Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Donner, Reik V.; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing the mechanism of drop formation at the interface of horizontal oil-water stratified flows is a fundamental problem eliciting a great deal of attention from different disciplines. We experimentally and theoretically investigate the formation and transition of horizontal oil-water stratified flows. We design a new multi-sector conductance sensor and measure multivariate signals from two different stratified flow patterns. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first characterize the flow behavior from an energy and frequency point of view. Then, we infer multivariate recurrence networks from the experimental data and investigate the cross-transitivity for each constructed network. We find that the cross-transitivity allows quantitatively uncovering the flow behavior when the stratified flow evolves from a stable state to an unstable one and recovers deeper insights into the mechanism governing the formation of droplets at the interface of stratified flows, a task that existing methods based on AOK TFR fail to work. These findings present a first step towards an improved understanding of the dynamic mechanism leading to the transition of horizontal oil-water stratified flows from a complex-network perspective.

Helping Residents Succeed on the Job: How To Design and Deliver an Effective Job Retention Program. Neighbor Networks.

ERIC Educational Resources Information Center

Department of Housing and Urban Development, Washington, DC. Office of Multifamily Housing.

This guide describes how Neighborhood Networks centers can identify, provide, and use local partners and center staff to deliver effective follow-up retention services to residents who have just made the transition from unemployment to employment. (Neighborhood Networks, a community-based initiative, encourages the development of resource and…

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.

Phase transitions in Pareto optimal complex networks

NASA Astrophysics Data System (ADS)

Seoane, Luís F.; Solé, Ricard

2015-09-01

The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.

Machine learning vortices at the Kosterlitz-Thouless transition

NASA Astrophysics Data System (ADS)

Beach, Matthew J. S.; Golubeva, Anna; Melko, Roger G.

2018-01-01

Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed-matter and statistical physics. Supervised algorithms trained on raw samples of microstates can successfully detect conventional phase transitions via learning a bulk feature such as an order parameter. In this paper, we investigate whether neural networks can learn to classify phases based on topological defects. We address this question on the two-dimensional classical XY model which exhibits a Kosterlitz-Thouless transition. We find significant feature engineering of the raw spin states is required to convincingly claim that features of the vortex configurations are responsible for learning the transition temperature. We further show a single-layer network does not correctly classify the phases of the XY model, while a convolutional network easily performs classification by learning the global magnetization. Finally, we design a deep network capable of learning vortices without feature engineering. We demonstrate the detection of vortices does not necessarily result in the best classification accuracy, especially for lattices of less than approximately 1000 spins. For larger systems, it remains a difficult task to learn vortices.

Percolation of a general network of networks.

PubMed

Gao, Jianxi; Buldyrev, Sergey V; Stanley, H Eugene; Xu, Xiaoming; Havlin, Shlomo

2013-12-01

Percolation theory is an approach to study the vulnerability of a system. We develop an analytical framework and analyze the percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the size of the failure, i.e., the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to an abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic Erdős-Rényi (ER) networks, where each network depends on the same number m of other networks, i.e., for a random regular network (RR) formed of interdependent ER networks. The dependency between nodes of different networks is taken as one-to-one correspondence, i.e., a node in one network can depend only on one node in the other network (no-feedback condition). In contrast to a treelike NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m and the topology of each network but not on n. We also analyzed the extremely vulnerable feedback condition of coupling, where the coupling between nodes of different networks is not one-to-one correspondence. In the case of NetONet formed of ER networks, percolation only exhibits two phases, a second order phase transition and collapse, and no first order percolation transition regime is found in the case of the no-feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when the coupling strength q (fraction of interdependency links) is large and a second order phase transition when q is small. Our insight on the resilience of coupled networks might help in designing robust interdependent systems.

How does network design constrain optimal operation of intermittent water supply?

NASA Astrophysics Data System (ADS)

Lieb, Anna; Wilkening, Jon; Rycroft, Chris

2015-11-01

Urban water distribution systems do not always supply water continuously or reliably. As pipes fill and empty, pressure transients may contribute to degraded infrastructure and poor water quality. To help understand and manage this undesirable side effect of intermittent water supply--a phenomenon affecting hundreds of millions of people in cities around the world--we study the relative contributions of fixed versus dynamic properties of the network. Using a dynamical model of unsteady transition pipe flow, we study how different elements of network design, such as network geometry, pipe material, and pipe slope, contribute to undesirable pressure transients. Using an optimization framework, we then investigate to what extent network operation decisions such as supply timing and inflow rate may mitigate these effects. We characterize some aspects of network design that make them more or less amenable to operational optimization.

The Potential Observation Network Design with Mesoscale Ensemble Sensitivities in Complex Terrain

DTIC Science & Technology

2012-03-01

in synoptic storms , extratropical transition and developing hurricanes. Because they rely on lagged covariances from a finite-sized ensemble, they...diagnose predictors of forecast error in synoptic storms , extratropical transition and developing hurricanes. Because they rely on lagged covariances...sensitivities can be used successfully to diagnose predictors of forecast error in synoptic storms (Torn and Hakim 2008), extratropical transition (Torn and

Computer-aided design of biological circuits using TinkerCell

PubMed Central

Bergmann, Frank T; Sauro, Herbert M

2010-01-01

Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field. PMID:21327060

Appplication of statistical mechanical methods to the modeling of social networks

NASA Astrophysics Data System (ADS)

Strathman, Anthony Robert

With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.

The mobility and safety of walk-and-ride systems.

DOT National Transportation Integrated Search

2015-03-01

In this project we investigate the effect of traffic calming measures, such as crosswalks and sidewalks on the overall cost and safety of a multimodal transportation network system design. Our design problem includes auto, transit, and walking as mod...

Towards a definition of life.

PubMed

Macklem, Peter T; Seely, Andrew

2010-01-01

This article offers a new definition of life as a "self-contained, self-regulating, self-organizing, self-reproducing, interconnected, open thermodynamic network of component parts which performs work, existing in a complex regime which combines stability and adaptability in the phase transition between order and chaos, as a plant, animal, fungus, or microbe." Open thermodynamic networks, which create and maintain order and are used by all organisms to perform work, import energy from and export entropy into the environment. Intra- and extracellular interconnected networks also confer order. Although life obeys the laws of physics and chemistry, the design of living organisms is not determined by these laws, but by Darwinian selection of the fittest designs. Over a short range of normalized energy consumption, open thermodynamic systems change from deeply ordered to chaotic, and life is found in this phase transition, where a dynamic balance between stability and adaptability allows for homeokinesis. Organisms and cells move within the phase transition with changes in metabolic rate. Seeds, spores and cryo-preserved tissue are well within the ordered regime, while health probably cannot be maintained with displacements into the chaotic regime. Understanding life in these terms may provide new insights into what constitutes health and lead to new theories of disease.

Computer-aided design of biological circuits using TinkerCell.

PubMed

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2010-01-01

Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze, and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field. © 2010 Landes Bioscience

Heuristic urban transportation network design method, a multilayer coevolution approach

NASA Astrophysics Data System (ADS)

Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun

2017-08-01

The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.

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.

Motion control in free-standing shape-memory actuators

NASA Astrophysics Data System (ADS)

Belmonte, Alberto; Lama, Giuseppe C.; Cerruti, Pierfrancesco; Ambrogi, Veronica; Fernández-Francos, Xavier; De la Flor, Silvia

2018-07-01

In this work, free-standing shape-memory thermally triggered actuators are developed by laminating ‘thiol-epoxy’-based glassy thermoset (GT) and stretched liquid-crystalline network (LCN) films. A sequential curing process was used to obtain GTs with tailored thermomechanical properties and network relaxation dynamics, and also to assemble the final actuator. The actuation extent, rate and time were studied by varying the GT and the heating rate in thermo-actuation with an experimental approach. The results demonstrate that it is possible to tailor the actuation rate and time by designing GT materials with a glass transition temperature close to that of the liquid-crystalline-to-isotropic phase transition of the LCN, thus making it possible to couple the two processes. Such coupling is also possible in rapid heating processes even when the glass transition temperature of the GT is clearly lower than the isotropization temperature of the LCN, depending on the network relaxation dynamics of the GT and the presence of thermal gradients within the actuators. Interestingly, varying the GT network relaxation dynamics does not affect the actuation extent. As predicted by the analytical model developed in our previous work, the modulus of the GT layer is mainly responsible for the actuation extent. Finally, to demonstrate the enhanced control of the actuation, specifically designed actuators were assembled in a three-dimensional actuating device able to make complex motions (including ‘S-type’ bending). This approach makes it possible to engineer advanced functional materials for application in self-adaptable structures and soft robotics.

Energy and time determine scaling in biological and computer designs

PubMed Central

Bezerra, George; Edwards, Benjamin; Brown, James; Forrest, Stephanie

2016-01-01

Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy–time minimization principle may govern the design of many complex systems that process energy, materials and information. This article is part of the themed issue ‘The major synthetic evolutionary transitions’. PMID:27431524

Taking Systems Medicine to Heart.

PubMed

Trachana, Kalliopi; Bargaje, Rhishikesh; Glusman, Gustavo; Price, Nathan D; Huang, Sui; Hood, Leroy E

2018-04-27

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine. © 2018 American Heart Association, Inc.

The transition to digital media in biocommunications.

PubMed

Lynch, P J

1996-01-01

As digital audiovisual media become dominant in biomedical communications, the skills of human interface design and the technology of client-server multimedia data networks will underlie and influence virtually every aspect of biocommunications professional practice. The transition to digital communications media will require financial, organizational, and professional changes in current biomedical communications departments, and will require a multi-disciplinary approach that will blur the boundaries of the current biocommunications professions.

Understanding transit ridership demand for a multi-destination, multimodal transit network in an American metropolitan area : lessons for increasing choice ridership while maintaining transit dependent ridership.

DOT National Transportation Integrated Search

2012-01-01

This study examines the factors underlying transit demand in the multi-destination, integrated bus and rail transit network for Atlanta, Georgia. Atlanta provides an opportunity to explore the consequences of a multi-destination transit network for b...

Evolutionary transitions in controls reconcile adaptation with continuity of evolution.

PubMed

Badyaev, Alexander V

2018-05-19

Evolution proceeds by accumulating functional solutions, necessarily forming an uninterrupted lineage from past solutions of ancestors to the current design of extant forms. At the population level, this process requires an organismal architecture in which the maintenance of local adaptation does not preclude the ability to innovate in the same traits and their continuous evolution. Representing complex traits as networks enables us to visualize a fundamental principle that resolves tension between adaptation and continuous evolution: phenotypic states encompassing adaptations traverse the continuous multi-layered landscape of past physical, developmental and functional associations among traits. The key concept that captures such traversing is network controllability - the ability to move a network from one state into another while maintaining its functionality (reflecting evolvability) and to efficiently propagate information or products through the network within a phenotypic state (maintaining its robustness). Here I suggest that transitions in network controllability - specifically in the topology of controls - help to explain how robustness and evolvability are balanced during evolution. I will focus on evolutionary transitions in degeneracy of metabolic networks - a ubiquitous property of phenotypic robustness where distinct pathways achieve the same end product - to suggest that associated changes in network controls is a common rule underlying phenomena as distinct as phenotypic plasticity, organismal accommodation of novelties, genetic assimilation, and macroevolutionary diversification. Capitalizing on well understood principles by which network structure translates into function of control nodes, I show that accumulating redundancy in one type of network controls inevitably leads to the emergence of another type of controls, forming evolutionary cycles of network controllability that, ultimately, reconcile local adaptation with continuity of evolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cytoskeletal Network Morphology Regulates Intracellular Transport Dynamics

PubMed Central

Ando, David; Korabel, Nickolay; Huang, Kerwyn Casey; Gopinathan, Ajay

2015-01-01

Intracellular transport is essential for maintaining proper cellular function in most eukaryotic cells, with perturbations in active transport resulting in several types of disease. Efficient delivery of critical cargos to specific locations is accomplished through a combination of passive diffusion and active transport by molecular motors that ballistically move along a network of cytoskeletal filaments. Although motor-based transport is known to be necessary to overcome cytoplasmic crowding and the limited range of diffusion within reasonable timescales, the topological features of the cytoskeletal network that regulate transport efficiency and robustness have not been established. Using a continuum diffusion model, we observed that the time required for cellular transport was minimized when the network was localized near the nucleus. In simulations that explicitly incorporated network spatial architectures, total filament mass was the primary driver of network transit times. However, filament traps that redirect cargo back to the nucleus caused large variations in network transport. Filament polarity was more important than filament orientation in reducing average transit times, and transport properties were optimized in networks with intermediate motor on and off rates. Our results provide important insights into the functional constraints on intracellular transport under which cells have evolved cytoskeletal structures, and have potential applications for enhancing reactions in biomimetic systems through rational transport network design. PMID:26488648

Association between quality domains and health care spending across physician networks

PubMed Central

Rahman, Farah; Guan, Jun; Glazier, Richard H.; Brown, Adalsteinn; Bierman, Arlene S.; Croxford, Ruth; Stukel, Therese A.

2018-01-01

One of the more fundamental health policy questions is the relationship between health care quality and spending. A better understanding of these relationships is needed to inform health systems interventions aimed at increasing quality and efficiency of care. We measured 65 validated quality indicators (QI) across Ontario physician networks. QIs were aggregated into domains representing six dimensions of care: screening and prevention, evidence-based medications, hospital-community transitions (7-day post-discharge visit with a primary care physician; 30-day post-discharge visit with a primary care physician and specialist), potentially avoidable hospitalizations and emergency department (ED) visits, potentially avoidable readmissions and unplanned returns to the ED, and poor cancer end of life care. Each domain rate was computed as a weighted average of QI rates, weighting by network population at risk. We also measured overall and sector-specific per capita healthcare network spending. We evaluated the associations between domain rates, and between domain rates and spending using weighted correlations, weighting by network population at risk, using an ecological design. All indicators were measured using Ontario health administrative databases. Large variations were seen in timely hospital-community transitions and potentially avoidable hospitalizations. Networks with timely hospital-community transitions had lower rates of avoidable admissions and readmissions (r = -0.89, -0.58, respectively). Higher physician spending, especially outpatient primary care spending, was associated with lower rates of avoidable hospitalizations (r = -0.83) and higher rates of timely hospital-community transitions (r = 0.81) and moderately associated with lower readmission rates (r = -0.46). Investment in effective primary care services may help reduce burden on the acute care sector and associated expenditures. PMID:29614131

Adaptive Probabilistic Protocols for Advanced Networks/Assuring the Integrity of Highly Decentralized Communications Systems

DTIC Science & Technology

2005-03-01

to obtain a protocol customized to the needs of a specific setting, under control of an automated theorem proving system that can guarantee...new “compositional” method for protocol design and implementation, in which small microprotocols are combined to obtain a protocol customized to the...and Network Centric Enterprise (NCES) visions. This final report documents a wide range of contributions and technology transitions, including: A

NASA/SPAN and DOE/ESnet-DECnet transition strategy for DECnet OSI/phase 5

NASA Technical Reports Server (NTRS)

Porter, Linda; Demar, Phil

1991-01-01

The technical issues are examined involved with the transition of very large DECnet networks from DECnet phase IV protocols to DECnet OSI/Phase V protocols. The networks involved are the NASA's Science Internet (NSI-DECnet) and the DOE's Energy Science network (ESnet-DECnet). These networks, along with the many universities and research institutions connected to them, combine to form a single DECnet network containing more than 20,000 transitions and crossing numerous organizational boundaries. Discussion of transition planning, including decisions about Phase V naming, addressing, and routing are presented. Also discussed are transition issues related to the use of non-DEC routers in the network.

Medical Devices Transition to Information Systems: Lessons Learned

PubMed Central

Charters, Kathleen G.

2012-01-01

Medical devices designed to network can share data with a Clinical Information System (CIS), making that data available within clinician workflow. Some lessons learned by transitioning anesthesia reporting and monitoring devices (ARMDs) on a local area network (LAN) to integration of anesthesia documentation within a CIS include the following categories: access, contracting, deployment, implementation, planning, security, support, training and workflow integration. Areas identified for improvement include: Vendor requirements for access reconciled with the organizations’ security policies and procedures. Include clauses supporting transition from stand-alone devices to information integrated into clinical workflow in the medical device procurement contract. Resolve deployment and implementation barriers that make the process less efficient and more costly. Include effective field communication and creative alternatives in planning. Build training on the baseline knowledge of trainees. Include effective help desk processes and metrics. Have a process for determining where problems originate when systems share information. PMID:24199054

Quantifying the underlying landscape and paths of cancer

PubMed Central

Li, Chunhe; Wang, Jin

2014-01-01

Cancer is a disease regulated by the underlying gene networks. The emergence of normal and cancer states as well as the transformation between them can be thought of as a result of the gene network interactions and associated changes. We developed a global potential landscape and path framework to quantify cancer and associated processes. We constructed a cancer gene regulatory network based on the experimental evidences and uncovered the underlying landscape. The resulting tristable landscape characterizes important biological states: normal, cancer and apoptosis. The landscape topography in terms of barrier heights between stable state attractors quantifies the global stability of the cancer network system. We propose two mechanisms of cancerization: one is by the changes of landscape topography through the changes in regulation strengths of the gene networks. The other is by the fluctuations that help the system to go over the critical barrier at fixed landscape topography. The kinetic paths from least action principle quantify the transition processes among normal state, cancer state and apoptosis state. The kinetic rates provide the quantification of transition speeds among normal, cancer and apoptosis attractors. By the global sensitivity analysis of the gene network parameters on the landscape topography, we uncovered some key gene regulations determining the transitions between cancer and normal states. This can be used to guide the design of new anti-cancer tactics, through cocktail strategy of targeting multiple key regulation links simultaneously, for preventing cancer occurrence or transforming the early cancer state back to normal state. PMID:25232051

Identifying critical transitions and their leading biomolecular networks in complex diseases.

PubMed

Liu, Rui; Li, Meiyi; Liu, Zhi-Ping; Wu, Jiarui; Chen, Luonan; Aihara, Kazuyuki

2012-01-01

Identifying a critical transition and its leading biomolecular network during the initiation and progression of a complex disease is a challenging task, but holds the key to early diagnosis and further elucidation of the essential mechanisms of disease deterioration at the network level. In this study, we developed a novel computational method for identifying early-warning signals of the critical transition and its leading network during a disease progression, based on high-throughput data using a small number of samples. The leading network makes the first move from the normal state toward the disease state during a transition, and thus is causally related with disease-driving genes or networks. Specifically, we first define a state-transition-based local network entropy (SNE), and prove that SNE can serve as a general early-warning indicator of any imminent transitions, regardless of specific differences among systems. The effectiveness of this method was validated by functional analysis and experimental data.

Analysis of Feeder Bus Network Design and Scheduling Problems

PubMed Central

Almasi, Mohammad Hadi; Karim, Mohamed Rehan

2014-01-01

A growing concern for public transit is its inability to shift passenger's mode from private to public transport. In order to overcome this problem, a more developed feeder bus network and matched schedules will play important roles. The present paper aims to review some of the studies performed on Feeder Bus Network Design and Scheduling Problem (FNDSP) based on three distinctive parts of the FNDSP setup, namely, problem description, problem characteristics, and solution approaches. The problems consist of different subproblems including data preparation, feeder bus network design, route generation, and feeder bus scheduling. Subsequently, descriptive analysis and classification of previous works are presented to highlight the main characteristics and solution methods. Finally, some of the issues and trends for future research are identified. This paper is targeted at dealing with the FNDSP to exhibit strategic and tactical goals and also contributes to the unification of the field which might be a useful complement to the few existing reviews. PMID:24526890

Comparative empirical analysis of flow-weighted transit route networks in R-space and evolution modeling

NASA Astrophysics Data System (ADS)

Huang, Ailing; Zang, Guangzhi; He, Zhengbing; Guan, Wei

2017-05-01

Urban public transit system is a typical mixed complex network with dynamic flow, and its evolution should be a process coupling topological structure with flow dynamics, which has received little attention. This paper presents the R-space to make a comparative empirical analysis on Beijing’s flow-weighted transit route network (TRN) and we found that both the Beijing’s TRNs in the year of 2011 and 2015 exhibit the scale-free properties. As such, we propose an evolution model driven by flow to simulate the development of TRNs with consideration of the passengers’ dynamical behaviors triggered by topological change. The model simulates that the evolution of TRN is an iterative process. At each time step, a certain number of new routes are generated driven by travel demands, which leads to dynamical evolution of new routes’ flow and triggers perturbation in nearby routes that will further impact the next round of opening new routes. We present the theoretical analysis based on the mean-field theory, as well as the numerical simulation for this model. The results obtained agree well with our empirical analysis results, which indicate that our model can simulate the TRN evolution with scale-free properties for distributions of node’s strength and degree. The purpose of this paper is to illustrate the global evolutional mechanism of transit network that will be used to exploit planning and design strategies for real TRNs.

Review on Selection and Suitability of Rail Transit Station Design Pertaining to Public Safety

NASA Astrophysics Data System (ADS)

Akabal, Farah Mohd; Masirin, Mohd Idrus Haji Mohd; Abidin Akasah, Zainal; Rohani, Munzilah Md

2017-08-01

Railway has emerged as a fast, convenient, safe, clean, and low-cost alternative to air and road transportation. Many countries have invested in rail transportation. In America, Europe and Asia, large investments are planned for rail transportation. This is because congestion problems can be reduced with the introduction of rail transportation. Rail transportation involves several components which are important to ensure the smooth and safe delivery of services such as locomotives, rail stations and railway tracks. Rail transit stations are places where trains stop to pick-up and drop-off passengers. Stations are vital for many to enable them to engage in work and social commitments. This paper focuses only on the rail transit station as it is one of the important components in rail transportation. It is also considered as a key public meeting place and space for interactions in a community. The role of rail transit station and the requirements of a good rail transit station are also described in this paper. Steps in selecting the location of rail transit station include the function and facilities in rail transit station are discussed with reference to best practices and handbooks. Selection of the appropriate rail transit station locations may help users indirectly. In addition, this paper will also elucidate on the design considerations for an efficient and effective rail transit station. Design selections for the rail transit station must be balanced between aesthetic value and functional efficiency. The right design selection may help conserve energy, assure and facilitate consumers even thought a rail transit station plays a smaller role in attracting consumers compared to a shopping complex or a residential building. This will contribute towards better and greener building for a green transportation facility. Thus, with this paper it is expected to assist the relevant authority to identify important elements in the selection and determination of suitable rail transit station design for the future. It is also important to ensure the design is appropriate from the selection and suitability perspective as design and operation will assist to facilitate the success of the national rail network and encourage the public to use rail transit system. A conducive and neatly design railway station will not only add to the passenger experience but also, as a supporting facility to the economic, social and environmental benefits of the rail industry.

Cascading failure in scale-free networks with tunable clustering

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Mapping networks of light-dark transition in LOV photoreceptors.

PubMed

Kaur Grewal, Rajdeep; Mitra, Devrani; Roy, Soumen

2015-11-15

In optogenetics, designing modules of long or short signaling state lifetime is necessary for control over precise cellular events. A critical parameter for designing artificial or synthetic photoreceptors is the signaling state lifetime of photosensor modules. Design and engineering of biologically relevant artificial photoreceptors is based on signaling mechanisms characteristic of naturally occurring photoreceptors. Therefore identifying residues important for light-dark transition is a definite first step towards rational design of synthetic photoreceptors. A thorough grasp of detailed mechanisms of photo induced signaling process would be immensely helpful in understanding the behaviour of organisms. Herein, we introduce the technique of differential networks. We identify key biological interactions, using light-oxygen-voltage domains of all organisms whose dark and light state crystal structures are simultaneously available. Even though structural differences between dark and light states are subtle (other than the covalent bond formation between flavin chromophore and active site Cysteine), our results successfully capture functionally relevant residues and are in complete agreement with experimental findings from literature. Additionally, using sequence-structure alignments, we predict functional significance of interactions found to be important from network perspective yet awaiting experimental validation. Our approach would not only help in minimizing extensive photo-cycle kinetics procedure but is also helpful in providing first-hand information on the fundamentals of photo-adaptation and rational design of synthetic photoreceptors in optogenetics. devrani.dbs@presiuniv.ac.in or soumen@jcbose.ac.in Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Passenger flow analysis of Beijing urban rail transit network using fractal approach

NASA Astrophysics Data System (ADS)

Li, Xiaohong; Chen, Peiwen; Chen, Feng; Wang, Zijia

2018-04-01

To quantify the spatiotemporal distribution of passenger flow and the characteristics of an urban rail transit network, we introduce four radius fractal dimensions and two branch fractal dimensions by combining a fractal approach with passenger flow assignment model. These fractal dimensions can numerically describe the complexity of passenger flow in the urban rail transit network and its change characteristics. Based on it, we establish a fractal quantification method to measure the fractal characteristics of passenger follow in the rail transit network. Finally, we validate the reasonability of our proposed method by using the actual data of Beijing subway network. It has been shown that our proposed method can effectively measure the scale-free range of the urban rail transit network, network development and the fractal characteristics of time-varying passenger flow, which further provides a reference for network planning and analysis of passenger flow.

Cytoskeletal Network Morphology Regulates Intracellular Transport Dynamics.

PubMed

Ando, David; Korabel, Nickolay; Huang, Kerwyn Casey; Gopinathan, Ajay

2015-10-20

Intracellular transport is essential for maintaining proper cellular function in most eukaryotic cells, with perturbations in active transport resulting in several types of disease. Efficient delivery of critical cargos to specific locations is accomplished through a combination of passive diffusion and active transport by molecular motors that ballistically move along a network of cytoskeletal filaments. Although motor-based transport is known to be necessary to overcome cytoplasmic crowding and the limited range of diffusion within reasonable timescales, the topological features of the cytoskeletal network that regulate transport efficiency and robustness have not been established. Using a continuum diffusion model, we observed that the time required for cellular transport was minimized when the network was localized near the nucleus. In simulations that explicitly incorporated network spatial architectures, total filament mass was the primary driver of network transit times. However, filament traps that redirect cargo back to the nucleus caused large variations in network transport. Filament polarity was more important than filament orientation in reducing average transit times, and transport properties were optimized in networks with intermediate motor on and off rates. Our results provide important insights into the functional constraints on intracellular transport under which cells have evolved cytoskeletal structures, and have potential applications for enhancing reactions in biomimetic systems through rational transport network design. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Formation of metallic cation-oxygen network for anomalous thermal expansion coefficients in binary phosphate glass

NASA Astrophysics Data System (ADS)

Onodera, Yohei; Kohara, Shinji; Masai, Hirokazu; Koreeda, Akitoshi; Okamura, Shun; Ohkubo, Takahiro

2017-05-01

Understanding glass structure is still challenging due to the result of disorder, although novel materials design on the basis of atomistic structure has been strongly demanded. Here we report on the atomic structures of the zinc phosphate glass determined by reverse Monte Carlo modelling based on diffraction and spectroscopic data. The zinc-rich glass exhibits the network formed by ZnOx (averaged x<4) polyhedra. Although the elastic modulus, refractive index and glass transition temperature of the zinc phosphate glass monotonically increase with the amount of ZnO, we find for the first time that the thermal expansion coefficient is very sensitive to the substitution of the phosphate chain network by a network consisting of Zn-O units in zinc-rich glass. Our results imply that the control of the structure of intermediate groups may enable new functionalities in the design of oxide glass materials.

Formation of metallic cation-oxygen network for anomalous thermal expansion coefficients in binary phosphate glass.

PubMed

Onodera, Yohei; Kohara, Shinji; Masai, Hirokazu; Koreeda, Akitoshi; Okamura, Shun; Ohkubo, Takahiro

2017-05-31

Understanding glass structure is still challenging due to the result of disorder, although novel materials design on the basis of atomistic structure has been strongly demanded. Here we report on the atomic structures of the zinc phosphate glass determined by reverse Monte Carlo modelling based on diffraction and spectroscopic data. The zinc-rich glass exhibits the network formed by ZnO x (averaged x<4) polyhedra. Although the elastic modulus, refractive index and glass transition temperature of the zinc phosphate glass monotonically increase with the amount of ZnO, we find for the first time that the thermal expansion coefficient is very sensitive to the substitution of the phosphate chain network by a network consisting of Zn-O units in zinc-rich glass. Our results imply that the control of the structure of intermediate groups may enable new functionalities in the design of oxide glass materials.

Detecting phase transitions in a neural network and its application to classification of syndromes in traditional Chinese medicine

NASA Astrophysics Data System (ADS)

Chen, J.; Xi, G.; Wang, W.

2008-02-01

Detecting phase transitions in neural networks (determined or random) presents a challenging subject for phase transitions play a key role in human brain activity. In this paper, we detect numerically phase transitions in two types of random neural network(RNN) under proper parameters.

Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

PubMed

Gong, Yubing; Wang, Baoying; Xie, Huijuan

2016-12-01

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate A p of STDP and become strongest at a certain A p value, and the A p value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when A p increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Mechanical Metamaterials with Negative Compressibility Transitions

NASA Astrophysics Data System (ADS)

Motter, Adilson

2015-03-01

When tensioned, ordinary materials expand along the direction of the applied force. In this presentation, I will explore network concepts to design metamaterials exhibiting negative compressibility transitions, during which the material undergoes contraction when tensioned (or expansion when pressured). Such transitions, which are forbidden in thermodynamic equilibrium, are possible during the decay of metastable, super-strained states. I will introduce a statistical physics theory for negative compressibility transitions, derive a first-principles model to predict these transitions, and present a validation of the model using molecular dynamics simulations. Aside from its immediate mechanical implications, our theory points to a wealth of analogous inverted responses, such as inverted susceptibility or heat-capacity transitions, allowed when considering realistic scales. This research was done in collaboration with Zachary Nicolaou, and was supported by the National Science Foundation and the Alfred P. Sloan Foundation.

Network traffic behaviour near phase transition point

NASA Astrophysics Data System (ADS)

Lawniczak, A. T.; Tang, X.

2006-03-01

We explore packet traffic dynamics in a data network model near phase transition point from free flow to congestion. The model of data network is an abstraction of the Network Layer of the OSI (Open Systems Interconnect) Reference Model of packet switching networks. The Network Layer is responsible for routing packets across the network from their sources to their destinations and for control of congestion in data networks. Using the model we investigate spatio-temporal packets traffic dynamics near the phase transition point for various network connection topologies, and static and adaptive routing algorithms. We present selected simulation results and analyze them.

Multisector Health Policy Networks in 15 Large US Cities

PubMed Central

Leider, J. P.; Carothers, Bobbi J.; Castrucci, Brian C.; Hearne, Shelley

2016-01-01

Context: Local health departments (LHDs) have historically not prioritized policy development, although it is one of the 3 core areas they address. One strategy that may influence policy in LHD jurisdictions is the formation of partnerships across sectors to work together on local public health policy. Design: We used a network approach to examine LHD local health policy partnerships across 15 large cities from the Big Cities Health Coalition. Setting/Participants: We surveyed the health departments and their partners about their working relationships in 5 policy areas: core local funding, tobacco control, obesity and chronic disease, violence and injury prevention, and infant mortality. Outcome Measures: Drawing on prior literature linking network structures with performance, we examined network density, transitivity, centralization and centrality, member diversity, and assortativity of ties. Results: Networks included an average of 21.8 organizations. Nonprofits and government agencies made up the largest proportions of the networks, with 28.8% and 21.7% of network members, whereas for-profits and foundations made up the smallest proportions in all of the networks, with just 1.2% and 2.4% on average. Mean values of density, transitivity, diversity, assortativity, centralization, and centrality showed similarity across policy areas and most LHDs. The tobacco control and obesity/chronic disease networks were densest and most diverse, whereas the infant mortality policy networks were the most centralized and had the highest assortativity. Core local funding policy networks had lower scores than other policy area networks by most network measures. Conclusion: Urban LHDs partner with organizations from diverse sectors to conduct local public health policy work. Network structures are similar across policy areas jurisdictions. Obesity and chronic disease, tobacco control, and infant mortality networks had structures consistent with higher performing networks, whereas core local funding networks had structures consistent with lower performing networks. PMID:26910868

Searching for exoplanets using artificial intelligence

NASA Astrophysics Data System (ADS)

Pearson, Kyle A.; Palafox, Leon; Griffith, Caitlin A.

2018-02-01

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.

AdaNET research project

NASA Technical Reports Server (NTRS)

Digman, R. Michael

1988-01-01

The components necessary for the success of the commercialization of an Ada Technology Transition Network are reported in detail. The organizational plan presents the planned structure for services development and technical transition of AdaNET services to potential user communities. The Business Plan is the operational plan for the AdaNET service as a commercial venture. The Technical Plan is the plan from which the AdaNET can be designed including detailed requirements analysis. Also contained is an analysis of user fees and charges, and a proposed user fee schedule.

Explosive synchronization transitions in complex neural networks.

PubMed

Chen, Hanshuang; He, Gang; Huang, Feng; Shen, Chuansheng; Hou, Zhonghuai

2013-09-01

It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes et al. Phys. Rev. Lett. 106, 128701 (2011)] and chaotic oscillators [Leyva et al. Phys. Rev. Lett. 108, 168702 (2012)]. Here, we investigate the effect of a microscopic correlation between the dynamics and the interacting topology of coupled FitzHugh-Nagumo oscillators on phase synchronization transition in Barabási-Albert (BA) scale-free networks and Erdös-Rényi (ER) random networks. We show that, if natural frequencies of the oscillations are positively correlated with node degrees and the width of the frequency distribution is larger than a threshold value, a strong hysteresis loop arises in the synchronization diagram of BA networks, indicating the evidence of an explosive transition towards synchronization of relaxation oscillators system. In contrast to the results in BA networks, in more homogeneous ER networks, the synchronization transition is always of continuous type regardless of the width of the frequency distribution. Moreover, we consider the effect of degree-mixing patterns on the nature of the synchronization transition, and find that the degree assortativity is unfavorable for the occurrence of such an explosive transition.

Explosive synchronization transitions in complex neural networks

NASA Astrophysics Data System (ADS)

Chen, Hanshuang; He, Gang; Huang, Feng; Shen, Chuansheng; Hou, Zhonghuai

2013-09-01

It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes et al. Phys. Rev. Lett. 106, 128701 (2011)] and chaotic oscillators [Leyva et al. Phys. Rev. Lett. 108, 168702 (2012)]. Here, we investigate the effect of a microscopic correlation between the dynamics and the interacting topology of coupled FitzHugh-Nagumo oscillators on phase synchronization transition in Barabási-Albert (BA) scale-free networks and Erdös-Rényi (ER) random networks. We show that, if natural frequencies of the oscillations are positively correlated with node degrees and the width of the frequency distribution is larger than a threshold value, a strong hysteresis loop arises in the synchronization diagram of BA networks, indicating the evidence of an explosive transition towards synchronization of relaxation oscillators system. In contrast to the results in BA networks, in more homogeneous ER networks, the synchronization transition is always of continuous type regardless of the width of the frequency distribution. Moreover, we consider the effect of degree-mixing patterns on the nature of the synchronization transition, and find that the degree assortativity is unfavorable for the occurrence of such an explosive transition.

Transitions in Smokers’ Social Networks After Quit Attempts: A Latent Transition Analysis

PubMed Central

Smith, Rachel A.; Piper, Megan E.; Roberts, Linda J.; Baker, Timothy B.

2016-01-01

Introduction: Smokers’ social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers’ social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Methods: Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members’ smoking habits, within network smoking, smoking buddies, and romantic partners’ smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Results: Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants’ social networks to less contact with smokers and larger networks in years 2 and 3. Conclusions: In the years following a smoking-cessation attempt, smokers’ social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Implications: Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person’s social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. PMID:27613925

Design Considerations: Falcon M Dwarf Habitable Exoplanet Survey

NASA Astrophysics Data System (ADS)

Polsgrove, Daniel; Novotny, Steven; Della-Rose, Devin J.; Chun, Francis; Tippets, Roger; O'Shea, Patrick; Miller, Matthew

2016-01-01

The Falcon Telescope Network (FTN) is an assemblage of twelve automated 20-inch telescopes positioned around the globe, controlled from the Cadet Space Operations Center (CSOC) at the US Air Force Academy (USAFA) in Colorado Springs, Colorado. Five of the 12 sites are currently installed, with full operational capability expected by the end of 2016. Though optimized for studying near-earth objects to accomplish its primary mission of Space Situational Awareness (SSA), the Falcon telescopes are in many ways similar to those used by ongoing and planned exoplanet transit surveys targeting individual M dwarf stars (e.g., MEarth, APACHE, SPECULOOS). The network's worldwide geographic distribution provides additional potential advantages. We have performed analytical and empirical studies exploring the viability of employing the FTN for a future survey of nearby late-type M dwarfs tailored to detect transits of 1-2REarth exoplanets in habitable-zone orbits . We present empirical results on photometric precision derived from data collected with multiple Falcon telescopes on a set of nearby (< 25 pc) M dwarfs using infrared filters and a range of exposure times, as well as sample light curves created from images gathered during known transits of varying transit depths. An investigation of survey design parameters is also described, including an analysis of site-specific weather data, anticipated telescope time allocation and the percentage of nearby M dwarfs with sufficient check stars within the Falcons' 11' x 11' field-of-view required to perform effective differential photometry. The results of this ongoing effort will inform the likelihood of discovering one (or more) habitable-zone exoplanets given current occurrence rate estimates over a nominal five-year campaign, and will dictate specific survey design features in preparation for initiating project execution when the FTN begins full-scale automated operations.

Blackmail propagation on small-world networks

NASA Astrophysics Data System (ADS)

Shao, Zhi-Gang; Jian-Ping Sang; Zou, Xian-Wu; Tan, Zhi-Jie; Jin, Zhun-Zhi

2005-06-01

The dynamics of the blackmail propagation model based on small-world networks is investigated. It is found that for a given transmitting probability λ the dynamical behavior of blackmail propagation transits from linear growth type to logistical growth one with the network randomness p increases. The transition takes place at the critical network randomness pc=1/N, where N is the total number of nodes in the network. For a given network randomness p the dynamical behavior of blackmail propagation transits from exponential decrease type to logistical growth one with the transmitting probability λ increases. The transition occurs at the critical transmitting probability λc=1/, where is the average number of the nearest neighbors. The present work will be useful for understanding computer virus epidemics and other spreading phenomena on communication and social networks.

Modulation of the brain's functional network architecture in the transition from wake to sleep

PubMed Central

Larson-Prior, Linda J.; Power, Jonathan D.; Vincent, Justin L.; Nolan, Tracy S.; Coalson, Rebecca S.; Zempel, John; Snyder, Abraham Z.; Schlaggar, Bradley L.; Raichle, Marcus E.; Petersen, Steven E.

2013-01-01

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes. PMID:21854969

Correlating Free-Volume Hole Distribution to the Glass Transition Temperature of Epoxy Polymers.

PubMed

Aramoon, Amin; Breitzman, Timothy D; Woodward, Christopher; El-Awady, Jaafar A

2017-09-07

A new algorithm is developed to quantify the free-volume hole distribution and its evolution in coarse-grained molecular dynamics simulations of polymeric networks. This is achieved by analyzing the geometry of the network rather than a voxelized image of the structure to accurately and efficiently find and quantify free-volume hole distributions within large scale simulations of polymer networks. The free-volume holes are quantified by fitting the largest ellipsoids and spheres in the free-volumes between polymer chains. The free-volume hole distributions calculated from this algorithm are shown to be in excellent agreement with those measured from positron annihilation lifetime spectroscopy (PALS) experiments at different temperature and pressures. Based on the results predicted using this algorithm, an evolution model is proposed for the thermal behavior of an individual free-volume hole. This model is calibrated such that the average radius of free-volumes holes mimics the one predicted from the simulations. The model is then employed to predict the glass-transition temperature of epoxy polymers with different degrees of cross-linking and lengths of prepolymers. Comparison between the predicted glass-transition temperatures and those measured from simulations or experiments implies that this model is capable of successfully predicting the glass-transition temperature of the material using only a PDF of the initial free-volume holes radii of each microstructure. This provides an effective approach for the optimized design of polymeric systems on the basis of the glass-transition temperature, degree of cross-linking, and average length of prepolymers.

Emergence of clustering in an acquaintance model without homophily

NASA Astrophysics Data System (ADS)

Bhat, Uttam; Krapivsky, P. L.; Redner, S.

2014-11-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

Design of activated serine-containing catalytic triads with atomic level accuracy

PubMed Central

Rajagopalan, Sridharan; Wang, Chu; Yu, Kai; Kuzin, Alexandre P.; Richter, Florian; Lew, Scott; Miklos, Aleksandr E.; Matthews, Megan L.; Seetharaman, Jayaraman; Su, Min; Hunt, John. F.; Cravatt, Benjamin F.; Baker, David

2014-01-01

A challenge in the computational design of enzymes is that multiple properties must be simultaneously optimized -- substrate-binding, transition state stabilization, and product release -- and this has limited the absolute activity of successful designs. Here, we focus on a single critical property of many enzymes: the nucleophilicity of an active site residue that initiates catalysis. We design proteins with idealized serine-containing catalytic triads, and assess their nucleophilicity directly in native biological systems using activity-based organophosphate probes. Crystal structures of the most successful designs show unprecedented agreement with computational models, including extensive hydrogen bonding networks between the catalytic triad (or quartet) residues, and mutagenesis experiments demonstrate that these networks are critical for serine activation and organophosphate-reactivity. Following optimization by yeast-display, the designs react with organophosphate probes at rates comparable to natural serine hydrolases. Co-crystal structures with diisopropyl fluorophosphate bound to the serine nucleophile suggest the designs could provide the basis for a new class of organophosphate captures agents. PMID:24705591

Phase transitions in semisupervised clustering of sparse networks

NASA Astrophysics Data System (ADS)

Zhang, Pan; Moore, Cristopher; Zdeborová, Lenka

2014-11-01

Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology. However, if we know the correct labels of some fraction α of the nodes, we can do better. We study the phase diagram of this semisupervised learning problem for networks generated by the stochastic block model. We use the cavity method and the associated belief propagation algorithm to study what accuracy can be achieved as a function of α . For k =2 groups, we find that the detectability transition disappears for any α >0 , in agreement with previous work. For larger k where a hard but detectable regime exists, we find that the easy/hard transition (the point at which efficient algorithms can do better than chance) becomes a line of transitions where the accuracy jumps discontinuously at a critical value of α . This line ends in a critical point with a second-order transition, beyond which the accuracy is a continuous function of α . We demonstrate qualitatively similar transitions in two real-world networks.

Information cascade on networks

NASA Astrophysics Data System (ADS)

Hisakado, Masato; Mori, Shintaro

2016-05-01

In this paper, we discuss a voting model by considering three different kinds of networks: a random graph, the Barabási-Albert (BA) model, and a fitness model. A voting model represents the way in which public perceptions are conveyed to voters. Our voting model is constructed by using two types of voters-herders and independents-and two candidates. Independents conduct voting based on their fundamental values; on the other hand, herders base their voting on the number of previous votes. Hence, herders vote for the majority candidates and obtain information relating to previous votes from their networks. We discuss the difference between the phases on which the networks depend. Two kinds of phase transitions, an information cascade transition and a super-normal transition, were identified. The first of these is a transition between a state in which most voters make the correct choices and a state in which most of them are wrong. The second is a transition of convergence speed. The information cascade transition prevails when herder effects are stronger than the super-normal transition. In the BA and fitness models, the critical point of the information cascade transition is the same as that of the random network model. However, the critical point of the super-normal transition disappears when these two models are used. In conclusion, the influence of networks is shown to only affect the convergence speed and not the information cascade transition. We are therefore able to conclude that the influence of hubs on voters' perceptions is limited.

Transition to synchrony in degree-frequency correlated Sakaguchi-Kuramoto model

NASA Astrophysics Data System (ADS)

Kundu, Prosenjit; Khanra, Pitambar; Hens, Chittaranjan; Pal, Pinaki

2017-11-01

We investigate transition to synchrony in degree-frequency correlated Sakaguchi-Kuramoto (SK) model on complex networks both analytically and numerically. We analytically derive self-consistent equations for group angular velocity and order parameter for the model in the thermodynamic limit. Using the self-consistent equations we investigate transition to synchronization in SK model on uncorrelated scale-free (SF) and Erdős-Rényi (ER) networks in detail. Depending on the degree distribution exponent (γ ) of SF networks and phase-frustration parameter, the population undergoes from first-order transition [explosive synchronization (ES)] to second-order transition and vice versa. In ER networks transition is always second order irrespective of the values of the phase-lag parameter. We observe that the critical coupling strength for the onset of synchronization is decreased by phase-frustration parameter in case of SF network where as in ER network, the phase-frustration delays the onset of synchronization. Extensive numerical simulations using SF and ER networks are performed to validate the analytical results. An analytical expression of critical coupling strength for the onset of synchronization is also derived from the self-consistent equations considering the vanishing order parameter limit.

A Polarization Reconfigurable Slot Antenna with a Novel Switchable Feeding Network

NASA Astrophysics Data System (ADS)

Xie, Peng; Wang, Guang Ming

2017-12-01

A polarization reconfigurable slot antenna is proposed in this paper. The antenna consists of a microstrip line-to-slotline transition structure, two radiation slots and a switchable feeding network. The feeding network is a gradually changed ring slot with six switching diodes on it. By controlling the diodes states, the antenna can generate y-direction polarization, z-direction polarization, left-hand circular polarization and right-hand circular polarization. Detailed design considerations of the proposed antenna, simulated and measured results are presented and discussed. Measured results agree well with simulated. The results proved that the antenna can realize polarization reconfiguration effectively at 5 GHz.

Transitions in Smokers' Social Networks After Quit Attempts: A Latent Transition Analysis.

PubMed

Bray, Bethany C; Smith, Rachel A; Piper, Megan E; Roberts, Linda J; Baker, Timothy B

2016-12-01

Smokers' social networks vary in size, composition, and amount of exposure to smoking. The extent to which smokers' social networks change after a quit attempt is unknown, as is the relation between quitting success and later network changes. Unique types of social networks for 691 smokers enrolled in a smoking-cessation trial were identified based on network size, new network members, members' smoking habits, within network smoking, smoking buddies, and romantic partners' smoking. Latent transition analysis was used to identify the network classes and to predict transitions in class membership across 3 years from biochemically assessed smoking abstinence. Five network classes were identified: Immersed (large network, extensive smoking exposure including smoking buddies), Low Smoking Exposure (large network, minimal smoking exposure), Smoking Partner (small network, smoking exposure primarily from partner), Isolated (small network, minimal smoking exposure), and Distant Smoking Exposure (small network, considerable nonpartner smoking exposure). Abstinence at years 1 and 2 was associated with shifts in participants' social networks to less contact with smokers and larger networks in years 2 and 3. In the years following a smoking-cessation attempt, smokers' social networks changed, and abstinence status predicted these changes. Networks defined by high levels of exposure to smokers were especially associated with continued smoking. Abstinence, however, predicted transitions to larger social networks comprising less smoking exposure. These results support treatments that aim to reduce exposure to smoking cues and smokers, including partners who smoke. Prior research has shown that social network features predict the likelihood of subsequent smoking cessation. The current research illustrates how successful quitting predicts social network change over 3 years following a quit attempt. Specifically, abstinence predicts transitions to networks that are larger and afford less exposure to smokers. This suggests that quitting smoking may expand a person's social milieu rather than narrow it. This effect, plus reduced exposure to smokers, may help sustain abstinence. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Exhaustive analysis of the modular structure of the spliceosomal assembly network: a petri net approach.

PubMed

Bortfeldt, Ralf H; Schuster, Stefan; Koch, Ina

2011-01-01

Spliceosomes are macro-complexes involving hundreds of proteins with many functional interactions. Spliceosome assembly belongs to the key processes that enable splicing of mRNA and modulate alternative splicing. A detailed list of factors involved in spliceosomal reactions has been assorted over the past decade, but, their functional interplay is often unknown and most of the present biological models cover only parts of the complete assembly process. It is a challenging task to build a computational model that integrates dispersed knowledge and combines a multitude of reaction schemes proposed earlier. Because for most reactions involved in spliceosome assembly kinetic parameters are not available, we propose a discrete modeling using Petri nets, through which we are enabled to get insights into the system's behavior via computation of structural and dynamic properties. In this paper, we compile and examine reactions from experimental reports that contribute to a functional spliceosome. All these reactions form a network, which describes the inventory and conditions necessary to perform the splicing process. The analysis is mainly based on system invariants. Transition invariants (T-invariants) can be interpreted as signaling routes through the network. Due to the huge number of T-invariants that arise with increasing network size and complexity, maximal common transition sets (MCTS) and T-clusters were used for further analysis. Additionally, we introduce a false color map representation, which allows a quick survey of network modules and the visual detection of single reactions or reaction sequences, which participate in more than one signaling route. We designed a structured model of spliceosome assembly, which combines the demands on a platform that i) can display involved factors and concurrent processes, ii) offers the possibility to run computational methods for knowledge extraction, and iii) is successively extendable as new insights into spliceosome function are reported by experimental reports. The network consists of 161 transitions (reactions) and 140 places (reactants). All reactions are part of at least one of the 71 T-invariants. These T-invariants define pathways, which are in good agreement with the current knowledge and known hypotheses on reaction sequences during spliceosome assembly, hence contributing to a functional spliceosome. We demonstrate that present knowledge, in particular of the initial part of the assembly process, describes parallelism and interaction of signaling routes, which indicate functional redundancy and reflect the dependency of spliceosome assembly initiation on different cellular conditions. The complexity of the network is further increased by two switches, which introduce alternative routes during A-complex formation in early spliceosome assembly and upon transition from the B-complex to the C-complex. By compiling known reactions into a complete network, the combinatorial nature of invariant computation leads to pathways that have previously not been described as connected routes, although their constituents were known. T-clusters divide the network into modules, which we interpret as building blocks in spliceosome maturation. We conclude that Petri net representations of large biological networks and system invariants, are well-suited as a means for validating the integration of experimental knowledge into a consistent model. Based on this network model, the design of further experiments is facilitated.

Exhaustive analysis of the modular structure of the spliceosomal assembly network: a Petri net approach.

PubMed

Bortfeldt, Ralf H; Schuster, Stefan; Koch, Ina

2010-01-01

Spliceosomes are macro-complexes involving hundreds of proteins with many functional interactions. Spliceosome assembly belongs to the key processes that enable splicing of mRNA and modulate alternative splicing. A detailed list of factors involved in spliceosomal reactions has been assorted over the past decade, but, their functional interplay is often unknown and most of the present biological models cover only parts of the complete assembly process. It is a challenging task to build a computational model that integrates dispersed knowledge and combines a multitude of reaction schemes proposed earlier.Because for most reactions involved in spliceosome assembly kinetic parameters are not available, we propose a discrete modeling using Petri nets, through which we are enabled to get insights into the system's behavior via computation of structural and dynamic properties. In this paper, we compile and examine reactions from experimental reports that contribute to a functional spliceosome. All these reactions form a network, which describes the inventory and conditions necessary to perform the splicing process. The analysis is mainly based on system invariants. Transition invariants (T-invariants) can be interpreted as signaling routes through the network. Due to the huge number of T-invariants that arise with increasing network size and complexity, maximal common transition sets (MCTS) and T-clusters were used for further analysis. Additionally, we introduce a false color map representation, which allows a quick survey of network modules and the visual detection of single reactions or reaction sequences, which participate in more than one signaling route. We designed a structured model of spliceosome assembly, which combines the demands on a platform that i) can display involved factors and concurrent processes, ii) offers the possibility to run computational methods for knowledge extraction, and iii) is successively extendable as new insights into spliceosome function are reported by experimental reports. The network consists of 161 transitions (reactions) and 140 places (reactants). All reactions are part of at least one of the 71 T-invariants. These T-invariants define pathways, which are in good agreement with the current knowledge and known hypotheses on reaction sequences during spliceosome assembly, hence contributing to a functional spliceosome. We demonstrate that present knowledge, in particular of the initial part of the assembly process, describes parallelism and interaction of signaling routes, which indicate functional redundancy and reflect the dependency of spliceosome assembly initiation on different cellular conditions. The complexity of the network is further increased by two switches, which introduce alternative routes during A-complex formation in early spliceosome assembly and upon transition from the B-complex to the C-complex. By compiling known reactions into a complete network, the combinatorial nature of invariant computation leads to pathways that have previously not been described as connected routes, although their constituents were known. T-clusters divide the network into modules, which we interpret as building blocks in spliceosome maturation. We conclude that Petri net representations of large biological networks and system invariants, are well-suited as a means for validating the integration of experimental knowledge into a consistent model. Based on this network model, the design of further experiments is facilitated.

78 FR 43234 - Agency Information Collection Activities; Submission to OMB for Reinstatement, With Change, of a...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-19

...) administrator, the Financial Crimes Enforcement Network (FinCEN) transitioned from a system originally designed... forms. FinCEN's objective is to have one electronically-filed dynamic and interactive BSA-SAR that will... and oversight of supervised institutions. \\2\\ The Board of Governors of the Federal Reserve System...

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

NASA Astrophysics Data System (ADS)

Xie, Huijuan; Gong, Yubing; Wang, Baoying

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin-Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

A universal indicator of critical state transitions in noisy complex networked systems

PubMed Central

Liang, Junhao; Hu, Yanqing; Chen, Guanrong; Zhou, Tianshou

2017-01-01

Critical transition, a phenomenon that a system shifts suddenly from one state to another, occurs in many real-world complex networks. We propose an analytical framework for exactly predicting the critical transition in a complex networked system subjected to noise effects. Our prediction is based on the characteristic return time of a simple one-dimensional system derived from the original higher-dimensional system. This characteristic time, which can be easily calculated using network data, allows us to systematically separate the respective roles of dynamics, noise and topology of the underlying networked system. We find that the noise can either prevent or enhance critical transitions, playing a key role in compensating the network structural defect which suffers from either internal failures or environmental changes, or both. Our analysis of realistic or artificial examples reveals that the characteristic return time is an effective indicator for forecasting the sudden deterioration of complex networks. PMID:28230166

Firing patterns transition and desynchronization induced by time delay in neural networks

NASA Astrophysics Data System (ADS)

Huang, Shoufang; Zhang, Jiqian; Wang, Maosheng; Hu, Chin-Kun

2018-06-01

We used the Hindmarsh-Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.

Tuning stochastic transition rates in a bistable genetic network.

NASA Astrophysics Data System (ADS)

Chickarmane, Vijay; Peterson, Carsten

2009-03-01

We investigate the stochastic dynamics of a simple genetic network, a toggle switch, in which the system makes transitions between the two alternative states. Our interest is in exploring whether such stochastic transitions, which occur due to the intrinsic noise such as transcriptional and degradation events, can be slowed down/speeded up, without changing the mean expression levels of the two genes, which comprise the toggle network. Such tuning is achieved by linking a signaling network to the toggle switch. The signaling network comprises of a protein, which can exist either in an active (phosphorylated) or inactive (dephosphorylated) form, and where its state is determined by one of the genetic network components. The active form of the protein in turn feeds back on the dynamics of the genetic network. We find that the rate of stochastic transitions from one state to the other, is determined essentially by the speed of phosphorylation, and hence the rate can be modulated by varying the phosphatase levels. We hypothesize that such a network architecture can be implemented as a general mechanism for controlling transition rates and discuss applications in population studies of two differentiated cell lineages, ex: the myeloid/erythroid lineage in hematopoiesis.

Bootstrap percolation on spatial networks

NASA Astrophysics Data System (ADS)

Gao, Jian; Zhou, Tao; Hu, Yanqing

2015-10-01

Bootstrap percolation is a general representation of some networked activation process, which has found applications in explaining many important social phenomena, such as the propagation of information. Inspired by some recent findings on spatial structure of online social networks, here we study bootstrap percolation on undirected spatial networks, with the probability density function of long-range links’ lengths being a power law with tunable exponent. Setting the size of the giant active component as the order parameter, we find a parameter-dependent critical value for the power-law exponent, above which there is a double phase transition, mixed of a second-order phase transition and a hybrid phase transition with two varying critical points, otherwise there is only a second-order phase transition. We further find a parameter-independent critical value around -1, about which the two critical points for the double phase transition are almost constant. To our surprise, this critical value -1 is just equal or very close to the values of many real online social networks, including LiveJournal, HP Labs email network, Belgian mobile phone network, etc. This work helps us in better understanding the self-organization of spatial structure of online social networks, in terms of the effective function for information spreading.

Dynamics of Propofol-Induced Loss of Consciousness Across Primate Neocortex.

PubMed

Ishizawa, Yumiko; Ahmed, Omar J; Patel, Shaun R; Gale, John T; Sierra-Mercado, Demetrio; Brown, Emery N; Eskandar, Emad N

2016-07-20

The precise neural mechanisms underlying transitions between consciousness and anesthetic-induced unconsciousness remain unclear. Here, we studied intracortical neuronal dynamics leading to propofol-induced unconsciousness by recording single-neuron activity and local field potentials directly in the functionally interconnecting somatosensory (S1) and frontal ventral premotor (PMv) network during a gradual behavioral transition from full alertness to loss of consciousness (LOC) and on through a deeper anesthetic level. Macaque monkeys were trained for a behavioral task designed to determine the trial-by-trial alertness and neuronal response to tactile and auditory stimulation. We show that disruption of coherent beta oscillations between S1 and PMv preceded, but did not coincide with, the LOC. LOC appeared to correspond to pronounced but brief gamma-/high-beta-band oscillations (lasting ∼3 min) in PMv, followed by a gamma peak in S1. We also demonstrate that the slow oscillations appeared after LOC in S1 and then in PMv after a delay, together suggesting that neuronal dynamics are very different across S1 versus PMv during LOC. Finally, neurons in both S1 and PMv transition from responding to bimodal (tactile and auditory) stimulation before LOC to only tactile modality during unconsciousness, consistent with an inhibition of multisensory integration in this network. Our results show that propofol-induced LOC is accompanied by spatiotemporally distinct oscillatory neuronal dynamics across the somatosensory and premotor network and suggest that a transitional state from wakefulness to unconsciousness is not a continuous process, but rather a series of discrete neural changes. How information is processed by the brain during awake and anesthetized states and, crucially, during the transition is not clearly understood. We demonstrate that neuronal dynamics are very different within an interconnecting cortical network (primary somatosensory and frontal premotor area) during the loss of consciousness (LOC) induced by propofol in nonhuman primates. Coherent beta oscillations between these regions are disrupted before LOC. Pronounced but brief gamma-band oscillations appear to correspond to LOC. In addition, neurons in both of these cortices transition from responding to both tactile and auditory stimulation before LOC to only tactile modality during unconsciousness. We demonstrate that propofol-induced LOC is accompanied by spatiotemporally distinctive neuronal dynamics in this network with concurrent changes in multisensory processing. Copyright © 2016 the authors 0270-6474/16/367718-09$15.00/0.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

NASA Astrophysics Data System (ADS)

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-08-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure.

PubMed

Schleussner, Carl-Friedrich; Donges, Jonathan F; Engemann, Denis A; Levermann, Anders

2016-08-11

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

NASA Astrophysics Data System (ADS)

Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting

2016-10-01

Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.

Role of Distance-Based Routing in Traffic Dynamics on Mobile Networks

NASA Astrophysics Data System (ADS)

Yang, Han-Xin; Wang, Wen-Xu

2013-06-01

Despite of intensive investigations on transportation dynamics taking place on complex networks with fixed structures, a deep understanding of networks consisting of mobile nodes is challenging yet, especially the lacking of insight into the effects of routing strategies on transmission efficiency. We introduce a distance-based routing strategy for networks of mobile agents toward enhancing the network throughput and the transmission efficiency. We study the transportation capacity and delivering time of data packets associated with mobility and communication ability. Interestingly, we find that the transportation capacity is optimized at moderate moving speed, which is quite different from random routing strategy. In addition, both continuous and discontinuous transitions from free flow to congestions are observed. Degree distributions are explored in order to explain the enhancement of network throughput and other observations. Our work is valuable toward understanding complex transportation dynamics and designing effective routing protocols.

Protective and Risky Social Network Factors for Drinking During the Transition From High School to College.

PubMed

Meisel, Matthew K; Barnett, Nancy P

2017-11-01

The transition from high school to college is a unique developmental period to examine the relationship between social networks and alcohol use, because during this transition, students enter new environments and alcohol use becomes more pervasive. The aim of this study was to examine the extent to which personal social networks change during this transition and to examine how changes in the composition of networks are related to alcohol use. Participants (N = 374, 57.8% female) reported on their alcohol use and provided information about individuals in their social network before and immediately after their first year of college. These network members were matched across the two observations and were classified as either carryover (i.e., named at both assessments), dropped (i.e., named at only the first assessment), or added (i.e., named at only the second assessment). We found robust turnover, such that only 22% of network members were retained from the first observation to the second. Furthermore, heavy drinking in high school was associated with retaining more friends during the transition to college, but once in college, adding more heavy drinkers as friends was associated with the greatest alcohol risk. These findings show how changes in the composition of the social network influence an individual's alcohol use during the transition to college. Results from this study could be used to improve interventions that address the composition of the social network as a whole, as well as the characteristics of each individual in their social network.

Natural and Synthetic Biohydrogels Design, Characterization, Network Structure Imaging and Modeling

NASA Astrophysics Data System (ADS)

Marmorat, Clement

Biocompatible hydrogels can be derived from materials that are naturally obtained, such as proteins or polysaccharides, or synthetic, such as poloxamers. In order to be classified as biocompatible, these water-swollen networks can not trigger a toxic response once introduced into a biological or physiological environment and, therefore, must be immunoneutral. Hyaluronic acid hydrogels can be great candidates for tissue engineering applications as long as the cross-linking chemistry and process does not affect the biocompatibility of the natural protein matrix. Thermoreversible hydrogels have the advantage of undergoing a sol/gel phase transition at specific temperatures. Thus, they are excellent candidates for biomedical applications such as drug delivery systems, wound healing coatings or cellular scaffolds. Although these hydrogels can be used in their natural form without further modification or chemical alteration, the original protein or polymer matrix is often strengthened by the use of a crosslinking agent to achieve a specific set of properties. In the case of gelatin fibril formation at low temperatures or the micellization of triblock copolymers in solution with temperature increase, the natural phase transition is modified when crosslinkers are introduced to alter the biohydrogels properties and, ultimately, disturb the system's equilibrium. By using spectroscopy techniques, rheology and cryo-imaging we investigated several biocompatible polymeric networks in their natural form as well as their engineered structures to better understand the mechanisms of gelation and artificial internal re-organization of the networks. Natural and synthetic biohydrogels were designed and their mechanical properties were characterized before imaging. Models that better describe the relationship between network configuration and resulting mechanical properties showed great agreement with experimental mesh size observations. Finally, a novel set of hybrid gels was developed and exhibited outstanding thermomechanical properties.

Physarum polycephalum Percolation as a Paradigm for Topological Phase Transitions in Transportation Networks

NASA Astrophysics Data System (ADS)

Fessel, Adrian; Oettmeier, Christina; Bernitt, Erik; Gauthier, Nils C.; Döbereiner, Hans-Günther

2012-08-01

We study the formation of transportation networks of the true slime mold Physarum polycephalum after fragmentation by shear. Small fragments, called microplasmodia, fuse to form macroplasmodia in a percolation transition. At this topological phase transition, one single giant component forms, connecting most of the previously isolated microplasmodia. Employing the configuration model of graph theory for small link degree, we have found analytically an exact solution for the phase transition. It is generally applicable to percolation as seen, e.g., in vascular networks.

Effect of correlations on controllability transition in network control

PubMed Central

Nie, Sen; Wang, Xu-Wen; Wang, Bing-Hong; Jiang, Luo-Luo

2016-01-01

The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links. More interestingly, the effect of the degree correlation on the transition point cannot be observed in dense networks for numerical controllability, which contrasts with the corresponding result for structural controllability. In particular, for directed random networks and scale-free networks, the influence of the degree correlation is determined by the types of correlations. Our approach provides an understanding of control problems in complex sparse networks. PMID:27063294

Design of sensor node platform for wireless biomedical sensor networks.

PubMed

Xijun, Chen; -H Meng, Max; Hongliang, Ren

2005-01-01

Design of low-cost, miniature, lightweight, ultra low-power, flexible sensor platform capable of customization and seamless integration into a wireless biomedical sensor network(WBSN) for health monitoring applications presents one of the most challenging tasks. In this paper, we propose a WBSN node platform featuring an ultra low-power microcontroller, an IEEE 802.15.4 compatible transceiver, and a flexible expansion connector. The proposed solution promises a cost-effective, flexible platform that allows easy customization, energy-efficient computation and communication. The development of a common platform for multiple physical sensors will increase reuse and alleviate costs of transition to a new generation of sensors. As a case study, we present an implementation of an ECG (Electrocardiogram) sensor.

MOSES: A Matlab-based open-source stochastic epidemic simulator.

PubMed

Varol, Huseyin Atakan

2016-08-01

This paper presents an open-source stochastic epidemic simulator. Discrete Time Markov Chain based simulator is implemented in Matlab. The simulator capable of simulating SEQIJR (susceptible, exposed, quarantined, infected, isolated and recovered) model can be reduced to simpler models by setting some of the parameters (transition probabilities) to zero. Similarly, it can be extended to more complicated models by editing the source code. It is designed to be used for testing different control algorithms to contain epidemics. The simulator is also designed to be compatible with a network based epidemic simulator and can be used in the network based scheme for the simulation of a node. Simulations show the capability of reproducing different epidemic model behaviors successfully in a computationally efficient manner.

Proposal for optimal placement platform of bikes using queueing networks.

PubMed

Mizuno, Shinya; Iwamoto, Shogo; Seki, Mutsumi; Yamaki, Naokazu

2016-01-01

In recent social experiments, rental motorbikes and rental bicycles have been arranged at nodes, and environments where users can ride these bikes have been improved. When people borrow bikes, they return them to nearby nodes. Some experiments have been conducted using the models of Hamachari of Yokohama, the Niigata Rental Cycle, and Bicing. However, from these experiments, the effectiveness of distributing bikes was unclear, and many models were discontinued midway. Thus, we need to consider whether these models are effectively designed to represent the distribution system. Therefore, we construct a model to arrange the nodes for distributing bikes using a queueing network. To adopt realistic values for our model, we use the Google Maps application program interface. Thus, we can easily obtain values of distance and transit time between nodes in various places in the world. Moreover, we apply the distribution of a population to a gravity model and we compute the effective transition probability for this queueing network. If the arrangement of the nodes and number of bikes at each node is known, we can precisely design the system. We illustrate our system using convenience stores as nodes and optimize the node configuration. As a result, we can optimize simultaneously the number of nodes, node places, and number of bikes for each node, and we can construct a base for a rental cycle business to use our system.

Mechanical critical phenomena and the elastic response of fiber networks

NASA Astrophysics Data System (ADS)

Mackintosh, Fred

The mechanics of cells and tissues are largely governed by scaffolds of filamentous proteins that make up the cytoskeleton, as well as extracellular matrices. Evidence is emerging that such networks can exhibit rich mechanical phase behavior. A classic example of a mechanical phase transition was identified by Maxwell for macroscopic engineering structures: networks of struts or springs exhibit a continuous, second-order phase transition at the isostatic point, where the number of constraints imposed by connectivity just equals the number of mechanical degrees of freedom. We present recent theoretical predictions and experimental evidence for mechanical phase transitions in in both synthetic and biopolymer networks. We show, in particular, excellent quantitative agreement between the mechanics of collagen matrices and the predictions of a strain-controlled phase transition in sub-isostatic networks.

Discriminative Cooperative Networks for Detecting Phase Transitions

NASA Astrophysics Data System (ADS)

Liu, Ye-Hua; van Nieuwenburg, Evert P. L.

2018-04-01

The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science setting so far. Here we introduce an unsupervised machine-learning scheme for detecting phase transitions with a pair of discriminative cooperative networks (DCNs). In this scheme, a guesser network and a learner network cooperate to detect phase transitions from fully unlabeled data. The new scheme is efficient enough for dealing with phase diagrams in two-dimensional parameter spaces, where we can utilize an active contour model—the snake—from computer vision to host the two networks. The snake, with a DCN "brain," moves and learns actively in the parameter space, and locates phase boundaries automatically.

Quality-by-Design (QbD): An integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and process design space development.

PubMed

Wu, Huiquan; White, Maury; Khan, Mansoor A

2011-02-28

The aim of this work was to develop an integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and design space development. A dynamic co-precipitation process by gradually introducing water to the ternary system of naproxen-Eudragit L100-alcohol was monitored at real-time in situ via Lasentec FBRM and PVM. 3D map of count-time-chord length revealed three distinguishable process stages: incubation, transition, and steady-state. The effects of high risk process variables (slurry temperature, stirring rate, and water addition rate) on both derived co-precipitation process rates and final chord-length-distribution were evaluated systematically using a 3(3) full factorial design. Critical process variables were identified via ANOVA for both transition and steady state. General linear models (GLM) were then used for parameter estimation for each critical variable. Clear trends about effects of each critical variable during transition and steady state were found by GLM and were interpreted using fundamental process principles and Nyvlt's transfer model. Neural network models were able to link process variables with response variables at transition and steady state with R(2) of 0.88-0.98. PVM images evidenced nucleation and crystal growth. Contour plots illustrated design space via critical process variables' ranges. It demonstrated the utility of integrated PAT approach for QbD development. Published by Elsevier B.V.

Unique sodium phosphosilicate glasses designed through extended topological constraint theory.

PubMed

Zeng, Huidan; Jiang, Qi; Liu, Zhao; Li, Xiang; Ren, Jing; Chen, Guorong; Liu, Fude; Peng, Shou

2014-05-15

Sodium phosphosilicate glasses exhibit unique properties with mixed network formers, and have various potential applications. However, proper understanding on the network structures and property-oriented methodology based on compositional changes are lacking. In this study, we have developed an extended topological constraint theory and applied it successfully to analyze the composition dependence of glass transition temperature (Tg) and hardness of sodium phosphosilicate glasses. It was found that the hardness and Tg of glasses do not always increase with the content of SiO2, and there exist maximum hardness and Tg at a certain content of SiO2. In particular, a unique glass (20Na2O-17SiO2-63P2O5) exhibits a low glass transition temperature (589 K) but still has relatively high hardness (4.42 GPa) mainly due to the high fraction of highly coordinated network former Si((6)). Because of its convenient forming and manufacturing, such kind of phosphosilicate glasses has a lot of valuable applications in optical fibers, optical amplifiers, biomaterials, and fuel cells. Also, such methodology can be applied to other types of phosphosilicate glasses with similar structures.

Land use planning using transit oriented development concept: Case study: Salaya station

NASA Astrophysics Data System (ADS)

Jarritthai, Supanee; Techpeeraparnich, Wasaporn

2017-10-01

The urban sprawl of Bangkok has increased with a motorization rate along with the expansion of the road network to adjacent cities. Nakhonpathom province, located at the southern edge of Bangkok has been affected by the urban sprawl. One of Nakhonpathom's Districts named "Salaya" Salaya has been quickly urbanized due to the establishment of many large academic institutes, such as universities, colleges and high schools as well as many commercial shopping malls. The period of 2013-2017, the Thai government introduced sustainable urban planning policy and promoted the use of public transportation systems. The Light Red Line railway extension of the Bangkok Metro Transit system will soon be constructed and the current Salaya Station will be replaced with new station. Many railway expansion projects will be built, should be designed by using transit-oriented development (TOD) scheme. This paper explores demographic information of the area, the demands of the community and relevant stakeholders for designing of the area using TOD. The proposed land use planning is designed based on the existing condition of the area as much as possible to meet the TOD standard and stakeholders' requirement. The result revealed that the guidelines of transit oriented development concept were of importance not only for planning of urban land use, supporting public transport, but also improving the quality of life.

The use of network analysis to study complex animal communication systems: a study on nightingale song.

PubMed

Weiss, Michael; Hultsch, Henrike; Adam, Iris; Scharff, Constance; Kipper, Silke

2014-06-22

The singing of song birds can form complex signal systems comprised of numerous subunits sung with distinct combinatorial properties that have been described as syntax-like. This complexity has inspired inquiries into similarities of bird song to human language; but the quantitative analysis and description of song sequences is a challenging task. In this study, we analysed song sequences of common nightingales (Luscinia megarhynchos) by means of a network analysis. We translated long nocturnal song sequences into networks of song types with song transitions as connectors. As network measures, we calculated shortest path length and transitivity and identified the 'small-world' character of nightingale song networks. Besides comparing network measures with conventional measures of song complexity, we also found a correlation between network measures and age of birds. Furthermore, we determined the numbers of in-coming and out-going edges of each song type, characterizing transition patterns. These transition patterns were shared across males for certain song types. Playbacks with different transition patterns provided first evidence that these patterns are responded to differently and thus play a role in singing interactions. We discuss potential functions of the network properties of song sequences in the framework of vocal leadership. Network approaches provide biologically meaningful parameters to describe the song structure of species with extremely large repertoires and complex rules of song retrieval.

The use of network analysis to study complex animal communication systems: a study on nightingale song

PubMed Central

Weiss, Michael; Hultsch, Henrike; Adam, Iris; Scharff, Constance; Kipper, Silke

2014-01-01

The singing of song birds can form complex signal systems comprised of numerous subunits sung with distinct combinatorial properties that have been described as syntax-like. This complexity has inspired inquiries into similarities of bird song to human language; but the quantitative analysis and description of song sequences is a challenging task. In this study, we analysed song sequences of common nightingales (Luscinia megarhynchos) by means of a network analysis. We translated long nocturnal song sequences into networks of song types with song transitions as connectors. As network measures, we calculated shortest path length and transitivity and identified the ‘small-world’ character of nightingale song networks. Besides comparing network measures with conventional measures of song complexity, we also found a correlation between network measures and age of birds. Furthermore, we determined the numbers of in-coming and out-going edges of each song type, characterizing transition patterns. These transition patterns were shared across males for certain song types. Playbacks with different transition patterns provided first evidence that these patterns are responded to differently and thus play a role in singing interactions. We discuss potential functions of the network properties of song sequences in the framework of vocal leadership. Network approaches provide biologically meaningful parameters to describe the song structure of species with extremely large repertoires and complex rules of song retrieval. PMID:24807258

Evaluating the effect of street network connectivity on first/last mile transit performance.

DOT National Transportation Integrated Search

2011-11-01

"This study defines a novel connectivity indicator (CI) to predict transit performance by : identifying the role that street network connectivity plays in influencing the service quality of : demand responsive feeder transit services. This new CI def...

Investigation of bus transit schedule behavior modeling using advanced techniques

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

Kalaputapu, R.; Demetsky, M.J.

This research focused on investigating the application of artificial neural networks (ANN) and the Box-Jenkins technique for developing and testing schedule behavior models using data obtained for a test route from Tidewater Regional Transit`s AVL system. The three ANN architectures investigated were: Feedforward Network, Elman Network and Jordan Network. In addition, five different model structures were investigated. The time-series methodology was adopted for developing the schedule behavior models. Finally, the role of a schedule behavior model within the framework of an intelligent transit management system is defined and the potential utility of the schedule behavior model is discussed using anmore » example application.« less

Environmental assessment of PSS, feedback on 2 years of experimentation

NASA Astrophysics Data System (ADS)

Allais, Romain; Gobert, Julie

2018-05-01

This communication details the sustainability assessment of the partial transition of business model from selling products to product renting for small household equipment (SHE). Perceived by the French SHE manufacturer as a strategic opportunity to meet customers' expectations and environmental regulation, 2-years experimentation was performed on a specific territory with the support of a network of new competencies (B-to-B-to-C market). Researchers were mandated for the sustainability assessment of such a transition but this communication focuses on the environmental performance of the experimentation. The results of the comparative LCA are presented and the main environmental impacts linked to this business model transition are specified and discussed. Then, different eco-design scenarios are explored and recommendations for this specific case are proposed.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

PubMed Central

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-01-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking. PMID:27510641

Defense Advanced Research Projects Agency: Key Factors Drive Transition of Technologies, but Better Training and Data Dissemination Can Increase Success

DTIC Science & Technology

2015-11-01

more detail. Table 1: Overview of DARPA Programs Selected for GAO Case Study Analyses Program name Program description Advanced Wireless Networks ...Selected DARPA Programs Program name According to DARPA portfolio-level database According to GAO analysis Advanced Wireless Networks for the Soldier...with potential transition partners Achievement of clearly defined technical goals Successful transition Advanced Wireless Networks for Soldier

Generating probabilistic Boolean networks from a prescribed transition probability matrix.

PubMed

Ching, W-K; Chen, X; Tsing, N-K

2009-11-01

Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state.

Effect of synapse dilution on the memory retrieval in structured attractor neural networks

NASA Astrophysics Data System (ADS)

Brunel, N.

1993-08-01

We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

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

Towards Determining the Optimal Density of Groundwater Observation Networks under Uncertainty

NASA Astrophysics Data System (ADS)

Langousis, Andreas; Kaleris, Vassilios; Kokosi, Angeliki; Mamounakis, Georgios

2016-04-01

Time series of groundwater level constitute one of the main sources of information when studying the availability of ground water reserves, at a regional level, under changing climatic conditions. To that extent, one needs groundwater observation networks that can provide sufficient information to estimate the hydraulic head at unobserved locations. The density of such networks is largely influenced by the structure of the aquifer, and in particular by the spatial distribution of hydraulic conductivity (i.e. layering), dependencies in the transition rates between different geologic formations, juxtapositional tendencies, etc. In this work, we: 1) use the concept of transition probabilities embedded in a Markov chain setting to conditionally simulate synthetic aquifer structures representative of geologic formations commonly found in the literature (see e.g. Hoeksema and Kitanidis, 1985), and 2) study how the density of observation wells affects the estimation accuracy of hydraulic heads at unobserved locations. The obtained results are promising, pointing towards the direction of establishing design criteria based on the statistical structure of the aquifer, such as the level of dependence in the transition rates of observed lithologies. Reference: Hoeksema, R.J. and P.K. Kitanidis (1985) Analysis of spatial structure of properties of selected aquifers, Water Resources Research, 21(4), 563-572. Acknowledgments: This work is sponsored by the Onassis Foundation under the "Special Grant and Support Program for Scholars' Association Members".

The Cognitive Social Network in Dreams: Transitivity, Assortativity, and Giant Component Proportion Are Monotonic.

PubMed

Han, Hye Joo; Schweickert, Richard; Xi, Zhuangzhuang; Viau-Quesnel, Charles

2016-04-01

For five individuals, a social network was constructed from a series of his or her dreams. Three important network measures were calculated for each network: transitivity, assortativity, and giant component proportion. These were monotonically related; over the five networks as transitivity increased, assortativity increased and giant component proportion decreased. The relations indicate that characters appear in dreams systematically. Systematicity likely arises from the dreamer's memory of people and their relations, which is from the dreamer's cognitive social network. But the dream social network is not a copy of the cognitive social network. Waking life social networks tend to have positive assortativity; that is, people tend to be connected to others with similar connectivity. Instead, in our sample of dream social networks assortativity is more often negative or near 0, as in online social networks. We show that if characters appear via a random walk, negative assortativity can result, particularly if the random walk is biased as suggested by remote associations. Copyright © 2015 Cognitive Science Society, Inc.

Turkey: Background and U.S. Relations

DTIC Science & Technology

2016-08-26

of $24B in foreign investment,” Anadolu Agency, August 11, 2016. 75 See, e.g., World Bank, Turkey’s Transitions: Integration, Inclusion ...security assistance programs for Turkey are designed to cultivate closeness in relationships and practices between Turkish military officers and...security officials and their U.S. counterparts. These programs also seek to counter terrorist and criminal networks that are active in the region

Photo-triggered solvent-free metamorphosis of polymeric materials.

PubMed

Honda, Satoshi; Toyota, Taro

2017-09-11

Liquefaction and solidification of materials are the most fundamental changes observed during thermal phase transitions, yet the design of organic and polymeric soft materials showing isothermal reversible liquid-nonliquid conversion remains challenging. Here, we demonstrate that solvent-free repeatable molecular architectural transformation between liquid-star and nonliquid-network polymers that relies on cleavage and reformation of a covalent bond in hexaarylbiimidazole. Liquid four-armed star-shaped poly(n-butyl acrylate) and poly(dimethyl siloxane) with 2,4,5-triphenylimidazole end groups were first synthesized. Subsequent oxidation of the 2,4,5-triphenylimidazoles into 2,4,5-triphenylimidazoryl radicals and their coupling with these liquid star polymers to form hexaarylbiimidazoles afforded the corresponding nonliquid network polymers. The resulting nonliquid network polymers liquefied upon UV irradiation and produced liquid star-shaped polymers with 2,4,5-triphenylimidazoryl radical end groups that reverted to nonliquid network polymers again by recoupling of the generated 2,4,5-triphenylimidazoryl radicals immediately after terminating UV irradiation.The design of organic and polymeric soft materials showing isothermal reversible liquid-nonliquid conversion is challenging. Here, the authors show solvent-free repeatable molecular architectural transformation between liquid-star and non-liquid-network polymers by the cleavage and reformation of covalent bonds in the polymer chain.

Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

NASA Astrophysics Data System (ADS)

Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

2017-03-01

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological function.

Stability analysis for virus spreading in complex networks with quarantine and non-homogeneous transition rates

NASA Astrophysics Data System (ADS)

Alarcon-Ramos, L. A.; Schaum, A.; Rodríguez Lucatero, C.; Bernal Jaquez, R.

2014-03-01

Virus propagations in complex networks have been studied in the framework of discrete time Markov process dynamical systems. These studies have been carried out under the assumption of homogeneous transition rates, yielding conditions for virus extinction in terms of the transition probabilities and the largest eigenvalue of the connectivity matrix. Nevertheless the assumption of homogeneous rates is rather restrictive. In the present study we consider non-homogeneous transition rates, assigned according to a uniform distribution, with susceptible, infected and quarantine states, thus generalizing the previous studies. A remarkable result of this analysis is that the extinction depends on the weakest element in the network. Simulation results are presented for large free-scale networks, that corroborate our theoretical findings.

Glass transition temperature and topological constraints of sodium borophosphate glass-forming liquids.

PubMed

Jiang, Qi; Zeng, Huidan; Liu, Zhao; Ren, Jing; Chen, Guorong; Wang, Zhaofeng; Sun, Luyi; Zhao, Donghui

2013-09-28

Sodium borophosphate glasses exhibit intriguing mixed network former effect, with the nonlinear compositional dependence of their glass transition temperature as one of the most typical examples. In this paper, we establish the widely applicable topological constraint model of sodium borophosphate mixed network former glasses to explain the relationship between the internal structure and nonlinear changes of glass transition temperature. The application of glass topology network was discussed in detail in terms of the unified methodology for the quantitative distribution of each coordinated boron and phosphorus units and glass transition temperature dependence of atomic constraints. An accurate prediction of composition scaling of the glass transition temperature was obtained based on topological constraint model.

β-Amino acid catalyzed asymmetric Michael additions: design of organocatalysts with catalytic acid/base dyad inspired by serine proteases.

PubMed

Yang, Hui; Wong, Ming Wah

2011-09-16

A new type of chiral β-amino acid catalyst has been computationally designed, mimicking the enzyme catalysis of serine proteases. Our catalyst approach is based on the bioinspired catalytic acid/base dyad, namely, a carboxyl and imidazole pair. DFT calculations predict that this designed organocatalyst catalyzes Michael additions of aldehydes to nitroalkenes with excellent enantioselectivities and remarkably high anti diastereoselectivities. The unusual stacked geometry of the enamine intermediate, hydrogen bonding network, and the adoption of an exo transition state are the keys to understand the stereoselectivity. © 2011 American Chemical Society

Extraordinary variability and sharp transitions in a maximally frustrated dynamic network

NASA Astrophysics Data System (ADS)

Liu, Wenjia; Schmittmann, Beate; Zia, R. K. P.

2013-03-01

Most previous studies of complex networks have focused on single, static networks. However, in the real world, networks are dynamic and interconnected. Inspired by the presence of extroverts and introverts in the general population, we investigate a highly simplified model of a social network, involving two types of nodes: one preferring the highest degree possible, and one preferring no connections whatsoever. There are only two control parameters in the model: the number of ``introvert'' and ``extrovert'' nodes, NI and NE. Our key findings are as follows: As a function of NI and NE, the system exhibits a highly unusual transition, displaying extraordinary fluctuations (as in 2nd order transitions) and discontinuous jumps (characteristic of 1st order transitions). Most remarkably, the system can be described by an Ising-like Hamiltonian with long-range multi-spin interactions and some of its properties can be obtained analytically. This is in stark contrast with other dynamic network models which rely almost exclusively on simulations. NSF-DMR-1005417/1244666 and and ICTAS Virginia Tech

Percolation of networks with directed dependency links

NASA Astrophysics Data System (ADS)

Niu, Dunbiao; Yuan, Xin; Du, Minhui; Stanley, H. Eugene; Hu, Yanqing

2016-04-01

The self-consistent probabilistic approach has proven itself powerful in studying the percolation behavior of interdependent or multiplex networks without tracking the percolation process through each cascading step. In order to understand how directed dependency links impact criticality, we employ this approach to study the percolation properties of networks with both undirected connectivity links and directed dependency links. We find that when a random network with a given degree distribution undergoes a second-order phase transition, the critical point and the unstable regime surrounding the second-order phase transition regime are determined by the proportion of nodes that do not depend on any other nodes. Moreover, we also find that the triple point and the boundary between first- and second-order transitions are determined by the proportion of nodes that depend on no more than one node. This implies that it is maybe general for multiplex network systems, some important properties of phase transitions can be determined only by a few parameters. We illustrate our findings using Erdős-Rényi networks.

In-Space Networking on NASA's SCAN Testbed

NASA Technical Reports Server (NTRS)

Brooks, David E.; Eddy, Wesley M.; Clark, Gilbert J.; Johnson, Sandra K.

2016-01-01

The NASA Space Communications and Navigation (SCaN) Testbed, an external payload onboard the International Space Station, is equipped with three software defined radios and a flight computer for supporting in-space communication research. New technologies being studied using the SCaN Testbed include advanced networking, coding, and modulation protocols designed to support the transition of NASAs mission systems from primarily point to point data links and preplanned routes towards adaptive, autonomous internetworked operations needed to meet future mission objectives. Networking protocols implemented on the SCaN Testbed include the Advanced Orbiting Systems (AOS) link-layer protocol, Consultative Committee for Space Data Systems (CCSDS) Encapsulation Packets, Internet Protocol (IP), Space Link Extension (SLE), CCSDS File Delivery Protocol (CFDP), and Delay-Tolerant Networking (DTN) protocols including the Bundle Protocol (BP) and Licklider Transmission Protocol (LTP). The SCaN Testbed end-to-end system provides three S-band data links and one Ka-band data link to exchange space and ground data through NASAs Tracking Data Relay Satellite System or a direct-to-ground link to ground stations. The multiple data links and nodes provide several upgradable elements on both the space and ground systems. This paper will provide a general description of the testbeds system design and capabilities, discuss in detail the design and lessons learned in the implementation of the network protocols, and describe future plans for continuing research to meet the communication needs for evolving global space systems.

Communication Needs Assessment for Distributed Turbine Engine Control

NASA Technical Reports Server (NTRS)

Culley, Dennis E.; Behbahani, Alireza R.

2008-01-01

Control system architecture is a major contributor to future propulsion engine performance enhancement and life cycle cost reduction. The control system architecture can be a means to effect net weight reduction in future engine systems, provide a streamlined approach to system design and implementation, and enable new opportunities for performance optimization and increased awareness about system health. The transition from a centralized, point-to-point analog control topology to a modular, networked, distributed system is paramount to extracting these system improvements. However, distributed engine control systems are only possible through the successful design and implementation of a suitable communication system. In a networked system, understanding the data flow between control elements is a fundamental requirement for specifying the communication architecture which, itself, is dependent on the functional capability of electronics in the engine environment. This paper presents an assessment of the communication needs for distributed control using strawman designs and relates how system design decisions relate to overall goals as we progress from the baseline centralized architecture, through partially distributed and fully distributed control systems.

Automated transit networks (ATN) : a review of the state of the industry and prospects for the future.

DOT National Transportation Integrated Search

2014-09-01

The concept of Automated Transit Networks (ATN) - in which fully automated vehicles on exclusive, grade-separated guideways : provide on-demand, primarily non-stop, origin-to-destination service over an area network has been around since the 1950...

Transition from amplitude to oscillation death in a network of oscillators

NASA Astrophysics Data System (ADS)

Nandan, Mauparna; Hens, C. R.; Pal, Pinaki; Dana, Syamal K.

2014-12-01

We report a transition from a homogeneous steady state (HSS) to inhomogeneous steady states (IHSSs) in a network of globally coupled identical oscillators. We perturb a synchronized population of oscillators in the network with a few local negative or repulsive mean field links. The whole population splits into two clusters for a certain number of repulsive mean field links and a range of coupling strength. For further increase of the strength of interaction, these clusters collapse into a HSS followed by a transition to IHSSs where all the oscillators populate either of the two stable steady states. We analytically determine the origin of HSS and its transition to IHSS in relation to the number of repulsive mean-field links and the strength of interaction using a reductionism approach to the model network. We verify the results with numerical examples of the paradigmatic Landau-Stuart limit cycle system and the chaotic Rössler oscillator as dynamical nodes. During the transition from HSS to IHSSs, the network follows the Turing type symmetry breaking pitchfork or transcritical bifurcation depending upon the system dynamics.

Transition from amplitude to oscillation death in a network of oscillators

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

Nandan, Mauparna; Department of Mathematics, National Institute of Technology, Durgapur 713209; Hens, C. R.

2014-12-01

We report a transition from a homogeneous steady state (HSS) to inhomogeneous steady states (IHSSs) in a network of globally coupled identical oscillators. We perturb a synchronized population of oscillators in the network with a few local negative or repulsive mean field links. The whole population splits into two clusters for a certain number of repulsive mean field links and a range of coupling strength. For further increase of the strength of interaction, these clusters collapse into a HSS followed by a transition to IHSSs where all the oscillators populate either of the two stable steady states. We analytically determinemore » the origin of HSS and its transition to IHSS in relation to the number of repulsive mean-field links and the strength of interaction using a reductionism approach to the model network. We verify the results with numerical examples of the paradigmatic Landau-Stuart limit cycle system and the chaotic Rössler oscillator as dynamical nodes. During the transition from HSS to IHSSs, the network follows the Turing type symmetry breaking pitchfork or transcritical bifurcation depending upon the system dynamics.« less

Proof of concept : GTFS data as a basis for optimization of Oregon's regional and statewide transit networks.

DOT National Transportation Integrated Search

2014-05-01

Assessing the current "state of health" of individual transit networks is a fundamental part of studies aimed at planning changes and/or upgrades to the transportation network serving a region. To be able to effect changes that benefit both the indiv...

Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks

PubMed Central

Kim, Minkyung; Kim, Seunghwan; Mashour, George A.; Lee, UnCheol

2017-01-01

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are “progressive and earlier” or “abrupt but delayed” account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations. PMID:28713258

Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks.

PubMed

Kim, Minkyung; Kim, Seunghwan; Mashour, George A; Lee, UnCheol

2017-01-01

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are "progressive and earlier" or "abrupt but delayed" account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations.

Implementing an Internet-based communication network for use during skilled nursing facility to emergency department care transitions: challenges and opportunities for improvement.

PubMed

Hustey, Fredric M; Palmer, Robert M

2012-03-01

To explore the feasibility of implementing an Internet-based communication network for communication of health care information during skilled nursing facility (SNF)-to-ED care transitions, and to identify potential barriers to system implementation. Qualitative. The largest SNF affiliated with the ED of an urban tertiary care center. Consecutive sample of all patients transferred from SNF to ED over 8 months between June 2007 and January 2008; ED and SNF care providers. The development and implementation of an Internet-based communication network for use during SNF-to-ED care transitions. This network was developed by adapting a preexisting Internet-based system that is widely used to facilitate placement of hospitalized patients into SNFs. Internet-based SNF and ED surveys were used to help identify barriers to implementation. There were 276/276 care transitions reviewed. The Internet-based communication network was used in 76 (28%) care transitions, with usage peaking at 40% near the end of the study. Barriers to success that were identified included lack of an electronic medical record (EMR) at the SNF; pervasive negative attitudes between ED and SNF personnel; time necessary for network use during care transitions; frustration by emergency physicians at low system usage rates by SNF personnel; and additional login requirements by ED personnel. Although implementing an Internet-based network for nursing home to ED communication may be feasible, significant barriers were identified in this study that are likely generalizable to other health care settings. Understanding such barriers is an essential first step toward building successful electronic communication networks in the future. Copyright © 2012 American Medical Directors Association, Inc. Published by Elsevier Inc. All rights reserved.

Robustness of network of networks under targeted attack.

PubMed

Dong, Gaogao; Gao, Jianxi; Du, Ruijin; Tian, Lixin; Stanley, H Eugene; Havlin, Shlomo

2013-05-01

The robustness of a network of networks (NON) under random attack has been studied recently [Gao et al., Phys. Rev. Lett. 107, 195701 (2011)]. Understanding how robust a NON is to targeted attacks is a major challenge when designing resilient infrastructures. We address here the question how the robustness of a NON is affected by targeted attack on high- or low-degree nodes. We introduce a targeted attack probability function that is dependent upon node degree and study the robustness of two types of NON under targeted attack: (i) a tree of n fully interdependent Erdős-Rényi or scale-free networks and (ii) a starlike network of n partially interdependent Erdős-Rényi networks. For any tree of n fully interdependent Erdős-Rényi networks and scale-free networks under targeted attack, we find that the network becomes significantly more vulnerable when nodes of higher degree have higher probability to fail. When the probability that a node will fail is proportional to its degree, for a NON composed of Erdős-Rényi networks we find analytical solutions for the mutual giant component P(∞) as a function of p, where 1-p is the initial fraction of failed nodes in each network. We also find analytical solutions for the critical fraction p(c), which causes the fragmentation of the n interdependent networks, and for the minimum average degree k[over ¯](min) below which the NON will collapse even if only a single node fails. For a starlike NON of n partially interdependent Erdős-Rényi networks under targeted attack, we find the critical coupling strength q(c) for different n. When q>q(c), the attacked system undergoes an abrupt first order type transition. When q≤q(c), the system displays a smooth second order percolation transition. We also evaluate how the central network becomes more vulnerable as the number of networks with the same coupling strength q increases. The limit of q=0 represents no dependency, and the results are consistent with the classical percolation theory of a single network under targeted attack.

An Examination of the Design, Development, and Implementation of an Internet Protocol Version 6 Network: The ADTRAN Inc. Case Study

ERIC Educational Resources Information Center

Perigo, Levi

2013-01-01

In this dissertation, the author examined the capabilities of Internet Protocol version 6 (IPv6) in regard to replacing Internet Protocol version 4 (IPv4) as the internetworking technology for Medium-sized Businesses (MBs) in the Information Systems (IS) field. Transition to IPv6 is inevitable, and, thus, organizations are adopting this protocol…

Neuronal network model of interictal and recurrent ictal activity

NASA Astrophysics Data System (ADS)

Lopes, M. A.; Lee, K.-E.; Goltsev, A. V.

2017-12-01

We propose a neuronal network model which undergoes a saddle node on an invariant circle bifurcation as the mechanism of the transition from the interictal to the ictal (seizure) state. In the vicinity of this transition, the model captures important dynamical features of both interictal and ictal states. We study the nature of interictal spikes and early warnings of the transition predicted by this model. We further demonstrate that recurrent seizures emerge due to the interaction between two networks.

Percolation Features on Climate Network under Attacks of El Niño Events

NASA Astrophysics Data System (ADS)

Lu, Z.

2015-12-01

Percolation theory under different attacks is one of the main research areas in complex networks but never be applied to investigate climate network. In this study, for the first time we construct a climate network of surface air temperature field to analyze its percolation features. Here, we regard El Niño event as a kind of naturally attacks generated from Pacific Ocean to attack its upper climate network. We find that El Niño event leads an abrupt percolation phase transition to the climate network which makes it splitting and unstable suddenly. Comparing the results of the climate network under three different forms of attacks, including most connected attack (MA), localized attack (LA) and random attack (RA) respectively, it is found that both MA and LA lead first-order transition and RA leads second-order transition to the climate network. Furthermore, we find that most real attacks consist of all these three forms of attacks. With El Niño event emerging, the ratios of LA and MA increase and dominate the style of attack while RA decreasing. It means the percolation phase transition due to El Niño events is close to first-order transition mostly affected by LA and MA. Our research may help us further understand two questions from perspective of percolation on network: (1) Why not all warming in Pacific Ocean but El Niño events could affect the climate. (2) Why the climate affected by El Niño events changes abruptly.

Conducting-insulating transition in adiabatic memristive networks

NASA Astrophysics Data System (ADS)

Sheldon, Forrest C.; Di Ventra, Massimiliano

2017-01-01

The development of neuromorphic systems based on memristive elements—resistors with memory—requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes. Our work sheds considerable light on the statistical properties of memristive networks that are presently studied both for unconventional computing and as models of neural networks.

Behavioral modeling of VCSELs for high-speed optical interconnects

NASA Astrophysics Data System (ADS)

Szczerba, Krzysztof; Kocot, Chris

2018-02-01

Transition from on-off keying to 4-level pulse amplitude modulation (PAM) in VCSEL based optical interconnects allows for an increase of data rates, at the cost of 4.8 dB sensitivity penalty. The resulting strained link budget creates a need for accurate VCSEL models for driver integrated circuit (IC) design and system level simulations. Rate equation based equivalent circuit models are convenient for the IC design, but system level analysis requires computationally efficient closed form behavioral models based Volterra series and neural networks. In this paper we present and compare these models.

Precise calculation of a bond percolation transition and survival rates of nodes in a complex network.

PubMed

Kawamoto, Hirokazu; Takayasu, Hideki; Jensen, Henrik Jeldtoft; Takayasu, Misako

2015-01-01

Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition.

Transport and percolation in complex networks

NASA Astrophysics Data System (ADS)

Li, Guanliang

To design complex networks with optimal transport properties such as flow efficiency, we consider three approaches to understanding transport and percolation in complex networks. We analyze the effects of randomizing the strengths of connections, randomly adding long-range connections to regular lattices, and percolation of spatially constrained networks. Various real-world networks often have links that are differentiated in terms of their strength, intensity, or capacity. We study the distribution P(σ) of the equivalent conductance for Erdoḧs-Rényi (ER) and scale-free (SF) weighted resistor networks with N nodes, for which links are assigned with conductance σ i ≡ e-axi, where xi is a random variable with 0 < xi < 1. We find, both analytically and numerically, that P(σ) for ER networks exhibits two regimes: (i) For σ < e-apc, P(σ) is independent of N and scales as a power law P(σ) ˜ sk/a-1 . Here pc = 1/ is the critical percolation threshold of the network and is the average degree of the network. (ii) For σ > e -apc, P(σ) has strong N dependence and scales as P(σ) ˜ f(σ, apc/N1/3). Transport properties are greatly affected by the topology of networks. We investigate the transport problem in lattices with long-range connections and subject to a cost constraint, seeking design principles for optimal transport networks. Our network is built from a regular d-dimensional lattice to be improved by adding long-range connections with probability Pij ˜ r-aij , where rij is the lattice distance between site i and j. We introduce a cost constraint on the total length of the additional links and find optimal transport in the system for α = d + 1, established here for d = 1, 2 and 3 for regular lattices and df for fractals. Remarkably, this cost constraint approach remains optimal, regardless of the strategy used for transport, whether based on local or global knowledge of the network structure. To further understand the role that long-range connections play in optimizing the transport of complex systems, we study the percolation of spatially constrained networks. We now consider originally empty lattices embedded in d dimensions by adding long-range connections with the same power law probability p(r) ˜ r -α. We find that, for α ≤ d, the percolation transition belongs to the universality class of percolation in ER networks, while for α > 2d it belongs to the universality class of percolation in regular lattices (for one-dimensional linear chain, there is no percolation transition). However for d < α < 2d, the percolation properties show new intermediate behavior different from ER networks, with critical exponents that depend on α.

Social Network Implications of Normative School Transitions in Non-Urban School Districts

ERIC Educational Resources Information Center

Temkin, Deborah A.; Gest, Scott D.; Osgood, D. Wayne; Feinberg, Mark; Moody, James

2018-01-01

This article expands research on normative school transitions (NSTs) from elementary to middle school or middle to high school by examining the extent to which they disrupt structures of friendship networks. Social network analysis is used to quantify aspects of connectedness likely relevant to student experiences of social support. Data were…

Dynamics, morphogenesis and convergence of evolutionary quantum Prisoner's Dilemma games on networks

PubMed Central

Yong, Xi

2016-01-01

The authors proposed a quantum Prisoner's Dilemma (PD) game as a natural extension of the classic PD game to resolve the dilemma. Here, we establish a new Nash equilibrium principle of the game, propose the notion of convergence and discover the convergence and phase-transition phenomena of the evolutionary games on networks. We investigate the many-body extension of the game or evolutionary games in networks. For homogeneous networks, we show that entanglement guarantees a quick convergence of super cooperation, that there is a phase transition from the convergence of defection to the convergence of super cooperation, and that the threshold for the phase transitions is principally determined by the Nash equilibrium principle of the game, with an accompanying perturbation by the variations of structures of networks. For heterogeneous networks, we show that the equilibrium frequencies of super-cooperators are divergent, that entanglement guarantees emergence of super-cooperation and that there is a phase transition of the emergence with the threshold determined by the Nash equilibrium principle, accompanied by a perturbation by the variations of structures of networks. Our results explore systematically, for the first time, the dynamics, morphogenesis and convergence of evolutionary games in interacting and competing systems. PMID:27118882

Critical forces for actin filament buckling and force transmission influence transport in actomyosin networks

NASA Astrophysics Data System (ADS)

Stam, Samantha; Gardel, Margaret

Viscoelastic networks of biopolymers coordinate the motion of intracellular objects during transport. These networks have nonlinear mechanical properties due to events such as filament buckling or breaking of cross-links. The influence of such nonlinear properties on the time and length scales of transport is not understood. Here, we use in vitro networks of actin and the motor protein myosin II to clarify how intracellular forces regulate active diffusion. We observe two transitions in the mean-squared displacement of cross-linked actin with increasing motor concentration. The first is a sharp transition from initially subdiffusive to diffusive-like motion that requires filament buckling but does not cause net contraction of the network. Further increase of the motor density produces a second transition to network rupture and ballistic actin transport. This corresponds with an increase in the correlation of motion and thus may be caused when forces propagate far enough for global motion. We conclude that filament buckling and overall network contraction require different amounts of force and produce distinct transport properties. These nonlinear transitions may act as mechanical switches that can be turned on to produce observed motion within cells.

All-optical OXC transition strategy from WDM optical network to elastic optical network.

PubMed

Chen, Xin; Li, Juhao; Guo, Bingli; Zhu, Paikun; Tang, Ruizhi; Chen, Zhangyuan; He, Yongqi

2016-02-22

Elastic optical network (EON) has been proposed recently as a spectrum-efficient optical layer to adapt to rapidly-increasing traffic demands instead of current deployed wavelength-division-multiplexing (WDM) optical network. In contrast with conventional WDM optical cross-connect (OXCs) based on wavelength selective switches (WSSs), the EON OXCs are based on spectrum selective switches (SSSs) which are much more expensive than WSSs, especially for large-scale switching architectures. So the transition cost from WDM OXCs to EON OXCs is a major obstacle to realizing EON. In this paper, we propose and experimentally demonstrate a transition OXC (TOXC) structure based on 2-stage cascading switching architectures, which make full use of available WSSs in current deployed WDM OXCs to reduce number and port count of required SSSs. Moreover, we propose a contention-aware spectrum allocation (CASA) scheme for EON built with the proposed TOXCs. We show by simulation that the TOXCs reduce the network capital expenditure transiting from WDM optical network to EON about 50%, with a minor traffic blocking performance degradation and about 10% accommodated traffic number detriment compared with all-SSS EON OXC architectures.

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

2013-09-01

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic.

PubMed

Gómez, José M; Verdú, Miguel

2017-03-06

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.

Percolation in real interdependent networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2015-07-01

The function of a real network depends not only on the reliability of its own components, but is affected also by the simultaneous operation of other real networks coupled with it. Whereas theoretical methods of direct applicability to real isolated networks exist, the frameworks developed so far in percolation theory for interdependent network layers are of little help in practical contexts, as they are suited only for special models in the limit of infinite size. Here, we introduce a set of heuristic equations that takes as inputs the adjacency matrices of the layers to draw the entire phase diagram for the interconnected network. We demonstrate that percolation transitions in interdependent networks can be understood by decomposing these systems into uncoupled graphs: the intersection among the layers, and the remainders of the layers. When the intersection dominates the remainders, an interconnected network undergoes a smooth percolation transition. Conversely, if the intersection is dominated by the contribution of the remainders, the transition becomes abrupt even in small networks. We provide examples of real systems that have developed interdependent networks sharing cores of `high quality’ edges to prevent catastrophic failures.

Exact coupling threshold for structural transition reveals diversified behaviors in interconnected networks.

PubMed

Darabi Sahneh, Faryad; Scoglio, Caterina; Van Mieghem, Piet

2015-10-01

An interconnected network features a structural transition between two regimes [F. Radicchi and A. Arenas, Nat. Phys. 9, 717 (2013)]: one where the network components are structurally distinguishable and one where the interconnected network functions as a whole. Our exact solution for the coupling threshold uncovers network topologies with unexpected behaviors. Specifically, we show conditions that superdiffusion, introduced by Gómez et al. [Phys. Rev. Lett. 110, 028701 (2013)], can occur despite the network components functioning distinctly. Moreover, we find that components of certain interconnected network topologies are indistinguishable despite very weak coupling between them.

Developmental engineering: a new paradigm for the design and manufacturing of cell-based products. Part II: from genes to networks: tissue engineering from the viewpoint of systems biology and network science.

PubMed

Lenas, Petros; Moos, Malcolm; Luyten, Frank P

2009-12-01

The field of tissue engineering is moving toward a new concept of "in vitro biomimetics of in vivo tissue development." In Part I of this series, we proposed a theoretical framework integrating the concepts of developmental biology with those of process design to provide the rules for the design of biomimetic processes. We named this methodology "developmental engineering" to emphasize that it is not the tissue but the process of in vitro tissue development that has to be engineered. To formulate the process design rules in a rigorous way that will allow a computational design, we should refer to mathematical methods to model the biological process taking place in vitro. Tissue functions cannot be attributed to individual molecules but rather to complex interactions between the numerous components of a cell and interactions between cells in a tissue that form a network. For tissue engineering to advance to the level of a technologically driven discipline amenable to well-established principles of process engineering, a scientifically rigorous formulation is needed of the general design rules so that the behavior of networks of genes, proteins, or cells that govern the unfolding of developmental processes could be related to the design parameters. Now that sufficient experimental data exist to construct plausible mathematical models of many biological control circuits, explicit hypotheses can be evaluated using computational approaches to facilitate process design. Recent progress in systems biology has shown that the empirical concepts of developmental biology that we used in Part I to extract the rules of biomimetic process design can be expressed in rigorous mathematical terms. This allows the accurate characterization of manufacturing processes in tissue engineering as well as the properties of the artificial tissues themselves. In addition, network science has recently shown that the behavior of biological networks strongly depends on their topology and has developed the necessary concepts and methods to describe it, allowing therefore a deeper understanding of the behavior of networks during biomimetic processes. These advances thus open the door to a transition for tissue engineering from a substantially empirical endeavor to a technology-based discipline comparable to other branches of engineering.

From scale-free to Erdos-Rényi networks.

PubMed

Gómez-Gardeñes, Jesús; Moreno, Yamir

2006-05-01

We analyze a model that interpolates between scale-free and Erdos-Rényi networks. The model introduced generates a one-parameter family of networks and allows one to analyze the role of structural heterogeneity. Analytical calculations are compared with extensive numerical simulations in order to describe the transition between these two important classes of networks. Finally, an application of the proposed model to the study of the percolation transition is presented.

System-wide analysis of the transcriptional network of human myelomonocytic leukemia cells predicts attractor structure and phorbol-ester-induced differentiation and dedifferentiation transitions

NASA Astrophysics Data System (ADS)

Sakata, Katsumi; Ohyanagi, Hajime; Sato, Shinji; Nobori, Hiroya; Hayashi, Akiko; Ishii, Hideshi; Daub, Carsten O.; Kawai, Jun; Suzuki, Harukazu; Saito, Toshiyuki

2015-02-01

We present a system-wide transcriptional network structure that controls cell types in the context of expression pattern transitions that correspond to cell type transitions. Co-expression based analyses uncovered a system-wide, ladder-like transcription factor cluster structure composed of nearly 1,600 transcription factors in a human transcriptional network. Computer simulations based on a transcriptional regulatory model deduced from the system-wide, ladder-like transcription factor cluster structure reproduced expression pattern transitions when human THP-1 myelomonocytic leukaemia cells cease proliferation and differentiate under phorbol myristate acetate stimulation. The behaviour of MYC, a reprogramming Yamanaka factor that was suggested to be essential for induced pluripotent stem cells during dedifferentiation, could be interpreted based on the transcriptional regulation predicted by the system-wide, ladder-like transcription factor cluster structure. This study introduces a novel system-wide structure to transcriptional networks that provides new insights into network topology.

The Fragility of Interdependency: Coupled Networks Switching Phenomena

NASA Astrophysics Data System (ADS)

Stanley, H. Eugene

2013-03-01

Recent disasters ranging from abrupt financial ``flash crashes'' and large-scale power outages to sudden death among the elderly dramatically exemplify the fact that the most dangerous vulnerability is hiding in the many interdependencies among different networks. In the past year, we have quantified failures in model of interconnected networks, and demonstrated the need to consider mutually dependent network properties in designing resilient systems. Specifically, we have uncovered new laws governing the nature of switching phenomena in coupled networks, and found that phenomena that are continuous ``second order'' phase transitions in isolated networks become discontinuous abrupt ``first order'' transitions in interdependent networks [S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, and S. Havlin, ``Catastrophic Cascade of Failures in Interdependent Networks,'' Nature 464, 1025 (2010); J. Gao, S. V. Buldyrev, H. E. Stanley, and S. Havlin, ``Novel Behavior of Networks Formed from Interdependent Networks,'' Nature Physics 8, 40 (2012). We conclude by discussing the network basis for understanding sudden death in the elderly, and the possibility that financial ``flash crashes'' are not unlike the catastrophic first-order failure incidents occurring in coupled networks. Specifically, we study the coupled networks that are responsible for financial fluctuations. It appears that ``trend switching phenomena'' that we uncover are remarkably independent of the scale over which they are analyzed. For example, we find that the same laws governing the formation and bursting of the largest financial bubbles also govern the tiniest finance bubbles, over a factor of 1,000,000,000 in time scale [T. Preis, J. Schneider, and H. E. Stanley, ``Switching Processes in Financial Markets,'' Proc. Natl. Acad. Sci. USA 108, 7674 (2011); T. Preis and H. E. Stanley, ``Bubble Trouble: Can a Law Describe Bubbles and Crashes in Financial Markets?'' Physics World 24, No. 5, 29 (May 2011)]. This work was carried out in collaboration with a number of colleagues, including T. Preis, J. J. Schneider, S. Havlin, R. Parshani, S. V. Buldyrev, J. Gao, and G. Paul-see ``When Networks Network,'' Science News, 22 Sept. 2012.

Synchronization properties of heterogeneous neuronal networks with mixed excitability type

NASA Astrophysics Data System (ADS)

Leone, Michael J.; Schurter, Brandon N.; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G.

2015-03-01

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

Field Measurements and Guidelines for the Application of Wireless Sensor Networks to the Environment and Security

PubMed Central

Gil Jiménez, Víctor P.; Armada, Ana García

2009-01-01

Frequently, Wireless Sensor Networks (WSN) are designed focusing on applications and omitting transmission problems in these wireless networks. In this paper, we present a measurement campaign that has been carried out using one of the most commonly used WSN platforms, the micaZ from Crossbow©. Based on these measurements, some guidelines to deploy a robust and reliable WSN are provided. The results are focused on security and environmental applications but can also be extrapolated to other scenarios. A main conclusion that can be extracted is that, from the transmission point of view, a dense WSN is one of the best choices to overcome many of the transmission problems such as the existence of a transitional region, redundance, forwarding, obstructions or interference with other systems. PMID:22303175

A general stochastic model for studying time evolution of transition networks

NASA Astrophysics Data System (ADS)

Zhan, Choujun; Tse, Chi K.; Small, Michael

2016-12-01

We consider a class of complex networks whose nodes assume one of several possible states at any time and may change their states from time to time. Such networks represent practical networks of rumor spreading, disease spreading, language evolution, and so on. Here, we derive a model describing the dynamics of this kind of network and a simulation algorithm for studying the network evolutionary behavior. This model, derived at a microscopic level, can reveal the transition dynamics of every node. A numerical simulation is taken as an ;experiment; or ;realization; of the model. We use this model to study the disease propagation dynamics in four different prototypical networks, namely, the regular nearest-neighbor (RN) network, the classical Erdös-Renyí (ER) random graph, the Watts-Strogátz small-world (SW) network, and the Barabási-Albert (BA) scalefree network. We find that the disease propagation dynamics in these four networks generally have different properties but they do share some common features. Furthermore, we utilize the transition network model to predict user growth in the Facebook network. Simulation shows that our model agrees with the historical data. The study can provide a useful tool for a more thorough understanding of the dynamics networks.

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.

Transition to parenthood: the role of social interaction and endogenous networks.

PubMed

Diaz, Belinda Aparicio; Fent, Thomas; Prskawetz, Alexia; Bernardi, Laura

2011-05-01

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdependencies across individuals' transition to parenthood and its timing, we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics: age, intended education, and parity. Agents endogenously form their network based on social closeness. Network members may then influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social interactions can explain the shift of first-birth probabilities in Austria during the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Multiple tipping points and optimal repairing in interacting networks

PubMed Central

Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

2016-01-01

Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

Detection of Intermediate-Period Transiting Planets with a Network of Small Telescopes: transitsearch.org

NASA Astrophysics Data System (ADS)

Seagroves, Scott; Harker, Justin; Laughlin, Gregory; Lacy, Justin; Castellano, Tim

2003-12-01

We describe a project (transitsearch.org) currently attempting to discover transiting intermediate-period planets orbiting bright parent stars, and we simulate that project's performance. The discovery of such a transit would be an important astronomical advance, bridging the critical gap in understanding between HD 209458b and Jupiter. However, the task is made difficult by intrinsically low transit probabilities and small transit duty cycles. This project's efficient and economical strategy is to photometrically monitor stars that are known (from radial velocity surveys) to bear planets, using a network of widely spaced observers with small telescopes. These observers, each individually capable of precision (1%) differential photometry, monitor candidates during the time windows in which the radial velocity solution predicts a transit if the orbital inclination is close to 90°. We use Monte Carlo techniques to simulate the performance of this network, performing simulations with different configurations of observers in order to optimize coordination of an actual campaign. Our results indicate that transitsearch.org can reliably rule out or detect planetary transits within the current catalog of known planet-bearing stars. A distributed network of skilled amateur astronomers and small college observatories is a cost-effective method for discovering the small number of transiting planets with periods in the range 10 days

Precise Calculation of a Bond Percolation Transition and Survival Rates of Nodes in a Complex Network

PubMed Central

Kawamoto, Hirokazu; Takayasu, Hideki; Jensen, Henrik Jeldtoft; Takayasu, Misako

2015-01-01

Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition. PMID:25885791

Social influence in small-world networks

NASA Astrophysics Data System (ADS)

Sun, Kai; Mao, Xiao-Ming; Ouyang, Qi

2002-12-01

We report on our numerical studies of the Axelrod model for social influence in small-world networks. Our simulation results show that the topology of the network has a crucial effect on the evolution of cultures. As the randomness of the network increases, the system undergoes a transition from a highly fragmented phase to a uniform phase. We also find that the power-law distribution at the transition point, reported by Castellano et al, is not a critical phenomenon; it exists not only at the onset of transition but also for almost any control parameters. All these power-law distributions are stable against perturbations. A mean-field theory is developed to explain these phenomena.

Mentoring in the Context of Latino Youth's Broader Village during Their Transition from High School

ERIC Educational Resources Information Center

Sanchez, Bernadette; Esparza, Patricia; Berardi, Luciano; Pryce, Julia

2011-01-01

The aims of this study were to examine the mentoring and social network experiences of Latino youth during the high school transition. A mixed-methods approach was used to examine participants' natural mentoring relationships before and after the transition along with the broader social networks of youth. A total of 32 Latino participants…

Falling Behind: Lingering Costs of the High School Transition for Youth Friendships and Grades

ERIC Educational Resources Information Center

Felmlee, Diane; McMillan, Cassie; Inara Rodis, Paulina; Osgood, D. Wayne

2018-01-01

This study investigates the influence of structural transitions to high school on adolescents' friendship networks and academic grades from 6th through 12th grade, in a direct comparison of students who do and do not transition. We utilize data from 14,462 youth in 51 networks from 26 districts (Promoting School-Community Partnerships to Enhance…

Percolation of spatially constraint networks

NASA Astrophysics Data System (ADS)

Li, Daqing; Li, Guanliang; Kosmidis, Kosmas; Stanley, H. E.; Bunde, Armin; Havlin, Shlomo

2011-03-01

We study how spatial constraints are reflected in the percolation properties of networks embedded in one-dimensional chains and two-dimensional lattices. We assume long-range connections between sites on the lattice where two sites at distance r are chosen to be linked with probability p(r)~r-δ. Similar distributions have been found in spatially embedded real networks such as social and airline networks. We find that for networks embedded in two dimensions, with 2<δ<4, the percolation properties show new intermediate behavior different from mean field, with critical exponents that depend on δ. For δ<2, the percolation transition belongs to the universality class of percolation in Erdös-Rényi networks (mean field), while for δ>4 it belongs to the universality class of percolation in regular lattices. For networks embedded in one dimension, we find that, for δ<1, the percolation transition is mean field. For 1<δ<2, the critical exponents depend on δ, while for δ>2 there is no percolation transition as in regular linear chains.

Bus-based park-and-ride system: a stochastic model on multimodal network with congestion pricing schemes

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Meng, Qiang

2014-05-01

This paper focuses on modelling the network flow equilibrium problem on a multimodal transport network with bus-based park-and-ride (P&R) system and congestion pricing charges. The multimodal network has three travel modes: auto mode, transit mode and P&R mode. A continuously distributed value-of-time is assumed to convert toll charges and transit fares to time unit, and the users' route choice behaviour is assumed to follow the probit-based stochastic user equilibrium principle with elastic demand. These two assumptions have caused randomness to the users' generalised travel times on the multimodal network. A comprehensive network framework is first defined for the flow equilibrium problem with consideration of interactions between auto flows and transit (bus) flows. Then, a fixed-point model with unique solution is proposed for the equilibrium flows, which can be solved by a convergent cost averaging method. Finally, the proposed methodology is tested by a network example.

Markov State Models of gene regulatory networks.

PubMed

Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L

2017-02-06

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Phase transitions in a multistate majority-vote model on complex networks

NASA Astrophysics Data System (ADS)

Chen, Hanshuang; Li, Guofeng

2018-06-01

We generalize the original majority-vote (MV) model from two states to arbitrary p states and study the order-disorder phase transitions in such a p -state MV model on complex networks. By extensive Monte Carlo simulations and a mean-field theory, we show that for p ≥3 the order of phase transition is essentially different from a continuous second-order phase transition in the original two-state MV model. Instead, for p ≥3 the model displays a discontinuous first-order phase transition, which is manifested by the appearance of the hysteresis phenomenon near the phase transition. Within the hysteresis loop, the ordered phase and disordered phase are coexisting, and rare flips between the two phases can be observed due to the finite-size fluctuation. Moreover, we investigate the type of phase transition under a slightly modified dynamics [Melo et al., J. Stat. Mech. (2010) P11032, 10.1088/1742-5468/2010/11/P11032]. We find that the order of phase transition in the three-state MV model depends on the degree heterogeneity of networks. For p ≥4 , both dynamics produce the first-order phase transitions.

Project Solaris, a Global Network of Autonomous Observatories: Design, Commissioning, and First Science Results

NASA Astrophysics Data System (ADS)

Kozłowski, S. K.; Sybilski, P. W.; Konacki, M.; Pawłaszek, R. K.; Ratajczak, M.; Hełminiak, K. G.; Litwicki, M.

2017-10-01

We present the design and commissioning of Project Solaris, a global network of autonomous observatories. Solaris is a Polish scientific undertaking aimed at the detection and characterization of circumbinary exoplanets and eclipsing binary stars. To accomplish this, a network of four fully autonomous observatories has been deployed in the Southern Hemisphere: Solaris-1 and Solaris-2 in the South African Astronomical Observatory in South Africa; Solaris-3 in Siding Spring Observatory in Australia; and Solaris-4 in Complejo Astronomico El Leoncito in Argentina. The four stations are nearly identical and are equipped with 0.5-m Ritchey-Crétien (f/15) or Cassegrain (f/9, Solaris-3) optics and high-grade 2 K × 2 K CCD cameras with Johnson and Sloan filter sets. We present the design and implementation of low-level security; data logging and notification systems; weather monitoring components; all-sky vision system, surveillance system; and distributed temperature and humidity sensors. We describe dedicated grounding and lighting protection system design and robust fiber data transfer interfaces in electrically demanding conditions. We discuss the outcomes of our design, as well as the resulting software engineering requirements. We describe our system’s engineering approach to achieve the required level of autonomy, the architecture of the custom high-level industry-grade software that has been designed and implemented specifically for the use of the network. We present the actual status of the project and first photometric results; these include data and models of already studied systems for benchmarking purposes (Wasp-4b, Wasp-64b, and Wasp-98b transits, PG 1663-018, an eclipsing binary with a pulsator) as well J024946-3825.6, an interesting low-mass binary system for which a complete model is provided for the first time.

The dynamic and geometric phase transition in the cellular network of pancreatic islet

NASA Astrophysics Data System (ADS)

Wang, Xujing

2013-03-01

The pancreatic islet is a micro-organ that contains several thousands of endocrine cells, majority of which being the insulin releasing β - cells . - cellsareexcitablecells , andarecoupledtoeachother through gap junctional channels. Here, using percolation theory, we investigate the role of network structure in determining the dynamics of the β-cell network. We show that the β-cell synchronization depends on network connectivity. More specifically, as the site occupancy is reducing, initially the β-cell synchronization is barely affected, until it reaches around a critical value, where the synchronization exhibit a sudden rapid decline, followed by an slow exponential tail. This critical value coincides with the critical site open probability for percolation transition. The dependence over bond strength is similar, exhibiting critical-behavior like dependence around a certain value of bond strength. These results suggest that the β-cell network undergoes a dynamic phase transition when the network is percolated. We further apply the findings to study diabetes. During the development of diabetes, the β - cellnetworkconnectivitydecreases . Siteoccupancyreducesfromthe reducing β-cell mass, and the bond strength is increasingly impaired from β-cell stress and chronic hyperglycemia. We demonstrate that the network dynamics around the percolation transition explain the disease dynamics around onset, including a long time mystery in diabetes, the honeymoon phenomenon.

Informal Networks in Youth Transitions in West Germany: Biographical Resource or Reproduction of Social Inequality?

ERIC Educational Resources Information Center

Walther, Andreas; Stauber, Barbara; Pohl, Axel

2005-01-01

This article deals with informal networks and their role in young people's strategies of coping with the uncertainties of transitions to work. The underlying hypothesis is that informal networks have a high potential in this regard that, however, is strongly differentiated according to class and education. Drawing on West German data from the…

Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.

PubMed

Xu, Kesheng; Maidana, Jean Paul; Castro, Samy; Orio, Patricio

2018-05-30

Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that - when isolated - can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.

Network inoculation: Heteroclinics and phase transitions in an epidemic model

NASA Astrophysics Data System (ADS)

Yang, Hui; Rogers, Tim; Gross, Thilo

2016-08-01

In epidemiological modelling, dynamics on networks, and, in particular, adaptive and heterogeneous networks have recently received much interest. Here, we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model, qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description, one of these corresponds to a local bifurcation, whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region, exposure of the system to a pathogen will lead to an outbreak that collapses but leaves the network in a configuration where the disease cannot reinvade, despite every agent returning to the susceptible class. We argue that this behaviour and the associated phase transitions can be expected to occur in a wide class of models of sufficient complexity.

Channel noise-induced temporal coherence transitions and synchronization transitions in adaptive neuronal networks with time delay

NASA Astrophysics Data System (ADS)

Gong, Yubing; Xie, Huijuan

2017-09-01

Using spike-timing-dependent plasticity (STDP), we study the effect of channel noise on temporal coherence and synchronization of adaptive scale-free Hodgkin-Huxley neuronal networks with time delay. It is found that the spiking regularity and spatial synchronization of the neurons intermittently increase and decrease as channel noise intensity is varied, exhibiting transitions of temporal coherence and synchronization. Moreover, this phenomenon depends on time delay, STDP, and network average degree. As time delay increases, the phenomenon is weakened, however, there are optimal STDP and network average degree by which the phenomenon becomes strongest. These results show that channel noise can intermittently enhance the temporal coherence and synchronization of the delayed adaptive neuronal networks. These findings provide a new insight into channel noise for the information processing and transmission in neural systems.

Physics of Blockchain Systems

NASA Astrophysics Data System (ADS)

Tessone, Claudio J.

Nowadays, most facets of human behaviour are pervaded by technical systems that facilitate our information and economic exchanges. In the last years, aiming at more resilient and scalable designs, these systems have transitioned towards decentralised concepts. Blockchain has disrupted the way of thinking distributed systems: This mechanism allows the secure diffusion of information across a network without the need of a central (trusted) authority to enforce the emergence of consensus. Indeed, as a primary example, the digital currency Bitcoin is implemented on top of a blockchain, and its value is solely assigned by a (largely speculative) market. This talk is divided into two parts. First, the analysis of Bitcoin as a closed economy: having followed a technocratic approach in its immutable design, it is the only case of an economy where all monetary transactions can be traced back with full detail. Interestingly, its fixed incentive scheme has created the emergence of large levels of centralisation and economic flow, drastically different from its original conception. Blockchain-based systems are underlain by decentralised peer-to-peer networks. While in the last years the number of its applications has increased enormously, little is known about their suitability in stressed working conditions. In the final part of this presentation we introduce a parsimonious modelling approach (an extension of the celebrated Gillespie algorithm) for these systems, identifying a phase transition from consensus in the diffusion of information to a frustrated (congested) state where the efficiency of these systems rapidly deteriorates. CJT acknowledges funding by University Research Priority Programme on Social Networks, University of Zurich (Switzerland).

Facile solid-state synthesis of Ni@C nanosheet-assembled hierarchical network for high-performance lithium storage

NASA Astrophysics Data System (ADS)

Gu, Jinghe; Li, Qiyun; Zeng, Pan; Meng, Yulin; Zhang, Xiukui; Wu, Ping; Zhou, Yiming

2017-08-01

Micro/nano-architectured transition-metal@C hybrids possess unique structural and compositional features toward lithium storage, and are thus expected to manifest ideal anodic performances in advanced lithium-ion batteries (LIBs). Herein, we propose a facile and scalable solid-state coordination and subsequent pyrolysis route for the formation of a novel type of micro/nano-architectured transition-metal@C hybrid (i.e., Ni@C nanosheet-assembled hierarchical network, Ni@C network). Moreover, this coordination-pyrolysis route has also been applied for the construction of bare carbon network using zinc salts instead of nickel salts as precursors. When applied as potential anodic materials in LIBs, the Ni@C network exhibits Ni-content-dependent electrochemical performances, and the partially-etched Ni@C network manifests markedly enhanced Li-storage performances in terms of specific capacities, cycle life, and rate capability than the pristine Ni@C network and carbon network. The proposed solid-state coordination and pyrolysis strategy would open up new opportunities for constructing micro/nano-architectured transition-metal@C hybrids as advanced anode materials for LIBs.

Rerouting Urban Waters: A Historic Examination of the Age of Imperviousness

NASA Astrophysics Data System (ADS)

Hopkins, K. G.; Bain, D. J.

2011-12-01

From the 1600's to the 1900's landscapes along the Eastern United States underwent dramatic changes, including transitions from forest to production agriculture and eventually urban development. Legacy effects from decisions on sewer and water infrastructure built during the early 1900's are emerging today in degraded urban waterways. Impervious cover is often a factor used to predict water impairment. However, does imperviousness age or change through the course of landscape evolution? This study reconstructs the history of imperviousness in the Panther Hollow watershed (161 ha, Pittsburgh, PA) to examine these changes. We reconstruct the importance of factors influencing effective imperviousness from the 1800's to present including; (1) pipe and road network technological transitions, (2) land cover changes, particularly the loss of forest cover, and (3) modifications to local topography. Analysis reveals effective imperviousness (impervious area in the basin directly connected to stream channels) increased dramatically after 1900. Prior to 1900, water and sewer infrastructure was very limited. Local drainage networks generally followed the natural topography and households accessed water supplies from wells, precipitation harvesting or surface water. Road networks were sparse and predominantly dirt or aggregate surfaces. Forests and large family farms dominated land cover. Around 1910 public water supply expanded, significantly increasing effective imperviousness due to installation of brick and ceramic sewer infrastructure that routed waste waters directly to stream channels. Road networks also expanded and began transitioning from dirt roads to brick and eventually asphalt. Shifting to impervious paving materials required the installation of stormwater drainage. New drainage systems altered historic flow paths by re-routed large quantities of water through macro-pore sewer networks to local waterways. While this improvement prevented flooding to roadways, it also created new flooding issues downstream of outfalls. Improvements to transit networks also increased mobility and connected towns together facilitating the expansion of development. Significant losses of urban tree canopy cover and the loss of water storage capacity in soils compounded issues, dramatically increasing effective imperviousness. From 1940 - 1960 concerns over polluted waterways resulted in the re-routing of sewage networks from streams to treatment facilities, decreasing sewage subsidies to effective imperviousness. However, connection of stormwater drainage networks to sewage infrastructure designed for earlier flow regimes and the increasing effective imperviousness resulted in frequent overflows of sewage directly to local waterways. Currently, aging infrastructure presents the opportunity to incorporate low impact development techniques in infrastructure repair. This has the potential to reduce effective imperviousness in urban areas by re-establishing lost hydrologic flow paths. This research indicates imperviousness as a parameter incorporates a complicated mix of processes. Examining the causal, mechanistic links between these systems can provide additional perspective on water impairments in urban landscapes throughout the course of landscape evolution.

Statistical Physics of Cascading Failures in Complex Networks

NASA Astrophysics Data System (ADS)

Panduranga, Nagendra Kumar

Systems such as the power grid, world wide web (WWW), and internet are categorized as complex systems because of the presence of a large number of interacting elements. For example, the WWW is estimated to have a billion webpages and understanding the dynamics of such a large number of individual agents (whose individual interactions might not be fully known) is a challenging task. Complex network representations of these systems have proved to be of great utility. Statistical physics is the study of emergence of macroscopic properties of systems from the characteristics of the interactions between individual molecules. Hence, statistical physics of complex networks has been an effective approach to study these systems. In this dissertation, I have used statistical physics to study two distinct phenomena in complex systems: i) Cascading failures and ii) Shortest paths in complex networks. Understanding cascading failures is considered to be one of the "holy grails" in the study of complex systems such as the power grid, transportation networks, and economic systems. Studying failures of these systems as percolation on complex networks has proved to be insightful. Previously, cascading failures have been studied extensively using two different models: k-core percolation and interdependent networks. The first part of this work combines the two models into a general model, solves it analytically, and validates the theoretical predictions through extensive computer simulations. The phase diagram of the percolation transition has been systematically studied as one varies the average local k-core threshold and the coupling between networks. The phase diagram of the combined processes is very rich and includes novel features that do not appear in the models which study each of the processes separately. For example, the phase diagram consists of first- and second-order transition regions separated by two tricritical lines that merge together and enclose a two-stage transition region. In the two-stage transition, the size of the giant component undergoes a first-order jump at a certain occupation probability followed by a continuous second-order transition at a smaller occupation probability. Furthermore, at certain fixed interdependencies, the percolation transition cycles from first-order to second-order to two-stage to first-order as the k-core threshold is increased. We setup the analytical equations describing the phase boundaries of the two-stage transition region and we derive the critical exponents for each type of transition. Understanding the shortest paths between individual elements in systems like communication networks and social media networks is important in the study of information cascades in these systems. Often, large heterogeneity can be present in the connections between nodes in these networks. Certain sets of nodes can be more highly connected among themselves than with the nodes from other sets. These sets of nodes are often referred to as 'communities'. The second part of this work studies the effect of the presence of communities on the distribution of shortest paths in a network using a modular Erdős-Renyi network model. In this model, the number of communities and the degree of modularity of the network can be tuned using the parameters of the model. We find that the model reaches a percolation threshold while tuning the degree of modularity of the network and the distribution of the shortest paths in the network can be used as an indicator of how the communities are connected.

"The Only Future Certainty Is that I'll Still Be Speaking to Her": Social Capital/Network for the Transition of Disadvantaged Young People

ERIC Educational Resources Information Center

Inui, Akio; Nishimura, Takayuki

2007-01-01

This paper examines the significance of social network and social capital in youth transition from school to work, with a focus on both instrumental and expressive aspects. In recent years the transition of Japanese young people has changed drastically, similar to young people in other industrialised countries. The individualisation of transition…

Remote network control plasma diagnostic system for Tokamak T-10

NASA Astrophysics Data System (ADS)

Troynov, V. I.; Zimin, A. M.; Krupin, V. A.; Notkin, G. E.; Nurgaliev, M. R.

2016-09-01

The parameters of molecular plasma in closed magnetic trap is studied in this paper. Using the system of molecular diagnostics, which was designed by the authors on the «Tokamak T-10» facility, the radiation of hydrogen isotopes at the plasma edge is investigated. The scheme of optical radiation registration within visible spectrum is described. For visualization, identification and processing of registered molecular spectra a new software is developed using MatLab environment. The software also includes electronic atlas of electronic-vibrational-rotational transitions for molecules of protium and deuterium. To register radiation from limiter cross-section a network control system is designed using the means of the Internet/Intranet. Remote control system diagram and methods are given. The examples of web-interfaces for working out equipment control scenarios and viewing of results are provided. After test run in Intranet, the remote diagnostic system will be accessible through Internet.

An Approach to Automated Fusion System Design and Adaptation

PubMed Central

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-01-01

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum. PMID:28300762

An Approach to Automated Fusion System Design and Adaptation.

PubMed

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-03-16

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.

SOS based robust H(∞) fuzzy dynamic output feedback control of nonlinear networked control systems.

PubMed

Chae, Seunghwan; Nguang, Sing Kiong

2014-07-01

In this paper, a methodology for designing a fuzzy dynamic output feedback controller for discrete-time nonlinear networked control systems is presented where the nonlinear plant is modelled by a Takagi-Sugeno fuzzy model and the network-induced delays by a finite state Markov process. The transition probability matrix for the Markov process is allowed to be partially known, providing a more practical consideration of the real world. Furthermore, the fuzzy controller's membership functions and premise variables are not assumed to be the same as the plant's membership functions and premise variables, that is, the proposed approach can handle the case, when the premise of the plant are not measurable or delayed. The membership functions of the plant and the controller are approximated as polynomial functions, then incorporated into the controller design. Sufficient conditions for the existence of the controller are derived in terms of sum of square inequalities, which are then solved by YALMIP. Finally, a numerical example is used to demonstrate the validity of the proposed methodology.

Network rewiring dynamics with convergence towards a star network

PubMed Central

Dick, G.; Parry, M.

2016-01-01

Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz (Nature 393, 440–442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach. PMID:27843396

Network rewiring dynamics with convergence towards a star network.

PubMed

Whigham, P A; Dick, G; Parry, M

2016-10-01

Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz ( Nature 393 , 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.

A Development System for Augmented Transition Network Grammars and a Large Grammar for Technical Prose. Technical Report No. 25.

ERIC Educational Resources Information Center

Mayer, John; Kieras, David E.

Using a system based on standard augmented transition network (ATN) parsing approach, this report describes a technique for the rapid development of natural language parsing, called High-Level Grammar Specification Language (HGSL). The first part of the report describes the syntax and semantics of HGSL and the network implementation of each of its…

Thiol-vinyl systems as shape memory polymers and novel two-stage reactive polymer systems

NASA Astrophysics Data System (ADS)

Nair, Devatha P.

2011-12-01

The focus of this research was to formulate, characterize and tailor the reaction methodologies and material properties of thiol-vinyl systems to develop novel polymer platforms for a range of engineering applications. Thiol-ene photopolymers were demonstrated to exhibit several advantageous characteristics for shape memory polymer systems for a range of biomedical applications. The thiol-ene shape memory polymer systems were tough and flexible as compared to the acrylic control systems with glass transition temperatures between 30 and 40 °C; ideal for actuation at body temperature. The thiol-ene polymers also exhibited excellent shape fixity and a rapid and distinct shape memory actuation response along with free strain recoveries of greater than 96% and constrained stress recoveries of 100%. Additionally, two-stage reactive thiol-acrylate systems were engineered as a polymer platform technology enabling two independent sets of polymer processing and material properties. There are distinct advantages to designing polymer systems that afford two distinct sets of material properties -- an intermediate polymer that would enable optimum handling and processing of the material (stage 1), while maintaining the ability to tune in different, final properties that enable the optimal functioning of the polymeric material (stage 2). To demonstrate the range of applicability of the two-stage reactive systems, three specific applications were demonstrated; shape memory polymers, lithographic impression materials, and optical materials. The thiol-acrylate reactions exhibit a wide range of application versatility due to the range of available thiol and acrylate monomers as well as reaction mechanisms such as Michael Addition reactions and free radical polymerizations. By designing a series of non-stoichiometeric thiol-acrylate systems, a polymer network is initially formed via a base catalyzed 'click' Michael addition reaction. This self-limiting reaction results in a Stage 1 polymer with excess acrylic functional groups within the network. At a later point in time, the photoinitiated, free radical polymerization of the excess acrylic functional groups results in a highly crosslinked, robust material system. By varying the monomers within the system as well as the stoichiometery of thiol to acrylate functional groups, the ability of the two-stage reactive systems to encompass a wide range of properties at the end of both the stage 1 and stage 2 polymerizations was demonstrated. The thiol-acrylate networks exhibited intermediate Stage 1 rubbery moduli and glass transition temperatures that range from 0.5 MPa and -10 ºC to 22 MPa and 22 ºC respectively. The same polymer networks can then attain glass transition temperatures that range from 5 ºC to 195 ºC and rubbery moduli of up to 200 MPa after the subsequent photocure stage. Two-stage reactive polymer composite systems were also formulated and characterized for thermomechanical and mechanical properties. Thermomechanical analysis showed that the fillers resulted in a significant increase in the modulus at both stage 1 and stage 2 polymerizations without a significant change in the glass transition temperatures (Tg). The two-stage reactive matrix composite formed with a hexafunctional acrylate matrix and 20 volume % silica particles showed a 125% increase in stage 1 modulus and 101% increase in stage 2 modulus, when compared with the modulus of the neat matrix. Finally, the two-stage reactive polymeric devices were formulated and designed as orthopedic suture anchors for arthroscopic surgeries and mechanically characterized. The Stage 1 device was designed to exhibit properties ideal for arthroscopic delivery and device placement with glass transition temperatures 25 -- 30 °C and rubbery moduli ˜ 95 MPa. The subsequent photopolymerization generated Stage 2 polymers designed to match the local bone environment with moduli ranging up to 2 GPa. Additionally, pull-out strengths of 140 N were demonstrated and are equivalent to the pull-strengths achieved by other commercially available suture anchors.

The challenges of a home-based nursing consultation business.

PubMed

Schulmeister, L

1999-03-01

The transition from working in a traditional setting to working at home alone can be challenging for new nurse consultants. Home-based consultants can use a variety of strategies to stay focused and connected, such as having a designated work area, limiting distractions, and networking. Nurse consultants can obtain information about business management from community resources, and computer on-line services offer a means of contacting other small-business owners. Ongoing business evaluations, which include professional accomplishments as well as an examination of income and expenses, help in planning. Home-based nurse consultants can increase the likelihood of business success by setting objectives, working diligently, and networking with others in the business community.

Social climber attachment in forming networks produces a phase transition in a measure of connectivity

NASA Astrophysics Data System (ADS)

Taylor, Dane; Larremore, Daniel B.

2012-09-01

The formation and fragmentation of networks are typically studied using percolation theory, but most previous research has been restricted to studying a phase transition in cluster size, examining the emergence of a giant component. This approach does not study the effects of evolving network structure on dynamics that occur at the nodes, such as the synchronization of oscillators and the spread of information, epidemics, and neuronal excitations. We introduce and analyze an alternative link-formation rule, called social climber (SC) attachment, that may be combined with arbitrary percolation models to produce a phase transition using the largest eigenvalue of the network adjacency matrix as the order parameter. This eigenvalue is significant in the analyses of many network-coupled dynamical systems in which it measures the quality of global coupling and is hence a natural measure of connectivity. We highlight the important self-organized properties of SC attachment and discuss implications for controlling dynamics on networks.

Flexible and evolutionary optical access networks

NASA Astrophysics Data System (ADS)

Hsueh, Yu-Li

Passive optical networks (PONs) are promising solutions that will open the first-mile bottleneck. Current PONs employ time division multiplexing (TDM) to share bandwidth among users, leading to low cost but limited capacity. In the future, wavelength division multiplexing (WDM) technologies will be deployed to achieve high performance. This dissertation describes several advanced technologies to enhance PON systems. A spectral shaping line coding scheme is developed to allow a simple and cost-effective overlay of high data-rate services in existing PONs, leaving field-deployed fibers and existing services untouched. Spectral shapes of coded signals can be manipulated to adapt to different systems. For a specific tolerable interference level, the optimal line code can be found which maximizes the data throughput. Experiments are conducted to demonstrate and compare several optimized line codes. A novel PON employing dynamic wavelength allocation to provide bandwidth sharing across multiple physical PONs is designed and experimentally demonstrated. Tunable lasers, arrayed waveguide gratings, and coarse/fine filtering combine to create a flexible optical access solution. The network's excellent scalability can bridge the gap between conventional TDM PONs and WDM PONs. Scheduling algorithms with quality of service support are also investigated. Simulation results show that the proposed architecture exhibits significant performance gain over conventional PON systems. Streaming video transmission is demonstrated on the prototype experimental testbed. The powerful architecture is a promising candidate for next-generation optical access networks. A new CDR circuit for receiving the bursty traffic in PONs is designed and analyzed. It detects data transition edges upon arrival of the data burst and quickly selects the best clock phase by a control logic circuit. Then, an analog delay-locked loop (DLL) keeps track of data transitions and removes phase errors throughout the burst. The combination of the fast phase detection mechanism and a feedback loop based on DLL allows both fast response and manageable jitter performance in the burst-mode application. A new efficient numerical algorithm is developed to analyze holey optical fibers. The algorithm has been verified against experimental data, and is exploited to design holey optical fibers optimized for the discrete Raman amplification.

Local and global synchronization transitions induced by time delays in small-world neuronal networks with chemical synapses.

PubMed

Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile

2015-02-01

Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.

Evolution of weighted complex bus transit networks with flow

NASA Astrophysics Data System (ADS)

Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei

2016-02-01

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

Exacerbated vulnerability of coupled socio-economic risk in complex networks

NASA Astrophysics Data System (ADS)

Zhang, Xin; Feng, Ling; Berman, Yonatan; Hu, Ning; Stanley, H. Eugene

2016-10-01

The study of risk contagion in economic networks has most often focused on the financial liquidities of institutions and assets. In practice the agents in a network affect each other through social contagion, i.e., through herd behavior and the tendency to follow leaders. We study the coupled risk between social and economic contagion and find it significantly more severe than when economic risk is considered alone. Using the empirical network from the China venture capital market we find that the system exhibits an extreme risk of abrupt phase transition and large-scale damage, which is in clear contrast to the smooth phase transition traditionally observed in economic contagion alone. We also find that network structure impacts market resilience and that the randomization of the social network of the market participants can reduce system fragility when there is herd behavior. Our work indicates that under coupled contagion mechanisms network resilience can exhibit a fundamentally different behavior, i.e., an abrupt transition. It also reveals the extreme risk when a system has coupled socio-economic risks, and this could be of interest to both policy makers and market practitioners.

Phase transitions in cooperative coinfections: Simulation results for networks and lattices

NASA Astrophysics Data System (ADS)

Grassberger, Peter; Chen, Li; Ghanbarnejad, Fakhteh; Cai, Weiran

2016-04-01

We study the spreading of two mutually cooperative diseases on different network topologies, and with two microscopic realizations, both of which are stochastic versions of a susceptible-infected-removed type model studied by us recently in mean field approximation. There it had been found that cooperativity can lead to first order transitions from spreading to extinction. However, due to the rapid mixing implied by the mean field assumption, first order transitions required nonzero initial densities of sick individuals. For the stochastic model studied here the results depend strongly on the underlying network. First order transitions are found when there are few short but many long loops: (i) No first order transitions exist on trees and on 2-d lattices with local contacts. (ii) They do exist on Erdős-Rényi (ER) networks, on d -dimensional lattices with d ≥4 , and on 2-d lattices with sufficiently long-ranged contacts. (iii) On 3-d lattices with local contacts the results depend on the microscopic details of the implementation. (iv) While single infected seeds can always lead to infinite epidemics on regular lattices, on ER networks one sometimes needs finite initial densities of infected nodes. (v) In all cases the first order transitions are actually "hybrid"; i.e., they display also power law scaling usually associated with second order transitions. On regular lattices, our model can also be interpreted as the growth of an interface due to cooperative attachment of two species of particles. Critically pinned interfaces in this model seem to be in different universality classes than standard critically pinned interfaces in models with forbidden overhangs. Finally, the detailed results mentioned above hold only when both diseases propagate along the same network of links. If they use different links, results can be rather different in detail, but are similar overall.

Phase Transition in Opinion Diffusion in Social Networks

DTIC Science & Technology

2012-05-01

the opinions of social agents diffuse in a network under a so-called hard-interaction model, in which the agents inter- act more strongly with...gent behavior. Index Terms— opinion diffusion , opinion dynamics, social net- works, phase transition, herding. 1. INTRODUCTION The study of the

Two coupled effects of sub micron silica particles on the mechanical relaxation behavior of ethylene-propylene-diene rubber chains.

PubMed

Gu, Zhen; Zhang, Xian; Ding, Xin; Bao, Chao; Fang, Fei; Li, Shiyuan; Zhou, Haifeng; Xue, Meng; Wang, Huan; Tian, Xingyou

2014-08-28

This article studied the influence of silica (SiO2) particles on the crosslinked network and the molecular mobility of ethylene-propylene-diene (EPDM) rubber chains by dynamic mechanical analysis (DMA). When SiO2 fraction is lower than 8 phr, the chain segments that participate in the glass-rubber transition (α transition) decrease with increasing the SiO2 content, while the whole crosslinked network is almost unaffected by the presence of SiO2. When the SiO2 fraction increases to about 20 phr, there appears a new tan δ peak (α' transition) above the α transition. This could be because the crosslinking reaction took place only on a small scale and the formed network became gradually incomplete when the content of the particles exceeded some critical value, and the α' transition is attributed primarily to the motion of non-elastic network chains loosely attached to the three-dimensional network. However, at SiO2 loadings higher than 40 phr, the crosslinking density was kept basically constant. The α' transition is hindered by a restriction of the chain mobility due to SiO2. The different changes of α' transition depended on the two coupled effects of SiO2, including restricting the chain mobility and decreasing the crosslinking density. Correspondingly, with increasing the mobility of EPDM chains and SiO2-induced strengthening, the mechanical properties of EPDM composite are dramatically improved. With the addition of 20 phr of SiO2 in the EPDM, a 113% increase in the elongation at break, a 510% increase in the fracture energy, and a 283% increase in the tensile strength are achieved.

Modeling and Simulating Passenger Behavior for a Station Closure in a Rail Transit Network

PubMed Central

Yin, Haodong; Han, Baoming; Li, Dewei; Wu, Jianjun; Sun, Huijun

2016-01-01

A station closure is an abnormal operational situation in which the entrances or exits of a rail transit station have to be closed for some time due to an unexpected incident. A novel approach is developed to estimate the impacts of the alternative station closure scenarios on both passenger behavioral choices at the individual level and passenger demand at the disaggregate level in a rail transit network. Therefore, the contributions of this study are two-fold: (1) A basic passenger behavior optimization model is mathematically constructed based on 0–1 integer programming to describe passengers’ responses to alternative origin station closure scenarios and destination station closure scenarios; this model also considers the availability of multi-mode transportation and the uncertain duration of the station closure; (2) An integrated solution algorithm based on the passenger simulation is developed to solve the proposed model and to estimate the effects of a station closure on passenger demand in a rail transit network. Furthermore, 13 groups of numerical experiments based on the Beijing rail transit network are performed as case studies with 2,074,267 records of smart card data. The comparisons of the model outputs and the manual survey show that the accuracy of our proposed behavior optimization model is approximately 80%. The results also show that our model can be used to capture the passenger behavior and to quantitatively estimate the effects of alternative closure scenarios on passenger flow demand for the rail transit network. Moreover, the closure duration and its overestimation greatly influence the individual behavioral choices of the affected passengers and the passenger demand. Furthermore, if the rail transit operator can more accurately estimate the closure duration (namely, as g approaches 1), the impact of the closure can be somewhat mitigated. PMID:27935963

Modeling and Simulating Passenger Behavior for a Station Closure in a Rail Transit Network.

PubMed

Yin, Haodong; Han, Baoming; Li, Dewei; Wu, Jianjun; Sun, Huijun

2016-01-01

A station closure is an abnormal operational situation in which the entrances or exits of a rail transit station have to be closed for some time due to an unexpected incident. A novel approach is developed to estimate the impacts of the alternative station closure scenarios on both passenger behavioral choices at the individual level and passenger demand at the disaggregate level in a rail transit network. Therefore, the contributions of this study are two-fold: (1) A basic passenger behavior optimization model is mathematically constructed based on 0-1 integer programming to describe passengers' responses to alternative origin station closure scenarios and destination station closure scenarios; this model also considers the availability of multi-mode transportation and the uncertain duration of the station closure; (2) An integrated solution algorithm based on the passenger simulation is developed to solve the proposed model and to estimate the effects of a station closure on passenger demand in a rail transit network. Furthermore, 13 groups of numerical experiments based on the Beijing rail transit network are performed as case studies with 2,074,267 records of smart card data. The comparisons of the model outputs and the manual survey show that the accuracy of our proposed behavior optimization model is approximately 80%. The results also show that our model can be used to capture the passenger behavior and to quantitatively estimate the effects of alternative closure scenarios on passenger flow demand for the rail transit network. Moreover, the closure duration and its overestimation greatly influence the individual behavioral choices of the affected passengers and the passenger demand. Furthermore, if the rail transit operator can more accurately estimate the closure duration (namely, as g approaches 1), the impact of the closure can be somewhat mitigated.

Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

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

Rossi, R; Gallagher, B; Neville, J

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied ourmore » model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.« less

Probing many-body localization with neural networks

NASA Astrophysics Data System (ADS)

Schindler, Frank; Regnault, Nicolas; Neupert, Titus

2017-06-01

We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.

A Networks Approach to Modeling Enzymatic Reactions.

PubMed

Imhof, P

2016-01-01

Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.

A systematic review of the status of children's school access in low- and middle-income countries between 1998 and 2013: using the INDEPTH Network platform to fill the research gaps

PubMed Central

Kamanda, Mamusu; Sankoh, Osman

2015-01-01

Background The framework for expanding children's school access in low- and middle-income countries (LMICs) has been directed by universal education policies as part of Education for All since 1990. In measuring progress to universal education, a narrow conceptualisation of access which dichotomises children's participation as being in or out of school has often been assumed. Yet, the actual promise of universal education goes beyond this simple definition to include retention, progression, completion, and learning. Objective Our first objective was to identify gaps in the literature on children's school access using the zones of exclusion of the Consortium for Research on Educational Access, Transition, and Equity as a framework. Second, we gave consideration to how these gaps can be met by using longitudinal and cross-country data from Health and Demographic Surveillance System (HDSS) sites within the International Network for the Demographic Evaluation of Population and Their Health (INDEPTH) in LMICs. Design Using Web of Science, we conducted a literature search of studies published in international peer-reviewed journals between 1998 and 2013 in LMICs. The phrases we searched included six school outcomes: school enrolment, school attendance, grade progression, school dropout, primary to secondary school transition, and school completion. From our search, we recorded studies according to: 1) school outcomes; 2) whether longitudinal data were used; and 3) whether data from more than one country were analysed. Results The area of school access most published is enrolment followed by attendance and dropout. Primary to secondary school transition and grade progression had the least number of publications. Of 132 publications which we found to be relevant to school access, 33 made use of longitudinal data and 17 performed cross-country analyses. Conclusions The majority of studies published in international peer-reviewed journals on children's school access between 1998 and 2013 were focused on three outcomes: enrolment, attendance, and dropout. Few of these studies used data collected over time or data collected from more than one country for comparative analyses. The contribution of the INDEPTH Network in helping to address these gaps in the literature lies in the longitudinal design of HDSS surveys and in the diversity of countries within the network. PMID:26562137

Modeling Developmental Transitions in Adaptive Resonance Theory

ERIC Educational Resources Information Center

Raijmakers, Maartje E. J.; Molenaar, Peter C. M.

2004-01-01

Neural networks are applied to a theoretical subject in developmental psychology: modeling developmental transitions. Two issues that are involved will be discussed: discontinuities and acquiring qualitatively new knowledge. We will argue that by the appearance of a bifurcation, a neural network can show discontinuities and may acquire…

Preparing for the Integration of Emerging Technologies.

ERIC Educational Resources Information Center

Dyrli, Odvard Egil; Kinnaman, Daniel E.

1994-01-01

Discussion of the process of integrating new technologies into schools considers the evolution of technology, including personal computers, CD-ROMs, hypermedia, and networking/communications; the transition from Industrial-Age to Information-Age schools; and the logical steps of transition. Sidebars discuss a networked multimedia pilot project and…

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

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

PubMed

Onaga, Tomokatsu; Gleeson, James P; Masuda, Naoki

2017-09-08

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

NASA Astrophysics Data System (ADS)

Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

2017-09-01

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Physics textbooks from the viewpoint of network structures

NASA Astrophysics Data System (ADS)

Králiková, Petra; Teleki, Aba

2017-01-01

We can observe self-organized networks all around us. These networks are, in general, scale invariant networks described by the Bianconi-Barabasi model. The self-organized networks (networks formed naturally when feedback acts on the system) show certain universality. These networks, in simplified models, have scale invariant distribution (Pareto distribution type I) and parameter α has value between 2 and 5. The textbooks are extremely important in the learning process and from this reason we studied physics textbook at the level of sentences and physics terms (bipartite network). The nodes represent physics terms, sentences, and pictures, tables, connected by links (by physics terms and transitional words and transitional phrases). We suppose that learning process are more robust and goes faster and easier if the physics textbook has a structure similar to structures of self-organized networks.

Coordinated and uncoordinated optimization of networks

NASA Astrophysics Data System (ADS)

Brede, Markus

2010-06-01

In this paper, we consider spatial networks that realize a balance between an infrastructure cost (the cost of wire needed to connect the network in space) and communication efficiency, measured by average shortest path length. A global optimization procedure yields network topologies in which this balance is optimized. These are compared with network topologies generated by a competitive process in which each node strives to optimize its own cost-communication balance. Three phases are observed in globally optimal configurations for different cost-communication trade offs: (i) regular small worlds, (ii) starlike networks, and (iii) trees with a center of interconnected hubs. In the latter regime, i.e., for very expensive wire, power laws in the link length distributions P(w)∝w-α are found, which can be explained by a hierarchical organization of the networks. In contrast, in the local optimization process the presence of sharp transitions between different network regimes depends on the dimension of the underlying space. Whereas for d=∞ sharp transitions between fully connected networks, regular small worlds, and highly cliquish periphery-core networks are found, for d=1 sharp transitions are absent and the power law behavior in the link length distribution persists over a much wider range of link cost parameters. The measured power law exponents are in agreement with the hypothesis that the locally optimized networks consist of multiple overlapping suboptimal hierarchical trees.

Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network

PubMed Central

Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun

2017-01-01

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links. PMID:28282899

Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network.

PubMed

Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun

2017-03-08

Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.

Design, Implementation, Building and Evaluation of a Torus Double Transitive Closure Network of Transputers.

DTIC Science & Technology

1988-06-01

especially ’Russell Whalen, Rosalie Johnson and Mike Williams, for all the aid lent to me during the lab research for this thesis. xi I. INTRODUCTION A...particular weather model. Finally is important to continue research in the field of graphic applications, especially that which pertains to the study of...San Bernardino, Caracas Distrito Federal, Armada-001-MSA, Venezuela, South America 14. Director de Educacion 01 Comandancia General de la Armada Ave

Theory driven research designs for explaining behavioural health risk transitions: the case of smoking.

PubMed

Dixon, Jane; Banwell, Cathy

2009-06-01

Recent social network analyses have suggested that common chronic disease risk factors are more mutable than expected; raising practical considerations for public health interventions. Within this context, it is timely to assess the alternative social science reasoning being offered to explain behavioural health risk transitions. This paper takes up this challenge by critically reviewing the major theories applied to the temporal trends and sub-population variations in affluent country smoking behaviour. Three explanations dominate: a materialist approach; Bourdieu's distinctive class-based cultures; and, the spread of norms and emotions within social networks. We note conceptual tension when integrated theories are adopted. We also report on the relative absence of theoretical interrogation for the persistent adoption of smoking behaviours among present and successive lower socio-economic status (SES) cohorts. While unequal rates of persistence within cohorts has received some attention, the ongoing adoption of a non-innovative and health damaging behaviour is not well understood. To this end, we suggest the incorporation of several underused concepts: namely Bourdieu's 'rules of the game' and 'symbolic violence' and 'mimesis', an aspect of social contagion. We conclude by describing the implications for social action of the alternative theories, and argue that theory driven research designs could deliver more efficacious evidence for interventions than the post hoc application of theories to existing data sets.

Towards designing robust coupled networks

NASA Astrophysics Data System (ADS)

Schneider, Christian M.; Yazdani, Nuri; Araújo, Nuno A. M.; Havlin, Shlomo; Herrmann, Hans J.

2013-06-01

Natural and technological interdependent systems have been shown to be highly vulnerable due to cascading failures and an abrupt collapse of global connectivity under initial failure. Mitigating the risk by partial disconnection endangers their functionality. Here we propose a systematic strategy of selecting a minimum number of autonomous nodes that guarantee a smooth transition in robustness. Our method which is based on betweenness is tested on various examples including the famous 2003 electrical blackout of Italy. We show that, with this strategy, the necessary number of autonomous nodes can be reduced by a factor of five compared to a random choice. We also find that the transition to abrupt collapse follows tricritical scaling characterized by a set of exponents which is independent on the protection strategy.

Optimizing Pedestrian-Friendly Walking Path for the First and Last Mile Transit Journey by Using the Analytical Network Process (anp) Decision Model and GIS Network Analysis

NASA Astrophysics Data System (ADS)

Naharudin, N.; Ahamad, M. S. S.; Sadullah, A. F. M.

2017-10-01

Every transit trip begins and ends with pedestrian travel. People need to walk to access the transit services. However, their choice to walk depends on many factors including the connectivity, level of comfort and safety. These factors can influence the pleasantness of riding the transit itself, especially during the first/last mile (FLM) journey. This had triggered few studies attempting to measure the pedestrian-friendliness a walking environment can offer. There were studies that implement the pedestrian experience on walking to assess the pedestrian-friendliness of a walking environment. There were also studies that use spatial analysis to measure it based on the path connectivity and accessibility to public facilities and amenities. Though both are good, but the perception-based studies and spatial analysis can be combined to derive more holistic results. This paper proposes a framework for selecting a pedestrian-friendly path for the FLM transit journey by using the two techniques (perception-based and spatial analysis). First, the degree of importance for the factors influencing a good walking environment will be aggregated by using Analytical Network Process (ANP) decision rules based on people's preferences on those factors. The weight will then be used as attributes in the GIS network analysis. Next, the network analysis will be performed to find a pedestrian-friendly walking route based on the priorities aggregated by ANP. It will choose routes passing through the preferred attributes accordingly. The final output is a map showing pedestrian-friendly walking path for the FLM transit journey.

Global epidemic invasion thresholds in directed cattle subpopulation networks having source, sink, and transit nodes

USDA-ARS?s Scientific Manuscript database

Through the characterization of a metapopulation cattle disease model on a directed network having source, transit, and sink nodes, we derive two global epidemic invasion thresholds. The first threshold defines the conditions necessary for an epidemic to successfully spread at the global scale. The ...

Generalized model for k -core percolation and interdependent networks

NASA Astrophysics Data System (ADS)

Panduranga, Nagendra K.; Gao, Jianxi; Yuan, Xin; Stanley, H. Eugene; Havlin, Shlomo

2017-09-01

Cascading failures in complex systems have been studied extensively using two different models: k -core percolation and interdependent networks. We combine the two models into a general model, solve it analytically, and validate our theoretical results through extensive simulations. We also study the complete phase diagram of the percolation transition as we tune the average local k -core threshold and the coupling between networks. We find that the phase diagram of the combined processes is very rich and includes novel features that do not appear in the models studying each of the processes separately. For example, the phase diagram consists of first- and second-order transition regions separated by two tricritical lines that merge and enclose a two-stage transition region. In the two-stage transition, the size of the giant component undergoes a first-order jump at a certain occupation probability followed by a continuous second-order transition at a lower occupation probability. Furthermore, at certain fixed interdependencies, the percolation transition changes from first-order → second-order → two-stage → first-order as the k -core threshold is increased. The analytic equations describing the phase boundaries of the two-stage transition region are set up, and the critical exponents for each type of transition are derived analytically.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Resilience of networks formed of interdependent modular networks

NASA Astrophysics Data System (ADS)

Shekhtman, Louis M.; Shai, Saray; Havlin, Shlomo

2015-12-01

Many infrastructure networks have a modular structure and are also interdependent with other infrastructures. While significant research has explored the resilience of interdependent networks, there has been no analysis of the effects of modularity. Here we develop a theoretical framework for attacks on interdependent modular networks and support our results through simulations. We focus, for simplicity, on the case where each network has the same number of communities and the dependency links are restricted to be between pairs of communities of different networks. This is particularly realistic for modeling infrastructure across cities. Each city has its own infrastructures and different infrastructures are dependent only within the city. However, each infrastructure is connected within and between cities. For example, a power grid will connect many cities as will a communication network, yet a power station and communication tower that are interdependent will likely be in the same city. It has previously been shown that single networks are very susceptible to the failure of the interconnected nodes (between communities) (Shai et al 2014 arXiv:1404.4748) and that attacks on these nodes are even more crippling than attacks based on betweenness (da Cunha et al 2015 arXiv:1502.00353). In our example of cities these nodes have long range links which are more likely to fail. For both treelike and looplike interdependent modular networks we find distinct regimes depending on the number of modules, m. (i) In the case where there are fewer modules with strong intraconnections, the system first separates into modules in an abrupt first-order transition and then each module undergoes a second percolation transition. (ii) When there are more modules with many interconnections between them, the system undergoes a single transition. Overall, we find that modular structure can significantly influence the type of transitions observed in interdependent networks and should be considered in attempts to make interdependent networks more resilient.

Using simple agent-based modeling to inform and enhance neighborhood walkability.

PubMed

Badland, Hannah; White, Marcus; Macaulay, Gus; Eagleson, Serryn; Mavoa, Suzanne; Pettit, Christopher; Giles-Corti, Billie

2013-12-11

Pedestrian-friendly neighborhoods with proximal destinations and services encourage walking and decrease car dependence, thereby contributing to more active and healthier communities. Proximity to key destinations and services is an important aspect of the urban design decision making process, particularly in areas adopting a transit-oriented development (TOD) approach to urban planning, whereby densification occurs within walking distance of transit nodes. Modeling destination access within neighborhoods has been limited to circular catchment buffers or more sophisticated network-buffers generated using geoprocessing routines within geographical information systems (GIS). Both circular and network-buffer catchment methods are problematic. Circular catchment models do not account for street networks, thus do not allow exploratory 'what-if' scenario modeling; and network-buffering functionality typically exists within proprietary GIS software, which can be costly and requires a high level of expertise to operate. This study sought to overcome these limitations by developing an open-source simple agent-based walkable catchment tool that can be used by researchers, urban designers, planners, and policy makers to test scenarios for improving neighborhood walkable catchments. A simplified version of an agent-based model was ported to a vector-based open source GIS web tool using data derived from the Australian Urban Research Infrastructure Network (AURIN). The tool was developed and tested with end-user stakeholder working group input. The resulting model has proven to be effective and flexible, allowing stakeholders to assess and optimize the walkability of neighborhood catchments around actual or potential nodes of interest (e.g., schools, public transport stops). Users can derive a range of metrics to compare different scenarios modeled. These include: catchment area versus circular buffer ratios; mean number of streets crossed; and modeling of different walking speeds and wait time at intersections. The tool has the capacity to influence planning and public health advocacy and practice, and by using open-access source software, it is available for use locally and internationally. There is also scope to extend this version of the tool from a simple to a complex model, which includes agents (i.e., simulated pedestrians) 'learning' and incorporating other environmental attributes that enhance walkability (e.g., residential density, mixed land use, traffic volume).

Star-coupled Hindmarsh-Rose neural network with chemical synapses

NASA Astrophysics Data System (ADS)

Usha, K.; Subha, P. A.

We analyze the patterns like synchrony, desynchrony, and Drum head mode in a network of Hindmarsh-Rose (HR) neurons interacting via chemical synapse in unidirectional and bidirectional star topology. A two-coupled system has been studied for synchronization by varying the coupling strength and the parameter describing the activation and inactivation of the fast ion channel. The transverse Lyapunov exponent spectrum is plotted to observe the point of transition from desynchrony to synchrony. The synchronized, desynchronized, and drum head mode regions are observed when the neurons are connected in unidirectional and bidirectional coupling configurations. A detailed analysis about the time evolution of membrane potential corresponding to each region is presented. The annihilation of synchronized region and the expansion of drum head mode region in bidirectional coupling is discussed using parameter space. Our work provides finer insight into the existence and stability of Drum head mode and is useful for designing communication networks.

Investigating the relationship between subjective drug craving and temporal dynamics of the default mode network, executive control network, and salience network in methamphetamine dependents using rsfMRI

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Somayyeh; Hossein-Zadeh, Gholam-Ali; Shahbabaie, Alireza; Ekhtiari, Hamed

2016-03-01

Resting state functional connectivity (rsFC) studies using fMRI provides a great deal of knowledge on the spatiotemporal organization of the brain. The relationships between and within a number of resting state functional networks, namely the default mode network (DMN), salience network (SN) and executive control network (ECN) have been intensely studied in basic and clinical cognitive neuroscience [1]. However, the presumption of spatial and temporal stationarity has mostly restricted the assessment of rsFC [1]. In this study, sliding window correlation analysis and k-means clustering were exploited to examine the temporal dynamics of rsFC of these three networks in 24 abstinent methamphetamine dependents. Afterwards, using canonical correlation analysis (CCA) the possible relationship between the level of self-reported craving and the temporal dynamics was examined. Results indicate that the rsFC transits between 6 discrete "FC states" in the meth dependents. CCA results show that higher levels of craving are associated with higher probability of transiting from state 4 to 6 (positive FC of DMN-ECN getting weak and negative FC of DMN-SN appearing) and staying in state 4 (positive FC of DMN-ECN), lower probability of staying in state 2 (negative FC of DMN-ECN), transiting from state 4 to 2 (change of positive FC of DMN-ECN to negative FC), and transiting from state 3 to 5 (appearance of negative FC of DMN-SN and positive FC of DMN-ECN with the presence of negative FC of SN-ECN). Quantitative measures of temporal dynamics in large-scale brain networks could bring new added values to increase potentials for applications of rsfMRI in addiction medicine.

Setting Access Permission through Transitive Relationship in Web-based Social Networks

NASA Astrophysics Data System (ADS)

Hong, Dan; Shen, Vincent Y.

The rising popularity of various social networking websites has created a huge problem on Internet privacy. Although it is easy to post photos, comments, opinions on some events, etc. on the Web, some of these data (such as a person’s location at a particular time, criticisms of a politician, etc.) are private and should not be accessed by unauthorized users. Although social networks facilitate sharing, the fear of sending sensitive data to a third party without knowledge or permission of the data owners discourages people from taking full advantage of some social networking applications. We exploit the existing relationships on social networks and build a ‘‘trust network’’ with transitive relationship to allow controlled data sharing so that the privacy and preferences of data owners are respected. The trust network linking private data owners, private data requesters, and intermediary users is a directed weighted graph. The permission value for each private data requester can be automatically assigned in this network based on the transitive relationship. Experiments were conducted to confirm the feasibility of constructing the trust network from existing social networks, and to assess the validity of permission value assignments in the query process. Since the data owners only need to define the access rights of their closest contacts once, this privacy scheme can make private data sharing easily manageable by social network participants.

Collective dynamics in heterogeneous networks of neuronal cellular automata

NASA Astrophysics Data System (ADS)

Manchanda, Kaustubh; Bose, Amitabha; Ramaswamy, Ramakrishna

2017-12-01

We examine the collective dynamics of heterogeneous random networks of model neuronal cellular automata. Each automaton has b active states, a single silent state and r - b - 1 refractory states, and can show 'spiking' or 'bursting' behavior, depending on the values of b. We show that phase transitions that occur in the dynamical activity can be related to phase transitions in the structure of Erdõs-Rényi graphs as a function of edge probability. Different forms of heterogeneity allow distinct structural phase transitions to become relevant. We also show that the dynamics on the network can be described by a semi-annealed process and, as a result, can be related to the Boolean Lyapunov exponent.

Communication: Analysing kinetic transition networks for rare events.

PubMed

Stevenson, Jacob D; Wales, David J

2014-07-28

The graph transformation approach is a recently proposed method for computing mean first passage times, rates, and committor probabilities for kinetic transition networks. Here we compare the performance to existing linear algebra methods, focusing on large, sparse networks. We show that graph transformation provides a much more robust framework, succeeding when numerical precision issues cause the other methods to fail completely. These are precisely the situations that correspond to rare event dynamics for which the graph transformation was introduced.

Multistable binary decision making on networks

NASA Astrophysics Data System (ADS)

Lucas, Andrew; Lee, Ching Hua

2013-03-01

We propose a simple model for a binary decision making process on a graph, motivated by modeling social decision making with cooperative individuals. The model is similar to a random field Ising model or fiber bundle model, but with key differences in behavior on heterogeneous networks. For many types of disorder and interactions between the nodes, we predict with mean field theory discontinuous phase transitions that are largely independent of network structure. We show how these phase transitions can also be understood by studying microscopic avalanches and describe how network structure enhances fluctuations in the distribution of avalanches. We suggest theoretically the existence of a “glassy” spectrum of equilibria associated with a typical phase, even on infinite graphs, so long as the first moment of the degree distribution is finite. This behavior implies that the model is robust against noise below a certain scale and also that phase transitions can switch from discontinuous to continuous on networks with too few edges. Numerical simulations suggest that our theory is accurate.

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

Design and Synthesis of Network-Forming Triblock Copolymers Using Tapered Block Interfaces

PubMed Central

Kuan, Wei-Fan; Roy, Raghunath; Rong, Lixia; Hsiao, Benjamin S.; Epps, Thomas H.

2012-01-01

We report a strategy for generating novel dual-tapered poly(isoprene-b-isoprene/styrene-b-styrene-b-styrene/methyl methacrylate-b-methyl methacrylate) [P(I-IS-S-SM-M)] triblock copolymers that combines anionic polymerization, atom transfer radical polymerization (ATRP), and Huisgen 1,3-dipolar cycloaddition click chemistry. The tapered interfaces between blocks were synthesized via a semi-batch feed using programmable syringe pumps. This strategy allows us to manipulate the transition region between copolymer blocks in triblock copolymers providing control over the interfacial interactions in our nanoscale phase-separated materials independent of molecular weight and block constituents. Additionally, we show the ability to retain a desirous and complex multiply-continuous network structure (alternating gyroid) in our dual-tapered triblock material. PMID:23066522

Examining the Impact of Pre-Induction Social Networking on the Student Transition into Higher Education

ERIC Educational Resources Information Center

Ribchester, Chris; Ross, Kim; Rees, Emma L. E.

2014-01-01

This research paper considers how bespoke online social networks have been used to support students' transition into higher education during the weeks immediately prior to formal "on-site" induction. An analysis of online activities showed some differences in the pattern of engagement between two contrasting departments (Geography and…

Mutually cooperative epidemics on power-law networks

NASA Astrophysics Data System (ADS)

Cui, Peng-Bi; Colaiori, Francesca; Castellano, Claudio

2017-08-01

The spread of an infectious disease can, in some cases, promote the propagation of other pathogens favoring violent outbreaks, which cause a discontinuous transition to an endemic state. The topology of the contact network plays a crucial role in these cooperative dynamics. We consider a susceptible-infected-removed-type model with two mutually cooperative pathogens: An individual already infected with one disease has an increased probability of getting infected by the other. We present a heterogeneous mean-field theoretical approach to the coinfection dynamics on generic uncorrelated power-law degree-distributed networks and validate its results by means of numerical simulations. We show that, when the second moment of the degree distribution is finite, the epidemic transition is continuous for low cooperativity, while it is discontinuous when cooperativity is sufficiently high. For scale-free networks, i.e., topologies with diverging second moment, the transition is instead always continuous. In this way we clarify the effect of heterogeneity and system size on the nature of the transition, and we validate the physical interpretation about the origin of the discontinuity.

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Glass Transition Temperature Measurement for Undercured Cyanate Ester Networks: Challenges, Tips, and Tricks (Briefing Charts)

DTIC Science & Technology

2014-01-29

DISTRIBUTION A: Approved for public release; distribution is unlimited. Thermosetting Polymers Have a TG Envelope – Not Just a TG 4 • The glass transition...glass transition temperature of a thermosetting polymer can vary over a wide range of temperatures depending on how the polymer is processed • A... thermosetting polymer with only one kind of network formation and negligible side reactions, the conversion may be determined at every point in the scan. • By

Community detection in networks with unequal groups.

PubMed

Zhang, Pan; Moore, Cristopher; Newman, M E J

2016-01-01

Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

Burst synchronization transitions in a neuronal network of subnetworks

NASA Astrophysics Data System (ADS)

Sun, Xiaojuan; Lei, Jinzhi; Perc, Matjaž; Kurths, Jürgen; Chen, Guanrong

2011-03-01

In this paper, the transitions of burst synchronization are explored in a neuronal network consisting of subnetworks. The studied network is composed of electrically coupled bursting Hindmarsh-Rose neurons. Numerical results show that two types of burst synchronization transitions can be induced not only by the variations of intra- and intercoupling strengths but also by changing the probability of random links between different subnetworks and the number of subnetworks. Furthermore, we find that the underlying mechanisms for these two bursting synchronization transitions are different: one is due to the change of spike numbers per burst, while the other is caused by the change of the bursting type. Considering that changes in the coupling strengths and neuronal connections are closely interlaced with brain plasticity, the presented results could have important implications for the role of the brain plasticity in some functional behavior that are associated with synchronization.

Social network analysis of child and adult interorganizational connections.

PubMed

Davis, Maryann; Koroloff, Nancy; Johnsen, Matthew

2012-01-01

Because most programs serve either children and their families or adults, a critical component of service and treatment continuity in mental health and related services for individuals transitioning into adulthood (ages 14-25) is coordination across programs on either side of the adult age divide. This study was conducted in Clark County, Washington, a community that had received a Partnership for Youth Transition grant from the Federal Center for Mental Health Services. Social Network Analysis methodology was used to describe the strength and direction of each organization's relationship to other organizations in the transition network. Interviews were conducted before grant implementation (n=103) and again four years later (n=99). The findings of the study revealed significant changes in the nature of relationships between organizations over time. While the overall density of the transition service network remained stable, specific ways of connecting did change. Some activities became more decentralized while others became more inclusive as evidenced by the increase in size of the largest K-core. This was particularly true for the activity of "receiving referrals." These changes reflected more direct contact between child and adult serving organizations. The two separate child and adult systems identified at baseline appeared more integrated by the end of the grant period. Having greater connectivity among all organizations regardless of ages served should benefit youth and young adults of transition age. This study provides further evidence that Social Network Analysis is a useful method for measuring change in service system integration over time.

Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets

NASA Astrophysics Data System (ADS)

Zucker, Shay; Giryes, Raja

2018-04-01

Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the presence of red (correlated) noise in the light curves obtained from the dedicated space telescopes. Based on the groundbreaking results deep learning achieves in many signal and image processing applications, we propose to use deep neural networks to solve this problem. We present a feasibility study, in which we applied a convolutional neural network on a simulated training set. The training set comprised light curves received from a hypothetical high-cadence space-based telescope. We simulated the red noise by using Gaussian Processes with a wide variety of hyper-parameters. We then tested the network on a completely different test set simulated in the same way. Our study proves that very difficult cases can indeed be detected. Furthermore, we show how detection trends can be studied and detection biases quantified. We have also checked the robustness of the neural-network performance against practical artifacts such as outliers and discontinuities, which are known to affect space-based high-cadence light curves. Future work will allow us to use the neural networks to characterize the transit model and identify individual transits. This new approach will certainly be an indispensable tool for the detection of habitable planets in the future planet-detection space missions such as PLATO.

A Model of Mental State Transition Network

NASA Astrophysics Data System (ADS)

Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo

Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

NASA Astrophysics Data System (ADS)

Scarpetta, Silvia; Apicella, Ilenia; Minati, Ludovico; de Candia, Antonio

2018-06-01

Many experimental results, both in vivo and in vitro, support the idea that the brain cortex operates near a critical point and at the same time works as a reservoir of precise spatiotemporal patterns. However, the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first-order transition, with many spatiotemporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate-and-fire neurons whose connections are the result of the learning of multiple spatiotemporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first-order transition between a low-spiking-rate disordered state (down), and a high-rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

Coupled protein-ligand dynamics in truncated hemoglobin N from atomistic simulations and transition networks.

PubMed

Cazade, Pierre-André; Berezovska, Ganna; Meuwly, Markus

2015-05-01

The nature of ligand motion in proteins is difficult to characterize directly using experiment. Specifically, it is unclear to what degree these motions are coupled. All-atom simulations are used to sample ligand motion in truncated Hemoglobin N. A transition network analysis including ligand- and protein-degrees of freedom is used to analyze the microscopic dynamics. Clustering of two different subsets of MD trajectories highlights the importance of a diverse and exhaustive description to define the macrostates for a ligand-migration network. Monte Carlo simulations on the transition matrices from one particular clustering are able to faithfully capture the atomistic simulations. Contrary to clustering by ligand positions only, including a protein degree of freedom yields considerably improved coarse grained dynamics. Analysis with and without imposing detailed balance agree closely which suggests that the underlying atomistic simulations are converged with respect to sampling transitions between neighboring sites. Protein and ligand dynamics are not independent from each other and ligand migration through globular proteins is not passive diffusion. Transition network analysis is a powerful tool to analyze and characterize the microscopic dynamics in complex systems. This article is part of a Special Issue entitled Recent developments of molecular dynamics. Copyright © 2014 Elsevier B.V. All rights reserved.

Group percolation in interdependent networks

NASA Astrophysics Data System (ADS)

Wang, Zexun; Zhou, Dong; Hu, Yanqing

2018-03-01

In many real network systems, nodes usually cooperate with each other and form groups to enhance their robustness to risks. This motivates us to study an alternative type of percolation, group percolation, in interdependent networks under attack. In this model, nodes belonging to the same group survive or fail together. We develop a theoretical framework for this group percolation and find that the formation of groups can improve the resilience of interdependent networks significantly. However, the percolation transition is always of first order, regardless of the distribution of group sizes. As an application, we map the interdependent networks with intersimilarity structures, which have attracted much attention recently, onto the group percolation and confirm the nonexistence of continuous phase transitions.

Indoor Subspacing to Implement Indoorgml for Indoor Navigation

NASA Astrophysics Data System (ADS)

Jung, H.; Lee, J.

2015-10-01

According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.

From quiescence to proliferation: Cdk oscillations drive the mammalian cell cycle

PubMed Central

Gérard, Claude; Goldbeter, Albert

2012-01-01

We recently proposed a detailed model describing the dynamics of the network of cyclin-dependent kinases (Cdks) driving the mammalian cell cycle (Gérard and Goldbeter, 2009). The model contains four modules, each centered around one cyclin/Cdk complex. Cyclin D/Cdk4–6 and cyclin E/Cdk2 promote progression in G1 and elicit the G1/S transition, respectively; cyclin A/Cdk2 ensures progression in S and the transition S/G2, while the activity of cyclin B/Cdk1 brings about the G2/M transition. This model shows that in the presence of sufficient amounts of growth factor the Cdk network is capable of temporal self-organization in the form of sustained oscillations, which correspond to the ordered, sequential activation of the various cyclin/Cdk complexes that control the successive phases of the cell cycle. The results suggest that the switch from cellular quiescence to cell proliferation corresponds to the transition from a stable steady state to sustained oscillations in the Cdk network. The transition depends on a finely tuned balance between factors that promote or hinder progression in the cell cycle. We show that the transition from quiescence to proliferation can occur in multiple ways that alter this balance. By resorting to bifurcation diagrams, we analyze the mechanism of oscillations in the Cdk network. Finally, we show that the complexity of the detailed model can be greatly reduced, without losing its key dynamical properties, by considering a skeleton model for the Cdk network. Using such a skeleton model for the mammalian cell cycle we show that positive feedback (PF) loops enhance the amplitude and the robustness of Cdk oscillations with respect to molecular noise. We compare the relative merits of the detailed and skeleton versions of the model for the Cdk network driving the mammalian cell cycle. PMID:23130001

Novel Holistic Approaches for Overcoming Therapy Resistance in Pancreatic and Colon Cancers.

PubMed

Sarkar, Fazlul H

2016-01-01

Gastrointestinal (GI) cancers, such as of the colon and pancreas, are highly resistant to both standard and targeted therapeutics. Therapy-resistant and heterogeneous GI cancers harbor highly complex signaling networks (the resistome) that resist apoptotic programming. Commonly used gemcitabine or platinum-based regimens fail to induce meaningful (i.e. disease-reversing) perturbations in the resistome, resulting in high rates of treatment failure. The GI cancer resistance networks are, in part, due to interactions between parallel signaling and aberrantly expressed microRNAs (miRNAs) that collectively promote the development and survival of drug-resistant cancer stem cells with epithelial-to-mesenchymal transition (EMT) characteristics. The lack of understanding of the resistance networks associated with this subpopulation of cells as well as reductionist, single protein-/pathway-targeted approaches have made 'effective drug design' a difficult task. We propose that the successful design of novel therapeutic regimens to target drug-resistant GI tumors is only possible if network-based drug avenues and agents, in particular 'natural agents' with no known toxicity, are correctly identified. Natural agents (dietary agents or their synthetic derivatives) can individually alter miRNA profiles, suppress EMT pathways and eliminate cancer stem-like cells that derive from pancreatic cancer and colon cancer, by partially targeting multiple yet meaningful networks within the GI cancer resistome. However, the efficacy of these agents as combinations (e.g. consumed in the diet) against this resistome has never been studied. This short review article provides an overview of the different challenges involved in the understanding of the GI resistome, and how novel computational biology can help in the design of effective therapies to overcome resistance. © 2015 S. Karger AG, Basel.

Learning phase transitions by confusion

NASA Astrophysics Data System (ADS)

van Nieuwenburg, Evert P. L.; Liu, Ye-Hua; Huber, Sebastian D.

2017-02-01

Classifying phases of matter is key to our understanding of many problems in physics. For quantum-mechanical systems in particular, the task can be daunting due to the exponentially large Hilbert space. With modern computing power and access to ever-larger data sets, classification problems are now routinely solved using machine-learning techniques. Here, we propose a neural-network approach to finding phase transitions, based on the performance of a neural network after it is trained with data that are deliberately labelled incorrectly. We demonstrate the success of this method on the topological phase transition in the Kitaev chain, the thermal phase transition in the classical Ising model, and the many-body-localization transition in a disordered quantum spin chain. Our method does not depend on order parameters, knowledge of the topological content of the phases, or any other specifics of the transition at hand. It therefore paves the way to the development of a generic tool for identifying unexplored phase transitions.

Learning phase transitions by confusion

NASA Astrophysics Data System (ADS)

van Nieuwenburg, Evert; Liu, Ye-Hua; Huber, Sebastian

Classifying phases of matter is a central problem in physics. For quantum mechanical systems, this task can be daunting owing to the exponentially large Hilbert space. Thanks to the available computing power and access to ever larger data sets, classification problems are now routinely solved using machine learning techniques. Here, we propose to use a neural network based approach to find transitions depending on the performance of the neural network after training it with deliberately incorrectly labelled data. We demonstrate the success of this method on the topological phase transition in the Kitaev chain, the thermal phase transition in the classical Ising model, and the many-body-localization transition in a disordered quantum spin chain. Our method does not depend on order parameters, knowledge of the topological content of the phases, or any other specifics of the transition at hand. It therefore paves the way to a generic tool to identify unexplored transitions.

Tunable impedance matching network fundamental limits and practical considerations

NASA Astrophysics Data System (ADS)

Allen, Wesley N.

As wireless devices continue to increase in utility while decreasing in dimension, design of the RF front-end becomes more complex. It is common for a single handheld device to operate on a plethora of frequency bands, utilize multiple antennae, and be subjected to a variety of environments. One complexity in particular which arises from these factors is that of impedance mismatch. Recently, tunable impedance matching networks have begun to be implemented to address this problem. This dissertation presents the first in-depth study on the frequency tuning range of tunable impedance matching networks. Both the fundamental limitations of ideal networks as well as practical considerations for design and implementation are addressed. Specifically, distributed matching networks with a single tuning element are investigated for use with parallel resistor-capacitor and series resistor-inductor loads. Analytical formulas are developed to directly calculate the frequency tuning range TR of ideal topologies. The theoretical limit of TR for these topologies is presented and discussed. Additional formulas are developed which address limitations in transmission line characteristic impedance and varactor range. Equations to predict loss due to varactor quality factor are demonstrated and the ability of parasitics to both increase and decrease TR are shown. Measured results exemplify i) the potential to develop matching networks with a small impact from parasitics, ii) the need for accurate knowledge of parasitics when designing near transition points in optimal parameters, iii) the importance of using a transmission line with the right characteristic impedance, and iv) the ability to achieve extremely low loss at the design frequency with a lossy varactor under the right conditions (measured loss of -0.07 dB). In the area of application, tunable matching networks are designed and measured for mobile handset antennas, demonstrating up to a 3 dB improvement in power delivered to a planar inverted-F antenna and up to 4--5.6 dB improvement in power delivered to the iPhone(TM) antenna. Additionally, a single-varactor matching network is measured to achieve greater tuning range than a two-varactor matching network (> 824--960 MHz versus 850--915 MHz) and yield higher power handling. Addressing miniaturization, an accurate model of metal loss in planar integrated inductors for low-loss substrates is developed and demonstrated. Finally, immediate future research directions are suggested: i) expanding the topologies, tuning elements, and loads analyzed; ii) performing a deep study into parasitics; and iii) investigating power handling with various varactor technologies.

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

PubMed Central

Chen, Hong; Li, Yang

2014-01-01

The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns. PMID:24982969

Phase transition of Boolean networks with partially nested canalizing functions

NASA Astrophysics Data System (ADS)

Jansen, Kayse; Matache, Mihaela Teodora

2013-07-01

We generate the critical condition for the phase transition of a Boolean network governed by partially nested canalizing functions for which a fraction of the inputs are canalizing, while the remaining non-canalizing inputs obey a complementary threshold Boolean function. Past studies have considered the stability of fully or partially nested canalizing functions paired with random choices of the complementary function. In some of those studies conflicting results were found with regard to the presence of chaotic behavior. Moreover, those studies focus mostly on ergodic networks in which initial states are assumed equally likely. We relax that assumption and find the critical condition for the sensitivity of the network under a non-ergodic scenario. We use the proposed mathematical model to determine parameter values for which phase transitions from order to chaos occur. We generate Derrida plots to show that the mathematical model matches the actual network dynamics. The phase transition diagrams indicate that both order and chaos can occur, and that certain parameters induce a larger range of values leading to order versus chaos. The edge-of-chaos curves are identified analytically and numerically. It is shown that the depth of canalization does not cause major dynamical changes once certain thresholds are reached; these thresholds are fairly small in comparison to the connectivity of the nodes.

Is the kinetoplast DNA a percolating network of linked rings at its critical point?

NASA Astrophysics Data System (ADS)

Michieletto, Davide; Marenduzzo, Davide; Orlandini, Enzo

2015-05-01

In this work we present a computational study of the kinetoplast genome, modelled as a large number of semiflexible unknotted loops, which are allowed to link with each other. As the DNA density increases, the systems shows a percolation transition between a gas of unlinked rings and a network of linked loops which spans the whole system. Close to the percolation transition, we find that the mean valency of the network, i.e. the average number of loops which are linked to any one loop, is around three, as found experimentally for the kinetoplast DNA (kDNA). Even more importantly, by simulating the digestion of the network by a restriction enzyme, we show that the distribution of oligomers, i.e. structures formed by a few loops which remain linked after digestion, quantitatively matches experimental data obtained from gel electrophoresis, provided that the density is, once again, close to the percolation transition. With respect to previous work, our analysis builds on a reduced number of assumptions, yet can still fully explain the experimental data. Our findings suggest that the kDNA can be viewed as a network of linked loops positioned very close to the percolation transition, and we discuss the possible biological implications of this remarkable fact.

Network Disruption in the Preclinical Stages of Alzheimer's Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment.

PubMed

López-Sanz, David; Garcés, Pilar; Álvarez, Blanca; Delgado-Losada, María Luisa; López-Higes, Ramón; Maestú, Fernando

2017-12-01

Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer's Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Social network based dynamic transit service through the OMITS system.

DOT National Transportation Integrated Search

2014-02-01

The Open Mode Integrated Transportation System (OMITS) forms a sustainable information infrastructure for communication within and between the mobile/Internet network, the roadway : network, and the users social network. It manipulates the speed g...

NSI directed to continue SPAN's functions

NASA Technical Reports Server (NTRS)

Rounds, Fred

1991-01-01

During a series of network management retreats in June and July 1990, representatives from NASA Headquarters Codes O and S agreed on networking roles and responsibilities for their respective organizations. The representatives decided that NASA Science Internet (NSI) will assume management of both the Space Physics Analysis Network (SPAN) and the NASA Science Network (NSN). SPAN is now known as the NSI/DECnet, and NSN is now known as the NSI/IP. Some management functions will be distributed between Ames Research Center (ARC) and Goddard Space Flight Center (GSFC). NSI at ARC has the lead role for requirements generation and networking engineering. Advanced Applications and the Network Information Center is being developed at GSFC. GSFC will lead the NSI User Services, but NSI at Ames will continue to provide the User Services during the transition. The transition will be made as transparent as possible for the users. DECnet service will continue, but is now directly managed by NSI at Ames. NSI will continue to work closely with routing center managers at other NASA centers, and has formed a transition team to address the change in management. An NSI/DECnet working group had also been formed as a separate engineering group within NSI to plan the transition to Phase 5, DECnet's approach to Open System Integration (OSI). Transition is not expected for a year or more due to delays in produce releases. Plans to upgrade speeds in tail circuits and the backbone are underway. The proposed baseline service for new connections is up to 56 Kbps; 9.6 Kbps lines will gradually be upgraded as requirements dictate. NSI is in the process of consolidating protocol traffic, tail circuits, and the backbone. Currently NSI's backbone is fractional T1; NSI will go to full T1 service as soon as it is feasible.

Some properties of asymmetric Hopfield neural networks with finite time of transition between states

NASA Astrophysics Data System (ADS)

Suleimenov, Ibragim; Mun, Grigoriy; Panchenko, Sergey; Pak, Ivan

2016-11-01

There were implemented samples of asymmetric Hopfield neural networks which have finite time of transition from one state to another. It was shown that in such systems, various oscillation modes could occur. It was revealed that the oscillation of the output signal of certain neuron could be treated as extra logical variable, which describes the state of the neuron. Asymmetric Hopfield neural networks are described in terms of ternary logic. Such logic may be employed in image recognition procedure.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

Hybrid phase transition into an absorbing state: Percolation and avalanches

NASA Astrophysics Data System (ADS)

Lee, Deokjae; Choi, S.; Stippinger, M.; Kertész, J.; Kahng, B.

2016-04-01

Interdependent networks are more fragile under random attacks than simplex networks, because interlayer dependencies lead to cascading failures and finally to a sudden collapse. This is a hybrid phase transition (HPT), meaning that at the transition point the order parameter has a jump but there are also critical phenomena related to it. Here we study these phenomena on the Erdős-Rényi and the two-dimensional interdependent networks and show that the hybrid percolation transition exhibits two kinds of critical behaviors: divergence of the fluctuations of the order parameter and power-law size distribution of finite avalanches at a transition point. At the transition point global or "infinite" avalanches occur, while the finite ones have a power law size distribution; thus the avalanche statistics also has the nature of a HPT. The exponent βm of the order parameter is 1 /2 under general conditions, while the value of the exponent γm characterizing the fluctuations of the order parameter depends on the system. The critical behavior of the finite avalanches can be described by another set of exponents, βa and γa. These two critical behaviors are coupled by a scaling law: 1 -βm=γa .

Hybrid Percolation Transition in Cluster Merging Processes: Continuously Varying Exponents

NASA Astrophysics Data System (ADS)

Cho, Y. S.; Lee, J. S.; Herrmann, H. J.; Kahng, B.

2016-01-01

Consider growing a network, in which every new connection is made between two disconnected nodes. At least one node is chosen randomly from a subset consisting of g fraction of the entire population in the smallest clusters. Here we show that this simple strategy for improving connection exhibits a more unusual phase transition, namely a hybrid percolation transition exhibiting the properties of both first-order and second-order phase transitions. The cluster size distribution of finite clusters at a transition point exhibits power-law behavior with a continuously varying exponent τ in the range 2 <τ (g )≤2.5 . This pattern reveals a necessary condition for a hybrid transition in cluster aggregation processes, which is comparable to the power-law behavior of the avalanche size distribution arising in models with link-deleting processes in interdependent networks.

Convolutional neural network-based classification system design with compressed wireless sensor network images.

PubMed

Ahn, Jungmo; Park, JaeYeon; Park, Donghwan; Paek, Jeongyeup; Ko, JeongGil

2018-01-01

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

Quantum entanglement percolation

NASA Astrophysics Data System (ADS)

Siomau, Michael

2016-09-01

Quantum communication demands efficient distribution of quantum entanglement across a network of connected partners. The search for efficient strategies for the entanglement distribution may be based on percolation theory, which describes evolution of network connectivity with respect to some network parameters. In this framework, the probability to establish perfect entanglement between two remote partners decays exponentially with the distance between them before the percolation transition point, which unambiguously defines percolation properties of any classical network or lattice. Here we introduce quantum networks created with local operations and classical communication, which exhibit non-classical percolation transition points leading to striking communication advantages over those offered by the corresponding classical networks. We show, in particular, how to establish perfect entanglement between any two nodes in the simplest possible network—the 1D chain—using imperfectly entangled pairs of qubits.

Where-Fi: a dynamic energy-efficient multimedia distribution framework for MANETs

NASA Astrophysics Data System (ADS)

Mohapatra, Shivajit; Carbunar, Bogdan; Pearce, Michael; Chaudhri, Rohit; Vasudevan, Venu

2008-01-01

Next generation mobile ad-hoc applications will revolve around users' need for sharing content/presence information with co-located devices. However, keeping such information fresh requires frequent meta-data exchanges, which could result in significant energy overheads. To address this issue, we propose distributed algorithms for energy efficient dissemination of presence and content usage information between nodes in mobile ad-hoc networks. First, we introduce a content dissemination protocol (called CPMP) for effectively distributing frequent small meta-data updates between co-located devices using multicast. We then develop two distributed algorithms that use the CPMP protocol to achieve "phase locked" wake up cycles for all the participating nodes in the network. The first algorithm is designed for fully-connected networks and then extended in the second to handle hidden terminals. The "phase locked" schedules are then exploited to adaptively transition the network interface to a deep sleep state for energy savings. We have implemented a prototype system (called "Where-Fi") on several Motorola Linux-based cell phone models. Our experimental results show that for all network topologies our algorithms were able to achieve "phase locking" between nodes even in the presence of hidden terminals. Moreover, we achieved battery lifetime extensions of as much as 28% for fully connected networks and about 20% for partially connected networks.

Analyzing the effects of transit network change on agency performance and riders in a decentralized, small-to-mid-sized US metropolitan area : a case study of Tallahassee, Florida.

DOT National Transportation Integrated Search

2013-05-01

On July 11, 2011, StarMetro, the local public transit agency in Tallahassee, Florida, restructured its entire bus network from a : downtown-focused radial system to a decentralized, grid-like system that local officials and agency leaders believed wo...

Self-Emergent Peer Support Using Online Social Networking during Cross-Border Transition

ERIC Educational Resources Information Center

Ding, Feng; Stapleton, Paul

2015-01-01

Transitioning from school to university is a major development for learners, often accompanied by difficulties. When overseas students arrive at university for the first time these challenges are multiplied. It is suggested, however, that these difficulties can be mitigated to a certain extent via the use of online social networks. The present…

Internet Protocol Transition Workbook

DTIC Science & Technology

1982-03-01

U N C-* INTERNET PROTOCOL TRANSITION WORKBOOK March 1982 Network Information Canter SRI International Menlo Park, CA 94025 t tv l...Feinler Network Information Center SRI International Menlo Park. California 94025 (415) 859-3695 FEINLEROSRI-NIC (Online mail) [Page ii] I.7 Internet ...31 Postel. J., " Internet Control Message Protocol - DARPA Internet Program Protocol Specification." RFC 792, USC/ Information Sciences Institute

Starting Online: Exploring the Use of a Social Networking Site to Facilitate Transition into Higher Education

ERIC Educational Resources Information Center

Knight, John; Rochon, Rebecca

2012-01-01

It has been widely recognised that transition into higher education (HE) can be challenging for incoming students. Literature identifies three main areas where students may benefit from support: social, practical and academic. This paper discusses a case study that explores the potential of a social networking environment to provide support in…

Enhancing the Transition to University by Facilitating Social and Study Networks: Results of a One-Day Workshop.

ERIC Educational Resources Information Center

Peat, Mary; Dalziel, James; Grant, Anthony M.

2000-01-01

Describes a one-day workshop developed at the University of Sydney (Australia) to facilitate social and study-related peer networks. Qualitative and quantitative analyses found that the workshops enhanced study, self-motivation, and general enjoyment of university life and were helpful in easing the transition of undergraduate students.…

Phase Transitions of an Epidemic Spreading Model in Small-World Networks

NASA Astrophysics Data System (ADS)

Hua, Da-Yin; Gao, Ke

2011-06-01

We propose a modified susceptible-infected-refractory-susceptible (SIRS) model to investigate the global oscillations of the epidemic spreading in Watts—Strogatz (WS) small-world networks. It is found that when an individual immunity does not change or decays slowly in an immune period, the system can exhibit complex transition from an infecting stationary state to a large amplitude sustained oscillation or an absorbing state with no infection. When the immunity decays rapidly in the immune period, the transition to the global oscillation disappears and there is no oscillation. Furthermore, based on the spatio-temporal evolution patterns and the phase diagram, it is disclosed that a long immunity period takes an important role in the emergence of the global oscillation in small-world networks.

Ensuring Patient Safety by using Colored Petri Net Simulation in the Design of Heterogeneous, Multi-Vendor, Integrated, Life-Critical Wireless (802.x) Patient Care Device Networks.

PubMed

Sloane, Elliot; Gehlot, Vijay

2005-01-01

Hospitals and manufacturers are designing and deploying the IEEE 802.x wireless technologies in medical devices to promote patient mobility and flexible facility use. There is little information, however, on the reliability or ultimate safety of connecting multiple wireless life-critical medical devices from multiple vendors using commercial 802.11a, 802.11b, 802.11g or pre-802.11n devices. It is believed that 802.11-type devices can introduce unintended life-threatening risks unless delivery of critical patient alarms to central monitoring systems and/or clinical personnel is assured by proper use of 802.11e Quality of Service (QoS) methods. Petri net tools can be used to simulate all possible states and transitions between devices and/or systems in a wireless device network, and can identify failure modes in advance. Colored Petri Net (CPN) tools are ideal, in fact, as they allow tracking and controlling each message in a network based on pre-selected criteria. This paper describes a research project using CPN to simulate and validate alarm integrity in a small multi-modality wireless patient monitoring system. A 20-monitor wireless patient monitoring network is created in two versions: one with non-prioritized 802.x CSM protocols and the second with simulated Quality of Service (QoS) capabilities similar to 802.11e (i.e., the second network allows message priority management.) In the standard 802.x network, dangerous heart arrhythmia and pulse oximetry alarms could not be reliably and rapidly communicated, but the second network's QoS priority management reduced that risk significantly.

Transition to Chaos in Random Neuronal Networks

NASA Astrophysics Data System (ADS)

Kadmon, Jonathan; Sompolinsky, Haim

2015-10-01

Firing patterns in the central nervous system often exhibit strong temporal irregularity and considerable heterogeneity in time-averaged response properties. Previous studies suggested that these properties are the outcome of the intrinsic chaotic dynamics of the neural circuits. Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e., connection variance) is sufficiently large. In the limit of an infinitely large network, there is a sharp transition from a fixed point to chaos, as the synaptic gain reaches a critical value. Near the onset, chaotic fluctuations are slow, analogous to the ubiquitous, slow irregular fluctuations observed in the firing rates of many cortical circuits. However, the existence of a transition from a fixed point to chaos in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work, we investigate rate-based dynamics of neuronal circuits composed of several subpopulations with randomly diluted connections. Nonzero connections are either positive for excitatory neurons or negative for inhibitory ones, while single neuron output is strictly positive with output rates rising as a power law above threshold, in line with known constraints in many biological systems. Using dynamic mean field theory, we find the phase diagram depicting the regimes of stable fixed-point, unstable-dynamic, and chaotic-rate fluctuations. We focus on the latter and characterize the properties of systems near this transition. We show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as the single population with Gaussian connectivity. In these architectures, the large mean excitatory and inhibitory inputs dynamically balance each other, amplifying the effect of the residual fluctuations. Importantly, the existence of a transition to chaos and its critical properties depend on the shape of the single-neuron nonlinear input-output transfer function, near firing threshold. In particular, for nonlinear transfer functions with a sharp rise near threshold, the transition to chaos disappears in the limit of a large network; instead, the system exhibits chaotic fluctuations even for small synaptic gain. Finally, we investigate transition to chaos in network models with spiking dynamics. We show that when synaptic time constants are slow relative to the mean inverse firing rates, the network undergoes a transition from fast spiking fluctuations with constant rates to a state where the firing rates exhibit chaotic fluctuations, similar to the transition predicted by rate-based dynamics. Systems with finite synaptic time constants and firing rates exhibit a smooth transition from a regime dominated by stationary firing rates to a regime of slow rate fluctuations. This smooth crossover obeys scaling properties, similar to crossover phenomena in statistical mechanics. The theoretical results are supported by computer simulations of several neuronal architectures and dynamics. Consequences for cortical circuit dynamics are discussed. These results advance our understanding of the properties of intrinsic dynamics in realistic neuronal networks and their functional consequences.

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.

A Framework for Assessing Feasibility of Transit-Oriented Development (TOD) Project Sites : Research Brief

DOT National Transportation Integrated Search

2017-09-01

This research answers the question: How can a transit agency choose among alternative TOD locations within a transit network? The ultimate objective of the research is to develop a decision support framework which can be used by transit agencies when...

From sparse to dense and from assortative to disassortative in online social networks

PubMed Central

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-01-01

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. PMID:24798703

From sparse to dense and from assortative to disassortative in online social networks.

PubMed

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-05-06

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Mitochondrial network complexity emerges from fission/fusion dynamics.

PubMed

Zamponi, Nahuel; Zamponi, Emiliano; Cannas, Sergio A; Billoni, Orlando V; Helguera, Pablo R; Chialvo, Dante R

2018-01-10

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

Complex quantum network geometries: Evolution and phase transitions

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Complex quantum network geometries: Evolution and phase transitions.

PubMed

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Dynamical modeling and analysis of large cellular regulatory networks

NASA Astrophysics Data System (ADS)

Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

2013-06-01

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Peru Subduction Zone Seismic Experiment (PeruSZE): Preliminary Results From a Seismic Network Between Mollendo and Lake Titicaca, Peru.

NASA Astrophysics Data System (ADS)

Guy, R.; Stubailo, I.; Skinner, S.; Phillips, K.; Foote, E.; Lukac, M.; Aguilar, V.; Tavera, H.; Audin, L.; Husker, A.; Clayton, R.; Davis, P. M.

2008-12-01

This work describes preliminary results from a 50 station broadband seismic network recently installed from the coast to the high Andes in Peru. UCLA's Center for Embedded Network Sensing (CENS) and Caltech's Tectonic Observatory are collaborating with the IRD (French L'Institut de Recherche pour le Developpement) and the Institute of Geophysics, in Lima Peru in a broadband seismic experiment that will study the transition from steep to shallow slab subduction. The currently installed line has stations located above the steep subduction zone at a spacing of about 6 km. In 2009 we plan to install a line of 50 stations north from this line along the crest of the Andes, crossing the transition from steep to shallow subduction. A further line from the end of that line back to the coast, completing a U shaped array, is in the planning phase. The network is wirelessly linked using multi-hop network software designed by computer scientists in CENS in which data is transmitted from station to station, and collected at Internet drops, from where it is transmitted over the Internet to CENS each night. The instrument installation in Peru is almost finished and we have been receiving data daily from 10 stations (out of total 50) since June 2008. The rest are recording on-site while the RF network is being completed. The software system provides dynamic link quality based routing, reliable data delivery, and a disruption tolerant shell interface for managing the system from UCLA without the need to travel to Peru. The near real-time data delivery also allows immediate detection of any problems at the sites. We are building a seismic data and GPS quality control toolset that would greatly minimize the station's downtime by alerting the users of any possible problems.

Finite-time scaling at the Anderson transition for vibrations in solids

NASA Astrophysics Data System (ADS)

Beltukov, Y. M.; Skipetrov, S. E.

2017-11-01

A model in which a three-dimensional elastic medium is represented by a network of identical masses connected by springs of random strengths and allowed to vibrate only along a selected axis of the reference frame exhibits an Anderson localization transition. To study this transition, we assume that the dynamical matrix of the network is given by a product of a sparse random matrix with real, independent, Gaussian-distributed nonzero entries and its transpose. A finite-time scaling analysis of the system's response to an initial excitation allows us to estimate the critical parameters of the localization transition. The critical exponent is found to be ν =1.57 ±0.02 , in agreement with previous studies of the Anderson transition belonging to the three-dimensional orthogonal universality class.

Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks

PubMed Central

Wang, Junjie; Li, Yishuai; Liu, Jingyu; He, Kun; Wang, Pu

2013-01-01

Based on large-scale human mobility data collected in San Francisco and Boston, the morning peak urban rail transit (URT) ODs (origin-destination matrix) were estimated and the most vulnerable URT segments, those capable of causing the largest service interruptions, were identified. In both URT networks, a few highly vulnerable segments were observed. For this small group of vital segments, the impact of failure must be carefully evaluated. A bipartite URT usage network was developed and used to determine the inherent connections between urban rail transits and their passengers' travel demands. Although passengers' origins and destinations were easy to locate for a large number of URT segments, a few show very complicated spatial distributions. Based on the bipartite URT usage network, a new layer of the understanding of a URT segment's vulnerability can be achieved by taking the difficulty of addressing the failure of a given segment into account. Two proof-of-concept cases are described here: Possible transfer of passenger flow to the road network is here predicted in the cases of failures of two representative URT segments in San Francisco. PMID:24260355

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Transition Features from Simplicity-Universality to Complexity-Diversification Under UHNTF

NASA Astrophysics Data System (ADS)

Fang, Jin-Qing; Li, Yong

2010-02-01

A large unified hybrid network model with a variable speed growth (LUHNM-VSG) is proposed as third model of the unified hybrid network theoretical framework (UHNTF). A hybrid growth ratio vg of deterministic linking number to random linking number and variable speed growth index α are introduced in it. The main effects of vg and α on topological transition features of the LUHNM-VSG are revealed. For comparison with the other models, we construct a type of the network complexity pyramid with seven levels, in which from the bottom level-1 to the top level-7 of the pyramid simplicity-universality is increasing but complexity-diversity is decreasing. The transition relations between them depend on matching of four hybrid ratios (dr, fd, gr, vg). Thus the most of network models can be investigated in the unification way via four hybrid ratios (dr, fd, gr, vg). The LUHNM-VSG as the level-1 of the pyramid is much better and closer to description of real-world networks as well as has potential application.

Stakeholders, Networks and Links in Early Childhood Policy: Network Analysis and the "Transition to School: Position Statement"

ERIC Educational Resources Information Center

Wallis, Jake; Dockett, Sue

2015-01-01

The importance of a positive start to school has been highlighted in a range of national and international research. This has stimulated considerable ongoing research attention, as well as initiatives across policy and practice, all with the aim of promoting a positive transition to school for all children. Despite the common interests across…

Morphological transitions of brain sphingomyelin are determined by the hydration protocol: ripples re-arrange in plane, and sponge-like networks disintegrate into small vesicles.

PubMed

Meyer, H W; Bunjes, H; Ulrich, A S

1999-06-01

The phase transition of hydrated brain sphingomyelin occurs at around 35 degrees C, which is close to the physiological temperature. Freeze-fracture electron microscopy is used to characterize different gel state morphologies in terms of solid-ordered and liquid-ordered phase states, according to the occurrence of ripples and other higher-dimensional bilayer deformations. Evidently, the natural mixed-chain sphingomyelin does not assume the flat L beta, phase but instead the rippled P beta, phase, with symmetric and asymmetric ripples as well as macroripples and an egg-carton pattern, depending on the incubation conditions. An unexpected difference was observed between samples that are hydrated above and below the phase transition temperature. When the lipid is hydrated at low temperature, a sponge-like network of bilayers is formed in the gel state, next to some normal lamellae. The network loses its ripples during cold-incubation, which indicates the formation of a liquid-ordered (lo) gel phase. Ripples re-appear upon warming and the sponge-like network disintegrates spontaneously and irreversibly into small vesicles above the phase transition.

Polaronic Transport in Phosphate Glasses Containing Transition Metal Ions

NASA Astrophysics Data System (ADS)

Henderson, Mark

The goal of this dissertation is to characterize the basic transport properties of phosphate glasses containing various amounts of TIs and to identify and explain any electronic phase transitions which may occur. The P2 O5-V2O5-WO3 (PVW) glass system will be analyzed to find the effect of TI concentration on conduction. In addition, the effect of the relative concentrations of network forming ions (SiO2 and P2O5) on transport will be studied in the P2O5-SiO2-Fe2O 3 (PSF) system. Also presented is a numerical study on a tight-binding model adapted for the purposes of modelling Gaussian traps, mimicking TI's, which are arranged in an extended network. The results of this project will contribute to the development of fundamental theories on the electronic transport in glasses containing mixtures of transition oxides as well as those containing multiple network formers without discernible phase separation. The present study on the PVW follows up on previous investigation into the effect on mixed transition ions in oxide glasses. Past research has focused on glasses containing transition metal ions from the 3d row. The inclusion of tungsten, a 5d transition metal, adds a layer of complexity through the mismatch of the energies of the orbitals contributing to localized states. The data have indicated that a transition reminiscent of a metal-insulator transition (MIT) occurs in this system as the concentration of tungsten increases. As opposed to some other MIT-like transitions found in phosphate glass systems, there seems to be no polaron to bipolaron conversion. Instead, the individual localization parameter for tungsten noticeably decreases dramatically at the transition point as well as the adiabaticity. Another distinctive feature of this project is the study of the PSF system, which contains two true network formers, phosphorous pentoxide (P2O 5) and silicon dioxide (SiO2). It is not usually possible to do a reliable investigation of the conduction properties of such glasses because the two network formers will tend to separate into different phases, making it difficult to obtain homogenous samples. The PSF system proved easier to study than other systems. The hopping in this system seems to be dominated by the Greaves mid-range mechanism. In addition, in samples containing the same proportion of iron, conductivities were found to not depend noticeably on composition, supporting the use of models focusing on the transition metal ions in calculating conductivity. Despite ostensibly changing the structural and metrical properties of the network, the ratio of the concentration of the network formers only appears to have an effect on the conductivity through changing the inter-atomic distance of iron. The numerical model adds to the evidence for the dominating contribution on the nearest-neighbor ordering of TI ions on the electrical properties of a glass; especially interesting is the reproducibility of the mixed-transition ion effect (MTE) in a numerical model where ensemble averages are taken over possible arrangements. It was also determined that the disorder arising from the spread between two types of traps can lead to a MIT as function of population. Finally, an outline of the notion of invariance in TI glasses is extended from work done by other authors, creating an opportunity for further research.

Reliable routing in transit networks.

DOT National Transportation Integrated Search

2013-07-02

The objectives of this project are (1) to make use of the newly emerging transit data sources : for evaluating the variations in transit services (especially headway), and (2) to help passengers : find optimal routing strategies to hedge against thes...

Transit Fare Prepayment Distribution Methods in Sacramento, CA

DOT National Transportation Integrated Search

1985-06-01

This demonstration tested the use of new methods to distribute transit fare prepayment (TFP) instruments at the Sacramento Regional Transit district (RT). Five new distribution methods were implemented to supplement a network of public, private, and ...

Gain control through divisive inhibition prevents abrupt transition to chaos in a neural mass model.

PubMed

Papasavvas, Christoforos A; Wang, Yujiang; Trevelyan, Andrew J; Kaiser, Marcus

2015-09-01

Experimental results suggest that there are two distinct mechanisms of inhibition in cortical neuronal networks: subtractive and divisive inhibition. They modulate the input-output function of their target neurons either by increasing the input that is needed to reach maximum output or by reducing the gain and the value of maximum output itself, respectively. However, the role of these mechanisms on the dynamics of the network is poorly understood. We introduce a novel population model and numerically investigate the influence of divisive inhibition on network dynamics. Specifically, we focus on the transitions from a state of regular oscillations to a state of chaotic dynamics via period-doubling bifurcations. The model with divisive inhibition exhibits a universal transition rate to chaos (Feigenbaum behavior). In contrast, in an equivalent model without divisive inhibition, transition rates to chaos are not bounded by the universal constant (non-Feigenbaum behavior). This non-Feigenbaum behavior, when only subtractive inhibition is present, is linked to the interaction of bifurcation curves in the parameter space. Indeed, searching the parameter space showed that such interactions are impossible when divisive inhibition is included. Therefore, divisive inhibition prevents non-Feigenbaum behavior and, consequently, any abrupt transition to chaos. The results suggest that the divisive inhibition in neuronal networks could play a crucial role in keeping the states of order and chaos well separated and in preventing the onset of pathological neural dynamics.

Transition Communities and the Glass Ceiling of Environmental Sustainability Policies at Three Universities

ERIC Educational Resources Information Center

Pardellas Santiago, Miguel; Meira Cartea, Pablo; Iglesias da Cunha, Lucía

2017-01-01

Purpose: This paper deals with the experiences of three European universities that have implemented transition initiatives, using the Transition Network's methodology to promote their sustainability plans. The Transition Communities' model for change is presented from a socio-educational perspective as an effective methodology for encouraging…

Potts Model in One-Dimension on Directed Small-World Networks

NASA Astrophysics Data System (ADS)

Aquino, Édio O.; Lima, F. W. S.; Araújo, Ascânio D.; Costa Filho, Raimundo N.

2018-06-01

The critical properties of the Potts model with q=3 and 8 states in one-dimension on directed small-world networks are investigated. This disordered system is simulated by updating it with the Monte Carlo heat bath algorithm. The Potts model on these directed small-world networks presents in fact a second-order phase transition with a new set of critical exponents for q=3 considering a rewiring probability p=0.1. For q=8 the system exhibits only a first-order phase transition independent of p.

Programming Chemical Reaction Networks Using Intramolecular Conformational Motions of DNA.

PubMed

Lai, Wei; Ren, Lei; Tang, Qian; Qu, Xiangmeng; Li, Jiang; Wang, Lihua; Li, Li; Fan, Chunhai; Pei, Hao

2018-06-22

The programmable regulation of chemical reaction networks (CRNs) represents a major challenge toward the development of complex molecular devices performing sophisticated motions and functions. Nevertheless, regulation of artificial CRNs is generally energy- and time-intensive as compared to natural regulation. Inspired by allosteric regulation in biological CRNs, we herein develop an intramolecular conformational motion strategy (InCMS) for programmable regulation of DNA CRNs. We design a DNA switch as the regulatory element to program the distance between the toehold and branch migration domain. The presence of multiple conformational transitions leads to wide-range kinetic regulation spanning over 4 orders of magnitude. Furthermore, the process of energy-cost-free strand exchange accompanied by conformational change discriminates single base mismatches. Our strategy thus provides a simple yet effective approach for dynamic programming of complex CRNs.

Novel amphiphilic poly(dimethylsiloxane) based polyurethane networks tethered with carboxybetaine and their combined antibacterial and anti-adhesive property

NASA Astrophysics Data System (ADS)

Jiang, Jingxian; Fu, Yuchen; Zhang, Qinghua; Zhan, Xiaoli; Chen, Fengqiu

2017-08-01

The traditional nonfouling materials are powerless against bacterial cells attachment, while the hydrophobic bactericidal surfaces always suffer from nonspecific protein adsorption and dead bacterial cells accumulation. Here, amphiphilic polyurethane (PU) networks modified with poly(dimethylsiloxane) (PDMS) and cationic carboxybetaine diol through simple crosslinking reaction were developed, which had an antibacterial efficiency of 97.7%. Thereafter, the hydrolysis of carboxybetaine ester into zwitterionic groups brought about anti-adhesive properties against bacteria and proteins. The surface chemical composition and wettability performance of the PU network surfaces were investigated by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS) and contact angle analysis. The surface distribution of PDMS and zwitterionic segments produced an obvious amphiphilic heterogeneous surface, which was demonstrated by atomic force microscopy (AFM). Enzyme-linked immunosorbent assays (ELISA) were used to test the nonspecific protein adsorption behaviors. With the advantages of the transition from excellent bactericidal performance to anti-adhesion and the combination of fouling resistance and fouling release property, the designed PDMS-based amphiphilic PU network shows great application potential in biomedical devices and marine facilities.

Building Careers, Transforming Institutions: Underrepresented Women and Minorities, Leadership Opportunities, and Interinstitutional Networking

NASA Astrophysics Data System (ADS)

Niemeier, D. A.; Smith, Vicki

Transforming universities does not occur exclusively as a result of the actions of current university leaders but additionally requires the collective efforts of women who are interested in mobility and opportunity for women across the board, and who are committed to changing the broad work environment for women in the academy. In engineering, the representation of women in mid-career and senior-level faculty positions remains very low, with even fewer women assuming leadership positions such as department chair or research center director. In this article, we examine outcomes of the National Science Foundation sponsored 1st Women in Engineering Leadership Conference in the fall of 2000. The conference was designed to enable women engineers to develop the types of network that can facilitate transition to leadership positions. With an analysis of data gathered from surveys at three points in time, we track the issues that were salient to women who were considering leadership roles (both obstacles to and aspirations for); identify the benefits accrued from participation in the conference and from subsequent networking activities; and propose future interventions that may enhance and promote interinstitutional networking.

Crystal Field Splitting is Limiting the Stability and Strength of Ultra-incompressible Orthorhombic Transition Metal Tetraborides

PubMed Central

Zhang, R. F.; Wen, X. D.; Legut, D.; Fu, Z. H.; Veprek, S.; Zurek, E.; Mao, H. K.

2016-01-01

The lattice stability and mechanical strengths of the supposedly superhard transition metal tetraborides (TmB4, Tm = Cr, Mn and Fe) evoked recently much attention from the scientific community due to the potential applications of these materials, as well as because of general scientific interests. In the present study, we show that the surprising stabilization of these compounds from a high symmetry to a low symmetry structure is accomplished by an in-plane rotation of the boron network, which maximizes the in-plane hybridization by crystal field splitting between d orbitals of Tm and p orbitals of B. Studies of mechanical and electronic properties of TmB4 suggest that these tetraborides cannot be intrinsically superhard. The mechanical instability is facilitated by a unique in-plane or out-of-plane weakening of the three-dimensional covalent bond network of boron along different shear deformation paths. These results shed a novel view on the origin of the stability and strength of orthorhombic TmB4, highlighting the importance of combinational analysis of a variety of parameters related to plastic deformation of the crystalline materials when attempting to design new ultra-incompressible, and potentially strong and hard solids. PMID:26976479

Phenotypic switching in bacteria

NASA Astrophysics Data System (ADS)

Merrin, Jack

Living matter is a non-equilibrium system in which many components work in parallel to perpetuate themselves through a fluctuating environment. Physiological states or functionalities revealed by a particular environment are called phenotypes. Transitions between phenotypes may occur either spontaneously or via interaction with the environment. Even in the same environment, genetically identical bacteria can exhibit different phenotypes of a continuous or discrete nature. In this thesis, we pursued three lines of investigation into discrete phenotypic heterogeneity in bacterial populations: the quantitative characterization of the so-called bacterial persistence, a theoretical model of phenotypic switching based on those measurements, and the design of artificial genetic networks which implement this model. Persistence is the phenotype of a subpopulation of bacteria with a reduced sensitivity to antibiotics. We developed a microfluidic apparatus, which allowed us to monitor the growth rates of individual cells while applying repeated cycles of antibiotic treatments. We were able to identify distinct phenotypes (normal and persistent) and characterize the stochastic transitions between them. We also found that phenotypic heterogeneity was present prior to any environmental cue such as antibiotic exposure. Motivated by the experiments with persisters, we formulated a theoretical model describing the dynamic behavior of several discrete phenotypes in a periodically varying environment. This theoretical framework allowed us to quantitatively predict the fitness of dynamic populations and to compare survival strategies according to environmental time-symmetries. These calculations suggested that persistence is a strategy used by bacterial populations to adapt to fluctuating environments. Knowledge of the phenotypic transition rates for persistence may provide statistical information about the typical environments of bacteria. We also describe a design of artificial genetic networks that would implement a more general theoretical model of phenotypic switching. We will use a new cloning strategy in order to systematically assemble a large number of genetic features, such as site-specific recombination components from the R64 plasmid, which invert several coexisting DNA segments. The inversion of these segments would lead to discrete phenotypic transitions inside a living cell. These artificial phenotypic switches can be controlled precisely in experiments and may serve as a benchmark for their natural counterparts.

An ant colony based algorithm for overlapping community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

2015-06-01

Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

Effects of frustration on explosive synchronization

NASA Astrophysics Data System (ADS)

Huang, Xia; Gao, Jian; Sun, Yu-Ting; Zheng, Zhi-Gang; Xu, Can

2016-12-01

In this study, we consider the emergence of explosive synchronization in scale-free networks by considering the Kuramoto model of coupled phase oscillators. The natural frequencies of oscillators are assumed to be correlated with their degrees and frustration is included in the system. This assumption can enhance or delay the explosive transition to synchronization. Interestingly, a de-synchronization phenomenon occurs and the type of phase transition is also changed. Furthermore, we provide an analytical treatment based on a star graph, which resembles that obtained in scale-free networks. Finally, a self-consistent approach is implemented to study the de-synchronization regime. Our findings have important implications for controlling synchronization in complex networks because frustration is a controllable parameter in experiments and a discontinuous abrupt phase transition is always dangerous in engineering in the real world.

Synchronization of mobile chaotic oscillator networks

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

Fujiwara, Naoya, E-mail: fujiwara@csis.u-tokyo.ac.jp; Kurths, Jürgen; Díaz-Guilera, Albert

We study synchronization of systems in which agents holding chaotic oscillators move in a two-dimensional plane and interact with nearby ones forming a time dependent network. Due to the uncertainty in observing other agents' states, we assume that the interaction contains a certain amount of noise that turns out to be relevant for chaotic dynamics. We find that a synchronization transition takes place by changing a control parameter. But this transition depends on the relative dynamic scale of motion and interaction. When the topology change is slow, we observe an intermittent switching between laminar and burst states close to themore » transition due to small noise. This novel type of synchronization transition and intermittency can happen even when complete synchronization is linearly stable in the absence of noise. We show that the linear stability of the synchronized state is not a sufficient condition for its stability due to strong fluctuations of the transverse Lyapunov exponent associated with a slow network topology change. Since this effect can be observed within the linearized dynamics, we can expect such an effect in the temporal networks with noisy chaotic oscillators, irrespective of the details of the oscillator dynamics. When the topology change is fast, a linearized approximation describes well the dynamics towards synchrony. These results imply that the fluctuations of the finite-time transverse Lyapunov exponent should also be taken into account to estimate synchronization of the mobile contact networks.« less

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

NASA Technical Reports Server (NTRS)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis.

PubMed

Fayyaz S, S Kiavash; Liu, Xiaoyue Cathy; Zhang, Guohui

2017-01-01

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George's transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis.

An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis

PubMed Central

Fayyaz S., S. Kiavash; Zhang, Guohui

2017-01-01

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis. PMID:28981544

Critical space-time networks and geometric phase transitions from frustrated edge antiferromagnetism

NASA Astrophysics Data System (ADS)

Trugenberger, Carlo A.

2015-12-01

Recently I proposed a simple dynamical network model for discrete space-time that self-organizes as a graph with Hausdorff dimension dH=4 . The model has a geometric quantum phase transition with disorder parameter (dH-ds) , where ds is the spectral dimension of the dynamical graph. Self-organization in this network model is based on a competition between a ferromagnetic Ising model for vertices and an antiferromagnetic Ising model for edges. In this paper I solve a toy version of this model defined on a bipartite graph in the mean-field approximation. I show that the geometric phase transition corresponds exactly to the antiferromagnetic transition for edges, the dimensional disorder parameter of the former being mapped to the staggered magnetization order parameter of the latter. The model has a critical point with long-range correlations between edges, where a continuum random geometry can be defined, exactly as in Kazakov's famed 2D random lattice Ising model but now in any number of dimensions.

Coevolving complex networks in the model of social interactions

NASA Astrophysics Data System (ADS)

Raducha, Tomasz; Gubiec, Tomasz

2017-04-01

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

Global Transportation Network : the heart of in-transit visibility

DOT National Transportation Integrated Search

1999-03-01

The Persian Gulf War highlighted problems concerning in-transit visibility (ITV). The lack of in-transit visibility resulted in over 20,000 of 40,000 containers entering the theater of operations being opened, inventoried, resealed, and shipped back ...

Searching for Exoplanets using Artificial Intelligence

NASA Astrophysics Data System (ADS)

Pearson, Kyle Alexander; Palafox, Leon; Griffith, Caitlin Ann

2017-10-01

In the last decade, over a million stars were monitored to detect transiting planets. The large volume of data obtained from current and future missions (e.g. Kepler, K2, TESS and LSST) requires automated methods to detect the signature of a planet. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called ``deep learning'' or ``deep nets'', are a state of the art machine learning technique designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms, the deep net learns to characterize the data instead of relying on hand-coded metrics that humans perceive as the most representative. Exoplanet transits have different shapes, as a result of, e.g. the planet's and stellar atmosphere and transit geometry. Thus, a simple template does not suffice to capture the subtle details, especially if the signal is below the noise or strong systematics are present. Current false-positive rates from the Kepler data are estimated around 12.3% for Earth-like planets and there has been no study of the false negative rates. It is therefore important to ask how the properties of current algorithms exactly affect the results of the Kepler mission and, future missions such as TESS, which flies next year. These uncertainties affect the fundamental research derived from missions, such as the discovery of habitable planets, estimates of their occurrence rates and our understanding about the nature and evolution of planetary systems.

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport

NASA Astrophysics Data System (ADS)

Riascos, A. P.; Mateos, José L.

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport.

PubMed

Riascos, A P; Mateos, José L

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

Loss surface of XOR artificial neural networks

NASA Astrophysics Data System (ADS)

Mehta, Dhagash; Zhao, Xiaojun; Bernal, Edgar A.; Wales, David J.

2018-05-01

Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these landscapes using optimization tools developed for potential energy landscapes in molecular science. The number of local minima and transition states (saddle points of index one), as well as the ratio of transition states to minima, grow rapidly with the number of nodes in the network. There is also a strong dependence on the regularization parameter, with the landscape becoming more convex (fewer minima) as the regularization term increases. We demonstrate that in our formulation, stationary points for networks with Nh hidden nodes, including the minimal network required to fit the XOR data, are also stationary points for networks with Nh+1 hidden nodes when all the weights involving the additional node are zero. Hence, smaller networks trained on XOR data are embedded in the landscapes of larger networks. Our results clarify certain aspects of the classification and sensitivity (to perturbations in the input data) of minima and saddle points for this system, and may provide insight into dropout and network compression.

Phase transition in NK-Kauffman networks and its correction for Boolean irreducibility

NASA Astrophysics Data System (ADS)

Zertuche, Federico

2014-05-01

In a series of articles published in 1986, Derrida and his colleagues studied two mean field treatments (the quenched and the annealed) for NK-Kauffman networks. Their main results lead to a phase transition curve Kc 2 pc(1-pc)=1 (0

Traffic Dimensioning and Performance Modeling of 4G LTE Networks

ERIC Educational Resources Information Center

Ouyang, Ye

2011-01-01

Rapid changes in mobile techniques have always been evolutionary, and the deployment of 4G Long Term Evolution (LTE) networks will be the same. It will be another transition from Third Generation (3G) to Fourth Generation (4G) over a period of several years, as is the case still with the transition from Second Generation (2G) to 3G. As a result,…

Do Transit-Oriented Developments (TODs) and Established Urban Neighborhoods Have Similar Walking Levels in Hong Kong?

PubMed

Lu, Yi; Gou, Zhonghua; Xiao, Yang; Sarkar, Chinmoy; Zacharias, John

2018-03-20

A sharp drop in physical activity and skyrocketing obesity rate has accompanied rapid urbanization in China. The urban planning concept of transit-oriented development (TOD) has been widely advocated in China to promote physical activity, especially walking. Indeed, many design features thought to promote walking-e.g., mixed land use, densification, and well-connected street network-often characterize both TODs and established urban neighborhoods. Thus, it is often assumed that TODs have similar physical activity benefits as established urban neighborhoods. To verify this assumption, this study compared walking behaviors in established urban neighborhoods and transit-oriented new towns in Hong Kong. To address the limitation of self-selection bias, we conducted a study using Hong Kong citywide public housing scheme, which assigns residents to different housing estates by flat availability and family size rather than personal preference. The results show new town residents walked less for transportation purpose than urban residents. New town residents far from the transit station (800-1200 m) walked less for recreational purpose than TOD residents close to a rail transit station (<400 m) or urban residents. The observed disparity in walking behaviors challenges the common assumption that TOD and established urban neighborhoods have similar impact on walking behavior. The results suggest the necessity for more nuanced planning strategies, taking local-level factors into account to promote walking of TOD residents who live far from transit stations.

Enabling Tussle-Agile Inter-networking Architectures by Underlay Virtualisation

NASA Astrophysics Data System (ADS)

Dianati, Mehrdad; Tafazolli, Rahim; Moessner, Klaus

In this paper, we propose an underlay inter-network virtualisation framework in order to enable tussle-agile flexible networking over the existing inter-network infrastructures. The functionalities that inter-networking elements (transit nodes, access networks, etc.) need to support in order to enable virtualisation are discussed. We propose the base architectures of each the abstract elements to support the required inter-network virtualisation functionalities.

Understanding transit ridership demand for a multi-destination, multimodal transit network in an American metropolitan area : lessons for increasing choice ridership while maintaining transit dependent ridership : [research brief].

DOT National Transportation Integrated Search

2012-01-01

Recent research indicates that multi-destination transit systems are far more effective in attracting passengers than central business district (CBD)-focused systems. However, the same research suggests that multi-destination systems appeal largely t...

Networking standards

NASA Technical Reports Server (NTRS)

Davies, Mark

1991-01-01

The enterprise network is currently a multivendor environment consisting of many defacto and proprietary standards. During the 1990s, these networks will evolve towards networks which are based on international standards in both Local Area Network (LAN) and Wide Area Network (WAN) space. Also, you can expect to see the higher level functions and applications begin the same transition. Additional information is given in viewgraph form.

A Facile and General Approach to Recoverable High-Strain Multishape Shape Memory Polymers.

PubMed

Li, Xingjian; Pan, Yi; Zheng, Zhaohui; Ding, Xiaobin

2018-03-01

Fabricating a single polymer network with no need to design complex structures to achieve an ideal combination of tunable high-strain multiple-shape memory effects and highly recoverable shape memory property is a great challenge for the real applications of advanced shape memory devices. Here, a facile and general approach to recoverable high-strain multishape shape memory polymers is presented via a random copolymerization of acrylate monomers and a chain-extended multiblock copolymer crosslinker. As-prepared shape memory networks show a large width at the half-peak height of the glass transition, far wider than current classical multishape shape memory polymers. A combination of tunable high-strain multishape memory effect and as high as 1000% recoverable strain in a single chemical-crosslinking network can be obtained. To the best of our knowledge, this is the first thermosetting material with a combination of highly recoverable strain and tunable high-strain multiple-shape memory effects. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Track and mode controller (TMC): a software executive for a high-altitude pointing and tracking experiment

NASA Astrophysics Data System (ADS)

Michnovicz, Michael R.

1997-06-01

A real-time executive has been implemented to control a high altitude pointing and tracking experiment. The track and mode controller (TMC) implements a table driven design, in which the track mode logic for a tracking mission is defined within a state transition diagram (STD). THe STD is implemented as a state transition table in the TMC software. Status Events trigger the state transitions in the STD. Each state, as it is entered, causes a number of processes to be activated within the system. As these processes propagate through the system, the status of key processes are monitored by the TMC, allowing further transitions within the STD. This architecture is implemented in real-time, using the vxWorks operating system. VxWorks message queues allow communication of status events from the Event Monitor task to the STD task. Process commands are propagated to the rest of the system processors by means of the SCRAMNet shared memory network. The system mode logic contained in the STD will autonomously sequence in acquisition, tracking and pointing system through an entire engagement sequence, starting with target detection and ending with aimpoint maintenance. Simulation results and lab test results will be presented to verify the mode controller. In addition to implementing the system mode logic with the STD, the TMC can process prerecorded time sequences of commands required during startup operations. It can also process single commands from the system operator. In this paper, the author presents (1) an overview, in which he describes the TMC architecture, the relationship of an end-to-end simulation to the flight software and the laboratory testing environment, (2) implementation details, including information on the vxWorks message queues and the SCRAMNet shared memory network, (3) simulation results and lab test results which verify the mode controller, and (4) plans for the future, specifically as to how this executive will expedite transition to a fully functional system.

An Evaluation and Demonstration of a Network Based Aircraft Telemetry System

NASA Technical Reports Server (NTRS)

Waldersen, Matt; Schnarr, Otto, III

2017-01-01

The primary topics of this presentation describe the testing of network based telemetry and RF modulation techniques. The overall intend is to aid the aerospace industry in transitioning to a network based telemetry system.

Randomized shortest-path problems: two related models.

PubMed

Saerens, Marco; Achbany, Youssef; Fouss, François; Yen, Luh

2009-08-01

This letter addresses the problem of designing the transition probabilities of a finite Markov chain (the policy) in order to minimize the expected cost for reaching a destination node from a source node while maintaining a fixed level of entropy spread throughout the network (the exploration). It is motivated by the following scenario. Suppose you have to route agents through a network in some optimal way, for instance, by minimizing the total travel cost-nothing particular up to now-you could use a standard shortest-path algorithm. Suppose, however, that you want to avoid pure deterministic routing policies in order, for instance, to allow some continual exploration of the network, avoid congestion, or avoid complete predictability of your routing strategy. In other words, you want to introduce some randomness or unpredictability in the routing policy (i.e., the routing policy is randomized). This problem, which will be called the randomized shortest-path problem (RSP), is investigated in this work. The global level of randomness of the routing policy is quantified by the expected Shannon entropy spread throughout the network and is provided a priori by the designer. Then, necessary conditions to compute the optimal randomized policy-minimizing the expected routing cost-are derived. Iterating these necessary conditions, reminiscent of Bellman's value iteration equations, allows computing an optimal policy, that is, a set of transition probabilities in each node. Interestingly and surprisingly enough, this first model, while formulated in a totally different framework, is equivalent to Akamatsu's model ( 1996 ), appearing in transportation science, for a special choice of the entropy constraint. We therefore revisit Akamatsu's model by recasting it into a sum-over-paths statistical physics formalism allowing easy derivation of all the quantities of interest in an elegant, unified way. For instance, it is shown that the unique optimal policy can be obtained by solving a simple linear system of equations. This second model is therefore more convincing because of its computational efficiency and soundness. Finally, simulation results obtained on simple, illustrative examples show that the models behave as expected.

Applications of neural networks to the studies of phase transitions of two-dimensional Potts models

NASA Astrophysics Data System (ADS)

Li, C.-D.; Tan, D.-R.; Jiang, F.-J.

2018-04-01

We study the phase transitions of two-dimensional (2D) Q-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We demonstrate that the ideas from NN can be adopted to study these considered phase transitions efficiently. In particular, even with a simple NN constructed in this investigation, we are able to obtain the relevant information of the nature of these phase transitions, namely whether they are first order or second order. Our results strengthen the potential applicability of machine learning in studying various states of matters. Subtlety of applying NN techniques to investigate many-body systems is briefly discussed as well.

Using simple agent-based modeling to inform and enhance neighborhood walkability

PubMed Central

2013-01-01

Background Pedestrian-friendly neighborhoods with proximal destinations and services encourage walking and decrease car dependence, thereby contributing to more active and healthier communities. Proximity to key destinations and services is an important aspect of the urban design decision making process, particularly in areas adopting a transit-oriented development (TOD) approach to urban planning, whereby densification occurs within walking distance of transit nodes. Modeling destination access within neighborhoods has been limited to circular catchment buffers or more sophisticated network-buffers generated using geoprocessing routines within geographical information systems (GIS). Both circular and network-buffer catchment methods are problematic. Circular catchment models do not account for street networks, thus do not allow exploratory ‘what-if’ scenario modeling; and network-buffering functionality typically exists within proprietary GIS software, which can be costly and requires a high level of expertise to operate. Methods This study sought to overcome these limitations by developing an open-source simple agent-based walkable catchment tool that can be used by researchers, urban designers, planners, and policy makers to test scenarios for improving neighborhood walkable catchments. A simplified version of an agent-based model was ported to a vector-based open source GIS web tool using data derived from the Australian Urban Research Infrastructure Network (AURIN). The tool was developed and tested with end-user stakeholder working group input. Results The resulting model has proven to be effective and flexible, allowing stakeholders to assess and optimize the walkability of neighborhood catchments around actual or potential nodes of interest (e.g., schools, public transport stops). Users can derive a range of metrics to compare different scenarios modeled. These include: catchment area versus circular buffer ratios; mean number of streets crossed; and modeling of different walking speeds and wait time at intersections. Conclusions The tool has the capacity to influence planning and public health advocacy and practice, and by using open-access source software, it is available for use locally and internationally. There is also scope to extend this version of the tool from a simple to a complex model, which includes agents (i.e., simulated pedestrians) ‘learning’ and incorporating other environmental attributes that enhance walkability (e.g., residential density, mixed land use, traffic volume). PMID:24330721

Phased-array-fed antenna configuration study, volume 2

NASA Technical Reports Server (NTRS)

Sorbello, R. M.; Zaghloul, A. I.; Lee, B. S.; Siddiqi, S.; Geller, B. D.

1983-01-01

Increased capacity in future satellite systems can be achieved through antenna systems which provide multiplicity of frequency reuses at K sub a band. A number of antenna configurations which can provide multiple fixed spot beams and multiple independent spot scanning beams at 20 GHz are addressed. Each design incorporates a phased array with distributed MMIC amplifiers and phasesifters feeding a two reflector optical system. The tradeoffs required for the design of these systems and the corresponding performances are presented. Five final designs are studied. In so doing, a type of MMIC/waveguide transition is described, and measured results of the breadboard model are presented. Other hardware components developed are described. This includes a square orthomode transducer, a subarray fed with a beamforming network to measure scanning performance, and another subarray used to study mutual coupling considerations. Discussions of the advantages and disadvantages of the final design are included.

A Qualitative Study of Secondary School Teachers’ Perception of Social Network Analysis Metrics in the Context of Alcohol Consumption among Adolescents

PubMed Central

Quiroga, Enedina; Benavides, Carmen; Martín, Vicente

2017-01-01

Adolescence is a transitional period during which a number of changes occur. Social relationships established during this period influence adolescent behaviour and affect academic performance or alcohol consumption habits, among other issues. Teachers are very important actors in observing and guiding the evolution of their students, and should therefore have the appropriate knowledge and tools to gain insight into the complex social relationships that exist in their classes. The use of social network analysis (SNA) techniques may be helpful in order to study and monitor the evolution of these social networks. This study tries to understand how teachers perceive SNA metrics from an intuitive point of view. Using this information, useful tools could be created that allow teachers to use SNA techniques to improve their understanding of student relationships. A number of interviews with different teachers were held in secondary schools in Spain, allowing SNA concepts to be related to the everyday terms used by the teachers to characterize their students. Results from the study have an impact on questionnaire design for gathering data from students in order to perform an SNA analysis and on the design of software applications that can help teachers to understand the results of this analysis. PMID:29292718

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.

Robustness and Vulnerability of Networks with Dynamical Dependency Groups.

PubMed

Bai, Ya-Nan; Huang, Ning; Wang, Lei; Wu, Zhi-Xi

2016-11-28

The dependency property and self-recovery of failure nodes both have great effects on the robustness of networks during the cascading process. Existing investigations focused mainly on the failure mechanism of static dependency groups without considering the time-dependency of interdependent nodes and the recovery mechanism in reality. In this study, we present an evolving network model consisting of failure mechanisms and a recovery mechanism to explore network robustness, where the dependency relations among nodes vary over time. Based on generating function techniques, we provide an analytical framework for random networks with arbitrary degree distribution. In particular, we theoretically find that an abrupt percolation transition exists corresponding to the dynamical dependency groups for a wide range of topologies after initial random removal. Moreover, when the abrupt transition point is above the failure threshold of dependency groups, the evolving network with the larger dependency groups is more vulnerable; when below it, the larger dependency groups make the network more robust. Numerical simulations employing the Erdős-Rényi network and Barabási-Albert scale free network are performed to validate our theoretical results.

fastBMA: scalable network inference and transitive reduction.

PubMed

Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E; Yeung, Ka Yee

2017-10-01

Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). © The Authors 2017. Published by Oxford University Press.

Gain control through divisive inhibition prevents abrupt transition to chaos in a neural mass model

PubMed Central

Papasavvas, Christoforos A.; Wang, Yujiang; Trevelyan, Andrew J.; Kaiser, Marcus

2016-01-01

Experimental results suggest that there are two distinct mechanisms of inhibition in cortical neuronal networks: subtractive and divisive inhibition. They modulate the input-output function of their target neurons either by increasing the input that is needed to reach maximum output or by reducing the gain and the value of maximum output itself, respectively. However, the role of these mechanisms on the dynamics of the network is poorly understood. We introduce a novel population model and numerically investigate the influence of divisive inhibition on network dynamics. Specifically, we focus on the transitions from a state of regular oscillations to a state of chaotic dynamics via period-doubling bifurcations. The model with divisive inhibition exhibits a universal transition rate to chaos (Feigenbaum behavior). In contrast, in an equivalent model without divisive inhibition, transition rates to chaos are not bounded by the universal constant (non-Feigenbaum behavior). This non-Feigenbaum behavior, when only subtractive inhibition is present, is linked to the interaction of bifurcation curves in the parameter space. Indeed, searching the parameter space showed that such interactions are impossible when divisive inhibition is included. Therefore, divisive inhibition prevents non-Feigenbaum behavior and, consequently, any abrupt transition to chaos. The results suggest that the divisive inhibition in neuronal networks could play a crucial role in keeping the states of order and chaos well separated and in preventing the onset of pathological neural dynamics. PMID:26465514

Generalized epidemic process on modular networks.

PubMed

Chung, Kihong; Baek, Yongjoo; Kim, Daniel; Ha, Meesoon; Jeong, Hawoong

2014-05-01

Social reinforcement and modular structure are two salient features observed in the spreading of behavior through social contacts. In order to investigate the interplay between these two features, we study the generalized epidemic process on modular networks with equal-sized finite communities and adjustable modularity. Using the analytical approach originally applied to clique-based random networks, we show that the system exhibits a bond-percolation type continuous phase transition for weak social reinforcement, whereas a discontinuous phase transition occurs for sufficiently strong social reinforcement. Our findings are numerically verified using the finite-size scaling analysis and the crossings of the bimodality coefficient.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Spiral Wave in Small-World Networks of Hodgkin-Huxley Neurons

NASA Astrophysics Data System (ADS)

Ma, Jun; Yang, Li-Jian; Wu, Ying; Zhang, Cai-Rong

2010-09-01

The effect of small-world connection and noise on the formation and transition of spiral wave in the networks of Hodgkin-Huxley neurons are investigated in detail. Some interesting results are found in our numerical studies. i) The quiescent neurons are activated to propagate electric signal to others by generating and developing spiral wave from spiral seed in small area. ii) A statistical factor is defined to describe the collective properties and phase transition induced by the topology of networks and noise. iii) Stable rotating spiral wave can be generated and keeps robust when the rewiring probability is below certain threshold, otherwise, spiral wave can not be developed from the spiral seed and spiral wave breakup occurs for a stable rotating spiral wave. iv) Gaussian white noise is introduced on the membrane of neurons to study the noise-induced phase transition on spiral wave in small-world networks of neurons. It is confirmed that Gaussian white noise plays active role in supporting and developing spiral wave in the networks of neurons, and appearance of smaller factor of synchronization indicates high possibility to induce spiral wave.

The Hetu'u Global Network: Measuring the Distance to the Sun Using the June 5th/6th Transit of Venus

ERIC Educational Resources Information Center

Faherty, Jacqueline K.; Rodriguez, David R.; Miller, Scott T.

2012-01-01

In the spirit of historic astronomical endeavors, we invited school groups across the globe to collaborate in a solar distance measurement using the rare June 5/6th transit of Venus. In total, we recruited 19 school groups spread over 6 continents and 10 countries to participate in our Hetu'u Global Network. Applying the methods of French…

a Numerical Investigation of the Jamming Transition in Traffic Flow on Diluted Planar Networks

NASA Astrophysics Data System (ADS)

Achler, Gabriele; Barra, Adriano

In order to develop a toy model for car's traffic in cities, in this paper we analyze, by means of numerical simulations, the transition among fluid regimes and a congested jammed phase of the flow of kinetically constrained hard spheres in planar random networks similar to urban roads. In order to explore as timescales as possible, at a microscopic level we implement an event driven dynamics as the infinite time limit of a class of already existing model (Follow the Leader) on an Erdos-Renyi two-dimensional graph, the crossroads being accounted by standard Kirchoff density conservations. We define a dynamical order parameter as the ratio among the moving spheres versus the total number and by varying two control parameters (density of the spheres and coordination number of the network) we study the phase transition. At a mesoscopic level it respects an, again suitable, adapted version of the Lighthill-Whitham model, which belongs to the fluid-dynamical approach to the problem. At a macroscopic level, the model seems to display a continuous transition from a fluid phase to a jammed phase when varying the density of the spheres (the amount of cars in a city-like scenario) and a discontinuous jump when varying the connectivity of the underlying network.

Simulation of Z(3) walls and string production via bubble nucleation in a quark-hadron transition

NASA Astrophysics Data System (ADS)

Gupta, Uma Shankar; Mohapatra, Ranjita K.; Srivastava, Ajit M.; Tiwari, Vivek K.

2010-10-01

We study the dynamics of confinement-deconfinement phase transition in the context of relativistic heavy-ion collisions within the framework of effective models for the Polyakov loop order parameter. We study the formation of Z(3) walls and associated strings in the initial transition from the confining (hadronic) phase to the deconfining [quark-gluon plasma (QGP)] phase via the so-called Kibble mechanism. Essential physics of the Kibble mechanism is contained in a sort of domain structure arising after any phase transition which represents random variation of the order parameter at distances beyond the typical correlation length. We implement this domain structure by using the Polyakov loop effective model with a first order phase transition and confine ourselves with temperature/time ranges so that the first order confinement-deconfinement transition proceeds via bubble nucleation, leading to a well defined domain structure. The formation of Z(3) walls and associated strings results from the coalescence of QGP bubbles expanding in the confining background. We investigate the evolution of the Z(3) wall and string network. We also calculate the energy density fluctuations associated with Z(3) wall network and strings which decay away after the temperature drops below the quark-hadron transition temperature during the expansion of QGP. We discuss evolution of these quantities with changing temperature via Bjorken’s hydrodynamical model and discuss possible experimental signatures resulting from the presence of Z(3) wall network and associate strings.

Simulation of Z(3) walls and string production via bubble nucleation in a quark-hadron transition

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

Gupta, Uma Shankar; Tiwari, Vivek K.; Mohapatra, Ranjita K.

2010-10-01

We study the dynamics of confinement-deconfinement phase transition in the context of relativistic heavy-ion collisions within the framework of effective models for the Polyakov loop order parameter. We study the formation of Z(3) walls and associated strings in the initial transition from the confining (hadronic) phase to the deconfining [quark-gluon plasma (QGP)] phase via the so-called Kibble mechanism. Essential physics of the Kibble mechanism is contained in a sort of domain structure arising after any phase transition which represents random variation of the order parameter at distances beyond the typical correlation length. We implement this domain structure by using themore » Polyakov loop effective model with a first order phase transition and confine ourselves with temperature/time ranges so that the first order confinement-deconfinement transition proceeds via bubble nucleation, leading to a well defined domain structure. The formation of Z(3) walls and associated strings results from the coalescence of QGP bubbles expanding in the confining background. We investigate the evolution of the Z(3) wall and string network. We also calculate the energy density fluctuations associated with Z(3) wall network and strings which decay away after the temperature drops below the quark-hadron transition temperature during the expansion of QGP. We discuss evolution of these quantities with changing temperature via Bjorken's hydrodynamical model and discuss possible experimental signatures resulting from the presence of Z(3) wall network and associate strings.« less

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

PubMed Central

Tse, Margaret J.; Chu, Brian K.; Roy, Mahua; Read, Elizabeth L.

2015-01-01

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks. PMID:26488666

Analysis of a Dynamic Multi-Track Airway Concept for Air Traffic Management

NASA Technical Reports Server (NTRS)

Wing, David J.; Smith, Jeremy C.; Ballin, Mark G.

2008-01-01

The Dynamic Multi-track Airways (DMA) Concept for Air Traffic Management (ATM) proposes a network of high-altitude airways constructed of multiple, closely spaced, parallel tracks designed to increase en-route capacity in high-demand airspace corridors. Segregated from non-airway operations, these multi-track airways establish high-priority traffic flow corridors along optimal routes between major terminal areas throughout the National Airspace System (NAS). Air traffic controllers transition aircraft equipped for DMA operations to DMA entry points, the aircraft use autonomous control of airspeed to fly the continuous-airspace airway and achieve an economic benefit, and controllers then transition the aircraft from the DMA exit to the terminal area. Aircraft authority within the DMA includes responsibility for spacing and/or separation from other DMA aircraft. The DMA controller is responsible for coordinating the entry and exit of traffic to and from the DMA and for traffic flow management (TFM), including adjusting DMA routing on a daily basis to account for predicted weather and wind patterns and re-routing DMAs in real time to accommodate unpredicted weather changes. However, the DMA controller is not responsible for monitoring the DMA for traffic separation. This report defines the mature state concept, explores its feasibility and performance, and identifies potential benefits. The report also discusses (a) an analysis of a single DMA, which was modeled within the NAS to assess capacity and determine the impact of a single DMA on regional sector loads and conflict potential; (b) a demand analysis, which was conducted to determine likely city-pair candidates for a nationwide DMA network and to determine the expected demand fraction; (c) two track configurations, which were modeled and analyzed for their operational characteristic; (d) software-prototype airborne capabilities developed for DMA operations research; (e) a feasibility analysis of key attributes in the concept design; (f) a near-term, transitional application of the DMA concept as a proving ground for new airborne technologies; and (g) conclusions. The analysis indicates that the operational feasibility of a national DMA network faces significant challenges, especially for interactions between DMAs and between DMA and non-DMA traffic. Provided these issues are resolved, sectors near DMAs could experience significant local capacity benefits.

Complex networks: Effect of subtle changes in nature of randomness

NASA Astrophysics Data System (ADS)

Goswami, Sanchari; Biswas, Soham; Sen, Parongama

2011-03-01

In two different classes of network models, namely, the Watts Strogatz type and the Euclidean type, subtle changes have been introduced in the randomness. In the Watts Strogatz type network, rewiring has been done in different ways and although the qualitative results remain the same, finite differences in the exponents are observed. In the Euclidean type networks, where at least one finite phase transition occurs, two models differing in a similar way have been considered. The results show a possible shift in one of the phase transition points but no change in the values of the exponents. The WS and Euclidean type models are equivalent for extreme values of the parameters; we compare their behaviour for intermediate values.

Limited ability driven phase transitions in the coevolution process in Axelrod's model

NASA Astrophysics Data System (ADS)

Wang, Bing; Han, Yuexing; Chen, Luonan; Aihara, Kazuyuki

2009-04-01

We study the coevolution process in Axelrod's model by taking into account of agents' abilities to access information, which is described by a parameter α to control the geographical range of communication. We observe two kinds of phase transitions in both cultural domains and network fragments, which depend on the parameter α. By simulation, we find that not all rewiring processes pervade the dissemination of culture, that is, a very limited ability to access information constrains the cultural dissemination, while an exceptional ability to access information aids the dissemination of culture. Furthermore, by analyzing the network characteristics at the frozen states, we find that there exists a stage at which the network develops to be a small-world network with community structures.

Increasing cellular coverage within integrated terrestrial/satellite mobile networks

NASA Technical Reports Server (NTRS)

Castro, Jonathan P.

1995-01-01

When applying the hierarchical cellular concept, the satellite acts as giant umbrella cell covering a region with some terrestrial cells. If a mobile terminal traversing the region arrives to the border-line or limits of a regular cellular ground service, network transition occurs and the satellite system continues the mobile coverage. To adequately assess the boundaries of service of a mobile satellite system an a cellular network within an integrated environment, this paper provides an optimized scheme to predict when a network transition may be necessary. Under the assumption of a classified propagation phenomenon and Lognormal shadowing, the study applies an analytical approach to estimate the location of a mobile terminal based on a reception of the signal strength emitted by a base station.

An internet-based communication network for information transfer during patient transitions from skilled nursing facility to the emergency department.

PubMed

Hustey, Fredric M; Palmer, Robert M

2010-06-01

To determine whether the implementation of an Internet-based communication system improves the amount of essential information conveyed between a skilled nursing facility (SNF) and the emergency department (ED) during patient care transitions. Interventional; before and after. ED of an urban teaching hospital with approximately 55,000 visits per year and a 55-bed subacute free-standing rehabilitation facility (the SNF). All patients transferred from the SNF to the ED over 16 months. An Internet-based communication network with SNF-ED transfer form for communication during patient care transitions. Nine elements of patient information assessed before and after intervention through chart review. changes in efficiency of information transfer and staff satisfaction. Two hundred thirty-four of 237 preintervention and all 276 postintervention care transitions were reviewed. The Internet communication network was used in 78 (26%) of all care transitions, peaking at 40% by the end of the study. There was more critical patient information (1.85 vs 4.29 of 9 elements; P<.001) contained within fewer pages of transfer documents (24.47 vs 5.15; P<.001) after the intervention. Staff satisfaction with communication was higher among ED physicians after the intervention. The use of an Internet-based system increased the amount of information communicated during SNF-ED care transitions and significantly reduced the number of pages in which this information was contained.

Fundamental ingredients for discontinuous phase transitions in the inertial majority vote model.

PubMed

Encinas, Jesus M; Harunari, Pedro E; de Oliveira, M M; Fiore, Carlos E

2018-06-19

Discontinuous transitions have received considerable interest due to the uncovering that many phenomena such as catastrophic changes, epidemic outbreaks and synchronization present a behavior signed by abrupt (macroscopic) changes (instead of smooth ones) as a tuning parameter is changed. However, in different cases there are still scarce microscopic models reproducing such above trademarks. With these ideas in mind, we investigate the key ingredients underpinning the discontinuous transition in one of the simplest systems with up-down Z 2 symmetry recently ascertained in [Phys. Rev. E 95, 042304 (2017)]. Such system, in the presence of an extra ingredient-the inertia- has its continuous transition being switched to a discontinuous one in complex networks. We scrutinize the role of three central ingredients: inertia, system degree, and the lattice topology. Our analysis has been carried out for regular lattices and random regular networks with different node degrees (interacting neighborhood) through mean-field theory (MFT) treatment and numerical simulations. Our findings reveal that not only the inertia but also the connectivity constitute essential elements for shifting the phase transition. Astoundingly, they also manifest in low-dimensional regular topologies, exposing a scaling behavior entirely different than those from the complex networks case. Therefore, our findings put on firmer bases the essential issues for the manifestation of discontinuous transitions in such relevant class of systems with Z 2 symmetry.

Defining and characterizing the critical transition state prior to the type 2 diabetes disease

PubMed Central

Zhu, Chunqing; Zhou, Xin; Chen, Pei; Fu, Tianyun; Hu, Zhongkai; Wu, Qian; Liu, Wei; Liu, Daowei; Yu, Yunxian; Zhang, Yan; McElhinney, Doff B.; Li, Yu-Ming; Culver, Devore S; Alfreds, Shaun T.; Stearns, Frank; Sylvester, Karl G.; Widen, Eric

2017-01-01

Background Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed through the longitudinal electronic medical record (EMR) analysis. Method We applied the transition-based network entropy methodology which previously identified a dynamic driver network (DDN) underlying the critical T2DM transition at the tissue molecular biological level. To profile pre-disease phenotypical changes that indicated a critical transition state, a cohort of 7,334 patients was assembled from the Maine State Health Information Exchange (HIE). These patients all had their first confirmative diagnosis of T2DM between January 1, 2013 and June 30, 2013. The cohort’s EMRs from the 24 months preceding their date of first T2DM diagnosis were extracted. Results Analysis of these patients’ pre-disease clinical history identified a dynamic driver network (DDN) and an associated critical transition state six months prior to their first confirmative T2DM state. Conclusions This 6-month window before the disease state provides an early warning of the impending T2DM, warranting an opportunity to apply proactive interventions to prevent or delay the new onset of T2DM. PMID:28686739

Glass-Glass Transitions by Means of an Acceptor-Donor Percolating Electric-Dipole Network

NASA Astrophysics Data System (ADS)

Zhang, Le; Lou, Xiaojie; Wang, Dong; Zhou, Yan; Yang, Yang; Kuball, Martin; Carpenter, Michael A.; Ren, Xiaobing

2017-11-01

We report the ferroelectric glass-glass transitions in KN (K+/Nb5 +) -doped BaTiO3 ferroelectric ceramics, which have been proved by x-ray diffraction profile and Raman spectra data. The formation of glass-glass transitions can be attributed to the existence of cubic (C )-tetragonal (T )-orthorhombic (O )-rhombohedral (R ) ferroelectric transitions in short-range order. These abnormal glass-glass transitions can perform very small thermal hysteresis (approximately 1.0 K ) with a large dielectric constant (approximately 3000), small remanent polarization Pr , and relative high maximum polarization Pm remaining over a wide temperature range (220-350 K) under an electrical stimulus, indicating the potential applications in dielectric recoverable energy-storage devices with high thermal reliability. Further phase field simulations suggest that these glass-glass transitions are induced by the formation of a percolating electric defect-dipole network (PEDN). This proper PEDN breaks the long-range ordered ferroelectric domain pattern and results in the local phase transitions at the nanoscale. Our work may further stimulate the fundamental physical theory and accelerate the development of dielectric energy-storing devices.

Small Molecules-Big Data.

PubMed

Császár, Attila G; Furtenbacher, Tibor; Árendás, Péter

2016-11-17

Quantum mechanics builds large-scale graphs (networks): the vertices are the discrete energy levels the quantum system possesses, and the edges are the (quantum-mechanically allowed) transitions. Parts of the complete quantum mechanical networks can be probed experimentally via high-resolution, energy-resolved spectroscopic techniques. The complete rovibronic line list information for a given molecule can only be obtained through sophisticated quantum-chemical computations. Experiments as well as computations yield what we call spectroscopic networks (SN). First-principles SNs of even small, three to five atomic molecules can be huge, qualifying for the big data description. Besides helping to interpret high-resolution spectra, the network-theoretical view offers several ideas for improving the accuracy and robustness of the increasingly important information systems containing line-by-line spectroscopic data. For example, the smallest number of measurements necessary to perform to obtain the complete list of energy levels is given by the minimum-weight spanning tree of the SN and network clustering studies may call attention to "weakest links" of a spectroscopic database. A present-day application of spectroscopic networks is within the MARVEL (Measured Active Rotational-Vibrational Energy Levels) approach, whereby the transitions information on a measured SN is turned into experimental energy levels via a weighted linear least-squares refinement. MARVEL has been used successfully for 15 molecules and allowed to validate most of the transitions measured and come up with energy levels with well-defined and realistic uncertainties. Accurate knowledge of the energy levels with computed transition intensities allows the realistic prediction of spectra under many different circumstances, e.g., for widely different temperatures. Detailed knowledge of the energy level structure of a molecule coming from a MARVEL analysis is important for a considerable number of modeling efforts in chemistry, physics, and engineering.

From partnerships to networks: new approaches for measuring U.S. National Heritage Area effectiveness.

PubMed

Laven, Daniel N; Krymkowski, Daniel H; Ventriss, Curtis L; Manning, Robert E; Mitchell, Nora J

2010-08-01

National Heritage Areas (NHAs) are an alternative and increasingly popular form of protected area management in the United States. NHAs seek to integrate environmental objectives with community and economic objectives at regional or landscape scales. NHA designations have increased rapidly in the last 20 years, generating a substantial need for evaluative information about (a) how NHAs work; (b) outcomes associated with the NHA process; and (c) the costs and benefits of investing public moneys into the NHA approach. Qualitative evaluation studies recently conducted at three NHAs have identified the importance of understanding network structure and function in the context of evaluating NHA management effectiveness. This article extends these case studies by examining quantitative network data from each of the sites. The authors analyze these data using both a descriptive approach and a statistically more robust approach known as exponential random graph modeling. Study findings indicate the presence of transitive structures and the absence of three-cycle structures in each of these networks. This suggests that these networks are relatively ''open,'' which may be desirable, given the uncertainty of the environments in which they operate. These findings also suggest, at least at the sites reported here, that the NHA approach may be an effective way to activate and develop networks of intersectoral organizational partners. Finally, this study demonstrates the utility of using quantitative network analysis to better understand the effectiveness of protected area management models that rely on partnership networks to achieve their intended outcomes.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

PubMed Central

Jang, Sumin; Choubey, Sandeep; Furchtgott, Leon; Zou, Ling-Nan; Doyle, Adele; Menon, Vilas; Loew, Ethan B; Krostag, Anne-Rachel; Martinez, Refugio A; Madisen, Linda; Levi, Boaz P; Ramanathan, Sharad

2017-01-01

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001 PMID:28296635

Modeling and visualizing cell type switching.

PubMed

Ghaffarizadeh, Ahmadreza; Podgorski, Gregory J; Flann, Nicholas S

2014-01-01

Understanding cellular differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression profiles and the potential cell types that they represent. A visualization tool called CellDiff3D creates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of the CellDiff3D technique, a new approach to quantify and envision all possible cell state transitions in any lineage network.

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network.

PubMed

Axelrod, Kevin; Sanchez, Alvaro; Gore, Jeff

2015-08-24

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition.

75 FR 9343 - Nomenclature Change Relating to the Network Distribution Center Transition

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-02

... POSTAL SERVICE 39 CFR Parts 111 and 121 Nomenclature Change Relating to the Network Distribution... (BMC) to network distribution centers (NDC), by replacing all text references to ``BMC'' with ``NDC...: Background: The BMC network was established in the 1970s to process Parcel Post[supreg], Bound Printed Matter...

Quantum phase transitions in a two-dimensional quantum XYX model: ground-state fidelity and entanglement.

PubMed

Li, Bo; Li, Sheng-Hao; Zhou, Huan-Qiang

2009-06-01

A systematic analysis is performed for quantum phase transitions in a two-dimensional anisotropic spin-1/2 antiferromagnetic XYX model in an external magnetic field. With the help of an innovative tensor network algorithm, we compute the fidelity per lattice site to demonstrate that the field-induced quantum phase transition is unambiguously characterized by a pinch point on the fidelity surface, marking a continuous phase transition. We also compute an entanglement estimator, defined as a ratio between the one-tangle and the sum of squared concurrences, to identify both the factorizing field and the critical point, resulting in a quantitative agreement with quantum Monte Carlo simulation. In addition, the local order parameter is "derived" from the tensor network representation of the system's ground-state wave functions.

The role of community structure on the nature of explosive synchronization.

PubMed

Lotfi, Nastaran; Rodrigues, Francisco A; Darooneh, Amir Hossein

2018-03-01

In this paper, we analyze explosive synchronization in networks with a community structure. The results of our study indicate that the mesoscopic structure of the networks could affect the synchronization of coupled oscillators. With the variation of three parameters, the degree probability distribution exponent, the community size probability distribution exponent, and the mixing parameter, we could have a fast or slow phase transition. Besides, in some cases, we could have communities which are synchronized inside but not with other communities and vice versa. We also show that there is a limit in these mesoscopic structures which suppresses the transition from the second-order phase transition and results in explosive synchronization. This could be considered as a tuning parameter changing the transition of the system from the second order to the first order.

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

Nigro, Valentina, E-mail: nigro@fis.uniroma3.it; Bruni, Fabio; Ricci, Maria Antonietta

The temperature dependence of the local intra-particle structure of colloidal microgel particles, composed of interpenetrated polymer networks, has been investigated by small-angle neutron scattering at different pH and concentrations, in the range (299÷315) K, where a volume phase transition from a swollen to a shrunken state takes place. Data are well described by a theoretical model that takes into account the presence of both interpenetrated polymer networks and cross-linkers. Two different behaviors are found across the volume phase transition. At neutral pH and T ≈ 307 K, a sharp change of the local structure from a water rich open inhomogeneousmore » interpenetrated polymer network to a homogeneous porous solid-like structure after expelling water is observed. Differently, at acidic pH, the local structure changes almost continuously. These findings demonstrate that a fine control of the pH of the system allows to tune the sharpness of the volume-phase transition.« less

Dual-phase evolution in complex adaptive systems

PubMed Central

Paperin, Greg; Green, David G.; Sadedin, Suzanne

2011-01-01

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle. PMID:21247947

Entropic determination of the phase transition in a coevolving opinion-formation model.

PubMed

Burgos, E; Hernández, Laura; Ceva, H; Perazzo, R P J

2015-03-01

We study an opinion formation model by the means of a coevolving complex network where the vertices represent the individuals, characterized by their evolving opinions, and the edges represent the interactions among them. The network adapts to the spreading of opinions in two ways: not only connected agents interact and eventually change their thinking but an agent may also rewire one of its links to a neighborhood holding the same opinion as his. The dynamics, based on a global majority rule, depends on an external parameter that controls the plasticity of the network. We show how the information entropy associated to the distribution of group sizes allows us to locate the phase transition between a phase of full consensus and another, where different opinions coexist. We also determine the minimum size of the most informative sampling. At the transition the distribution of the sizes of groups holding the same opinion is scale free.

Dual-phase evolution in complex adaptive systems.

PubMed

Paperin, Greg; Green, David G; Sadedin, Suzanne

2011-05-06

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle.

Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

PubMed

Chen, Shi; Ilany, Amiyaal; White, Brad J; Sanderson, Michael W; Lanzas, Cristina

2015-01-01

Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.

Long-Term Observations of Atmospheric CO2, O3 and BrO over the Transitioning Arctic Ocean Pack-ice: The O-Buoy Chemical Network

NASA Astrophysics Data System (ADS)

Matrai, P.

2016-02-01

Autonomous, sea ice-tethered O-Buoys have been deployed (2009-2016) across the Arctic sea ice for long-term atmospheric measurements (http://www.o-buoy.org). O-Buoys (15) provide in-situ concentrations of three sentinel atmospheric chemicals, ozone, CO2 and BrO, as well as meteorological parameters and imagery, over the frozen ocean. O-Buoys were designed to transmit daily data over a period of 2 years while deployed in sea ice, as part of automated ice-drifting stations that include snow/ice measurement systems (e.g. Ice Mass Balance buoys) and oceanographic measurements (e.g. Ice Tethered Profilers). Seasonal changes in Arctic atmospheric chemistry are influenced by changes in the characteristics and presence of the sea ice vs. open water as well as air mass trajectories, especially during the winter-spring and summer-fall transitions when sea ice is melting and freezing, respectively. The O-Buoy Chemical Network provides the unique opportunity to observe these transition periods in real-time with high temporal resolution, and to compare them with those collected on land-based monitoring stations located. Due to the logistical challenges of measurements over the Arctic Ocean region, most long term, in-situ observations of atmospheric chemistry have been made at coastal or island sites around the periphery of the Arctic Ocean, leaving large spatial and temporal gaps that O-Buoys overcome. Advances in floatation, communications, power management, and sensor hardware have been made to overcome the challenges of diminished Arctic sea ice. O-Buoy data provide insights into enhanced seasonal, interannual and spatial variability in atmospheric composition, atmospheric boundary layer control on the amount of halogen activation, enhancement of the atmospheric CO2 signal over the more variable and porous pack ice, and to develop an integrated picture of the coupled ocean/ice/atmosphere system. As part of the Arctic Observing Network, we provide data to the community (www.aoncadis.org).

Modeling resting-state functional networks when the cortex falls asleep: local and global changes.

PubMed

Deco, Gustavo; Hagmann, Patric; Hudetz, Anthony G; Tononi, Giulio

2014-12-01

The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

Thermoreversible Folding as a Route to the Unique Shape-Memory Character in Ductile Polymer Networks.

PubMed

McBride, Matthew K; Podgorski, Maciej; Chatani, Shunsuke; Worrell, Brady T; Bowman, Christopher N

2018-06-21

Ductile, cross-linked films were folded as a means to program temporary shapes without the need for complex heating cycles or specialized equipment. Certain cross-linked polymer networks, formed here with the thiol-isocyanate reaction, possessed the ability to be pseudoplastically deformed below the glass transition, and the original shape was recovered during heating through the glass transition. To circumvent the large forces required to plastically deform a glassy polymer network, we have utilized folding, which localizes the deformation in small creases, and achieved large dimensional changes with simple programming procedures. In addition to dimension changes, three-dimensional objects such as swans and airplanes were developed to demonstrate applying origami principles to shape memory. We explored the fundamental mechanical properties that are required to fold polymer sheets and observed that a yield point that does not correspond to catastrophic failure is required. Unfolding occurred during heating through the glass transition, indicating the vitrification of the network that maintained the temporary, folded shape. Folding was demonstrated as a powerful tool to simply and effectively program ductile shape-memory polymers without the need for thermal cycling.

Supra-molecular networks for CO2 capture

NASA Astrophysics Data System (ADS)

Sadowski, Jerzy; Kestell, John

Utilizing capabilities of low-energy electron microscopy (LEEM) for non-destructive interrogation of the real-time molecular self-assembly, we have investigated supramolecular systems based on carboxylic acid-metal complexes, such as trimesic and mellitic acid, doped with transition metals. Such 2D networks can act as host systems for transition-metal phthalocyanines (MPc; M = Fe, Ti, Sc). The electrostatic interactions of CO2 molecules with transition metal ions can be tuned by controlling the type of TM ion and the size of the pore in the host network. We further applied infrared reflection-absorption spectroscopy (IRRAS) to determine of the molecular orientation of the functional groups and the whole molecule in the 2D monolayers of carboxylic acid. The kinetics and mechanism of the CO2 adsorption/desorption on the 2D molecular network, with and without the TM ion doping, have been also investigated. This research used resources of the Center for Functional Nanomaterials, which is the U.S. DOE Office of Science User Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704.

The Influence of Social Network Characteristics on Peer Clustering in Smoking: A Two-Wave Panel Study of 19- and 23-Year-Old Swedes.

PubMed

Miething, Alexander; Rostila, Mikael; Edling, Christofer; Rydgren, Jens

2016-01-01

The present study examines how the composition of social networks and perceived relationship content influence peer clustering in smoking, and how the association changes during the transition from late adolescence to early adulthood. The analysis was based on a Swedish two-wave survey sample comprising ego-centric network data. Respondents were 19 years old in the initial wave, and 23 when the follow-up sample was conducted. 17,227 ego-alter dyads were included in the analyses, which corresponds to an average response rate of 48.7 percent. Random effects logistic regression models were performed to calculate gender-specific average marginal effects of social network characteristics on smoking. The association of egos' and alters' smoking behavior was confirmed and found to be stronger when correlated in the female sample. For females, the associations decreased between age 19 and 23. Interactions between network characteristics and peer clustering in smoking showed that intense social interactions with smokers increase egos' smoking probability. The influence of network structures on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. The study confirmed peer clustering in smoking and revealed that females' smoking behavior in particular is determined by social interactions. Female smokers' propensity to interact with other smokers was found to be associated with the quality of peer relationships, frequent social interactions, and network density. The influence of social networks on peer clustering in smoking decreased during the transition from late adolescence to early adulthood.

The Influence of Social Network Characteristics on Peer Clustering in Smoking: A Two-Wave Panel Study of 19- and 23-Year-Old Swedes

PubMed Central

Rostila, Mikael; Edling, Christofer; Rydgren, Jens

2016-01-01

Objectives The present study examines how the composition of social networks and perceived relationship content influence peer clustering in smoking, and how the association changes during the transition from late adolescence to early adulthood. Methods The analysis was based on a Swedish two-wave survey sample comprising ego-centric network data. Respondents were 19 years old in the initial wave, and 23 when the follow-up sample was conducted. 17,227 ego-alter dyads were included in the analyses, which corresponds to an average response rate of 48.7 percent. Random effects logistic regression models were performed to calculate gender-specific average marginal effects of social network characteristics on smoking. Results The association of egos’ and alters’ smoking behavior was confirmed and found to be stronger when correlated in the female sample. For females, the associations decreased between age 19 and 23. Interactions between network characteristics and peer clustering in smoking showed that intense social interactions with smokers increase egos’ smoking probability. The influence of network structures on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. Conclusions The study confirmed peer clustering in smoking and revealed that females’ smoking behavior in particular is determined by social interactions. Female smokers’ propensity to interact with other smokers was found to be associated with the quality of peer relationships, frequent social interactions, and network density. The influence of social networks on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. PMID:27727314

Rational Design of Porous Conjugated Polymers and Roles of Residual Palladium for Photocatalytic Hydrogen Production.

PubMed

Li, Lianwei; Cai, Zhengxu; Wu, Qinghe; Lo, Wai-Yip; Zhang, Na; Chen, Lin X; Yu, Luping

2016-06-22

Developing highly efficient photocatalyts for water splitting is one of the grand challenges in solar energy conversion. Here, we report the rational design and synthesis of porous conjugated polymer (PCP) that photocatalytically generates hydrogen from water splitting. The design mimics natural photosynthetics systems with conjugated polymer component to harvest photons and the transition metal part to facilitate catalytic activities. A series of PCPs have been synthesized with different light harvesting chromophores and transition metal binding bipyridyl (bpy) sites. The photocatalytic activity of these bpy-containing PCPs can be greatly enhanced due to the improved light absorption, better wettability, local ordering structure, and the improved charge separation process. The PCP made of strong and fully conjugated donor chromophore DBD (M4) shows the highest hydrogen production rate at ∼33 μmol/h. The results indicate that copolymerization between a strong electron donor and weak electron acceptor into the same polymer chain is a useful strategy for developing efficient photocatalysts. This study also reveals that the residual palladium in the PCP networks plays a key role for the catalytic performance. The hydrogen generation activity of PCP photocatalyst can be further enhanced to 164 μmol/h with an apparent quantum yield of 1.8% at 350 nm by loading 2 wt % of extra platinum cocatalyst.

Evaluation of the North Country on the Job Network: a model of facilitated care for injured workers in rural upstate New York.

PubMed

Lipscomb, Hester J; Moon, Samuel D; Li, Leiming; Pompeii, Lisa; Kennedy, Margaret Q

2002-03-01

We describe the evaluation of a community-based program designed to facilitate access to care and return to work for injured workers in a rural, medically underserved area in upstate New York. Providers are recruited to provide easily accessible care and are oriented to concepts of transitional duty and rapid return to work as medically appropriate; companies are recruited with the agreement to provide transitional work for injured employees. Registered nurses, hired by the local hospital, serve as case coordinators to facilitate care and coordinate communications among all parties. Over 3000 injured workers received care through the program in the first 56 months, with a decline in the number of transitional days over time. The number of days that the cases remain open has steadily declined, and the number of return-to-work cases has increased. The success of this initiative provides an excellent background for continued improvement in delivery of care to injured workers and proactive efforts at improving workplace safety and health in a rural area.

Superconcentrated electrolytes for a high-voltage lithium-ion battery

PubMed Central

Wang, Jianhui; Yamada, Yuki; Sodeyama, Keitaro; Chiang, Ching Hua; Tateyama, Yoshitaka; Yamada, Atsuo

2016-01-01

Finding a viable electrolyte for next-generation 5 V-class lithium-ion batteries is of primary importance. A long-standing obstacle has been metal-ion dissolution at high voltages. The LiPF6 salt in conventional electrolytes is chemically unstable, which accelerates transition metal dissolution of the electrode material, yet beneficially suppresses oxidative dissolution of the aluminium current collector; replacing LiPF6 with more stable lithium salts may diminish transition metal dissolution but unfortunately encounters severe aluminium oxidation. Here we report an electrolyte design that can solve this dilemma. By mixing a stable lithium salt LiN(SO2F)2 with dimethyl carbonate solvent at extremely high concentrations, we obtain an unusual liquid showing a three-dimensional network of anions and solvent molecules that coordinate strongly to Li+ ions. This simple formulation of superconcentrated LiN(SO2F)2/dimethyl carbonate electrolyte inhibits the dissolution of both aluminium and transition metal at around 5 V, and realizes a high-voltage LiNi0.5Mn1.5O4/graphite battery that exhibits excellent cycling durability, high rate capability and enhanced safety. PMID:27354162

Non-equilibrium phase transition in mesoscopic biochemical systems: from stochastic to nonlinear dynamics and beyond

PubMed Central

Ge, Hao; Qian, Hong

2011-01-01

A theory for an non-equilibrium phase transition in a driven biochemical network is presented. The theory is based on the chemical master equation (CME) formulation of mesoscopic biochemical reactions and the mathematical method of large deviations. The large deviations theory provides an analytical tool connecting the macroscopic multi-stability of an open chemical system with the multi-scale dynamics of its mesoscopic counterpart. It shows a corresponding non-equilibrium phase transition among multiple stochastic attractors. As an example, in the canonical phosphorylation–dephosphorylation system with feedback that exhibits bistability, we show that the non-equilibrium steady-state (NESS) phase transition has all the characteristics of classic equilibrium phase transition: Maxwell construction, a discontinuous first-derivative of the ‘free energy function’, Lee–Yang's zero for a generating function and a critical point that matches the cusp in nonlinear bifurcation theory. To the biochemical system, the mathematical analysis suggests three distinct timescales and needed levels of description. They are (i) molecular signalling, (ii) biochemical network nonlinear dynamics, and (iii) cellular evolution. For finite mesoscopic systems such as a cell, motions associated with (i) and (iii) are stochastic while that with (ii) is deterministic. Both (ii) and (iii) are emergent properties of a dynamic biochemical network. PMID:20466813

Numerical simulation of flow for viscoelastic neutrophil models in a rectangular capillary network: effects of capillary shape and cell stiffness on transit time.

PubMed

Shirai, Atsushi; Fujita, Ryo; Hayase, Toshiyuki

2007-01-01

The concentration of neutrophils in the pulmonary microvasculature is higher than in large systemic vessels. It is thought that the high concentration of neutrophils facilitates their effective recruitment to sites of inflammation. Thus, in order to understand the role of neutrophils in the immune system, it is important to clarify their flow characteristics in the pulmonary microvasculature. In a previous study, we developed a model to simulate the flow of neutrophils in a capillary network, in which the cells were modeled as spheres of a Maxwell material with a cortical tension and the capillary segments were modeled as arc-shaped constrictions in straight pipes. In the present paper, the flow of neutrophils in a simplified alveolar capillary network model is investigated for various constriction shapes and cell stiffnesses. Finally, it is shown that both the coefficient of variation of the transit time of the cells, which is the standard deviation divided by the mean transit time, and the mean transit time increase as the capillary segments become steep or tight, or when the cells become hard. The mean value of the transit time exceeds the median for all of the conditions that occur in real lungs, although the difference between them is small.

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Consumers don’t play dice, influence of social networks and advertisements

NASA Astrophysics Data System (ADS)

Groot, Robert D.

2006-05-01

Empirical data of supermarket sales show stylised facts that are similar to stock markets, with a broad (truncated) Lévy distribution of weekly sales differences in the baseline sales [R.D. Groot, Physica A 353 (2005) 501]. To investigate the cause of this, the influence of social interactions and advertisements are studied in an agent-based model of consumers in a social network. The influence of network topology was varied by using a small-world network, a random network and a Barabási-Albert network. The degree to which consumers value the opinion of their peers was also varied. On a small-world and random network we find a phase transition between an open market and a locked-in market that is similar to condensation in liquids. At the critical point, fluctuations become large and buying behaviour is strongly correlated. However, on the small world network the noise distribution at the critical point is Gaussian, and critical slowing down occurs which is not observed in supermarket sales. On a scale-free network, the model shows a transition between a gas-like phase and a glassy state, but at the transition point the noise amplitude is much larger than what is seen in supermarket sales. To explore the role of advertisements, a model is studied where imprints are placed on the minds of consumers that ripen when a decision for a product is made. The correct distribution of weekly sales returns follows naturally from this model, as well as the noise amplitude, the correlation time and cross-correlation of sales fluctuations. For particular parameter values, simulated sales correlation shows power-law decay in time. The model predicts that social interaction helps to prevent aversion, and that products are viewed more positively when their consumption rate is higher.

Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites.

PubMed

Hadadi, Noushin; Hafner, Jasmin; Soh, Keng Cher; Hatzimanikatis, Vassily

2017-01-01

Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Proceedings of the International Symposium on Topological Aspects of Critical Systems and Networks

NASA Astrophysics Data System (ADS)

Yakubo, Kousuke; Amitsuka, Hiroshi; Ishikawa, Goo; Machino, Kazuo; Nakagaki, Toshiyuki; Tanda, Satoshi; Yamada, Hideto; Kichiji, Nozomi

2007-07-01

I. General properties of networks. Physics of network security / Y.-C. Lai, X. Wand and C. H. Lai. Multi-state interacting particle systems on scale-free networks / N. Masuda and N. Konno. Homotopy Reduction of Complex Networks 18 / Y. Hiraoka and T. Ichinomiya. Analysis of the Susceptible-Infected-Susceptible Model on Complex Network / T. Ichinomiya -- II. Complexity in social science. Innovation and Development in a Random Lattice / J. Lahtinen. Long-tailed distributions in biological systems: revisit to Lognormals / N. Kobayashi ... [et al.]. Two-class structure of income distribution in the USA:exponential bulk and power-law tail / V. M. Yakovenko and A. Christian Silva. Power Law distributions in two community currencies / N. Kichiji and M. Nishibe -- III. Patterns in biological objects. Stoichiometric network analysis of nonlinear phenomena in rection mechanism for TWC converters / M. Marek ... [et al.]. Collective movement and morphogenesis of epithelial cells / H. Haga and K. Kawabata. Indecisive behavior of amoeba crossing an environmental barrier / S. Takagi ... [et al.]. Effects of amount of food on path selection in the transport network of an amoeboid organism / T. Nakagaki ... [et al.]. Light scattering study in double network gels / M. Fukunaya ... [et al.].Blood flow velocity in the choroid in punctate inner choroidopathy and Vogt-Koyanagi-Harada disease; amd multifractal analysis of choroidal blood flow in age-related macular degeneration / K. Yoshida ... [et al.]. Topological analysis of placental arteries: correlation with neonatal growth / H. Yamada and K. Yakubo -- IV. Criticality in pure and applied physics. Droplets in Disordered Metallic Quantum Critical Systems / A. H. Castro Neto and B. A. Jones. Importance of static disorder and inhomogeneous cooperative dynamics in heavy-fermion metals / O. O. Bernal. Competition between spin glass and Antiferromagnetic phases in heavy fermion materials / S. Sullow. Emergent Phases via Fermi surface reconstruction near the metamagnetic quantum critical point in U (RU1-xRhx)2Si2 / K. H. Kim ... [et al.]. Continuous Evolution of the Fermi Surface of CeRu2Si2 across the metamagnetic transition / R. Daou, C. Bergemann and S. R. Julian. Phase transition between the itinerant and the localized f-electron states in heavy fermion antiferromagnet Ce(Ru0.9Rh0.1)2(Si1-yGey) / Relation between magnetism and metal-Insulator transition in Mn-doped SrRuO3 / M. Yokoyama ... [et al.]. Magnetization study of pairing and Vortex states in Sr2RuO4 / K. Tenya ... [et al.]. Single-site effects of Pr ions doped in ThRu2Si2 / A. Morishita ... [et al.]. / A. Morishita ... [et al.]. 51V-NMR studies of Heisenberg Triangular System V15 Cluster / Y. Furukawa ... [et al.]Menger sponge-like fractal body created with a designed template method / H. Mayama and K. Tsujii. Nonlinear lattice relaxation mechanism for photoexcited dimetal-hallide chain compounds / J.Ohara and S. Yamamoto. Real space renormalization group analysis with the replica method for the two-dimensional ising spin glass / T. Hasegawa and K. Nemoto. Quantum Network models and their symmetry properties / T. Ohtsuki and K. M. Slevin. Fractality of critical percolation networks / M. Mitobe and K. Yakubo. Ising phase transition on curved surfaces / Y. Sakaniwa, I. Hasegawa and H. Shima. Quantum confinement in deformed cylindrical surfaces / H. Taira and H. Shima. Topological spin currents due to nonadiabatic quantum pumping / K. Yakubo and M. Morikawa. Charge density wave state in topological crystal / T. Nogawa and K. Nemoto. Spatiotemporal mapping of symmetrical surface acoustic fields on crystals and periodic microstructures / T. Tachizaki ... [et al.]. Clean optical vortex beam generation for large topological charge / J. Hamazaki, Y. Mineta and R. Morita. Spherically symmetric Black Hole in a topological universe: a toy model / K. Konno ... [et al.].

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

Firewall systems: the next generation

NASA Astrophysics Data System (ADS)

McGhie, Lynda L.

1996-01-01

To be competitive in today's globally connected marketplace, a company must ensure that their internal network security methodologies and supporting policies are current and reflect an overall understanding of today's technology and its resultant threats. Further, an integrated approach to information security should ensure that new ways of sharing information and doing business are accommodated; such as electronic commerce, high speed public broadband network services, and the federally sponsored National Information Infrastructure. There are many challenges, and success is determined by the establishment of a solid and firm baseline security architecture that accommodate today's external connectivity requirements, provides transitional solutions that integrate with evolving and dynamic technologies, and ultimately acknowledges both the strategic and tactical goals of an evolving network security architecture and firewall system. This paper explores the evolution of external network connectivity requirements, the associated challenges and the subsequent development and evolution of firewall security systems. It makes the assumption that a firewall is a set of integrated and interoperable components, coming together to form a `SYSTEM' and must be designed, implement and managed as such. A progressive firewall model will be utilized to illustrates the evolution of firewall systems from earlier models utilizing separate physical networks, to today's multi-component firewall systems enabling secure heterogeneous and multi-protocol interfaces.

Advanced network planning for bus rapid transit : the "Quickway" model as a modal alternative to "Light Rail Lite"

DOT National Transportation Integrated Search

2008-02-01

Transit planning in the United States has tended toward viewing BRT as an analogue to light rail transit, with similar operating patterns. This model, referred to as Light Rail Lite, is compared to international best practices, which have often...

Non-consensus Opinion Models on Complex Networks

NASA Astrophysics Data System (ADS)

Li, Qian; Braunstein, Lidia A.; Wang, Huijuan; Shao, Jia; Stanley, H. Eugene; Havlin, Shlomo

2013-04-01

Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual's original opinion when determining their future opinion (NCO W model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not only within single networks but also between networks, and because the rules of opinion formation within a network may differ from those between networks, we study here the opinion dynamics in coupled networks. Each network represents a social group or community and the interdependent links joining individuals from different networks may be social ties that are unusually strong, e.g., married couples. We apply the non-consensus opinion (NCO) rule on each individual network and the global majority rule on interdependent pairs such that two interdependent agents with different opinions will, due to the influence of mass media, follow the majority opinion of the entire population. The opinion interactions within each network and the interdependent links across networks interlace periodically until a steady state is reached. We find that the interdependent links effectively force the system from a second order phase transition, which is characteristic of the NCO model on a single network, to a hybrid phase transition, i.e., a mix of second-order and abrupt jump-like transitions that ultimately becomes, as we increase the percentage of interdependent agents, a pure abrupt transition. We conclude that for the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar. We also find that the effect of interdependent links is more pronounced in interdependent scale free networks than in interdependent Erdős Rényi networks.

Relaxation dynamics of maximally clustered networks

NASA Astrophysics Data System (ADS)

Klaise, Janis; Johnson, Samuel

2018-01-01

We study the relaxation dynamics of fully clustered networks (maximal number of triangles) to an unclustered state under two different edge dynamics—the double-edge swap, corresponding to degree-preserving randomization of the configuration model, and single edge replacement, corresponding to full randomization of the Erdős-Rényi random graph. We derive expressions for the time evolution of the degree distribution, edge multiplicity distribution and clustering coefficient. We show that under both dynamics networks undergo a continuous phase transition in which a giant connected component is formed. We calculate the position of the phase transition analytically using the Erdős-Rényi phenomenology.

Quantifying Complexity in Quantum Phase Transitions via Mutual Information Complex Networks

NASA Astrophysics Data System (ADS)

Valdez, Marc Andrew; Jaschke, Daniel; Vargas, David L.; Carr, Lincoln D.

2017-12-01

We quantify the emergent complexity of quantum states near quantum critical points on regular 1D lattices, via complex network measures based on quantum mutual information as the adjacency matrix, in direct analogy to quantifying the complexity of electroencephalogram or functional magnetic resonance imaging measurements of the brain. Using matrix product state methods, we show that network density, clustering, disparity, and Pearson's correlation obtain the critical point for both quantum Ising and Bose-Hubbard models to a high degree of accuracy in finite-size scaling for three classes of quantum phase transitions, Z2, mean field superfluid to Mott insulator, and a Berzinskii-Kosterlitz-Thouless crossover.

Optimal navigation for characterizing the role of the nodes in complex networks

NASA Astrophysics Data System (ADS)

Cajueiro, Daniel O.

2010-05-01

In this paper, we explore how the approach of optimal navigation (Cajueiro (2009) [33]) can be used to evaluate the centrality of a node and to characterize its role in a network. Using the subway network of Boston and the London rapid transit rail as proxies for complex networks, we show that the centrality measures inherited from the approach of optimal navigation may be considered if one desires to evaluate the centrality of the nodes using other pieces of information beyond the geometric properties of the network. Furthermore, evaluating the correlations between these inherited measures and classical measures of centralities such as the degree of a node and the characteristic path length of a node, we have found two classes of results. While for the London rapid transit rail, these inherited measures can be easily explained by these classical measures of centrality, for the Boston underground transportation system we have found nontrivial results.

Fluctuation relations between hierarchical kinetically equivalent networks with Arrhenius-type transitions and their roles in systems and structural biology.

PubMed

Deng, De-Ming; Lu, Yi-Ta; Chang, Cheng-Hung

2017-06-01

The legality of using simple kinetic schemes to determine the stochastic properties of a complex system depends on whether the fluctuations generated from hierarchical equivalent schemes are consistent with one another. To analyze this consistency, we perform lumping processes on the stochastic differential equations and the generalized fluctuation-dissipation theorem and apply them to networks with the frequently encountered Arrhenius-type transition rates. The explicit Langevin force derived from those networks enables us to calculate the state fluctuations caused by the intrinsic and extrinsic noises on the free energy surface and deduce their relations between kinetically equivalent networks. In addition to its applicability to wide classes of network related systems, such as those in structural and systems biology, the result sheds light on the fluctuation relations for general physical variables in Keizer's canonical theory.

Once upon a (slow) time in the land of recurrent neuronal networks….

PubMed

Huang, Chengcheng; Doiron, Brent

2017-10-01

The brain must both react quickly to new inputs as well as store a memory of past activity. This requires biology that operates over a vast range of time scales. Fast time scales are determined by the kinetics of synaptic conductances and ionic channels; however, the mechanics of slow time scales are more complicated. In this opinion article we review two distinct network-based mechanisms that impart slow time scales in recurrently coupled neuronal networks. The first is in strongly coupled networks where the time scale of the internally generated fluctuations diverges at the transition between stable and chaotic firing rate activity. The second is in networks with finitely many members where noise-induced transitions between metastable states appear as a slow time scale in the ongoing network firing activity. We discuss these mechanisms with an emphasis on their similarities and differences. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ionic supramolecular networks fully based on chemicals coming from renewable sources.

PubMed

Aboudzadeh, Ali; Fernandez, Mercedes; Muñoz, Maria Eugenia; Santamaría, Antxon; Mecerreyes, David

2014-02-01

New supramolecular ionic networks are synthesized by proton transfer reaction between a bio-based fatty diamine molecule (Priamine 1074) and a series of naturally occurring carboxylic acids such as malonic acid, citric acid, tartaric acid, and 2,5-furandicarboxylic acid. The resulting solid soft material exhibits a thermoreversible transition becoming a viscoelastic liquid at high temperatures. All the networks show an elastic behavior at low temperatures/high frequencies, with elastic modulus values ranging from 4.5 × 10(6) to 4.5 × 10(7) Pa and soft network to liquid transitions T(nl) between -10 and 60 °C. The supramolecular ionic network based on cationic Priamine 1074 and anionic citrate shows promising self-healing properties at room temperature as well as relatively high ionic conductivity values close to 10(-6) S cm(-1). © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

NASA Astrophysics Data System (ADS)

Sun, Xiaofeng; Shen, Shuhan; Lin, Xiangguo; Hu, Zhanyi

2017-10-01

High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.

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

Optimal topology to minimizing congestion in connected communication complex network

NASA Astrophysics Data System (ADS)

Benyoussef, M.; Ez-Zahraouy, H.; Benyoussef, A.

In this paper, a new model of the interdependent complex network is proposed, based on two assumptions that (i) the capacity of a node depends on its degree, and (ii) the traffic load depends on the distribution of the links in the network. Based on these assumptions, the presented model proposes a method of connection not based on the node having a higher degree but on the region containing hubs. It is found that the final network exhibits two kinds of degree distribution behavior, depending on the kind and the way of the connection. This study reveals a direct relation between network structure and traffic flow. It is found that pc the point of transition between the free flow and the congested phase depends on the network structure and the degree distribution. Moreover, this new model provides an improvement in the traffic compared to the results found in a single network. The same behavior of degree distribution found in a BA network and observed in the real world is obtained; except for this model, the transition point between the free phase and congested phase is much higher than the one observed in a network of BA, for both static and dynamic protocols.

The protein folding network

NASA Astrophysics Data System (ADS)

Rao, Francesco; Caflisch, Amedeo

2004-03-01

Networks are everywhere. The conformation space of a 20-residue antiparallel beta-sheet peptide [1], sampled by molecular dynamics simulations, is mapped to a network. Conformations are nodes of the network, and the transitions between them are links. As previously found for the World-Wide Web as well as for social and biological networks , the conformation space contains highly connected hubs like the native state which is the most populated free energy basin. Furthermore, the network shows a hierarchical modularity [2] which is consistent with the funnel mechanism of folding [3] and is not observed for a random heteropolymer lacking a native state. Here we show that the conformation space network describes the free energy landscape without requiring projections into arbitrarily chosen reaction coordinates. The network analysis provides a basis for understanding the heterogeneity of the folding transition state and the existence of multiple pathways. [1] P. Ferrara and A. Caflisch, Folding simulations of a three-stranded antiparallel beta-sheet peptide, PNAS 97, 10780-10785 (2000). [2] Ravasz, E. and Barabási, A. L. Hierarchical organization in complex networks. Phys. Rev. E 67, 026112 (2003). [3] Dill, K. and Chan, H From Levinthal to pathways to funnels. Nature Struct. Biol. 4, 10-19 (1997)

Incorporating social network effects into cost-effectiveness analysis: a methodological contribution with application to obesity prevention

PubMed Central

Konchak, Chad; Prasad, Kislaya

2012-01-01

Objectives To develop a methodology for integrating social networks into traditional cost-effectiveness analysis (CEA) studies. This will facilitate the economic evaluation of treatment policies in settings where health outcomes are subject to social influence. Design This is a simulation study based on a Markov model. The lifetime health histories of a cohort are simulated, and health outcomes compared, under alternative treatment policies. Transition probabilities depend on the health of others with whom there are shared social ties. Setting The methodology developed is shown to be applicable in any healthcare setting where social ties affect health outcomes. The example of obesity prevention is used for illustration under the assumption that weight changes are subject to social influence. Main outcome measures Incremental cost-effectiveness ratio (ICER). Results When social influence increases, treatment policies become more cost effective (have lower ICERs). The policy of only treating individuals who span multiple networks can be more cost effective than the policy of treating everyone. This occurs when the network is more fragmented. Conclusions (1) When network effects are accounted for, they result in very different values of incremental cost-effectiveness ratios (ICERs). (2) Treatment policies can be devised to take network structure into account. The integration makes it feasible to conduct a cost-benefit evaluation of such policies. PMID:23117559

A vision of network-centric military communications

NASA Astrophysics Data System (ADS)

Conklin, Ross, Jr.; Burbank, Jack; Nichols, Robert, Jr.

2005-05-01

This paper presents a vision for a future capability-based military communications system that considers user requirements. Historically, the military has developed and fielded many specialized communications systems. While these systems solved immediate communications problems, they were not designed to operate with other systems. As information has become more important to the execution of war, the "stove-pipe" nature of the communications systems deployed by the military is no longer acceptable. Realizing this, the military has begun the transformation of communications to a network-centric communications paradigm. However, the specialized communications systems were developed in response to the widely varying environments related to military communications. These environments, and the necessity for effective communications within these environments, do not disappear under the network-centric paradigm. In fact, network-centric communications allows for one message to cross many of these environments by transiting multiple networks. The military would also like one communications approach that is capable of working well in multiple environments. This paper presents preliminary work on the creation of a framework that allows for a reconfigurable device that is capable of adapting to the physical and network environments. The framework returns to the Open Systems Interconnect (OSI) architecture with the addition of a standardized intra-layer control interface for control information exchange, a standardized data interface and a proposed device architecture based on the software radio.

An integrated GIS-based data model for multimodal urban public transportation analysis and management

NASA Astrophysics Data System (ADS)

Chen, Shaopei; Tan, Jianjun; Ray, C.; Claramunt, C.; Sun, Qinqin

2008-10-01

Diversity is one of the main characteristics of transportation data collected from multiple sources or formats, which can be extremely complex and disparate. Moreover, these multimodal transportation data are usually characterised by spatial and temporal properties. Multimodal transportation network data modelling involves both an engineering and research domain that has attracted the design of a number of spatio-temporal data models in the geographic information system (GIS). However, the application of these specific models to multimodal transportation network is still a challenging task. This research addresses this challenge from both integrated multimodal data organization and object-oriented modelling perspectives, that is, how a complex urban transportation network should be organized, represented and modeled appropriately when considering a multimodal point of view, and using object-oriented modelling method. We proposed an integrated GIS-based data model for multimodal urban transportation network that lays a foundation to enhance the multimodal transportation network analysis and management. This modelling method organizes and integrates multimodal transit network data, and supports multiple representations for spatio-temporal objects and relationship as both visual and graphic views. The data model is expressed by using a spatio-temporal object-oriented modelling method, i.e., the unified modelling language (UML) extended to spatial and temporal plug-in for visual languages (PVLs), which provides an essential support to the spatio-temporal data modelling for transportation GIS.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

NASA Astrophysics Data System (ADS)

Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Explosive death of conjugate coupled Van der Pol oscillators on networks

NASA Astrophysics Data System (ADS)

Zhao, Nannan; Sun, Zhongkui; Yang, Xiaoli; Xu, Wei

2018-06-01

Explosive death phenomenon has been gradually gaining attention of researchers due to the research boom of explosive synchronization, and it has been observed recently for the identical or nonidentical coupled systems in all-to-all network. In this work, we investigate the emergence of explosive death in networked Van der Pol (VdP) oscillators with conjugate variables coupling. It is demonstrated that the network structures play a crucial role in identifying the types of explosive death behaviors. We also observe that the damping coefficient of the VdP system not only can determine whether the explosive death state is generated but also can adjust the forward transition point. We further show that the backward transition point is independent of the network topologies and the damping coefficient, which is well confirmed by theoretical analysis. Our results reveal the generality of explosive death phenomenon in different network topologies and are propitious to promote a better comprehension for the oscillation quenching behaviors.

An exploration of the Facebook social networks of smokers and non-smokers.

PubMed

Fu, Luella; Jacobs, Megan A; Brookover, Jody; Valente, Thomas W; Cobb, Nathan K; Graham, Amanda L

2017-01-01

Social networks influence health behavior, including tobacco use and cessation. To date, little is known about whether and how the networks of online smokers and non-smokers may differ, or the potential implications of such differences with regards to intervention efforts. Understanding how social networks vary by smoking status could inform public health efforts to accelerate cessation or slow the adoption of tobacco use. These secondary analyses explore the structure of ego networks of both smokers and non-smokers collected as part of a randomized control trial conducted within Facebook. During the trial, a total of 14,010 individuals installed a Facebook smoking cessation app: 9,042 smokers who were randomized in the trial, an additional 2,881 smokers who did not meet full eligibility criteria, and 2,087 non-smokers. The ego network for all individuals was constructed out to second-degree connections. Four kinds of networks were constructed: friendship, family, photo, and group networks. From these networks we measured edges, isolates, density, mean betweenness, transitivity, and mean closeness. We also measured diameter, clustering, and modularity without ego and isolates. Logistic regressions were performed with smoking status as the response and network metrics as the primary independent variables and demographics and Facebook utilization metrics as covariates. The four networks had different characteristics, indicated by different multicollinearity issues and by logistic regression output. Among Friendship networks, the odds of smoking were higher in networks with lower betweenness (p = 0.00), lower transitivity (p = 0.00), and larger diameter (p = 0.00). Among Family networks, the odds of smoking were higher in networks with more vertices (p = .01), less transitivity (p = .04), and fewer isolates (p = .01). Among Photo networks, none of the network metrics were predictive of smoking status. Among Group networks, the odds of smoking were higher when diameter was smaller (p = .04). Together, these findings suggested that compared to non-smokers, smokers in this sample had less connected, more dispersed Facebook Friendship networks; larger but more fractured Family networks with fewer isolates; more compact Group networks; and Photo networks that were similar in network structure to those of non-smokers. This study illustrates the importance of examining structural differences in online social networks as a critical component for network-based interventions and lays the foundation for future research that examines the ways that social networks differ based on individual health behavior. Interventions that seek to target the behavior of individuals in the context of their social environment would be well served to understand social network structures of participants.

An exploration of the Facebook social networks of smokers and non-smokers

PubMed Central

2017-01-01

Background Social networks influence health behavior, including tobacco use and cessation. To date, little is known about whether and how the networks of online smokers and non-smokers may differ, or the potential implications of such differences with regards to intervention efforts. Understanding how social networks vary by smoking status could inform public health efforts to accelerate cessation or slow the adoption of tobacco use. Objectives These secondary analyses explore the structure of ego networks of both smokers and non-smokers collected as part of a randomized control trial conducted within Facebook. Methods During the trial, a total of 14,010 individuals installed a Facebook smoking cessation app: 9,042 smokers who were randomized in the trial, an additional 2,881 smokers who did not meet full eligibility criteria, and 2,087 non-smokers. The ego network for all individuals was constructed out to second-degree connections. Four kinds of networks were constructed: friendship, family, photo, and group networks. From these networks we measured edges, isolates, density, mean betweenness, transitivity, and mean closeness. We also measured diameter, clustering, and modularity without ego and isolates. Logistic regressions were performed with smoking status as the response and network metrics as the primary independent variables and demographics and Facebook utilization metrics as covariates. Results The four networks had different characteristics, indicated by different multicollinearity issues and by logistic regression output. Among Friendship networks, the odds of smoking were higher in networks with lower betweenness (p = 0.00), lower transitivity (p = 0.00), and larger diameter (p = 0.00). Among Family networks, the odds of smoking were higher in networks with more vertices (p = .01), less transitivity (p = .04), and fewer isolates (p = .01). Among Photo networks, none of the network metrics were predictive of smoking status. Among Group networks, the odds of smoking were higher when diameter was smaller (p = .04). Together, these findings suggested that compared to non-smokers, smokers in this sample had less connected, more dispersed Facebook Friendship networks; larger but more fractured Family networks with fewer isolates; more compact Group networks; and Photo networks that were similar in network structure to those of non-smokers. Conclusions This study illustrates the importance of examining structural differences in online social networks as a critical component for network-based interventions and lays the foundation for future research that examines the ways that social networks differ based on individual health behavior. Interventions that seek to target the behavior of individuals in the context of their social environment would be well served to understand social network structures of participants. PMID:29095958

QPA-CLIPS: A language and representation for process control

NASA Technical Reports Server (NTRS)

Freund, Thomas G.

1994-01-01

QPA-CLIPS is an extension of CLIPS oriented towards process control applications. Its constructs define a dependency network of process actions driven by sensor information. The language consists of three basic constructs: TASK, SENSOR, and FILTER. TASK's define the dependency network describing alternative state transitions for a process. SENSOR's and FILTER's define sensor information sources used to activate state transitions within the network. Deftemplate's define these constructs and their run-time environment is an interpreter knowledge base, performing pattern matching on sensor information and so activating TASK's in the dependency network. The pattern matching technique is based on the repeatable occurrence of a sensor data pattern. QPA-CIPS has been successfully tested on a SPARCStation providing supervisory control to an Allen-Bradley PLC 5 controller driving molding equipment.

Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons

NASA Astrophysics Data System (ADS)

Costa, Ariadne; Brochini, Ludmila; Kinouchi, Osame

2017-08-01

Networks of stochastic spiking neurons are interesting models in the area of Theoretical Neuroscience, presenting both continuous and discontinuous phase transitions. Here we study fully connected networks analytically, numerically and by computational simulations. The neurons have dynamic gains that enable the network to converge to a stationary slightly supercritical state (self-organized supercriticality or SOSC) in the presence of the continuous transition. We show that SOSC, which presents power laws for neuronal avalanches plus some large events, is robust as a function of the main parameter of the neuronal gain dynamics. We discuss the possible applications of the idea of SOSC to biological phenomena like epilepsy and dragon king avalanches. We also find that neuronal gains can produce collective oscillations that coexists with neuronal avalanches, with frequencies compatible with characteristic brain rhythms.

Partial information, market efficiency, and anomalous continuous phase transition

NASA Astrophysics Data System (ADS)

Yang, Guang; Zheng, Wenzhi; Huang, Jiping

2014-04-01

It is a common belief in economics and social science that if there is more information available for agents to gather in a human system, the system can become more efficient. The belief can be easily understood according to the well-known efficient market hypothesis. In this work, we attempt to challenge this belief by investigating a complex adaptive system, which is modeled by a market-directed resource-allocation game with a directed random network. We conduct a series of controlled human experiments in the laboratory to show the reliability of the model design. As a result, we find that even under a small information concentration, the system can still almost reach the optimal (balanced) state. Furthermore, the ensemble average of the system’s fluctuation level goes through a continuous phase transition. This behavior means that in the second phase if too much information is shared among agents, the system’s stability will be harmed instead, which differs from the belief mentioned above. Also, at the transition point, the ensemble fluctuations of the fluctuation level remain at a low value. This phenomenon is in contrast to the textbook knowledge about continuous phase transitions in traditional physical systems, namely, fluctuations will rise abnormally around a transition point since the correlation length becomes infinite. Thus, this work is of potential value to a variety of fields, such as physics, economics, complexity science, and artificial intelligence.

Stratosphere-Troposphere Coupling in the Northern Hemisphere analyzed with climate network measures

NASA Astrophysics Data System (ADS)

Kirsch, C.; Donner, R. V.

2017-12-01

The Stratosphere-Troposphere Coupling (STC) is a climate phenomenon providing additional predictive skills for extended-range weather forecasting. The variability of the winter stratospheric polar vortex can particularly influence the tropospheric circulation and, hence, mid-to-high latitude weather for a few weeks or months by strong or weak vortex signals propagating downward with time. This study investigates the STC with climate networks. For this purpose, we use the geopotential height field between 20°N and 90°N at 37 vertical levels from the ERA-Interim reanalysis data from 1979 until 2016. There are two main research questions: (i) Is it possible to define a new, more robust index of the variability of the polar vortex than the currently used NAM index by exploiting climate network properties? (ii) What additional information on STC is provided by climate networks? By calculating the transitivity of evolving climate networks at 10 hPa height, we obtain a new characteristic measure for tracing evolving patterns in stratospheric variability. A higher value than the baseline transitivity indicates an anomalous (strong or weak) polar vortex. Displayed for all vertical levels, the transitivity also exhibits the downward propagation of pressure anomalies into the troposphere. Beyond these findings, we observe additional peaks in the transitivity that does not coincide with weak and strong vortex events. These peaks could be used for identifying the change between winter and summer circulation, also called final warming. We will discuss how these results could potentially affect the predictability of tropospheric weather during boreal spring.

A Longitudinal Study of Social Relationships and Networks in the Transition to and within Adulthood for Vulnerable Young Adults at Ages 24, 29 and 34 Years: Compensation, Reinforcement or Cumulative Disadvantages?

ERIC Educational Resources Information Center

Bele, Irene Velsvik; Kvalsund, Rune

2016-01-01

This longitudinal study, spanning from 1995 through 2012, followed vulnerable youth from upper secondary school (T1) as they made the transition to their early twenties (T2), late twenties (T3) and mid-thirties (T4). We investigated their social network relationships in different phases of adult life, focusing mainly on factors that explain…

Inference for Transition Network Grammars,

DTIC Science & Technology

1976-01-01

If the arc Is followed. language L(G) is said to be structurally complete if The power of an augmented transition network (Am) is each rewriting rule ...Clearly, a context-sensitive grammar can be represented as a context—free grarmar plus a set of transformationDbbbbb Eabbbbbb Dbb~~bb Ebbbbbb rules ...are the foun— as a CFG (base) and a set of transformationa l rules . datIons of grammars of different complexities. The The CSL Is obtained by appl

Gel-expanded to gel-condensed transition in neurofilament networks revealed by direct force measurements

NASA Astrophysics Data System (ADS)

Beck, Roy; Deek, Joanna; Jones, Jayna B.; Safinya, Cyrus R.

2010-01-01

Neurofilaments (NF)-the principal cytoskeletal constituent of myelinated axons in vertebrates-consist of three molecular-weight subunit proteins NF-L (low), NF-M (medium) and NF-H (high), assembled to form mature filaments with protruding unstructured C-terminus side arms. Liquid-crystal gel networks of side-arm-mediated neurofilament assemblies have a key role in the mechanical stability of neuronal processes. Disruptions of the neurofilament network, owing to neurofilament over-accumulation or incorrect side-arm interactions, are a hallmark of motor-neuron diseases including amyotrophic lateral sclerosis. Using synchrotron X-ray scattering, we report on a direct measurement of forces in reconstituted neurofilament gels under osmotic pressure (P). With increasing pressure near physiological salt and average phosphorylation conditions, NF-LMH, comprising the three subunits near in vivo composition, or NF-LH gels, undergo for P>Pc~10kPa, an abrupt non-reversible gel-expanded to gel-condensed transition. The transition indicates side-arm-mediated attractions between neurofilaments consistent with an electrostatic model of interpenetrating chains. In contrast, NF-LM gels remain in a collapsed state for PPc. These findings, which delineate the distinct roles of NF-M and NF-H in regulating neurofilament interactions, shed light on possible mechanisms for disruptions of optimal mechanical network properties.

Critical behavior of the XY-rotor model on regular and small-world networks

NASA Astrophysics Data System (ADS)

De Nigris, Sarah; Leoncini, Xavier

2013-07-01

We study the XY rotors model on small networks whose number of links scales with the system size Nlinks˜Nγ, where 1≤γ≤2. We first focus on regular one-dimensional rings in the microcanonical ensemble. For γ<1.5 the model behaves like a short-range one and no phase transition occurs. For γ>1.5, the system equilibrium properties are found to be identical to the mean field, which displays a second-order phase transition at a critical energy density ɛ=E/N,ɛc=0.75. Moreover, for γc≃1.5 we find that a nontrivial state emerges, characterized by an infinite susceptibility. We then consider small-world networks, using the Watts-Strogatz mechanism on the regular networks parametrized by γ. We first analyze the topology and find that the small-world regime appears for rewiring probabilities which scale as pSW∝1/Nγ. Then considering the XY-rotors model on these networks, we find that a second-order phase transition occurs at a critical energy ɛc which logarithmically depends on the topological parameters p and γ. We also define a critical probability pMF, corresponding to the probability beyond which the mean field is quantitatively recovered, and we analyze its dependence on γ.

A computer-assisted motivational social network intervention to reduce alcohol, drug and HIV risk behaviors among Housing First residents.

PubMed

Kennedy, David P; Hunter, Sarah B; Chan Osilla, Karen; Maksabedian, Ervant; Golinelli, Daniela; Tucker, Joan S

2016-03-15

Individuals transitioning from homelessness to housing face challenges to reducing alcohol, drug and HIV risk behaviors. To aid in this transition, this study developed and will test a computer-assisted intervention that delivers personalized social network feedback by an intervention facilitator trained in motivational interviewing (MI). The intervention goal is to enhance motivation to reduce high risk alcohol and other drug (AOD) use and reduce HIV risk behaviors. In this Stage 1b pilot trial, 60 individuals that are transitioning from homelessness to housing will be randomly assigned to the intervention or control condition. The intervention condition consists of four biweekly social network sessions conducted using MI. AOD use and HIV risk behaviors will be monitored prior to and immediately following the intervention and compared to control participants' behaviors to explore whether the intervention was associated with any systematic changes in AOD use or HIV risk behaviors. Social network health interventions are an innovative approach for reducing future AOD use and HIV risk problems, but little is known about their feasibility, acceptability, and efficacy. The current study develops and pilot-tests a computer-assisted intervention that incorporates social network visualizations and MI techniques to reduce high risk AOD use and HIV behaviors among the formerly homeless. CLINICALTRIALS. NCT02140359.

Next generation information communication infrastructure and case studies for future power systems

NASA Astrophysics Data System (ADS)

Qiu, Bin

As power industry enters the new century, powerful driving forces, uncertainties and new functions are compelling electric utilities to make dramatic changes in their information communication infrastructure. Expanding network services such as real time measurement and monitoring are also driving the need for more bandwidth in the communication network. These needs will grow further as new remote real-time protection and control applications become more feasible and pervasive. This dissertation addresses two main issues for the future power system information infrastructure: communication network infrastructure and associated power system applications. Optical networks no doubt will become the predominant data transmission media for next generation power system communication. The rapid development of fiber optic network technology poses new challenges in the areas of topology design, network management and real time applications. Based on advanced fiber optic technologies, an all-fiber network is investigated and proposed. The study will cover the system architecture and data exchange protocol aspects. High bandwidth, robust optical networks could provide great opportunities to the power system for better service and efficient operation. In the dissertation, different applications are investigated. One of the typical applications is the SCADA information accessing system. An Internet-based application for the substation automation system will be presented. VLSI (Very Large Scale Integration) technology is also used for one-line diagrams auto-generation. High transition rate and low latency optical network is especially suitable for power system real time control. In the dissertation, a new local area network based Load Shedding Controller (LSC) for isolated power system will be presented. By using PMU (Phasor Measurement Unit) and fiber optic network, an AGE (Area Generation Error) based accurate wide area load shedding scheme will also be proposed. The objective is to shed the load in the limited area with minimum disturbance.

Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization.

PubMed

Zhao, Zhi-Qin; Han, Guo-Sheng; Yu, Zu-Guo; Li, Jinyan

2015-08-01

Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request. Copyright © 2015 Elsevier Ltd. All rights reserved.

76 FR 33656 - Television Broadcasting Services; Nashville, TN

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-09

.... SUMMARY: The Commission grants a petition for rulemaking filed by NewsChannel 5 Network, LLC (``News... Nashville. According to NewsChannel 5, after WTVF(TV) transitioned from its pre-transition digital channel...

The Public Telecommunications Network: A Concept in Transition.

ERIC Educational Resources Information Center

Noam, Eli M.

1987-01-01

Argues that the traditional centralized model of the public telecommunication networks is being undermined by a host of centrifugal forces, and that a new and open network concept is emerging that breaks down the dichotomy between the telecommunications sector and the rest of the economy. (JD)

Three key residues form a critical contact network in a protein folding transition state

NASA Astrophysics Data System (ADS)

Vendruscolo, Michele; Paci, Emanuele; Dobson, Christopher M.; Karplus, Martin

2001-02-01

Determining how a protein folds is a central problem in structural biology. The rate of folding of many proteins is determined by the transition state, so that a knowledge of its structure is essential for understanding the protein folding reaction. Here we use mutation measurements-which determine the role of individual residues in stabilizing the transition state-as restraints in a Monte Carlo sampling procedure to determine the ensemble of structures that make up the transition state. We apply this approach to the experimental data for the 98-residue protein acylphosphatase, and obtain a transition-state ensemble with the native-state topology and an average root-mean-square deviation of 6Å from the native structure. Although about 20 residues with small positional fluctuations form the structural core of this transition state, the native-like contact network of only three of these residues is sufficient to determine the overall fold of the protein. This result reveals how a nucleation mechanism involving a small number of key residues can lead to folding of a polypeptide chain to its unique native-state structure.

Combinatorial study of degree assortativity in networks.

PubMed

Estrada, Ernesto

2011-10-01

Why are some networks degree-degree correlated (assortative), while most of the real-world ones are anticorrelated (disassortative)? Here, we prove, by combinatorial methods, that the assortativity of a network depends only on three structural factors: transitivity (clustering coefficient), intermodular connectivity, and branching. Then, a network is assortative if the contributions of the first two factors are larger than that of the third. Highly branched networks are likely to be disassortative.

Parents' early healthcare transition experiences with preterm and acutely ill infants: a scoping review.

PubMed

Ballantyne, M; Orava, T; Bernardo, S; McPherson, A C; Church, P; Fehlings, D

2017-11-01

Parents undergo multiple transitions following the birth of an ill infant: their infant's illness-health trajectory, neonatal intensive care unit hospitalization and transfers from one healthcare setting to another, while also transitioning to parenthood. The objective of this review was to map and synthesize evidence on the experiences and needs of parents of preterm or ill infants as they transition within and between healthcare settings following birth. The scoping review followed Arskey and O'Malley's () framework, enhanced by Levac et al. (). Relevant studies were identified through a comprehensive search strategy of scientific and grey literature databases, online networks, Web of Science and citation lists of relevant articles. Inclusion criteria encompassed a focus on infants undergoing a healthcare transition, and the experiences and needs of parents during transition. Studies were appraised for design quality, and data relevant to parent experiences were extracted and underwent thematic analysis. A total of 7773 records were retrieved, 90 full texts reviewed and 11 articles synthesized that represented a total sample of 435 parents of preterm or ill infants. Parents reported on their experiences in response to their infant's transition within and between hospitals and across levels of neonatal intensive care unit, intermediate and community hospital care. Ten studies used qualitative research methods, while one employed quantitative survey methods. Four key themes were identified: that of parent distress throughout transition, parenting at a distance, sources of stress and sources of support. Parents' stress resulted from not being informed or involved in the transition decision, inadequate communication and perceived differences in cultures of care across healthcare settings. Opportunities to improve parents' early transition experiences include enhanced engagement, communication, information-sharing and shared decision-making between health care providers and parents. Future areas of research should focus on early transition interventions to advance parent capacity, confidence and closeness as the primary nurturer. © 2017 John Wiley & Sons Ltd.

Thermosetting epoxy resin/thermoplastic system with combined shape memory and self-healing properties

NASA Astrophysics Data System (ADS)

Yao, Yongtao; Wang, Jingjie; Lu, Haibao; Xu, Ben; Fu, Yongqing; Liu, Yanju; Leng, Jinsong

2016-01-01

A novel and facile strategy was proposed to construct a thermosetting/thermoplastic system with both shape memory and self-healing properties based on commercial epoxy resin and poly(ɛ-caprolactone)-PCL. Thermoplastic material is capable of re-structuring and changing the stiffness/modulus when the temperature is above melting temperature. PCL microfiber was used as a plasticizer in epoxy resin-based blends, and served as a ‘hard segment’ to fix a temporary shape of the composites during shape memory cycles. In this study, the electrospun PCL membrane with a porous network structure enabled a homogenous PCL fibrous distribution and optimized interaction between fiber and epoxy resin. The self-healing capability is achieved by phase transition during curing of the composites. The mechanism of the shape memory effect of the thermosetting (rubber)/thermoplastic composite is attributed to the structural design of the thermoplastic network inside the thermosetting resin/rubber matrix.

California's transition from conventional snowpack measurements to a developing remote sensing capability for water supply forecasting

NASA Technical Reports Server (NTRS)

Brown, A. J.; Peterson, N.

1980-01-01

California's Snow Survey Program and water supply forecasting procedures are described. A review is made of current activities and program direction on such matters as: the growing statewide network of automatic snow sensors; restrictions on the gathering hydrometeorological data in areas designated as wilderness; the use of satellite communications, which both provides a flexible network without mountaintop repeaters and satisfies the need for unobtrusiveness in wilderness areas; and the increasing operational use of snow covered area (SCA) obtained from satellite imagery, which, combined with water equivalent from snow sensors, provides a high correlation to the volumes and rates of snowmelt runoff. Also examined are the advantages of remote sensing; the anticipated effects of a new input of basin wide index of water equivalent, such as the obtained through microwave techniques, on future forecasting opportunities; and the future direction and goals of the California Snow Surveys Program.

Space network scheduling benchmark: A proof-of-concept process for technology transfer

NASA Technical Reports Server (NTRS)

Moe, Karen; Happell, Nadine; Hayden, B. J.; Barclay, Cathy

1993-01-01

This paper describes a detailed proof-of-concept activity to evaluate flexible scheduling technology as implemented in the Request Oriented Scheduling Engine (ROSE) and applied to Space Network (SN) scheduling. The criteria developed for an operational evaluation of a reusable scheduling system is addressed including a methodology to prove that the proposed system performs at least as well as the current system in function and performance. The improvement of the new technology must be demonstrated and evaluated against the cost of making changes. Finally, there is a need to show significant improvement in SN operational procedures. Successful completion of a proof-of-concept would eventually lead to an operational concept and implementation transition plan, which is outside the scope of this paper. However, a high-fidelity benchmark using actual SN scheduling requests has been designed to test the ROSE scheduling tool. The benchmark evaluation methodology, scheduling data, and preliminary results are described.

NASA Langley Research Center's distributed mass storage system

NASA Technical Reports Server (NTRS)

Pao, Juliet Z.; Humes, D. Creig

1993-01-01

There is a trend in institutions with high performance computing and data management requirements to explore mass storage systems with peripherals directly attached to a high speed network. The Distributed Mass Storage System (DMSS) Project at NASA LaRC is building such a system and expects to put it into production use by the end of 1993. This paper presents the design of the DMSS, some experiences in its development and use, and a performance analysis of its capabilities. The special features of this system are: (1) workstation class file servers running UniTree software; (2) third party I/O; (3) HIPPI network; (4) HIPPI/IPI3 disk array systems; (5) Storage Technology Corporation (STK) ACS 4400 automatic cartridge system; (6) CRAY Research Incorporated (CRI) CRAY Y-MP and CRAY-2 clients; (7) file server redundancy provision; and (8) a transition mechanism from the existent mass storage system to the DMSS.

Local vs. global redundancy - trade-offs between resilience against cascading failures and frequency stability

NASA Astrophysics Data System (ADS)

Plietzsch, A.; Schultz, P.; Heitzig, J.; Kurths, J.

2016-05-01

When designing or extending electricity grids, both frequency stability and resilience against cascading failures have to be considered amongst other aspects of energy security and economics such as construction costs due to total line length. Here, we compare an improved simulation model for cascading failures with state-of-the-art simulation models for short-term grid dynamics. Random ensembles of realistic power grid topologies are generated using a recent model that allows for a tuning of global vs local redundancy. The former can be measured by the algebraic connectivity of the network, whereas the latter can be measured by the networks transitivity. We show that, while frequency stability of an electricity grid benefits from a global form of redundancy, resilience against cascading failures rather requires a more local form of redundancy and further analyse the corresponding trade-off.

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

PubMed Central

Ye, Ping; Peyser, Brian D; Spencer, Forrest A; Bader, Joel S

2005-01-01

Background In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. Results We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). Conclusion Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed). PMID:16283923

Consensus, Polarization and Clustering of Opinions in Social Networks

DTIC Science & Technology

2013-06-01

values of τ , and consensus at larger values. Fig. 6 compares the phase transitions for three different network configurations: RGG, Erdos- Renyi graph and...Erdos- Renyi graph [25] is generated uniformly at random from the collection of all graphs which have n = 50 nodes and M = 120 edges. The small- world...0.6 0.8 1 Threshold τ N or m al iz ed A lg eb ra ic C on ne ct iv ity RGG Erdos− Renyi Small−World Fig. 6. Phase transitions using three

The Transition from Middle Childhood to Early Adolescence: Sex Differences in the Social Networks and Perceived Self-Competence.

ERIC Educational Resources Information Center

Feiring, Candice; Lewis, Michael

1991-01-01

Examines the development of social networks from middle childhood to adolescence based on a longitudinal sample of 100 children. Age changes, sex differences, and the relation between network characteristics and self-perceived competence are considered. Adolescent girls' social networks are larger than boys' and are also more related to specific…

Structural and Supportive Changes in Couples' Family and Friendship Networks across the Transition to Parenthood.

ERIC Educational Resources Information Center

Bost, Kelly K.; Cox, Martha J.; Burchinal, Margaret R.; Payne, Chris

2002-01-01

Examines patterns of change in family and friend network with parenthood in 137 couples surveyed before the birth of their first child. Husbands and wives who reported larger network sizes and support prior to their first child's birth were more likely to report larger networks after birth. Changes in parents' social systems were related to…

In-situ immobilization of quantum dots in polysaccharide-based nanogels for integration of optical pH-sensing, tumor cell imaging, and drug delivery.

PubMed

Wu, Weitai; Aiello, Michael; Zhou, Ting; Berliner, Alexandra; Banerjee, Probal; Zhou, Shuiqin

2010-04-01

We report a class of polysaccharide-based hybrid nanogels that can integrate the functional building blocks for optical pH-sensing, cancer cell imaging, and controlled drug release into a single nanoparticle system, which can offer broad opportunities for combined diagnosis and therapy. The hybrid nanogels were prepared by in-situ immobilization of CdSe quantum dots (QDs) in the interior of the pH and temperature dual responsive hydroxypropylcellulose-poly(acrylic acid) (HPC-PAA) semi-interpenetrating polymer networks. The-OH groups of the HPC chains are designed to sequester the precursor Cd(2+) ions into the nanogels as well as stabilize the in-situ formed CdSe QDs. The pH-sensitive PAA network chains are designed to induce a pH-responsive volume phase transition of the hybrid nanogels. The developed HPC-PAA-CdSe hybrid nanogels combine a strong trap emission at 741nm for sensing physicochemical environment in a pH dependent manner and a visible excitonic emission at 592nm for mouse melanoma B16F10 cell imaging. The hybrid nanogels also provide excellent stability as a drug carrier, which cannot only provide a high drug loading capacity for a model anticancer drug temozolomide, but also offer a pH-triggered sustained-release of the drug molecules in the gel network. Copyright 2010 Elsevier Ltd. All rights reserved.

Disruption of Hydrogen-Bonding Network Eliminates Water Anomalies Normally Observed on Cooling to Its Calorimetric Glass Transition

DOE PAGES

Borreguero, Jose M.; Mamontov, Eugene

2017-04-11

Here, the calorimetric glass-transition temperature of water is 136 K, but extrapolation of thermodynamic and relaxation properties of water from ambient temperature to below its homogeneous nucleation temperature T H = 235 K predicts divergence at T S = 228 K. The “no-man’s land” between the T H and glassy water crystallization temperature of 150 K, which is encountered on warming up from the vitrified state, precludes a straightforward reconciliation of the two incompatible temperature dependences of water properties, above 235 K and below 150 K. The addition of lithium chloride to water allows bypassing both T H and Tmore » S on cooling, resulting in the dynamics with no features except the calorimetric glass transition, still at 136 K. We show that lithium chloride prevents hydrogen-bonding network completion in water on cooling, as manifested, in particular, in changing microscopic diffusion mechanism of the water molecules. Thus thermodynamic and relaxation peculiarities exhibited by pure water on cooling to its glass transition, such as the existence of the T H and T S, must be associated specifically with the hydrogen-bonding network.« less

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network

PubMed Central

Axelrod, Kevin; Sanchez, Alvaro; Gore, Jeff

2015-01-01

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition. DOI: http://dx.doi.org/10.7554/eLife.07935.001 PMID:26302311

A method of network topology optimization design considering application process characteristic

NASA Astrophysics Data System (ADS)

Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo

2018-03-01

Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

A stability-based mechanism for hysteresis in the walk–trot transition in quadruped locomotion

PubMed Central

Aoi, Shinya; Katayama, Daiki; Fujiki, Soichiro; Tomita, Nozomi; Funato, Tetsuro; Yamashita, Tsuyoshi; Senda, Kei; Tsuchiya, Kazuo

2013-01-01

Quadrupeds vary their gaits in accordance with their locomotion speed. Such gait transitions exhibit hysteresis. However, the underlying mechanism for this hysteresis remains largely unclear. It has been suggested that gaits correspond to attractors in their dynamics and that gait transitions are non-equilibrium phase transitions that are accompanied by a loss in stability. In the present study, we used a robotic platform to investigate the dynamic stability of gaits and to clarify the hysteresis mechanism in the walk–trot transition of quadrupeds. Specifically, we used a quadruped robot as the body mechanical model and an oscillator network for the nervous system model to emulate dynamic locomotion of a quadruped. Experiments using this robot revealed that dynamic interactions among the robot mechanical system, the oscillator network, and the environment generate walk and trot gaits depending on the locomotion speed. In addition, a walk–trot transition that exhibited hysteresis was observed when the locomotion speed was changed. We evaluated the gait changes of the robot by measuring the locomotion of dogs. Furthermore, we investigated the stability structure during the gait transition of the robot by constructing a potential function from the return map of the relative phase of the legs and clarified the physical characteristics inherent to the gait transition in terms of the dynamics. PMID:23389894

A stability-based mechanism for hysteresis in the walk-trot transition in quadruped locomotion.

PubMed

Aoi, Shinya; Katayama, Daiki; Fujiki, Soichiro; Tomita, Nozomi; Funato, Tetsuro; Yamashita, Tsuyoshi; Senda, Kei; Tsuchiya, Kazuo

2013-04-06

Quadrupeds vary their gaits in accordance with their locomotion speed. Such gait transitions exhibit hysteresis. However, the underlying mechanism for this hysteresis remains largely unclear. It has been suggested that gaits correspond to attractors in their dynamics and that gait transitions are non-equilibrium phase transitions that are accompanied by a loss in stability. In the present study, we used a robotic platform to investigate the dynamic stability of gaits and to clarify the hysteresis mechanism in the walk-trot transition of quadrupeds. Specifically, we used a quadruped robot as the body mechanical model and an oscillator network for the nervous system model to emulate dynamic locomotion of a quadruped. Experiments using this robot revealed that dynamic interactions among the robot mechanical system, the oscillator network, and the environment generate walk and trot gaits depending on the locomotion speed. In addition, a walk-trot transition that exhibited hysteresis was observed when the locomotion speed was changed. We evaluated the gait changes of the robot by measuring the locomotion of dogs. Furthermore, we investigated the stability structure during the gait transition of the robot by constructing a potential function from the return map of the relative phase of the legs and clarified the physical characteristics inherent to the gait transition in terms of the dynamics.

Integrated renewable energy networks

NASA Astrophysics Data System (ADS)

Mansouri Kouhestani, F.; Byrne, J. M.; Hazendonk, P.; Brown, M. B.; Spencer, L.

2015-12-01

This multidisciplinary research is focused on studying implementation of diverse renewable energy networks. Our modern economy now depends heavily on large-scale, energy-intensive technologies. A transition to low carbon, renewable sources of energy is needed. We will develop a procedure for designing and analyzing renewable energy systems based on the magnitude, distribution, temporal characteristics, reliability and costs of the various renewable resources (including biomass waste streams) in combination with various measures to control the magnitude and timing of energy demand. The southern Canadian prairies are an ideal location for developing renewable energy networks. The region is blessed with steady, westerly winds and bright sunshine for more hours annually than Houston Texas. Extensive irrigation agriculture provides huge waste streams that can be processed biologically and chemically to create a range of biofuels. The first stage involves mapping existing energy and waste flows on a neighbourhood, municipal, and regional level. Optimal sites and combinations of sites for solar and wind electrical generation, such as ridges, rooftops and valley walls, will be identified. Geomatics based site and grid analyses will identify best locations for energy production based on efficient production and connectivity to regional grids.

Phase-Change Thermoplastic Elastomer Blends for Tunable Shape Memory by Physical Design

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

Mineart, Kenneth P.; Tallury, Syamal S.; Li, Tao

Shape-memory polymers (SMPs) change shape upon exposure to an environmental stimulus.1-3 They are of considerable importance in the ongoing development of stimuli-responsive biomedical4,5 and deployable6 devices, and their function depends on the presence of two components.7 The first provides mechanical rigidity to ensure retention of one or more temporary strain states and also serves as a switch capable of releasing a temporary strain state. The second, a network-forming component, is required to restore the polymer to a prior strain state upon stimulation. In thermally-activated SMPs, the switching element typically relies on a melting or glass transition temperature,1-3,7 and broad ormore » multiple switches permit several temporary strain states.8-10 Chemical integration of network-forming and switching species endows SMPs with specific properties.8,10,11 Here, we demonstrate that phase-change materials incorporated into network-forming macromolecules yield shape-memory polymer blends (SMPBs) with physically tunable switching temperatures and recovery kinetics for use in multi-responsive laminates and shape-change electronics.« less

Phase transitions in tumor growth VI: Epithelial-Mesenchymal transition

NASA Astrophysics Data System (ADS)

Guerra, A.; Rodriguez, D. J.; Montero, S.; Betancourt-Mar, J. A.; Martin, R. R.; Silva, E.; Bizzarri, M.; Cocho, G.; Mansilla, R.; Nieto-Villar, J. M.

2018-06-01

Herewith we discuss a network model of the epithelial-mesenchymal transition (EMT) based on our previous proposed framework. The EMT appears as a "first order" phase transition process, analogous to the transitions observed in the chemical-physical field. Chiefly, EMT should be considered a transition characterized by a supercritical Andronov-Hopf bifurcation, with the emergence of limit cycle and, consequently, a cascade of saddle-foci Shilnikov's bifurcations. We eventually show that the entropy production rate is an EMT-dependent function and, as such, its formalism reminds the van der Waals equation.

Deep Neural Network Detects Quantum Phase Transition

NASA Astrophysics Data System (ADS)

Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki

2018-03-01

We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.

The geometry of chaotic dynamics — a complex network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Heitzig, J.; Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J.

2011-12-01

Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ɛ-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ɛ-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ɛ-recurrence networks exhibit an important link between dynamical systems and graph theory.

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

PubMed

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

2015-01-01

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

AllAboard: Visual Exploration of Cellphone Mobility Data to Optimise Public Transport.

PubMed

Di Lorenzo, G; Sbodio, M; Calabrese, F; Berlingerio, M; Pinelli, F; Nair, R

2016-02-01

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizens' mobility and use data-driven insights to better plan and manage services. With large scale data on mobility patterns, operators can move away from the costly, mostly survey based, transportation planning processes, to a more data-centric view, that places the instrumented user at the center of development. In this framework, using mobile phone data to perform transit analysis and optimization represents a new frontier with significant societal impact, especially in developing countries. In this paper we present AllAboard, an intelligent tool that analyses cellphone data to help city authorities in visually exploring urban mobility and optimizing public transport. This is performed within a self contained tool, as opposed to the current solutions which rely on a combination of several distinct tools for analysis, reporting, optimisation and planning. An interactive user interface allows transit operators to visually explore the travel demand in both space and time, correlate it with the transit network, and evaluate the quality of service that a transit network provides to the citizens at very fine grain. Operators can visually test scenarios for transit network improvements, and compare the expected impact on the travellers' experience. The system has been tested using real telecommunication data for the city of Abidjan, Ivory Coast, and evaluated from a data mining, optimisation and user prospective.

Framework for developing a spatial walkability index (SWI) for the light-rail transit (LRT) stations in Kuala Lumpur city centre using analytical network process (ANP) and GIS

NASA Astrophysics Data System (ADS)

Naharudin, Nabilah; Ahamad, Mohd Sanusi S.; Sadullah, Ahmad Farhan Mohd

2017-10-01

In support to the nation's goal of developing a liveable city, Malaysian government aims to improve the mobility in Kuala Lumpur by providing good quality transit services across the city. However, the public starts to demand for more than just a connectivity between two points. They want their transit journey to be comfortable and pleasant from the very first mile. The key here is the first and last mile (FLM) of the transit service which defines their journey to access the station itself. The question is, does the existing transit services' FLM satisfy public's needs? Therefore, many studies had emerged in attempt to assess the pedestrian-friendliness. While most of them did base on the pedestrian's perceptions, there were also studies that spatially measured the connectivity and accessibility to various landuses and point of interests. While both can be a good method, their integration could actually produce a better assessment. However, till date, only a few studies had attempted to do so. This paper proposes a framework to develop a Spatial Walkability Index (SWI) by integrating a multicriteria evaluation technique, Analytical Network Process (ANP) and network analysis on geographical information system (GIS) platform. First, ANP will aggregate the degree of importance for each walkability criteria based on the pedestrian's perceptions. Then, the network analysis will use the weighted criteria as attributes to find the walkable routes within half mile radius from each station. The index will be calculated by rationing the total length of walkable routes in respect to the available footpath. The final outcome is a percentage of walkable FLM transit routes for each station which will be named as the SWI. It is expected that the developed framework can be applied in other cities across the globe. It can also be improvised to suit the demand and purpose there.

Toward observationally constrained high space and time resolution CO2 urban emission inventories

NASA Astrophysics Data System (ADS)

Maness, H.; Teige, V. E.; Wooldridge, P. J.; Weichsel, K.; Holstius, D.; Hooker, A.; Fung, I. Y.; Cohen, R. C.

2013-12-01

The spatial patterns of greenhouse gas (GHG) emission and sequestration are currently studied primarily by sensor networks and modeling tools that were designed for global and continental scale investigations of sources and sinks. In urban contexts, by design, there has been very limited investment in observing infrastructure, making it difficult to demonstrate that we have an accurate understanding of the mechanism of emissions or the ability to track processes causing changes in those emissions. Over the last few years, our team has built a new high-resolution observing instrument to address urban CO2 emissions, the BErkeley Atmospheric CO2 Observing Network (BEACON). The 20-node network is constructed on a roughly 2 km grid, permitting direct characterization of the internal structure of emissions within the San Francisco East Bay. Here we present a first assessment of BEACON's promise for evaluating the effectiveness of current and upcoming local emissions policy. Within the next several years, a variety of locally important changes are anticipated--including widespread electrification of the motor vehicle fleet and implementation of a new power standard for ships at the port of Oakland. We describe BEACON's expected performance for detecting these changes, based on results from regional forward modeling driven by a suite of projected inventories. We will further describe the network's current change detection capabilities by focusing on known high temporal frequency changes that have already occurred; examples include a week of significant freeway traffic congestion following the temporary shutdown of the local commuter rail (the Bay Area Rapid Transit system).

The Hetu'u Global Network: Measuring the Distance to the Sun with the Transit of Venus

NASA Astrophysics Data System (ADS)

Rodriguez, David; Faherty, J.

2013-01-01

In the spirit of historic astronomical endeavors, we invited school groups across the globe to collaborate in a solar distance measurement using the 2012 transit of Venus. In total, our group (stationed at Easter Island, Chile) recruited 19 school groups spread over 6 continents and 10 countries to participate in our Hetu’u Global Network. Applying the methods of French astronomer Joseph-Nicolas Delisle, we used individual second and third Venus-Sun contact times to calculate the distance to the Sun. Ten of the sites in our network had amiable weather; 8 of which measured second contact and 5 of which measured third contact leading to consistent solar distance measurements of 152+/-30 million km and 163+/-30 million km respectively. The distance to the Sun at the time of the transit was 152.25 million km; therefore, our measurements are also consistent within 1-sigma of the known value. The goal of our international school group network was to inspire the next generation of scientists using the excitement and accessibility of such a rare astronomical event. In the process, we connected hundreds of participating students representing a diverse, multi-cultural group with differing political, economic, and racial backgrounds.

A stochastic Markov chain model to describe lung cancer growth and metastasis.

PubMed

Newton, Paul K; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila A; Nieva, Jorge; Kuhn, Peter

2012-01-01

A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.

The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools

PubMed Central

Boyd, Andrew D; Li, Jianrong ‘John’; Burton, Mike D; Jonen, Michael; Gardeux, Vincent; Achour, Ikbel; Luo, Roger Q; Zenku, Ilir; Bahroos, Neil; Brown, Stephen B; Vanden Hoek, Terry; Lussier, Yves A

2013-01-01

Objective Applying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties. Materials and Methods Datasets were the Center for Medicaid and Medicare Services ICD-9-CM to ICD-10-CM mapping files, general equivalence mappings, and statewide Medicaid emergency department billing. Diagnoses were represented as nodes and their mappings as directional relationships. The complex network was synthesized as an aggregate of simpler motifs and tabulation per clinical specialty. Results We identified five mapping motif categories: identity, class-to-subclass, subclass-to-class, convoluted, and no mapping. Convoluted mappings indicate that multiple ICD-9-CM and ICD-10-CM codes share complex, entangled, and non-reciprocal mappings. The proportions of convoluted diagnoses mappings (36% overall) range from 5% (hematology) to 60% (obstetrics and injuries). In a case study of 24 008 patient visits in 217 emergency departments, 27% of the costs are associated with convoluted diagnoses, with ‘abdominal pain’ and ‘gastroenteritis’ accounting for approximately 3.5%. Discussion Previous qualitative studies report that administrators and clinicians are likely to be challenged in understanding and managing their practice because of the ICD-10-CM transition. We substantiate the complexity of this transition with a thorough quantitative summary per clinical specialty, a case study, and the tools to apply this methodology easily to any clinical practice in the form of a web portal and analytic tables. Conclusions Post-transition, successful management of frequent diseases with convoluted mapping network patterns is critical. The http://lussierlab.org/transition-to-ICD10CM web portal provides insight in linking onerous diseases to the ICD-10 transition. PMID:23645552

Time delay and long-range connection induced synchronization transitions in Newman-Watts small-world neuronal networks.

PubMed

Qian, Yu

2014-01-01

The synchronization transitions in Newman-Watts small-world neuronal networks (SWNNs) induced by time delay τ and long-range connection (LRC) probability P have been investigated by synchronization parameter and space-time plots. Four distinct parameter regions, that is, asynchronous region, transition region, synchronous region, and oscillatory region have been discovered at certain LRC probability P = 1.0 as time delay is increased. Interestingly, desynchronization is observed in oscillatory region. More importantly, we consider the spatiotemporal patterns obtained in delayed Newman-Watts SWNNs are the competition results between long-range drivings (LRDs) and neighboring interactions. In addition, for moderate time delay, the synchronization of neuronal network can be enhanced remarkably by increasing LRC probability. Furthermore, lag synchronization has been found between weak synchronization and complete synchronization as LRC probability P is a little less than 1.0. Finally, the two necessary conditions, moderate time delay and large numbers of LRCs, are exposed explicitly for synchronization in delayed Newman-Watts SWNNs.

Time Delay and Long-Range Connection Induced Synchronization Transitions in Newman-Watts Small-World Neuronal Networks

PubMed Central

Qian, Yu

2014-01-01

The synchronization transitions in Newman-Watts small-world neuronal networks (SWNNs) induced by time delay and long-range connection (LRC) probability have been investigated by synchronization parameter and space-time plots. Four distinct parameter regions, that is, asynchronous region, transition region, synchronous region, and oscillatory region have been discovered at certain LRC probability as time delay is increased. Interestingly, desynchronization is observed in oscillatory region. More importantly, we consider the spatiotemporal patterns obtained in delayed Newman-Watts SWNNs are the competition results between long-range drivings (LRDs) and neighboring interactions. In addition, for moderate time delay, the synchronization of neuronal network can be enhanced remarkably by increasing LRC probability. Furthermore, lag synchronization has been found between weak synchronization and complete synchronization as LRC probability is a little less than 1.0. Finally, the two necessary conditions, moderate time delay and large numbers of LRCs, are exposed explicitly for synchronization in delayed Newman-Watts SWNNs. PMID:24810595

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

Chen, Hang, E-mail: hangchen@mit.edu; Thill, Peter; Cao, Jianshu

In biochemical systems, intrinsic noise may drive the system switch from one stable state to another. We investigate how kinetic switching between stable states in a bistable network is influenced by dynamic disorder, i.e., fluctuations in the rate coefficients. Using the geometric minimum action method, we first investigate the optimal transition paths and the corresponding minimum actions based on a genetic toggle switch model in which reaction coefficients draw from a discrete probability distribution. For the continuous probability distribution of the rate coefficient, we then consider two models of dynamic disorder in which reaction coefficients undergo different stochastic processes withmore » the same stationary distribution. In one, the kinetic parameters follow a discrete Markov process and in the other they follow continuous Langevin dynamics. We find that regulation of the parameters modulating the dynamic disorder, as has been demonstrated to occur through allosteric control in bistable networks in the immune system, can be crucial in shaping the statistics of optimal transition paths, transition probabilities, and the stationary probability distribution of the network.« less

Transition to Grandmotherhood.

ERIC Educational Resources Information Center

Fischer, Lucy Rose

1983-01-01

Examined grandparenthood as a role relationship. Grandmothers, in describing transition to grandparenthood, tended to emphasize emotional/symbolic investment in grandchildren rather than instrumental/interactional dimensions of relationships. The data suggest that grandparental role conceptions are modified by family network variable: Ambiguity in…

Efficient transportation for Vermont : optimal statewide transit networks.

DOT National Transportation Integrated Search

2011-01-01

"Public transit systems are receiving increased attention as viable solutions to problems with : transportation system robustness, energy-efficiency and equity. The over-reliance on a single : mode, the automobile, is a threat to system robustness. I...

Memory of the unjamming transition during cyclic tiltings of a granular pile.

PubMed

Deboeuf, S; Dauchot, O; Staron, L; Mangeney, A; Vilotte, J-P

2005-11-01

Discrete numerical simulations are performed to study the evolution of the microstructure and the response of a granular packing during successive loading-unloading cycles, consisting of quasistatic rotations in the gravity field between opposite inclination angles. We show that internal variables--e.g., stress and fabric of the pile--exhibit hysteresis during these cycles due to the exploration of different metastable configurations. Interestingly, the hysteretic behavior of the pile strongly depends on the maximal inclination of the cycles, giving evidence of the irreversible modifications of the pile state occurring close to the unjamming transition. More specifically, we show that for cycles with maximal inclination larger than the repose angle, the weak-contact network carries the memory of the unjamming transition. These results demonstrate the relevance of a two-phase description--strong- and weak-contact networks--for a granular system, as soon as it has approached the unjamming transition.

Design of band-notched antenna with DG-CEBG

NASA Astrophysics Data System (ADS)

Jaglan, Naveen; Kanaujia, Binod Kumar; Gupta, Samir Dev; Srivastava, Shweta

2018-01-01

Ultra-wideband (UWB) disc monopole antenna with crescent shaped slot for double band-notched features is presented. Planned antenna discards worldwide interoperability for microwave access (WiMAX) band (3.3-3.6 GHz) and wireless local area network (WLAN) band (5-6 GHz). Defected ground compact electromagnetic band gap (DG-CEBG) designs are used to accomplish band notches in WiMAX and WLAN bands. Defected ground planes are utilised to achieve compactness in electromagnetic band gap (EBG) structures. The proposed WiMAX and WLAN DG-CEBG designs show a compactness of around 46% and 50%, respectively, over mushroom EBG structures. Parametric analyses of DG-CEBG design factors are carried out to control the notched frequencies. Stepwise notch transition from upper to lower frequencies is presented with incremental inductance augmentation. The proposed antenna is made-up on low-cost FR-4 substrate of complete extents as (42 × 50 × 1.6) mm3.Fabricated sample antenna shows excellent consistency in simulated and measured outcomes.

Innovative Commercialization Efforts Underway at the National Renewable Energy Laboratory

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

Cheesbrough, Kate; Bader, Meghan

New clean energy and energy efficiency technology solutions hold the promise of significant reductions in energy consumption. However, proven barriers for these technologies, including the technological and commercialization valleys of death, result in promising technologies falling to the wayside. To address these gaps, NREL's Innovation & Entrepreneurship Center designs and manages advanced programs aimed at supporting the development and commercialization of early stage clean energy technologies with the goal of accelerating new technologies to market. These include: Innovation Incubator (IN2) in partnership with Wells Fargo: this technology incubator supports energy efficiency building-related startups to overcome market gaps by providing accessmore » to technical support at NREL; Small Business Voucher Pilot: this program offers paid vouchers for applicants to access a unique skill, capability, or facility at any of the 17 DOE National Laboratories to bring next-generation clean energy technologies to market; Energy Innovation Portal: NREL designed and developed the Energy Innovation Portal, providing access to EERE focused intellectual property available for licensing from all of the DOE National Laboratories; Lab-Corps: Lab-Corps aims to better train and empower national lab researchers to understand market drivers and successfully transition their discoveries into high-impact, real world technologies in the private sector; Incubatenergy Network: the Network provides nationwide coordination of clean energy business incubators, share best practices, support clean energy entrepreneurs, and help facilitate a smoother transition to a more sustainable clean energy economy; Industry Growth Forum: the Forum is the perfect venue for clean energy innovators to maximize their exposure to receptive capital and strategic partners. Since 2003, presenting companies have collectively raised more than $5 billion in growth financing.« less

NSIUWG: Science networking retreat

NASA Technical Reports Server (NTRS)

Hart, Jim

1991-01-01

The purpose of this session was to study and identify alternatives to be recommended for the science networking areas of vision; roles and responsibilities; and technical approach and transition. This presentation is represented by charts and viewgraphs only.

Social Networking Web Sites as a Tool for Student Transitions: Purposive Use of Social Networking Web Sites for the First-Year Experience

ERIC Educational Resources Information Center

Nalbone, David P.; Kovach, Ronald J.; Fish, Jessica N.; McCoy, Kelsey M.; Jones, Kathryn E.; Wright, Hillary Rawlings

2016-01-01

The current study investigated the potential role that social networking Web sites (e.g., Facebook) played in creating both actual and virtual learning communities within the first-year seminar. Researchers conducted a 2-year longitudinal study to assess whether students who were connected within a university-founded virtual network persisted in…

Endemic infections are always possible on regular networks

NASA Astrophysics Data System (ADS)

Del Genio, Charo I.; House, Thomas

2013-10-01

We study the dependence of the largest component in regular networks on the clustering coefficient, showing that its size changes smoothly without undergoing a phase transition. We explain this behavior via an analytical approach based on the network structure, and provide an exact equation describing the numerical results. Our work indicates that intrinsic structural properties always allow the spread of epidemics on regular networks.

Post-transcriptional regulatory network of epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions

PubMed Central

2014-01-01

Epithelial-to-mesenchymal transition (EMT) and its reverse process, mesenchymal-to-epithelial transition (MET), play important roles in embryogenesis, stem cell biology, and cancer progression. EMT can be regulated by many signaling pathways and regulatory transcriptional networks. Furthermore, post-transcriptional regulatory networks regulate EMT; these networks include the long non-coding RNA (lncRNA) and microRNA (miRNA) families. Specifically, the miR-200 family, miR-101, miR-506, and several lncRNAs have been found to regulate EMT. Recent studies have illustrated that several lncRNAs are overexpressed in various cancers and that they can promote tumor metastasis by inducing EMT. MiRNA controls EMT by regulating EMT transcription factors or other EMT regulators, suggesting that lncRNAs and miRNA are novel therapeutic targets for the treatment of cancer. Further efforts have shown that non-coding-mediated EMT regulation is closely associated with epigenetic regulation through promoter methylation (e.g., miR-200 or miR-506) and protein regulation (e.g., SET8 via miR-502). The formation of gene fusions has also been found to promote EMT in prostate cancer. In this review, we discuss the post-transcriptional regulatory network that is involved in EMT and MET and how targeting EMT and MET may provide effective therapeutics for human disease. PMID:24598126

Analyzing phase diagrams and phase transitions in networked competing populations

NASA Astrophysics Data System (ADS)

Ni, Y.-C.; Yin, H. P.; Xu, C.; Hui, P. M.

2011-03-01

Phase diagrams exhibiting the extent of cooperation in an evolutionary snowdrift game implemented in different networks are studied in detail. We invoke two independent payoff parameters, unlike a single payoff often used in most previous works that restricts the two payoffs to vary in a correlated way. In addition to the phase transition points when a single payoff parameter is used, phase boundaries separating homogeneous phases consisting of agents using the same strategy and a mixed phase consisting of agents using different strategies are found. Analytic expressions of the phase boundaries are obtained by invoking the ideas of the last surviving patterns and the relative alignments of the spectra of payoff values to agents using different strategies. In a Watts-Strogatz regular network, there exists a re-entrant phenomenon in which the system goes from a homogeneous phase into a mixed phase and re-enters the homogeneous phase as one of the two payoff parameters is varied. The non-trivial phase diagram accompanying this re-entrant phenomenon is quantitatively analyzed. The effects of noise and cooperation in randomly rewired Watts-Strogatz networks are also studied. The transition between a mixed phase and a homogeneous phase is identify to belong to the directed percolation universality class. The methods used in the present work are applicable to a wide range of problems in competing populations of networked agents.

Analysis Tools for Interconnected Boolean Networks With Biological Applications.

PubMed

Chaves, Madalena; Tournier, Laurent

2018-01-01

Boolean networks with asynchronous updates are a class of logical models particularly well adapted to describe the dynamics of biological networks with uncertain measures. The state space of these models can be described by an asynchronous state transition graph, which represents all the possible exits from every single state, and gives a global image of all the possible trajectories of the system. In addition, the asynchronous state transition graph can be associated with an absorbing Markov chain, further providing a semi-quantitative framework where it becomes possible to compute probabilities for the different trajectories. For large networks, however, such direct analyses become computationally untractable, given the exponential dimension of the graph. Exploiting the general modularity of biological systems, we have introduced the novel concept of asymptotic graph , computed as an interconnection of several asynchronous transition graphs and recovering all asymptotic behaviors of a large interconnected system from the behavior of its smaller modules. From a modeling point of view, the interconnection of networks is very useful to address for instance the interplay between known biological modules and to test different hypotheses on the nature of their mutual regulatory links. This paper develops two new features of this general methodology: a quantitative dimension is added to the asymptotic graph, through the computation of relative probabilities for each final attractor and a companion cross-graph is introduced to complement the method on a theoretical point of view.

Water-network percolation transitions in hydrated yeast

NASA Astrophysics Data System (ADS)

Sokołowska, Dagmara; Król-Otwinowska, Agnieszka; Mościcki, Józef K.

2004-11-01

We discovered two percolation processes in succession in dc conductivity of bulk baker’s yeast in the course of dehydration. Critical exponents characteristic for the three-dimensional network for heavily hydrated system, and two dimensions in the light hydration limit, evidenced a dramatic change of the water network dimensionality in the dehydration process.

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…

Massively Open Online Course for Educators (MOOC-Ed) Network Dataset

ERIC Educational Resources Information Center

Kellogg, Shaun; Edelmann, Achim

2015-01-01

This paper presents the Massively Open Online Course for Educators (MOOC-Ed) network dataset. It entails information on two online communication networks resulting from two consecutive offerings of the MOOC called "The Digital Learning Transition in K-12 Schools" in spring and fall 2013. The courses were offered to educators from the USA…

The Virginia Community Cadre Network: Community Reintegration of Persons with Spinal Cord Injury.

ERIC Educational Resources Information Center

Wilson, Walter C.; Thompson, Donald D.

1983-01-01

The Community Cadre Network in Virginia is a local support network for helping individuals with spinal cord injuries make the transition from an institutional setting to home and community life. Cadre members are people who have been through the experience of traumatic injury, rehabilitation, and return home. (SEW)

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

Critical phenomena in communication/computation networks with various topologies and suboptimal to optimal resource allocation

NASA Astrophysics Data System (ADS)

Cogoni, Marco; Busonera, Giovanni; Anedda, Paolo; Zanetti, Gianluigi

2015-01-01

We generalize previous studies on critical phenomena in communication networks [1,2] by adding computational capabilities to the nodes. In our model, a set of tasks with random origin, destination and computational structure is distributed on a computational network, modeled as a graph. By varying the temperature of a Metropolis Montecarlo, we explore the global latency for an optimal to suboptimal resource assignment at a given time instant. By computing the two-point correlation function for the local overload, we study the behavior of the correlation distance (both for links and nodes) while approaching the congested phase: a transition from peaked to spread g(r) is seen above a critical (Montecarlo) temperature Tc. The average latency trend of the system is predicted by averaging over several network traffic realizations while maintaining a spatially detailed information for each node: a sharp decrease of performance is found over Tc independently of the workload. The globally optimized computational resource allocation and network routing defines a baseline for a future comparison of the transition behavior with respect to existing routing strategies [3,4] for different network topologies.

Life's attractors : understanding developmental systems through reverse engineering and in silico evolution.

PubMed

Jaeger, Johannes; Crombach, Anton

2012-01-01

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study-the gap gene network in dipterans (flies, midges, and mosquitoes)-to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

Age-related changes in the ease of dynamical transitions in human brain activity.

PubMed

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

2018-06-01

Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural systems, the age-related decline in executive functions should be underpinned by alteration of large-scale neural dynamics. However, the effects of age on brain dynamics have not been firmly formulated. Here, we investigate such age-related changes in brain dynamics by applying "energy landscape analysis" to publicly available functional magnetic resonance imaging data from healthy younger and older human adults. We quantified the ease of dynamical transitions between different major patterns of brain activity, and estimated it for the default mode network (DMN) and the cingulo-opercular network (CON) separately. We found that the two age groups shared qualitatively the same trajectories of brain dynamics in both the DMN and CON. However, in both of networks, the ease of transitions was significantly smaller in the older than the younger group. Moreover, the ease of transitions was associated with the performance in executive function tasks in a doubly dissociated manner: for the younger adults, the ability of executive functions was mainly correlated with the ease of transitions in the CON, whereas that for the older adults was specifically associated with the ease of transitions in the DMN. These results provide direct biological evidence for age-related changes in macroscopic brain dynamics and suggest that such neural dynamics play key roles when individuals carry out cognitively demanding tasks. © 2018 Wiley Periodicals, Inc.

Order parameter analysis of synchronization transitions on star networks

NASA Astrophysics Data System (ADS)

Chen, Hong-Bin; Sun, Yu-Ting; Gao, Jian; Xu, Can; Zheng, Zhi-Gang

2017-12-01

The collective behaviors of populations of coupled oscillators have attracted significant attention in recent years. In this paper, an order parameter approach is proposed to study the low-dimensional dynamical mechanism of collective synchronizations, by adopting the star-topology of coupled oscillators as a prototype system. The order parameter equation of star-linked phase oscillators can be obtained in terms of the Watanabe-Strogatz transformation, Ott-Antonsen ansatz, and the ensemble order parameter approach. Different solutions of the order parameter equation correspond to the diverse collective states, and different bifurcations reveal various transitions among these collective states. The properties of various transitions in the star-network model are revealed by using tools of nonlinear dynamics such as time reversibility analysis and linear stability analysis.

A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State

PubMed Central

2016-01-01

Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods. PMID:27891146

A systematic review of the status of children's school access in low- and middle-income countries between 1998 and 2013: using the INDEPTH Network platform to fill the research gaps.

PubMed

Kamanda, Mamusu; Sankoh, Osman

2015-01-01

The framework for expanding children's school access in low- and middle-income countries (LMICs) has been directed by universal education policies as part of Education for All since 1990. In measuring progress to universal education, a narrow conceptualisation of access which dichotomises children's participation as being in or out of school has often been assumed. Yet, the actual promise of universal education goes beyond this simple definition to include retention, progression, completion, and learning. Our first objective was to identify gaps in the literature on children's school access using the zones of exclusion of the Consortium for Research on Educational Access, Transition, and Equity as a framework. Second, we gave consideration to how these gaps can be met by using longitudinal and cross-country data from Health and Demographic Surveillance System (HDSS) sites within the International Network for the Demographic Evaluation of Population and Their Health (INDEPTH) in LMICs. Using Web of Science, we conducted a literature search of studies published in international peer-reviewed journals between 1998 and 2013 in LMICs. The phrases we searched included six school outcomes: school enrolment, school attendance, grade progression, school dropout, primary to secondary school transition, and school completion. From our search, we recorded studies according to: 1) school outcomes; 2) whether longitudinal data were used; and 3) whether data from more than one country were analysed. The area of school access most published is enrolment followed by attendance and dropout. Primary to secondary school transition and grade progression had the least number of publications. Of 132 publications which we found to be relevant to school access, 33 made use of longitudinal data and 17 performed cross-country analyses. The majority of studies published in international peer-reviewed journals on children's school access between 1998 and 2013 were focused on three outcomes: enrolment, attendance, and dropout. Few of these studies used data collected over time or data collected from more than one country for comparative analyses. The contribution of the INDEPTH Network in helping to address these gaps in the literature lies in the longitudinal design of HDSS surveys and in the diversity of countries within the network.

The future of transit in West Virginia.

DOT National Transportation Integrated Search

2013-12-01

The Future of Transit in West Virginia is a study of the current system of public transportation in West Virginia and : an examination of issues, priorities and projections of the public transportation network in the coming years. The : purpose...

Crashworthiness evaluation of mass transit buses.

DOT National Transportation Integrated Search

2008-11-01

Mass transit bus systems are an integral part of the national transportation network, serving more than 20.6 billion passenger-miles per year with a relatively low fatality rate. Bus occupant injuries are evenly distributed among crashes on all sides...

Analysis of local bus markets – volumes I - III: technical brief.

DOT National Transportation Integrated Search

2017-07-04

Despite having an extensive network of public transit, traffic congestion and transportation-related greenhouse gas (GHG) emissions are significant concerns in New Jersey. This research examines the congestion and GHG impacts of transit by exclusivel...

Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network.

PubMed

Ding, Rui; Ujang, Norsidah; Hamid, Hussain Bin; Wu, Jianjun

2015-01-01

Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

Efficient room temperature hydrogen sensor based on UV-activated ZnO nano-network

NASA Astrophysics Data System (ADS)

Kumar, Mohit; Kumar, Rahul; Rajamani, Saravanan; Ranwa, Sapana; Fanetti, Mattia; Valant, Matjaz; Kumar, Mahesh

2017-09-01

Room temperature hydrogen sensors were fabricated from Au embedded ZnO nano-networks using a 30 mW GaN ultraviolet LED. The Au-decorated ZnO nano-networks were deposited on a SiO2/Si substrate by a chemical vapour deposition process. X-ray diffraction (XRD) spectrum analysis revealed a hexagonal wurtzite structure of ZnO and presence of Au. The ZnO nanoparticles were interconnected, forming nano-network structures. Au nanoparticles were uniformly distributed on ZnO surfaces, as confirmed by FESEM imaging. Interdigitated electrodes (IDEs) were fabricated on the ZnO nano-networks using optical lithography. Sensor performances were measured with and without UV illumination, at room temperate, with concentrations of hydrogen varying from 5 ppm to 1%. The sensor response was found to be ˜21.5% under UV illumination and 0% without UV at room temperature for low hydrogen concentration of 5 ppm. The UV-photoactivated mode enhanced the adsorption of photo-induced O- and O2- ions, and the d-band electron transition from the Au nanoparticles to ZnO—which increased the chemisorbed reaction between hydrogen and oxygen. The sensor response was also measured at 150 °C (without UV illumination) and found to be ˜18% at 5 ppm. Energy efficient low cost hydrogen sensors can be designed and fabricated with the combination of GaN UV LEDs and ZnO nanostructures.

Growing Actin Networks Form Lamellipodium and Lamellum by Self-Assembly

PubMed Central

Huber, Florian; Käs, Josef; Stuhrmann, Björn

2008-01-01

Many different cell types are able to migrate by formation of a thin actin-based cytoskeletal extension. Recently, it became evident that this extension consists of two distinct substructures, designated lamellipodium and lamellum, which differ significantly in their kinetic and kinematic properties as well as their biochemical composition. We developed a stochastic two-dimensional computer simulation that includes chemical reaction kinetics, G-actin diffusion, and filament transport to investigate the formation of growing actin networks in migrating cells. Model parameters were chosen based on experimental data or theoretical considerations. In this work, we demonstrate the system's ability to form two distinct networks by self-organization. We found a characteristic transition in mean filament length as well as a distinct maximum in depolymerization flux, both within the first 1–2 μm. The separation into two distinct substructures was found to be extremely robust with respect to initial conditions and variation of model parameters. We quantitatively investigated the complex interplay between ADF/cofilin and tropomyosin and propose a plausible mechanism that leads to spatial separation of, respectively, ADF/cofilin- or tropomyosin-dominated compartments. Tropomyosin was found to play an important role in stabilizing the lamellar actin network. Furthermore, the influence of filament severing and annealing on the network properties is explored, and simulation data are compared to existing experimental data. PMID:18708450

Use of Social Media by Fathers of Premature Infants.

PubMed

Kim, Hyung Nam; Wyatt, Tami H; Li, Xueping; Gaylord, Mark

Although parents of premature infants experience many challenges when transitioning home from the neonatal intensive care unit, healthcare providers and social support systems tend to focus on mothers and infants rather than fathers. Unfortunately, very little is known about paternal concerns and needs as compared with maternal ones. The lack of understanding about paternal needs may lead to inadequate designs of neonatal intensive care unit family support programs with less involved fathers, all of which contribute to increased burdens on mothers and poor health outcomes for their infants. Although information technology (IT) might have the potential to increase support for the fathers of preterm infants, only a few studies have examined systematically how IT applications can be beneficial. This study aims to advance the understanding of needs and concerns of fathers with preterm infants and how fathers use the IT applications (eg, social networking Web sites) to support themselves. We observed qualitatively various social networking Web sites (ie, 29 Web sites) where fathers share their experiences about preterm infants. We discovered that fathers used various social media to discuss their concerns and, in turn, obtained informational, companionship, and emotional supports. On the basis of our analysis, we provide insights into a father-centered technology intervention design.

Multisector Health Policy Networks in 15 Large US Cities.

PubMed

Harris, Jenine K; Leider, J P; Carothers, Bobbi J; Castrucci, Brian C; Hearne, Shelley

2016-01-01

Local health departments (LHDs) have historically not prioritized policy development, although it is one of the 3 core areas they address. One strategy that may influence policy in LHD jurisdictions is the formation of partnerships across sectors to work together on local public health policy. We used a network approach to examine LHD local health policy partnerships across 15 large cities from the Big Cities Health Coalition. We surveyed the health departments and their partners about their working relationships in 5 policy areas: core local funding, tobacco control, obesity and chronic disease, violence and injury prevention, and infant mortality. Drawing on prior literature linking network structures with performance, we examined network density, transitivity, centralization and centrality, member diversity, and assortativity of ties. Networks included an average of 21.8 organizations. Nonprofits and government agencies made up the largest proportions of the networks, with 28.8% and 21.7% of network members, whereas for-profits and foundations made up the smallest proportions in all of the networks, with just 1.2% and 2.4% on average. Mean values of density, transitivity, diversity, assortativity, centralization, and centrality showed similarity across policy areas and most LHDs. The tobacco control and obesity/chronic disease networks were densest and most diverse, whereas the infant mortality policy networks were the most centralized and had the highest assortativity. Core local funding policy networks had lower scores than other policy area networks by most network measures. Urban LHDs partner with organizations from diverse sectors to conduct local public health policy work. Network structures are similar across policy areas jurisdictions. Obesity and chronic disease, tobacco control, and infant mortality networks had structures consistent with higher performing networks, whereas core local funding networks had structures consistent with lower performing networks.

The influence of passenger flow on the topology characteristics of urban rail transit networks

NASA Astrophysics Data System (ADS)

Hu, Yingyue; Chen, Feng; Chen, Peiwen; Tan, Yurong

2017-05-01

Current researches on the network characteristics of metro networks are generally carried out on topology networks without passenger flows running on it, thus more complex features of the networks with ridership loaded on it cannot be captured. In this study, we incorporated the load of metro networks, passenger volume, into the exploration of network features. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a metro network. To this end, section load was selected as an edge weight to demonstrate the influence of ridership on the network, and a weighted calculation method for complex network indicators and robustness were proposed to capture the unique behaviors of a metro network with passengers flowing in it. The proposed method was applied on Beijing Subway. Firstly, the passenger volume in terms of daily origin and destination matrix was extracted from exhausted transit smart card data. Using the established approach and the matrix as weighting, common indicators of complex network including clustering coefficient, betweenness and degree were calculated, and network robustness were evaluated under potential attacks. The results were further compared to that of unweighted networks, and it suggests indicators of the network with consideration of passenger volumes differ from that without ridership to some extent, and networks tend to be more vulnerable than that without load on it. The significance sequence for the stations can be changed. By introducing passenger flow weighting, actual operation status of the network can be reflected more accurately. It is beneficial to determine the crucial stations and make precautionary measures for the entire network’s operation security.

Physics of Financial Markets: Can we Understand the Unpredictable Phenomenon of Flash Crashes

NASA Astrophysics Data System (ADS)

Stanley, H. Eugene

2015-03-01

Dangerous vulnerability is hiding in complex systems. Indeed, disasters ranging from abrupt financial ``flash crashes'' and large-scale power outages to sudden death among the elderly dramatically exemplify this fact. While we can understand the cause of most events in complex systems, sudden unexpected ``black swans'' whether in economics or in the ``physicists world'' cry out for insight. To design more resilient systems we will describe recent results seeking understanding of these black swans. In many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic ``phase-flipping'' phenomena. The fraction of active nodes switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behavior analogous to phase transitions near a critical point [A. Majdandzic, B. Podobnik, S. V. Buldyrev, D. Y. Kenett, S. Havlin, and H. E. Stanley, ``Spontaneous Recovery in Dynamic Networks,'' Nature Physics 10, 34 (2014)]. This work was carried out in collaboration with a number of colleagues, chief among whom are A. Majdanzic, B. Podobnik, S. V. Buldyrev, D. Y. Kenett, and S. Havlin.

A networks-based discrete dynamic systems approach to volcanic seismicity

NASA Astrophysics Data System (ADS)

Suteanu, Mirela

2013-04-01

The detection and relevant description of pattern change concerning earthquake events is an important, but challenging task. In this paper, earthquake events related to volcanic activity are considered manifestations of a dynamic system evolving over time. The system dynamics is seen as a succession of events with point-like appearance both in time and in space. Each event is characterized by a position in three-dimensional space, a moment of occurrence, and an event size (magnitude). A weighted directed network is constructed to capture the effects of earthquakes on subsequent events. Each seismic event represents a node. Relations among events represent edges. Edge directions are given by the temporal succession of the events. Edges are also characterized by weights reflecting the strengths of the relation between the nodes. Weights are calculated as a function of (i) the time interval separating the two events, (ii) the spatial distance between the events, (iii) the magnitude of the earliest event among the two. Different ways of addressing weight components are explored, and their implications for the properties of the produced networks are analyzed. The resulting networks are then characterized in terms of degree- and weight distributions. Subsequently, the distribution of system transitions is determined for all the edges connecting related events in the network. Two- and three-dimensional diagrams are constructed to reflect transition distributions for each set of events. Networks are thus generated for successive temporal windows of different size, and the evolution of (a) network properties and (b) system transition distributions are followed over time and compared to the timeline of documented geologic processes. Applications concerning volcanic seismicity on the Big Island of Hawaii show that this approach is capable of revealing novel aspects of change occurring in the volcanic system on different scales in time and in space.

Synergistic interactions promote behavior spreading and alter phase transitions on multiplex networks

NASA Astrophysics Data System (ADS)

Liu, Quan-Hui; Wang, Wei; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-02-01

Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system.

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

PubMed Central

Fretter, Christoph; Lesne, Annick; Hilgetag, Claus C.; Hütt, Marc-Thorsten

2017-01-01

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience. PMID:28186182

Structural Transitions in Densifying Networks

NASA Astrophysics Data System (ADS)

Lambiotte, R.; Krapivsky, P. L.; Bhat, U.; Redner, S.

2016-11-01

We introduce a minimal generative model for densifying networks in which a new node attaches to a randomly selected target node and also to each of its neighbors with probability p . The networks that emerge from this copying mechanism are sparse for p <1/2 and dense (average degree increasing with number of nodes N ) for p ≥1/2 . The behavior in the dense regime is especially rich; for example, individual network realizations that are built by copying are disparate and not self-averaging. Further, there is an infinite sequence of structural anomalies at p =2/3 , 3/4 , 4/5 , etc., where the N dependences of the number of triangles (3-cliques), 4-cliques, undergo phase transitions. When linking to second neighbors of the target can occur, the probability that the resulting graph is complete—all nodes are connected—is nonzero as N →∞ .

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs.

PubMed

Fretter, Christoph; Lesne, Annick; Hilgetag, Claus C; Hütt, Marc-Thorsten

2017-02-10

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.

Overarching framework for data-based modelling

NASA Astrophysics Data System (ADS)

Schelter, Björn; Mader, Malenka; Mader, Wolfgang; Sommerlade, Linda; Platt, Bettina; Lai, Ying-Cheng; Grebogi, Celso; Thiel, Marco

2014-02-01

One of the main modelling paradigms for complex physical systems are networks. When estimating the network structure from measured signals, typically several assumptions such as stationarity are made in the estimation process. Violating these assumptions renders standard analysis techniques fruitless. We here propose a framework to estimate the network structure from measurements of arbitrary non-linear, non-stationary, stochastic processes. To this end, we propose a rigorous mathematical theory that underlies this framework. Based on this theory, we present a highly efficient algorithm and the corresponding statistics that are immediately sensibly applicable to measured signals. We demonstrate its performance in a simulation study. In experiments of transitions between vigilance stages in rodents, we infer small network structures with complex, time-dependent interactions; this suggests biomarkers for such transitions, the key to understand and diagnose numerous diseases such as dementia. We argue that the suggested framework combines features that other approaches followed so far lack.

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

NASA Astrophysics Data System (ADS)

Fretter, Christoph; Lesne, Annick; Hilgetag, Claus C.; Hütt, Marc-Thorsten

2017-02-01

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.

The investigation of order–disorder transition process of ZSM-5 induced by spark plasma sintering

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

Wang, Liang; Wang, Lianjun, E-mail: wanglj@dhu.edu.cn; Jiang, Wan

2014-04-01

Based on the amorphization of zeolites, an order–disorder transition method was used to prepare silica glass via Spark Plasma Sintering (SPS). In order to get a better understanding about the mechanism of amorphization induced by SPS, the intermediate products in this process were prepared and characterized by different characterization techniques. X-ray diffraction and High-energy synchrotron X-ray scattering show a gradual transformation from ordered crystal to glass. Local structural changes in glass network including Si–O bond length, O–Si–O bond angle, size of rings, coordination were detected by Infrared spectroscopy and {sup 29}Si magic-angle spinning nuclear magnetic resonance (NMR) spectroscopy. Topologically ordered,more » amorphous material with a different intermediate-range structure can be obtained by precise control of intermediate process which can be expected to optimize and design material. - Graphical abstract: Low-density, ordered zeolites collapse to the rigid amorphous glass through spark plasma sintering. The intermediate-range structure formed in the process of order–disorder transition may give rise to specific property. - Highlights: • Order–disorder transition process of ZSM-5 induced by spark plasma sintering was investigated using several methods including XRD, High-energy synchrotron X-ray scattering, SAXS, IR, NMR, ect. • Order–disorder transition induced by SPS was compared with TIA and PIA. • Three stages has been divided during the whole process. • The collapse temperature range which may give rise to intermediate-range structure has been located.« less

Fisher information at the edge of chaos in random Boolean networks.

PubMed

Wang, X Rosalind; Lizier, Joseph T; Prokopenko, Mikhail

2011-01-01

We study the order-chaos phase transition in random Boolean networks (RBNs), which have been used as models of gene regulatory networks. In particular we seek to characterize the phase diagram in information-theoretic terms, focusing on the effect of the control parameters (activity level and connectivity). Fisher information, which measures how much system dynamics can reveal about the control parameters, offers a natural interpretation of the phase diagram in RBNs. We report that this measure is maximized near the order-chaos phase transitions in RBNs, since this is the region where the system is most sensitive to its parameters. Furthermore, we use this study of RBNs to clarify the relationship between Shannon and Fisher information measures.

Adolescent Mothers in a Transitional Living Facility: An Exploratory Study of Support Networks and Attachment Patterns

ERIC Educational Resources Information Center

Schwartz, Ann E.; McRoy, Ruth G.; Downs, A. Chris

2004-01-01

Most of the research literature on attachment and adolescent transitions has addressed youth in family settings. This article explores these issues with a sample of 25 pregnant and parenting teens living in a transitional shelter. Using case records and interview data as well as results of standardized measures of depression, self-esteem, child…

How old is streamwater? Open questions in catchment transit time conceptualization, modeling and analysis

Treesearch

J.J. McDonnell; K. McGuire; P. Aggarwal; K.J. Beven; D. Biondi; G. Destouni; S. Dunn; A. James; J. Kirchner; P. Kraft; S. Lyon; P. Maloszewski; B. Newman; L. Pfister; A. Rinaldo; A. Rodhe; T. Sayama; J. Seibert; K. Solomon; C. Soulsby; M. Stewart; D. Tetzlaff; C. Tobin; P. Troch; M. Weiler; A. Western; A. Wörman; S. Wrede

2010-01-01

The time water spends travelling subsurface through a catchment to the stream network (i.e. the catchment water transit time) fundamentally describes the storage, flow pathway heterogeneity and sources of water in a catchment. The distribution of transit times reflects how catchments retain and release water and solutes that in turn set biogeochemical conditions and...

A Framework for Monitoring Transition Systems. OECD Education Working Papers, No. 20

ERIC Educational Resources Information Center

van der Velden, Rolf K.; Wolbers, Maarten H.

2008-01-01

With its publication of the Thematic Review on the Transition from Initial Education to Working Life in 2000, OECD has laid the foundation for the development of indicators regarding the transition from education to work. One of the core activities of OECD's Network B in 2005 and 2006 was to further develop these indicators by establishing a…

Identification of key residues for protein conformational transition using elastic network model.

PubMed

Su, Ji Guo; Xu, Xian Jin; Li, Chun Hua; Chen, Wei Zu; Wang, Cun Xin

2011-11-07

Proteins usually undergo conformational transitions between structurally disparate states to fulfill their functions. The large-scale allosteric conformational transitions are believed to involve some key residues that mediate the conformational movements between different regions of the protein. In the present work, a thermodynamic method based on the elastic network model is proposed to predict the key residues involved in protein conformational transitions. In our method, the key functional sites are identified as the residues whose perturbations largely influence the free energy difference between the protein states before and after transition. Two proteins, nucleotide binding domain of the heat shock protein 70 and human/rat DNA polymerase β, are used as case studies to identify the critical residues responsible for their open-closed conformational transitions. The results show that the functionally important residues mainly locate at the following regions for these two proteins: (1) the bridging point at the interface between the subdomains that control the opening and closure of the binding cleft; (2) the hinge region between different subdomains, which mediates the cooperative motions between the corresponding subdomains; and (3) the substrate binding sites. The similarity in the positions of the key residues for these two proteins may indicate a common mechanism in their conformational transitions.

Identifying early-warning signals of critical transitions with strong noise by dynamical network markers

PubMed Central

Liu, Rui; Chen, Pei; Aihara, Kazuyuki; Chen, Luonan

2015-01-01

Identifying early-warning signals of a critical transition for a complex system is difficult, especially when the target system is constantly perturbed by big noise, which makes the traditional methods fail due to the strong fluctuations of the observed data. In this work, we show that the critical transition is not traditional state-transition but probability distribution-transition when the noise is not sufficiently small, which, however, is a ubiquitous case in real systems. We present a model-free computational method to detect the warning signals before such transitions. The key idea behind is a strategy: “making big noise smaller” by a distribution-embedding scheme, which transforms the data from the observed state-variables with big noise to their distribution-variables with small noise, and thus makes the traditional criteria effective because of the significantly reduced fluctuations. Specifically, increasing the dimension of the observed data by moment expansion that changes the system from state-dynamics to probability distribution-dynamics, we derive new data in a higher-dimensional space but with much smaller noise. Then, we develop a criterion based on the dynamical network marker (DNM) to signal the impending critical transition using the transformed higher-dimensional data. We also demonstrate the effectiveness of our method in biological, ecological and financial systems. PMID:26647650

Local structural mechanism for frozen-in dynamics in metallic glasses

NASA Astrophysics Data System (ADS)

Liu, X. J.; Wang, S. D.; Wang, H.; Wu, Y.; Liu, C. T.; Li, M.; Lu, Z. P.

2018-04-01

The nature of the glass transition is a fundamental and long-standing intriguing issue in the condensed-matter physics and materials science community. In particular, the structural response by which a liquid is arrested dynamically to form a glass or amorphous solid upon approaching its freezing temperature [the glass transition temperature (Tg)] remains unclear. Various structural scenarios in terms of the percolation theory have been proposed recently to understand such a phenomenon; however, there is still no consensus on what the general percolation entity is and how the entity responds to the sudden slowdown dynamics during the glass transition. In this paper, we demonstrate that one-dimensional local linear ordering (LLO) is a universal structural motif associated with the glass transition for various metallic glasses. The quantitative evolution of LLO with temperature indicates that a percolating LLO network forms to serve as the backbone of the rigid glass solid when the temperature approaches the freezing point, resulting in the frozen-in dynamics accompanying the glass transition. The percolation transition occurs by pinning different LLO networks together, which only needs the introduction of a small number of "joint" atoms between them, and therefore the energy expenditure is very low.

Dynamics of the sol—gel transition in organic—inorganic nanocomposites

NASA Astrophysics Data System (ADS)

Judeinstein, P.; Oliveira, P. W.; Krug, H.; Schmidt, H.

1994-03-01

Two different techniques have been used to follow the gelation of photochromic organic—inorganic nanocomposites. The variations of molecular and macromolecular motions in these complex systems have been analyzed. Photo-correlation spectroscopy probes the formation of the gel network. Forced Rayleigh scattering experiences the microstructure of the mixtures via the measurement of the translational diffusion coefficient of entrapped photoreactive targets. In the different mixtures, a drop of the network mobility could be observed around the sol to gel conversion, while the entrapped molecules do not experience the macroscopic transition.

Synthetic quorum sensing in model microcapsule colonies

NASA Astrophysics Data System (ADS)

Shum, Henry; Balazs, Anna C.

2017-08-01

Biological quorum sensing refers to the ability of cells to gauge their population density and collectively initiate a new behavior once a critical density is reached. Designing synthetic materials systems that exhibit quorum sensing-like behavior could enable the fabrication of devices with both self-recognition and self-regulating functionality. Herein, we develop models for a colony of synthetic microcapsules that communicate by producing and releasing signaling molecules. Production of the chemicals is regulated by a biomimetic negative feedback loop, the “repressilator” network. Through theory and simulation, we show that the chemical behavior of such capsules is sensitive to both the density and number of capsules in the colony. For example, decreasing the spacing between a fixed number of capsules can trigger a transition in chemical activity from the steady, repressed state to large-amplitude oscillations in chemical production. Alternatively, for a fixed density, an increase in the number of capsules in the colony can also promote a transition into the oscillatory state. This configuration-dependent behavior of the capsule colony exemplifies quorum-sensing behavior. Using our theoretical model, we predict the transitions from the steady state to oscillatory behavior as a function of the colony size and capsule density.

Experimental testing of a new integrated model of the budding yeast Start transition

PubMed Central

Adames, Neil R.; Schuck, P. Logan; Chen, Katherine C.; Murali, T. M.; Tyson, John J.; Peccoud, Jean

2015-01-01

The cell cycle is composed of bistable molecular switches that govern the transitions between gap phases (G1 and G2) and the phases in which DNA is replicated (S) and partitioned between daughter cells (M). Many molecular details of the budding yeast G1–S transition (Start) have been elucidated in recent years, especially with regard to its switch-like behavior due to positive feedback mechanisms. These results led us to reevaluate and expand a previous mathematical model of the yeast cell cycle. The new model incorporates Whi3 inhibition of Cln3 activity, Whi5 inhibition of SBF and MBF transcription factors, and feedback inhibition of Whi5 by G1–S cyclins. We tested the accuracy of the model by simulating various mutants not described in the literature. We then constructed these novel mutant strains and compared their observed phenotypes to the model’s simulations. The experimental results reported here led to further changes of the model, which will be fully described in a later article. Our study demonstrates the advantages of combining model design, simulation, and testing in a coordinated effort to better understand a complex biological network. PMID:26310445

IPv6 Tactical Network Management

DTIC Science & Technology

2009-09-01

is transitioning to IPv6 networks. While the benefits provided by IPv6 are numerous, its challenges lie in managing a network on the scale...operability, and usability in a tactical network is under way. New challenges are also presented by the need to integrate into the IPv6 segment new...Accessing this information also presents challenges . Feasibility studies are conducted to show that, for these devices, the IPv6 domain is at least

On the degelation of networks – Case of the radiochemical degradation of methyl methacrylate – ethylene glycol dimethacrylate copolymers

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

Richaud, Emmanuel; Gilormini, Pierre; Verdu, Jacques

2016-05-18

Methyl methacrylate networks were synthetized and submitted to radiochemical degradation. Ageing was monitored by means of sol-gel analysis and glass transition temperature measurements. Networks were shown to undergo exclusively chain scission process leading to the degelation of network. The critical conversion degree corresponding to degelation (loss of all elastically active chains) is discussed regarding a statistical theory.

Mining the Temporal Dimension of the Information Propagation

NASA Astrophysics Data System (ADS)

Berlingerio, Michele; Coscia, Michele; Giannotti, Fosca

In the last decade, Social Network Analysis has been a field in which the effort devoted from several researchers in the Data Mining area has increased very fast. Among the possible related topics, the study of the information propagation in a network attracted the interest of many researchers, also from the industrial world. However, only a few answers to the questions “How does the information propagates over a network, why and how fast?” have been discovered so far. On the other hand, these answers are of large interest, since they help in the tasks of finding experts in a network, assessing viral marketing strategies, identifying fast or slow paths of the information inside a collaborative network. In this paper we study the problem of finding frequent patterns in a network with the help of two different techniques: TAS (Temporally Annotated Sequences) mining, aimed at extracting sequential patterns where each transition between two events is annotated with a typical transition time that emerges from input data, and Graph Mining, which is helpful for locally analyzing the nodes of the networks with their properties. Finally we show preliminary results done in the direction of mining the information propagation over a network, performed on two well known email datasets, that show the power of the combination of these two approaches.

Representation of transit ITS in network-based travel models

DOT National Transportation Integrated Search

2005-03-01

The increased use of Intelligent Transportation Systems (ITS) technology in public transit has two major impacts on travel forecasting. First, the technology will often result in an improved volume and quality of data that may be used for planning. S...

Amorphous-amorphous transition in a porous coordination polymer.

PubMed

Ohtsu, Hiroyoshi; Bennett, Thomas D; Kojima, Tatsuhiro; Keen, David A; Niwa, Yasuhiro; Kawano, Masaki

2017-07-04

The amorphous state plays a key role in porous coordination polymer and metal-organic framework phase transitions. We investigate a crystalline-to-amorphous-to-amorphous-to-crystalline (CAAC) phase transition in a Zn based coordination polymer, by X-ray absorption fine structure (XAFS) and X-ray pair distribution function (PDF) analysis. We show that the system shows two distinct amorphous phases upon heating. The first involves a reversible transition to a desolvated form of the original network, followed by an irreversible transition to an intermediate phase which has elongated Zn-I bonds.

Geometric phase transition in the cellular network of the pancreatic islets may underlie the onset of type 1diabetes

NASA Astrophysics Data System (ADS)

Wang, Xujing

Living systems are characterized by complexity in structure and emergent dynamic orders. In many aspects the onset of a chronic disease resembles phase transition in a dynamic system: quantitative changes accumulate largely unnoticed until a critical threshold is reached, which causes abrupt qualitative changes of the system. In this study we investigate this idea in a real example, the insulin-producing pancreatic islet β-cells and the onset of type 1 diabetes. Within each islet, the β-cells are electrically coupled to each other, and function as a network with synchronized actions. Using percolation theory we show how normal islet function is intrinsically linked to network connectivity, and the critical point where the islet cellular network loses site percolation, is consistent with laboratory and clinical observations of the threshold β-cell loss that causes islet functional failure. Numerical simulations confirm that the islet cellular network needs to be percolated for β-cells to synchronize. Furthermore, the interplay between site percolation and bond strength predicts the existence of a transient phase of islet functional recovery after disease onset and introduction of treatment, potentially explaining a long time mystery in the clinical study of type 1 diabetes: the honeymoon phenomenon. Based on these results, we hypothesized that the onset of T1D may be the result of a phase transition of the islet β-cell network. We further discuss the potential applications in identifying disease-driving factors, and the critical parameters that are predictive of disease onset.

Dynamics of blood flow in a microfluidic ladder network

NASA Astrophysics Data System (ADS)

Maddala, Jeevan; Zilberman-Rudenko, Jevgenia; McCarty, Owen

The dynamics of a complex mixture of cells and proteins, such as blood, in perturbed shear flow remains ill-defined. Microfluidics is a promising technology for improving the understanding of blood flow under complex conditions of shear; as found in stent implants and in tortuous blood vessels. We model the fluid dynamics of blood flow in a microfluidic ladder network with dimensions mimicking venules. Interaction of blood cells was modeled using multiagent framework, where cells of different diameters were treated as spheres. This model served as the basis for predicting transition regions, collision pathways, re-circulation zones and residence times of cells dependent on their diameters and device architecture. Based on these insights from the model, we were able to predict the clot formation configurations at various locations in the device. These predictions were supported by the experiments using whole blood. To facilitate platelet aggregation, the devices were coated with fibrillar collagen and tissue factor. Blood was perfused through the microfluidic device for 9 min at a physiologically relevant venous shear rate of 600 s-1. Using fluorescent microscopy, we observed flow transitions near the channel intersections and at the areas of blood flow obstruction, which promoted larger thrombus formation. This study of integrating model predictions with experimental design, aids in defining the dynamics of blood flow in microvasculature and in development of novel biomedical devices.

Effects of a Group Intervention on the Career Network Ties of Finnish Adolescents

ERIC Educational Resources Information Center

Jokisaari, Markku; Vuori, Jukka

2011-01-01

The authors evaluated how a group-based career intervention affected career network ties among Finnish adolescents as they made educational choices and prepared for their transition to secondary education. They examined the career-related network ties of 868 students during their last year in comprehensive school (junior high school) in a…

A New Addiction for Teacher Candidates: Social Networks

ERIC Educational Resources Information Center

Cam, Emre; Isbulan, Onur

2012-01-01

With the transition to being a knowledge-based society, the internet usage has become an irreplaceable part of life. As socials networks have come into our lives, the internet usage has taken a different dimension. People can affiliate to social networks in order to make friends, exchange information, find partners, and to play games. The process…

Infant Transport Monitoring

NASA Technical Reports Server (NTRS)

1978-01-01

The photo sequence illustrates the movement of an ill infant to a special care hospital by means of a new Pediatric Monitoring and Transport System, in which NASA technology and technical assistance are being applied to an urgent medical problem. Development of the system is a collaborative effort involving several organizations, principally, NASA Ames Research Center and Children's Hospital Medical Center, Oakland, California. Key to the system's efficacy is a custom-designed ambulance-to-hospital and hospital-to-hospital communications network, including two-way voice capability and space-derived biotelemetry; it allows a specialist at the destination hospital to monitor continuously the vital signs of the patient during transit.

Evaluation of the Public Transportation Network : Diffusion of Innovative Transit Practices

DOT National Transportation Integrated Search

1988-08-01

This report presents an evaluation of the Public Transportation Network (PTN), a technical assistance program established by the Urban Mass Transportation Administration in 1983 to help public transportation agencies adopt better ways of managing and...

Exploring activity-driven network with biased walks

NASA Astrophysics Data System (ADS)

Wang, Yan; Wu, Ding Juan; Lv, Fang; Su, Meng Long

We investigate the concurrent dynamics of biased random walks and the activity-driven network, where the preferential transition probability is in terms of the edge-weighting parameter. We also obtain the analytical expressions for stationary distribution and the coverage function in directed and undirected networks, all of which depend on the weight parameter. Appropriately adjusting this parameter, more effective search strategy can be obtained when compared with the unbiased random walk, whether in directed or undirected networks. Since network weights play a significant role in the diffusion process.

Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare.

PubMed

Will, Thorsten; Helms, Volkhard

2017-04-04

Differential analysis of cellular conditions is a key approach towards understanding the consequences and driving causes behind biological processes such as developmental transitions or diseases. The progress of whole-genome expression profiling enabled to conveniently capture the state of a cell's transcriptome and to detect the characteristic features that distinguish cells in specific conditions. In contrast, mapping the physical protein interactome for many samples is experimentally infeasible at the moment. For the understanding of the whole system, however, it is equally important how the interactions of proteins are rewired between cellular states. To overcome this deficiency, we recently showed how condition-specific protein interaction networks that even consider alternative splicing can be inferred from transcript expression data. Here, we present the differential network analysis tool PPICompare that was specifically designed for isoform-sensitive protein interaction networks. Besides detecting significant rewiring events between the interactomes of grouped samples, PPICompare infers which alterations to the transcriptome caused each rewiring event and what is the minimal set of alterations necessary to explain all between-group changes. When applied to the development of blood cells, we verified that a reasonable amount of rewiring events were reported by the tool and found that differential gene expression was the major determinant of cellular adjustments to the interactome. Alternative splicing events were consistently necessary in each developmental step to explain all significant alterations and were especially important for rewiring in the context of transcriptional control. Applying PPICompare enabled us to investigate the dynamics of the human protein interactome during developmental transitions. A platform-independent implementation of the tool PPICompare is available at https://sourceforge.net/projects/ppicompare/ .

A Markovian event-based framework for stochastic spiking neural networks.

PubMed

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

NASA Astrophysics Data System (ADS)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

Phase Transitions in Living Neural Networks

NASA Astrophysics Data System (ADS)

Williams-Garcia, Rashid Vladimir

Our nervous systems are composed of intricate webs of interconnected neurons interacting in complex ways. These complex interactions result in a wide range of collective behaviors with implications for features of brain function, e.g., information processing. Under certain conditions, such interactions can drive neural network dynamics towards critical phase transitions, where power-law scaling is conjectured to allow optimal behavior. Recent experimental evidence is consistent with this idea and it seems plausible that healthy neural networks would tend towards optimality. This hypothesis, however, is based on two problematic assumptions, which I describe and for which I present alternatives in this thesis. First, critical transitions may vanish due to the influence of an environment, e.g., a sensory stimulus, and so living neural networks may be incapable of achieving "critical" optimality. I develop a framework known as quasicriticality, in which a relative optimality can be achieved depending on the strength of the environmental influence. Second, the power-law scaling supporting this hypothesis is based on statistical analysis of cascades of activity known as neuronal avalanches, which conflate causal and non-causal activity, thus confounding important dynamical information. In this thesis, I present a new method to unveil causal links, known as causal webs, between neuronal activations, thus allowing for experimental tests of the quasicriticality hypothesis and other practical applications.

Coherence time of over a second in a telecom-compatible quantum memory storage material

NASA Astrophysics Data System (ADS)

Rančić, Miloš; Hedges, Morgan P.; Ahlefeldt, Rose L.; Sellars, Matthew J.

2018-01-01

Quantum memories for light will be essential elements in future long-range quantum communication networks. These memories operate by reversibly mapping the quantum state of light onto the quantum transitions of a material system. For networks, the quantum coherence times of these transitions must be long compared to the network transmission times, approximately 100 ms for a global communication network. Due to a lack of a suitable storage material, a quantum memory that operates in the 1,550 nm optical fibre communication band with a storage time greater than 1 μs has not been demonstrated. Here we describe the spin dynamics of 167Er3+: Y2SiO5 in a high magnetic field and demonstrate that this material has the characteristics for a practical quantum memory in the 1,550 nm communication band. We observe a hyperfine coherence time of 1.3 s. We also demonstrate efficient spin pumping of the entire ensemble into a single hyperfine state, a requirement for broadband spin-wave storage. With an absorption of 70 dB cm-1 at 1,538 nm and Λ transitions enabling spin-wave storage, this material is the first candidate identified for an efficient, broadband quantum memory at telecommunication wavelengths.