fibers were unaffected, suggesting that Cx46 and Cx50 form homomeric channels. We also demonstrate that gap junction plaques rest in lacunae of the membrane-associated actin-spectrin network, suggesting that disruption of the actin-spectrin network in DKO fibers may interfere with gap junction plaque accretion into micrometer-sized domains or alter the stability of large plaques. This is the first work to reveal that normal gap junction plaque localization and size are associated with normal lens coupling conductance. Copyright © 2015 the American Physiological Society.

Introducing ab initio based neural networks for transition-rate prediction in kinetic Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Messina, Luca; Castin, Nicolas; Domain, Christophe; Olsson, Pär

2017-02-01

The quality of kinetic Monte Carlo (KMC) simulations of microstructure evolution in alloys relies on the parametrization of point-defect migration rates, which are complex functions of the local chemical composition and can be calculated accurately with ab initio methods. However, constructing reliable models that ensure the best possible transfer of physical information from ab initio to KMC is a challenging task. This work presents an innovative approach, where the transition rates are predicted by artificial neural networks trained on a database of 2000 migration barriers, obtained with density functional theory (DFT) in place of interatomic potentials. The method is tested on copper precipitation in thermally aged iron alloys, by means of a hybrid atomistic-object KMC model. For the object part of the model, the stability and mobility properties of copper-vacancy clusters are analyzed by means of independent atomistic KMC simulations, driven by the same neural networks. The cluster diffusion coefficients and mean free paths are found to increase with size, confirming the dominant role of coarsening of medium- and large-sized clusters in the precipitation kinetics. The evolution under thermal aging is in better agreement with experiments with respect to a previous interatomic-potential model, especially concerning the experiment time scales. However, the model underestimates the solubility of copper in iron due to the excessively high solution energy predicted by the chosen DFT method. Nevertheless, this work proves the capability of neural networks to transfer complex ab initio physical properties to higher-scale models, and facilitates the extension to systems with increasing chemical complexity, setting the ground for reliable microstructure evolution simulations in a wide range of alloys and applications.

Impact of ageing on problem size and proactive interference in arithmetic facts solving.

PubMed

Archambeau, Kim; De Visscher, Alice; Noël, Marie-Pascale; Gevers, Wim

2018-02-01

Arithmetic facts (AFs) are required when solving problems such as "3 × 4" and refer to calculations for which the correct answer is retrieved from memory. Currently, two important effects that modulate the performance in AFs have been highlighted: the problem size effect and the proactive interference effect. The aim of this study is to investigate possible age-related changes of the problem size effect and the proactive interference effect in AF solving. To this end, the performance of young and older adults was compared in a multiplication production task. Furthermore, an independent measure of proactive interference was assessed to further define the architecture underlying this effect in multiplication solving. The results indicate that both young and older adults were sensitive to the effects of interference and of the problem size. That is, both interference and problem size affected performance negatively: the time needed to solve a multiplication problem increases as the level of interference and the size of the problem increase. Regarding the effect of ageing, the problem size effect remains constant with age, indicating a preserved AF network in older adults. Interestingly, sensitivity to proactive interference in multiplication solving was less pronounced in older than in younger adults suggesting that part of the proactive interference has been overcome with age.

GLM Validation Studies in Colorado

NASA Astrophysics Data System (ADS)

Rutledge, S. A.; Reimel, K.; Fuchs, B.; Xu, W.

2017-12-01

On 8 May 2017 the Geostationary Lightning Mapper (GLM) calibration/validation field campaign completed a mission over the domain of the Colorado Lightning Mapping Array (LMA). This "gold mine day" produced a mixture of normal polarity and anomalous storms of varying intensity. A case study analysis has been completed for a portion of three individual storms from this day. By utilizing a cell tracking algorithm and lightning flash attribution program, individual lightning flashes detected by the GLM, LMA, the National Lightning Detection Network (NLDN), and Earth Networks Total Lightning Network (ENTLN) are attributed to individual storm cells. The focus of this analysis is the detection efficiency of GLM. We will discuss how the GLM detection efficiency changes as a result of storm morphology and lightning flash characteristics. Lightning flash size, flash height, and the amount of ice present between the lightning flash altitude and the top of the cloud all appear to play a role in how well GLM detects lightning flashes. Since GLM shares the same concept as its predecessor TRMM LIS (optically-based lightning detection), the evaluation of TRMM LIS against LMA network-detected lightning provides insights into the GLM detection efficiency. We have collected observations by LIS and LMA coincident in time and space during 2008-2014. The sample includes 400 LIS overpasses with both LIS and LMA detecting flashes within 150 km radius of the center of the LMA array during the 120 second LIS observing time period (analysis presently confined to the Alabama LMA network). The overall LIS detection efficiency (DE, defined as the ratio of flash rates between LIS and LMA) is 0.45, with higher DE for lower flash rate cases. LIS showed a DE of nearly 100% for cases with flash rates < 10 fl/min, but had a DE of only 20-30% for high flash rates within intense storms (> 300 fl/min). We further separated the dataset into day and night, and found that the night-time DE (0.6) increased by 20% compared to day-time DE (0.5). LIS DE also increased as a function of LMA-derived flash size, possibly due to stronger radiance from larger flashes. LIS DE was the lowest ( 40%) for flashes with sizes smaller than a single LIS pixel (< 16 km2). These results may be applicable to GLM as well.

Computer Simulations of Bottlebrush Melts and Soft Networks

NASA Astrophysics Data System (ADS)

Cao, Zhen; Carrillo, Jan-Michael; Sheiko, Sergei; Dobrynin, Andrey

We have studied dense bottlebrush systems in a melt and network state using a combination of the molecular dynamics simulations and analytical calculations. Our simulations show that the bottlebrush macromolecules in a melt behave as ideal chains with the effective Kuhn length bK. The bottlebrush induced bending rigidity is due to redistribution of the side chains upon backbone bending. Kuhn length of the bottlebrushes increases with increasing the side-chain degree of polymerization nsc as bK ~nsc0 . 46 . This model of bottlebrush macromolecules is extended to describe mechanical properties of bottlebrush networks in linear and nonlinear deformation regimes. In the linear deformation regime, the network shear modulus scales with the degree of polymerization of the side chains as G0 ~nsc + 1 - 1 as long as the ratio of the Kuhn length to the size of the fully extended bottlebrush backbone between crosslinks, Rmax, is smaller than unity, bK /Rmax < < 1 . Bottlebrush networks with bK /Rmax ~ 1 demonstrate behavior similar to that of networks of semiflexible chains with G0 ~nsc- 0 . 5 . In the nonlinear deformation regime, the deformation dependent shear modulus is a universal function of the first strain invariant I1 and bottlebrush backbone deformation ratio β describing stretching ability of the bottlebrush backbone between crosslinks. Nsf DMR-1409710 DMR-1436201.

Dynamics of neural cryptography

NASA Astrophysics Data System (ADS)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-05-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

Dynamics of neural cryptography.

PubMed

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-05-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

Dynamics of neural cryptography

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

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-05-15

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently,more » synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.« less

Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix

PubMed Central

Zhang, Pengwei; Hu, Liming; Meegoda, Jay N.; Gao, Shengyan

2015-01-01

The gas flow in shale matrix is of great research interests for optimized shale gas extraction. The gas flow in the nano-scale pore may fall in flow regimes such as viscous flow, slip flow and Knudsen diffusion. A 3-dimensional nano-scale pore network model was developed to simulate dynamic gas flow, and to describe the transient properties of flow regimes. The proposed pore network model accounts for the various size distributions and low connectivity of shale pores. The pore size, pore throat size and coordination number obey normal distribution, and the average values can be obtained from shale reservoir data. The gas flow regimes were simulated using an extracted pore network backbone. The numerical results show that apparent permeability is strongly dependent on pore pressure in the reservoir and pore throat size, which is overestimated by low-pressure laboratory tests. With the decrease of reservoir pressure, viscous flow is weakening, then slip flow and Knudsen diffusion are gradually becoming dominant flow regimes. The fingering phenomenon can be predicted by micro/nano-pore network for gas flow, which provides an effective way to capture heterogeneity of shale gas reservoir. PMID:26310236

Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix.

PubMed

Zhang, Pengwei; Hu, Liming; Meegoda, Jay N; Gao, Shengyan

2015-08-27

The gas flow in shale matrix is of great research interests for optimized shale gas extraction. The gas flow in the nano-scale pore may fall in flow regimes such as viscous flow, slip flow and Knudsen diffusion. A 3-dimensional nano-scale pore network model was developed to simulate dynamic gas flow, and to describe the transient properties of flow regimes. The proposed pore network model accounts for the various size distributions and low connectivity of shale pores. The pore size, pore throat size and coordination number obey normal distribution, and the average values can be obtained from shale reservoir data. The gas flow regimes were simulated using an extracted pore network backbone. The numerical results show that apparent permeability is strongly dependent on pore pressure in the reservoir and pore throat size, which is overestimated by low-pressure laboratory tests. With the decrease of reservoir pressure, viscous flow is weakening, then slip flow and Knudsen diffusion are gradually becoming dominant flow regimes. The fingering phenomenon can be predicted by micro/nano-pore network for gas flow, which provides an effective way to capture heterogeneity of shale gas reservoir.

Optimization and resilience of complex supply-demand networks

NASA Astrophysics Data System (ADS)

Zhang, Si-Ping; Huang, Zi-Gang; Dong, Jia-Qi; Eisenberg, Daniel; Seager, Thomas P.; Lai, Ying-Cheng

2015-06-01

Supply-demand processes take place on a large variety of real-world networked systems ranging from power grids and the internet to social networking and urban systems. In a modern infrastructure, supply-demand systems are constantly expanding, leading to constant increase in load requirement for resources and consequently, to problems such as low efficiency, resource scarcity, and partial system failures. Under certain conditions global catastrophe on the scale of the whole system can occur through the dynamical process of cascading failures. We investigate optimization and resilience of time-varying supply-demand systems by constructing network models of such systems, where resources are transported from the supplier sites to users through various links. Here by optimization we mean minimization of the maximum load on links, and system resilience can be characterized using the cascading failure size of users who fail to connect with suppliers. We consider two representative classes of supply schemes: load driven supply and fix fraction supply. Our findings are: (1) optimized systems are more robust since relatively smaller cascading failures occur when triggered by external perturbation to the links; (2) a large fraction of links can be free of load if resources are directed to transport through the shortest paths; (3) redundant links in the performance of the system can help to reroute the traffic but may undesirably transmit and enlarge the failure size of the system; (4) the patterns of cascading failures depend strongly upon the capacity of links; (5) the specific location of the trigger determines the specific route of cascading failure, but has little effect on the final cascading size; (6) system expansion typically reduces the efficiency; and (7) when the locations of the suppliers are optimized over a long expanding period, fewer suppliers are required. These results hold for heterogeneous networks in general, providing insights into designing optimal and resilient complex supply-demand systems that expand constantly in time.

Framework for waveband switching in multigranular optical networks: part I-multigranular cross-connect architectures [Invited

NASA Astrophysics Data System (ADS)

Cao, Xiaojun; Anand, Vishal; Qiao, Chunming

2006-12-01

Optical networks using wavelength-division multiplexing (WDM) are the foremost solution to the ever-increasing traffic in the Internet backbone. Rapid advances in WDM technology will enable each fiber to carry hundreds or even a thousand wavelengths (using dense-WDM, or DWDM, and ultra-DWDM) of traffic. This, coupled with worldwide fiber deployment, will bring about a tremendous increase in the size of the optical cross-connects, i.e., the number of ports of the wavelength switching elements. Waveband switching (WBS), wherein wavelengths are grouped into bands and switched as a single entity, can reduce the cost and control complexity of switching nodes by minimizing the port count. This paper presents a detailed study on recent advances and open research issues in WBS networks. In this study, we investigate in detail the architecture for various WBS cross-connects and compare them in terms of the number of ports and complexity and also in terms of how flexible they are in adjusting to dynamic traffic. We outline various techniques for grouping wavelengths into bands for the purpose of WBS and show how traditional wavelength routing is different from waveband routing and why techniques developed for wavelength-routed networks (WRNs) cannot be simply applied to WBS networks. We also outline how traffic grooming of subwavelength traffic can be done in WBS networks. In part II of this study [Cao , submitted to J. Opt. Netw.], we study the effect of wavelength conversion on the performance of WBS networks with reconfigurable MG-OXCs. We present an algorithm for waveband grouping in wavelength-convertible networks and evaluate its performance. We also investigate issues related to survivability in WBS networks and show how waveband and wavelength conversion can be used to recover from failures in WBS networks.

Coronal Heating, Spicules, and Solar-B

NASA Technical Reports Server (NTRS)

Moore, Ron; Falconer, David; Porter, Jason; Hathaway, David; Yamauchi, Yohei

2003-01-01

Falconer et al. investigated the heating of the quiet corona by measuring the increase of coronal luminosity with the amount of the magnetic flux in the underlying network at solar minimum when there were no active regions on the face of the Sun. The coronal luminosity was measured from Fe IX/X - Fe XII pairs of coronal images from SOHO/EIT, under the assumption that practically all of the coronal luminosity in these very quiet regions came from plasma in the temperature range 0.9 x 10(exp 6) K is less than or equal to T is less than or equal to 1.3 x 10(exp 6) K. The network magnetic flux content was measured from SOHO/MDI magnetograms. It was found that luminosity of the corona in these quiet regions increased roughly in proportion to the square root of the magnetic flux content of the network and roughly in proportion to the length of the perimeter of the network flux clumps. From 1) this result; 2) the observed occurrence of many fine-scale explosive events (e.g., spicules) at the edges of network flux clumps; and 3) a demonstration that it is energetically feasible for the heating of the corona in quiet regions to be driven by explosions of granule-sized sheared-core magnetic bipoles embedded in the edges of the network flux clumps, Falconer et al. infer that in quiet regions that are not influenced by active regions the corona is mainly heated by such magnetic activity in the edges of the network flux clumps. From their observational results together with their feasibility analysis, Falconer et al. predict that 1) At the edges of the network flux clumps there are many transient sheared core bipoles of the size and lifetime of granules and having transverse field strengths greater than approx. 100 G; 2) Approx. 30 of these bipoles are present per supergranule; and 3) Most spicules are produced by explosions of these bipoles. The photospheric vector magnetograms, chromospheric filtergrams, and EUV spectra from Solar-B are expected to have sufficient sensitivity, spatial resolution, and cadence to test these predictions. The Falconer et al. (2003) inferred mixed-polarity magnetic flux at the base of spicules is compatible with the observed magnetic structure of Ha macrospicules recently found by Yamuchi et al. (2003).

On the structural properties of small-world networks with range-limited shortcut links

NASA Astrophysics Data System (ADS)

Jia, Tao; Kulkarni, Rahul V.

2013-12-01

We explore a new variant of Small-World Networks (SWNs), in which an additional parameter (r) sets the length scale over which shortcuts are uniformly distributed. When r=0 we have an ordered network, whereas r=1 corresponds to the original Watts-Strogatz SWN model. These limited range SWNs have a similar degree distribution and scaling properties as the original SWN model. We observe the small-world phenomenon for r≪1, indicating that global shortcuts are not necessary for the small-world effect. For limited range SWNs, the average path length changes nonmonotonically with system size, whereas for the original SWN model it increases monotonically. We propose an expression for the average path length for limited range SWNs based on numerical simulations and analytical approximations.

An Evaluation of TCP with Larger Initial Windows

NASA Technical Reports Server (NTRS)

Allman, Mark; Hayes, Christopher; Ostermann, Shawn

1998-01-01

Transmission Control Protocol (TCP's) slow start algorithm gradually increases the amount of data a sender injects into the network, which prevents the sender from overwhelming the network with an inappropriately large burst of traffic. However, the slow start algorithm can make poor use of the available band-width for transfers which are small compared to the bandwidth-delay product of the link, such as file transfers up to few thousand characters over satellite links or even transfers of several hundred bytes over local area networks. This paper evaluates a proposed performance enhancement that raises the initial window used by TCP from 1 MSS-sized segment to roughly 4 KB. The paper evaluates the impact of using larger initial windows on TCP transfers over both the shared Internet and dialup modem links.

Electrochemically Smart Bimetallic Materials Featuring Group 11 Metals: In-situ Conductive Network Generation and Its Impact on Cell Capacity

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

Takeuchi, Esther

2016-11-30

Our results for this program “Electrochemically smart bimetallic materials featuring Group 11 metals: in-situ conductive matrix generation and its impact on battery capacity, power and reversibility” have been highly successful: 1) we demonstrated material structures which generated in-situ conductive networks through electrochemical activation with increases in conductivity up to 10,000 fold, 2) we pioneered in situ analytical methodology to map the cathodes at several stages of discharge through the use of Energy Dispersive X-ray Diffraction (EDXRD) to elucidate the kinetic dependence of the conductive network formation, and 3) we successfully designed synthetic methodology for direct control of material properties includingmore » crystallite size and surface area which showed significant impact on electrochemical behavior.« less

A stochastic-field description of finite-size spiking neural networks

PubMed Central

Longtin, André

2017-01-01

Neural network dynamics are governed by the interaction of spiking neurons. Stochastic aspects of single-neuron dynamics propagate up to the network level and shape the dynamical and informational properties of the population. Mean-field models of population activity disregard the finite-size stochastic fluctuations of network dynamics and thus offer a deterministic description of the system. Here, we derive a stochastic partial differential equation (SPDE) describing the temporal evolution of the finite-size refractory density, which represents the proportion of neurons in a given refractory state at any given time. The population activity—the density of active neurons per unit time—is easily extracted from this refractory density. The SPDE includes finite-size effects through a two-dimensional Gaussian white noise that acts both in time and along the refractory dimension. For an infinite number of neurons the standard mean-field theory is recovered. A discretization of the SPDE along its characteristic curves allows direct simulations of the activity of large but finite spiking networks; this constitutes the main advantage of our approach. Linearizing the SPDE with respect to the deterministic asynchronous state allows the theoretical investigation of finite-size activity fluctuations. In particular, analytical expressions for the power spectrum and autocorrelation of activity fluctuations are obtained. Moreover, our approach can be adapted to incorporate multiple interacting populations and quasi-renewal single-neuron dynamics. PMID:28787447

Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams

PubMed Central

Jaeger, Kristin L.; Olden, Julian D.; Pelland, Noel A.

2014-01-01

Protecting hydrologic connectivity of freshwater ecosystems is fundamental to ensuring species persistence, ecosystem integrity, and human well-being. More frequent and severe droughts associated with climate change are poised to significantly alter flow intermittence patterns and hydrologic connectivity in dryland streams of the American Southwest, with deleterious effects on highly endangered fishes. By integrating local-scale hydrologic modeling with emerging approaches in landscape ecology, we quantify fine-resolution, watershed-scale changes in habitat size, spacing, and connectance under forecasted climate change in the Verde River Basin, United States. Model simulations project annual zero-flow day frequency to increase by 27% by midcentury, with differential seasonal consequences on continuity (temporal continuity at discrete locations) and connectivity (spatial continuity within the network). A 17% increase in the frequency of stream drying events is expected throughout the network with associated increases in the duration of these events. Flowing portions of the river network will diminish between 8% and 20% in spring and early summer and become increasingly isolated by more frequent and longer stretches of dry channel fragments, thus limiting the opportunity for native fishes to access spawning habitats and seasonally available refuges. Model predictions suggest that midcentury and late century climate will reduce network-wide hydrologic connectivity for native fishes by 6–9% over the course of a year and up to 12–18% during spring spawning months. Our work quantifies climate-induced shifts in stream drying and connectivity across a large river network and demonstrates their implications for the persistence of a globally endemic fish fauna. PMID:25136090

Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams.

PubMed

Jaeger, Kristin L; Olden, Julian D; Pelland, Noel A

2014-09-23

Protecting hydrologic connectivity of freshwater ecosystems is fundamental to ensuring species persistence, ecosystem integrity, and human well-being. More frequent and severe droughts associated with climate change are poised to significantly alter flow intermittence patterns and hydrologic connectivity in dryland streams of the American Southwest, with deleterious effects on highly endangered fishes. By integrating local-scale hydrologic modeling with emerging approaches in landscape ecology, we quantify fine-resolution, watershed-scale changes in habitat size, spacing, and connectance under forecasted climate change in the Verde River Basin, United States. Model simulations project annual zero-flow day frequency to increase by 27% by midcentury, with differential seasonal consequences on continuity (temporal continuity at discrete locations) and connectivity (spatial continuity within the network). A 17% increase in the frequency of stream drying events is expected throughout the network with associated increases in the duration of these events. Flowing portions of the river network will diminish between 8% and 20% in spring and early summer and become increasingly isolated by more frequent and longer stretches of dry channel fragments, thus limiting the opportunity for native fishes to access spawning habitats and seasonally available refuges. Model predictions suggest that midcentury and late century climate will reduce network-wide hydrologic connectivity for native fishes by 6-9% over the course of a year and up to 12-18% during spring spawning months. Our work quantifies climate-induced shifts in stream drying and connectivity across a large river network and demonstrates their implications for the persistence of a globally endemic fish fauna.

Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

NASA Astrophysics Data System (ADS)

Siddiqui, Maheen; Wedemann, Roseli S.; Jensen, Henrik Jeldtoft

2018-01-01

We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.

Two-year outcome of team-based intensive case management for patients with schizophrenia.

PubMed

Aberg-Wistedt, A; Cressell, T; Lidberg, Y; Liljenberg, B; Osby, U

1995-12-01

Two-year outcomes of patients with schizophrenic disorders who were assigned to an intensive, team-based case management program and patients who received standard psychiatric services were assessed. The case management model featured increased staff contact time with patients, rehabilitation plans based on patients' expressed needs, and patients' attendance at team meetings where their rehabilitation plan was discussed. Forty patients were randomly assigned to either the case management group or the control group that received standard services. Patients' use of emergency and inpatient services, their quality of life, the size of their social networks, and their relatives' burden of care were assessed at assignment to the study groups and at two-year follow-up. Patients in the case management group had significantly fewer emergency visits compared with the two years before the study, and their relatives reported significantly reduced burden of care associated with relationships with psychiatric services over the two-year period. The size of patients' social networks increased for the case management group and decreased for the control group. A team-based intensive case management model is an effective intervention in the rehabilitation of patients with chronic schizophrenia.

Real-time image processing for particle tracking velocimetry

NASA Astrophysics Data System (ADS)

Kreizer, Mark; Ratner, David; Liberzon, Alex

2010-01-01

We present a novel high-speed particle tracking velocimetry (PTV) experimental system. Its novelty is due to the FPGA-based, real-time image processing "on camera". Instead of an image, the camera transfers to the computer using a network card, only the relevant information of the identified flow tracers. Therefore, the system is ideal for the remote particle tracking systems in research and industrial applications, while the camera can be controlled and data can be transferred over any high-bandwidth network. We present the hardware and the open source software aspects of the PTV experiments. The tracking results of the new experimental system has been compared to the flow visualization and particle image velocimetry measurements. The canonical flow in the central cross section of a a cubic cavity (1:1:1 aspect ratio) in our lid-driven cavity apparatus is used for validation purposes. The downstream secondary eddy (DSE) is the sensitive portion of this flow and its size was measured with increasing Reynolds number (via increasing belt velocity). The size of DSE estimated from the flow visualization, PIV and compressed PTV is shown to agree within the experimental uncertainty of the methods applied.

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

PubMed Central

Azad, Ariful; Ouzounis, Christos A; Kyrpides, Nikos C; Buluç, Aydin

2018-01-01

Abstract Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license. PMID:29315405

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

DOE PAGES

Azad, Ariful; Pavlopoulos, Georgios A.; Ouzounis, Christos A.; ...

2018-01-05

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times andmore » memory demands. In this paper, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ~70 million nodes with ~68 billion edges in ~2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. Finally, HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.« less

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

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

Azad, Ariful; Pavlopoulos, Georgios A.; Ouzounis, Christos A.

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times andmore » memory demands. In this paper, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ~70 million nodes with ~68 billion edges in ~2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. Finally, HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.« less

Insect-Flower Interaction Network Structure Is Resilient to a Temporary Pulse of Floral Resources from Invasive Rhododendron ponticum

PubMed Central

Tiedeken, Erin Jo; Stout, Jane C.

2015-01-01

Invasive alien plants can compete with native plants for resources, and may ultimately decrease native plant diversity and/or abundance in invaded sites. This could have consequences for native mutualistic interactions, such as pollination. Although invasive plants often become highly connected in plant-pollinator interaction networks, in temperate climates they usually only flower for part of the season. Unless sufficient alternative plants flower outside this period, whole-season floral resources may be reduced by invasion. We hypothesized that the cessation of flowering of a dominant invasive plant would lead to dramatic, seasonal compositional changes in plant-pollinator communities, and subsequent changes in network structure. We investigated variation in floral resources, flower-visiting insect communities, and interaction networks during and after the flowering of invasive Rhododendron ponticum in four invaded Irish woodland sites. Floral resources decreased significantly after R. ponticum flowering, but the magnitude of the decrease varied among sites. Neither insect abundance nor richness varied between the two periods (during and after R. ponticum flowering), yet insect community composition was distinct, mostly due to a significant reduction in Bombus abundance after flowering. During flowering R. ponticum was frequently visited by Bombus; after flowering, these highly mobile pollinators presumably left to find alternative floral resources. Despite compositional changes, however, network structural properties remained stable after R. ponticum flowering ceased: generality increased, but quantitative connectance, interaction evenness, vulnerability, H’2 and network size did not change. This is likely because after R. ponticum flowering, two to three alternative plant species became prominent in networks and insects increased their diet breadth, as indicated by the increase in network-level generality. We conclude that network structure is robust to seasonal changes in floral abundance at sites invaded by alien, mass-flowering plant species, as long as alternative floral resources remain throughout the season to support the flower-visiting community. PMID:25764085

Is networking different with doctors working part-time? Differences in social networks of part-time and full-time doctors.

PubMed

Heiligers, Phil J M; de Jong, Judith D; Groenewegen, Peter P; Hingstman, Lammert; Völker, Beate; Spreeuwenberg, Peter

2008-10-04

Part-time working is a growing phenomenon in medicine, which is expected to influence informal networks at work differently compared to full-time working. The opportunity to meet and build up social capital at work has offered a basis for theoretical arguments. Twenty-eight teams of medical specialists in the Netherlands, including 226 individuals participated in this study. Interviews with team representatives and individual questionnaires were used. Data were gathered on three types of networks: relationships of consulting, communication and trust. For analyses, network and multilevel applications were used. Differences between individual doctors and between teams were both analysed, taking the dependency structure of the data into account, because networks of individual doctors are not independent. Teams were divided into teams with and without doctors working part-time. Contrary to expectations we found no impact of part-time working on the size of personal networks, neither at the individual nor at the team level. The same was found regarding efficient reachability. Whereas we expected part-time doctors to choose their relations as efficiently as possible, we even found the opposite in intended relationships of trust, implying that efficiency in reaching each other was higher for full-time doctors. But we found as expected that in mixed teams with part-time doctors the frequency of regular communication was less compared to full-time teams. Furthermore, as expected the strength of the intended relationships of trust of part-time and full-time doctors was equally high. From these findings we can conclude that part-time doctors are not aiming at efficiency by limiting the size of networks or by efficient reachability, because they want to contact their colleagues directly in order to prevent from communication errors. On the other hand, together with the growth of teams, we found this strategy, focussed on reaching all colleagues, was diminishing. And our data confirmed that formalisation was increasing together with the growth of teams.

State space truncation with quantified errors for accurate solutions to discrete Chemical Master Equation

PubMed Central

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-01-01

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEG), we truncate the state space by limiting the total molecular copy numbers in each MEG. We further describe a theoretical framework for analysis of the truncation error in the steady state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of 1) the birth and death model, 2) the single gene expression model, 3) the genetic toggle switch model, and 4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate out theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large number of stochastic networks. PMID:27105653

State Space Truncation with Quantified Errors for Accurate Solutions to Discrete Chemical Master Equation

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

Cao, Youfang; Terebus, Anna; Liang, Jie

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEGs), we truncate the state space by limiting the total molecular copy numbers in each MEG. Wemore » further describe a theoretical framework for analysis of the truncation error in the steady-state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of (1) the birth and death model, (2) the single gene expression model, (3) the genetic toggle switch model, and (4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady-state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate our theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large number of stochastic networks.« less

State Space Truncation with Quantified Errors for Accurate Solutions to Discrete Chemical Master Equation

DOE PAGES

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-04-22

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEGs), we truncate the state space by limiting the total molecular copy numbers in each MEG. Wemore » further describe a theoretical framework for analysis of the truncation error in the steady-state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of (1) the birth and death model, (2) the single gene expression model, (3) the genetic toggle switch model, and (4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady-state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate our theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large number of stochastic networks.« less

Voids in mixed-cation silicate glasses: Studies by positron annihilation lifetime and Fourier transform infrared spectroscopies

NASA Astrophysics Data System (ADS)

Reben, M.; Golis, E.; Filipecki, J.; Sitarz, M.; Kotynia, K.; Jeleń, P.; Grelowska, I.

2014-08-01

PALS in comparison with FTIR studies have been applied to investigate the structure of different oxide glasses. Three components of the positron lifetime τ (τ1 para- and τ3 ortho-positronium and τ2 intermediate lifetime component) and their intensities were obtained. The results of the calculation of mean values of positron lifetimes for the investigated glasses showed the existence of a long-living component on the positron annihilation lifetime spectra. From the Tao-Eldrup formula we can estimate the size of free volume. On the basis of the measurements we can conclude that the size and fraction of free volume reaches the biggest value for the fused silica glass. The degree of network polymerisation increases void size.

Network-scale dynamics of sediment mixtures: how do tectonics affect surface bed texture and channel slope?

NASA Astrophysics Data System (ADS)

Gasparini, N. M.; Bras, R. L.; Tucker, G. E.

2003-04-01

An alluvial channel's slope and bed texture are intimately linked. Along with fluvial discharge, these variables are the key players in setting alluvial transport rates. We know that both channel slope and mean grain size usually decrease downstream, but how sensitive are these variables to tectonic changes? Are basin concavity and downstream fining drastically disrupted during transitions from one tectonic regime to another? We explore these questions using the CHILD numerical landscape evolution model to generate alluvial networks composed of a sand and gravel mixture. The steady-state and transient patterns of both channel slope and sediment texture are investigated. The steady-state patterns in slope and sediment texture are verified independently by solving the erosion equations under equilibrium conditions, i.e. the case when the erosion rate is equal to the uplift rate across the entire landscape. The inclusion of surface texture as a free parameter (as opposed to just channel slope) leads to some surprising results. In all cases, an increase in uplift rate results in channel beds which are finer at equilibrium (for a given drainage area). Higher uplift rates imply larger equilibrium transport rates; this leads to finer channels that have a smaller critical shear stress to entrain material, and therefore more material can be transported for a given discharge (and channel slope). Changes in equilibrium slopes are less intuitive. An increase in uplift rates can cause channel slopes to increase, remain the same, or decrease, depending on model parameter values. In the surprising case in which equilibrium channel slopes decrease with increasing uplift rates, we suggest that surface texture changes more than compensate for the required increase in transport rates, causing channel slopes to decrease. These results highlight the important role of sediment grain size in determining transport rates and caution us against ignoring this important variable in fluvial networks.

Do learning collaboratives strengthen communication? A comparison of organizational team communication networks over time.

PubMed

Bunger, Alicia C; Lengnick-Hall, Rebecca

Collaborative learning models were designed to support quality improvements, such as innovation implementation by promoting communication within organizational teams. Yet the effect of collaborative learning approaches on organizational team communication during implementation is untested. The aim of this study was to explore change in communication patterns within teams from children's mental health organizations during a year-long learning collaborative focused on implementing a new treatment. We adopt a social network perspective to examine intraorganizational communication within each team and assess change in (a) the frequency of communication among team members, (b) communication across organizational hierarchies, and (c) the overall structure of team communication networks. A pretest-posttest design compared communication among 135 participants from 21 organizational teams at the start and end of a learning collaborative. At both time points, participants were asked to list the members of their team and rate the frequency of communication with each along a 7-point Likert scale. Several individual, pair-wise, and team level communication network metrics were calculated and compared over time. At the individual level, participants reported communicating with more team members by the end of the learning collaborative. Cross-hierarchical communication did not change. At the team level, these changes manifested differently depending on team size. In large teams, communication frequency increased, and networks grew denser and slightly less centralized. In small teams, communication frequency declined, growing more sparse and centralized. Results suggest that team communication patterns change minimally but evolve differently depending on size. Learning collaboratives may be more helpful for enhancing communication among larger teams; thus, managers might consider selecting and sending larger staff teams to learning collaboratives. This study highlights key future research directions that can disentangle the relationship between learning collaboratives and team networks.